# Canonical covariate columns

This file is the authoritative register of covariate column names used in nlmixr2lib models. Every covariate referenced inside a model's `model()` block must have an entry here. The register is seeded from a full audit of `inst/modeldb/` and extended whenever a new paper introduces a covariate that isn't yet registered.

## How to use this register

1. **Before adding a covariate to a new model**, search this file (by canonical name and by source alias) for the concept you need.
2. **If the canonical name exists**, use it exactly. Document any source-paper rename in the model's `covariateData[[name]]$source_name` field.
3. **If the source paper uses an alias listed under an existing canonical name**, use the canonical name and note in `covariateData[[name]]$notes` whether the values must be transformed (e.g., `SEXM -> SEXF` inverts values; the effect coefficient sign / reference category must be inverted as well).
4. **If the covariate is not in this register at all**, propose a new entry with a canonical name, description, units, type, scope, and source aliases. Verify with the user before committing. The addition is part of the model's PR.
5. **Do not modify existing model files when you discover an alias**; simply document the mapping in the register. Retrofitting existing models is a separate effort.

## Scope: general vs specific

Each entry has a `Scope:` field declaring whether it is **general** (any model may use it without warning) or **specific** (only the models listed under `Example models` may use it; other usage is flagged by `checkModelConventions()`). This prevents accidentally reusing a covariate name whose meaning is tied to a particular paper.

- **Scope: general** -- the covariate has a stable, paper-independent meaning and any future model may use it. Examples: `WT`, `AGE`, `SEXF`, `CREAT`, `ADA_POS`, `CRP`, `CRCL`.
- **Scope: specific** -- the covariate's semantics depend on a particular study's design (a specific study indicator, a drug-product variant, a composite race grouping, a tumor-type decomposition, etc.). If a new paper needs the same concept with different semantics, register a new canonical name; if the concept matches, extend the `Example models` list (and consider promoting to general).

When adding or updating an entry, choose the most conservative scope: if in doubt, start with `specific` and promote when a second model legitimately ratifies the name.

## Case convention

Covariate column names should be ALL CAPS. Current non-all-caps canonical names are `dilution` and `nonECZTRA` (both scope: specific), preserved from their source files with "future rename" notes. New entries should default to all caps.

## Entry schema

```yaml
- name: <CANONICAL_NAME>
  description: <one-sentence definition>
  units: <unit string, or "(binary)" / "(categorical)">
  type: continuous | binary | categorical | count
  scope: general | specific
  reference_category: <the 0 group for binary/categorical, or NULL>
  source_aliases:
    - <ALIAS_NAME> (<transformation if any>) -- used in <model.R>
  example_models:
    - <model.R>
  notes: <free text>
```

---

## Demographics

### WT (**canonical for body weight (baseline or time-varying)**)
- **Description:** Body weight (baseline or time-varying).
- **Units:** kg
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with allometric scaling `(WT / ref_wt)^exponent`. Reference weights observed: 70 kg (adults), 75 kg, 84.8 kg, 56 kg (Kloprogge 2014 quinine cohort typical), 5 kg (infants), 25 kg (Wang 2012 Chinese pediatric epilepsy cohort median).
- **Source aliases:**
  - `WEIG` -- weight column abbreviation used by Wang 2012 (Acta Pharmacol Sin 33:845-851); same biological quantity in kg, no value transformation. Used in `Wang_2012_levetiracetam.R`.
- **Example models:** `Clegg_2024_nirsevimab.R`, `Hu_2026_clesrovimab.R`, `Zhu_2017_lebrikizumab.R`, `Kovalenko_2020_dupilumab.R`, `CarlssonPetri_2021_liraglutide.R`, `Cirincione_2017_exenatide.R`, `Grimm_2023_gantenerumab.R`, `Grimm_2023_trontinemab.R`, `Kyhl_2016_nalmefene.R`, `Soehoel_2022_tralokinumab.R`, `Xie_2019_agomelatine.R`, `PK_2cmt_mAb_Davda_2014.R`, `phenylalanine_charbonneau_2021.R`, `Chua_2025_mirikizumab.R`, `Jackson_2022_ixekizumab.R`, `Kotani_2022_astegolimab.R`, `Ma_2020_sarilumab_anc.R`, `Ma_2020_sarilumab_das28crp.R`, `Moein_2022_etrolizumab.R`, `Tiraboschi_2025_amlitelimab.R`, `Robbie_2012_palivizumab.R`, `Bajaj_2017_nivolumab.R`, `Quartino_2019_trastuzumab.R`, `Wang_2020_ontamalimab.R`, `Fau_2020_isatuximab.R`, `Okada_2025_rocatinlimab.R`, `Kunisawa_2014_olprinone.R`, `Xu_2020_daratumumab.R` (reference 78.6 kg; power exponents 0.451 on linear CL and 0.375 on V1), `Dirks_2008_cetuximab.R` (reference 60 kg; additive linear-deviation effect 0.0083 per kg on V1 of a two-compartment Michaelis-Menten model for cetuximab in SCCHN; source column WGT), `Struemper_2017_belimumab.R` (reference 67 kg; fixed allometric exponents 0.75 on CL and Q, 1.00 on Vc, 0.8 on Vp; baseline-only, source column BWT), `MedellinGaribay_2015_gentamicin.R` (linear (not allometric) weight scaling on both CL and Vc: CL = theta1 * BW + theta5 * (CRCL/75), Vc = theta2 * BW; no reference weight used because the scaling is linear, not divisive; source column BW; cohort mean 6.4 +/- 2.2 kg, infants 1-24 months), `Wahlby_2004_pefloxacin.R` (time-varying; reference 65 kg; linear-exp form on V with coefficient 0.014), `Decker_2024_baricitinib.R` (baseline-only, source column WTE = weight at entry; reference 74 kg carried from the upstream adult rheumatoid-arthritis model rather than taken from the pediatric cohort; exponents fixed at 0.75 on all clearance arms and 1 on both volumes).
- **Notes:** Universal. Verify time-varying vs. baseline-only against the source paper.

### AGE (**canonical for subject age**)
- **Description:** Subject age in years.
- **Units:** years
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a
- **Source aliases:** none.
- **Example models:** `Archary_2019_lamivudine.R`, `Budha_2023_tislelizumab.R`, `Chakraborty_2012_canakinumab.R`, `Chen_2020_luspatercept.R`, `Conrado_2014_alzheimer.R`, `Diepstraten_2013_propofol.R`, `Gandhi_2021_abatacept.R`, `Goel_2016_Sonidegib.R`, `Hennig_2013_tobra.R`, `Hong_2025_datopotamab.R`, `Ide_2020_elotuzumab.R`, `Koopman_2023_factorix.R`, `Kuchimanchi_2024_dostarlimab.R`, `Kunarajah_2017_doxorubicin.R`, `Kyhl_2016_nalmefene.R`, `Lahu_2010_roflumilast.R`, `Li_2006_meropenem.R`, `Li_2017_cediranib.R`, `Li_2019_abatacept.R`, `Lin_2024_casirivimab.R`, `Martinez_2019_alirocumab.R`, `Melhem_2022_dostarlimab.R`, `Mulyukov_2018_ranibizumab.R`, `NA_NA_tte_gompertz.R`, `NA_NA_tte_lognormal.R`, `Retlich_2015_linagliptin.R`, `Rosario_2015_vedolizumab.R`, `Svensson_2016_bedaquiline.R`, `Thakre_2022_risankizumab.R`, `Wu_2024_inotuzumab.R`, `Yassen_2025_asundexian.R`, `Yu_2022_ofatumumab.R`, `Zhong_2026_abatacept.R`, `Zhou_2021_belimumab.R`, `Zhu_2017_lebrikizumab.R`.
- **Notes:** Zhu 2017 normalizes as `AGE/40`.

### AGE_GE70 (**canonical for age >= 70 years indicator**)
- **Description:** 1 = subject is aged 70 years or older at baseline, 0 = subject is younger than 70 years. Time-fixed at study entry per subject. Used when the source paper reports an age effect as a threshold indicator (>= 70 y vs < 70 y) rather than as a continuous covariate on `AGE`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (< 70 years).
- **Source aliases:**
  - `AGE >= 70` -- derived from the continuous `AGE` column via `AGE_GE70 = as.integer(AGE >= 70)`; used in `Overgaard_2016_liraglutide.R` (Overgaard 2016 Fig. 2 / Table S1 covariate label "Age >= 70 years").
- **Example models:** `Overgaard_2016_liraglutide.R` (multiplicative log-scale effect on CL/F, coefficient -0.10 per Table S1).
- **Notes:** Sibling to the `ECOG_GE1` / `ECOG_GE2` binary threshold family; captures a paper-defined age band as a decomposed binary indicator rather than a continuous `AGE` term. When the source paper describes the age band as ">= 70 y" (as in Overgaard 2016), use `AGE_GE70`; for other thresholds (e.g., >= 65 y, >= 75 y) register a sibling canonical (`AGE_GE65`, `AGE_GE75`). The underlying continuous `AGE` may still be recorded alongside for downstream re-derivation.

### LBM (**canonical for lean body mass**)
- **Description:** Lean body mass.
- **Units:** kg
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a
- **Source aliases:**
  - `LBW` (lean body weight) -- synonym; same biological quantity (total body weight minus body fat). Hemophilia popPK literature typically uses `LBW` (Hume or James formula) where mAb / general literature uses `LBM`. Used in `Garmann_2017_BAY81_8973.R` (reference 51.1 kg).
- **Example models:** `Kyhl_2016_nalmefene.R` (reference 56.28 kg, exponent 0.626 on CL), `Garmann_2017_BAY81_8973.R` (alias `LBW`; reference 51.1 kg, exponents 0.610 on CL and 0.950 on Vc), `Schoemaker_2017_brivaracetam.R` (alias `LBW`; paediatric cohort, reference 50 kg adult typical value, fixed theoretical allometric exponents 0.750 on CL/F and 1.00 on V/F).

### FFM (**canonical for fat-free mass**)
- **Description:** Fat-free mass derived from body weight, height, and sex via the Janmahasatian et al. formula (Clin Pharmacokinet 2005;44:1051-1065).
- **Units:** kg
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(FFM / ref)^exponent`. Reference values observed: 40.69 kg (Zhou 2021 belimumab pooled adult+pediatric SLE), 45 kg (Aguiar 2021, Crohn's disease cohort median).
- **Source aliases:** none; `FFM` is the universal abbreviation.
- **Example models:** `Zhou_2021_belimumab.R` (reference 40.69 kg; exponents 0.673 on CL and 0.891 on V1), `Aguiar_2021_ustekinumab.R` (reference 45 kg; power exponents 0.598 on CL, 0.590 on Vc, 0.586 on Vp).
- **Notes:** Distinct from `LBM` (lean body mass) which is sometimes computed by the Boer or Hume formulae. When the source paper reports the body-composition formula it used (e.g., Janmahasatian for FFM), record it in `covariateData[[FFM]]$notes`. FFM is preferred over total body weight when scaling monoclonal-antibody PK because mAb distribution is largely confined to extracellular fluid; muscle / lean tissue tracks extracellular volume better than total weight in heavier patients.

### IBW (**canonical for ideal body weight**)
- **Description:** Ideal body weight in kg, typically derived from height and sex using the Devine formula or its variants. Time-fixed at baseline unless the source paper states otherwise. Used in size-normalisation of clearance / dose-rate in adult popPK models where the source paper reports IBW as the preferred size descriptor over total body weight (e.g., when overweight subjects pull clearance scaling away from the typical pattern).
- **Units:** kg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with a linear ratio `(IBW / ref)` for clearance / dose-rate normalisation. Reference values observed: 60 kg (Holford 1992 tacrine adult Alzheimer's population mean IBW).
- **Source aliases:**
  - `IBW` -- standard abbreviation used directly in Holford 1992.
- **Example models:** `Holford_1992_tacrine.R` (reference 60 kg; tacrine "clearance" relative to IBW = 60 enters as the dose-rate normalisation factor `(60 / IBW)` driving the tacrine effect-compartment input; the Holford-Peace Devine variant is documented in `covariateData[[IBW]]$notes`), `VanWart_2004_garenoxacin.R` (reference 64.2 kg; additive linear-deviation effect 0.764 mL/min per (IBW - 64.2) kg on CL/F, plus an in-model obesity flag derived as `WT > 1.3 * IBW` per Van Wart 2004's >130% IBW obesity definition), `Shoji_2011_pregabalin.R` (reference 62 kg; power-of-ratio scaling on CL/F (exponent 0.354) and V/F (exponent 0.819) per Shoji 2011 Table 3 final model; the IBW formula is the Devine-family variant cited as reference [12] of the paper), `Dirks_2008_cetuximab.R` (reference 64 kg; additive linear-deviation effect 0.0108 per kg on Vmax of a two-compartment Michaelis-Menten model for cetuximab in SCCHN; the source dataset supplied IBW pre-computed, so the Devine-family variant used is not identified in the paper).
- **Notes:** Specific scope until a second adult-popPK model ratifies the name; at that point promote to `general`. Per-model `covariateData[[IBW]]$notes` should record the formula the source paper used. The Holford-Peace 1992 variant is: men IBW (kg) = 52 + 0.75 * (height_cm - 152); women IBW (kg) = 49 + 0.67 * (height_cm - 152). The classic Devine 1974 formula is: men 50 + 2.3 * (height_in - 60); women 45.5 + 2.3 * (height_in - 60). Other variants (Robinson 1983, Miller 1983, Hamwi 1964) exist; the per-paper choice should be recorded so a user simulating against IBW can match the source's derivation. When the source dataset supplies IBW pre-computed, the column name is typically `IBW` directly. When only `HT` + `SEXF` are provided, the user must compute IBW externally using the source-paper formula before passing it to the model.

### HT (**canonical for body height at baseline**)
- **Description:** Subject body height at baseline. Time-fixed unless the source paper states otherwise.
- **Units:** cm
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with a linear-deviation form `(HT - ref)` or with a power-style scaling. Reference values observed: 167 cm (Naik 2016, vortioxetine adult MDD/GAD population median); 165 cm (Zhang 2018 flurbiprofen and Angeli 2016 healthy non-menopausal women).
- **Source aliases:**
  - `HGT` -- height (cm) abbreviation appearing in some NONMEM control streams.
  - `HEIGHT` -- spelled-out form (used by Angeli 2016).
- **Example models:** `Naik_2016_vortioxetine.R` (reference 167 cm; linear-additive effect 0.40 L/hr per (HT - 167) cm on CL/F, retained over weight and BMI in stepwise selection because it produced the larger reduction in CL IIV), `Zhang_2018_flurbiprofen.R` (reference 165 cm; linear-multiplicative effect `1 + theta_height * (HT - 165)` on the effect-compartment equilibration rate Ke alongside a paired linear-multiplicative WT effect), `Angeli_2016_iron_hepcidin.R` (reference 165 cm; power-law multiplier `(HT / 165)^32.70` on the hepcidin post-menses rebound parameter `krel_hep`; the very large exponent reflects the narrow height range across the cohort, 158-173 cm at the 10th-90th percentile), `Wu_2014_FEV1_asthma.R` (per-observation HT in cm enters directly into the exponential FEV1 disease-progression model: `FEV1 = exp(theta_age*AGE + theta_ht*HT - theta_int) + ...` with `theta_ht = 0.0169 1/cm`; paediatric population, ages 5-13 y).
- **Notes:** Height is sometimes retained as a size covariate when allometric scaling on weight performs less well; it is also an input to BSA, BMI, FFM, and LBM derivations, so a model that retains `HT` alongside one of those derived covariates should document the dependency in `covariateData[[HT]]$notes`. Promoted from `specific` to `general` on 2026-06-03 with the Angeli 2016 iron / hepcidin extraction (the third adult-popPK model to register HT, satisfying the original promotion condition documented when the canonical was introduced).

### BSA (**canonical for body surface area**)
- **Description:** Body surface area (typically computed by DuBois, Mosteller, or Haycock from height and weight).
- **Units:** m^2
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(BSA / ref)^exponent` or with linear centering `(1 + slope * (BSA - ref))`. Reference values observed: 1.70 m^2 (Yamada 2025, adult oncology), 1.11 m^2 (Park 2023, paediatric HSCT cohort median).
- **Source aliases:** none.
- **Example models:** `Yamada_2025_zolbetuximab.R` (reference 1.70 m^2; power scaling, exponents 1.06 on clearances and 0.968 on volumes), `Roepcke_2023_rezafungin.R` (reference 1.90 m^2 = the pooled-cohort median; separately estimated exponents 0.882 on CL, 1.56 on the central volume V1, and 1.17 on the shared peripheral volume V23; BSA replaced body weight and BMI, which the paper notes were highly correlated with it and would have fit comparably; the BSA formula used is unspecified in the source), `Park_2023_mycophenolic_acid.R` (reference 1.11 m^2; centered-linear scaling `Vd/F = 89.83 * (1 + 0.854 * (BSA - 1.11))` on the apparent volume of distribution in paediatric HSCT recipients; founding centered-linear example).
- **Notes:** Oncology mAbs dosed by BSA (mg/m^2) often use BSA in place of body weight for allometric-style scaling. Document the BSA computation formula (DuBois / Mosteller / Haycock) the source paper used; if unstated, record "unspecified."

### BMI (**canonical for body mass index**)
- **Description:** Body mass index at baseline.
- **Units:** kg/m^2
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with a linear-deviation form (`1 + e * (BMI - ref)`) or a power form (`(BMI / ref)^e`). Document the reference value in `covariateData[[BMI]]$notes`.
- **Source aliases:** none known.
- **Example models:** `Chua_2025_mirikizumab.R` (reference 24.75 kg/m^2; linear-deviation effect on logit of bioavailability), `NA_NA_lidocaine.R` (DDMODEL00000281; binary stratification at threshold 27.93 kg/m^2 adding +0.939 to the GX rate constant K30 in the BMI > 27.93 cohort), `Struemper_2017_belimumab.R` (kg/m^2, reference 24.7; power exponent -0.610 on Vc; baseline-only, source column BBMI).
- **Notes:** Universal clinical-trial demographic. Derived as `WT / (height_m)^2`; assume time-fixed at baseline unless the source paper states otherwise.

### BMIZ (**canonical for body-mass-index z-score (age- and sex-standardised)**)
- **Description:** Age- and sex-standardised body-mass-index z-score (number of standard deviations above or below the reference-population mean BMI for the subject's age and sex). Distinct from raw `BMI` (kg/m^2): `BMIZ` is unitless and centred at 0 in the reference population, so the reference value used in linear-deviation effects is 0 (not a population BMI in kg/m^2). Time-varying when the source paper carries a per-visit z-score; document baseline-vs-time-varying status in `covariateData[[BMIZ]]$notes`.
- **Units:** unitless (z-score; standard-deviation units)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with a linear-deviation form `(1 + e * (BMIZ - 0))` so the reference is 0 (population mean for the subject's age/sex). Effect coefficients are interpreted as fractional change per 1 z-score-unit deviation from the reference.
- **Source aliases:**
  - `BMI` -- when a paper uses the column name `BMI` for what is actually a z-score (e.g., the Harun 2019 NMTRAN control stream column `BMI` is documented in the dataset header as "body-mass index z-score"). The canonical column is `BMIZ`; the source-paper column name is recorded in `covariateData[[BMIZ]]$source_name`.
- **Example models:** `Harun_2019_cysticFibrosis.R` (time-varying per-visit BMI z-score; linear-deviation effect on baseline FEV1% predicted with reference 0 and coefficient +0.0382 per z-score unit).
- **Notes:** Distinct from `BMI` (raw kg/m^2 used in adult populations). Paediatric and adolescent studies routinely report BMI as a z-score relative to a growth reference (WHO 2007 Growth Reference for school-aged children, CDC 2000, etc.); document the reference standard the source paper used in `covariateData[[BMIZ]]$notes`. Specific scope until a second paediatric model ratifies the name; at that point promote to `general`.

### SEXF (**canonical for sex**)
- **Description:** Biological sex indicator, 1 = female, 0 = male.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (male).
- **Source aliases:**
  - `SEXM` (values inverted: `SEXF = 1 - SEXM`; effect coefficient sign and reference category both invert) -- used in `CarlssonPetri_2021_liraglutide.R`.
  - `SEX` with `"M"`/`"F"` strings -- derive `SEXF = as.integer(SEX == "F")`.
  - `SEX` with `1`=male / `2`=female numeric coding -- derive `SEXF = as.integer(SEX == 2)`. Used in `Netterberg_2017_docetaxel.R` and `NA_NA_miridesap.R` (DDMODEL00000262 source bundle; Sahota 2015 NONMEM convention).
  - `FEM` (1 = female, 0 = male; same orientation as canonical, no transformation) -- used in `Guiastrennec_2016_gastric_emptying.R`.
- **Example models:** `Zhu_2017_lebrikizumab.R` (canonical), `CarlssonPetri_2021_liraglutide.R` (alias `SEXM`), `Bajaj_2017_nivolumab.R` (male-indicator source; effect applied as `exp(coef * (1 - SEXF))` to preserve the paper's female-reference CL_REF / VC_REF), `Fau_2020_isatuximab.R` (exponential effect on Vc; reference category 0 = male), `Netterberg_2017_docetaxel.R` (multiplicative effect on baseline ANC: `BACOV *= (1 + theta * SEXF)`; source column `SEX` with 1 = male, 2 = female encoding, decomposed via `SEXF = as.integer(SEX == 2)`), `NA_NA_miridesap.R` (DDMODEL00000262 / Sahota 2015; multiplicative effect on baseline SAP via `SAP_BASE_ref * (1 + e_sexf_sap0 * SEXF)` with `e_sexf_sap0 = -0.30`; female baseline is ~30% lower than male), `Xu_2020_daratumumab.R` (additive shift on V1 `(1 + e_sexf_vc * SEXF)` with `e_sexf_vc = -0.205`: female V1 is 20.5% lower than male, reference category 0 = male), `Guiastrennec_2016_gastric_emptying.R` (multiplicative +40.7% strengthening of the caloric-feedback slope SLPCAL on gastric emptying in females; `SLPCAL_eff = SLPCAL * (1 + 0.407 * SEXF)`), `Wada_2023_sparsentan.R` (male-indicator source with a FEMALE reference subject, so the published +0.139 log-scale male effect on CL/F is applied as `exp(e_sexf_cl * (1 - SEXF))` to preserve the verbatim coefficient while keeping the canonical 1 = female orientation -- the same construction as `Bajaj_2017_nivolumab.R`; men had ~15% higher CL/F).
- **Notes:** When translating a model that used `SEXM`, flag the sign/reference-category inversion to the user.

### PREG (**canonical for pregnancy status indicator**)
- **Description:** 1 = pregnant, 0 = non-pregnant. Time-fixed per subject in trial cohorts that enrol pregnant and non-pregnant women in parallel; not a time-varying flag.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-pregnant).
- **Source aliases:** none known; source NONMEM control streams typically use `PREG` directly.
- **Example models:** `Birgersson_2019_artesunate.R` (multiplicative effect on dihydroartemisinin clearance; the published structural CLM = 190 L/h is reported with the source-paper reference category PREG = 1, so the model file applies the effect via `(1 + e_preg_cl_dha * (1 - PREG))` with `e_preg_cl_dha = -0.214` to preserve verbatim source values; non-pregnant women have ~21% lower CLM relative to pregnant women).
- **Notes:** Use this canonical for adult clinical-trial models that test a pregnancy-vs-non-pregnancy contrast (typical settings: malaria-in-pregnancy PK, antiviral-in-pregnancy PK). Trimester or gestational-age stratification within the pregnant cohort should use a separate canonical (e.g., gestational-age weeks via `GA` or a trimester indicator, ratified separately when needed). The canonical convention is reference category 0 (non-pregnant) following the broader pharmacology default; source papers that use the pregnant cohort as the reference (Birgersson 2019) preserve their published structural values via a `(1 - PREG)` form on the effect coefficient. Ratified canonically on 2026-05-07.

### CHILD (**canonical for child age-cohort indicator**)
- **Description:** 1 = subject is a child, 0 = not a child.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (not child, i.e., adult baseline).
- **Source aliases:**
  - `PED` -- used in the Schoemaker 2018 LEV / BRV pediatric extrapolation (DDMODEL00000239) as the pediatric-vs-adult indicator that gates the Markov-amplitude term, the overdispersion IIV, and the four pediatric offsets on log baseline rate / mixture / placebo / Emax / EC50.
- **Example models:** `CarlssonPetri_2021_liraglutide.R`, `Schoemaker_2018_levetiracetam.R` (DDMODEL00000239).
- **Notes:** Age-group indicator used alongside `ADOLESCENT`; paper's age cutoffs must be captured in `covariateData[[CHILD]]$notes`.

### ADOLESCENT (**canonical for adolescent age-cohort indicator**)
- **Description:** 1 = subject is an adolescent, 0 = not.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0.
- **Example models:** `CarlssonPetri_2021_liraglutide.R`.
- **Notes:** Paired with `CHILD`. Document age cutoffs.

### FM (**canonical for fat mass**)
- **Description:** Fat mass in kg derived as the complement of fat-free mass, FM = TBW - FFM, where FFM is computed by the Janmahasatian et al. (2005) formula (or, equivalently, by direct measurement via bioelectrical-impedance analysis / DXA when the source paper specifies it). Time-fixed at baseline.
- **Units:** kg
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(FM / ref)^exponent`. Reference values observed: 19 kg (Robarge 2017 efavirenz; cohort median calculated FM).
- **Source aliases:** `FAT`, `FATMASS`.
- **Example models:** `Robarge_2017_efavirenz.R` (reference 19 kg; exponent 1 fixed on V_p/F).
- **Notes:** Natural complement of `FFM`. Use both in the same model when the paper scales different disposition parameters by FFM vs FM (e.g., Robarge 2017 efavirenz: CL/F by FFM, V_p/F by FM, motivated by lipophilic partitioning into adipose tissue). When the source paper reports the body-composition formula it used (Janmahasatian for FFM is the most common; direct DXA / BIA also acceptable), record it in `covariateData[[FM]]$notes`.


### VISCERAL_ABDOMINAL_FAT (**canonical for visceral abdominal fat area from imaging**)
- **Description:** Visceral abdominal fat area measured from a single computed-tomographic (CT) slice at the L2 / L3 vertebral level (standard IVGTT / metabolic-imaging protocol; Jensen et al. 1995). Captures the metabolically active visceral adipose-tissue depot that drives insulin resistance independently of subcutaneous fat. Time-fixed at baseline.
- **Units:** cm^2 (single CT slice). Document per-model via `covariateData[[VISCERAL_ABDOMINAL_FAT]]$units` when a paper uses a different anatomical level (L3 / L4, L4 / L5) or a different summary form (volumetric L1-L5 cm^3, MRI-derived).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with mean-centred linear-in-log form `log(SI) = log(SI_ref) + theta * (VISCERAL_ABDOMINAL_FAT - ref)`. Reference values observed: 141.8 cm^2 / CT-slice (Denti 2010 Table 1 pooled-population mean across 204 healthy adults aged 18-87).
- **Source aliases:**
  - `VAF` -- universal abbreviation in the IVGTT / glucose-metabolism literature (Denti 2010, Basu 2003, Basu 2006). The canonical column is `VISCERAL_ABDOMINAL_FAT`; the source paper's column name is recorded in `covariateData[[VISCERAL_ABDOMINAL_FAT]]$source_name`.
- **Example models:** `Denti_2010_glucoseMinimal.R` (introduces canonical; reference 141.8 cm^2 / CT-slice; linear-in-log effect coefficient -0.00208 per cm^2 deviation on insulin sensitivity `lsi`).
- **Notes:** Specific scope because the value reported is tied to the imaging modality (single-slice CT at L2 / L3 in the founding example). Volumetric CT, multi-slice averages, and MRI-derived volumetric adiposity from future papers can ratify under the same canonical with the assay method and units recorded per-model in `covariateData[[VISCERAL_ABDOMINAL_FAT]]$notes`. Distinct from `BODYFAT_PCT` (whole-body adiposity fraction by DEXA) and from `FFM` / `LBM` (fat-free mass / lean body mass). The H3-name uses underscores to preserve readability; future authors may abbreviate the source column as `VAF` in their own datasets and declare `source_name = "VAF"`. Operator-ratified 2026-06-07 via the Denti 2010 extraction sidecar.


### BODYFAT_PCT (**canonical for percent total body fat (whole-body adiposity fraction)**)
- **Description:** Percent total body fat, measured as the fraction of total body mass classified as fat tissue by an imaging assay (typically dual-energy X-ray absorptiometry, DEXA). Expressed as a percentage (0-100). Time-fixed at baseline unless the source paper states otherwise. Captures whole-body adiposity, distinct from regional adiposity (visceral, abdominal, subcutaneous) and from fat-free mass.
- **Units:** % (percent; 0 = no body fat, 100 = all body fat). Document per-model via `covariateData[[BODYFAT_PCT]]$units` when a paper uses a fractional 0-1 scale.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with mean-centred linear-in-log form `log(VD) = log(VD_ref) + theta * (BODYFAT_PCT - ref)`, or as the direct determinant of an adipose-compartment volume in whole-body PBPK. Reference values observed: 32.39 % (Denti 2010 Table 1 pooled-population mean across 204 healthy adults); 25 % (Levitt PKQuest standard human, i.e. 17.5 kg adipose of a 70 kg total, used by `Pei_2023_tacrolimus_pbpk.R`).
- **Source aliases:**
  - `%TBF` -- paper symbol in the IVGTT / glucose-metabolism literature (Denti 2010, Basu 2003). The leading `%` character is dataset-incompatible (NMTRAN / R column-name rules); the canonical column is `BODYFAT_PCT` and the paper's symbol is recorded in `covariateData[[BODYFAT_PCT]]$source_name`.
  - `PTBF` -- numeric-safe rendering of the paper symbol used in some derived datasets.
- **Example models:** `Denti_2010_glucoseMinimal.R` (introduces canonical; reference 32.39 %; linear-in-log effect coefficient -0.0101 per percentage-point deviation on glucose distribution volume `lvd`), `Pei_2023_tacrolimus_pbpk.R` (whole-body PBPK organ-volume scaling: the adipose compartment weight is set directly as `WT * BODYFAT_PCT / 100` and the remaining lean organ weights and blood flows scale by `WT * (1 - BODYFAT_PCT / 100) / 52.5 kg`; Pei 2023 Methods 2.5 names "the proportion of adipose tissue" as one of the two determinants of organ volume and Table 5 reports its local sensitivity).
- **Notes:** Promoted from `Scope: specific` to `Scope: general` on 2026-08-05 alongside the Pei 2023 tacrolimus PBPK extraction. The original specific scope reflected the founding example's DEXA assay, but percent total body fat is an assay-independent body-composition quantity and the second registered use (a whole-body PBPK adipose-compartment volume) is unrelated to how the value was measured; the assay method belongs in the per-model `covariateData[[BODYFAT_PCT]]$notes`, not in the scope. Bioimpedance- or skinfold-derived `%BF` from future papers ratifies under the same canonical on the same terms. Distinct from `FFM` (fat-free mass in kg) and from `VISCERAL_ABDOMINAL_FAT` (regional visceral adiposity in cm^2 / CT slice). The negative effect coefficient on glucose distribution volume captures the physiological observation that glucose distributes preferentially into lean rather than adipose tissue, so per-kg distribution volume decreases as fat fraction rises. Operator-ratified 2026-06-07 via the Denti 2010 extraction sidecar.


### WT_BASE (**canonical for per-subject baseline body weight (time-fixed, paired with time-varying WT)**)
- **Description:** Per-subject baseline body weight, time-fixed. Used when a source paper applies Wahlby 2004's extended covariate-model split (Br J Clin Pharmacol 2004;58(4):367-377) and the data column carries the subject's baseline weight as a constant alongside a time-varying `WT` column. Operationally, `WT_BASE` is equal to `WT` at the subject's first observation and remains constant for all subsequent records; the within-subject delta is computed in `model()` as `(WT - WT_BASE)`.
- **Units:** kg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- typically used as `(WT_BASE - ref)` in an exponential or linear-deviation effect on a structural parameter, where `ref` is the cohort median baseline weight.
- **Source aliases:**
  - `BWT` (baseline weight) -- Wahlby 2004 source-column convention; used in `Wahlby_2004_pefloxacin.R`.
- **Example models:** `Wahlby_2004_pefloxacin.R` (reference 65 kg; saturating "up to median WT" qualifier from Wahlby 2004 Methods plateaus the effect above 65 kg, encoded as `min(WT_BASE, 65) - 65`).
- **Notes:** Specific scope because the BCOV/DCOV decomposition pattern is paper-defined: `WT_BASE` exists conceptually as a per-subject snapshot of baseline weight that the original modeller chose to enter separately from the time-varying weight column. Promote to `general` if a second paper ratifies the same baseline-weight column with consistent semantics. Distinct from time-fixed adult-cohort weight (where the per-subject weight is just constant by data design); use `WT_BASE` only when the source paper deliberately separates the BCOV from the time-varying COV. Ratified canonically alongside the Wahlby 2004 extraction.


### BSA_BASE (**canonical for per-subject baseline body surface area (time-fixed)**)
- **Description:** Per-subject baseline body surface area, time-fixed. Used when a source paper enters the subject's baseline BSA as a constant alongside a time-varying BSA column under Wahlby 2004's extended covariate-model decomposition (Br J Clin Pharmacol 2004;58(4):367-377). Time-fixed at the subject's first observation.
- **Units:** m^2
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- typically used as a linear scaling factor on a volume parameter.
- **Source aliases:**
  - `BBSA` (baseline BSA) -- Wahlby 2004 source-column convention; used in `Wahlby_2004_gentamicin.R`.
- **Example models:** `Wahlby_2004_gentamicin.R` (replaces time-varying BSA in the final-model V1 equation V1 = 8.63 * BSA_BASE * (ALB/34)^-0.41 because BBSA was the better predictor than time-varying BSA, RSE 2.5% vs 110% on delta-BSA).
- **Notes:** Specific scope because the BCOV/DCOV split is a paper-defined modelling choice (Wahlby 2004 demonstrated that BBSA was the relevant predictor for gentamicin V1 because delta-BSA was uninformative). Promote to `general` if a second paper ratifies the baseline-BSA column with consistent semantics. Ratified canonically alongside the Wahlby 2004 extraction.

### BLOOD_GROUP_O (**canonical for ABO blood group O indicator**)
- **Description:** 1 = subject has ABO blood group O (either O-positive or O-negative Rh subtype); 0 = subject has ABO blood group A, B, or AB (non-O). Time-fixed per subject. Enters factor VIII (FVIII) and von Willebrand factor (VWF) population PK models as a surrogate for baseline VWF plasma concentration: blood group O individuals have ~25% lower circulating VWF than non-O individuals due to accelerated VWF clearance by the ASGR / CLEC4M receptor system (VWF's ABH glycan antigens are recognized differently by clearance receptors depending on the ABO glycosyltransferase phenotype). Because VWF binds and stabilizes circulating FVIII, lower VWF in blood group O individuals translates to faster FVIII clearance.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-O ABO blood group: A, B, or AB).
- **Source aliases:**
  - `blood group` (paper-prose 0/1 indicator, 1 = O) -- used in `Hazendonk_2016_factor_viii.R` (Hazendonk 2016 Table 5: `1.26 ^ blood_group` on CL, so blood group O subjects have 26% higher FVIII clearance than non-O).
- **Example models:** `Hazendonk_2016_factor_viii.R` (multiplicative effect on CL: `(1 + 0.26 * BLOOD_GROUP_O)` -- equivalent to `1.26 ^ BLOOD_GROUP_O` when binary; reference category 0 = non-O ABO blood group).
- **Notes:** General scope because ABO blood group is a universally recorded clinical variable that recurs in FVIII / VWF / VWF-related-protein PK models (Hazendonk 2016 Discussion cites the well-established 25% lower baseline VWF in blood group O as the mechanism; also documented in Gill 1987, Miller 1985). The canonical is orthogonal to `VWF` (the actual plasma VWF concentration): when both are available, papers typically retain VWF and drop BLOOD_GROUP_O because VWF captures the effect more directly (e.g., Nestorov 2014 uses VWF directly; Hazendonk 2016 uses BLOOD_GROUP_O because VWF measurements were only available for ~50% of the cohort). Founding example: Hazendonk 2016. Ratified canonically on 2026-07-11 alongside the Hazendonk 2016 perioperative FVIII extraction.


## Pediatric / maturation

### PAGE (**canonical for postmenstrual age**)
- **Description:** Postmenstrual age in months (`GA_weeks / 4.35 + postnatal_months`). Time-varying.
- **Units:** months
- **Type:** continuous
- **Scope:** general
- **Example models:** `Clegg_2024_nirsevimab.R`, `Robbie_2012_palivizumab.R`.

### PNA (**canonical for postnatal age**)
- **Description:** Postnatal age (chronological since birth). Time-varying.
- **Units:** months
- **Type:** continuous
- **Scope:** general
- **Source aliases:**
  - `PNA` -- used in Zhao 2018 (paper Methods 'Population pharmacokinetic-pharmacogenetic modelling' and Table 2 report PNA in DAYS; the canonical PNA carries months, so Zhao 2018's `F_PNA = (PNA_days / 38)^0.472` is reparameterised inside `model()` as `F_PNA = (PNA_months / 1.249)^0.472` using the conversion `PNA_months = PNA_days / 30.4375` and reference `1.249 months = 38 days / 30.4375`).
- **Example models:** `Hu_2026_clesrovimab.R`, `Zhao_2018_omeprazole.R` (power effect on the formation clearance of 5-hydroxy-omeprazole: `(PNA / 1.249)^0.472`; PNA reference 1.249 months / 38 days from Zhao 2018 Table 2 cohort median).

### GA (**canonical for gestational age at birth**)
- **Description:** Gestational age at birth. Time-fixed per subject.
- **Units:** weeks
- **Type:** continuous
- **Scope:** general
- **Example models:** `Hu_2026_clesrovimab.R`, `Clegg_2024_nirsevimab.R` (folded into PAGE).

### WT_BIRTH (**canonical for birth weight**)
- **Description:** Body weight at birth. Time-fixed per subject. Distinct from current body weight (`WT`), which is time-varying.
- **Units:** kg
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with linear-deviation `(1 + e * (WT_BIRTH - ref))` or power scaling. Reference value observed: 2.59 kg (Voller 2017 newborn cohort).
- **Source aliases:**
  - `BWEIGHT` -- used in `Voller_2017_phenobarbital.R` (Voller 2017 source data column for birth weight in kg).
  - `Bwb` -- used in `Wu_2025_paracetamol.R` (Wu 2025 source-paper symbol for birth weight in kg; same biological quantity, no value transformation).
- **Example models:** `Voller_2017_phenobarbital.R` (linear-deviation effect on CL: `clbw = 1 + 0.369 * (WT_BIRTH - 2.59)`), `Wu_2025_paracetamol.R` (power-law scaling of PTNA CL_birth and GFR_birth terms with reference 1.75 kg).
- **Notes:** Time-fixed at birth; characterises pre-/term-newborn cohorts. Pairs with `GA` (gestational age at birth) when both are reported. The conventional clinical-PK abbreviation `BWT` is intentionally NOT used as the canonical name because it is already used across the codebase (Gandhi 2021, Li 2019, Chen 2022, Wojciechowski 2022, Lu 2019) as a source-name alias for body weight (`WT`). The `WT_BIRTH` form keeps the `WT` root consistent with the existing body-weight canonical and avoids the `BWT` ambiguity.

### AGE_DPF (**canonical for zebrafish-larval age in days post-fertilization**)
- **Description:** Age of a zebrafish (Danio rerio) larva in days post-fertilization (dpf). Time-fixed per subject in destructive-sampling designs (each larva is harvested at exactly one observation, so its dpf is fixed at the value assigned at exposure-start).
- **Units:** days post-fertilization (dpf)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used in van Wijk 2019 with a step-form effect on the absorption rate (`IF (AGE_DPF > 3) k12 = k12_3 * (1 + e_age_dpf_k12)`) and a per-day power-form effect on the elimination rate (`k25 = k25_3 * (1 + e_age_dpf_k25)^(AGE_DPF - 3)`). Reference age is 3 dpf (the youngest cohort in the study).
- **Source aliases:**
  - `AGE` -- van Wijk 2019 NONMEM column. Renamed to canonical `AGE_DPF` because the human-PK canonical `AGE` denotes subject age in years; the zebrafish dpf semantic is incompatible and would silently corrupt any future model that mixed them. Same orientation, no value transformation.
- **Example models:** `vanWijk_2019_paracetamol.R`.
- **Notes:** Specific scope because the meaning is bounded to zebrafish-larval-development PK studies. Distinct from canonical `AGE` (human age in years), `PNA` (postnatal age in months), `PAGE` (postmenstrual age in months), and `GA` (gestational age in weeks) -- none of those are appropriate for a non-mammalian organism whose developmental clock is anchored at fertilization rather than birth. Integer values 3, 4, 5 in the van Wijk 2019 dataset, but treated as a continuous covariate in the elimination-rate power form. Future zebrafish-PK or other non-mammalian-developmental-age models should reuse this canonical only when the covariate is indeed dpf-anchored; other developmental-time conventions (e.g., somite-stage, hpf, dph) would warrant separate canonicals. Ratified canonically on 2026-05-07.

### ASPHYXIA (**canonical for perinatal asphyxia indicator (low 5-minute Apgar score)**)
- **Description:** Binary indicator of perinatal asphyxia at birth defined by a 5-minute Apgar score < 5; `1` = 5-minute Apgar score was less than 5 (i.e., the neonate met the paper's perinatal-asphyxia criterion), `0` = 5-minute Apgar score was 5 or higher. Time-fixed per subject (an event at birth, not a time-varying physiologic state). Used by neonatal popPK models that report a structural-PK shift in subjects who experienced perinatal asphyxia relative to those who did not.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no perinatal asphyxia; 5-minute Apgar >= 5). Effect form in Grasela 1985 is a linear-deviation increment on the volume of distribution: `V = V_typical * (1 + e_asphyxia_vc * ASPHYXIA)` with `e_asphyxia_vc = 0.13` (+13% V in subjects with 5-minute Apgar < 5; p < 0.05).
- **Source aliases:**
  - None registered (Grasela 1985 abstract names the covariate `asphyxia` in prose; the abstract does not report the source NONMEM column label).
- **Example models:** `Grasela_1985_phenobarbital.R` (Grasela & Donn 1985; preterm-neonate phenobarbital popPK; +13% on V when 5-minute Apgar < 5; no detected effect on CL).
- **Notes:** Distinct from the continuous Apgar score itself (which has not been registered as a canonical because no model in nlmixr2lib uses the continuous form as of this entry). The < 5 cutoff at 5 minutes is the perinatal-asphyxia convention Grasela 1985 used; future papers that adopt a different Apgar cutoff (e.g., < 7 at 1 minute, or a different time point) should register a separate canonical (e.g., `APGAR1_LT7`) rather than reusing `ASPHYXIA` with relaxed semantics. Ratified canonically alongside the Grasela 1985 phenobarbital extraction.

## Mother-infant dyad partner

Ratified 2026-08-06 with the library's first lactation / mother-to-infant transfer model (`Wattanakul_2024_primaquine.R` / `Wattanakul_2024_primaquine_motherinfant.R`; operator sidecar `oare_PMC11078975` request-001 / response-001, question q3, option A).

In a dyad model the modelled SUBJECT is the mother, so the general `WT`, `AGE`, and `PAGE` canonicals already denote her. Describing the breastfed infant with those same names would put two different people's demographics on one record under one canonical name, and nothing in the column name would say which person it referred to. The `_INFANT` suffix marks a column as belonging to the dyad PARTNER rather than to the modelled subject. It parallels the `infant_<canonical>` compartment namespace in `compartment-names.md`.

Use these columns only in a genuine dyad model, i.e. one that carries maternal and infant quantities simultaneously. A standalone paediatric popPK model whose subject is the infant uses the plain `WT` / `AGE` / `PAGE` canonicals, because there is no second person to disambiguate against.

### WT_INFANT (**canonical for breastfed-infant body weight in a mother-infant dyad model**)
- **Description:** Body weight of the breastfed infant paired with the modelled lactating subject. Distinct from `WT`, which in a dyad model is the mother's body weight.
- **Units:** kg
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used both to size the maternal breast-milk compartment (milk ingested per feed = daily milk intake per kg x `WT_INFANT` / feeds per day) and, in a full dyad model, to scale the infant's clearances and volumes allometrically from the mother's, `(WT_INFANT / WT)^0.75` on clearance and `(WT_INFANT / WT)` on volume.
- **Source aliases:**
  - `INFWT` -- NONMEM `$INPUT` column name in `Wattanakul_2024_primaquine.R` / `Wattanakul_2024_primaquine_motherinfant.R` (`INFWT ; INFANT BODY WEIGHT (KG)`); same quantity in kg, no value transformation.
- **Example models:** `Wattanakul_2024_primaquine.R` (enters only through the breast-milk compartment volume `V_M = (0.15 L/kg/day * WT_INFANT) / feeds-per-day`; cohort median 6.8 kg, range 4.13-10.8, giving milk-compartment volumes of 0.062-0.162 L), `Wattanakul_2024_primaquine_motherinfant.R` (additionally scales the infant's apparent clearances and volumes from the mother's estimates).
- **Notes:** Baseline in the founding models, but time-varying in principle -- infant weight changes materially over a multi-month lactation period, and dyad simulations that sweep infant age (Wattanakul 2024 uses WHO weight-for-age curves with z-scores -3 to +3 from birth to 24 months) pair each `AGE_INFANT` with the corresponding `WT_INFANT`. Verify baseline vs. time-varying against the source paper.

### AGE_INFANT (**canonical for breastfed-infant postnatal age in a mother-infant dyad model**)
- **Description:** Postnatal age of the breastfed infant paired with the modelled lactating subject. Distinct from `AGE`, which in a dyad model is the mother's age.
- **Units:** months
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- typically enters an enzyme-maturation function on the infant's clearance. When the maturation function is written in postmenstrual age, the model derives PMA internally by adding an assumed term gestation to `AGE_INFANT` rather than requiring a separate column, because individual gestational ages are rarely reported in lactation studies; the assumed gestation must be stated in the model file.
- **Source aliases:**
  - `INFAGE` -- NONMEM `$INPUT` column name in `Wattanakul_2024_primaquine_motherinfant.R` (`INFAGE ; INFANT AGE (MONTHS)`); same quantity in months, no value transformation.
- **Example models:** `Wattanakul_2024_primaquine_motherinfant.R` (drives monoamine-oxidase-A maturation on the infant's primaquine clearance, `MF = PMA / (TM50 + PMA)` with `PMA = AGE_INFANT + 9.2` months for assumed full-term gestation of 40 weeks and `TM50 = 7.6` months; cohort median 5.0 months, range 1.6-21.7).
- **Notes:** Months, not years -- lactation cohorts are dominated by infants under two years old, where a year scale loses resolution, and paediatric maturation functions are conventionally written in months or weeks of postmenstrual age. This matches the units of the general `PAGE` canonical. Distinct from `PAGE`: `AGE_INFANT` is postnatal, so a model that needs postmenstrual age must add the gestational term explicitly.

## Nutritional status

### MAL_NOURISH (**canonical for malnutrition status indicator**)
- **Description:** 1 = subject is malnourished at study entry per a paper-defined anthropometric criterion (e.g., WHO height-for-age and weight-for-age Z-scores both < -2.0 in Tikiso 2021); 0 = not malnourished. Subject-level baseline indicator; the time-decaying recovery during nutritional supplementation is carried by the paired `T_NUT_SUPP` column rather than as a time-varying value of `MAL_NOURISH`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not malnourished).
- **Source aliases:**
  - `MAL` -- used in `Tikiso_2021_abacavir.R` (the dataset's paper-defined indicator, 1 = malnourished, 0 = not malnourished).
  - `NUT` -- used in `Catalan-Latorre_2018_taurine_rat.R` (the dataset's paper-defined indicator, 1 = undernourished UN, 0 = well-nourished WN).
- **Example models:** `Tikiso_2021_abacavir.R` (gates the time-decaying malnutrition effect: `mal_decay = MAL_NOURISH * exp(-T_NUT_SUPP * log(2) / 12.2)`, which then drives multiplicative shifts of `+115%` on F and `-64%` on CL at the start of nutritional supplementation, decaying with a 12.2-day half-life).
  - `Tikiso_2021_abacavir.R` (gates the time-decaying malnutrition effect: `mal_decay = MAL_NOURISH * exp(-T_NUT_SUPP * log(2) / 12.2)`, which then drives multiplicative shifts of `+115%` on F and `-64%` on CL at the start of nutritional supplementation, decaying with a 12.2-day half-life).
  - `Catalan-Latorre_2018_taurine_rat.R` (static baseline indicator -- no `T_NUT_SUPP` pairing because there was no nutritional rehabilitation phase in the preclinical Wistar-rat study; `MAL_NOURISH = 1` reduces the saturable tubular secretion Vmax of taurine by 9.4% relative to well-nourished animals).
- **Notes:** Specific scope because the malnutrition definition (WHO Z-score thresholds in Tikiso 2021; end-of-adaptation body weight below 80% of the well-nourished mean AND serum albumin below 23 g/L in Catalan-Latorre 2018; mid-upper arm circumference, weight-for-height vs height-for-age, etc.) is paper-defined; per-model `covariateData[[MAL_NOURISH]]$notes` must document the criterion used. Pairs with `T_NUT_SUPP` (days on nutritional supplementation) when the model uses a time-decaying recovery function; otherwise `MAL_NOURISH` alone serves as a static baseline indicator. Distinct from generic body-weight Z-scores (which are continuous anthropometric metrics rather than a binarised malnutrition indicator).

### T_NUT_SUPP (**canonical for time on nutritional supplementation**)
- **Description:** Time elapsed since the start of nutritional / refeeding supplementation, in days. 0 at the start of supplementation; increases with time. Used by population PK models that describe the time-varying recovery of PK parameters during nutritional rehabilitation in malnourished cohorts.
- **Units:** days
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as the argument of an exponential decay function `exp(-T_NUT_SUPP * log(2) / T_half)` whose half-life `T_half` is an estimated parameter (12.2 days in Tikiso 2021).
- **Source aliases:**
  - `TNUTRI` -- used in `Tikiso_2021_abacavir.R` (the dataset's paper-defined column for days since start of nutritional supplementation; same orientation as the canonical, 0 = start of supplementation, increasing with time on supplementation).
- **Example models:** `Tikiso_2021_abacavir.R` (paired with `MAL_NOURISH`; drives the recovery decay `exp(-T_NUT_SUPP * log(2) / 12.2)` of the malnutrition effect on F and CL).
- **Notes:** Specific scope because the underlying recovery dynamics are tied to the nutritional-rehabilitation protocol of the source study. For non-malnourished subjects (`MAL_NOURISH == 0`) the value is irrelevant because the malnutrition effect is gated by `MAL_NOURISH`; supply 0 as a default. For fully-recovered malnourished subjects, supply a large value (e.g., `>= 100` days, well beyond the 12.2-day Tikiso 2021 half-life) so the decay function reaches near zero and the effect vanishes.

### DRINK_OGTT (**canonical for oral-glucose-tolerance-test-only drink indicator**)
- **Description:** Binary indicator that the postprandial test drink is glucose-only (oral glucose tolerance test, OGTT). 1 = the drink is glucose-only with no fat content (e.g., 25 / 75 / 125 g OGTT); 0 = otherwise (water, or any drink containing fat). Per-occasion (per-test-drink-administration) covariate -- a single subject in a crossover challenge protocol receives different drink types across occasions, so the indicator varies per dose-record rather than per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (water or fat-containing drink). Mutually exclusive with `DRINK_FAT` -- both indicators cannot be 1 simultaneously.
- **Source aliases:** paper narrative "glucose solution" / "OGTT" cohort labels driving the gastric-emptying-onset T50OGTT selection in Guiastrennec 2016.
- **Example models:** `Guiastrennec_2016_gastric_emptying.R` (selects the OGTT-specific half-onset time T50OGTT = 15.7 min for the gastric-emptying delay Hill function; water is recovered when DRINK_OGTT = DRINK_FAT = 0 with the onset factor pinned to 1).
- **Notes:** Specific scope because the OGTT / fat-containing partition is tied to the Guiastrennec 2016 postprandial-challenge design (Studies B and C OGTT arms). Set to 1 for Study B's 25 / 75 / 125 g OGTT drinks and the Study C 75 g OGTT arm; set to 0 for Study A water and for all fat-containing drinks. Pairs with `DRINK_FAT`: the two indicators jointly select the appropriate gastric-emptying-delay T50 parameter (T50OGTT vs T50Fat) for the Hill onset function. Ratified canonically alongside the Guiastrennec 2016 gastric-emptying / CCK / GBE extraction.

### DRINK_FAT (**canonical for fat-containing test-drink indicator**)
- **Description:** Binary indicator that the postprandial test drink contains fat (any nonzero fat content). 1 = the drink contains fat (e.g., the Study C low / medium / high-fat isocaloric drinks, or the Study D medium-high-fat drink); 0 = otherwise (water or glucose-only OGTT drinks). Per-occasion covariate -- a single subject in a crossover challenge protocol receives different drink types across occasions.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (water or glucose-only drink). Mutually exclusive with `DRINK_OGTT` -- both indicators cannot be 1 simultaneously.
- **Source aliases:** paper narrative "low-fat" / "medium-fat" / "high-fat" / "medium-high-fat" cohort labels driving the gastric-emptying-onset T50Fat selection in Guiastrennec 2016.
- **Example models:** `Guiastrennec_2016_gastric_emptying.R` (selects the fat-specific half-onset time T50Fat = 23.1 min for the gastric-emptying delay Hill function).
- **Notes:** Specific scope because the fat-containing partition is tied to the Guiastrennec 2016 postprandial-challenge design. Pairs with `DRINK_OGTT`: the two indicators jointly select the appropriate gastric-emptying-delay T50 parameter (T50OGTT vs T50Fat) for the Hill onset function. Ratified canonically alongside the Guiastrennec 2016 gastric-emptying / CCK / GBE extraction.

### GLN_BL (**canonical for baseline (pre-dose) endogenous plasma L-glutamine concentration**)
- **Description:** Per-subject pre-dose plasma concentration of endogenous L-glutamine, measured before the first study dose. L-glutamine is the most abundant free amino acid in plasma and is present at several hundred umol/L in every subject, so an exogenous oral dose is superimposed on a large endogenous pool. In popPK models of *administered* L-glutamine the baseline plays a dual role: it is subtracted from every measured concentration to isolate the exogenous increment being modelled (so it defines the zero of the modelled concentration scale), and it enters the clearance covariate equation, because a higher endogenous pool competes with exogenous glutamine for metabolism and other elimination pathways.
- **Units:** umol/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a power (Eq. 5) term normalised to a standard baseline, `(GLN_BL / GLN_BL_standard)^theta`. Sadaf 2024 uses `GLN_BL_standard` = 683 umol/L. Record the per-model normalisation constant in `covariateData[[GLN_BL]]$notes`, since it is a modelling choice rather than a physiological constant.
- **Source aliases:**
  - `Glu_BSL` -- used in `Sadaf_2024_glutamine.R` (Sadaf 2024 Table 2; the paired standard value is written `Glu_BSLstardard` in the table footnote, a typographical variant of "standard"). Note the `Glu` stem here abbreviates *glutamine*, not glutamate: Sadaf 2024 measured both analytes and screened baseline L-glutamate separately (it was not retained), so a future extraction of that paper's sibling analyte must not reuse this alias.
- **Example models:** `Sadaf_2024_glutamine.R` (power effect on apparent oral clearance: `cl = 78.5 * (WT/70)^0.75 * (GLN_BL/683)^-0.96`, Sadaf 2024 Table 2 theta2 = -0.96; the same per-subject value is the quantity subtracted from each measurement before fitting).
- **Notes:** Specific scope until a second L-glutamine or amino-acid supplementation model ratifies the canonical. Member of the `<ANALYTE>_BL` baseline-concentration family (`HGB_BL`, `FERRITIN_BL`, `INS_BL`, `PKK_BL`, `TRACP5B_BL`, `TRAST_BL`); the closest structural precedent is `AT_BL_UDL`, which likewise carries the endogenous baseline of the very substance being dosed and likewise serves both as a power covariate normalised to a reference and as the endogenous anchor of the observation scale. Distinct from `AA_*` general amino-acid panel entries (none registered at the time of writing) and from the *dosed* amount, which is `DOSE_GLN_GKG`. Time-fixed per subject in Sadaf 2024, which verified that the pre-dose baseline did not drift across the four study visits; a study in which the endogenous pool does shift over time should carry it as time-varying and say so in the model's `covariateData` notes. Ratified canonically alongside the Sadaf 2024 L-glutamine sickle-cell-disease extraction.

## Pregnancy / hormonal status

### EGA (**canonical for maternal estimated gestational age during pregnancy**)
- **Description:** The mother's estimated gestational age at the time of an observation or dose, in weeks. Time-varying across a pregnancy (though usually effectively constant within a single treatment cycle). `EGA = 0` is the non-pregnant anchor: semi-physiological pregnancy models are constructed so that every gestational relation collapses to its non-pregnant value at `EGA = 0`, which makes the covariate a continuous pregnant-vs-non-pregnant contrast rather than a within-pregnancy-only stratifier.
- **Units:** weeks
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- `EGA = 0` is the non-pregnant reference value, not a category. Enters as the argument of the gestational physiology polynomials (serum albumin, alpha-1-acid glycoprotein, GFR, haematocrit, CYP3A4 activity, plasma volume, extracellular water, total body water), each of which is written so that it returns the non-pregnant value at `EGA = 0`.
- **Source aliases:**
  - `EGA` -- Janssen 2023 symbol for estimated gestational age; same orientation and units, no transformation.
  - `GAWK`, `GESTAGE`, `GAWEEK` -- common NONMEM column spellings for maternal gestational age in weeks; same orientation, no transformation.
- **Example models:** `Janssen_2023_docetaxel.R`, `Janssen_2023_paclitaxel.R`, `Janssen_2023_doxorubicin.R`, `Janssen_2023_epirubicin.R` (semi-physiological enriched cytotoxic models in which `EGA` drives all ten gestational physiology relations reprinted as Janssen 2023 Eqs 1-17).
- **Notes:** Distinct from `GA` (**gestational age at birth**), which is a time-fixed maturity descriptor of the **neonate** used in paediatric models -- `EGA` describes the **mother** and varies over her pregnancy. Also distinct from `PREG` (binary pregnancy status) and from `TPP` (time postpartum, which is 0 during pregnancy and increases after delivery). `PREG` gives a step contrast; `EGA` gives the continuous within-pregnancy trajectory that `PREG` explicitly defers to (see the `PREG` entry's note that "trimester or gestational-age stratification within the pregnant cohort should use a separate canonical ... ratified separately when needed"). When a source paper reports trimesters rather than weeks, record the week value used per trimester in `covariateData[[EGA]]$notes` rather than introducing a trimester-indicator canonical.

### TPP (**canonical for time postpartum (time after delivery)**)
- **Description:** Time elapsed since delivery, in weeks. 0 during pregnancy and at delivery; increases after delivery. Used by popPK models that describe the time-varying postpartum recovery of pregnancy-induced PK changes (e.g., renal blood flow, hepatic enzyme activity) that return gradually to the prepregnant baseline over weeks to months rather than instantaneously at delivery.
- **Units:** weeks
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- enters as the argument of a sigmoidal recovery function `(TPP^gamma) / (TPP^gamma + T50^gamma)` whose `T50` (weeks-to-50%-recovery) and `gamma` (shape parameter) are estimated. For pregnant visits (`TPP = 0`) the sigmoid evaluates to 0 and the pregnancy-state PK parameter is unchanged; for far-postpartum visits (`TPP >> T50`) the sigmoid approaches 1 and the parameter reaches its asymptotic non-pregnant value.
- **Source aliases:**
  - `TPP` -- de Kock 2017 NONMEM column for time after delivery in weeks; same orientation, no transformation; assigned 0 for samples collected during pregnancy.
- **Example models:** `deKock_2017_sulfadoxinePyrimethamine.R` (sigmoidal effect on sulfadoxine CL with asymptotic fractional change -0.757, T50 = 6.35 weeks, gamma = 4.90; the sigmoid approaches its asymptote ~13 weeks postpartum, consistent with the literature for return of GFR and renal blood flow to prepregnant values within 6-12 weeks postpartum).
- **Notes:** Paired with `PREG` (pregnancy status indicator) when the source paper models pregnancy as a step contrast on one PK parameter and as a sigmoidal time-decay on another. During pregnancy `TPP = 0`; after delivery `TPP > 0`. Document the postpartum sampling window in `covariateData[[TPP]]$notes` per model. Distinct from `T_NUT_SUPP` (time on nutritional supplementation, days) and `GA` (gestational age at birth, weeks) -- those are different timescale covariates anchored to different events. Ratified canonically on 2026-05-18 alongside the de Kock 2017 sulfadoxine/pyrimethamine extraction.

### TERM_BIRTH (**canonical for term-vs-preterm birth indicator**)
- **Description:** Binary indicator of term-vs-preterm birth status; `1` = term birth (>= 37 weeks gestation), `0` = preterm birth (< 37 weeks gestation). Time-fixed per subject. In Allegaert 2015 (paracetamol PK in young women) the indicator is used to select between two typical-value clearances for the sulphate-formation pathway.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (preterm). Effect form is the symmetric `CL = TERM_BIRTH * theta_term + (1 - TERM_BIRTH) * theta_preterm` selection -- so neither category is "the multiplicative reference", and both per-stratum clearances are estimated parameters.
- **Source aliases:**
  - `TERM` (Allegaert 2015 NONMEM column; same orientation, no transformation) -- used in `Allegaert_2015_paracetamol.R`.
- **Example models:** `Allegaert_2015_paracetamol.R`.
- **Notes:** Distinct from `GA` (continuous gestational age in weeks): `TERM_BIRTH` is the binarized version with the conventional 37-week cutoff. Use `GA` when the source paper carries gestational age as a continuous covariate; use `TERM_BIRTH` only when the paper itself dichotomizes. Do not derive `TERM_BIRTH` from `GA` programmatically inside `model()` -- the term-cutoff convention belongs in data assembly, not the model file.

### CONMED_BIRTHCONTROL (**canonical for oral-contraceptive use indicator**)
- **Description:** Binary indicator of oral hormonal contraceptive use; `1` = currently taking an oral contraceptive (estrogen-progestin or progestin-only pill), `0` = not on hormonal contraception. Time-varying as women cycle on/off contraception across study occasions.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no oral contraceptive). Effect form in Allegaert 2015 is multiplicative: `CL_glucuronide *= theta_CONMED_BIRTHCONTROL` when `CONMED_BIRTHCONTROL == 1`, with `theta_CONMED_BIRTHCONTROL = 1.46` (estrogen-driven UGT2B7 induction).
- **Source aliases:**
  - `BC` (Allegaert 2015 NONMEM column; same orientation, no transformation) -- used in `Allegaert_2015_paracetamol.R`.
  - `OC` (Csajka 2005 NONMEM column; same orientation, no transformation) -- used in `Csajka_2005_ephedrine_caffeine.R`.
- **Example models:** `Allegaert_2015_paracetamol.R`, `Csajka_2005_ephedrine_caffeine.R`, `Angeli_2016_iron_hepcidin.R` (multiplicative effect on iron elimination `kout_iron`; women on oral contraception have ~18% lower iron elimination, consistent with the paper's narrative that contraception limits menstrual blood loss; `e_conmed_birthcontrol_kout_iron = -0.20`).
- **Notes:** Specific scope because the canonical encoding pools all oral contraceptive types (combined / progestin-only) into a single binary; future models that need to distinguish formulations should register a finer-grained canonical (e.g., `CONMED_BIRTHCONTROL_COMBINED`, `CONMED_BIRTHCONTROL_PROGESTIN`). The full-word canonical name was chosen over a shorter `BC_USE` form for clarity in source traces.

### DIS_EOPE (**canonical for early-onset pre-eclampsia indicator**)
- **Description:** Binary indicator of early-onset pre-eclampsia (eoPE); `1` = eoPE diagnosed before 34 weeks gestation, `0` = not eoPE. Time-fixed per subject within the gestational PK study window. Used by population PK models that compare drug disposition in pregnant women with vs without early-onset pre-eclampsia.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no eoPE). Effect form in Schoenmakers 2025 is multiplicative on CL: `CL_eoPE = CL * ThetaPE^DIS_EOPE` with `ThetaPE = 0.617` (38% reduction in betamethasone CL when eoPE is present); encoded in nlmixr2 as the log-additive shift `cl <- exp(lcl + etalcl + e_eope_cl * DIS_EOPE)` with `e_eope_cl = log(0.617)`.
- **Source aliases:**
  - `PE` -- common abbreviation in obstetric pharmacology papers when the cohort restriction is to early-onset PE only; used in `Schoenmakers_2025_betamethasone.R` (paper notation: `eoPE` / `ThetaPE`).
- **Example models:** `Schoenmakers_2025_betamethasone.R` (multiplicative effect on CL, encoded via the log-additive form on `lcl`; reduces apparent betamethasone clearance from 15.6 L/h to 9.6 L/h).
- **Notes:** Distinct from a broader `PREECL` indicator that would pool early-onset, late-onset and postpartum pre-eclampsia. The "early-onset" specifier corresponds to diagnosis before 34 weeks gestation, the conventional clinical cutoff (Phipps 2019 Nat Rev Nephrol). Future papers that enrol mixed early-/late-onset cohorts or that report PE status without the 34-week stratification should register a separate canonical (e.g., `PREECL` for any-onset PE, or `LOPE` for late-onset PE) rather than reusing `DIS_EOPE` with relaxed semantics. Distinct from `PREG` (pregnancy status indicator): `DIS_EOPE` is a complication-of-pregnancy stratifier within a pregnant cohort, whereas `PREG` discriminates pregnant-vs-non-pregnant subjects. Ratified canonically on 2026-05-11 alongside the Schoenmakers 2025 betamethasone extraction.

### SCORE_HIGHAM (**canonical for Higham pictorial blood-loss assessment chart score**)
<!-- AUDIT 2026-06-19: renamed from `HIGHAM` to `SCORE_HIGHAM` per the canonical-register standardization audit. The prior name `HIGHAM` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Higham's score (Pictorial Blood-loss Assessment Chart, PBAC), a semi-quantitative pictorial scoring of menstrual blood loss summed across all sanitary protection items used during a menstrual period. Higher scores correspond to heavier menstrual bleeding; a per-menstrual-period score >= 100 is the conventional clinical threshold for menorrhagia (Higham 1990 BJOG). Time-fixed per subject when computed as the mean of the last few cycles preceding the study (the Angeli 2016 use), time-varying when scored per cycle.
- **Units:** (score; unitless)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a power-law multiplier `(SCORE_HIGHAM / tHIGHAM)^exponent` on hepcidin synthesis and elimination rate constants in Angeli 2016, with the reference value `tHIGHAM` set near the population median (96.6 in the Angeli 2016 HEPMEN cohort).
- **Source aliases:**
  - `HIGHAM` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `HIGHAM` -- prior canonical name (pre-2026-06-19 SCORE_ family standardization).
  - `HiS` -- Angeli 2016 paper notation; same orientation, no transformation.
- **Example models:** `Angeli_2016_iron_hepcidin.R` (per-subject mean of the three most recent cycles' Higham scores; enters ksynH and koutH via power-law effects with exponents 0.66 and 0.83 respectively).
- **Notes:** Specific scope because the Higham / PBAC score is a reproductive-health-specific instrument. Promote to general if a second menstrual-cycle or iron-status paper registers the same instrument. Distinct from generic blood-loss quantities reported in mL (which would warrant a separate `MENSTRUAL_BLOOD_LOSS_ML` canonical). The threshold for menorrhagia is 100 (Higham 1990 BJOG 97:734); the Angeli 2016 cohort had a mean of 96.6 with SD 60.5 (Table I), so most subjects sat near the menorrhagia threshold. Ratified canonically on 2026-06-03 alongside the Angeli 2016 iron and hepcidin extraction. Renamed from `HIGHAM` to `SCORE_HIGHAM` on 2026-06-19 per the canonical-register standardization audit (clinical-instrument SCORE_ family normalization).

### DLOSS (**canonical for menstrual-period duration**)
- **Description:** Per-subject length of the menstrual period (days of menstrual bleeding) used as a time bound on the increased-elimination phase in joint iron / hepcidin turnover models. Time-fixed per subject within a single menstrual cycle (the Angeli 2016 use); set to the individual's observed menses length over the study cycle rather than estimated from the data.
- **Units:** days
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the model as the upper bound of the loss-phase indicator window `t < DLOSS`. Reference values observed: Angeli 2016 inclusion criterion required menses length between 3 and 5 days (Methods p. 491); the per-subject value was fixed to the observed individual menses length.
- **Source aliases:**
  - `dloss` -- Angeli 2016 paper notation; same orientation, no transformation.
- **Example models:** `Angeli_2016_iron_hepcidin.R` (per-subject menses length in days; bounds the loss phase during which `kloss` adds to both `kout_iron` and `kout_hep`).
- **Notes:** Specific scope because the menses-length covariate is meaningful only inside joint iron / hepcidin turnover models that include a discrete loss-phase indicator. Promote to general if a second reproductive-cycle PK / PD paper uses an equivalent per-subject menses-length time-bound. Distinct from `TPP` (time postpartum, weeks; unbounded postpartum time after delivery) and from a hypothetical `CYCLE_LENGTH` covariate (full menstrual cycle length in days, which would warrant a separate canonical). Ratified canonically on 2026-06-03 alongside the Angeli 2016 iron and hepcidin extraction.

### DAY_DELIVERY (**canonical for day-of-delivery indicator**)
- **Description:** Binary indicator that the PK sample was collected on the day of delivery (the labour-and-childbirth occasion). `1` = sample taken on the day of delivery; `0` = otherwise (pregnant non-delivery, or non-pregnant). In Hirt 2007 the indicator is mutually exclusive with `PREG = 1`: per the paper, on the day of delivery the coding was `PREG = 0` and `DAY_DELIVERY = 1`, so the two indicators jointly partition women into non-pregnant (PREG = 0, DAY_DELIVERY = 0), pregnant non-delivery (PREG = 1, DAY_DELIVERY = 0), and delivery (PREG = 0, DAY_DELIVERY = 1) cohorts. Time-fixed per sampling occasion.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not day of delivery).
- **Source aliases:**
  - `DEL` -- Hirt 2007 NONMEM symbol for the day-of-delivery indicator; same orientation, no value transformation.
- **Example models:** `Hirt_2007_nelfinavir.R` (multiplicative effect `1 + 1.92 * DAY_DELIVERY` applied to both maternal nelfinavir clearance `CL_Nm_No` and distribution volume `V`; gates a body-weight power effect `(WT/73)^2.81` on `CL_Nm_No` within the delivery cohort only).
- **Notes:** Distinct from `PREG` (pregnant non-delivery, 1 = pregnant; see entry above), `TPP` (time postpartum, a continuous time-since-delivery covariate for postpartum recovery), and `TERM_BIRTH` (term-vs-preterm birth at any postnatal time). Use `DAY_DELIVERY` when the source paper carries a contrasted day-of-delivery cohort against pregnant non-delivery women in the same PK analysis (placental-transfer / labour-PK studies). Specific scope until a second model ratifies the name; promote to general when a future placental-transfer or labour-PK paper uses the same indicator. Ratified canonically alongside the Hirt 2007 nelfinavir extraction.


## Vital signs

### HR (**canonical for heart rate**)
- **Description:** Subject heart rate, in beats per minute. Captured in popPK studies where hemodynamic state modifies hepatic blood flow and hence clearance of high-extraction-ratio drugs (e.g., propofol). May be time-varying when serial intra-operative or intensive-monitoring values are recorded; many studies summarise as the per-subject median across the observation window and treat the covariate as time-fixed. Document baseline-vs-time-varying status in `covariateData[[HR]]$notes` per model.
- **Units:** beats/min
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(HR / ref)^exponent` or linear-deviation forms `(1 + e * (HR - ref))`. Reference values observed: 158 beats/min (Ngamprasertwong 2016; population reference encoded in the Table 2 equation `CL = theta1 * (HR/158)^theta2`).
- **Source aliases:**
  - `HR` -- same orientation as the canonical, no value transformation; used in `Ngamprasertwong_2016_propofol_sheep.R` (per-subject median HR over the propofol-infusion observation window, treated as time-fixed in line with the cohort-typical sheep hemodynamic state).
- **Example models:** `Ngamprasertwong_2016_propofol_sheep.R` (power effect on maternal propofol clearance: `CL_indiv = theta1 * (HR/158)^theta2` with `theta2 = 0.764`; clearance increases with heart rate, plausibly reflecting heart-rate-driven increases in hepatic blood flow that govern propofol's high hepatic-extraction-ratio elimination).
- **Notes:** General scope because heart rate is a universally applicable vital sign suitable for any model where hemodynamic state modulates clearance. Future models can use a different reference HR (typical adult human is ~70 beats/min vs the sheep cohort 158 beats/min); document the reference in `covariateData[[HR]]$notes`. Distinct from `HR_BAND` or `HRV` (not yet registered) which would be a heart-rate-band stratifier or heart-rate variability metric, respectively. Ratified canonically on 2026-05-23 alongside the Ngamprasertwong 2016 propofol maternal-fetal sheep extraction.

### SBP (**canonical for systolic blood pressure**)
- **Description:** Subject systolic blood pressure, in mmHg. Captured at baseline or serially during a study. Universal vital sign used in popPK / disease-progression models where hemodynamic state serves as either (a) a prognostic covariate on non-PK endpoints (overall survival, tumor dynamics), or (b) a covariate on clearance for drugs whose disposition is sensitive to blood pressure. Document baseline-vs-time-varying status per model in `covariateData[[SBP]]$notes`.
- **Units:** mmHg
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(SBP / ref)^exponent`, linear-deviation forms `(1 + e * (SBP - ref))`, or additive-log forms `beta * log(SBP)`. Reference values observed: 120 mmHg (Terranova 2022 avelumab JAVELIN Gastric 100 cohort median; also the population-adult clinical normal midpoint).
- **Source aliases:**
  - `Log systolic blood pressure` -- Terranova 2022 Table S2 OS TTE coefficient label; enters as `log(SBP) * beta` (an additive-log form on log-median OS).
  - `SBP` -- source-paper column name; same orientation, no value transformation.
- **Example models:** `Terranova_2022_TGD_OS_gastric.R` (additive-log effect on log-median OS: `log_median_OS += 0.532 * log(SBP)`, screened by ML and retained in the parametric TTE model despite the credible interval including zero, in keeping with the paper's hypothesis-generating scope).
- **Notes:** General scope because systolic BP is a universally applicable vital sign. Sibling of `HR` (heart rate, beats/min) and `DBP_REL` (relative change in diastolic BP from baseline, unitless). The two BP siblings are physiologically related but independent covariate columns: some source papers report SBP only, some report DBP or DBP-change only, some report both. `SBP` here is the raw absolute value; if a future model needs a relative-change (`SBP_REL`) or a diastolic sibling (`DBP`) it should register a new sibling canonical rather than overloading this entry. Ratified canonically on 2026-07-24 alongside the Terranova 2022 avelumab JAVELIN Gastric 100 extraction (agcand_13066655 sidecar request-001 q1=A).

### BODYTEMP (**canonical for body temperature**)
- **Description:** Subject body temperature (typically axillary or oral) at the relevant clinical observation. Captured at study admission in acute-infection PK studies (fever as a marker of acute illness severity); may be time-varying when serial temperature measurements are recorded across visits. Document baseline-vs-time-varying status in `covariateData[[BODYTEMP]]$notes` per model.
- **Units:** degC
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with linear-deviation forms `(1 + e * (BODYTEMP - ref))` or exponential forms `exp(e * (BODYTEMP - ref))`. Reference values observed: 36.9 degC (Kloprogge 2013 lumefantrine; pooled-cohort median in Ugandan pregnant + non-pregnant women with uncomplicated P. falciparum malaria), 37.2 degC (Kloprogge 2014 quinine; pregnant Ugandan women with uncomplicated P. falciparum malaria, cohort median at admission).
- **Source aliases:**
  - `TEMP` -- common short form in malaria / infectious-disease NONMEM control streams; used in `Kloprogge_2013_lumefantrine.R` and `Kloprogge_2014_quinine.R` (same orientation as the canonical, no value transformation).
- **Example models:** `Kloprogge_2013_lumefantrine.R` (linear-deviation effect on mean absorption transit time MTT: `MTT_indiv = TVMTT * (1 + e_bodytemp_mtt * (BODYTEMP - 36.9))` with `e_bodytemp_mtt = 0.165` per degC; mean transit time increases ~16.5% per degC over 36.0-39.8 degC, plausibly reflecting reduced gut motility / prolonged absorption in feverish malaria patients), `Kloprogge_2014_quinine.R` (exponential effect on elimination clearance: `CL_indiv = TVCL * exp(e_bodytemp_cl * (BODYTEMP - 37.2))` with `e_bodytemp_cl = -0.243` per degC, centered at the cohort median; clearance decreases ~21.6% per degC increase in admission body temperature over 36.0-38.9 degC, reflecting reduced metabolic CYP3A4 activity during acute febrile malaria).
- **Notes:** General scope because body temperature is a universally applicable vital sign; the Kloprogge 2013 reference value 36.9 degC is cohort-specific (Ugandan malaria cohort median) and future models should document their own reference in `covariateData[[BODYTEMP]]$notes`. Units are degrees Celsius; convert from Fahrenheit (degF) at data-assembly time, not inside `model()`. Distinct from `BODYTEMP_FEBRILE` (not yet registered) which would be a binary fever indicator if a future paper dichotomises at the conventional 37.5 / 38.0 degC threshold. Ratified canonically on 2026-05-16 alongside the Kloprogge 2013 lumefantrine extraction.

### PACO2 (**canonical for arterial blood carbon dioxide tension (PaCO2)**)
- **Description:** Arterial blood partial pressure of carbon dioxide, a routinely-measured blood-gas variable. Elevated in hypercapnia and respiratory acidosis (chronic obstructive pulmonary disease, restrictive lung disease, hypoventilation, severe asthma exacerbation). Used in population-PK analyses of theophylline and other drugs whose hepatic metabolism is sensitive to acid-base status / pulmonary disease severity. Typically captured at study admission via an automatic blood-gas analyzer on an arterial puncture sample; document baseline-vs-time-varying status in `covariateData[[PACO2]]$notes` per model.
- **Units:** mmHg
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used as a continuous covariate in exponential / log-linear effect forms `exp(e_paco2_param * PACO2)` or centered `exp(e_paco2_param * (PACO2 - ref))`. Reference values observed: 42.5 mmHg (Yano 1993 stable chronic airway obstruction cohort mean; also the laboratory normal-range midpoint of 35-45 mmHg in adults).
- **Source aliases:**
  - `PaCO2` -- the standard physiological abbreviation; used directly in Yano 1993 Table I and Eqs 7-8.
- **Example models:** `Yano_1993_theophylline.R` (log-linear effect on both CL and Vd of theophylline: `CL = exp(lcl + e_hepimp_cl * HEPIMP + e_paco2_cl * PACO2 + etalcl)` with `e_paco2_cl = 0.0233` per mmHg, and `Vd = exp(lvc + e_paco2_vc * PACO2 + etalvc)` with `e_paco2_vc = 0.00934` per mmHg; both estimated by forward-selection likelihood-ratio testing).
- **Notes:** Companion to arterial PaO2, arterial blood pH, serum bicarbonate, and base excess in respiratory-physiology covariate panels (not yet registered; future models retaining these as final covariates should propose them as new canonicals at extraction time). Yano 1993 found PaCO2 to be the most informative blood-gas covariate when entered in a forward-selection competition against PaO2, pH, hematocrit, and albumin. General scope because PaCO2 is a routinely-measured laboratory variable suitable for any model where pulmonary disease severity or acid-base status modulates drug disposition. Ratified canonically on 2026-06-07 alongside the Yano 1993 theophylline extraction.

### PEEP (**canonical for positive end expiratory pressure**)
- **Description:** Positive end expiratory pressure (PEEP) applied at the end of the expiratory phase during positive-pressure mechanical ventilation. Airway pressure setting maintained above atmospheric to prevent alveolar collapse; range 0-20 mmHg (0-25 cmH2O) in adult ICU cohorts. Time-varying within an ICU stay as ventilator settings are adjusted; source papers may use it as an admission value or a time-varying covariate -- document per-model in `covariateData[[PEEP]]$notes`. Higher PEEP raises intrathoracic pressure, decreases venous return and cardiac output, and can compromise hepatic blood flow (total hepatic blood flow decreases 4%, 12%, 32% at PEEP 10, 15, 20 mmHg respectively).
- **Units:** mmHg (the unit reported in Swart 2004 Table 3). Some ICU literature uses cmH2O (1 cmH2O = 0.736 mmHg); when the source paper uses cmH2O the convention should be documented in `covariateData[[PEEP]]$notes` and values converted to mmHg for consistency across models.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- typically used with centred linear scaling. Reference values observed: 5 mmHg (Swart 2004 lorazepam CL formula centring value; representative of routine "protective" PEEP in mechanically-ventilated ICU adults).
- **Source aliases:**
  - `PEEP` -- printed column label in Swart 2004 Table 3 and formula label in Table 4; used in `Swart_2004_lorazepam.R`.
- **Example models:** `Swart_2004_lorazepam.R` (centred linear effect on CL for non-alcohol-abuse subjects with reference 5: `cl = 4.13 - (PEEP - 5) * 0.417` L/h; each 1 mmHg above 5 lowers CL by 0.417 L/h, capturing decreased hepatic blood flow at higher PEEP; alcohol-abuse subjects instead take a flat CL = 0.74 L/h with no PEEP effect).
- **Notes:** Specific scope because PEEP is meaningful only for mechanically-ventilated cohorts. Distinct from `MECH_VENT` (binary invasive-mechanical-ventilation status) which is a treatment-status flag, not a quantitative pressure -- `MECH_VENT` cannot substitute for `PEEP` because every subject in a PEEP-covariate model already has `MECH_VENT = 1`. The Swart 2004 rationale (Discussion) links PEEP to hepatic blood flow, so PEEP as a covariate is expected to matter most for low-hepatic-extraction-ratio drugs (lorazepam ER ~ 0.06). Future ICU popPK papers reusing PEEP with the same quantitative encoding can extend `Example models`; scope may be promoted to general after multiple papers. Per-model `covariateData[[PEEP]]$notes` must document whether the value is admission PEEP, time-varying PEEP, or a cohort summary. Ratified canonically on 2026-07-26 alongside the Swart 2004 lorazepam / midazolam extraction.


### CARDIAC_OUTPUT (**canonical for cardiac output**)
- **Description:** Subject cardiac output, in litres of blood pumped per minute. Captured in popPK / physiological-PK studies where hemodynamic state modulates drug distribution (volumes) and / or clearance, most often via uptake or elimination at organs whose effective flow is set by cardiac output (lung uptake for inhaled anesthetics, hepatic flow for high-extraction drugs such as propofol, fentanyl). May be time-fixed per subject (cohort-typical or per-subject median over the observation window) or time-varying when serial intra-operative measurements are recorded; document baseline-vs-time-varying status in `covariateData[[CARDIAC_OUTPUT]]$notes` per model.
- **Units:** L/min
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used most commonly with linear-deviation forms `(param + e * (CARDIAC_OUTPUT - ref))` or power scaling `(CARDIAC_OUTPUT / ref)^exponent`. Reference values observed: 5 L/min (Hendrickx 2006; population centering for the WT=70 / CARDIAC_OUTPUT=5 typical-adult anchor in the Hendrickx covariate equations).
- **Source aliases:**
  - `CO` -- common short form in inhaled-anesthetic and hemodynamic popPK NONMEM control streams; same orientation as the canonical, no value transformation.
- **Example models:** none yet (canonical pre-registered ahead of the Hendrickx 2006 inhaled-anesthetic task; see notes).
- **Notes:** General scope because cardiac output is a universally applicable hemodynamic measurement. Distinct from `HR` (heart rate, beats/min) -- the two are physiologically related (`CARDIAC_OUTPUT = HR * stroke_volume`) but they are independent covariate columns and a paper may report either or both. Distinct from `BFR` (blood flow rate through the extracorporeal dialysis circuit, mL/min) which is dialysis-circuit-specific. Distinct from `ECMO_PUMP_SPEED` (ECMO centrifugal-pump rotational speed) which characterises an externally-imposed cardiac-output augmentation rather than the patient's intrinsic cardiac output. The full-word `CARDIAC_OUTPUT` canonical was chosen over the bare `CO` source-data abbreviation per operator instruction (Hendrickx 2006 sidecar response, task `frompeople-744`); `CO` is preserved as the recognised source alias so paper NONMEM control streams using the bare two-letter form translate without value transformation. Pre-registered on 2026-06-10 ahead of the Hendrickx 2006 (BMC Anesthesiology 6:7, doi:10.1186/1471-2253-6-7) inhaled-anesthetic extraction; the Hendrickx 2006 model files were not built because the source data were generated by the Gas Man(R) physiological simulator rather than measured in real patients (the paper fits empirical 3-compartment mammillary parameters to Gas Man-simulated alveolar-concentration curves for four inhaled anesthetics, with no IIV or residual error parameters reported), but the canonical is registered so future real-patient hemodynamic / inhaled-anesthetic popPK papers can use it without re-litigating the name.


### DBP_REL (**canonical for relative change in diastolic blood pressure from baseline**)
- **Description:** Relative (fractional) change in diastolic blood pressure from the per-subject baseline, unitless (e.g., 0.10 = +10% above baseline). Typically a time-varying driver simulated from an upstream diastolic-blood-pressure indirect-response model as `DBP_REL(t) = (dbp(t) - dbp0) / dbp0`, then consumed as a covariate by a downstream model. Distinct from a raw diastolic-BP value (mmHg); this is the normalized deviation.
- **Units:** fraction (unitless; positive when dBP is elevated above baseline)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters a log-linear hazard term `exp(e_dbprel_haz * DBP_REL)`.
- **Source aliases:**
  - `DBPREL` -- Hansson 2013 e85 NONMEM column for the relative dBP change; same orientation, no value transformation.
- **Example models:** `Hansson_2013_sunitinib_os.R` (time-varying relative dBP change, simulated from `Hansson_2013_sunitinib_dbp` and entered as an overall-survival hazard modulator: a greater relative dBP increase lowers the hazard).
- **Notes:** Ratified 2026-06-28. Specific scope because the value semantics are tied to the upstream Hansson 2013 indirect-response dBP model's relative-change definition; a future model needing the same relative-dBP-change driver should add itself to the Example models list (and consider promotion) rather than reuse the name with different semantics. Structurally analogous to `RCFB1MAX` (relative change from baseline in SUVmax driving the Schindler 2016 OS hazard). The related raw-state PD output is the `dbp` compartment in `compartment-names.md`; the placebo-arm indicator paired with that model family is `PLACEBO`.

### QTC_BL (**canonical for subject's heart-rate-corrected QT interval at pre-dose baseline**)
- **Description:** Subject's pre-dose baseline QT interval corrected for heart rate, treated as a per-subject time-fixed covariate. Used in concentration-DeltaQTc PD models to capture each subject's intrinsic QTc level entering the linear-mixed-effects intercept as a centered term `e_qtc_bl_e0 * (QTC_BL - QTC_BL_ref)`. The QT correction method (Bazett, Fridericia, individual, study-population-specific) is model-specific and documented per-model in `covariateData[[QTC_BL]]$notes`; the canonical name does NOT carry the correction method, on the same precedent as `HGB_BL` (units / convention documented per-model) and `IGG` (units documented per-model). Distinct from `HR` (heart rate at observation, beats/min) and `BODYTEMP` (vital-sign covariate): `QTC_BL` is a derived per-subject ECG summary statistic, not a per-observation vital-sign reading.
- **Units:** ms (the standard ECG / QT interval unit; document the correction method per-model in `covariateData[[QTC_BL]]$notes`).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- enters as a centered linear deviation `e_qtc_bl_e0 * (QTC_BL - QTC_BL_ref)`. Reference values observed: 390 ms (Darpo 2014; rounded standard reflecting the male-dominated 28 men / 11 women cohort with day-0 QTcI ranges 384-394 ms (men) / 398-412 ms (women) and similar QTcF ranges; the specific cohort median was not quoted in the paper). Future popPK-DeltaQTc models centering on a different reference (e.g., 400 ms in older / sicker cohorts where QTc is elevated) should document the per-model reference in `covariateData[[QTC_BL]]$notes`.
- **Source aliases:**
  - `baseline QTcI` -- used in `Darpo_2014_racSotalol_QTcI.R` (day-0 individually-corrected QT interval, ms; values transformed via centering against the cohort-median reference inside `model()`).
  - `baseline QTcF` -- used in `Darpo_2014_racSotalol_QTcF.R` (day-0 Fridericia-corrected QT interval, ms; same covariate canonical, correction method documented per-model).
- **Example models:** `Darpo_2014_racSotalol_QTcI.R`, `Darpo_2014_racSotalol_QTcF.R` (PD-only linear mixed-effects E-R model for DeltaQTcI / DeltaQTcF after single 160 mg oral rac-sotalol; QTC_BL is each subject's pre-dose day-0 mean QTc by the corresponding correction method, time-fixed for the analysis).
- **Notes:** General scope because pre-dose QTc baseline is a universally applicable ECG-derived per-subject covariate in any concentration-DeltaQTc model. The QT correction method (Bazett, Fridericia, individual, study-population-specific) is model-specific and is the per-model documentation responsibility of `covariateData[[QTC_BL]]$notes`; do NOT register parallel canonicals like `QTCI_BL` or `QTCF_BL` for different correction methods (the unit, role, and centering pattern are identical -- only the precise QT-to-QTc conversion formula differs upstream of the model). Future concentration-DeltaQTc papers using a non-zero centering reference different from the Darpo 2014 rounded standard 390 ms should document the per-model reference in `covariateData[[QTC_BL]]$notes`. Ratified canonically on 2026-06-30 alongside the Darpo 2014 rac-sotalol concentration-QTc extraction.


## Renal / hepatic function

### URINE_FLOW (**canonical for instantaneous urine flow rate**)
- **Description:** Instantaneous urine flow rate (mL/h) measured over the urine collection interval that includes the current observation. Time-varying.
- **Units:** mL/h
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with a centered-linear-effect form `CL_renal = base + theta_URINE_FLOW * (URINE_FLOW - URINE_FLOW_ref)` with `URINE_FLOW_ref = 100 mL/h` in Allegaert 2015. A value of `0` is a sentinel for "no urine collected during the interval" (i.e., the urine pathway contribution is dropped); the linear-effect term is gated by `URINE_FLOW > 0` and not extrapolated below the centering reference.
- **Source aliases:**
  - `UF` (Allegaert 2015 NONMEM column; same orientation, no transformation) -- used in `Allegaert_2015_paracetamol.R`.
- **Example models:** `Allegaert_2015_paracetamol.R`.
- **Notes:** Specific scope because the centered-linear effect form with the `URINE_FLOW == 0` sentinel-zero rule reflects an Allegaert-specific convention rather than a universally-agreed-upon parameterization. A second model that uses a different effect form (e.g., direct `URINE_FLOW / URINE_FLOW_ref` proportional scaling, no zero-sentinel) should register its own canonical (e.g., `URINE_FLOW_PROP`) rather than reusing `URINE_FLOW` with conflicting semantics. The full-word canonical name was chosen over the bare `UF` source-data abbreviation for clarity in source traces.

### URINE_VOL_24H (**canonical for 24-hour residual diuresis (total urine volume per day)**)
- **Description:** Total urine volume collected over a 24-hour interval, in mL per 24 h. Used as an indicator of preserved native renal function in CRRT-dependent / oliguric / anuric critically ill patients, where it characterises the residual non-CRRT renal-excretion pathway. Time-fixed per study day in the founding example (a single 24-hour summary collected by nursing staff during the day of PK sampling). Distinct from `URINE_FLOW` (instantaneous urine flow rate in mL/h measured per collection interval).
- **Units:** mL/24h
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with an additive linear effect `CL = CL_base + e_URINE_VOL_24H_cl * (URINE_VOL_24H / 100)` centered at 100 mL/24h (the anuria cutoff), or as a binary preserved-diuresis gate `(URINE_VOL_24H > 100)` multiplying a renal-clearance arm. Clinical cutoffs used in the founding example: anuria < 100 mL/24h, oliguria 100-500 mL/24h, preserved diuresis > 500 mL/24h.
- **Source aliases:**
  - `residual diuresis` (Ulldemolins 2015 prose); typical NONMEM column abbreviation `DIUR`. Same value orientation, no transformation.
- **Example models:** `Ulldemolins_2015_meropenem.R` (critically ill adults with septic shock and continuous renal replacement therapy; reference 100 mL/24h; additive linear effect 0.22 L/h per (URINE_VOL_24H / 100) on meropenem total CL on top of the CRRT-mediated baseline 3.68 L/h), `Huppe_2023_fosfomycin.R` (critically ill adults with renal insufficiency on continuous venovenous hemodialysis; used as the binary preserved-diuresis gate `(URINE_VOL_24H > 100)` that switches the renal clearance arm off entirely in anuric patients, per the source's own definition of preserved diuresis as residual diuresis exceeding 100 mL/24h).
- **Notes:** Promoted from `specific` to `general` alongside the Huppe 2023 fosfomycin extraction, the second model to retain a 24-hour urine-volume covariate with consistent semantics. The two registered effect forms are both multiplicative-or-additive in the same 100 mL/24h anuria cutoff and so share this entry: Ulldemolins 2015 uses the additive linear `base + slope * (URINE_VOL_24H / 100)`, and Huppe 2023 uses the binary gate `(URINE_VOL_24H > 100)` on a renal-clearance arm. Distinct from `URINE_FLOW` because (a) `URINE_VOL_24H` is a 24-hour cumulative volume (mL/24h), not an instantaneous rate (mL/h), and (b) the additive-linear effect form `base + slope * (URINE_VOL_24H / 100)` is structurally different from the centered-linear-effect-with-sentinel-zero form used by `URINE_FLOW`. A future model that uses a proportional or multiplicative effect form on a 24-hour volume should evaluate whether the semantics still match this entry (proportional / multiplicative forms are compatible); a centered-linear-with-sentinel form (analogous to the URINE_FLOW convention) would diverge and should register a sibling canonical. The full-word `URINE_VOL_24H` form was chosen over the shorter `URINE_24H` per operator instruction (sidecar request 001 of task `frompeople-536`). Ratified canonically on 2026-06-27 alongside the Ulldemolins 2015 meropenem extraction.

### CRCL (**canonical for BSA-normalized renal function (creatinine-based estimate OR tracer-measured GFR)**)
- **Description:** BSA-normalized renal function expressed in mL/min/1.73 m^2. Accepts either (a) a creatinine-based estimate -- MDRD- or CKD-EPI-estimated glomerular filtration rate, or a measured creatinine clearance that has been BSA-normalized as `1.73 x CrCl / BSA` -- or (b) a tracer-measured glomerular filtration rate using an exogenous filtration marker (iohexol clearance, inulin clearance, 99mTc-DTPA clearance, 51Cr-EDTA clearance), which is the clinical gold standard for measured GFR. All variants enter popPK models with the same operational role (a covariate on clearance) and the same units; the per-model `covariateData[[CRCL]]$description` and `notes` must state which assay the source paper used.
- **Units:** mL/min/1.73 m^2
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(CRCL / ref)^exponent` or with linear centering `(1 + slope * (CRCL - ref))`. Reference values observed: 76 mL/min/1.73 m^2 (Hamren 2008, iohexol-clearance-measured GFR), 80 mL/min/1.73 m^2 (Cirincione 2017, MDRD eGFR), 90 mL/min/1.73 m^2 (Li 2019, calculated GFR; Bajaj 2017, CKD-EPI eGFR), 100 mL/min/1.73 m^2 (Xu 2019, measured-CrCl BSA-normalized), 141 mL/min/1.73 m^2 (Jung 2024, bedside Schwartz eGFR in critically ill children, whose renal function skews supranormal).
- **Source aliases:**
  - `eGFR` -- estimated glomerular filtration rate; the estimating equation varies by cohort and must be documented per model. MDRD variant used in `Cirincione_2017_exenatide.R` and `Kotani_2022_astegolimab.R`; CKD-EPI variant in `Bajaj_2017_nivolumab.R`; the paediatric bedside Schwartz variant `0.413 x height (cm) / Scr (mg/dL)` in `Jung_2024_vancomycin.R`.
  - `EGFR` -- all-caps variant.
  - `CRCL_BSA` -- BSA-normalized creatinine clearance (measured CrCl / BSA x 1.73); used in `Xu_2019_sarilumab.R`.
  - `1.73*CrCl/BSA` -- the formula form appearing in Xu 2019 Eq. for Vm.
  - `cGFR` -- calculated/estimated GFR, BSA-normalized; used in `Li_2019_abatacept.R`.
  - `CLCR` -- source-paper column name; underlying assay form varies. Used in `Delattre_2010_amikacin.R` (raw Cockcroft-Gault, NOT BSA-normalized; median 55.5 mL/min in critically ill septic adults) and in `MedellinGaribay_2015_gentamicin.R` (Schwartz formula `CLCR = K * length / SCr` with K in {0.33, 0.45, 0.55}, BSA-normalized to mL/min/1.73 m^2). Document the assay form per model in `covariateData[[CRCL]]$description`.
  - `CLiohexol` -- plasma iohexol clearance, the clinical gold-standard tracer-measured glomerular filtration rate; used in `Hamren_2008_tesaglitazar.R` (tesaglitazar / acyl glucuronide interconversion popPK in subjects with varying renal function). Iohexol is an exogenous contrast agent cleared exclusively by glomerular filtration (no tubular secretion or reabsorption); its clearance is a direct measurement of GFR.
  - `CLinulin` -- plasma inulin clearance, the historical gold standard for measured GFR; analogous to iohexol clearance but requires constant intravenous infusion of inulin.
  - `CL_DTPA` -- plasma 99mTc-DTPA clearance, a radiolabelled tracer of glomerular filtration commonly used in clinical practice.
  - `CL_EDTA` -- plasma 51Cr-EDTA clearance, another radiolabelled glomerular-filtration tracer.
- **Example models:** `Cirincione_2017_exenatide.R` (MDRD eGFR), `Xu_2019_sarilumab.R` (measured CrCl BSA-normalized), `Kotani_2022_astegolimab.R` (MDRD eGFR), `Li_2019_abatacept.R` (cGFR), `Bajaj_2017_nivolumab.R` (CKD-EPI eGFR, reference 90 mL/min/1.73 m^2), `NA_NA_lidocaine.R` (DDMODEL00000281; binary stratification at threshold 52.7 mL/min adding -0.319 to the GX rate constant K30 in the CRCL <= 52.7 cohort; the source `.ctl` does not state the BSA-normalisation method), `Delattre_2010_amikacin.R` (raw Cockcroft-Gault mL/min, NOT BSA-normalized; reference 55.5 mL/min population median; additive linear effect 1.42 L/h per (CRCL/55.5) on CL), `MedellinGaribay_2015_gentamicin.R` (Schwartz BSA-normalized CLCR; reference 75 mL/min/1.73 m^2 (population mean 76.7); additive linear effect 0.06 L/h per (CRCL/75) on CL in infants 1-24 months), `Georges_2009_ceftazidime.R` (raw MDRD-eGFR mL/min, NOT BSA-normalized; mean 121 mL/min; additive linear effect 0.024 L/h per mL/min on CL, no centering), `Hamren_2008_tesaglitazar.R` (iohexol-clearance-measured GFR; reference 76 mL/min/1.73 m^2; linear centered slope 0.99 %/(mL/min/1.73 m^2) on renal clearance of parent tesaglitazar AND simple linear normalised scaling `(CRCL / 76)` on the saturable Michaelis-Menten Vmax of acyl-glucuronide renal elimination; founding tracer-GFR example), `Chen_2023_nemonoxacin.R` (raw Cockcroft-Gault mL/min, NOT BSA-normalized; cohort 114.4 +/- 29.2 mL/min, median 116.2, range 50.7-200.7; additive linear effect 0.026 L/h per mL/min on a 10.3 L/h intercept with no centering, per Chen 2023 Eq. 8 `CL = (10.3 + 0.026 x CLcr) x (BW/70)^0.75`; source column `CLcr`), `Wada_2023_sparsentan.R` (raw Cockcroft-Gault mL/min, NOT BSA-normalized; reference 112 mL/min overall cohort median, range 26-361; power effect `(CRCL / 112)^0.222` on CL/F, so lower renal function raises sparsentan exposure even though urinary excretion accounts for only 2.18% of the dose -- the paper notes the association may be partly confounded by sex, which is a Cockcroft-Gault input and also a retained covariate on CL/F), `Shu_2024_posaconazole.R` (raw Cockcroft-Gault mL/min, NOT BSA-normalized; reference 103.81 mL/min cohort median, range 14.44-240.64 in Chinese HSCT recipients spanning ages 3-65; power effect `(CRCL / 103.81)^0.68` on CL/F for posaconazole oral suspension, of which only about 13% of a dose is renally excreted -- as with Wada 2023 the effect is more plausibly a marker of general physiological reserve than a mechanistic renal-elimination pathway, and the authors flag it as a departure from earlier posaconazole popPK models).
- **Notes:** All estimation methods (creatinine-based MDRD / CKD-EPI / measured CrCl, and tracer-based iohexol / inulin / DTPA / EDTA clearance) produce values in the same units and are operationally interchangeable as a covariate on clearance. Document the method explicitly in each model's `covariateData[[CRCL]]$description` so future reviewers can trace the source assay; the assay choice affects accuracy of the absolute GFR value but does not change the covariate's role as a linear / power scalar on renal clearance. Tracer-measured GFR is the clinical gold standard (unaffected by muscle mass, malnutrition, and tubular-secretion confounders that perturb creatinine-based estimates) but is rarely available outside research and renal-impairment-cohort studies; creatinine-based estimates dominate routine clinical popPK datasets. Tracer-measured-GFR scope codified on 2026-06-17 alongside the Hamren 2008 tesaglitazar extraction (per operator decision in sidecar request 001).

### KBF (**canonical for native kidney (renal) blood flow**)
- **Description:** Whole-organ blood flow perfusing the kidneys, used as a physiological system input in mechanistic kidney / renal PBPK models, where it sets the peritubular vascular flow that carries drug past the tubular epithelium. May be measured (para-aminohippurate or isovalerylglycine renal clearance, phase-contrast MRI, Doppler ultrasound) or assumed from a population physiological default. Distinct from the extracorporeal-circuit flows `BFR` / `DFR` / `Q_CVVH` / `QBL` / `QEFF`, which describe dialysis or ECMO hardware rather than native renal perfusion, and from `CARDIAC_OUTPUT` (whole-body hemodynamic output, of which renal blood flow is roughly one fifth).
- **Units:** mL/min (raw whole-organ flow, NOT BSA-normalized; the Huang & Isoherranen mechanistic kidney framework consumes it internally as L/h via the 1 mL/min = 0.06 L/h conversion)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- enters the model directly as the peritubular vascular flow, not as a ratio or centered deviation. Healthy-adult physiological default in the founding framework: 60 L/h (= 1000 mL/min), scaled proportionally with GFR in the population version of the model (Huang & Isoherranen 2020 distributed MATLAB code, `Q_kidney0 = 60 * Proportional_Reduction`). Granda 2024 replaces that population default with a per-subject measurement, which is the paper's central methodological contribution.
- **Source aliases:**
  - `Isovalerylglycine CL_r` -- Granda 2024 Table 3 column heading; the renal clearance of the endogenous solute isovalerylglycine used as the KBF surrogate (high extraction ratio, low protein binding, clearance more than fourfold GFR). Same orientation and units, no value transformation.
  - `Q_kidney` / `Qk` / `Q_kidney0` -- Huang & Isoherranen 2018 / 2020 and Chang 2023 symbol for the same quantity, expressed in L/h (divide by 0.06 to obtain the canonical mL/min).
  - `RBF` / `RPF` -- renal blood flow / renal plasma flow shorthand common in renal-physiology and PAH-clearance literature. `RPF` is PLASMA flow and must be converted to blood flow (`KBF = RPF / (1 - haematocrit)`) before use.
- **Example models:** `Granda_2024_kynurenicacid_pbpk.R`, `Granda_2024_tenofovir_pbpk.R`, `Granda_2024_oseltamivircarboxylate_pbpk.R` (all three: per-subject KBF estimated from measured isovalerylglycine renal clearance, mL/min; cohort mean 575 +/- 387, range 76-1692 across 27 adults spanning CKD stages 1-5; enters as the peritubular blood flow `qk <- KBF * 0.06` in the 35-state mechanistic kidney PBPK).
- **Notes:** Ratified 2026-08-05 alongside the Granda 2024 mechanistic-kidney-PBPK extraction (sidecar request 001, operator answer A). General scope because native kidney blood flow is a paper-independent physiological quantity that any renal-PBPK or renal-physiology model may use. The canonical name states the quantity rather than a paper symbol, per the standing canonical-naming rule. Note the physiological constraint the founding models enforce: secretory clearance cannot exceed kidney blood flow, so a subject whose measured secretory-marker clearance exceeds their measured KBF cannot be fitted (Granda 2024 participants 38, 41 and 45, whose fitted secretory clearance therefore railed at the 1000 L/h search bound); a model consuming KBF should surface that violation rather than silently saturating. The companion per-subject quantity in Granda 2024 -- unbound intrinsic secretory clearance -- is deliberately NOT registered as a covariate: it is a fitted model parameter (`lclintsec`), not a measurement, per the same sidecar decision.

### CREAT (**canonical for serum creatinine**)
- **Description:** Serum creatinine concentration (baseline or time-varying).
- **Units:** umol/L or mg/dL -- document the unit used in each model via `covariateData[[CREAT]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(CREAT / ref)^exponent`.
- **Source aliases:**
  - `CRE` (umol/L, reference 70.73) -- used in `Thakre_2022_risankizumab.R`.
  - `SCR` -- common clinical-PK abbreviation; also Llanos-Paez 2020 source column for the patient's individual serum creatinine.
- **Example models:** `Thakre_2022_risankizumab.R`, `Hennig_2013_tobra.R` (umol/L; paired with `CREAT_REF` for the SCR_mean / SCR ratio used in the Hennig 2013 renal-function factor), `Llanos_2017_gentamicin.R` (umol/L; standardized per-patient against `CREAT_REF` rather than a fixed cohort reference), `Llanos-Paez_2020_gentamicin.R` (umol/L; used as the patient's `SCR_i` in the renal-function ratio `(CREAT_REF / CREAT)^0.58` on CL).
- **Notes:** `CREAT` chosen over the shorter `CRE`/`SCR` as the NONMEM/clinical-PK convention that is unambiguous. Per-model reference values must be documented in `covariateData[[CREAT]]$notes`.

### CREAT_REF (**canonical for sex/age/size-expected normal-mean serum creatinine**)
- **Description:** Externally-computed reference serum creatinine for the individual (the expected normal SCR for a healthy person of the same sex, age and body size). Used as the numerator of a ratio against the patient's measured `CREAT` to define a renal-function factor on clearance.
- **Units:** umol/L or mg/dL -- must match the unit of the paired `CREAT` column. Document via `covariateData[[CREAT_REF]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as `(CREAT_REF / CREAT)^exponent` so that a patient with measured SCR equal to the population-expected normal SCR has factor 1.
- **Source aliases:**
  - `SCR_mean` -- used in `Hennig 2013` (Eq. 5: `f_SCR = (SCR_mean / SCR)^theta_SCR`); also Llanos-Paez 2020 paper notation for the Ceriotti 2008 age/sex-matched physiological mean SCR.
  - `Scrmean` -- Llanos-Paez 2017 paper notation; computed from Ceriotti et al. 2008 age- and sex-stratified medians (Clin Chem 54:559-566, doi:10.1373/clinchem.2007.099648).
  - `SCR_standardised` -- Germovsek 2018 paper notation; PMA-adjusted standardisation of raw SCR per the paper's reference 28 (a previously-developed Standing-style PMA stratification).
  - `CCR,adj` -- Ruhs 2012 paper notation; age- and gender-adjusted reference creatinine derived from the paper's reference [23] (a Schwartz-style paediatric maturation adjustment); the main text does not give the explicit formula.
- **Example models:** `Hennig_2013_tobra.R`, `Llanos_2017_gentamicin.R` (umol/L; computed externally per Ceriotti et al. 2008), `Llanos-Paez_2020_gentamicin.R` (umol/L; ratio `(CREAT_REF / CREAT)^0.58` multiplies the maturation-scaled CL), `Germovsek_2018_meropenem.R` (umol/L; ratio `(CREAT_REF / CREAT)^0.40` multiplies the maturation-scaled CL for renal meropenem clearance in neonates and young infants; PMA-stratification reference per Germovsek 2018 Methods reference 28), `Ruhs_2012_methotrexate.R` (mg/dL; age- and gender-adjusted CCR,adj per the paper's reference [23]; ratio `(CREAT_REF / CREAT)^0.314` multiplies the BSA-scaled MTX CL in paediatric ALL patients).
- **Notes:** Specific scope because the formula used to derive the reference value is paper-defined (Hennig 2013 cites a combination of Ceriotti 2008, Junge 2004 and Johansson 2011 reference-interval relationships; Llanos-Paez 2017 and 2020 both use Ceriotti 2008); a future paper that uses a different reference-SCR derivation (e.g., a CKD-EPI-style adult-only reference, or a Schwartz-derived paediatric-only reference) should pin its formula in `covariateData[[CREAT_REF]]$notes` so that a user assembling a virtual cohort can reproduce it. When no covariate data are available to compute `CREAT_REF`, set `CREAT_REF = CREAT` so the renal-function factor evaluates to 1 (matching the Hennig 2013 'covariate set to 1 for missing data' rule). Ratified canonically on 2026-05-08 alongside the Hennig 2013 tobramycin extraction.

### BUN (**canonical for blood urea nitrogen**)
- **Description:** Blood urea nitrogen concentration (baseline or time-varying). Reflects the nitrogenous waste burden being cleared by the kidneys; rises with reduced glomerular filtration, dehydration, increased protein catabolism, or GI bleeding. Distinct from `CREAT` (the other commonly-reported renal-function marker) because urea reabsorption is flow-dependent in the renal tubule, so BUN is sensitive to volume status as well as GFR.
- **Units:** mg/dL or mmol/L -- document the unit used in each model via `covariateData[[BUN]]$units` (1 mmol/L urea ~= 2.80 mg/dL BUN).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used either as a linear / hinge effect on a PK parameter, or with power scaling `(BUN / ref)^exponent`. Reference values observed: 7 mg/dL (Hall 2017 MARS hinge knot for ka; not a population median); 4.2 mmol/L (Chen 2017 cohort median for the tacrolimus power-of-ratio CL effect).
- **Source aliases:** none known.
- **Example models:** `Hall_2017_dapsone.R` (mg/dL; population median 13 mg/dL [range 7-28]; enters the MARS-based covariate model on the absorption rate constant via the basis function `BF1 = max(0, BUN - 7)`, which interacts with a weight hinge to drive `Ka`), `Chen_2017_tacrolimus.R` (mmol/L; cohort median 4.2 mmol/L [range 1.7-10.4]; enters CL/F via standard power-of-ratio scaling `(BUN/4.2)^1.42` in low-dose oral tacrolimus for Chinese myasthenia-gravis patients).
- **Notes:** Promoted to `general` scope on 2026-06-03 alongside the Chen 2017 tacrolimus extraction, the second model registering BUN. Hall 2017 enters BUN only through a piecewise-linear MARS hinge (`max(0, BUN - 7) * max(0, 63.7 - WT)`), not as a power scaling -- the per-model `covariateData[[BUN]]$notes` documents this is part of a machine-learning-driven hinge model, not a standard popPK covariate transform. Chen 2017 uses the conventional centred power-of-ratio form `(BUN/ref)^exponent` referenced to the cohort median; the positive exponent there is interpreted via a urea-driven protein-carbamylation mechanism that reduces albumin binding of the ~99%-protein-bound tacrolimus. Ratified canonically on 2026-05-18 alongside the Hall 2017 dapsone extraction.

### UACR (**canonical for urine albumin-to-creatinine ratio**)
- **Description:** Urine albumin-to-creatinine ratio (UACR), a renal-damage biomarker quantifying albuminuria. UACR is the spot-urine albumin concentration divided by the spot-urine creatinine concentration; it approximates 24-hour urinary albumin excretion (in mg/day) because creatinine excretion is roughly constant at ~1 g/day in an adult. UACR is the principal albuminuria measure in KDIGO chronic-kidney-disease (CKD) staging, with thresholds A1 (< 30 mg/g, normal-to-mildly increased), A2 (30-300 mg/g, moderately increased), and A3 (> 300 mg/g, severely increased). Distinct from the function markers `CRCL` / `CREAT` / `BUN`: UACR captures upstream glomerular albumin leak (kidney *damage*), whereas CRCL / CREAT / BUN capture downstream filtration rate (kidney *function*). Both axes are routinely reported together in diabetic-kidney-disease (DKD) and CKD popPK / PD studies. Time-fixed at baseline by default; document baseline-vs-time-varying status per model via `covariateData[[UACR]]$notes`.
- **Units:** mg/g (urine albumin in mg per gram of urine creatinine). Equivalent to mg/mmol scaled by 8.84 (1 mg/g ~= 0.113 mg/mmol). Document the unit used in each model via `covariateData[[UACR]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used either as a centred linear effect on `(UACR - ref)` or as a power scaling `(UACR / ref)^exponent`. Reference values observed: 800 mg/g (Goulooze 2022 serum-potassium analysis centring value) and 850 mg/g (Goulooze 2022 UACR / eGFR analysis centring value); both are close to the FIDELIO-DKD cohort median of 852 mg/g.
- **Source aliases:**
  - `UACR0` -- Goulooze 2022 paper convention for baseline UACR (the `0` suffix denotes the baseline / pre-randomisation value).
- **Example models:** `Goulooze_2022_finerenone.R` (centred linear effects on Emax and on the disease-progression slope TSLOPE, both centred at 800 mg/g; coefficients 9.31e-5 g/mg on Emax and 1.14e-3 g/mg on TSLOPE per Goulooze 2022 Table 1), `Goulooze_2022_finerenone_uacr.R` (power scaling `(UACR / 850)^0.877` on the baseline of the UACR disease-progression state), `Goulooze_2022_finerenone_egfr.R` (same scaling on the embedded UACR sub-model baseline, plus `(UACR / 850)^0.306` on the magnitude of the inter-individual variability of the eGFR decline rate -- a covariate on a variance term rather than on a typical value).
- **Notes:** Promoted from specific to general scope alongside the Goulooze 2022 UACR / eGFR extraction, which was the second paper to register the column (as the original entry anticipated). Distinct from the `uacr` COMPARTMENT, which is the modelled UACR time course; this column is the time-fixed observed baseline. Ratified canonically alongside the Goulooze 2022 finerenone extraction.

### SOD (**canonical for serum sodium concentration**)
- **Description:** Serum (or plasma) sodium concentration. Most commonly captured at study entry as a baseline biochemistry value, but can be time-varying when serial electrolytes are recorded. Reflects fluid / electrolyte balance; hyponatraemia is common in severe acute illness, severe malnutrition, GI losses, SIADH and adrenal insufficiency, while hypernatraemia accompanies dehydration and excessive sodium intake.
- **Units:** mmol/L (equivalent to mEq/L for sodium).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with a centered-linear-deviation form `(1 + e_sod_<param> * (SOD - ref))`. Reference values observed: 136 mmol/L (Thuo 2011 ciprofloxacin; cohort median in Kenyan children with severe malnutrition, normal-range lower bound).
- **Source aliases:**
  - `Na+` / `NA` / `SODIUM` -- common source-paper printed forms; renamed to canonical `SOD` when assembling input data. Used in `Thuo_2011_ciprofloxacin.R` (the paper writes "Na+ (mmol/L)" in Table 1 and structural-model equations).
- **Example models:** `Thuo_2011_ciprofloxacin.R` (linear centered-deviation effects on apparent CL and apparent Vc: `1 + 0.0368*(SOD - 136)` and `1 + 0.0291*(SOD - 136)`; reference 136 mmol/L is the cohort median).
- **Notes:** General scope because serum sodium is a universally applicable serum-electrolyte covariate. Reference value is paper-specific (cohort median); future models should document their own reference in `covariateData[[SOD]]$notes`. Distinct from any "sodium content of dosed formulation" concept (e.g., sodium-rich oral rehydration solution) -- that would warrant a separate canonical (`DOSE_NA_MGML`, etc.) if a future model retains it. Ratified canonically on 2026-05-21 alongside the Thuo 2011 ciprofloxacin extraction.

### POT (**canonical for serum potassium concentration**)
- **Description:** Serum (or plasma) potassium concentration. Most commonly captured at study entry as a baseline biochemistry value, but can be time-varying when serial electrolytes are recorded. Central to mineralocorticoid-receptor-antagonist, potassium-sparing-diuretic and RAAS-inhibitor programmes, where hyperkalaemia is the dose-limiting toxicity and drives titration and discontinuation rules; KDIGO and ESC hyperkalaemia thresholds are 5.5 and 6.0 mmol/L. Direct sibling of `SOD` (serum sodium). Distinct from the `serumK` COMPARTMENT, which is the modelled potassium time course when potassium is the PD output rather than an explanatory covariate.
- **Units:** mmol/L (equivalent to mEq/L for potassium).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(POT / ref)^exponent` or linear centring `(1 + slope * (POT - ref))`. Reference values observed: 4.4 mmol/L (Goulooze 2022 FIDELIO-DKD, the cohort median).
- **Source aliases:**
  - `K0` -- Goulooze 2022 convention for observed baseline serum potassium (the `0` suffix denotes the baseline / pre-randomisation value).
  - `K`, `K+`, `POTAS`, `SERUM_K` -- common source-paper printed forms.
- **Example models:** `Goulooze_2022_finerenone_egfr.R` (power scaling `(POT / 4.4)^-1.38` on DSLOPE, the slope of the acute finerenone effect on eGFR; ESM Table S2 theta_K0,DSLOPE = -1.38, reproducing the paper's Table 2 range of +31.9% to -18.4% over the 3.6-5.1 mmol/L 5th-95th percentile band).
- **Notes:** General scope because serum potassium is a universally applicable serum-electrolyte covariate, matching the scope granted to `SOD`. Reference value is paper-specific (cohort median); future models should document their own reference in `covariateData[[POT]]$notes`. Ratified canonically alongside the Goulooze 2022 finerenone UACR / eGFR extraction.

## Renal-replacement-therapy (RRT) family -- section-header policy

All RRT-related canonicals follow the `RRT_<MODALITY>_<KIND>` shape, where `MODALITY` is the specific RRT type (`HEMODIAL` for intermittent hemodialysis, `CRRT` for continuous / extended modalities, `PERIT_DIAL` for peritoneal dialysis) and `KIND` is `STATUS` for subject-level treatment-status indicators (time-fixed within the analysis window) or `ACTIVE` for per-time-point session gates (time-varying within subject, 1 only during an active session). The 2026-06-19 canonical-register standardization audit renamed the prior `HEMODIAL`, `HEMODIALYSIS`, and `CRRT_STATUS` canonicals into this family to make the modality + kind contrast explicit at the column name; pre-2026-06-19 names are preserved as `source_aliases` so existing data CSVs continue to work for one release cycle.

### RRT_HEMODIAL_STATUS (**canonical for intermittent-hemodialysis treatment-status indicator**)
- **Description:** 1 = the subject was undergoing intermittent hemodialysis during the modeled period; 0 = no intermittent hemodialysis. Treatment-status flag rather than a measured renal-function value; used as a multiplicative covariate on PK parameters that change with chronic dialysis (typically CL and Vc -- intermittent hemodialysis decreases vancomycin-class CL and reduces interstitial volume overload, lowering Vc). Per-subject indicator in the source data; in Goti 2018 it is treated as time-fixed at the subject level (the cohort either was or was not receiving intermittent hemodialysis during the admission), and Goti 2018 explicitly notes that actual hemodialysis-session timing was not used because of documentation limitations.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no intermittent hemodialysis).
- **Source aliases:**
  - `HEMODIAL` -- prior canonical name (pre-2026-06-19 RRT_<modality>_<kind> standardization).
  - `DIAL` -- used in `Goti_2018_vancomycin.R` (binary indicator on CL and Vc in a 2-compartment vancomycin popPK model). Goti 2018 Methods notes the indicator was created for the routine-TDM cohort (n = 336 hemodialysis subjects of 1812 total) and that all hemodialysis procedures were intermittent and used high-flux membranes.
- **Example models:** `Goti_2018_vancomycin.R` (multiplicative factors on CL and Vc: `0.7^RRT_HEMODIAL_STATUS` on CL and `0.5^RRT_HEMODIAL_STATUS` on Vc, so dialysis subjects have 30% lower CL and 50% lower central volume than non-dialysis subjects).
- **Notes:** Specific to intermittent hemodialysis (IHD). Distinct from peritoneal dialysis (PD) and from continuous renal replacement therapy (CRRT), each of which has different drug-extraction kinetics and would warrant its own canonical (`RRT_PERIT_DIAL_STATUS`, `RRT_CRRT_STATUS`) if a future paper retains them as covariates. Goti 2018 treats `RRT_HEMODIAL_STATUS` as time-fixed per subject because session-level dialysis timing was not reliably documented in the source EHR data; a future paper that resolves drug clearance during versus between dialysis sessions would use a time-varying form (`RRT_HEMODIAL_ACTIVE`) or a separate per-session covariate. When pairing `RRT_HEMODIAL_STATUS` with `CRCL`, note that the Cockcroft-Gault CRCL of an anuric hemodialysis patient is by convention very low or set per institution to a small floor value (Goti 2018 truncated CRCL > 150 mL/min to 150 mL/min and corrected SCr < 1 mg/dL in elderly subjects); residual renal function in hemodialysis subjects is highly variable and the dialysis indicator captures the bulk PK shift on top of the CRCL covariate. Ratified canonically on 2026-05-16 alongside the Goti 2018 vancomycin extraction. Renamed from `HEMODIAL` to `RRT_HEMODIAL_STATUS` on 2026-06-19 per the canonical-register standardization audit (RRT family normalization).

### RRT_CRRT_STATUS (**canonical for continuous / extended renal-replacement-therapy treatment-status indicator**)
- **Description:** 1 = the subject was undergoing a continuous or extended (long-session) extracorporeal renal-replacement-therapy modality during the modeled period (continuous venovenous hemofiltration CVVH / CVVHF, continuous venovenous hemodiafiltration CVVHDF, sustained low-efficiency dialysis SLED, extended daily diafiltration EDD-f, or similar slow-clearance / long-duration extracorporeal therapies); 0 = no such therapy. Treatment-status flag rather than a measured renal-function value; used as a multiplicative or piecewise covariate on PK parameters that change when slow extracorporeal solute removal is active (typically CL). Per-subject indicator in the source data; in Shekar 2014 it is treated as time-fixed at the subject level (the cohort either was or was not receiving RRT during the entire PK sampling period; all RRT patients were on CVVH or EDD-f continuously / daily during sampling) -- the indicator captures the subject-level RRT status, not session-level on/off timing.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no continuous / extended RRT).
- **Source aliases:**
  - `CRRT_STATUS` -- prior canonical name (pre-2026-06-19 RRT_<modality>_<kind> standardization).
  - `RRT` -- used in `Shekar_2014_meropenem.R` (binary indicator selecting between the RRT-fixed-CL term and the CrCL-driven non-RRT CL term in a 2-compartment meropenem popPK model). Shekar 2014 Methods describes the RRT cohort as mixed CVVH (control RRT subjects, true CRRT) and EDD-f (ECMO RRT subjects, extended daily diafiltration; pharmacokinetically CRRT-like for slow-clearance solutes such as meropenem) and the model treats the modalities as a single binary covariate without distinguishing them.
- **Example models:** `Shekar_2014_meropenem.R` (piecewise CL: `TVCL = exp(lcl) * RRT_CRRT_STATUS + e_crcl_cl * CRCL_in_Lh * (1 - RRT_CRRT_STATUS)`, with CRCL in raw Cockcroft-Gault mL/min converted to L/h inside `model()`; 5/11 ECMO patients and 5/10 controls were on RRT).
- **Notes:** Distinct from `RRT_HEMODIAL_STATUS` (intermittent hemodialysis IHD only) and from `RRT_HEMODIAL_ACTIVE` (per-time-point session gate in within-subject time-varying dialysis-clearance models such as Liesenfeld 2013 dabigatran). Anticipated as a future canonical `RRT_PERIT_DIAL_STATUS` for peritoneal dialysis. Shekar 2014 ratification uses a mixed CVVH + EDD-f cohort because the source paper treats them identically as a single binary RRT covariate; a future paper that retains modality as a separate covariate (e.g. CVVH vs SLED vs CVVHDF) would either reuse `RRT_CRRT_STATUS` with finer per-modality columns layered on top, or warrant its own modality-specific canonical (`RRT_CVVH_STATUS`, `RRT_SLED_STATUS`, etc.). When pairing `RRT_CRRT_STATUS` with `CRCL`, note that Cockcroft-Gault CrCL is conventionally not defined / not reported for RRT-dependent subjects; Shekar 2014 records CrCL only for non-RRT subjects and the model formula switches off the CrCL term when `RRT_CRRT_STATUS = 1`. Ratified canonically on 2026-05-18 alongside the Shekar 2014 meropenem extraction. Renamed from `CRRT_STATUS` to `RRT_CRRT_STATUS` on 2026-06-19 per the canonical-register standardization audit (RRT family normalization).

### ALB (**canonical for serum albumin**)
- **Description:** Serum albumin concentration.
- **Units:** **g/L** (SI; canonical as of 2026-06-19). US-convention papers report in g/dL; convert via `ALB_gL = ALB_gdL * 10` on data ingestion. Models calibrated to g/dL values must apply an inline conversion in `model()` (e.g., `alb_gdL <- ALB * 0.1  # SI g/L -> US-convention g/dL`) so the structural coefficients stay aligned with their original calibration.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(ALB / ref)^exponent`.
- **Source aliases:**
  - `BALB` (baseline albumin) -- used in `Zhou_2021_belimumab.R`. Maps directly to `ALB`; baseline-vs-time-varying status documented in per-model notes.
  - `HSA` (human serum albumin) -- used in `Fauchet_2015_lopinavir_unbound.R` where the column name follows the paper's protein-binding-equation notation distinguishing serum albumin from alpha-1 acid glycoprotein. Maps directly to canonical `ALB`; no value transformation. Reported in g/L; converted to umol/L inside `model()` via molecular weight 66500 g/mol for the K_HSA linear-binding term.
- **Example models:** `Fasanmade_2009_infliximab.R` (g/dL, reference 4.1), `Thakre_2022_risankizumab.R` (g/L, reference 45), `Chua_2025_mirikizumab.R`, `Moein_2022_etrolizumab.R`, `Tiraboschi_2025_amlitelimab.R`, `Yamada_2025_zolbetuximab.R`, `Li_2019_abatacept.R` (g/dL, reference 4.0; the Li 2019 Methods states 'mg/dL' which is a publication typo -- see the model's `covariateData[[ALB]]$notes`), `Quartino_2019_trastuzumab.R` (g/dL, reference 4; source column `ALBU`; negative exponent -0.998 on linear CL), `Wang_2020_ontamalimab.R` (g/L, reference 39), `Zhou_2021_belimumab.R` (g/L, reference 40; baseline-only, source column `BALB`), `Okada_2025_rocatinlimab.R` (g/L, reference 44; source column `ALBU`; power exponent -1.30 on linear CL), `Xu_2020_daratumumab.R` (g/L, reference 37.0; power exponent -1.149 on linear CL), `Struemper_2017_belimumab.R` (g/L, reference 41; baseline-only, source column `BALB`; power exponent -0.736 on linear CL), `Fauchet_2015_lopinavir_unbound.R` (g/L; source column `HSA`; enters the saturable-binding submodel via a linear K_HSA * [ALB] * Cunbound term with K_HSA = 0.036 L/umol, not as a power scaling on CL), `Roepcke_2023_rezafungin.R` (g/dL, reference 3.2; power exponent -0.708 on the shared peripheral volume V23 rather than on CL; model() applies the inline `alb_gdL <- ALB * 0.1` SI-to-US conversion required by this entry's Units note. Roepcke 2023 Table 2 prints the exponent magnitude without a sign and the footnote-a equation supplies the minus; the negative orientation is corroborated by Table 3, where the low-albumin patient cohort has the larger steady-state volume of distribution).
- **Notes:** Ratified canonically on 2026-04-19 after cross-model review. Canonical units standardized to **g/L (SI)** on 2026-06-19 per the canonical-register standardization audit. The per-model `covariateData[[ALB]]$units` field is load-bearing for model-file source-trace; effect-coefficient magnitude is meaningless without the unit. Per-model conversion to US-convention g/dL (where the structural coefficients were calibrated) is required in `model()` via an inline `alb_gdL <- ALB * 0.1` line - see the per-model `notes` for the conversion factor used. Conversion factor: 1 g/dL = 10 g/L.

### TPRO (**canonical for total serum protein**)
- **Description:** Total serum protein concentration (sum of albumin + globulins; baseline or time-varying).
- **Units:** **g/L** (SI; canonical as of 2026-06-19). US-convention papers report in g/dL; convert via `TPRO_gL = TPRO_gdL * 10` on data ingestion. Models calibrated to g/dL must apply an inline conversion in `model()` (e.g., `tpro_gdL <- TPRO * 0.1`). 1 g/dL = 10 g/L.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(TPRO / ref)^exponent`. Reference value observed: 74 g/L (Frey 2010 pooled-cohort median).
- **Source aliases:**
  - `PROT` -- Frey 2010 abbreviation in the final-model equation.
  - `TP` -- common clinical-chemistry abbreviation.
- **Example models:** `Frey_2010_tocilizumab.R` (g/L, reference 74; exponent -1.1 on V1).
- **Notes:** Distinct from `ALB` (serum albumin, the largest single component of total protein). Frey 2010 retains both `TPRO` and `ALB` on V1 as separate covariates with opposite signs (TPRO negative, ALB positive) and notes there is no clear mechanistic explanation; the joint effect may reflect serum-volume modifications. `TPRO` ratified canonically on 2026-04-28 alongside the Frey 2010 extraction.

### FU (**canonical for per-subject measured fraction unbound of parent drug in plasma**)
- **Description:** Individual subject's measured fraction unbound of the parent drug in plasma, typically determined by ultrafiltration of a per-subject ex-vivo plasma sample (occasionally by equilibrium dialysis). Per-subject value; the source paper documents whether the measurement was at baseline, at steady state, or time-averaged. Distinct from binding-protein concentrations (`ALB` / `AAG` / `TPRO`), which would derive fu indirectly through a binding equation -- `FU` is the direct measurement.
- **Units:** % (percent; e.g., 0.1 = 0.1% fraction unbound = 0.001 expressed as a fraction). The per-model `covariateData[[FU]]$units` field documents whether the value is a percent or a unitless fraction; effect-coefficient magnitudes are meaningless without the unit.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with linear centering `(1 + slope * (FU - ref))` or with power scaling `(FU / ref)^exponent`. Reference values observed: 0.1% (Hamren 2008 tesaglitazar cohort median imputed for three subjects with missing fu measurements; range 0.06-0.2%).
- **Source aliases:**
  - `f_u` -- Hamren 2008 paper notation (italicised `fu`).
  - `FRAC_UNBOUND` -- common NONMEM column-name form when the value is reported as a unitless fraction.
- **Example models:** `Hamren_2008_tesaglitazar.R` (%, reference 0.1%; linear centered effect 555 %/unit fu on the parent metabolic clearance CLmt -- per-subject fu measurements at day 42 by ultrafiltration drive the realised CLmt range from approximately 1.5 to 3.0 L/h across the cohort fu range 0.06-0.2%; founding example).
- **Notes:** Rare in popPK literature -- only papers that bother to do per-subject ultrafiltration measurements report this covariate. Most popPK papers either (a) assume a fixed population-typical fu (in which case use the parameter-side `fu` paper-named parameter, not this covariate) or (b) parameterise the effect through measured binding-protein concentrations (`ALB`, `AAG`, `TPRO`). Use `FU` only when the source paper provides individual measured unbound fractions and uses them as a covariate. Ratified canonically on 2026-06-17 alongside the Hamren 2008 tesaglitazar extraction (per operator decision in sidecar request 001).

### CSF_TPRO (**canonical for cerebrospinal-fluid total protein**)
- **Description:** Total protein concentration measured in cerebrospinal fluid (baseline or time-varying). CNS-compartment analogue of `TPRO`; used in popPK models of CSF-penetrating drugs as a surrogate for blood-brain-barrier integrity (elevated CSF protein indicates inflammation or barrier breakdown and typically correlates with increased CNS penetration of small-molecule drugs).
- **Units:** **g/L** (SI; canonical as of 2026-06-19). US-convention reporting in mg/dL converts via `CSF_TPRO_gL = CSF_TPRO_mgdL * 0.01`. 1 g/L = 100 mg/dL.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- enters either as an additive term on the logit-scale CSF uptake / barrier parameter, or as a power scaling `(CSF_TPRO / ref)^exponent` on a penetration fraction. Reference value observed: 1.2 g/L (Germovsek 2018 typical-infant value; sick-neonate cohort median).
- **Source aliases:**
  - `CSF_protein` -- Germovsek 2018 paper notation.
  - `CSFPROT` / `CSF_PROT` -- compact column-name forms common in NONMEM control streams.
- **Example models:** `Germovsek_2018_meropenem.R` (g/L, reference 1.2; additive on the logit CSF barrier parameter with coefficient theta_CSFproteins = -0.17 per g/L deviation from 1.2; ratified canonically on 2026-05-21 alongside the Germovsek 2018 meropenem extraction).
- **Notes:** Distinct from `TPRO` (serum total protein) -- the two are biologically independent because the blood-brain barrier prevents free equilibration of serum protein into CSF. Normal CSF protein is approximately 0.15-0.45 g/L in healthy adults; sick neonates and meningitis patients can reach several g/L. The covariate is typically time-varying because CSF protein evolves over the course of CNS inflammation; missing values are commonly imputed to the cohort median when the source paper does not report a per-sample CSF protein measurement.

### IGG (**canonical for serum immunoglobulin G**)
- **Description:** Serum total immunoglobulin G concentration (baseline or time-varying). Used in mAb PK analyses as a competition-for-FcRn-recycling covariate on therapeutic-mAb clearance -- high endogenous IgG is hypothesized to displace the therapeutic mAb from FcRn salvage and increase its catabolic clearance.
- **Units:** **g/L** (SI; canonical as of 2026-06-19). US-convention papers report in mg/dL; convert via `IGG_gL = IGG_mgdL * 0.01` on data ingestion. 1 g/L = 100 mg/dL. Models calibrated to mg/dL must apply an inline conversion in `model()` (e.g., `igg_mgdL <- IGG * 100`).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(IGG / ref)^exponent`. Reference values observed: 14.8 g/L (Zhou 2021), 9.65 g/L (Yang 2021).
- **Source aliases:**
  - `BIGG` (baseline IgG) -- used in `Zhou_2021_belimumab.R`.
  - `IGGBL` (baseline IgG) -- used in `Yang_2021_cemiplimab.R`.
- **Example models:** `Zhou_2021_belimumab.R` (g/L, reference 14.8; baseline-only; exponent 0.293 on CL), `Yang_2021_cemiplimab.R` (g/L, reference 9.65; small positive exponent 0.184 on shared CL/Q), `Struemper_2017_belimumab.R` (g/L, reference 13.7; baseline-only; exponent 0.347 on CL).
- **Notes:** Mechanistically meaningful for monoclonal-antibody PK because endogenous IgG competes with the therapeutic mAb for FcRn-mediated recycling. The per-model `covariateData[[IGG]]$units` field is load-bearing (1 g/L ~= 100 mg/dL). Baseline-vs-time-varying status documented in `covariateData[[IGG]]$notes`. Distinct from `lIgG0` / IgG-as-a-state in mechanistic FcRn-competition TMDD models (e.g., `Valenzuela_2025_nipocalimab.R`), where IgG is a dynamic state, not a baseline covariate; use `IGG` only when the source paper treats IgG as a static (baseline) covariate column.

### IGM (**canonical for serum immunoglobulin M**)
- **Description:** Serum total immunoglobulin M (IgM) concentration (baseline). Used in IgRT population-PK analyses as a proxy for B-cell antibody-producing capacity / humoral function -- IgM is the first antibody produced after B-cell activation, so circulating IgM reflects ongoing B-cell activity prior to class-switching to IgG.
- **Units:** **g/L** (SI; canonical as of 2026-06-19). US-convention reporting in mg/dL converts via `IGM_gL = IGM_mgdL * 0.01`. 1 g/L = 100 mg/dL.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(IGM / ref)^exponent`. Reference values observed: 0.21 g/L (Cheng 2026, pooled PID + SAD pediatric cohort median).
- **Source aliases:** none known.
- **Example models:** `Cheng_2026_immunoglobulin.R` (g/L, reference 0.21; baseline-only; power exponent 0.11 on baseline IgG (CBAS) -- IgM enters as a humoral-capacity proxy that informs the endogenous-IgG baseline rather than directly modifying clearance).
- **Notes:** IgM is the immune-globulin class produced by activated B cells before class-switching, so it remains detectable in patients with hypogammaglobulinaemia who still have residual B-cell function. Scope: specific because the relevance of IgM as a covariate depends on the paper's mechanistic interpretation (in Cheng 2026 it acts on the endogenous-IgG baseline; future use cases may differ). Promote to general if a second paper retains IgM with consistent semantics. Ratified canonically on 2026-04-28.

### TBILI (**canonical for total bilirubin**)
- **Description:** Total serum bilirubin concentration.
- **Units:** **umol/L** (SI; canonical as of 2026-06-19). US-convention papers report in mg/dL; convert via `TBILI_umolL = TBILI_mgdL * 17.1` on data ingestion. 1 mg/dL = 17.1 umol/L. Models calibrated to mg/dL must apply an inline conversion in `model()` (e.g., `tbili_mgdL <- TBILI / 17.1  # SI umol/L -> US-convention mg/dL`).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(TBILI / ref)^exponent`.
- **Source aliases:**
  - `BIL` (legacy NONMEM short label for total bilirubin) -- used in `NA_NA_lidocaine.R` (DDMODEL00000281; binarised at threshold 0.53 mg/dL with `BIL_HIGH = as.integer(BIL > 0.53)`).
  - `BILT` (Urien 2005 capecitabine paper's NONMEM short label for "total bilirubin") -- used in `Urien_2005_capecitabine.R` (umol/L, reference 8.8; power scaling on the capecitabine non-transformation CL10 and on the 5'-DFUR -> 5-FU rate constant K34).
- **Example models:** `Yamada_2025_zolbetuximab.R` (mg/dL, reference 0.38; small positive exponent 0.0347 on V1), `NA_NA_lidocaine.R` (mg/dL, source column `BIL`; binary effect at threshold 0.53 mg/dL on the GX elimination rate constant K30), `Urien_2005_capecitabine.R` (umol/L, reference 8.8; source column `BILT`; positive exponent +0.32 on capecitabine non-transformation CL10 and negative exponent -0.36 on the 5'-DFUR -> 5-FU rate constant K34).
- **Notes:** Hepatic-function marker. Unit varies by paper (US convention mg/dL, SI convention umol/L; 1 mg/dL ~= 17.1 umol/L). The per-model `covariateData[[TBILI]]$units` field is load-bearing.

### DBIL (**canonical for direct (conjugated) bilirubin**)
- **Description:** Direct (conjugated) serum bilirubin concentration. Distinct from `TBILI`: direct bilirubin is the water-soluble glucuronide-conjugated fraction processed by hepatocytes and excreted in bile, so a rise in DBIL specifically flags impaired biliary excretion / cholestasis or intrahepatic shunting, whereas total bilirubin also captures unconjugated (indirect) hyperbilirubinaemia from haemolysis or Gilbert-type conjugation defects.
- **Units:** **umol/L** (SI; canonical as of 2026-06-19). US-convention papers report in mg/dL; convert via `DBIL_umolL = DBIL_mgdL * 17.1` on data ingestion. 1 mg/dL = 17.1 umol/L. Models calibrated to mg/dL must apply an inline conversion in `model()` (e.g., `dbil_mgdL <- DBIL / 17.1`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(DBIL / ref)^exponent`. Reference values observed: 2.6 umol/L (Chen 2015 voriconazole Chinese ICU cohort population median).
- **Source aliases:** none known.
- **Example models:** `Chen_2015_voriconazole.R` (umol/L, reference 2.6; negative exponent -0.40 on CL: `CL = TVCL * (DBIL / 2.6)^-0.40`).
- **Notes:** Hepatic-function / cholestasis-specific marker. Unit varies by paper (US convention mg/dL, SI convention umol/L; 1 mg/dL ~= 17.1 umol/L). Distinct entry from `TBILI` because direct vs total are not interchangeable: total = direct + indirect, and the two fractions track different pathophysiologic processes. Scope kept `specific` pending a second model that ratifies DBIL with consistent semantics; promote to `general` once corroborated.

### AST (**canonical for aspartate aminotransferase**)
- **Description:** Serum aspartate aminotransferase activity (baseline or time-varying).
- **Units:** **U/L** (SI; canonical as of 2026-06-19; IU/L is used interchangeably). Document per-model via `covariateData[[AST]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(AST / ref)^exponent`.
- **Source aliases:**
  - `SGOT` (serum glutamic-oxaloacetic transaminase; the legacy clinical-chemistry name for AST) -- used in `Quartino_2019_trastuzumab.R`.
- **Example models:** `Lu_2014_trastuzumabemtansine.R` (U/L, reference 27; small positive exponent 0.071 on CL), `Quartino_2019_trastuzumab.R` (IU/L, reference 24; source column `SGOT`; positive exponent 0.205 on linear CL).
- **Notes:** Hepatic-function marker. Commonly reported alongside `ALT` and `TBILI`; register a separate `ALT` canonical if a future paper requires it. `SGOT` is the older lab-reporting name; values and units are identical to `AST`.

### ALT (**canonical for alanine aminotransferase**)
- **Description:** Serum alanine aminotransferase activity (baseline or time-varying).
- **Units:** **U/L** (SI; canonical as of 2026-06-19; IU/L is used interchangeably). Document per-model via `covariateData[[ALT]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(ALT / ref)^exponent`.
- **Source aliases:**
  - `SGPT` (serum glutamic-pyruvic transaminase; the legacy clinical-chemistry name for ALT, paralleling `SGOT` -> `AST`) -- used in `NA_NA_lidocaine.R` (DDMODEL00000281; binarised at threshold 11 with `SGPT_HIGH = as.integer(SGPT > 11)`).
- **Example models:** `Nikanjam_2019_siltuximab.R` (U/L, reference 19; small negative exponent -0.096 on CL), `Melhem_2022_dostarlimab.R` (U/L, reference 18; small negative exponent -0.0585 on CL, time-varying), `NA_NA_lidocaine.R` (source column `SGPT`; binary effects at threshold 11 on the GX rate constant K30 and on the 2,6-xylidide rate constant K40).
- **Notes:** Hepatic-function marker. Commonly reported alongside `AST` and `TBILI`. Ratified canonically on 2026-04-24. `SGPT` is the older lab-reporting name; values and units are identical to `ALT`.

### ALP (**canonical for alkaline phosphatase**)
- **Description:** Serum alkaline phosphatase activity (baseline or time-varying). Liver-function / cholestasis marker; often used in popPK covariate models either as a continuous concentration with power scaling or as a binary above/below upper-limit-of-normal (ULN) indicator. When binarized inline, document the ULN threshold used (typically ~120 U/L for adults; varies by lab, age, and sex).
- **Units:** **U/L** (SI; canonical as of 2026-06-19; IU/L is used interchangeably). Document per-model via `covariateData[[ALP]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(ALP / ref)^exponent`, with a linear-deviation form, or binarized inline as `alp_high <- (ALP > uln)` for a binary >ULN indicator.
- **Source aliases:** none known.
- **Example models:** `Gupta_2016_lenvatinib.R` (binarized inline as `alp_high <- (ALP > 120)`; the source paper enters `ALP` as a 0/1 NONMEM indicator with `ALP = 1` when the ratio ALP/ULN > 1; multiplicative effect on CL/F: `0.883^alp_high`), `Wada_2023_sparsentan.R` (continuous power scaling on CL/F normalised to the 68 U/L overall cohort median: `(ALP / 68)^-0.208`; elevated alkaline phosphatase lowers apparent clearance, and ALKP was the only one of four hepatic markers -- ALKP, ALT, AST, total bilirubin -- retained after backward elimination in a study where hepatic impairment itself was not tested as a covariate).
- **Notes:** Liver-function / cholestasis marker; routine clinical-chemistry covariate. Commonly tested alongside `ALT` / `AST` / `GGT` / `TBILI`. Ratified canonically alongside the Gupta 2016 lenvatinib extraction.

### PHOS (**canonical for serum phosphate**)
- **Description:** Serum inorganic phosphate concentration, reported clinically as elemental phosphorus (baseline or time-varying). A routine serum-chemistry analyte and the pharmacodynamic biomarker of fibroblast growth factor receptor (FGFR) inhibition: FGFR1 blockade suppresses FGF23 signalling in the proximal tubule, reducing urinary phosphate excretion and raising serum phosphate, so on-treatment hyperphosphatemia is an on-target class effect of the FGFR-inhibitor drug class. Used both as a baseline covariate (predicting the on-treatment rise) and, in its change-from-baseline form, as an exposure surrogate.
- **Units:** **mmol/L** (SI; canonical). US-convention papers report in mg/dL; convert via `PHOS_mmolL = PHOS_mgdL * 0.3229` on data ingestion (elemental phosphorus, atomic mass 30.974 g/mol, so 1 mg/dL = 0.32285 mmol/L and 1 mmol/L = 3.0975 mg/dL). Models calibrated against mg/dL values must either apply an inline conversion in `model()` (e.g., `phos_mgdL <- PHOS * 3.0975`) or carry the coefficient rescaled to per-mmol/L units, with the conversion shown in the model file -- the same treatment `ALB` receives (see the `ALB` entry). Adult reference range is roughly 2.5-4.5 mg/dL (0.81-1.45 mmol/L).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- entered so far in an uncentered linear form `theta * PHOS`. Reference values observed: baseline serum phosphate was not tabulated by Gong 2023, but the observed quartile boundaries of the *change from baseline* at 13.5 mg once daily were 0.5, 2.1, 2.7, 3.5, and 6.3 mg/dL (0.16, 0.68, 0.87, 1.13, and 2.03 mmol/L; Gong 2023 Figure 3a).
- **Source aliases:**
  - `PHOS` -- the NONMEM `$INPUT` column name in the Gong 2023 Appendix S1 control stream, in mg/dL. Maps directly to the canonical name; unit conversion to SI mmol/L is required.
  - `PO4` / `PHOSPHORUS` / `IPHOS` (inorganic phosphorus) -- common clinical-laboratory reporting names for the same analyte.
- **Example models:** `Gong_2023_pemigatinib_phosphate.R` (mmol/L; uncentered linear coefficient -0.5730 mg/dL per mmol/L on the change-from-baseline endpoint, rescaled from the published -0.185 per mg/dL; the model's endpoint is retained in the paper's mg/dL so Table S2 remains usable verbatim).
- **Notes:** Distinct from `ALP` (alkaline phosphatase, an enzyme activity) and from `CPK` (creatine phosphokinase) despite the shared word stem -- `PHOS` is the electrolyte. Housed in the renal / hepatic function section because phosphate homeostasis is renally regulated and because source papers report it inside the same serum-chemistry covariate panel as `ALB` / `ALP` / `ALT` / `AST` / `TBILI` (Gong 2023 tests exactly that panel); the register has no separate electrolyte section. When a source paper models the *change from baseline* in serum phosphate as an endpoint rather than as a covariate, that is a model output, not a covariate column -- register only the baseline (or time-varying) input value here. Ratified canonically alongside the Gong 2023 pemigatinib extraction (operator decision, sidecar request 001 q1: canonical units SI mmol/L, matching the 2026-06-19 SI standardization applied to `ALB`).

### GGT (**canonical for gamma-glutamyltransferase**)
- **Description:** Serum gamma-glutamyltransferase activity (baseline or time-varying); hepatic / cholestatic biliary-enzyme marker.
- **Units:** **U/L** (SI; canonical as of 2026-06-19; IU/L is used interchangeably). Document per-model via `covariateData[[GGT]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with linear-deviation form `1 + theta * (GGT - ref)` or power scaling `(GGT / ref)^exponent`. Reference values observed: 33 U/L (Retlich 2015 popPK linagliptin median), 32.3 U/L (Retlich 2015 popPK/PD linagliptin median).
- **Source aliases:** none known.
- **Example models:** `Retlich_2015_linagliptin.R` (U/L, reference 33; linear-deviation effect on linagliptin CL with coefficient -0.0339 % per U/L deviation. The PK/PD layer uses GGT (reference 32.3 U/L) as a piecewise covariate on baseline DPP-4 activity BSL with a linear-deviation effect below GGT = 175 U/L and a constant +21.3% effect above the threshold).
- **Notes:** Liver-function / cholestasis marker; routine clinical-chemistry covariate. Commonly tested alongside `ALT` / `AST` / `ALP` / `TBILI`. The piecewise above/below-threshold form in Retlich 2015 reflects empirical saturation of the GGT-vs-DPP-4-activity relationship at extreme values. Ratified canonically alongside the Retlich 2015 linagliptin extraction.

### LDH (**canonical for serum lactate dehydrogenase**)
- **Description:** Serum lactate dehydrogenase activity (baseline or time-varying). General-purpose marker of tissue / cellular turnover; in oncology PK analyses it is interpreted as a disease-burden / cell-turnover proxy.
- **Units:** **U/L** (SI; canonical as of 2026-06-19; IU/L is used interchangeably). Document per-model via `covariateData[[LDH]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(LDH / ref)^exponent` or with an additive linear-on-log form `exp(coef * (log(LDH) - log(ref)))` (algebraically equivalent to `(log(LDH) / log(ref))^coef`). Reference values observed: 217 U/L (Sanghavi 2020).
- **Source aliases:**
  - `BLDH` (baseline LDH) -- used in `Sanghavi_2020_ipilimumab.R`.
- **Example models:** `Sanghavi_2020_ipilimumab.R` (linear-on-log form on CL with reference 217 U/L; coefficient 0.703), `NA_NA_lidocaine.R` (DDMODEL00000281; binary stratification at threshold 195 U/L switching the typical-value baseline of the 2,6-xylidide rate constant K40).
- **Notes:** Universal lab marker. Sanghavi 2020 log-transforms LDH because the distribution is heavily right-skewed (range 74-6,245 U/L over a median of 217); other papers may use a simple `(LDH/ref)^exponent` form. Document the functional form in `covariateData[[LDH]]$notes`.

### HEPIMP_MILD (**canonical for mild hepatic impairment indicator**)
- **Description:** 1 = mild hepatic impairment per the National Cancer Institute Organ Dysfunction Working Group (NCI ODWG) criteria, 0 = normal hepatic function or non-mild category. NCI ODWG mild = total bilirubin <= ULN with AST > ULN, OR total bilirubin > 1.0xULN to <= 1.5xULN with any AST.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (normal hepatic function; the moderate / severe categories are typically pooled into the reference for population PK analyses where mild impairment is the only category with non-trivial sample size).
- **Source aliases:**
  - `HEPIMP` (with values `1 = mild / 0 = others`) -- used in `Lin_2024_casirivimab.R`.
  - `Child-Pugh A` -- used in `Desai_2016_isavuconazole.R` and in the `Luo_2024_*_pbpk.R` CES1 semi-PBPK family, where the three `HEPIMP_MILD` / `HEPIMP_MOD` / `HEPIMP_SEV` indicators are mutually exclusive and select the Child-Pugh A / B / C physiology columns respectively, with all three 0 selecting the normal-hepatic-function column.
- **Example models:** `Lin_2024_casirivimab.R` (NCI ODWG mild; multiplicative fractional change on CL), `Lu_2022_patritumab.R` (NCI ODWG mild; paired with `HEPIMP_MOD_OR_MISSING`; multiplicative fractional effect 0.706 on CLDXd for mild impairment vs the normal-hepatic-function reference), `Desai_2016_isavuconazole.R` (Child-Pugh A; log-additive shift `e_hepimp_mild_cl = log(1.55 / 2.54) = -0.494` on CL and `e_hepimp_mild_q = log(38.8 / 33.678) = +0.142` on Q vs the healthy reference, paired with `HEPIMP_MOD` for the parallel Child-Pugh B stratum).
- **Notes:** Use this column when a model dichotomizes hepatic-impairment status as "mild vs. others" (i.e., normal + the rare moderate/severe cases pooled into the reference). For models that test moderate or severe as separate categories, register additional canonicals `HEPIMP_MOD` / `HEPIMP_SEV` rather than overloading this entry.

### HEPIMP_MOD_OR_MISSING (**canonical for composite moderate-or-data-missing hepatic impairment indicator**)
- **Description:** 1 = moderate hepatic impairment per the NCI ODWG criteria OR baseline hepatic-function data missing/unknown; 0 = normal hepatic function or any other (non-moderate, non-missing) category. Composite indicator used by source papers that pool the moderate-impairment subgroup with patients whose hepatic-function data are missing because both subgroups are individually too small to estimate as separate effects.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (normal hepatic function; mild impairment is typically captured by a separate `HEPIMP_MILD` indicator paired with this column, so all-zero corresponds to NCI ODWG group 1 = normal).
- **Source aliases:**
  - `HEPIMP_MOD_MISSING` -- prior canonical name (pre-2026-06-19 OR-disambiguation standardization).
  - `HEPATIC` / `HEPATIC_MOD_MISSING` -- informal NONMEM names for the composite group.
- **Example models:** `Lu_2022_patritumab.R` (paired with `HEPIMP_MILD`; multiplicative fractional effect 0.532 on CLDXd; the composite group pools n = 6 moderate-impairment patients with n = 6 missing/unknown patients per Lu 2022 Table S5).
- **Notes:** Specific scope because the composition of the "moderate or missing" group is paper-defined and the missing/unknown subgroup may have a different distribution of true hepatic-function status across studies. Use only when the source paper explicitly pools the moderate-impairment cases with missing-data cases under a single coefficient; for models that estimate moderate impairment separately (without pooling missing data), use the `HEPIMP_MOD` canonical instead. Ratified canonically on 2026-04-28. Renamed from `HEPIMP_MOD_MISSING` to `HEPIMP_MOD_OR_MISSING` on 2026-06-19 per the canonical-register standardization audit (operator decision: insert `OR` so the composite-group meaning is explicit in the name; the prior form could be misread as "moderate, with missing-data flag" rather than "moderate OR missing pooled").

### B2M (**canonical for serum beta-2-microglobulin**)
- **Description:** Serum beta-2-microglobulin concentration. Low-molecular-weight (~12 kDa) protein freely filtered at the glomerulus and reabsorbed in the proximal tubule; serum levels rise with renal impairment, with increased plasma-cell turnover in multiple myeloma, and with broader lymphoid-cell turnover. Used in oncology PK analyses both as a renal-function proxy and as a tumor-burden / disease-severity covariate.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(B2M / ref)^exponent`. Reference values observed: 3.90 mg/L (Fau 2020 multiple-myeloma cohort median).
- **Source aliases:** none; `B2M` is the universal abbreviation.
- **Example models:** `Fau_2020_isatuximab.R` (mg/L, reference 3.90; exponent 0.343 on the steady-state linear clearance CLinf).
- **Notes:** In multiple myeloma B2M is part of the International Staging System (ISS); in routine PK analyses it is interpreted simultaneously as a renal-function and disease-burden marker. Document the interpretation per-model via `covariateData[[B2M]]$notes`.

### CYSC (**canonical for serum cystatin C**)
- **Description:** Serum cystatin C concentration. Low-molecular-weight (~13 kDa) protease-inhibitor protein produced at a near-constant rate by all nucleated cells; freely filtered at the glomerulus and almost entirely reabsorbed and catabolized in the proximal tubule, so serum cystatin C reflects glomerular filtration rate (GFR) more directly than serum creatinine. Used as a renal-function covariate on the clearance of renally-eliminated drugs, especially in populations where creatinine-based estimates of GFR are unreliable (low muscle mass, normal-creatinine concentrations masking impaired GFR, ICU patients).
- **Units:** mg/L
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with either power scaling `(CYSC / ref)^exponent` (Chung 2013) or centred-linear scaling on the reciprocal `1 + e * (1/CYSC - ref_inv)` (Viberg 2006). Reference values observed: 0.91 mg/L (Chung 2013 vancomycin Korean adults with SCr <= 1.2 mg/dL; cohort median); 1.32 mg/L equivalent to 1/CYSC = 0.758 (mg/L)^-1 (Viberg 2006 cefuroxime adult patients with broad renal-function range; population-typical).
- **Source aliases:**
  - `Cystatin C` / `cystatin` -- Chung 2013 paper narrative and Table 2 footnote.
  - `CysC` -- Viberg 2006 paper narrative and Table 4 footnote.
- **Example models:** `Chung_2013_vancomycin.R` (mg/L, reference 0.91; power exponent -0.780 on CL: `CL_pop * (CYSC / 0.91)^-0.780`), `Viberg_2006_cefuroxime.R` (mg/L; centred-linear effect on 1/CYSC with coefficient 1.43 per (mg/L)^-1 and reference 0.758 (mg/L)^-1 on CL: `CL_pop * (1 + 1.43 * (1/CYSC - 0.758))`).
- **Notes:** Cystatin C is freely filtered at the glomerulus and is not secreted by the renal tubule (unlike creatinine), so it is less sensitive to muscle mass, body composition, and tubular-secretion blockers. Reference ranges 0.57-0.97 mg/L for adult females and 0.65-1.10 mg/L for adult males (Chung 2013 Methods; Roche Cobas 6000 particle-enhanced immunoturbidimetric assay). Distinct from `CREAT` (serum creatinine) -- the two are commonly reported alongside each other and can enter the same model as separate covariates (as in Chung 2013, where CYSC explains 62% of CL variability vs SCr 13%). The functional form (power on CYSC vs centred-linear on 1/CYSC) is paper-specific and lives in the model file; the canonical column is the underlying biomarker concentration in mg/L.

### HEPIMP (**canonical for hepatic-impairment indicator (NCI ODWG classification)**)
- **Description:** Baseline hepatic-impairment indicator per the National Cancer Institute Organ Dysfunction Working Group (NCI ODWG) classification: 1 = mild or worse hepatic impairment (group >= 2 = mild, moderate, or severe), 0 = normal hepatic function (group 1).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (normal hepatic function, NCI ODWG group 1).
- **Source aliases:**
  - `BHPTGRPN` (categorical: 1 = normal, 2 = mild, 3 = moderate, 4 = severe; 9999 = missing) -- used in `Lu_2019_polatuzumab.R`. Decompose: `HEPIMP = as.integer(BHPTGRPN > 1.5 & BHPTGRPN != 9999)`.
  - `HEP_IMP` -- retired canonical name; replaced by `HEPIMP` for consistency with the `HEPIMP_MILD` family.
- **Example models:** `Lu_2019_polatuzumab.R` (multiplicative effect on FRAC_NS = 1.19, applied as `1.19^HEPIMP`).
- **Notes:** NCI ODWG classification (Ramalingam SS et al., J Clin Oncol 2010;28:4507) groups subjects by total bilirubin and AST: group 1 = normal, group 2 = mild (TBILI <= ULN and AST > ULN, or TBILI > 1-1.5 x ULN), group 3 = moderate (TBILI > 1.5-3 x ULN), group 4 = severe (TBILI > 3 x ULN). Source papers typically pool groups 2-4 versus group 1 for a binary indicator because the impaired-liver subgroups are individually small. If a future model needs finer resolution (separate effects for mild vs moderate-or-worse), add a parallel `HEPIMP_MOD` canonical rather than overloading this one.

### NASF (**canonical for nonalcoholic steatohepatitis severity score combining NAS and fibrosis staging**)
- **Description:** Integer composite severity score for nonalcoholic steatohepatitis (NASH), summing the NAFLD activity score (NAS: steatosis 0-3 + hepatocyte ballooning 0-2 + lobular inflammation 0-3, total 0-8) and the fibrosis staging score (0 = absent, 1 = perisinusoidal / pericellular, 2 = periportal, 3 = bridging, 4 = cirrhosis). Total range 0-12; healthy subjects without biopsy-confirmed NASH are assigned NASF = 0 by convention. Scores below 5 reflect a benign form of NAFLD; scores >= 5 reflect biopsy-confirmed NASH.
- **Units:** (count, 0-12)
- **Type:** count
- **Scope:** specific
- **Reference category:** n/a -- used with a linear effect on `log(NASF / 4)` for NASF >= 4 and zero contribution for NASF < 4 (so NASF = 4 and any NASF < 4 reduce to the typical-value reference). The cutoff of 4 distinguishes patients with a benign form of NAFLD from those with biopsy-confirmed NASH (Pierre 2017 Methods 'Covariate analysis' and references 31 and 34).
- **Source aliases:**
  - `NASF` -- used in `Pierre_2017_morphine.R` (Pierre 2017 Methods 'Covariate analysis').
- **Example models:** `Pierre_2017_morphine.R` (linear effect on `log(NASF / 4)` for NASF >= 4 with coefficient -0.628 on M3G clearance: `CL_M3G_i = CL_M3G_pop * (1 + e_nasf_cl_m3g * log(NASF / 4))` for NASF >= 4 and `CL_M3G_i = CL_M3G_pop` for NASF < 4; higher NASF reduces M3G clearance via reduced biliary excretion and increased basolateral efflux of M3G into systemic circulation).
- **Notes:** The NAFLD activity score (NAS) component is the histology score described by Bondini 2007 / Kleiner 2005 and references therein; the fibrosis staging is the Brunt / NASH-CRN system. The combined NASF score is the noninvasive staging proposed by Santiago-Rolon 2015 (Proc R Health Sci J 34:189-194) and used by Angulo 2007 as the NAFLD Fibrosis Score cutoff. Scope: specific because the precise cutoff (NASF >= 4) and the linear-on-log functional form are Pierre 2017's modeling choice; future papers may model NASF or its components differently. Distinct from `HEPIMP*` (NCI ODWG oncology-trial hepatic-impairment categories) and from continuous liver enzymes (ALT, AST, ALP) -- NASF is a biopsy-derived disease-severity ordinal specific to NAFLD / NASH. Ratified canonically on 2026-05-18 alongside the Pierre 2017 morphine extraction.

### HEPIMP_SEV (**canonical for severe hepatic impairment indicator**)
- **Description:** 1 = severe hepatic impairment, 0 = normal hepatic function or less-than-severe category. The classification scheme that defines "severe" is paper-specific and must be documented in per-model `covariateData[[HEPIMP_SEV]]$notes`. Two schemes are commonly encountered:
  - **NCI ODWG group 4**: total bilirubin > 3 x ULN with any AST (Ramalingam SS et al., J Clin Oncol 2010;28:4507).
  - **Child-Pugh Class C**: composite score 10-15 across bilirubin, albumin, INR, ascites, and encephalopathy.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-severe category: normal, mild, or moderate; the model typically uses HEPIMP_SEV alongside other severity-specific indicators that partition the non-severe pool further).
- **Source aliases:**
  - `Child-Pugh Class C` -- used in `vanderWalt_2013_dapagliflozin.R` (covariate effects on CLP_M15 and V2M; the paper dichotomizes severe hepatic impairment per the Child-Pugh classification).
- **Example models:** `vanderWalt_2013_dapagliflozin.R` (Child-Pugh Class C; multiplicative fractional effects -0.422 on the dapagliflozin -> D3OG metabolic clearance and +1.33 on the D3OG central volume of distribution; paper text "With severe HI (Child-Pugh Class C), CLP M15 decreased by 41% and V2M increased by 134%").
- **Notes:** Use this column when a model dichotomizes severe hepatic impairment as a separate indicator from milder categories. The classification scheme (NCI ODWG vs Child-Pugh vs other) is paper-specific and must be documented per-model. For composite "moderate-or-severe" pooled indicators, use the parallel `HEPIMP_MODSEV` canonical rather than overloading this entry. Companion to `HEPIMP_MILD` (mild only) and `HEPIMP_MODSEV` (moderate + severe pooled); the SKILL.md anticipates each severity level as its own canonical when the source paper tests them as separate covariates.

### HEPIMP_MODSEV (**canonical for composite moderate-or-severe hepatic impairment indicator**)
- **Description:** 1 = moderate or severe hepatic impairment, 0 = normal hepatic function or mild impairment. Composite indicator used by source papers that pool the moderate and severe subgroups because the severe subgroup alone is too small to support a separate covariate-effect estimate. Distinct from `HEPIMP_MOD_OR_MISSING` (which pools moderate cases with missing-data cases, not with severe cases). The classification scheme that defines the cut points is paper-specific and must be documented in per-model `covariateData[[HEPIMP_MODSEV]]$notes`. Two schemes are commonly encountered:
  - **NCI ODWG groups 3-4 pooled**: total bilirubin > 1.5 x ULN with any AST.
  - **Child-Pugh Class B or C pooled**: composite score >= 7.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (normal hepatic function or mild impairment; the indicator is mutually exclusive with HEPIMP_MILD, so all-zero on both indicators corresponds to the normal-function reference and HEPIMP_MILD=1 with HEPIMP_MODSEV=0 corresponds to mild-only).
- **Source aliases:**
  - `Child-Pugh Class B,C` -- used in `vanderWalt_2013_dapagliflozin.R` (covariate effects on V3P and CLM; the paper dichotomizes moderate-or-severe hepatic impairment per the Child-Pugh classification).
- **Example models:** `vanderWalt_2013_dapagliflozin.R` (Child-Pugh Class B or C; multiplicative fractional effects -0.600 on the dapagliflozin peripheral volume of distribution V3P and -0.293 on the D3OG renal clearance CLM; paper text "Moderate or severe HI (Child-Pugh Class B or C) decreased CLM and the peripheral volume of distribution of dapagliflozin (V3P) by 29 and 60%, respectively").
- **Notes:** Use this column when a model pools moderate-and-severe hepatic impairment under a single coefficient (typically because the severe subgroup alone is too small to estimate as its own effect). The classification scheme (NCI ODWG vs Child-Pugh vs other) is paper-specific and must be documented per-model. Companion to `HEPIMP_MILD` (mild only) and `HEPIMP_SEV` (severe only). Distinct from `HEPIMP_MOD_OR_MISSING` (which pools moderate cases with subjects whose hepatic-function data are missing/unknown, not with severe cases). The composite mod-or-sev pooling is a different load-bearing convention than the mod-or-missing pooling, so the two canonicals must remain separate.

### LIVER_RESECT_MAJOR (**canonical for major hepatic-resection indicator**)
- **Description:** 1 = major hepatic resection (three or more liver segments resected), 0 = minor hepatic resection (two liver segments resected). Time-fixed per subject, describing the surgical exposure rather than a chronic hepatic-impairment state. Distinct from the `HEPIMP_*` family (which classifies chronic liver disease by NCI ODWG / Child-Pugh criteria) because major hepatic resection produces a transient reduction in metabolic capacity that recovers within 2-3 weeks in patients with normal underlying liver function (Ollier 2015 Discussion citing Fortner et al., Ann Surg 1987).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (minor resection; two liver segments)
- **Source aliases:**
  - `NSH` -- used in `Ollier_2015_ropivacaine.R` (Ollier 2015 paper notation, dichotomised: NSH = 0 for 2 segments, NSH = 1 for 3, 4 or 5 segments per Methods 'Covariate model').
- **Example models:** `Ollier_2015_ropivacaine.R` (multiplicative exponential effect on free-ropivacaine clearance: `Cl = exp(lcl + etalcl) * exp(beta_NSH * LIVER_RESECT_MAJOR)` with `beta_NSH = log(620/1310) = -0.7480`; Cl drops from 1310 L/h at LIVER_RESECT_MAJOR = 0 to 620 L/h at LIVER_RESECT_MAJOR = 1, a 53% reduction).
- **Notes:** Specific scope pending a second liver-resection-popPK model to ratify the name. The dichotomisation at >= 3 segments reflects the Ollier 2015 analytical choice; a future model that parameterises resection extent as a continuous count of segments should introduce a separate canonical rather than repurpose LIVER_RESECT_MAJOR. Do not confuse with the `HEPIMP_*` family: `HEPIMP_MILD` / `HEPIMP_MOD` / `HEPIMP_SEV` etc. describe chronic hepatic impairment classified from labs (bilirubin, AST) or Child-Pugh scores, whereas LIVER_RESECT_MAJOR is a surgical-exposure covariate that acts on drug clearance for the transient postoperative window.

### RENALIMP_MOD (**canonical for moderate renal impairment indicator**)
- **Description:** 1 = moderate renal impairment, 0 = normal renal function, mild renal impairment, or any non-moderate category. Binary categorical indicator used by source papers that test renal-impairment status as a discrete covariate rather than via continuous creatinine clearance (`CRCL`). The classification scheme that defines "moderate" is paper-specific and must be documented in per-model `covariateData[[RENALIMP_MOD]]$notes`. Two schemes are commonly encountered:
  - **Cockcroft-Gault CrCl 30-50 mL/min** (Morris 2011 ZP-006 study eligibility criteria).
  - **FDA / EMA labeling: CrCl 30-59 mL/min** (US FDA Guidance for Industry: PK in Patients with Impaired Renal Function, 2020).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (normal renal function or mild renal impairment; severe and end-stage categories are typically pooled with the reference or excluded from the source cohort).
- **Source aliases:**
  - `RENAL` (binary 1 = moderate / 0 = healthy or mild) -- used in `Morris_2011_telapristone.R` (Morris 2011 Methods "Covariate Analysis", paragraph after Equation for the proportional covariate model).
- **Example models:** `Morris_2011_telapristone.R` (proportional fractional effect on the telapristone absorption rate constant Ka; coefficient -0.744 yielding `Ka = Ka_typ * (1 + e_renalimp_mod_ka * RENALIMP_MOD)`, i.e., a 74% decrease in Ka in moderate-renal-impaired subjects vs the reference healthy/mild-renal cohort), `CarlssonPetri_2018_semaglutide.R`.
- **Notes:** Use this column when a model dichotomizes moderate renal impairment as a separate indicator (typically because severe / ESRD subjects are not in the source cohort and the moderate group is the only renal-impairment stratum with non-trivial sample size). The classification scheme (Cockcroft-Gault vs MDRD vs CKD-EPI; mL/min vs mL/min/1.73 m^2) is paper-specific and must be documented per-model. Companion to `CRCL` (continuous Cockcroft-Gault clearance), `RENALIMP_MILD` and `RENALIMP_SEV` (parallel canonicals reserved for future extractions following the `HEPIMP_*` family precedent). The Morris 2011 Discussion notes that the effect is "not directly attributed to renal impairment (in terms of decrease glomerular filtration rate/changes in creatinine clearance)" but "attributed to some other disease precluding/resulting in renal impairment or other pathophysiologic states induced by renal impairment (e.g., delayed gastric emptying as a result of diabetes)", so the binary indicator captures the cohort allocation rather than a mechanistic GFR effect. Ratified canonically on 2026-06-09 alongside the Morris 2011 telapristone extraction.

### RENALIMP_MILD (**canonical for mild renal impairment indicator**)
- **Description:** 1 = mild renal impairment, 0 = normal renal function or any non-mild category. Binary categorical indicator used by source papers that test renal-impairment status as a discrete covariate rather than via continuous creatinine clearance (`CRCL`). The classification scheme that defines "mild" is paper-specific and must be documented in per-model `covariateData[[RENALIMP_MILD]]$notes`. A common scheme (used by Carlsson Petri 2018 and mirrored in FDA / EMA labeling guidance for renal-impairment PK) is eGFR 60-89 mL/min/1.73 m^2 as mild versus eGFR >= 90 mL/min/1.73 m^2 as normal.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (normal renal function; the moderate / severe categories are typically encoded as separate `RENALIMP_MOD` / `RENALIMP_SEV` indicators alongside this one, with all three set to 0 for the normal-renal-function reference).
- **Source aliases:**
  - `RENAL` (multi-level categorical with mild as one level; decompose to binary indicators) -- used in `CarlssonPetri_2018_semaglutide.R` (Carlsson Petri 2018 Methods 'Renal impairment groups were categorized [by estimated glomerular filtration rate (eGFR)] as normal function (eGFR >= 90 ml/min/1.73 m^2), mild (eGFR = 60-89 ml/min/1.73 m^2), moderate (eGFR = 30-59 ml/min/1.73 m^2), or severe (eGFR < 30 ml/min/1.73 m^2) renal impairment').
- **Example models:** `CarlssonPetri_2018_semaglutide.R` (multiplicative CL/F ratio 0.948 for mild renal impairment vs normal reference, encoded as `e_renal_mild_cl^RENALIMP_MILD` per Table S3).
- **Notes:** Sister canonical to `RENALIMP_MOD` and `RENALIMP_SEV`; the trio decomposes a four-level renal-function classification (normal / mild / moderate / severe) into three binary indicators with normal as the implicit reference. Follows the `HEPIMP_*` family precedent (mild / moderate / severe as parallel binary canonicals). Companion to continuous `CRCL`. Founded alongside the CarlssonPetri_2018_semaglutide extraction where all three renal-impairment categories were tested simultaneously.

### RENALIMP_SEV (**canonical for severe renal impairment indicator**)
- **Description:** 1 = severe renal impairment, 0 = normal renal function or any non-severe category. Binary categorical indicator used by source papers that test renal-impairment status as a discrete covariate rather than via continuous creatinine clearance (`CRCL`). The classification scheme that defines "severe" is paper-specific and must be documented in per-model `covariateData[[RENALIMP_SEV]]$notes`. A common scheme (used by Carlsson Petri 2018 and mirrored in FDA / EMA labeling guidance for renal-impairment PK) is eGFR < 30 mL/min/1.73 m^2 as severe.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (normal renal function; typically paired with `RENALIMP_MILD` and `RENALIMP_MOD` so the trio of indicators is mutually exclusive and normal renal function is the shared reference).
- **Source aliases:**
  - `RENAL` (multi-level categorical with severe as one level; decompose to binary indicators) -- used in `CarlssonPetri_2018_semaglutide.R`.
- **Example models:** `CarlssonPetri_2018_semaglutide.R` (multiplicative CL/F ratio 0.920 for severe renal impairment vs normal reference, encoded as `e_renal_sev_cl^RENALIMP_SEV` per Table S3; the Discussion notes the severe stratum came only from SUSTAIN 6 and might be confounded by trial effect, and that a dedicated renal-impairment clinical pharmacology trial found no clinically relevant effect).
- **Notes:** Sister canonical to `RENALIMP_MILD` and `RENALIMP_MOD`; the trio decomposes a four-level renal-function classification (normal / mild / moderate / severe) into three binary indicators with normal as the implicit reference. Follows the `HEPIMP_*` family precedent. Companion to continuous `CRCL`. Founded alongside the CarlssonPetri_2018_semaglutide extraction.

### CPK (**canonical for serum creatine phosphokinase / creatine kinase**)
- **Description:** Serum creatine phosphokinase (also called creatine kinase, CK) activity (baseline or time-varying). Skeletal-muscle / cardiac-muscle injury and turnover marker; in macrophage-targeted PK/PD analyses (axatilimab, anti-CSF-1R) it is interpreted as a Kupffer-cell / tissue-macrophage clearance surrogate because Kupffer cells participate in the elimination of circulating muscle-derived enzymes.
- **Units:** **U/L** (SI; canonical as of 2026-06-19; IU/L is used interchangeably). Document per-model via `covariateData[[CPK]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(CPK / ref)^exponent`. Reference values observed: 63 U/L (Yang 2024 axatilimab; pooled-cohort median).
- **Source aliases:**
  - `BLCPK` (baseline CPK) -- informal usage in Yang 2024.
- **Example models:** `Yang_2024_axatilimab.R` (baseline-only covariate on baseline NCMC concentration `BL_NCMC` with power exponent 0.376; reference 63 U/L).
- **Notes:** Muscle-origin enzyme distinct from `AST` / `ALT` (hepatic) and `LDH` (general tissue turnover). Yang 2024 uses CPK alongside `AST` and `LDH` as tracked safety biomarkers. Per-model `covariateData[[CPK]]$notes` should document baseline-vs-time-varying status and the clinical interpretation in the source population (skeletal-muscle injury, macrophage-clearance surrogate, or both). Distinct from any model state variable representing CPK time-course dynamics -- covariate column is the pre-dose laboratory observation.

### DIAL (**DEPRECATED -- superseded by `RRT_HEMODIAL_ACTIVE`**)
- **Description:** Deprecated entry. The 2026-06-09 audit renamed this canonical to `HEMODIALYSIS`, and the 2026-06-19 audit then renamed it again to the current `RRT_HEMODIAL_ACTIVE` (see that entry below for full documentation). The `DIAL` form is retained ONLY as a `source_alias` of `RRT_HEMODIAL_ACTIVE` so that pre-2026-06-09 data CSVs continue to load.
- **Notes:** Do not use for new models. See `RRT_HEMODIAL_ACTIVE` for the current canonical.

### BFR (**canonical for blood flow rate through the extracorporeal circuit during dialysis**)
- **Description:** Instantaneous blood flow rate through the extracorporeal circuit during an active dialysis session. Time-varying within subject; meaningful only when `DIAL = 1` -- in the interdialytic period the value is sentinel and the Michaels-equation term is gated off by `DIAL`.
- **Units:** mL/min
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- enters the Michaels equation together with `DFR` and a hemodialyzer mass-transfer-area coefficient. Values investigated in the ratification source were 200, 300, and 400 mL/min (Liesenfeld 2013 Methods, Study Design; Table 1).
- **Source aliases:** none known.
- **Example models:** `Liesenfeld_2013_dabigatran.R`.
- **Notes:** Pairs with `DIAL` (binary on/off gate) and `DFR` (dialysate flow rate). Ratified canonically on 2026-05-16 alongside the Liesenfeld 2013 dabigatran extraction.

### DFR (**canonical for dialysate flow rate through the extracorporeal circuit during dialysis**)
- **Description:** Instantaneous dialysate flow rate through the extracorporeal circuit during an active dialysis session. Time-varying within subject; meaningful only when `DIAL = 1`.
- **Units:** mL/min
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- enters the Michaels equation together with `BFR`. The ratification source fixed DFR at 700 mL/min throughout (Liesenfeld 2013 Methods, Study Design) and additionally simulated 500 mL/min (Methods, Simulations).
- **Source aliases:** none known.
- **Example models:** `Liesenfeld_2013_dabigatran.R`.
- **Notes:** Pairs with `DIAL` and `BFR`. Ratified canonically on 2026-05-16 alongside the Liesenfeld 2013 dabigatran extraction.

### ECMO_PUMP_SPEED (**canonical for extracorporeal-membrane-oxygenation centrifugal-pump rotational speed**)
- **Description:** Rotational speed of the extracorporeal-membrane-oxygenation (ECMO) centrifugal blood pump during VA-ECMO or VV-ECMO support. Continuous covariate; treated as time-fixed per subject in Yang 2017 (the per-subject pump speed reported in the source data was the prevailing speed during the PK sampling window; pump-speed adjustments during sampling were not modelled as time-varying). For future models that resolve session-level changes in pump speed, the covariate is naturally time-varying and the per-model `covariateData[[ECMO_PUMP_SPEED]]$notes` should document the time resolution.
- **Units:** RPM (revolutions per minute)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- enters as a power-centered effect `(ECMO_PUMP_SPEED / ref)^exponent`. The reference value is paper-specific (median pump speed in the source cohort): Yang 2017 uses 2350 RPM (cohort median; Table 1 / Results: "median ECMO pump speeds of 2350 RPM").
- **Source aliases:** none known.
- **Example models:** `Yang_2017_remifentanil.R` (power effect on remifentanil CL: `(ECMO_PUMP_SPEED / 2350)^2.04`; higher pump speed associated with higher CL, hypothesised mechanism is increased spontaneous drug degradation at high centrifugal-pump shear).
- **Notes:** Distinct from blood flow rate (BFR, mL/min) and from dialysate flow rate (DFR, mL/min) which characterise renal-replacement-therapy circuits. ECMO circuits use a centrifugal pump whose rotational speed sets the cardiac-output augmentation; the resulting blood flow rate (LPM) is a separate measured quantity that depends on circuit resistance and patient hemodynamics. Yang 2017 tested both ECMO pump speed (RPM) and ECMO flow rate (LPM) and only pump speed was retained as a significant covariate (ECMO flow rate was not significantly associated with PK parameters). Ratified canonically on 2026-05-23 alongside the Yang 2017 remifentanil extraction.

### Q_CVVH (**canonical for continuous venovenous hemofiltration circuit flow rate during ECMO**)
- **Description:** Flow rate through the continuous venovenous hemofiltration (CVVH) filter when it is placed parallel to the ECMO circuit. The filter contributes solute removal that adds to the drug's apparent CL whenever CVVH is active; the column is zero in subjects whose ECMO circuit has no CVVH filter or whose CVVH is currently off. Continuous covariate; naturally time-varying although the source paper treats the per-subject median as time-fixed (the median Q_CVVH per individual did not vary much during sampling).
- **Units:** mL/min
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- enters as a power-centered effect `(Q_CVVH / ref)^exponent` with the source paper additionally gating the effect to 1 when `Q_CVVH = 0` (no CVVH active). The reference value is paper-specific (cohort median during CVVH operation): Ahsman 2010 uses 193 mL/min (cohort median; Table 1).
- **Source aliases:**
  - `QCVVH` -- used in `Ahsman_2010_cefotaxime.R` (paper notation, subscript dropped).
- **Example models:** `Ahsman_2010_cefotaxime.R` (power effect on DACT metabolite CL: `(Q_CVVH / 193)^0.72` when CVVH active, 1 otherwise).
- **Notes:** Distinct from `ECMO_PUMP_SPEED` (the centrifugal-pump rotational speed setting cardiac-output augmentation) and from `BFR` / `DFR` (blood-flow / dialysate-flow rates characterising standalone hemodialysis circuits): Q_CVVH is the slow-clearance flow through a CVVH filter integrated INTO an ECMO circuit. Distinct from `RRT_CRRT_STATUS` (binary on / off indicator for continuous RRT) which captures presence-of-therapy rather than its rate. Future ECMO + CVVH popPK extractions that quantify CVVH flow should reuse this canonical and extend the example list. Ratified canonically alongside the Ahsman 2010 cefotaxime extraction.


### T_ECMO (**canonical for time since ECMO cannulation start (beginning of extracorporeal circulation)**)
- **Description:** Wall-clock time elapsed since the patient was cannulated onto ECMO (the beginning of extracorporeal circulation). Zero before cannulation; positive and monotonically increasing throughout the ECMO support period. Time-varying. Whether the value continues to increase after decannulation, resets at decannulation, or is censored at decannulation is paper-specific and must be documented in `covariateData[[T_ECMO]]$notes`; when the source model uses a saturating Hill / Emax form (Kleiber 2017) it does not matter whether T_ECMO continues past decannulation because the effect is already saturated.
- **Units:** hour
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- enters as a sigmoidal Emax multiplier `1 + Emax * T_ECMO^gamma / (T50_EC^gamma + T_ECMO^gamma)` (Kleiber 2017 Eq 9) or a saturating half-life form (Ahsman 2010 Eq 10-style). At `T_ECMO = 0` (pre-ECMO baseline) the Hill ratio evaluates to 0 and the multiplier collapses to 1.
- **Source aliases:**
  - `tec` / `t_EC` -- used in `Kleiber_2017_clonidine.R` (paper notation, Eq 9 and Table 3).
- **Example models:** `Kleiber_2017_clonidine.R` (sigmoidal Emax effect on V: `V x (1 + 0.55 * T_ECMO^18.5 / (51.7^18.5 + T_ECMO^18.5))`; V approaches 1.55 * V_pop * (WT/70) on full ECMO and recovers to V_pop * (WT/70) before ECMO).
- **Notes:** Conceptually the on-ECMO mirror of `T_POST_ECMO` -- `T_ECMO` measures time since cannulation, `T_POST_ECMO` measures time since decannulation. A model that uses BOTH typically gates each by an additional ECMO-on indicator or by the natural floor (`T_ECMO = 0` before cannulation, `T_POST_ECMO = 0` before-and-during ECMO). Kleiber 2017 uses only `T_ECMO`; Ahsman 2010 uses only `T_POST_ECMO`. Distinct from on-ECMO duration covariates (per-run summary of total ECMO hours) and from `ECMO_PUMP_SPEED` (instantaneous centrifugal-pump rotational speed). Anticipated by the existing `T_POST_ECMO` entry's Notes (which referenced `t_EC` as "tested but not retained" in Ahsman 2010); ratified canonically alongside the Kleiber 2017 clonidine extraction where the covariate is retained.


### T_POST_ECMO (**canonical for time after ECMO decannulation (end of extracorporeal circulation)**)
- **Description:** Wall-clock time elapsed since the patient was decannulated from ECMO (the end of extracorporeal circulation). Zero before and during ECMO support; becomes positive after decannulation and increases linearly with time. Naturally time-varying. Used as a power-centred recovery-time covariate on PK parameters whose values change as the patient transitions off ECMO back to native circulation.
- **Units:** hour
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- enters as a power-centered effect `(T_POST_ECMO / ref)^exponent` with the source paper additionally gating the effect to 1 when `T_POST_ECMO = 0` (before or during ECMO). The reference value is paper-specific: Ahsman 2010 uses 100 h (Appendix).
- **Source aliases:**
  - `tEND` -- used in `Ahsman_2010_cefotaxime.R` (paper notation, subscript dropped).
- **Example models:** `Ahsman_2010_cefotaxime.R` (power effect on parent CTX CL: `(T_POST_ECMO / 100)^0.16` when post-ECMO, 1 otherwise; power effect on DACT metabolite CL: `(T_POST_ECMO / 100)^0.53` when post-ECMO, 1 otherwise).
- **Notes:** Conceptually a time-since-event covariate similar to `TPP` (time postpartum) and `T_ENTRY` (per-subject study-entry time) but anchored to ECMO decannulation. Distinct from `t_EC` (time since ECMO START / beginning of extracorporeal circulation, which the source paper tested but did not retain) and from on-ECMO duration covariates. The gating-at-zero convention is structural: during ECMO the recovery-time covariate is undefined / not yet started, so the (0 / ref)^exponent expression would collapse pathologically and the implementer must short-circuit the multiplier to 1. Future ECMO popPK extractions that include a post-decannulation recovery phase should reuse this canonical and extend the example list. Ratified canonically alongside the Ahsman 2010 cefotaxime extraction.


### FILT_AGE_HI (**canonical for dialysis-filter-age above-48-hour indicator**)
- **Description:** Binary indicator that the in-use extracorporeal renal-replacement-therapy (CVVHDF / CVVH / SLED / EDD-f / IHD) hemofilter or dialyser membrane has been in continuous use for more than 48 hours at the time of dosing / sampling. 1 = filter is older than 48 h; 0 = filter is within the first 48 h of use (the fresh-filter reference). Subject-level binary in the source data (Patel 2011 had 1 of 10 patients on a > 48 h filter at the start of CVVHDF treatment; the rest had fresh filters), but the covariate is naturally time-varying within subject as the filter ages and is replaced -- record the per-model time resolution in `covariateData[[FILT_AGE_HI]]$notes`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (fresh filter, less than 48 h in use). Models that apply a multiplicative reduction to the dialysis clearance arm should encode the effect as `cl_dialysis * e_filt_age_hi_<arm>^FILT_AGE_HI` so the FILT_AGE_HI = 0 case recovers the fresh-filter baseline.
- **Source aliases:** none known; Patel 2011 retains the effect via the parameter `ffCL_CVVHDF` (a multiplicative factor on the CVVHDF clearance arm) rather than naming a data-set column. When ratified the column was assigned the canonical name FILT_AGE_HI in the model file.
- **Example models:** `Patel_2011_fluconazole.R` (multiplicative factor `e_filt_age_hi_cl_renal^FILT_AGE_HI` on the CVVHDF clearance arm: `e_filt_age_hi_cl_renal = 0.368`, so > 48 h filters give 36.8% of fresh-filter CVVHDF efficiency; bootstrap 95% CI 0.326-0.426 from Patel 2011 Table 2; Delta-OBJ = -11.46 vs base model).
- **Notes:** Distinct from `RRT_CRRT_STATUS` (subject-level binary indicator that ANY continuous / extended renal-replacement therapy is occurring) and from `RRT_HEMODIAL_ACTIVE` (per-time-point on/off gate for the dialysis-clearance term in within-subject time-varying dialysis-clearance models). `FILT_AGE_HI` gates WITHIN the CRRT-on subset by membrane age -- meaningful only when RRT_CRRT_STATUS = 1 or RRT_HEMODIAL_ACTIVE = 1. The > 48 h threshold is paper-specific (Patel 2011 Methods note that membrane fouling and clot formation in the hemofilter pores progressively reduce dialysis efficiency, with the inflexion typically observed near 48 h). Future extractions that retain a finer filter-age structure (continuous filter_age_hours or multiple thresholds) should reuse FILT_AGE_HI for the binary > 48 h case and add a separate canonical for the continuous form. Scope kept specific pending a second model that ratifies the same > 48 h threshold; promote to general once corroborated. Ratified canonically on 2026-06-09 alongside the Patel 2011 fluconazole extraction.


### RRT_HEMODIAL_ACTIVE (**canonical for hemodialysis-active indicator (time-varying per-session gate)**)
- **Description:** Within-subject time-varying indicator for whether an extracorporeal renal-replacement-therapy session (intermittent hemodialysis, hemofiltration, or hemodiafiltration) is currently running. 1 during the session; 0 in the interdialytic interval and in non-dialysed subjects.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no dialysis running). Models that compose an additional `cl_hemodialysis` term should gate it by `RRT_HEMODIAL_ACTIVE = 1` so that the interdialytic clearance reduces to the intrinsic body clearance.
- **Source aliases:**
  - `HEMODIALYSIS` -- prior canonical name (2026-06-09 -- 2026-06-19); the 2026-06-19 RRT family standardization renamed it to `RRT_HEMODIAL_ACTIVE` to align with the sibling `RRT_HEMODIAL_STATUS` / `RRT_CRRT_STATUS` shape.
  - `DIAL` -- legacy form (used in Liesenfeld 2013, Jacobs 2016, Veinstein 2013 prior to the 2026-06-09 rename to the more explicit `HEMODIALYSIS`).
  - `IHD_ON`, `HD_ACTIVE`, `RRT_ACTIVE` -- variant abbreviations used in adjacent ESRD / CRRT popPK literature.
- **Example models:**
  - `Liesenfeld_2013_dabigatran.R` (Michaels-equation gate; `cl_total <- cl + RRT_HEMODIAL_ACTIVE * Michaels(BFR, DFR, KoA)`; the dialysis arm is derived from blood flow rate, dialysate flow rate, and a hemodialyzer mass-transfer-area coefficient).
  - `Jacobs_2016_colistin.R` (additive on/off gate of fixed device-level hemodialysis-clearance constants for CMS and colistin; `cl_tot <- cl + RRT_HEMODIAL_ACTIVE * cl_hd_cms`).
  - `Veinstein_2013_gentamicin.R` (additive arm with an estimated primary `ini()` parameter; `cl_total <- cl + RRT_HEMODIAL_ACTIVE * cl_hemodialysis`).
- **Notes:** Distinct from a renal-impairment indicator (subject-level baseline class) and from `RRT_HEMODIAL_STATUS` (the subject-level treatment-status indicator) -- `RRT_HEMODIAL_ACTIVE` is the per-time-point gate that turns the dialysis-clearance term on and off WITHIN a single hemodialysis subject's record. Pair with `BFR` and `DFR` when the dialysis clearance depends on flow rates; pair with a filter-specific mass-transfer coefficient (estimated `lkoa` in the model, not a covariate) when the Michaels parameterisation is used. Pair with `lcl_hemodialysis` (canonical) when the dialysis arm is a primary estimated structural parameter rather than a derived expression. Ratified canonically on 2026-05-16 (as `DIAL`), renamed to `HEMODIALYSIS` on 2026-06-09 alongside the Veinstein 2013 gentamicin extraction, then renamed again to `RRT_HEMODIAL_ACTIVE` on 2026-06-19 per the canonical-register standardization audit (RRT family normalization: the `RRT_<modality>_<kind>` pattern makes the modality + per-session-vs-subject-level distinction explicit at the column name).


### RRT_CRRT_ACTIVE (**canonical for continuous-renal-replacement-therapy-active indicator (time-varying gate)**)
- **Description:** Within-subject time-varying indicator for whether a continuous or extended extracorporeal renal-replacement-therapy modality is currently running -- continuous venovenous hemodialysis (CVVHD), hemofiltration (CVVH), hemodiafiltration (CVVHDF), sustained low-efficiency dialysis (SLED), or extended daily diafiltration (EDD-f). 1 while the therapy is running; 0 while it is interrupted and in subjects never treated. Completes the `RRT_<modality>_<kind>` family as the continuous-modality counterpart of `RRT_HEMODIAL_ACTIVE`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no continuous RRT running). Models that compose an additional dialysis-clearance term should gate it by `RRT_CRRT_ACTIVE = 1` so that clearance during an interruption reduces to the intrinsic body clearance.
- **Source aliases:**
  - `CVVHD` -- Huppe 2023 prose and figure legends.
  - `CRRT_ON`, `CVVH_ACTIVE` -- variant abbreviations used in adjacent critical-care popPK literature.
- **Example models:** `Huppe_2023_fosfomycin.R` (Michaels-equation gate on the dialysis clearance arm: `cl_cvvhd <- RRT_CRRT_ACTIVE * 60 * Michaels(BFR, DFR, K0A)`; each of the 15 patients contributed one PK profile during CVVHD and one after CVVHD was interrupted for tubing-system replacement, so the covariate genuinely varies within subject).
- **Notes:** Distinct from `RRT_CRRT_STATUS`, which is the SUBJECT-LEVEL treatment-status flag for the same modality family (Shekar 2014 records whether a patient was on RRT at all during sampling, not when a session was running). Distinct from `RRT_HEMODIAL_ACTIVE`, which is the per-session gate for INTERMITTENT hemodialysis (Liesenfeld 2013, Jacobs 2016, Veinstein 2013). The intermittent-versus-continuous split is retained at the ACTIVE level for the same reason the register already retains it at the STATUS level: the two modality classes have materially different drug-extraction kinetics -- intermittent hemodialysis runs high blood and dialysate flows for a few hours, whereas continuous modalities run flows an order of magnitude lower around the clock, so a coefficient or mass-transfer coefficient calibrated on one does not transfer to the other. Pair with `BFR` and `DFR` when the dialysis clearance is derived from circuit flow rates via the Michaels equation, and with `lkoa` as the estimated filter mass-transfer-area coefficient in `model()` (not a covariate). Note that `BFR` and `DFR` are canonically both mL/min, while continuous-modality papers commonly report dialysate flow in L/h -- convert on ingestion and document any in-`model()` back-conversion, as `Huppe_2023_fosfomycin.R` must do.

### HEPIMP_MOD (**canonical for moderate hepatic impairment indicator**)
- **Description:** 1 = moderate hepatic impairment, 0 = normal hepatic function or any other (non-moderate) category. The classification scheme that defines "moderate" is paper-specific and must be documented in per-model `covariateData[[HEPIMP_MOD]]$notes`. Two schemes are commonly encountered:
  - **NCI ODWG group 3**: total bilirubin > 1.5-3 x ULN with any AST (Ramalingam SS et al., J Clin Oncol 2010;28:4507).
  - **Child-Pugh Class B**: composite score 7-9 across bilirubin, albumin, INR, ascites, and encephalopathy.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-moderate category: normal, mild, or severe; the model typically uses HEPIMP_MOD alongside HEPIMP_MILD and/or HEPIMP_SEV to partition the non-moderate pool further).
- **Source aliases:**
  - `HEP2` -- used in `Desai_2016_isavuconazole.R` (paper's liver-function index HEP2 for moderate Child-Pugh B; same orientation as the canonical).
- **Example models:** `Desai_2016_isavuconazole.R` (Child-Pugh Class B; log-additive shift `e_hepimp_mod_cl = log(1.326 / 2.54) = -0.650` on CL and `e_hepimp_mod_q = log(63.554 / 33.678) = +0.635` on Q vs the healthy reference, paired with `HEPIMP_MILD` for the parallel Child-Pugh A stratum; encodes the paper's three-typical-value form `CL = theta_g * exp(eta_jCL)` as a healthy-reference baseline plus group-specific log shifts).
- **Notes:** Use this column when a model dichotomizes moderate hepatic impairment as a separate indicator from milder or more-severe categories. The classification scheme (NCI ODWG vs Child-Pugh vs other) is paper-specific and must be documented per-model. Companion to `HEPIMP_MILD` (mild only) and `HEPIMP_SEV` (severe only). Distinct from `HEPIMP_MOD_OR_MISSING` (pools moderate cases with missing-data cases) and `HEPIMP_MODSEV` (pools moderate with severe). Anticipated by the `HEPIMP_MILD` entry's Notes: "For models that test moderate or severe as separate categories, register additional canonicals HEPIMP_MOD / HEPIMP_SEV rather than overloading this entry." HEPIMP_SEV was registered separately; HEPIMP_MOD completes the parallel.


### CRCL_BASE (**canonical for per-subject baseline creatinine clearance (time-fixed)**)
- **Description:** Per-subject baseline renal function (creatinine clearance or BSA-normalized eGFR), time-fixed. Used when a source paper enters the subject's baseline renal-function value as a separate constant column alongside a time-varying `CRCL` column under Wahlby 2004's extended covariate-model decomposition (Br J Clin Pharmacol 2004;58(4):367-377). The within-subject delta is computed in `model()` as `(CRCL - CRCL_BASE)`.
- **Units:** mL/min or mL/min/1.73 m^2 -- match the units of the paired `CRCL` column and document per-model via `covariateData[[CRCL_BASE]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used either as `(CRCL_BASE - ref)` in a linear or linear-exp effect, or as the ratio `(CRCL_BASE / ref)`, where `ref` is a cohort or upstream-analysis median baseline renal function. Reference values observed: 71.7 mL/min (Wahlby 2004 BCLC median), 93 mL/min/1.73 m^2 (Decker 2024, median eGFR of the upstream adult rheumatoid-arthritis analysis).
- **Source aliases:**
  - `BCLC` (baseline creatinine clearance) -- Wahlby 2004 source-column convention; used in `Wahlby_2004_gentamicin.R` (raw mL/min) and `Wahlby_2004_pefloxacin.R` (concept tested but not retained in final model).
  - `baseline eGFR` -- Decker 2024 source-table wording for the time-fixed bedside-Schwartz pediatric eGFR entering the renal-clearance arm as `(baseline eGFR / 93)`; used in `Decker_2024_baricitinib.R`.
- **Example models:** `Wahlby_2004_gentamicin.R` (raw mL/min; reference 71.7 mL/min = BCLC median; coefficient 0.0098 per mL/min on CL alongside paired DCLC effect 0.0068 per mL/min), `Decker_2024_baricitinib.R` (bedside-Schwartz pediatric eGFR in mL/min/1.73 m^2; enters the apparent renal clearance arm multiplicatively as the ratio `(CRCL_BASE / 93)` rather than as a centred linear deviation, paired with the delta effect 0.00586 per mL/min/1.73 m^2 on `(CRCL - CRCL_BASE)`; the reference 93 is the median eGFR of the upstream adult analysis, not a statistic of the pediatric cohort, whose mean baseline eGFR is 119).
- **Notes:** Promoted to `general` scope on 2026-08-04 alongside the Decker 2024 baricitinib extraction, the second model registering the column with consistent Wahlby-style BCOV/DCOV semantics. The two ratifying papers differ in the functional form applied to the baseline arm -- Wahlby 2004 uses a centred linear deviation `(CRCL_BASE - 71.7)`, Decker 2024 a multiplicative ratio `(CRCL_BASE / 93)` -- and in the underlying assay (Cockcroft-Gault-style raw CrCl vs bedside-Schwartz pediatric eGFR); both are within scope because the column's role is identical (the time-fixed baseline half of a time-varying renal-function covariate). Document the assay, the units, and the functional form per model in `covariateData[[CRCL_BASE]]$notes`. Distinct from time-fixed adult-cohort CRCL (where per-subject CRCL is just constant by data design); use `CRCL_BASE` only when the source paper deliberately separates the BCOV from the time-varying COV. Ratified canonically alongside the Wahlby 2004 extraction.


### TBILI_BASE (**canonical for per-subject baseline total bilirubin (time-fixed)**)
- **Description:** Per-subject baseline total serum bilirubin, time-fixed. Used when a source paper enters the subject's baseline TBILI as a constant column alongside the time-varying `TBILI` column under Wahlby 2004's extended covariate-model decomposition (Br J Clin Pharmacol 2004;58(4):367-377). The within-subject delta is computed in `model()` as `(TBILI - TBILI_BASE)`.
- **Units:** mg/dL or umol/L -- match the units of the paired `TBILI` column and document per-model via `covariateData[[TBILI_BASE]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- typically used as `(TBILI_BASE - ref)` in a linear or linear-exp effect.
- **Source aliases:**
  - `BBIL` (baseline total bilirubin) -- Wahlby 2004 source-column convention; used in `Wahlby_2004_pefloxacin.R` and `Wahlby_2004_paclitaxel_myelosuppression.R`.
- **Example models:** `Wahlby_2004_pefloxacin.R` (umol/L; centered at 25 umol/L; coefficient -0.0068 per umol/L on CL via exp[e * (TBILI_BASE - 25)]), `Wahlby_2004_paclitaxel_myelosuppression.R` (umol/L; used together with TBILI as the within-subject delta TBILI - TBILI_BASE entering the Slope-DBIL effect).
- **Notes:** Specific scope because the BCOV/DCOV decomposition is paper-defined. Promote to `general` if a second paper ratifies. Distinct from `DBIL` (direct/conjugated bilirubin, a clinical-chemistry concept) -- TBILI_BASE is a methodologic baseline-snapshot column, not a separate biomarker. Ratified canonically alongside the Wahlby 2004 extraction.


### ALP_BASE (**canonical for per-subject baseline alkaline phosphatase (time-fixed)**)
- **Description:** Per-subject baseline serum alkaline phosphatase, time-fixed. Used when a source paper enters the subject's baseline ALP as a constant column alongside the time-varying `ALP` column under Wahlby 2004's extended covariate-model decomposition (Br J Clin Pharmacol 2004;58(4):367-377). The within-subject delta (or log-ratio) is computed in `model()` from `ALP` and `ALP_BASE`.
- **Units:** U/L (IU/L; interchangeable). Match the units of the paired `ALP` column and document per-model via `covariateData[[ALP_BASE]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- typically used inside a log-ratio `log(ALP / ALP_BASE)` or a linear delta `(ALP - ALP_BASE)`.
- **Source aliases:**
  - `BALKP` (baseline alkaline phosphatase) -- Wahlby 2004 source-column convention; used in `Wahlby_2004_voriconazole.R`.
- **Example models:** `Wahlby_2004_voriconazole.R` (IU/L; population median 136 IU/L; paired with time-varying ALP to form log(ALP/ALP_BASE), the paper's log(DALKP) term, with coefficient 0.59 on CL inside Eq 7 Final-Model column).
- **Notes:** Specific scope because the BCOV/DCOV decomposition is paper-defined. Promote to `general` if a second paper ratifies. Ratified canonically alongside the Wahlby 2004 extraction.


### PERIT_DIAL (**canonical for peritoneal-dialysis treatment-status indicator**)
- **Description:** 1 = the subject was undergoing chronic peritoneal dialysis (PD) during the modeled period; 0 = not on peritoneal dialysis. Treatment-status flag rather than a measured renal-function value; used as a multiplicative covariate on PK parameters that change between dialysis modalities (typically central volume Vc -- chronic peritoneal dialysis is associated with higher extracellular fluid volume than intermittent hemodialysis, so plasma volume markers are larger in PD subjects). Per-subject indicator in the source data; typically time-fixed at the subject level (chronic dialysis modality is a months-to-years-stable patient characteristic, not a per-session attribute).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (not on peritoneal dialysis). In a chronic-dialysis cohort comparing PD vs HD modalities, the reference category corresponds to the HD subjects; `PERIT_DIAL = 1` selects the PD subgroup.
- **Source aliases:**
  - `DIA` -- used in `Takama_2007_darbepoetin.R` (dialysis-modality indicator: `DIA = 0` for hemodialysis, `DIA = 1` for peritoneal dialysis; maps directly onto `PERIT_DIAL` with no value transformation). Takama 2007 Table 4 and Methods describe the covariate equation `V1 = theta_V1 * [1 + theta_V1_WT * (WT - 54) + theta_V1_DIA * DIA]` where `theta_V1_DIA = 0.170` increments V1 by +17% in PD subjects relative to HD subjects.
- **Example models:** `Takama_2007_darbepoetin.R` (+17% multiplicative-deviation effect on central volume V1; chronic-dialysis cohort comparing 63 HD vs 68 PD Japanese adult patients).
- **Notes:** Distinct from `RRT_HEMODIAL_STATUS` (intermittent-hemodialysis IHD indicator; `RRT_HEMODIAL_STATUS = 1` selects HD subjects) and from `RRT_CRRT_STATUS` (continuous / extended RRT indicator). In a chronic-dialysis cohort where every subject is on either HD or PD, `RRT_HEMODIAL_STATUS` and `PERIT_DIAL` are perfectly anti-correlated (`PERIT_DIAL = 1 - RRT_HEMODIAL_STATUS`); the choice of which to use as the covariate is dictated by which modality the source paper treats as the reference category. Takama 2007 took HD as the reference and reports a +17% effect on V1 for PD, so `PERIT_DIAL` is the natural canonical for that paper (V1 increases when `PERIT_DIAL = 1`). Anticipated in the `RRT_HEMODIAL_STATUS` register-entry notes ("would warrant its own canonical (`RRT_PERIT_DIAL_STATUS`, `RRT_CRRT_STATUS`) if a future paper retains them as covariates") and ratified canonically on 2026-06-12 alongside the Takama 2007 darbepoetin alfa extraction. Future audit may rename this to `RRT_PERIT_DIAL_STATUS` for consistency with the RRT_<modality>_<kind> family; left unchanged for now to avoid disturbing the Takama 2007 ingestion before broader peritoneal-dialysis coverage in the registry.


## Hematology

### HGB (**canonical for hemoglobin**)
- **Description:** Blood hemoglobin concentration.
- **Units:** g/L or g/dL -- document the unit used in each model via `covariateData[[HGB]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(HGB / ref)^exponent`.
- **Source aliases:** none; `HGB` is the common NONMEM / clinical-PK abbreviation.
- **Example models:** `Yamada_2025_zolbetuximab.R` (g/L, reference 118; exponent -0.374 on V1).
- **Notes:** Unit varies by paper (SI g/L, US g/dL; 1 g/dL = 10 g/L). The per-model `covariateData[[HGB]]$units` field is load-bearing.

### HGB_BL (**canonical for per-subject baseline hemoglobin concentration (initial-condition use)**)
- **Description:** Pre-treatment (or per-subject anchor-time) hemoglobin concentration used as the initial condition for a hemoglobin state variable in indirect-response / turnover anaemia models. Distinct from `HGB` (which is the time-varying or baseline-only hemoglobin value when entering a covariate effect on a structural PK parameter): `HGB_BL` is a static per-subject covariate that supplies the steady-state baseline for an Hb-state ODE, typically together with a `kin = kout * HGB_BL` synthesis-rate-from-baseline coupling so the Hb state starts at steady state. Parallel to `FERRITIN_BL` (baseline ferritin) and `WT_BASE` (baseline weight).
- **Units:** g/L, g/dL, or mmol/L -- document the unit used in each model via `covariateData[[HGB_BL]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- subject-level baseline supplied as a covariate column. Reference values observed: 8.3 mmol/L (Mulder 2025 chronic-HEV SOT cohort median).
- **Source aliases:**
  - `HBBASE` -- used in `Mulder_2025_ribavirin.R`.
- **Example models:** `Mulder_2025_ribavirin.R` (mmol/L; initial condition for the `hb_state` compartment; per-subject `kin = kout * HGB_BL` so the Hb state is at steady state pre-RBV; the source paper substitutes the cohort typical (median) baseline for the one subject without an individual baseline value).
- **Notes:** Specific scope because the initial-condition idiom is paper-defined (Mulder 2025 specifies `kin` per subject from baseline rather than estimating a typical Hb0). Promote to `general` if a second paper ratifies the same baseline-Hb-as-initial-condition pattern. The unit varies widely across papers (SI g/L, US g/dL, Dutch / European convention mmol/L; 1 g/dL = 10 g/L; 1 mmol/L of tetrameric haemoglobin ~ 16.114 g/L); the per-model `covariateData[[HGB_BL]]$units` field is load-bearing.

### WBC (**canonical for white blood cell count**)
- **Description:** Total white blood cell count (baseline or time-varying). In chronic lymphocytic leukaemia (CLL) populations the value is elevated because circulating leukaemic B-cells make up the majority of the count, so WBC can serve as a biomarker of target-cell burden rather than general hematology.
- **Units:** 10^9 cells/L (equivalent to 10^3 cells/uL). Document per-model via `covariateData[[WBC]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(WBC / ref)^exponent`. Reference values observed: 10 x 10^9/L (Mould 2007, typical CLL Vmax normalization).
- **Source aliases:** none known (`WBC` is the universal clinical-PK abbreviation).
- **Example models:** `Mould_2007_alemtuzumab.R` (reference 10 x 10^9/L; exponent 0.194 on Vmax), `Dirks_2008_cetuximab.R` (reference 6.8 x 10^9/L; additive linear-deviation effect 0.0216 per 10^9/L on Vmax; baseline only -- cetuximab does not deplete circulating leukocytes, so WBC is treated as a static covariate).
- **Notes:** Time-varying in treatment studies where the drug depletes the leukaemic clone (e.g., alemtuzumab in CLL): WBC must be supplied at every observation time in the event dataset. In diseases where WBC is not therapeutically targeted the column can be treated as a baseline-only covariate; record the per-model convention in `covariateData[[WBC]]$notes`.

### NLR (**canonical for neutrophil-to-lymphocyte ratio**)
- **Description:** Ratio of absolute neutrophil count to absolute lymphocyte count from a complete blood count with differential. Used as a peripheral inflammation marker. May be reported as baseline only or as a time-varying covariate.
- **Units:** ratio (unitless)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(NLR / ref)^exponent` or exponential effects. Reference values observed: 2.11 (Lin 2024, median in pooled COVID-19 + non-infected cohort).
- **Source aliases:** none; `NLR` is the universal abbreviation in clinical-PK and inflammation-biomarker literature.
- **Example models:** `Lin_2024_casirivimab.R` (time-varying; reference 2.11; small positive exponent +0.029 on CL).
- **Notes:** Document baseline-vs-time-varying status in `covariateData[[NLR]]$notes`. Although it derives from `WBC` differential counts, register it as its own canonical because the ratio (not the absolute counts) is what the model uses.

### HCT (**canonical for hematocrit**)
- **Description:** Hematocrit -- packed red blood cell volume fraction (baseline or time-varying).
- **Units:** % (volume fraction times 100). Document per-model via `covariateData[[HCT]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(HCT / ref)^exponent`. Reference values observed: 45 % (Nestorov 2014, study-population median for severe hemophilia A adults).
- **Source aliases:** none; `HCT` is the universal NONMEM / clinical-PK abbreviation.
- **Example models:** `Nestorov_2014_factorviii.R` (reference 45 %, exponent -0.419 on V1).
- **Notes:** Higher HCT (more red-cell volume) leaves a smaller plasma fraction within total body volume; for plasma-restricted distribution (e.g., factor VIII activity, which circulates in plasma) the central volume of distribution decreases as HCT rises, so the exponent is negative. Document baseline-vs-time-varying status in `covariateData[[HCT]]$notes`. Distinct from `HGB` (mass concentration of hemoglobin); the two correlate but enter different mechanistic relationships.

### THB_MASS (**canonical for total hemoglobin mass**)
- **Description:** Subject-level total hemoglobin mass in grams. Plasma-volume-independent quantity measured by the optimised CO-rebreathing method (Schmidt 2005, Pottgiesser 2008); distinct from `HGB` (mass concentration in plasma) and `HCT` (volume fraction). Used in erythropoiesis / RBC-regeneration models as the steady-state set point Base that drives the negative-feedback term and seeds the steady-state initial conditions of the precursor compartments.
- **Units:** g
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- subject-level baseline supplied as a covariate column. Reference values observed: 885.42 g (Tetschke 2018 Table 1 example subject); Pottgiesser 2008 cohort mean ~870 g across 28 estimable adult-male volunteers.
- **Source aliases:** `Base` (Tetschke 2018 paper symbol).
- **Example models:** `Tetschke_2018_erythropoiesis.R` (reference 885.42 g; Pottgiesser 2008 dataset of 29 healthy adult male volunteers).
- **Notes:** Specific scope because total hemoglobin mass requires the optimised CO-rebreathing method to obtain (Schmidt 2005), which is a specialised technique not present in routine clinical labs; promote to `general` if a second model registers this quantity. Distinct from `HGB` (g/L or g/dL plasma concentration) and `HCT` (RBC volume fraction): `THB_MASS` is the absolute body-pool mass and is not perturbed by short-term plasma-volume fluctuations (Pottgiesser 2008 Section 3.2 explicitly motivates the choice of mass over concentration). Sex-dimorphic: typical value in adult males is meaningfully higher than in adult females; document the sex composition of the population in `covariateData[[THB_MASS]]$notes`.

### LYMPH_ABS (**canonical for absolute peripheral-blood lymphocyte count (total)**)
- **Description:** Total absolute peripheral-blood lymphocyte count (all lymphocyte subsets pooled: T, B, and NK cells). Baseline or time-varying; document the time resolution per model via `covariateData[[LYMPH_ABS]]$notes`. Used as a continuous covariate on clearance and other PK parameters when the source paper carries the count as an exogenous exposure covariate rather than as a modelled PD state. Common in allo-HSCT / GvHD-prophylaxis popPK studies where circulating-lymphocyte burden is a mechanistic driver for lymphocyte-targeting biologics (integrin blockers, T-cell / B-cell-depleting mAbs).
- **Units:** cells/uL (equivalent to 10^9 cells/L = K/uL x 1000; document the per-paper reporting unit in `covariateData[[LYMPH_ABS]]$units`). Reporting conventions vary: some papers report as `K/uL` (i.e., 10^3 cells/uL, numerically identical to 10^9 cells/L) and some as `cells/uL` -- both encode the same numerical value once the K/uL suffix is expanded. Prefer `cells/uL` as the canonical unit for consistency with `CD4_ABS`, `CD19_ABS`, and `NEUT`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- typically enters as a power term `(LYMPH_ABS / ref)^exponent` against a paper-specific reference. Reference values observed: 100 cells/uL (Waterhouse 2024, `0.1 K/uL`, exponent -0.0180 on CL; the paper further replaces any zero lymphocyte counts with `0.01 K/uL` = 10 cells/uL to avoid a log-of-zero in the power form).
- **Source aliases:**
  - `LYMPH` -- Waterhouse 2024 NM-TRAN column, time-varying absolute lymphocyte count in K/uL.
  - `ALC` -- clinical laboratory abbreviation for "absolute lymphocyte count"; common in hematology / transplant papers. Same semantics; no value transformation.
- **Example models:** `Waterhouse_2024_vedolizumab.R` (time-varying absolute lymphocyte count on CL: `cl *= (LYMPH_ABS / 100)^-0.0180`; reference 100 cells/uL = 0.1 K/uL per paper Table 2; zeros replaced with 10 cells/uL to avoid `log(0)`).
- **Notes:** Distinct from `CD4_ABS` (a subset -- CD4+ T-lymphocytes only), `CD19_ABS` (a subset -- CD19+ B-lymphocytes only), and `NLR` (a ratio, neutrophil-to-lymphocyte). `LYMPH_ABS` is the total pooled lymphocyte count and is the correct canonical for papers that measure "absolute lymphocyte count" without further flow-cytometry subsetting. Distinct from `WBC` (total white blood cell count, of which lymphocytes are one differential fraction). Zero-value handling: allo-HSCT / lymphocyte-depleting-therapy cohorts routinely have observed lymphocyte counts of exactly 0 K/uL at some time points; source papers commonly substitute a small floor (e.g., Waterhouse 2024 uses 0.01 K/uL) before evaluating a power-form covariate effect, per Waterhouse 2024 Supplement Equation S2. Document any per-paper zero-floor convention in `covariateData[[LYMPH_ABS]]$notes`. Ratified canonically on 2026-07-25 alongside the Waterhouse 2024 vedolizumab extraction.

### NEUT (**canonical for absolute neutrophil count**)
- **Description:** Absolute neutrophil count, typically as a baseline covariate (entered via centred-deviation `(NEUT - ref)` or power scaling `(NEUT / ref)^exponent`) or, in semi-mechanistic myelosuppression models, as a per-subject initial-condition value for the proliferation, transit, and circulating compartments.
- **Units:** cells/mm^3 (equivalent to cells/uL; i.e., the same value reported in 10^9/L x 1000). Document per-model via `covariateData[[NEUT]]$units` if the source paper uses a different unit (e.g., `10^9 cells/L` for `Ozawa_2007_docetaxel.R` per the paper's Table-3 reporting unit).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used in centred-deviation form `exp(coef * (NEUT - ref))`, in power scaling `(NEUT / ref)^exponent`, or as a direct per-subject initial-condition assignment in semi-mechanistic Friberg-family models. Reference values observed: 4133 cells/mm^3 (BAST PTTE 2017 simulated cohort median; `NA_NA_tte_gompertz.R` Event 1 base hazard model); 5 x 10^9/L (Ozawa 2007 typical Japanese cancer cohort, used as the initial condition for the proliferation, transit, and circulation compartments).
- **Source aliases:**
  - `BASE` -- per-subject baseline ANC supplied as a NONMEM data column (used in `Ozawa_2007_docetaxel.R`; Appendix I $INPUT).
  - `ANC` -- the absolute-neutrophil-count source-column spelling (10^9/L); time-varying hazard modulator in `Hansson_2013_sunitinib_os.R` (the overall-survival Weibull hazard term `exp(e_anc_haz * ANC + ...)`). No value transformation beyond the per-paper unit (document `10^9/L` in `covariateData[["ANC"]]$units`).
- **Example models:** `NA_NA_tte_gompertz.R` (BAST PTTE 2017 / DDMODEL00000243 Event 1 hazard model; centred at NEUT = 4133/mm^3; coefficient -1.56e-4 on the NONMEM rescaled scale, equivalent to `exp(-1.56e-4 * (NEUT - 4133))` on the hazard), `Ozawa_2007_docetaxel.R` (Friberg-extension myelosuppression PD; per-subject baseline ANC supplied via the `NEUT` column, used as the initial condition for the proliferating, three transit, and circulating compartments per the Methods text 'Circ (t = 0) was fixed at its observed value'), `Hansson_2013_sunitinib_os.R` (time-varying ANC as `ANC`, simulated from the upstream Hansson 2013 myelosuppression model and consumed as an OS-hazard predictor).
- **Notes:** General scope because absolute neutrophil count is a routine clinical-laboratory measurement that recurs across cytotoxic-chemotherapy myelosuppression models (centred-deviation hazard models, Friberg-family per-subject baseline initial conditions, and time-varying ANC outputs). Promoted to general scope on 2026-05-10 to support `Ozawa_2007_docetaxel.R`. The NEUT canonical units are cells/mm^3, but the reporting unit `10^9 cells/L` (numerically NEUT_per_mm3 / 1000) is also common in oncology papers; per-model `covariateData[[NEUT]]$units` documents the per-paper unit. Distinct from `WBC` (total white blood cell count, of which neutrophils are the largest fraction in healthy adults) -- `NEUT` is a specific differential-count subfraction. Also distinct from `NLR` (neutrophil-to-lymphocyte ratio), which is a derived ratio.

### FERRITIN_BL (**canonical for baseline serum ferritin concentration**)
- **Description:** Pre-treatment (or per-subject anchor-time) serum ferritin concentration. Two complementary uses across iron-related models: (1) as the per-subject initial condition for a ferritin state variable in iron-overload disease-progression models (`ferritin(0) <- FERRITIN_BL`; Bellanti 2015), and (2) as a static per-subject covariate entering a power-law multiplier on hepcidin turnover rate constants in iron-status / menstrual-cycle turnover models (Angeli 2016). Distinct from a state-output ferritin trajectory: `FERRITIN_BL` is a static per-subject covariate (one value, supplied at simulation start); a state-output ferritin would evolve over time per a disease ODE.
- **Units:** ug/L (clinical reporting convention in iron-related papers; numerically equivalent to ng/mL). Document per-model via `covariateData[[FERRITIN_BL]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- subject-level baseline supplied as a covariate column. Reference values observed: 2260 ug/L (Bellanti 2015 thalassaemia cohort median; range 393-8500 ug/L across 27 transfusion-dependent beta-thalassaemia major patients); 53 ug/L (Angeli 2016 healthy non-menopausal women; mean 53.14, range at 10th-90th percentile 18.6-97.4 ug/L).
- **Source aliases:** none known.
- **Example models:** `Bellanti_2015_deferoxamine.R` (ug/L; initial condition for the ferritin compartment; n=27 transfusion-dependent beta-thalassaemia major paediatric / adolescent cohort, median 2260, range 393-8500), `Angeli_2016_iron_hepcidin.R` (ug/L; per-subject end-of-cycle baseline used as a power-law multiplier `(FERRITIN_BL / 53)^exponent` on hepcidin elimination `kout_hep` (exponent -0.60) and on the hepcidin post-menses rebound `krel_hep` (exponent -1.95); higher baseline ferritin -> slower hepcidin elimination and a smaller rebound, consistent with iron-regulatory feedback).
- **Notes:** Iron storage protein; elevated in transfusional iron overload (beta-thalassaemia major, sickle-cell disease on chronic transfusion, MDS on transfusion support) and in some inflammatory states (acute-phase reactant); near normal in healthy adults (typically ~30-300 ug/L). Promoted from `specific` to `general` on 2026-06-03 with the Angeli 2016 iron / hepcidin extraction (the second model to register `FERRITIN_BL` with consistent baseline-ferritin semantics across two distinct mechanistic uses).

### RBC (**canonical for red blood cell (erythrocyte) count**)
- **Description:** Red blood cell (erythrocyte) count (baseline or time-varying).
- **Units:** 10^12 cells/L (clinical reporting convention; equivalent to 10^6 cells/uL). Document per-model via `covariateData[[RBC]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with linear-deviation `(RBC - ref)` or power scaling `(RBC / ref)^exponent`. Reference values observed: 4.40 x 10^12/L (Zhang 2012 healthy Chinese adult cohort median across the four dosing groups in Table 1); 3.7 x 10^12/L (Jiang 2023 postoperative Chinese GIST cohort central value, Table 1).
- **Source aliases:**
  - `RBC*` -- used in `Zhang_2012_bivalirudin.R` (the asterisk in Zhang 2012 Table 1 denotes the per-subject baseline demographic value).
- **Example models:** `Zhang_2012_bivalirudin.R` (linear-deviation effect on EC50: `EC50_i = theta_EC50 * exp(eta_EC50) * (1 + 1.70 * (RBC - 4.40))`), `Jiang_2023_imatinib.R` (power scaling on apparent oral clearance: `CL/F = 9.72 * (RBC / 3.7)^0.49 * ...`; the paper attributes the positive association to imatinib binding to and distributing into red blood cells, and reports that halving RBC lowers CL/F by 29%).
- **Notes:** Universal CBC component, distinct from `HGB` (hemoglobin mass concentration), `HCT` (hematocrit volume fraction), and `WBC` (white blood cell count). Document time-varying-vs-baseline-only status in `covariateData[[RBC]]$notes`. RBC and HCT correlate via mean corpuscular volume (HCT ~ RBC * MCV / 10); models that retain both should record the dependency in `notes`.


## Coagulation / hemostasis biomarkers

### AT_BL_UDL (**canonical for per-subject baseline plasma antithrombin activity**)
- **Description:** Per-subject baseline (predose) plasma antithrombin (AT) activity level, expressed as units of AT activity per deciliter. Because the Stachrom chromogenic AT assay is calibrated to pooled normal plasma, `units/dL` is numerically equivalent to `% of normal` (100 units/dL = 100% of normal). Time-fixed per subject: the single predose value measured before the first AT-concentrate dose. Used both as a covariate on the volume of distribution (`(AT_BL_UDL / 60)^exponent`) and as an additive endogenous baseline in the observation equation to represent ongoing endogenous AT production alongside dosed exogenous AT (Dansirikul B3 method).
- **Units:** units/dL (equivalent to % of normal on chromogenic anti-FIIa or anti-FXa AT-activity assays).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(AT_BL_UDL / ref)^exponent` on VD in the source paper's final model. Reference values observed: 60 units/dL (Moffett 2017 cohort baseline mean 59 +/- 17, cohort median approximately 60). Normal healthy paediatric range 85-130 units/dL; the cohort baseline mean 59 is depressed relative to healthy range because the patients under study had consumptive AT depletion secondary to critical illness / concurrent UFH.
- **Source aliases:**
  - `BASE` -- used in `Moffett_2017_antithrombin.R` (Moffett 2017 Table 3 baseline antithrombin activity covariate). The paper reports the covariate value as `units/dL`; the same numerical value is equivalent to `% of normal` on the STA-Stachrom AT chromogenic assay.
- **Example models:** `Moffett_2017_antithrombin.R` (power effect on VD: `VD_dL = 67.9 * (WT/70)^1 * (AT_BL_UDL / 60)^(-0.389)`; reference 60 units/dL from Moffett 2017 Table 3. Also enters the observation equation as an additive endogenous baseline: `Cc_measured = central / vc + AT_BL_UDL`, per Dansirikul 2008 B3 method described in Moffett 2017 Methods "Data handling").
- **Notes:** Specific scope until a second paediatric or adult AT / anticoagulation-supplementation model ratifies the canonical. Distinct from `INR_BASE` (baseline international normalized ratio, an integrated coagulation-cascade readout) and from other coagulation biomarkers (`VWF`, `FVIIIRECENT`) which measure different clotting-cascade components. The dual role -- both a VD covariate AND an additive constant in the observation equation -- reflects the Dansirikul B3 method's approach to modelling exogenous drug administration on top of an endogenous baseline. When simulating, the same subject-level AT_BL_UDL value must be supplied for both effects; the additive baseline term captures the ongoing endogenous production of AT during the drug-derived-concentration decay window. Ratified canonically on 2026-06-21 alongside the Moffett 2017 antithrombin extraction.

### INR_BASE (**canonical for baseline international normalized ratio**)
- **Description:** Pre-medication baseline INR (international normalized ratio of prothrombin time). Time-fixed per subject (measured once, before the first warfarin dose). Used directly in the warfarin K-PD INR equation as an additive constant (`INR = INR_BASE + inrmax * (1 - (coag_s3 + coag_l3)/2)` per Xia 2024 supplement Section 1.1) so the simulated INR returns to the subject-specific baseline when the drug is removed.
- **Units:** (unitless ratio; INR has no units)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- subject-specific baseline. Default simulation value documented per-model in `covariateData[[INR_BASE]]$notes`; the Xia 2024 simulation uses the total-cohort mean of 1.13 (Table 1).
- **Source aliases:**
  - `INR_BASE`, `BL_INR`, `INRBASE` -- pre-medication INR column in NONMEM data sets; document the source-column name per-model in `covariateData[[INR_BASE]]$source_name`.
- **Example models:** `Xia_2024_warfarin.R` (additive baseline in the INR observation equation; cohort mean 1.13, SD 0.59 per Xia 2024 Table 1).
- **Notes:** Distinct from a time-varying INR observation (the model's observed `INR` variable). Healthy subjects with no anticoagulation typically have INR around 1.0; the Hamberg / Xia 2024 model treats deviations from 1.0 as a subject-specific covariate rather than an estimated parameter so the model returns to the observed baseline when warfarin is withdrawn. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin extraction.

### PTR (**canonical for prothrombin time ratio (PT relative to baseline)**)
- **Description:** Prothrombin time ratio -- the ratio of measured prothrombin time to the pre-treatment baseline prothrombin time of the same subject (or trial-arm population mean). Unitless. Used as a time-varying input biomarker that drives downstream pharmacodynamic / event-rate models for direct oral factor-Xa inhibitors and related anticoagulant pharmacology.
- **Units:** (unitless ratio)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- continuous. PTR = 1 corresponds to no FXa-inhibitor effect (placebo / pre-dose baseline) and reproduces the placebo log-odds in `Yoshioka_2018_FXa_inhibitors_mbma`. Must be > 0 because downstream PD equations evaluate `log(PTR)`.
- **Source aliases:**
  - `PTR`, `PT_RATIO`, `x` -- in Yoshioka 2018 Eq. 1 / Eq. 2 the symbol `x` is used for the PT ratio; document the source-column name per-model in `covariateData[[PTR]]$source_name`.
- **Example models:** `Yoshioka_2018_FXa_inhibitors_mbma.R` (model-based meta-analysis: per-arm population-mean PTR is input; outputs are per-arm event probability of ischemic stroke/SE and major bleeding).
- **Notes:** Distinct from `INR_BASE` (a time-fixed baseline INR scalar used as an additive constant in warfarin K-PD models). PTR is time-varying and must be supplied externally (typically computed from an upstream popPK -> PT-ratio model for the FXa inhibitor of interest, e.g., Girgis 2014 rivaroxaban, Leil 2014 / Chang 2016 apixaban, Krekels 2016 / Koretsune 2015 edoxaban). Yoshioka 2018 corrects all PT measurements to RecombiplasTin reagent equivalence per Gosselin 2016 before computing the ratio; downstream models that consume PTR should document the reagent-correction convention they assume. Scope: specific until a second model ratifies the canonical name.

### VWF (**canonical for von Willebrand factor concentration**)
- **Description:** Plasma concentration (or activity) of von Willebrand factor (VWF) -- the multimeric carrier protein that binds and protects circulating factor VIII (FVIII) from proteolytic degradation and rapid clearance. Used as a covariate on FVIII (and FVIII-Fc) clearance because the vast majority (>95%) of circulating FVIII is in complex with VWF.
- **Units:** IU/dL (equivalent to % of pooled normal plasma); document per-model via `covariateData[[VWF]]$units`. Some sources report `VWF:Ag` (antigen) versus `VWF:RCo` (ristocetin cofactor activity); record which assay was used in `covariateData[[VWF]]$notes`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(VWF / ref)^exponent`. Reference values observed: 118 IU/dL (Nestorov 2014, study-population median).
- **Source aliases:** none; `VWF` is the universal abbreviation. Source papers may write `vWF` (lowercase v) or specify the assay (`VWF:Ag`).
- **Example models:** `Nestorov_2014_factorviii.R` (reference 118 IU/dL, exponent -0.343 on CL; VWF antigen).
- **Notes:** Higher VWF protects FVIII from clearance, so the exponent on CL is negative. VWF is time-varying within an individual (acute-phase response, age, blood group, etc.), but most published population PK models use baseline-only VWF when the within-subject dynamics are not characterized; document the per-model convention in `covariateData[[VWF]]$notes`.

### FVIIIRECENT (**canonical for most recently measured FVIII:C activity**)
- **Description:** The patient's most recently measured plasma factor VIII coagulant activity (FVIII:C), obtained at most 1 day prior to a desmopressin (DDAVP) test / treatment administration and in the absence of any treatment effect on the measurement. Used as a per-occasion covariate that indexes the patient's current endogenous FVIII synthetic capacity, which the source paper found to be more predictive of DDAVP-triggered FVIII:C response than the alternative `FVIII-lowest` (ever-lowest measurement) covariate. Distinct from the model's observed FVIII:C time profile after DDAVP -- FVIIIRECENT is the single pre-dose anchor value.
- **Units:** IU/mL (1 IU/mL = 100% of pooled normal plasma FVIII activity; equivalent to 100 IU/dL).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(FVIIIRECENT / ref)^exponent`. Reference value observed: 0.15 IU/mL (Schütte 2018, study-population median FVIII-recent in Table 1).
- **Source aliases:**
  - `FVIII-recent`, `FVIII_recent`, `fviii_recent`, `FVIIIRECENT` -- the recently-measured FVIII:C column. Document the source-column name per-model in `covariateData[[FVIIIRECENT]]$source_name`.
- **Example models:** `Schutte_2018_desmopressin.R` (reference 0.15 IU/mL; exponents +0.74 on baseline FVIII, -0.61 on V1, -0.73 on CL; Schütte 2018 Table 2 final covariate model).
- **Notes:** Specific scope until a second nonsevere haemophilia A / DDAVP-response model registers the canonical. Time-fixed per desmopressin episode (one pre-dose measurement per occasion). The source paper additionally defines a binary missing-data branch with flat correction factors (1.2 / 1.1 / 0.78 on baseline FVIII / V1 / CL) when FVIIIRECENT was unavailable for a fitted subject; per the standing nlmixr2lib pattern, the missing branch is documented in the validation vignette deviations rather than encoded as a separate `MISSING_FVIIIRECENT` covariate, and simulation users are expected to supply FVIIIRECENT for every simulated patient. Distinct from `FVIII-lowest` (ever-lowest historical FVIII:C), which Schütte 2018 tested but did NOT retain in the final covariate model. Ratified canonically on 2026-05-30 alongside the Schütte 2018 desmopressin extraction.

### DDIMER (**canonical for plasma D-dimer concentration**)
- **Description:** Plasma D-dimer protein concentration, the fibrin-degradation peptide produced by plasmin-mediated cleavage of cross-linked fibrin. Used in vascular / coagulation-pathology models as a circulating biomarker of fibrin turnover, intra-aneurysmal thrombus burden, or systemic fibrinolytic activity.
- **Units:** ng/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used either with log10-transformed proportional scaling `log10(DDIMER) / median(log10(DDIMER))` (Sherer 2012) or with categorical strata (Sherer 2012 sensitivity analysis groups: <=150, 151-300, 301-900, >900 ng/mL). Reference values observed: 326 ng/mL (Sherer 2012 cohort median; log10 approx 2.513).
- **Source aliases:**
  - `C^(D-dimer)` -- used in `Sherer_2012_AAA.R` (the symbol in Sherer 2012 Methods equation page 2).
- **Example models:** `Sherer_2012_AAA.R` (proportional log10-transformed covariate on the baseline AAA growth rate `beta1` (`e_ddimer_b1 = 0.90 mm/year`) and on the first derivative of growth rate with size `beta2` (`e_ddimer_b2 = 0.37/year`)).
- **Notes:** Specific scope until a second model registers the canonical. Time-fixed (baseline-only) in Sherer 2012 because the HIMS cohort had a single follow-up D-dimer measurement; the source paper flags this as a limitation. Cohort interquartile range 142-785 ng/mL; extrapolation outside this range is not validated by the source. The log10 transformation reflects Sherer 2012's finding that "differences in AAA growth were predominantly driven by patients with the highest plasma D-dimer concentrations." Distinct from the time-varying biomarker columns in indirect-response / TMDD models -- DDIMER enters Sherer 2012 as a baseline regression covariate, not as a dynamic exposure / response variable. Ratified canonically on 2026-05-16 alongside the Sherer 2012 extraction.

### AAA_DIAM (**canonical for baseline abdominal aortic aneurysm diameter**)
- **Description:** Abdominal aortic aneurysm (AAA) maximum infrarenal diameter, ascertained by ultrasound at study entry. Used in vascular disease-progression models as the per-subject baseline severity covariate that anchors the typical-value regression for individual-level growth parameters.
- **Units:** mm
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used in proportional form `AAA_DIAM / median(AAA_DIAM)` so the effect coefficients represent the contribution at the cohort median. Reference value observed: 32.7 mm (Sherer 2012 cohort median; q1 30.8, q3 36.0).
- **Source aliases:**
  - `Y(0)` -- used in `Sherer_2012_AAA.R` (the symbol in Sherer 2012 Methods equation page 2; the baseline screening ultrasound diameter).
- **Example models:** `Sherer_2012_AAA.R` (proportional covariate on all three individual-level parameters: `e_aaadiam_b0 = 32.6 mm` on baseline size beta0, `e_aaadiam_b1 = 2.03 mm/year` on baseline growth rate beta1, and `e_aaadiam_b2 = 0.59/year` on the first derivative of growth rate with size beta2).
- **Notes:** Specific scope until a second model registers the canonical. Time-fixed (baseline-only) per subject -- the value is the single screening ultrasound diameter; the time-evolving AAA diameter during follow-up is the model's observation, not the covariate. Sherer 2012 inclusion criterion (HIMS cohort): 30-49 mm small AAA, so extrapolation outside this range to <30 mm (non-aneurysmal aorta) or >=50 mm (surgical-referral threshold) is not validated by the source. Ratified canonically on 2026-05-16 alongside the Sherer 2012 extraction.

### FIB (**canonical for plasma fibrinogen concentration**)
- **Description:** Plasma fibrinogen concentration (Clauss assay or equivalent). Acute-phase protein synthesised in the liver; values rise in inflammation, infection, trauma, and pregnancy and fall in disseminated intravascular coagulation, severe hepatic failure, and congenital afibrinogenaemia. Used as a per-subject (or time-varying) covariate that proxies hepatic acute-phase response or inflammatory burden on the clearance of drugs whose elimination correlates with these processes.
- **Units:** umol/L (Taubert 2016 reports values in umol/L; the more common clinical units are g/L or mg/dL -- document per-model via `covariateData[[FIB]]$units`. Approximate conversion using a molecular weight of 340 kDa: 1 umol/L ~ 0.34 g/L.)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(FIB / ref)^exponent`. Reference values observed: 13.0 umol/L (Taubert 2016 patient-group-1 median; a critically-ill ICU cohort with elevated baseline fibrinogen).
- **Source aliases:**
  - `Fibrinogen` -- the spelled-out column header in Taubert 2016 Table 1 and supplementary Table S3; same biological quantity, no value transformation.
- **Example models:** `Taubert_2016_linezolid.R` (umol/L; reference 13.0; positive power exponent 0.04 on CL; quantitatively a weak effect at the cohort range but selected by stepwise covariate modeling).
- **Notes:** Distinct from `FVIIIRECENT` and `VWF` (specific coagulation-factor activities used in haemophilia / DDAVP-response models) -- `FIB` is the routine plasma-fibrinogen concentration used as an acute-phase / inflammatory marker in popPK analyses. Distinct from `CRP`, `IL6`, `LDH`, and other acute-phase / cell-turnover biomarkers; `FIB` may correlate with these but is a separate measurement. Ratified canonically on 2026-06-30 alongside the Taubert 2016 linezolid extraction.

## Disease severity scores

### SCORE_EASI (**canonical for Eczema Area and Severity Index**)
<!-- AUDIT 2026-06-19: renamed from `EASI` to `SCORE_EASI` per the canonical-register standardization audit. The prior name `EASI` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Eczema Area and Severity Index score (atopic-dermatitis severity composite; bounded continuous, scale 0-72 with higher values = more severe disease).
- **Units:** (score)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- healthy volunteers have SCORE_EASI = 0. Effect enters as an additive term in models that pool AD patients with HV (e.g., `Tiraboschi_2025_amlitelimab.R`).
- **Source aliases:**
  - `EASI` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `BEASI` (baseline SCORE_EASI) -- used in `Tiraboschi_2025_amlitelimab.R`.
- **Example models:** `Tiraboschi_2025_amlitelimab.R`.
- **Notes:** When used as a time-invariant baseline covariate (`BEASI`), document in `covariateData[[SCORE_EASI]]$notes`. Canonical name is `SCORE_EASI` without the `B` prefix to match the `AGE` / `WT` / `ALB` pattern where baseline vs time-varying status is recorded in notes rather than the column name.

### SCORE_MGADL (**canonical for Myasthenia Gravis Activities of Daily Living score**)
<!-- AUDIT 2026-06-19: renamed from `MGADL` to `SCORE_MGADL` per the canonical-register standardization audit. The prior name `MGADL` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Myasthenia Gravis Activities of Daily Living score -- eight-item patient-reported outcome measure (each item 0-3), total 0-24, higher values = greater symptom severity and functional limitation.
- **Units:** (score)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- healthy participants (no gMG) have `SCORE_MGADL = 0` by definition. Effect enters as a baseline covariate on MG-ADL response parameters in gMG cohorts.
- **Source aliases:**
  - `MGADL` -- prior canonical name (pre-2026-06-19 SCORE_ family standardization).
- **Example models:** `Valenzuela_2025_nipocalimab.R` (reference 7 points; power-form effect on `IDecplacebo` and on the slope between MG-ADL change and IgG reduction).
- **Notes:** Baseline-only in Valenzuela 2025 (the observation is the absolute change from baseline MG-ADL). When used time-varying (e.g., in pure PD models driven by disease-progression dynamics), document in `covariateData[[SCORE_MGADL]]$notes`. Canonical name is `SCORE_MGADL` without a `BL` prefix to match the `SCORE_EASI` / `AGE` / `WT` / `ALB` pattern.

### SCORE_BCVA (**canonical for best-corrected visual acuity**)
<!-- AUDIT 2026-06-19: renamed from `BCVA` to `SCORE_BCVA` per the canonical-register standardization audit. The prior name `BCVA` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Best-corrected visual acuity score measured on the Early Treatment Diabetic Retinopathy Study (ETDRS) chart, expressed as the number of letters read correctly (0-100; higher values = better vision). Used as a baseline severity covariate in ophthalmology PK/PD models of anti-VEGF treatment.
- **Units:** ETDRS letters (0-100)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as a baseline input to set the initial condition of an indirect-response SCORE_BCVA state or as a power-form effect on response parameters. Reference value observed: 55 letters (Mulyukov 2018 narrative: mean study-population baseline SCORE_BCVA).
- **Source aliases:**
  - `BCVA` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `BVA` (baseline visual acuity) -- used in `Mulyukov_2018_ranibizumab.R`.
- **Example models:** `Mulyukov_2018_ranibizumab.R` (baseline SCORE_BCVA used as the center for the initial-condition draw `g0 = SCORE_BCVA + eta_g0`).
- **Notes:** Ophthalmology-specific. Baseline-only in Mulyukov 2018 (carried once per subject and used only as the starting SCORE_BCVA for the indirect-response model). Canonical name drops the `B` prefix to match the `SCORE_EASI` / `AGE` / `WT` / `ALB` pattern (baseline-vs-time-varying status recorded in `covariateData[[SCORE_BCVA]]$notes`). Scope is `specific` until a second ophthalmology model ratifies the name; at that point promote to `general`.

### PREV_AE_SCORE (**canonical for previous-time-step ordinal adverse-event score**)
- **Description:** Ordinal adverse-event score recorded at the immediately preceding observation time, used as a Markov-state covariate that conditions the current-time logit / probability calculation on the previous outcome. Integer 0..N where N is the maximum AE grade in the source-paper grading scheme; convention is that the value is 0 at the first observation (no prior AE).
- **Units:** (ordinal score; document per-model the scale in `covariateData[[PREV_AE_SCORE]]$notes`)
- **Type:** count
- **Scope:** general
- **Reference category:** n/a -- typically used as a categorical conditioner (`IF (PREV_AE_SCORE == 0) ...`) or via piecewise-FPS indicator decomposition; the natural reference is `PREV_AE_SCORE = 0` (no prior AE).
- **Source aliases:**
  - `PREVSCOR` -- used in `Girard_2012_pimasertib.R` (CTCAE 0..3 ocular-AE score).
  - `PDV` -- used in `Yoshida_2024_fazpilodemab.R` (GIAE grade 0 / 1 / 2-3 combined, carried via the NONMEM `IF (EVID.EQ.0.AND.TYPE.EQ.1) PDV=DV` idiom in the DTMM control stream).
- **Example models:** `Girard_2012_pimasertib.R` (Markov-state covariate that selects per-previous-score logit thresholds `b01`/`b11`/`b21` and `b02`/`b12`/`b22` and per-previous-score `emax` levels; reset to 0 at TIME = 0 per source `IF (TIME.EQ.0) PREVSCOR=0`), `Yoshida_2024_fazpilodemab.R` (Markov-state covariate that selects per-previous-grade DTMM intercepts `b1_g{0,1,2}_ae` / `b2b1_g{0,1,2}_ae` and per-previous-grade discontinuation intercepts `b_dc_g{0,1,2}`; pooled grades 2 and 3 into a single PREV_AE_SCORE = 2 category per the source paper Methods page 545).
- **Notes:** Scope promoted from `specific` to `general` on 2026-06-30 alongside the Yoshida 2024 fazpilodemab extraction (the second model to use this canonical). The ordinal scale itself is paper-specific (different AE-grading schemes, different number of categories, different grouping rules -- Girard 2012 collapses CTCAE grades 1+2 into a single "1-2" category and treats grades >=3 as a third stratum; Yoshida 2024 pools CTCAE grades 2 and 3 into a single PREV_AE_SCORE = 2 category) and must be documented per-model in `covariateData[[PREV_AE_SCORE]]$notes`; the COVARIATE CONCEPT (previous-time-step ordinal AE grade as a Markov-state conditioner) is general. When assembling the simulation event table, set `PREV_AE_SCORE = 0` at the first observation of every subject and update each subsequent observation to the previous observation's sampled score -- matching the NONMEM `IF (TIME.EQ.0) PREVSCOR=0` / `PREVSCOR = DV` carry-forward idiom. Distinct from `PAIN` (continuous baseline pain score) and from `SCORE_MGADL` / `SCORE_EASI` (continuous severity scores not modelled as Markov states).

### SCORE_CDR_SOB (**canonical for Clinical Dementia Rating - Sum of Boxes score**)
<!-- AUDIT 2026-06-19: renamed from `CDR_SOB` to `SCORE_CDR_SOB` per the canonical-register standardization audit. The prior name `CDR_SOB` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Clinical Dementia Rating scale - Sum of Boxes (CDR-SOB) score, the unweighted sum of the six CDR-box scores (memory, orientation, judgement and problem solving, community affairs, home and hobbies, personal care; each scored 0 / 0.5 / 1 / 2 / 3). Total ranges 0-18; higher values indicate more severe cognitive and functional impairment. Widely used as a primary efficacy endpoint in Alzheimer's-disease (AD) clinical trials and as a disease-progression biomarker in mild-cognitive-impairment (MCI) / prodromal-AD populations.
- **Units:** (CDR-SOB units, 0-18 score)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- the source paper centres covariate effects on dataset medians (e.g., Delor 2013 uses SCORE_CDR_SOB / 2 in the DOT power-form and SCORE_CDR_SOB - 1 in the mixture-logit additive form).
- **Source aliases:**
  - `CDR_SOB` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `CDR_bsl` -- used in `Delor_2013_alzheimer.R` (baseline CDR-SOB at study entry).
  - `CDR` -- alternative bare-name often seen in ADNI / CAMD-style NONMEM datasets.
- **Example models:** `Delor_2013_alzheimer.R` (time-fixed baseline covariate; enters both the per-subject DOT power form (`(SCORE_CDR_SOB / 2)^e_cdr_sob_dot` with `e_cdr_sob_dot = -0.072`) and the per-subject slow-progression mixture-logit additive form (`+ e_cdr_sob_slow * (SCORE_CDR_SOB - 1)` with `e_cdr_sob_slow = -1.27`)).
- **Notes:** Canonical name is `SCORE_CDR_SOB` without a `_BL` suffix to match the `SCORE_EASI` / `SCORE_MGADL` / `SCORE_BCVA` pattern (baseline-vs-time-varying status recorded in `covariateData[[SCORE_CDR_SOB]]$notes`). The CDR sum-of-boxes form is distinct from the global CDR rating (`CDR_GLOBAL`, a 0 / 0.5 / 1 / 2 / 3 ordinal); the sum-of-boxes is preferred in disease-progression modelling for its finer granularity. Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

### SCORE_ADAS_COG (**canonical for ADAS-cog total cognitive subscale score**)
<!-- AUDIT 2026-06-19: renamed from `ADAS_COG` to `SCORE_ADAS_COG` per the canonical-register standardization audit. The prior name `ADAS_COG` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Alzheimer's Disease Assessment Scale - cognitive subscale (ADAS-cog) total score. The total-11 form ranges 0-70; the modernised total-13 form ranges 0-85. Higher values = more cognitive impairment. The ADAS-cog is the most widely used cognitive endpoint in AD clinical trials and disease-progression modelling.
- **Units:** (ADAS-cog units; document the form (total-11 / total-13) per-model in `covariateData[[SCORE_ADAS_COG]]$units`)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- covariate effects typically centred on a dataset median (e.g., Delor 2013 centres on SCORE_ADAS_COG = 12.67).
- **Source aliases:**
  - `ADAS_COG` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `ADAS_bsl` -- used in `Delor_2013_alzheimer.R` (baseline ADAS-cog total-11 at study entry).
  - `ADAS`, `ADAS_COG_11`, `ADAS_COG_13` -- alternative bare-name forms seen across ADNI / CAMD datasets.
- **Example models:** `Delor_2013_alzheimer.R` (time-fixed baseline covariate; ADAS-cog total-11 form; enters the per-subject DOT power form `(SCORE_ADAS_COG / 12.67)^e_adas_cog_dot` with `e_adas_cog_dot = -0.0439`).
- **Notes:** Canonical name is `SCORE_ADAS_COG` (no `_BL` suffix; baseline-vs-time-varying recorded in notes). The total-11 vs total-13 form must be documented per-model in `covariateData[[SCORE_ADAS_COG]]$units` because the same numeric SCORE_ADAS_COG value has different clinical interpretation across the two forms. Conrado 2014 uses ADAS-cog as the modelled observation (response variable) rather than as a baseline covariate; that model file therefore does not list SCORE_ADAS_COG in its `covariateData`. Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

### SCORE_MMSE (**canonical for Mini Mental State Examination score**)
<!-- AUDIT 2026-06-19: renamed from `MMSE` to `SCORE_MMSE` per the canonical-register standardization audit. The prior name `MMSE` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Mini Mental State Examination total score (0-30; higher values = better cognitive function). A widely used cognitive-screening instrument in AD and MCI populations.
- **Units:** (SCORE_MMSE units, 0-30 score)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- covariate effects typically centred on a dataset median (e.g., Delor 2013 centres on SCORE_MMSE = 26).
- **Source aliases:**
  - `MMSE` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `MMSE_bsl` -- used in `Delor_2013_alzheimer.R` (baseline SCORE_MMSE at study entry).
- **Example models:** `Delor_2013_alzheimer.R` (time-fixed baseline covariate; modifies the per-subject disease-progression acceleration parameter alpha via a power form `(SCORE_MMSE / 26)^e_mmse_alpha` with `e_mmse_alpha = -2.01`).
- **Notes:** Canonical name is `SCORE_MMSE` (no `_BL` suffix; baseline-vs-time-varying recorded in notes). SCORE_MMSE is the inverse-direction counterpart of SCORE_CDR_SOB / SCORE_ADAS_COG (SCORE_MMSE high = healthy; SCORE_CDR_SOB / SCORE_ADAS_COG high = impaired); covariate-effect coefficient signs are therefore typically opposite to those for SCORE_CDR_SOB / SCORE_ADAS_COG. Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

### SCORE_FAQ (**canonical for Functional Assessment Questionnaire score**)
<!-- AUDIT 2026-06-19: renamed from `FAQ` to `SCORE_FAQ` per the canonical-register standardization audit. The prior name `FAQ` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Functional Assessment Questionnaire (Pfeffer SCORE_FAQ) total score: sum of ten functional-activities items (each scored 0 = normal to 3 = dependent), total 0-30; higher values = greater functional impairment. Used as a functional-status covariate alongside cognitive scores in AD and MCI populations.
- **Units:** (SCORE_FAQ units, 0-30 score)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- covariate effects typically centred on a dataset median (e.g., Delor 2013 centres on SCORE_FAQ = 1).
- **Source aliases:**
  - `FAQ` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `FAQ_bsl` -- used in `Delor_2013_alzheimer.R` (baseline SCORE_FAQ at study entry).
- **Example models:** `Delor_2013_alzheimer.R` (time-fixed baseline covariate; enters the per-subject slow-progression mixture-logit additive form `+ e_faq_slow * (SCORE_FAQ - 1)` with `e_faq_slow = -0.341`).
- **Notes:** Canonical name is `SCORE_FAQ` (no `_BL` suffix; baseline-vs-time-varying recorded in notes). Distinct from the cognitive scores (SCORE_CDR_SOB / SCORE_ADAS_COG / SCORE_MMSE): SCORE_FAQ measures instrumental activities of daily living rather than cognitive performance, and adds incremental information about disease-stage severity in MCI cohorts. Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

### SCORE_RHPNM (**canonical for normalized hippocampal volume (head-size and age-adjusted)**)
<!-- AUDIT 2026-06-19: renamed from `RHPNM` to `SCORE_RHPNM` per the canonical-register standardization audit. The prior name `RHPNM` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Normalised hippocampal volume: the subject's average left+right hippocampal volume divided by the value expected for a healthy subject of the same age and estimated intracranial volume (head size). 1.0 corresponds to the healthy reference; values below 1.0 indicate hippocampal atrophy. Derived from MRI volumetry and a healthy-subject regression on age and intracranial volume.
- **Units:** (unitless ratio; 1.0 = healthy reference)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- covariate effects typically centred on SCORE_RHPNM = 1 (the healthy reference, e.g., Delor 2013).
- **Source aliases:**
  - `RHPNM` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `SCORE_RHPNM` -- used in `Delor_2013_alzheimer.R` (baseline normalized hippocampal volume; Delor 2013 derivation: `RHPNMbsl_i = HIPVbsl_i / HPNMbsl_i` where `HPNMbsl_i = Age_i * (-26.6268 + EICVbsl_i * 0.0016 + 3340.4395)`).
- **Example models:** `Delor_2013_alzheimer.R` (time-fixed baseline covariate; enters the per-subject slow-progression mixture-logit additive form `+ e_rhpnm_slow * (SCORE_RHPNM - 1)` with `e_rhpnm_slow = 7.5`, a strongly positive effect indicating that less atrophic hippocampi (SCORE_RHPNM closer to 1) are associated with a higher probability of being in the slow-progressing subpopulation).
- **Notes:** Distinct from the raw hippocampal volume (which would be a `HIPV` canonical not yet registered; raw HIPV is confounded with head size and age, hence the need for the normalisation). The Delor 2013 paper notes that the same effect is only marginally significant with unnormalised HIPV (P = 0.02) but strongly significant with the normalised form (SCORE_RHPNM). The exact age / EICV regression coefficients are paper-specific and any future model adopting this canonical should re-derive the normalisation for its own population or document why the Delor 2013 regression is reused. Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

### SCORE_ROSS (**canonical for modified Ross score of paediatric heart-failure severity**)
- **Description:** Modified Ross score, a clinical heart-failure severity instrument for infants and young children. Integer sum of per-domain ordinal items across feeding / exertional, respiratory (tachypnoea, retractions), cardiac auscultation (S3, hepatomegaly), and perfusion domains. Scale 0-12 in the modified Reithmann / Ross / Connolly form used across paediatric heart-failure trials; lower values = milder heart failure, higher values = more severe. The paediatric analogue of the adult-only NYHA functional classification.
- **Units:** (score; integer 0-12)
- **Type:** count
- **Scope:** general
- **Reference category:** n/a -- typically entered as an exponential effect `exp(theta * (SCORE_ROSS - ref))` on a PK parameter, centred on a dataset median. Per-model reference value must be documented in `covariateData[[SCORE_ROSS]]$notes`. The exponential form is required over a power form because the score can legitimately be 0 (per Steichert 2025 Section 2.2.2 rationale).
- **Source aliases:**
  - `Ross score` -- paper-prose form.
- **Example models:** `Steichert_2025_enalapril_enalaprilat_pediatric.R` (reference SCORE_ROSS = 4, the analysed-population weighted median; exponential effect `theta = -0.15` (RSE 26.3%) on the apparent volume of distribution of enalaprilat `Vd_ENAAT/F`).
- **Notes:** Distinct from `RACHS1` (Risk Adjustment for Congenital Heart Surgery, a perioperative surgery-risk category derived from procedure type) and from the adult-only NYHA classification. Count type rather than continuous because the score is an integer sum of per-domain ordinal items. Different modifications of the Ross score exist across paediatric cardiology (Reithmann 1989, Ross 1992 derivation; Connolly 2001, Laer / LENA 2015+ form); the specific modification used must be documented in `covariateData[[SCORE_ROSS]]$notes` per model.

### ACUTE_MED_DAYS (**canonical for baseline number of days/month of acute migraine medication use**)
- **Description:** Baseline number of days per month on which acute migraine medication (triptans or ergot compounds) was used during the 28-day run-in period prior to first dose. Enters as a piecewise-linear shift on baseline migraine or moderate-to-severe headache days in migraine exposure-response models.
- **Units:** days/month
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- piecewise-linear shift with breakpoint at 5 d/mo: contributes 0 below 5 and `slope * (ACUTE_MED_DAYS - 5)` above 5 (Fiedler-Kelly 2020). The 5-day breakpoint reflects the clinical guideline for medication-overuse headache.
- **Source aliases:** "Baseline days/month of acute medications" -- used in `FiedlerKelly_2020_fremanezumab_em.R` and `FiedlerKelly_2020_fremanezumab_cm.R`.
- **Example models:** `FiedlerKelly_2020_fremanezumab_em.R` (slope 0.438 d/d, episodic migraine), `FiedlerKelly_2020_fremanezumab_cm.R` (slope 0.460 d/d, chronic migraine).
- **Notes:** Specific scope because the variable is migraine-domain-bound. Time-fixed per subject (baseline-only). When future migraine E-R models register additional aliases or alternative breakpoints, document them per-model and consider promoting to `general`.

### DIS_AMYLOID_LOAD (**canonical for systemic-amyloidosis whole-body amyloid load grade**)
- **Description:** Ordinal whole-body amyloid load score in patients with systemic amyloidosis. Integer 0-3: 0 = no amyloid (healthy volunteers), 1 = small amyloid load, 2 = moderate amyloid load, 3 = large amyloid load. The grading combines organ-by-organ amyloid presence (liver, spleen, bone, adrenals, gut, heart) and SAP-scintigraphy uptake into a single per-patient severity grade at baseline. Time-fixed per subject.
- **Units:** (categorical 0-3)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** 0 (no amyloid). Sahota 2015 Eq. 2 also treats category 1 as part of the reference for the V4 effect (categories 0 and 1 share a V4 multiplier of 1); only categories 2 and 3 carry a non-zero effect via the cumulative parameters `e_amload2_vp_sap` and `e_amload2_vp_sap + e_amload3_vp_sap` respectively.
- **Source aliases:**
  - `AMLOAD` -- prior canonical name (pre-2026-06-19 DIS_ prefix standardization) and source-paper column in `Sahota_2015_miridesap.R`.
- **Example models:** `NA_NA_miridesap.R` (DDMODEL00000262; Sahota 2015 Eq. 2 multiplicative effect on SAP peripheral volume V4: V4 = V4_ref * (1 + e_amload2_vp_sap * I(DIS_AMYLOID_LOAD>=2) + e_amload3_vp_sap * I(DIS_AMYLOID_LOAD>=3)); reported effects e_amload2_vp_sap = 6.39 / e_amload3_vp_sap = 26.39 yielding ~7.4x V4 at moderate load and ~33.8x at large load), `Sahota_2015_miridesap.R` (paper-only extraction of the same Sahota 2015 final model with identical Eq. 2 effect on V4; values 6.39 / 26.39 taken from Table 2).
- **Notes:** Scope: specific because the grading scheme is amyloidosis-specific (Sahota 2015 Methods: "The whole body amyloid load covariate, AMLOAD, was a categorical score: 0 for no amyloid in healthy volunteers, 1 for small, 2 for moderate, and 3 for large"). The cumulative monotonic parameterisation in Sahota 2015 Eq. 2 encodes a positive-only step at each grade increment. Co-used with `DIS_AMYLOID_LIVER` for the binary hepatic-involvement modifier. Ratified canonically on 2026-05-15 alongside the DDMODEL00000262 / Sahota 2015 extraction. Renamed from `AMLOAD` to `DIS_AMYLOID_LOAD` on 2026-06-19 per the canonical-register standardization audit (operator decision to apply the `DIS_<concept>` prefix uniformly to disease-state indicators and to spell out amyloidosis rather than the shortened `AM` prefix that collides with chemistry symbols).

### DIS_AMYLOID_LIVER (**canonical for hepatic amyloid involvement indicator**)
- **Description:** Binary indicator for the presence of amyloid in the liver as a separate organ involvement from the overall whole-body amyloid load. 1 = liver amyloid present at baseline; 0 = no liver amyloid. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no liver amyloid).
- **Source aliases:**
  - `AMLIVER` -- prior canonical name (pre-2026-06-19 DIS_ prefix standardization) and source-paper column in `Sahota_2015_miridesap.R`.
- **Example models:** `NA_NA_miridesap.R` (DDMODEL00000262; Sahota 2015 Eq. 2 multiplicative effect on SAP intercompartmental clearance Q4: Q4 = Q4_ref * (1 + e_amliver_q4 * DIS_AMYLOID_LIVER); reported effect 4.01, yielding ~5x Q4 in patients with hepatic amyloid), `Sahota_2015_miridesap.R` (paper-only extraction of the same Sahota 2015 final model with identical Eq. 2 effect on Q4; value 4.01 from Table 2).
- **Notes:** Scope: specific because the covariate is amyloidosis-specific (Sahota 2015 Methods names AMLIVER alongside AMSPLEEN / AMHEART as organ-specific amyloid-involvement binary indicators; only AMLIVER was retained as a covariate in the final model). Used in combination with the global `DIS_AMYLOID_LOAD` grade so the model can express both general amyloid burden and the specific hepatic-clearance modifier (the SAP-CPHPC complex is cleared by the liver, motivating the hepatic-amyloid-specific Q4 effect). Ratified canonically on 2026-05-15 alongside the DDMODEL00000262 / Sahota 2015 extraction. Renamed from `AMLIVER` to `DIS_AMYLOID_LIVER` on 2026-06-19 per the canonical-register standardization audit (operator decision to apply the `DIS_<concept>` prefix uniformly to disease-state indicators and to spell out amyloidosis rather than the shortened `AM` prefix that collides with chemistry symbols).

## Critical-illness severity

### DIS_CRITILL (**canonical for binary critical-illness / ICU-admission indicator**)
- **Description:** Binary indicator that the subject is critically ill (equivalently, admitted to an intensive care unit) at the time of the modelled treatment. 1 = critically ill; 0 = not critically ill (ward inpatient or outpatient). Time-fixed per subject in the papers registered so far. Used when a population PK analysis pools critically ill and non-critically-ill subjects in one dataset and tests the contrast as a PK covariate -- critical illness perturbs clearance and volume through capillary leak, altered protein binding, augmented or impaired renal clearance, and organ support, but is frequently reported as a bare yes/no cohort flag with no accompanying severity score.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not critically ill).
- **Source aliases:**
  - `critically ill` -- the covariate name used in `Nguyen_2021_ganciclovir.R` (Yang 2023 Table 3 footnote: "critically ill: 1 for critically ill patients and 0 for others").
- **Example models:** `Nguyen_2021_ganciclovir.R` (multiplicative power factor on clearance: `cl = 2.55 * (WT/11.7)^0.75 * (CRCL/167)^0.763 * 0.806^DIS_CRITILL`, so a critically ill child has clearance 19.4% lower than a non-critically-ill child of the same weight and eGFR; Yang 2023 Table 3).
- **Notes:** Distinct from the continuous ICU severity scores `SAPS_II` and `APACHE_II` (which quantify how severely ill a patient is, and presuppose ICU admission), from `ORG_FAIL_COUNT` (integer count of failing organs), from `MECH_VENT` (a specific organ support), and from the ICU-admission-etiology indicators `ICU_ADM_POLYTRAUMA` / `ICU_ADM_POSTSURG` / `ICU_ADM_MEDICAL` (which classify *why* an already-admitted patient is in the ICU and are undefined for a non-ICU subject). `DIS_CRITILL` is the parent flag those etiology indicators presuppose: a dataset carrying any `ICU_ADM_*` indicator has `DIS_CRITILL = 1` for those subjects. Do **not** derive `DIS_CRITILL` from `ORG_FAIL_COUNT > 0` or from `DIS_SEPSIS` -- a critically ill patient may have neither organ failure nor sepsis, and the founding paper reports the bare binary with no organ-failure count or sepsis diagnosis behind it. This canonical is only needed where a model contrasts critically ill against non-critically-ill subjects **within one dataset**; a study whose entire cohort is critically ill (e.g. `Li_2021_ganciclovir.R`, `Krens_2020_ganciclovir.R`, `Horvatits_2014_ganciclovir.R`) records critical illness as a population property in `population$disease_state`, not as a covariate column. Scope: specific; promote to general if a second paper pools critically ill with non-critically-ill subjects using the same bare-binary semantics. Registered as `DIS_CRITILL` in the `DIS_<condition>` family rather than under the `ICU_` prefix per operator decision (sidecar request 001, question 1, 2026-07-30), because "critically ill" as papers use it is a clinical state rather than strictly an admission location. Ratified canonically alongside the Nguyen 2021 ganciclovir extraction.


### ORG_FAIL_COUNT (**canonical for number of organs failing in critically ill patients**)
- **Description:** Integer count of failing organs in a critically ill patient at a given observation day, ascertained per-day and reported as the worst-of-day count. The count is decomposed into mutually exclusive strata 0 / 1 / 2 / 3 / >=4 (Vet 2016 used the strata 0, 1, 2, 3, and 4-or-5) that select per-stratum typical clearance values; the strata are not collapsed onto a single linear or power covariate effect because the underlying organ-failure mechanisms (cardiovascular, pulmonary, renal, hepatic, neurologic, hematologic) impair drug elimination heterogeneously.
- **Units:** (count)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** 0 (no organs failing). Per-stratum typical CL values are estimated for ORG_FAIL_COUNT = 1, 2, 3, and >=4; ORG_FAIL_COUNT = 0 sets the baseline typical CL (frequently FIXED at the baseline value, as in Vet 2016).
- **Source aliases:**
  - `ORGF` -- used in `Vet_2016_midazolam.R` (DDMODEL00000249 NMTRAN `$INPUT` column; values 0..>=4). Renamed to canonical `ORG_FAIL_COUNT` when assembling input data for the packaged model.
- **Example models:** `Vet_2016_midazolam.R` (per-stratum typical CL values: ORG_FAIL_COUNT=0 fixed at 1.6 L/h for a 5 kg child with CRP=32 mg/L; ORG_FAIL_COUNT=1 -> 1.29 L/h; ORG_FAIL_COUNT=2 -> 0.957 L/h; ORG_FAIL_COUNT=3 -> 0.842 L/h; ORG_FAIL_COUNT>=4 -> 0.678 L/h).
- **Notes:** Specific scope because the variable is critical-care-population-bound (PICU / ICU). Time-varying within subject (re-evaluated each ICU day). The Vet 2016 organ-failure ascertainment follows the Wilkinson 1987 paediatric multiple organ system failure (MOSF) criteria -- operator-confirmed per-paper criteria should be documented in each model's `covariateData[["ORG_FAIL_COUNT"]]$notes`. Decompose inside `model()` into binary indicators (`orgf1 <- (ORG_FAIL_COUNT == 1)`, `orgf2 <- (ORG_FAIL_COUNT == 2)`, `orgf3 <- (ORG_FAIL_COUNT == 3)`, `orgf_ge4 <- (ORG_FAIL_COUNT >= 4)`) and select per-stratum CL with mutually-exclusive multiplicative-flag arithmetic. Ratified canonically on 2026-05-06.


### SAPS_II (**canonical for new Simplified Acute Physiology Score II at ICU admission**)
- **Description:** New Simplified Acute Physiology Score II (SAPS II) at intensive-care-unit admission. Validated 17-item ICU severity-of-illness score (Le Gall, Lemeshow & Saulnier, JAMA 1993;270:2957-2963) computed from age, vital signs, laboratory values, type of admission, and chronic-disease history during the first 24 hours after ICU admission; higher scores indicate greater severity and a higher predicted hospital mortality. Range theoretically 0-163; in adult ICU cohorts admission scores typically span ~10-100 with cohort means in the 35-65 range. Time-fixed per ICU stay.
- **Units:** points
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(SAPS_II / ref)^exponent`. Reference value observed: 50 points (Abboud 2009 typical-subject reference for the septic-shock cohort, mean SAPS II = 64 +/- 23).
- **Source aliases:**
  - `SAPS II` (with whitespace, as printed in the source paper's prose) -- used in `Abboud_2009_epinephrine.R`.
- **Example models:** `Abboud_2009_epinephrine.R` (power exponent -0.67 on epinephrine CL with reference 50; higher SAPS II is associated with lower clearance), `Polito_2016_fludrocortisone.R` (power exponents +0.019 on apparent oral fludrocortisone CL/F and +0.036 on the absorption lag time Tlag, both with reference 53 = the cohort median SAPS II among the 14 patients with detectable plasma concentrations; higher SAPS II is associated with longer absorption lag and faster apparent oral clearance).
- **Notes:** Specific scope because the column is critical-care-population-bound (adult ICU) and the score's clinical meaning depends on the SAPS-II derivation rules; future ICU models reusing the score with the same definition can extend `Example models` rather than registering a new canonical. Should not be confused with SAPS I or SAPS 3 (different scoring rules / item sets) -- register those under separate canonicals if a future paper uses them. Ratified canonically on 2026-05-18 alongside the Abboud 2009 epinephrine extraction.

### APACHE_II (**canonical for Acute Physiology and Chronic Health Evaluation II score at ICU admission**)
- **Description:** Acute Physiology and Chronic Health Evaluation II (APACHE II) score at intensive-care-unit admission. ICU severity-of-illness score (Knaus WA, Draper EA, Wagner DP, Zimmerman JE. Crit Care Med 1985;13(10):818-829) computed from the worst values of 12 acute-physiology variables during the first 24 hours after ICU admission, plus points for age and chronic health status. Integer range 0-71; higher scores indicate greater severity and higher predicted hospital mortality. Adult ICU cohorts typically span 5-40 with means in the 15-30 range. Time-fixed per ICU stay.
- **Units:** points
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- typically used with centred linear or power scaling. Reference values observed: 26 points (Swart 2004 midazolam learning-group mean APACHE II, used as the centring value in `Q = 40.8 - (APACHE - 26) * 2.75`).
- **Source aliases:**
  - `APACHE II-score on admission to ICU` -- printed row label in Swart 2004 Table 1; used in `Swart_2004_midazolam.R`.
  - `APACHE` -- shortened name in Swart 2004 Table 5 formula `Q = 40.8 - (APACHE - 26) * 2.75`.
- **Example models:** `Swart_2004_midazolam.R` (centred linear effect on Q with reference 26: `q = 40.8 - (APACHE_II - 26) * 2.75`; each 1-point increase above 26 lowers Q by 2.75 L/h, capturing decreased intercompartmental clearance in more severely ill patients).
- **Notes:** Specific scope because the score's clinical meaning is critical-care-population-bound (adult ICU) and the Knaus 1985 derivation rules must apply. Distinct from `SAPS_II` (Le Gall 1993, 17-item Simplified Acute Physiology Score II) -- APACHE II and SAPS II are different scoring systems with different item weights and calibration; a single ICU popPK paper may report one, the other, or both. Following the precedent set by `SAPS_II` Notes ("Should not be confused with SAPS I or SAPS 3 ... register those under separate canonicals if a future paper uses them"), APACHE II gets its own canonical. Future ICU popPK papers using APACHE II can extend `Example models` rather than registering a new canonical; scope may be promoted to general after multiple corroborating papers. Ratified canonically on 2026-07-26 alongside the Swart 2004 lorazepam / midazolam extraction.

### LACT (**canonical for serum lactate concentration**)
- **Description:** Serum (or plasma / arterial-blood) L-lactate concentration. Routine acid-base / tissue-perfusion marker in critically ill patients; rising values reflect tissue hypoperfusion, anaerobic glycolysis, hepatic dysfunction, or metabolic disturbance. Distinct from `LDH` (lactate dehydrogenase enzyme activity), which is a related but mechanistically different cell-turnover marker. Time-varying within an ICU stay (often updated several times per day) although source papers may use it as a per-day or per-subject summary -- document per-model in `covariateData[[LACT]]$notes`.
- **Units:** mmol/L (SI; equivalent to mEq/L; commonly reported as mg/dL = mmol/L * 9.0)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(LACT / ref)^exponent`. Reference values observed: 1.91 mmol/L (Taubert 2016 patient-group-1 day-1 median; a critically-ill ICU cohort spanning 0.53-10.7 mmol/L).
- **Source aliases:**
  - `Lactate` -- the spelled-out column header in Taubert 2016 Table 1 and supplementary Table S3; same biological quantity, no value transformation.
- **Example models:** `Taubert_2016_linezolid.R` (mmol/L; reference 1.91; negative power exponent -0.21 on CL; lower lactate is associated with higher clearance, consistent with reduced cardiac output / impaired tissue perfusion at higher lactate).
- **Notes:** Distinct from `LDH` (serum lactate dehydrogenase enzyme activity in U/L); a popular point of confusion because both encode "lactate" in the lab-marker abbreviation. Critically-ill PK models that include both should keep the two columns separate. Taubert 2016 reports that lactate correlated significantly with the cardiovascular SOFA sub-score (i.e., lactate acts here as a perfusion / cardiac-output proxy more than as a direct biochemical driver of linezolid elimination). General scope because serum lactate is a universally applicable critical-care covariate. Ratified canonically on 2026-06-30 alongside the Taubert 2016 linezolid extraction.

### RACHS1 (**canonical for Risk Adjustment for Congenital Heart Surgery 1 (RACHS-1) category**)
- **Description:** Integer 1-6 RACHS-1 surgical-risk category, ascertained pre-operatively from the type of congenital heart defect and the planned operation. Higher categories indicate greater perioperative risk of in-hospital mortality (Jenkins 2002 Pediatrics). Time-fixed per subject.
- **Units:** (categorical; 1-6 integer)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** Paper-specific. Oualha 2014 uses RACHS-1 = 2 as the low-risk reference stratum on SV*SVR_max (categories 1 are absent from the cohort; 3 and 4 are pooled as the higher-risk stratum).
- **Source aliases:**
  - `RACHS-1` -- the publication's printed form with a hyphen, not a valid R identifier; renamed to `RACHS1` when assembling input data.
- **Example models:** `Oualha_2014_epinephrine.R` (decomposed inside `model()` into a binary indicator `rachs1_high <- (RACHS1 >= 3)` that selects an additive log-shift on SV*SVR_max from 0.44 to 0.26 for the high-risk pool).
- **Notes:** Specific scope because the variable is paediatric-cardiac-surgery-population-bound and the reference category depends on which RACHS-1 strata the cohort contains (Oualha 2014 has categories 2-4 only; a paper with categories 1-6 would need a different decomposition). Decompose inside `model()` into mutually exclusive binary indicators matching the source's pooling (e.g., `rachs1_high <- (RACHS1 >= 3)`) and document the pooling rule in `covariateData[["RACHS1"]]$notes`.

### CVP (**canonical for central venous pressure**)
- **Description:** Central venous pressure (CVP), measured in mmHg through a central venous catheter. Time-varying when monitored continuously; in cardiovascular Emax PD models it enters the mean-arterial-pressure equation (MAP = HR * SV*SVR + CVP) as an additive constant rather than a fitted covariate effect.
- **Units:** mmHg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used additively in the MAP equation. Cohort medians observed: 11 mmHg (Oualha 2014, range 8-15).
- **Source aliases:** none.
- **Example models:** `Oualha_2014_epinephrine.R` (enters Eq. 7 as the additive offset in MAP = HR * SV*SVR + CVP; the vignette defaults to the cohort median 11 mmHg when CVP is not supplied per subject).
- **Notes:** Specific scope because CVP is meaningful only for haemodynamic-PD models that resolve mean arterial pressure into its component cardiac-output and venous-return terms. The Oualha 2014 final model does not test CVP as a fitted covariate on PK or PD parameters; the column is used only as the structural offset in the MAP equation. Future haemodynamic models that fit a covariate effect on CVP itself can re-use the canonical name.

### MORTRISK_HIGH (**canonical for high-mortality-risk composite indicator**)
- **Description:** 1 = subject meets the paper-defined "high mortality risk" composite criteria at study entry; 0 = subject is in the low or intermediate risk strata. Subject-level baseline indicator. The defining criteria are paper-specific: in Thuo 2011 (severe malnutrition cohort, following Berkley et al. Arch Dis Child 2003), a child is "high risk" if any one of the following is present: depressed conscious state, bradycardia (heart rate < 80 bpm), evidence of shock (capillary refill time >= 2 s, temperature gradient or weak pulse), or hypoglycaemia (blood glucose < 3 mmol/L); "intermediate risk" is any one of deep acidotic breathing, severe dehydration with diarrhoea, lethargy, hyponatraemia (Na < 125 mmol/L), or hypokalaemia (K < 2.5 mmol/L); "low risk" is none of the above. The canonical column collapses the intermediate and low strata into the 0 reference because the Thuo 2011 model only retains the high-vs-not-high contrast as a covariate on CL.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (low or intermediate mortality risk).
- **Source aliases:**
  - `HIGHRISK` / `RISK` (Thuo 2011 NONMEM notation; paper writes "high risk" in prose and structural equations as `[1 + theta3 * (high risk)]`) -- used in `Thuo_2011_ciprofloxacin.R`.
- **Example models:** `Thuo_2011_ciprofloxacin.R` (multiplicative fractional effect on apparent CL: `1 + (-0.283) * MORTRISK_HIGH`, i.e., a 28.3% reduction in apparent oral clearance for high-risk children; the standardised CL falls from 42.7 L/h/70 kg in low/intermediate-risk to 30.6 L/h/70 kg in high-risk; the paper attributes the contrast to delayed gastric emptying / impaired gut absorption in critically ill malnourished children).
- **Notes:** Specific scope because the underlying definition of "high mortality risk" is paper-defined (Thuo 2011 follows Berkley 2003's three-stratum risk score for severely malnourished children). Distinct from `SAPS_II` (continuous adult-ICU severity score), `RACHS1` (paediatric cardiac surgery risk category), and `ORG_FAIL_COUNT` (integer organ-failure count) -- each is its own canonical with paper-specific scoring rules. Future paediatric-severe-malnutrition popPK papers that retain the Berkley 2003 three-stratum score (or a close variant) can reuse this canonical; per-model `covariateData[[MORTRISK_HIGH]]$notes` must document the exact criteria the source paper used. Ratified canonically on 2026-05-21 alongside the Thuo 2011 ciprofloxacin extraction.

### MECH_VENT (**canonical for invasive-mechanical-ventilation status indicator**)
- **Description:** 1 = subject is receiving invasive mechanical ventilation during the modeled period; 0 = not. Treatment-status indicator (not a physiologic measurement) captured at study entry or as a time-fixed per-subject flag for the ICU admission. Captures the bulk fluid-distribution and hemodynamic shifts associated with positive-pressure ventilation -- increased plasma renin activity, aldosterone, and antidiuretic hormone -- which can change PK distribution volumes in critically ill patients.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not mechanically ventilated).
- **Source aliases:**
  - `mechanical ventilation` -- printed phrase in Georges 2009 Table 1 (`no/yes` count column); renamed to canonical `MECH_VENT` when assembling input data. Used in `Georges_2009_ceftazidime.R`.
- **Example models:** `Georges_2009_ceftazidime.R` (selector effect on V1 via the multiplicative log-effect form: `vc = exp(lvc + e_mech_vent_vc * MECH_VENT + etalvc)`, with `lvc = log(18.9)` at MECH_VENT = 0 and `e_mech_vent_vc = log(9.02 / 18.9)` recovering TVV1 = 9.02 L at MECH_VENT = 1; Discussion attributes the V1 contraction to PPV-induced hormonal fluid shifts).
- **Notes:** Specific scope because the indicator is ICU-population-bound and the mode-of-ventilation conventions vary across cohorts (Georges 2009 uses a binary IMV/no-IMV partition without distinguishing non-invasive ventilation, tracheostomy delivery, or weaning trial state). Distinct from `RRT_HEMODIAL_STATUS` / `RRT_CRRT_STATUS` (renal-replacement-therapy treatment-status flags) and from `SAPS_II` (continuous severity score). When a future critically-ill popPK paper reuses MECH_VENT with the same definition (binary, time-fixed at ICU admission), extend the `Example models` list rather than registering a new canonical; promotion to general scope is appropriate after a second corroborating paper. Per-model `covariateData[[MECH_VENT]]$notes` must document whether the indicator is time-fixed at admission or re-evaluated as ventilation status changes during the ICU stay. Ratified canonically on 2026-06-10 alongside the Georges 2009 ceftazidime extraction.


### ICU_ADM_POLYTRAUMA (**canonical for ICU admission etiology indicator: polytrauma**)
- **Description:** 1 = subject's ICU admission etiology is polytrauma (severe multi-system blunt or penetrating trauma); 0 = otherwise. One of three mutually-exclusive ICU admission etiology indicators (`ICU_ADM_POLYTRAUMA + ICU_ADM_POSTSURG + ICU_ADM_MEDICAL = 1` for every subject). Time-fixed per subject (the admission category is recorded once at ICU entry). Used as a categorical-stratum covariate on PK distribution parameters that differ between trauma-induced fluid shifts and elective surgical or medical patient profiles.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (other admission etiology -- ICU_ADM_POSTSURG = 1 or ICU_ADM_MEDICAL = 1).
- **Source aliases:**
  - `Admission for polytrauma` -- printed row label in Georges 2009 Table 1 (count column); renamed to canonical `ICU_ADM_POLYTRAUMA` when assembling input data. Used in `Georges_2009_ceftazidime.R`.
- **Example models:** `Georges_2009_ceftazidime.R` (selector effect on V2: TVV2 = theta6 = 57.1 L at ICU_ADM_POLYTRAUMA = 1; multiplicative log-effect form encodes the shift relative to the ICU_ADM_MEDICAL reference stratum as `e_icu_adm_polytrauma_vp = log(57.1 / 13.6)`).
- **Notes:** Specific scope because the polytrauma stratum definition is cohort-dependent (Georges 2009 partitions polytrauma vs postsurgical vs medical-reason at the time of ICU admission per Toulouse-cohort criteria; other ICU populations may use different boundaries -- e.g., trauma + sepsis combination, isolated head injury, burns separated from polytrauma). The three ICU_ADM_* indicators must be mutually exclusive in the input data; the canonical mapping convention is that exactly one of them is 1 per subject. Distinct from `MORTRISK_HIGH` (severity score), `SAPS_II` (continuous severity), and `ORG_FAIL_COUNT` (organ-failure count) -- all of which describe critical-illness severity rather than admission etiology. Future adult-ICU popPK papers that retain the same three-stratum etiology partition can extend the `Example models` list and the scope may be promoted to general after a second corroborating paper. Ratified canonically on 2026-06-10 alongside the Georges 2009 ceftazidime extraction.


### ICU_ADM_POSTSURG (**canonical for ICU admission etiology indicator: postsurgical**)
- **Description:** 1 = subject's ICU admission etiology is postsurgical (elective or urgent surgical recovery, not polytrauma); 0 = otherwise. One of three mutually-exclusive ICU admission etiology indicators (see `ICU_ADM_POLYTRAUMA` for the partition definition). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (other admission etiology -- ICU_ADM_POLYTRAUMA = 1 or ICU_ADM_MEDICAL = 1).
- **Source aliases:**
  - `Admission for postsurgical` -- printed row label in Georges 2009 Table 1 (count column); renamed to canonical `ICU_ADM_POSTSURG` when assembling input data. Used in `Georges_2009_ceftazidime.R`.
- **Example models:** `Georges_2009_ceftazidime.R` (selector effect on V2: TVV2 = theta7 = 25.7 L at ICU_ADM_POSTSURG = 1; multiplicative log-effect form encodes the shift relative to the ICU_ADM_MEDICAL reference stratum as `e_icu_adm_postsurg_vp = log(25.7 / 13.6)`).
- **Notes:** See `ICU_ADM_POLYTRAUMA` for shared scope rationale and partition rules. Specific scope because the postsurgical stratum definition is cohort-dependent and the partition into the three Georges 2009 categories is not universal across ICU popPK papers (some cohorts merge postsurgical with medical or split it into elective vs urgent). Ratified canonically on 2026-06-10 alongside the Georges 2009 ceftazidime extraction.


### ICU_ADM_MEDICAL (**canonical for ICU admission etiology indicator: medical**)
- **Description:** 1 = subject's ICU admission etiology is a medical reason (sepsis, pneumonia, cardiac, neurologic, or other non-surgical / non-polytrauma medical condition); 0 = otherwise. One of three mutually-exclusive ICU admission etiology indicators (see `ICU_ADM_POLYTRAUMA` for the partition definition). Time-fixed per subject. Commonly serves as the structural reference category in models that compare polytrauma / postsurgical strata against the medical baseline, because medical-reason patients in adult ICU cohorts tend to have distribution volumes closer to non-critically-ill individuals (per Georges 2009 Discussion).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (other admission etiology -- ICU_ADM_POLYTRAUMA = 1 or ICU_ADM_POSTSURG = 1). When this stratum is chosen as the multiplicative-effect reference (as in Georges 2009), `e_icu_adm_medical_<param> = 0` is implicit and only the two non-reference effects are estimated.
- **Source aliases:**
  - `Admission for medical reason` -- printed row label in Georges 2009 Table 1 (count column); renamed to canonical `ICU_ADM_MEDICAL` when assembling input data. Used in `Georges_2009_ceftazidime.R`.
- **Example models:** `Georges_2009_ceftazidime.R` (selector effect on V2: TVV2 = theta8 = 13.6 L at ICU_ADM_MEDICAL = 1; this is the structural reference of the multiplicative parameterisation -- `exp(lvp)` recovers 13.6 L when both other indicators are zero).
- **Notes:** See `ICU_ADM_POLYTRAUMA` for shared scope rationale. Specific scope. The Georges 2009 Discussion notes 'Patients with a medical reason for admission presented a volume of distribution in the same order of magnitude as those of healthy subjects' which motivates the choice of this stratum as the structural reference for V2 in the model file. Ratified canonically on 2026-06-10 alongside the Georges 2009 ceftazidime extraction.


### HIE_POST (**canonical for post hypoxic-ischemic event indicator**)
- **Description:** 1 = subject has undergone a hypoxic-ischemic (HI) event and is in the post-insult state (sustained cerebral injury and / or multi-organ stress that alters drug disposition); 0 = no HI event. Time-varying within a subject as the indicator flips from 0 to 1 at the documented HI insult time and remains 1 thereafter for the modeled observation window. Common in neonatal-encephalopathy / perinatal-asphyxia popPK studies (and potentially in adult cardiac-arrest / traumatic-arrest popPK studies) where the HI event causes persistent reductions in hepatic and renal drug clearance via CYP450 / UGT pathway impairment, decreased cardiac output, and multi-organ dysfunction. The exact definition of "post-HI state" (e.g., calculated from a continuous severity score thresholded to binary, or simply set to 1 from the documented insult time onward) is paper-specific; document per-model in `covariateData[[HIE_POST]]$notes`. Used as a binary state indicator on PK parameters (most commonly CL).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no HI event; pre-insult or never-insulted).
- **Source aliases:**
  - `AED` -- used in `Ezzati_2014_dexmedetomidine_piglet.R` (the paper defines `AED` as "acute energy depletion", computed by the time integral of beta-nucleotide-triphosphate (NTP) peak-area changes during hypoxia-ischemia; in the final model the AED severity score is used as a binary 0 / 1 state flag for whether the subject experienced the HI insult, NOT as a continuous covariate -- 9 piglets had AED = 1 throughout dosing and 1 control piglet had AED = 0). NOTE: the paper's `AED` column-name encodes a calculated NTP-integral severity score that the model dichotomises to a 0 / 1 state flag; downstream users assembling event tables should set `HIE_POST = 1` from the HI insult time onward for all HI-exposed subjects.
- **Example models:** `Ezzati_2014_dexmedetomidine_piglet.R` (multiplicative factor on dexmedetomidine CL post-insult: `CL = CLstd * (Wt/70)^0.75 * (1 + Ftemp * (TEMP - 37)) * (1 + (FAED - 1) * HIE_POST)` with `FAED = 0.558` -- clearance is multiplied by 0.558 in the post-HI state, i.e. CL is reduced TO 55.8% of the pre-insult value, equivalent to a 44.2% reduction relative to the pre-insult typical value).
- **Notes:** General scope because hypoxic-ischemic injury is a recurring exposure across neonatal-encephalopathy, perinatal-asphyxia, and potentially adult cardiac-arrest popPK studies. Distinct from `BURN_RECENT` (acute hypermetabolic phase indicator with different physiology and a time-windowed threshold) and from `DIS_*` disease-state indicators (which describe chronic conditions rather than acute insult states). Distinct from `ORG_FAIL_COUNT` (integer organ-failure count) and `MORTRISK_HIGH` (mortality risk score) -- HIE_POST is a binary state flag for a specific insult exposure, not a downstream severity or outcome score. Future papers that report a continuous HI severity score (e.g., AED integral, Sarnat staging, MRI biomarker) could either (a) re-use `HIE_POST` as a binary derived from the continuous score, or (b) register a sibling continuous canonical (e.g., `HIE_SEVERITY`) -- operator decides per paper. Ratified canonically on 2026-06-10 alongside the Ezzati 2014 dexmedetomidine piglet extraction.


## Interferon / biomarker panels

### BGENE21 (**canonical for 21-gene type I interferon signature score**)
- **Description:** Baseline 21-gene type I interferon signature score -- a composite transcriptomic score summarising the expression of 21 interferon-regulated genes in whole blood relative to a healthy-donor reference, used as a biomarker of type I IFN pathway activation in SLE and related autoimmune conditions.
- **Units:** unitless fold-change score (relative to healthy-donor reference).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(BGENE21 / ref)^exponent`. Reference values observed: 32 in Narwal 2013 (study-population median was 33), 12.04 in Zheng 2016 (median of the SLE phase IIb cohort, range 0.32-38.59).
- **Source aliases:** none.
- **Example models:** `Narwal_2013_sifalimumab.R` (reference 32, exponent 0.0558 on CL), `Zheng_2016_sifalimumab.R` (reference 12.04, power effect on CL with exponent 0.09).
- **Notes:** Specific to drugs whose mechanism targets the type I IFN pathway (e.g., anti-IFN-alpha antibodies like sifalimumab, anifrolumab). Higher BGENE21 indicates stronger target engagement / disease activity and is associated with increased drug clearance via target-mediated mechanisms. The 21-gene panel composition is tied to the MedImmune/AstraZeneca SLE development programme; a different IFN gene signature (e.g., a 4-gene or 5-gene panel) should be registered under its own canonical name (`BGENE4`, `IFN_SIG`, ...) to avoid conflating panel definitions.

### BGENE21_HIGH (**canonical for binary high-vs-low IFN-21-gene indicator**)
- **Description:** 1 = subject's baseline 21-gene type I IFN signature score is at or above the paper-specified high/low cut-off, 0 = below cut-off.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (low).
- **Source aliases:** none.
- **Example models:** `Almquist_2022_anifrolumab.R` (binary high-IFN indicator on CL).
- **Notes:** Pair with continuous `BGENE21` when the paper reports both. The high/low cut-off is paper-specific (commonly the population median) and must be documented in `covariateData[[BGENE21_HIGH]]$notes` for every model that uses this covariate. Operator decision (2026-04-28): use `BGENE21_HIGH` (not `IFNGS_HIGH`) so the link to the existing `BGENE21` register entry is explicit while the binary nature stays visible in the column name.

## Inflammation markers

### EOS (**canonical for blood eosinophil count**)
- **Description:** Blood eosinophil count (baseline or time-varying).
- **Units:** cells/uL
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(EOS / ref)^exponent`.
- **Source aliases:**
  - `BEOS` (baseline EOS) -- used in `Kotani_2022_astegolimab.R`.
- **Example models:** `Kotani_2022_astegolimab.R` (reference 180 cells/uL, baseline).
- **Notes:** Used as a surrogate of inflammatory burden that correlates with protein turnover and therefore mAb clearance. Canonical name drops the `B` prefix to match the `SCORE_EASI` / `AGE` / `WT` / `ALB` pattern; baseline-vs-time-varying status is documented in `covariateData[[EOS]]$notes`.

### BLBCELL (**canonical for baseline CD19+ B cell count**)
- **Description:** Baseline CD19+ B cell count (cells/uL) measured by fluorescence-activated cell sorting (FACS) prior to first dose. Used as a covariate / scaling biomarker for B-cell-targeted antibody PK-PD models (e.g., anti-CD20 mAbs in multiple sclerosis or B cell malignancies).
- **Units:** cells/uL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(BLBCELL / ref)^exponent`. Reference value observed: 200 cells/uL (Yu 2022, median of the pooled five-study cohort).
- **Source aliases:**
  - `Bcell0` -- used in `Yu_2022_ofatumumab.R`.
  - `BBCC` (NHL Phase I/Ib/II convention; values in 10^6 cells/L = cells/uL) -- used in `Lu_2019_polatuzumab.R`.
- **Example models:** `Yu_2022_ofatumumab.R` (power effect on the maximum B-cell-lysis stimulatory effect Emax, exponent 0.275, reference 200 cells/uL), `Lu_2019_polatuzumab.R` (two distinct effects: power on CL_INF with input floored at 1 cell/uL, and a thresholded power on CL_T with the BLBCELL/121-cells/uL ratio floored at 1), `Lu_2017_polatuzumab_neuropathy.R` (carries forward the Lu 2019 acMMAE PK-side effects via the inlined acMMAE popPK layer; not used directly by the Lu 2017 TTE PD layer).
- **Notes:** Distinct from a *time-varying* B cell count, which is the PD response variable rather than a covariate. Scope: specific because the clinically relevant baseline depends on the surface marker (CD19, CD20, CD22) and whether the panel reports total B cells or memory/naive subsets -- register a new canonical name if a future paper uses a different marker. Both Yu 2022 (anti-CD20 ofatumumab) and Lu 2019 (anti-CD79b polatuzumab vedotin) use CD19+ counts, so the canonical is reused; subtype-specific differences are documented in each model's `covariateData[[BLBCELL]]$notes`.

### BL_PARP_PBL (**canonical for baseline poly(ADP-ribose) polymerase activity in peripheral blood lymphocytes**)
- **Description:** Subject-specific baseline (pre-dose) poly(ADP-ribose) polymerase (PARP) activity in peripheral blood lymphocytes (PBL), measured as picomoles of PAR polymer formed per 10^6 PBL by an enzyme-activity assay. Used as a covariate / scaling biomarker on the maximal-inhibition residual activity (Emin) parameter in PARP-inhibitor PK/PD models.
- **Units:** pmol/10^6 PBL (document per-model via `covariateData[[BL_PARP_PBL]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(BL_PARP_PBL / ref)^exponent`. Reference value observed: 90.8 pmol/10^6 PBL (Wang 2015 rucaparib; population typical baseline E0 used as a stand-in for the unreported study-cohort median because the paper reports only the typical E0 and the exponent value, not the numeric BLB_median).
- **Source aliases:**
  - `BLB` (Wang 2015's notation for "baseline level in blood / PBL") -- used in `Wang_2015_rucaparib.R`.
- **Example models:** `Wang_2015_rucaparib.R` (power effect on residual maximum-inhibition parameter Emin; exponent 0.620 with the form `Emin = TV(Emin) * (BL_PARP_PBL / BLB_median)^alpha`; PBL paired with a separate tumor-tissue PARP activity covariate that is not yet a registered canonical because the units differ -- pmol/mg protein for tumor vs pmol/10^6 PBL for blood).
- **Notes:** Specific scope because the column is meaningful only for PARP-inhibitor PK/PD models (rucaparib, olaparib, niraparib, talazoparib, veliparib, etc.) and the units are tied to the PBL-specific assay format. A future tumor-tissue PARP activity covariate would need a separate canonical because the units differ (pmol/mg protein) and the biology of cellular PARP activity per mg of protein is not numerically interchangeable with PBL-normalized PARP activity. The Wang 2015 model uses BL_PARP_PBL only on Emin (residual maximum inhibition) and not on E0 or IC50; per-paper effects must be documented in each model's `covariateData[[BL_PARP_PBL]]$notes`. The paper does not publish the numeric study-cohort median of BLB used to center the covariate; the model file uses 90.8 pmol/10^6 PBL (the population typical baseline E0 reported in Wang 2015 Table 2) as a defensible default reference and documents the assumption in the vignette's Assumptions and deviations section.

### BL_TGN_RBC (**canonical for baseline erythrocyte 6-thioguanine-nucleotide concentration**)
- **Description:** Subject-specific baseline (first observed) concentration of 6-thioguanine nucleotides (TGN), the active metabolites of 6-mercaptopurine, inside red blood cells. Reported clinically as "E-TGN". Used as the initial condition of the `rbc_tgn` compartment in thiopurine PK/PD models whose observation record begins during ongoing maintenance therapy, when the red-cell pool has already accumulated and cannot be initialised at zero.
- **Units:** umol/L. Document per-model via `covariateData[[BL_TGN_RBC]]$units`. Papers frequently report E-TGN as nmol per mmol hemoglobin; convert to umol/L using the assay's assumed hemoglobin molecular weight and erythrocyte hemoglobin concentration and record the conversion in `$notes` (Gebhard 2023 uses MW 64458 g/mol and 330 g Hb/L erythrocytes).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- supplied as an ODE initial condition (`rbc_tgn(0) = BL_TGN_RBC`), not as a scaling covariate. Cohort median 0.83 umol/L, range 0-7.6 umol/L (Gebhard 2023 Table 1).
- **Source aliases:**
  - `INITGN` -- used in `Gebhard_2023_mercaptopurine.R` and `Gebhard_2023_mercaptopurine_anc.R` (Gebhard 2023: "`X_E^6MP(0) = INITGN` ... with `INITGN` being the first observation in the data set at time point 0").
- **Example models:** `Gebhard_2023_mercaptopurine.R`, `Gebhard_2023_mercaptopurine_anc.R`.
- **Notes:** Ratified 2026-07-30 (sidecar `oare_PMC10359452` request-001 q5, option A) as a member of the existing `BL_<biomarker>` family; the `_RBC` cell-compartment suffix mirrors the `_PBL` suffix of `BL_PARP_PBL`. Distinct from `CONMED_MP`, which is a concomitant-6-mercaptopurine-medication FLAG, not a measured red-cell concentration. Specific scope because the column is meaningful only for thiopurine models that carry an intracellular red-cell TGN state. A model that can start its estimation at a drug-free time point should initialise `rbc_tgn(0) = 0` and not carry this covariate; Gebhard 2023's own Discussion names that as the preferred remedy, but the published model as estimated requires the observed initial value.

### BL_MTX_RBC (**canonical for baseline erythrocyte methotrexate concentration**)
- **Description:** Subject-specific baseline (first observed) concentration of methotrexate -- predominantly as methotrexate polyglutamates -- inside red blood cells. Reported clinically as "E-MTX". Used as the initial condition of the `rbc_mtx` compartment in low-dose-methotrexate PK models whose observation record begins during ongoing maintenance therapy, when the red-cell pool has already accumulated and cannot be initialised at zero.
- **Units:** umol/L. Document per-model via `covariateData[[BL_MTX_RBC]]$units`. Papers frequently report E-MTX as nmol per mmol hemoglobin; convert to umol/L and record the conversion in `$notes` (Gebhard 2023 uses MW 64458 g/mol and 330 g Hb/L erythrocytes).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- supplied as an ODE initial condition (`rbc_mtx(0) = BL_MTX_RBC`), not as a scaling covariate. Cohort median 0.026 umol/L, range 0-0.10 umol/L (Gebhard 2023 Table 1).
- **Source aliases:**
  - `INIMTX` -- used in `Gebhard_2023_methotrexate.R` (Gebhard 2023: "`X_E^MTX(0) = INIMTX` with `INIMTX` being the first observation in the data set at time point 0").
- **Example models:** `Gebhard_2023_methotrexate.R`.
- **Notes:** Ratified 2026-07-30 (sidecar `oare_PMC10359452` request-001 q5, option A) alongside `BL_TGN_RBC`. Distinct from `CONMED_MTX`, which is a concomitant-methotrexate-medication FLAG, not a measured red-cell concentration. Note that the red-cell methotrexate half-life is long (30-40 days in the literature Gebhard 2023 cites), so this initial condition remains influential over a much longer horizon than a typical plasma baseline.

### DOSE_MTX_MGM2 (**canonical for administered methotrexate dose per body-surface area**)
- **Description:** The methotrexate dose administered on the current dose record, normalised to body-surface area and expressed in mg/m^2. Per-dose-record covariate; constant within an inter-dose interval and updated when the prescriber alters the dose.
- **Units:** mg/m^2
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters a saturable dose-dependent bioavailability term `F = 1 - Imax * DOSE_MTX_MGM2 / (D50 + DOSE_MTX_MGM2)` (Gebhard 2023 Supplementary Table S1, `Imax = 0.77`, `D50 = 15.01 mg/m^2`, both fixed from the literature source Table S1 cites). Cohort median weekly dose 15.0 mg/m^2, range 1.3-45.0 mg/m^2 (Gebhard 2023 Table 1).
- **Source aliases:**
  - `MTX` -- used in `Gebhard_2023_methotrexate.R` (the bare symbol Gebhard 2023 Supplementary Table S1 uses inside the bioavailability formula, annotated "MTX in mg/m2").
- **Example models:** `Gebhard_2023_methotrexate.R`.
- **Notes:** Well-formed member of the auto-approved `DOSE_<DRUG>_<UNITS>` family (cf. `DOSE_EMPA_MGD`, `DOSE_PHT_MGKGD`). A dedicated column is required rather than the general `DOSE` canonical because `Gebhard_2023_methotrexate.R` supplies its `amt` in umol/m^2 (the unit the model's concentration states demand) while the published bioavailability formula is calibrated in mg/m^2; carrying both in one `DOSE` column would silently mix units. Convert with the methotrexate molecular weight 454.44 g/mol: `amt [umol/m^2] = DOSE_MTX_MGM2 [mg/m^2] * 1000 / 454.44`. Ratified 2026-07-30 alongside the Gebhard 2023 extraction.

### CSF1 (**canonical for colony-stimulating factor 1 / macrophage-colony-stimulating factor concentration**)
- **Description:** Plasma colony-stimulating factor 1 (CSF-1, also known as macrophage colony-stimulating factor, M-CSF) concentration (baseline or time-varying). The hematopoietic cytokine that signals through CSF-1R to drive monocyte / macrophage differentiation and survival; used as both a target-engagement biomarker (anti-CSF-1R mAbs increase circulating free CSF-1) and a baseline covariate on PK / PD parameters in CSF-1R-pathway PopPK/PD models.
- **Units:** pg/mL (= ng/L; the two labels are numerically equivalent). Document per-model via `covariateData[[CSF1]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(CSF1 / ref)^exponent`. Reference values observed: 549 pg/mL (Yang 2024 axatilimab; pooled-cohort median).
- **Source aliases:** none formally; informal aliases include `BLCSF1` (baseline CSF-1) and `BL_CSF1` (model-parameter notation in Monolix / NONMEM control streams).
- **Example models:** `Yang_2024_axatilimab.R` (baseline-only covariate on linear clearance `CL` with power exponent 0.912 and on the model parameter `BL_CSF1` with power exponent 0.656; reference 549 pg/mL).
- **Notes:** Specific scope because the column is meaningful only for CSF-1R-pathway-targeting drugs (axatilimab and future anti-CSF-1R molecules). Distinct from any CSF-1 model state representing time-course dynamics -- covariate column is the pre-dose laboratory observation, typically measured by an ELISA assay (Yang 2024 used the R&D Systems Quantikine ELISA). Per-model `covariateData[[CSF1]]$notes` should document the assay used and any LOQ-related imputation for samples below the assay's limit of detection.

### CRP (**canonical for C-reactive protein**)
- **Description:** C-reactive protein concentration. Covers both standard and high-sensitivity (hs-CRP) assays and both baseline and time-varying usages. Each model's `covariateData[[CRP]]$description` and `notes` must state the assay type (standard vs hs-CRP) and whether the column carries a baseline-only or time-varying value, including the paper-specific reference value used for power scaling.
- **Units:** mg/L (document per-model via `covariateData[[CRP]]$units`).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(CRP / ref)^exponent` or exponential effects `exp(coef * (CRP - ref))`. Reference values observed: 4.23 mg/L (Moein 2022, IBD standard assay), 4.31 mg/L (Moein 2022 Table 3 median), 5.21 mg/L (Thakre 2022, baseline hs-CRP), 7.41 mg/L (Chua 2025, baseline standard assay), 14.2 mg/L (Xu 2019, baseline standard assay), 15.7 mg/L (Ma 2020, baseline standard assay), 0.837 mg/dL = 8.37 mg/L (Wang 2020, IBD standard assay; the model carries the source unit mg/dL).
- **Source aliases:**
  - `hsCRP` -- high-sensitivity CRP (mixed-case preserved from earlier register drafts).
  - `HSCRP` -- all-caps variant.
  - `CRPHS` -- used in `Thakre_2022_risankizumab.R` (baseline, high-sensitivity assay).
  - `BLCRP` -- baseline CRP; used in `Xu_2019_sarilumab.R` and `Ma_2020_sarilumab_das28crp.R`.
- **Example models:** `Thakre_2022_risankizumab.R`, `Xu_2019_sarilumab.R`, `Chua_2025_mirikizumab.R`, `Moein_2022_etrolizumab.R`, `Ma_2020_sarilumab_das28crp.R`, `Wang_2020_ontamalimab.R` (mg/dL, reference 0.837).
- **Notes:** The prior separate `hsCRP`, `BLCRP`, and standard-assay `CRP` canonicals were merged on 2026-04-20 to a single general-scope `CRP` canonical. Assay type (standard vs hs-CRP), baseline-vs-time-varying status, and the paper-specific reference value all live in each model's `covariateData[[CRP]]$description` / `notes`. Only aggregate values from hs-validated assays as CRP when the downstream analysis relies on low-range sensitivity; for most inflammatory-disease cohorts (IBD, RA/PsA), baseline CRP is well above the hs-sensitivity range and the distinction is moot.

### AAG (**canonical for alpha-1 acid glycoprotein concentration**)
- **Description:** Serum alpha-1 acid glycoprotein (AAG; orosomucoid; ORM1) concentration, an acute-phase plasma glycoprotein that binds basic and lipophilic drugs (including taxanes such as docetaxel and paclitaxel). Elevated in cancer, inflammation, and infection; influences free-drug fraction and downstream PD effects in cytotoxic-chemotherapy myelosuppression models.
- **Units:** g/L (= mg/mL; 1 g/L is the conventional clinical-PK reporting unit). Document per-model via `covariateData[[AAG]]$units` if a different unit is used.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used in piecewise-linear, power, or exponential effect forms (e.g., the Kloft 2006 cytotoxic-chemotherapy myelosuppression family fits a piecewise-linear effect with breakpoint at the cohort median 1.34 g/L: separate slopes apply for `AAG <= 1.34` and `AAG > 1.34`). Reference values observed: 1.34 g/L (Kloft 2006 / Netterberg 2017, cohort median in mixed adult-cancer cohort).
- **Source aliases:**
  - `AAG` -- used in `Netterberg_2017_docetaxel.R` (per the bundle's NM-TRAN $INPUT block; matching Kloft 2006).
  - `AGP1` -- used in `Ozawa_2007_docetaxel.R` (Appendix I $INPUT block); reported in mg/dL with conversion to canonical g/L via `AAG_g_per_L = AGP_mg_per_dL / 100`.
- **Example models:** `Netterberg_2017_docetaxel.R` (piecewise-linear effects on baseline ANC with separate low-AAG and high-AAG slopes around median 1.34 g/L; linear effect on the drug-effect slope SL via `(1 + theta * (AAG - 1.34))`), `Ozawa_2007_docetaxel.R` (multiplicative power-form effect on the linear drug-effect slope: `SLOPE = theta_SLOPE * (AAG / 0.94)^e_aag_slope` with `e_aag_slope = -1.38`; reference value 0.94 g/L from the published NONMEM control stream `AGPm = 94` mg/dL).
- **Notes:** General scope because serum AAG is a routine clinical-laboratory measurement that recurs across cytotoxic-chemotherapy population-PK / PD analyses (Bruno 1996/1998 docetaxel popPK uses AAG as a CL covariate; Kloft 2006 and downstream Friberg-family myelosuppression models use it on baseline ANC and drug-effect slope; Ozawa 2007 uses a power-form effect on the drug-effect slope only). Time-fixed at baseline unless the source paper states otherwise. The Kloft 2006 piecewise-linear breakpoint at 1.34 g/L corresponds to the population median in their pooled cancer cohort; the Ozawa 2007 normalisation reference 0.94 g/L is the cohort median used inside that paper's NONMEM control stream. Future papers may use different breakpoints / reference values, so document the per-model reference in `covariateData[[AAG]]$notes`. Distinct from `CRP` (a different acute-phase reactant with different binding properties).

### IL6 (**canonical for serum interleukin-6 concentration**)
- **Description:** Serum (or plasma) interleukin-6 (IL-6) concentration. Pro-inflammatory cytokine; elevated in rheumatoid arthritis, Castleman's disease, sepsis, COVID-19, and other inflammatory conditions. Both baseline and time-varying usages are covered; document per-model in `covariateData[[IL6]]$notes` whether the column is baseline-only or time-varying.
- **Units:** pg/mL (= ng/L; the two labels are numerically equivalent).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(IL6 / ref)^exponent` or with the log-transformed form `(log(IL6 * 1000) / log(ref * 1000))^exponent` that some legacy NONMEM analyses adopt. Reference values observed: 20 pg/mL (Frey 2013 baseline; the formula `(log(IL-6 * 1000)/9.9)^exponent` is the algebraic equivalent of `(log(IL-6) / log(20))^exponent` after the constant-factor rescaling that ties the reference log to 9.9 = log(20000)).
- **Source aliases:**
  - `BLIL6`, `bIL6`, `IL6_BASE` -- baseline IL-6 (used in some NONMEM control streams; canonical drops the `BL` prefix per the `EOS` / `SCORE_EASI` / `AGE` convention with baseline-vs-time-varying status documented in per-model notes).
  - `IL-6`, `IL_6` -- punctuation variants seen in publication tables and figures.
- **Example models:** `Frey_2013_tocilizumab.R` (baseline IL-6, reference 20 pg/mL; log-transformed power effects on EC50, BASE, and the DMARD background-effect parameter).
- **Notes:** Anti-IL-6 / anti-IL-6R drugs (tocilizumab, sarilumab, siltuximab) often see large transient increases in measured IL-6 after dosing -- when the column is time-varying for these drugs, the assay typically detects total (free + drug-bound) IL-6 because the antibody complex slows clearance of IL-6. Document the assay type (free vs. total IL-6) and whether the value is pre-dose (baseline-only) or post-dose / time-varying in each model's `covariateData[[IL6]]$notes`. Frey 2013's formula relies on the relative log-IL-6 ratio rather than the linear concentration, so the column units must be pg/mL exactly (not ng/mL) for the published exponents to apply unchanged.

### IL22 (**canonical for serum interleukin-22 concentration**)
- **Description:** Serum (or plasma) interleukin-22 (IL-22) concentration. Effector cytokine produced downstream of IL-23 receptor signalling by Th17 / Th22 and innate lymphoid cells; elevated in Crohn's disease and ulcerative colitis and correlated with disease activity. Both baseline and time-varying usages are covered; document per-model in `covariateData[[IL22]]$notes` whether the column is baseline-only or time-varying.
- **Units:** pg/mL (= ng/L; the two labels are numerically equivalent).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used as the driver variable of a sigmoid Imax / Emax term rather than as a multiplicative covariate. Reference (half-maximal) values observed: 22.8 pg/mL (Zhang 2023 brazikumab IB50 on the CDAI production-rate inhibition).
- **Source aliases:**
  - `BIL22`, `BLIL22`, `IL22_BASE` -- baseline IL-22 (used in NONMEM `$INPUT` columns; the canonical drops the `B` / `BL` prefix per the `IL6` / `CRP` / `SCORE_EASI` convention, with baseline-vs-time-varying status documented in per-model notes).
  - `IL-22`, `IL_22` -- punctuation variants seen in publication tables and figures.
- **Example models:** `Zhang_2023_brazikumab_il22.R` (baseline serum IL-22 as the per-subject predictive-biomarker driver of the brazikumab drug effect on the CDAI input rate: `idrug = imax * IL22^hill / (ec50^hill + IL22^hill)` with `imax = 0.297`, `ec50` (the paper's IB50) `= 22.8 pg/mL`, `hill` (the paper's gamma) fixed at 20; the effect is gated by `ON_TREATMENT` because the drug term is zero in the placebo arm).
- **Notes:** Ratified 2026-07-29 alongside the Zhang 2023 brazikumab extraction (sidecar request-001 / response-001, option A). Sibling of `IL6`, but a biologically distinct cytokine and NOT an alias of it: IL-22 sits downstream of IL-23 whereas IL-6 is an independent pro-inflammatory mediator, and the two are measured on separate assays. Anti-IL-23 antibodies (brazikumab, risankizumab, guselkumab, mirikizumab) block IL-23 signalling and thereby suppress downstream IL-22 production, so for those drugs a post-dose IL-22 column is pharmacodynamically driven rather than a stable baseline covariate -- record the assay (Zhang 2023 used the R&D Systems Quantikine ELISA, quantifiable range 10-800 pg/mL in 100% serum) and the baseline-vs-time-varying status in each consuming model's `covariateData[[IL22]]$notes`. Distinct from `CRP`, which Zhang 2023 fits as a parallel alternative predictive biomarker in a separate model file.

## Cardiometabolic / target biomarkers

### HDLC (**canonical for high-density lipoprotein cholesterol**)
- **Description:** Serum high-density lipoprotein cholesterol concentration (baseline or time-varying).
- **Units:** mg/dL or mmol/L -- document the unit used in each model via `covariateData[[HDLC]]$units` (1 mmol/L ~= 38.67 mg/dL for cholesterol).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(HDLC / ref)^exponent`. Reference value observed: 54 mg/dL (Frey 2010 pooled-cohort median).
- **Source aliases:**
  - `HDL-C` -- Frey 2010 spelling with hyphen.
  - `HDL_C` -- common alternative spelling.
- **Example models:** `Frey_2010_tocilizumab.R` (mg/dL, reference 54; small negative exponent -0.2 on linear CL; the paper interprets the effect as a body-size surrogate rather than a mechanism).
- **Notes:** Cardiometabolic lipid-panel covariate. In Frey 2010 it correlates with body size (HDL-C is lower in larger patients) and the small CL effect (-14% to +15% across the observed 23-135 mg/dL range) was retained but not interpreted as mechanistic.

### TCHOL (**canonical for total serum cholesterol**)
- **Description:** Serum (or plasma) total cholesterol concentration (baseline or time-varying). Sum of HDL, LDL, VLDL, and other lipoprotein-associated cholesterol fractions; distinct from `HDLC` which captures only the HDL fraction.
- **Units:** mmol/L or mg/dL -- document the unit used in each model via `covariateData[[TCHOL]]$units` (1 mmol/L ~= 38.67 mg/dL for cholesterol).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with linear-deviation forms `1 + theta * (TCHOL - ref)` or power scaling `(TCHOL / ref)^exponent`. Reference value observed: 3 mmol/L (Archary 2018, severely malnourished pediatric LPV cohort, baseline mean 2.7-2.9 mmol/L).
- **Source aliases:**
  - `CHOL` -- Archary 2018 NONMEM column abbreviation; the universal short form.
  - `TC` -- alternative abbreviation common in lipid-panel literature.
- **Example models:** `Archary_2018_lopinavir.R` (mmol/L, reference 3; linear effect on apparent CL/F: `1 + 0.207 * (TCHOL - 3)`; serves as a surrogate for nutritional / hepatic-function recovery rather than a mechanistic effect).
- **Notes:** In severe acute malnutrition, total cholesterol tracks lipid-pool repletion and hepatic recovery and may serve as a surrogate covariate when the actual driver of bioavailability or clearance variability is not directly measured. Lopinavir is highly protein-bound (to albumin and AAG) and lipophilic, so circulating cholesterol can correlate with binding capacity for lipophilic drugs. Distinct from `HDLC` (HDL fraction only) and from `CRP` / `ALB` / `AAG` (separate canonicals for related malnutrition / inflammation markers).

### TRIG (**canonical for serum triglyceride concentration**)
- **Description:** Serum triglyceride concentration (baseline or time-varying).
- **Units:** mmol/L or mg/dL -- document the unit used in each model via `covariateData[[TRIG]]$units` (1 mmol/L ~= 88.5 mg/dL for triglyceride).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with linear-deviation form `(1 + theta * (TRIG - ref))` or power scaling `(TRIG / ref)^exponent`. Reference value observed: 5.3 mmol/L (Archary 2019 lamivudine equation centring -- the equation centring in the source is reported as the cohort average; cohort median in Archary 2019 Table 1 is 2.2-2.3 mmol/L, see model-file Errata).
- **Source aliases:** none known.
- **Example models:** `Archary_2019_lamivudine.R` (mmol/L, reference 5.3; linear-deviation effect on Vc/F with coefficient -0.13 per mmol/L deviation from the reference; lower triglyceride implies higher apparent central volume).
- **Notes:** Cardiometabolic lipid-panel covariate. In Archary 2019 the inverse triglyceride--Vc relationship is interpreted as a nutritional-status / hydrophilic-drug-distribution surrogate: triglycerides rise during nutritional rehabilitation, and lamivudine (a hydrophilic drug) shows decreasing apparent volume as nutritional status improves. The linear-deviation form preserves the source's published parameterization; per-model `covariateData[[TRIG]]$units` is load-bearing because the centring reference and slope are unit-specific.

### LDLC (**canonical for low-density lipoprotein cholesterol**)
- **Description:** Serum low-density lipoprotein cholesterol concentration (baseline or time-varying). The pharmacologically meaningful endpoint for lipid-lowering therapies (statins, PCSK9 inhibitors, ANGPTL3 inhibitors); also serves as a baseline covariate or as the time-varying PD response in indirect-response exposure-response models.
- **Units:** mg/dL or mmol/L -- document the unit used in each model via `covariateData[[LDLC]]$units` (1 mmol/L ~= 38.67 mg/dL for cholesterol).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(LDLC / ref)^exponent` for the baseline-LDLC covariate role, or with no reference (used directly as the PD response state) when it is the modelled output. Reference values observed: 211 mg/dL (Pu 2021 HoFH typical-patient definition).
- **Source aliases:**
  - `LDL-C` -- common spelling with hyphen.
  - `LDL_C` -- common alternative spelling.
  - `LDLBL` (baseline LDL-C) -- used in `Pu_2021_evinacumab.R` (Pu 2021 NM-TRAN $INPUT column for centred baseline LDL-C as a covariate on IC50).
- **Example models:** `Pu_2021_evinacumab.R` (mg/dL, baseline reference 211 mg/dL; power exponent -1.17 on IC50, where higher baseline LDL-C predicts a smaller IC50 and therefore greater sensitivity to evinacumab; LDL-C is also the PD output state initialised at the baseline value).
- **Notes:** Cardiometabolic lipid-panel covariate. Distinct from `HDLC` (high-density lipoprotein cholesterol) and from any total-cholesterol or non-HDL-C derivation. When LDL-C is both the response variable AND a covariate (as in Pu 2021, where baseline LDLC drives IC50 and the time-varying state is the modelled PD), document the dual role in `covariateData[[LDLC]]$notes`.

### ANGPTL3 (**canonical for angiopoietin-like protein 3 concentration**)
- **Description:** Total serum angiopoietin-like protein 3 (ANGPTL3) concentration. ANGPTL3 is the pharmacological target for anti-ANGPTL3 monoclonal antibodies (evinacumab) and antisense oligonucleotides (vupanorsen). Baseline ANGPTL3 acts as a soluble-target biomarker that contributes to target-mediated drug disposition; higher baseline target predicts a higher saturable Vmax.
- **Units:** mg/L (equivalent to ug/mL). Document per-model via `covariateData[[ANGPTL3]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(ANGPTL3 / ref)^exponent`. Reference value observed: 0.08 mg/L (Pu 2021 typical-patient median).
- **Source aliases:**
  - `ANGBL` (baseline ANGPTL3) -- used in `Pu_2021_evinacumab.R` (Pu 2021 NM-TRAN $INPUT column for centred baseline ANGPTL3).
  - `ANGBL0` -- alternative Pu 2021 raw column.
- **Example models:** `Pu_2021_evinacumab.R` (mg/L, baseline reference 0.08 mg/L; power exponent +0.405 on Vmax, where higher baseline target predicts a faster saturable elimination -- biologically consistent with evinacumab being co-cleared along with bound ANGPTL3).
- **Notes:** Specific scope because the column is meaningful only for drugs whose mechanism involves ANGPTL3 (anti-ANGPTL3 mAbs / ASOs). Reusing the name for another anti-ANGPTL3 agent is acceptable (extend the example-models list). The assay in Pu 2021 detects both free and target-bound ANGPTL3 after acid pretreatment of serum; document the assay type (free vs. total) per model in `covariateData[[ANGPTL3]]$notes`.

### FPCSK9 (**canonical for free (unbound) proprotein convertase subtilisin/kexin type 9 concentration**)
- **Description:** Free (unbound, non-drug-bound) serum proprotein convertase subtilisin/kexin type 9 (PCSK9) concentration. For anti-PCSK9 monoclonal antibodies (alirocumab, evolocumab, bococizumab) the free-PCSK9 pool is the pharmacologically active target fraction; drug-target binding reduces FPCSK9 relative to total PCSK9.
- **Units:** ng/mL (document per-model if a paper reports a different unit).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with linear-deviation forms `TVPARAM + theta * (FPCSK9 / ref)` or power-form `(FPCSK9 / ref)^theta`. Reference values observed: 72.9 ng/mL (Martinez 2019 time-varying median).
- **Source aliases:** none known.
- **Example models:** `Martinez_2019_alirocumab.R` (time-varying; additive-linear effect on `Km` with slope -0.541 per (FPCSK9/72.9), reference 72.9 ng/mL).
- **Notes:** Specific scope because the column is meaningful only for drugs whose mechanism is PCSK9 inhibition; reusing the name for a different anti-PCSK9 agent is acceptable (add to Example models). For non-PCSK9 drugs that use a similar target-concentration biomarker, register a new canonical (e.g., `FIL6R`, `FTNF`) rather than overloading `FPCSK9`. Per-model `covariateData[[FPCSK9]]$notes` should state whether the value is baseline-only or time-varying and how missing values were imputed (Martinez 2019 used LOCF).

### SBCMA (**canonical for soluble B-cell maturation antigen concentration**)
- **Description:** Baseline serum (or plasma) concentration of soluble B-cell maturation antigen (sBCMA), the shed extracellular domain of the BCMA receptor (TNFRSF17). Serves as a soluble-target biomarker for BCMA-directed therapeutics (anti-BCMA antibody-drug conjugates such as belantamab mafodotin, BCMA-targeted bispecifics, and BCMA CAR-T) -- sBCMA is elevated in multiple-myeloma and reflects tumour burden, and contributes to target-mediated drug disposition by sequestering circulating drug.
- **Units:** ng/mL (equivalent to ug/L; 1 ng/mL = 1 ug/L). Document per-model via `covariateData[[SBCMA]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(SBCMA / ref)^exponent`. Reference value observed: 50 ng/mL (Papathanasiou 2025 typical-patient definition).
- **Source aliases:**
  - `SBCMABL` (baseline soluble BCMA) -- used in `Papathanasiou_2025_belantamab.R`.
- **Example models:** `Papathanasiou_2025_belantamab.R` (ng/mL, reference 50; power exponents on initial CL +0.113, on ADC Vc +0.0401, on Imax +0.160), `Collins_2023_belantamab_mprotein.R` (ng/mL, reference 100; power exponent -0.414 on the effect-compartment rate constant KEO of the serum M-protein tumour-growth-inhibition model).
- **Notes:** Specific scope because the column is meaningful only for drugs whose mechanism involves the BCMA receptor (and thus a circulating soluble-target pool). Reusing the name for another anti-BCMA agent is acceptable (extend the example-models list). For other oncology TMDD targets register a new canonical (e.g., `HER2_ECD` already exists for HER2; an analogous `SCD20`, `SCD38` would follow the same pattern). Multiple myeloma populations show sBCMA spanning roughly 2 to 2,000 ng/mL, so the (SBCMA/50)^exponent form should be evaluated with care over the full clinical range.

### HBA1C (**canonical for glycated hemoglobin**)
- **Description:** Glycated hemoglobin (HbA1c, %). Routine clinical laboratory measurement on whole blood reflecting average glycemia over the preceding ~2-3 months; reported in National Glycohemoglobin Standardization Program (NGSP) units.
- **Units:** % (NGSP). Document the unit standard (NGSP vs IFCC mmol/mol) per-model via `covariateData[[HBA1C]]$units` when a paper reports IFCC units (IFCC mmol/mol = 10.93 * NGSP% - 23.50).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with a power form `(HBA1C / ref)^exponent` or linear-deviation form. Reference value observed: 5.88% (Oniki 2018 NAFLD-risk dataset baseline mean).
- **Source aliases:**
  - `HbA1c` -- used in `Oniki_2018_nafld_risk.R` (dataset column for glycated hemoglobin in %).
- **Example models:** `Oniki_2018_nafld_risk.R` (%, reference 5.88; power exponent -3.34 for `(HBA1C / 5.88)` on the (BMI50 - 17) half-saturation offset of the sigmoidal logit-of-NAFLD function, Oniki 2018 Eq. 4 / Figure 2c).
- **Notes:** General scope because HbA1c is a routine, paper-independent glycemic-control laboratory measurement. Companion glycemic covariate to `FPG` (baseline fasting plasma glucose), which is routinely reported alongside HbA1c in T2DM / metabolic-syndrome populations. Distinct from `GLU` (time-varying within-subject glucose regressor). Ratified canonically alongside the Oniki 2018 NAFLD-risk extraction.

## Bone biomarkers

### TRACP5B_BL (**canonical for baseline serum tartrate-resistant acid phosphatase 5b concentration**)
- **Description:** Pre-treatment serum tartrate-resistant acid phosphatase 5b (TRACP-5b) concentration, an osteoclast-derived bone-resorption biomarker. Time-fixed per subject. Used as the per-subject anchor for the steady-state marker pool in indirect-response bone-turnover models (`Kin = TRACP5B_BL * Kout`, `marker(0) <- TRACP5B_BL`) and as a continuous covariate on disease-progression and BMD-coupling parameters (power-model standardisation to the cohort median).
- **Units:** mU/dL (clinical reporting convention used in Japanese osteoporosis cohorts and the Osteolinks TRACP-5b kit; document per-model via `covariateData[[TRACP5B_BL]]$units`). Some clinical contexts report TRACP-5b in U/L; 1 U/L = 100 mU/dL only if the assays are calibrated equivalently -- per-model unit documentation is load-bearing.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(TRACP5B_BL / ref)^exponent`. Reference value observed: 400 mU/dL (Mori 2018 cohort -- mean baseline 401.1 mU/dL across N = 306 Japanese primary-osteoporosis patients; the paper says continuous covariates were standardised to their cohort median but the numeric median is not published, so the rounded cohort mean serves as the documented reference).
- **Source aliases:**
  - `TRACP-5b` -- text-form of the marker name used in Mori 2018; transliterated to `TRACP5B_BL` for the per-subject baseline canonical name.
- **Example models:** `Mori_2018_zoledronicAcid.R` (mU/dL, reference 400; power exponents -1.534 on EKD50, -1.350 on Slope, -1.319 on T50; and -1.112 on Scale in the active-treatment arm only -- the Scale covariate is gated by `ON_TREATMENT`).
- **Notes:** Specific scope because the cohort-median reference (400 mU/dL) is tied to the Mori 2018 Japanese primary-osteoporosis cohort and the Osteolinks assay; future bone-resorption-marker extractions using a different assay (different antibodies, different normalisation) should ratify the same canonical and document the per-model assay / reference in `covariateData[[TRACP5B_BL]]$notes`. Ratified canonically alongside the Mori 2018 zoledronic-acid BMD extraction. Distinct from any time-varying serum TRACP-5b state output (which would be a model output rather than a covariate column).

### BMD_BL (**canonical for baseline lumbar-spine bone mineral density**)
- **Description:** Pre-treatment lumbar-spine (L2-L4) bone mineral density measured by dual-energy X-ray absorptiometry (DXA). Time-fixed per subject. Used as the per-subject initial condition for a BMD state compartment (`bmd(0) <- BMD_BL`) and as the per-subject deviation reference inside the BMD effect-compartment ODE (`d/dt(bmd) <- Ke0 * [Scale * (marker - Marker0) - (bmd - BMD_BL)]`).
- **Units:** g/cm^2 (the standard DXA areal-BMD unit; document per-model via `covariateData[[BMD_BL]]$units`). Some clinical contexts report BMD as the dimensionless T-score; the canonical column stores the raw areal density.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as a per-subject anchor; no power-form covariate effect in the example model.
- **Source aliases:**
  - `BMD0` -- in-text mathematical symbol used by Mori 2018 for the baseline BMD value entering the BMD ODE; the covariate column name is `BMD_BL`.
- **Example models:** `Mori_2018_zoledronicAcid.R` (g/cm^2, used as the initial condition `bmd(0) <- BMD_BL` and the deviation reference inside the BMD ODE; per-subject median 0.677 g/cm^2 across the Mori 2018 cohort).
- **Notes:** Specific scope because the anatomical site (lumbar spine L2-L4) and the modality (DXA Hologic instrument) are tied to the Mori 2018 ZONE-study protocol; future BMD extractions from other anatomical sites (femoral neck, total hip, distal radius) should ratify a sibling canonical (e.g., `BMD_FN_BL`, `BMD_TH_BL`) rather than overloading this column. Ratified canonically alongside the Mori 2018 zoledronic-acid BMD extraction. Companion bone biomarker to `TRACP5B_BL`.

## Kallikrein-kinin system biomarkers and hereditary-angioedema disease-activity covariates

### PKK (**canonical for plasma prekallikrein concentration as a time-varying PD driver**)
- **Description:** Plasma prekallikrein concentration used as a time-varying regressor / exposure driver in downstream exposure-response models for hereditary-angioedema (HAE) prophylactic therapies (e.g., prekallikrein-lowering antisense oligonucleotides such as donidalorsen). Averaging convention is per-model (e.g., per-4-week average `PKKavg,4W` in Singh 2025); the canonical column stores the appropriate exposure summary at each observation row and is consumed with LOCF (piecewise-constant) or `linear()` interpolation semantics as documented per model.
- **Units:** mg/L (document per-model via `covariateData[[PKK]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the sigmoidal Emax attack-rate model as `PKK^Hill / (EC50^Hill + PKK^Hill)` (Singh 2025 page 3 equation). Set to `PKK_BL` at the pre-dose baseline row and to model-predicted values thereafter (either from an on-disk companion popPK/PD like `Diep_2026_donidalorsen` or from observed data for placebo arms).
- **Source aliases:**
  - `PKKavg,4W` -- Singh 2025 Section 2.2 and Table 1 (per-4-week average plasma prekallikrein).
- **Example models:** `Singh_2025_donidalorsen.R` (per-4-week average PKK reads at each observation row; drives the sigmoidal Emax term).
- **Notes:** Specific scope because `PKK` is meaningful only for HAE / kallikrein-kinin-pathway PD models that consume prekallikrein as an exogenous exposure driver. Companion baseline canonical `PKK_BL`. The companion popPK/PD backbone for donidalorsen simulations is `Diep_2026_donidalorsen` (indirect-response model with donidalorsen-driven inhibition of PKK production). Ratified canonically alongside the Singh 2025 donidalorsen exposure-response extraction.

### PKK_BL (**canonical for per-subject baseline plasma prekallikrein concentration**)
- **Description:** Per-subject baseline (pre-dose) plasma prekallikrein concentration, time-fixed per subject. For donidalorsen-treated cohorts the value is typically the initial condition of an upstream popPK/PD model (e.g., `Diep_2026_donidalorsen`'s baseline PKK = `rbase`, derived from `lrbase + etalrbase` with HAE-status adjustment); for placebo arms the observed baseline PKK is used directly. Enters downstream exposure-response models as a covariate on EC50 via a power form.
- **Units:** mg/L (document per-model via `covariateData[[PKK_BL]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(PKK_BL / ref)^exponent`. Reference value observed: 122 mg/L (Singh 2025 page 3 equation `(BLPKK/122)^bPKK`; matches the Section 2.3 simulation baseline PKK).
- **Source aliases:**
  - `BLPKK` -- Singh 2025 page 3 equation and Table 1 abbreviation.
- **Example models:** `Singh_2025_donidalorsen.R` (per-subject baseline PKK reference 122 mg/L; power exponent `e_pkk_bl_ec50 = 0.13` on the EC50^Hill denominator term of the sigmoidal Emax attack-rate model).
- **Notes:** Specific scope for the same reasons as `PKK` (HAE / kallikrein-kinin-pathway-bound). Sister to `PKK` (time-varying). Distinct from the general-purpose `_BL` baseline-biomarker family (`TRACP5B_BL`, `BMD_BL`, `HGB_BL`, `INS_BL`, `FERRITIN_BL`) whose members serve broader clinical contexts; `PKK_BL` is narrowly the prekallikrein-pathway baseline. Ratified canonically alongside the Singh 2025 donidalorsen exposure-response extraction.

### HAERATE_BL (**canonical for baseline per-4-week normalized hereditary-angioedema attack rate**)
- **Description:** Per-subject baseline HAE attack rate normalised to a 4-week window, computed from the number of investigator-confirmed HAE attacks during a defined screening / run-in period divided by the contributed days and multiplied by 28 days. Time-fixed per subject. Used as a per-subject covariate on Emax in HAE exposure-response models (higher baseline attack burden -> proportionally higher on-treatment maximum attack rate).
- **Units:** attacks per 4 weeks (document per-model via `covariateData[[HAERATE_BL]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(HAERATE_BL / ref)^exponent`. Reference value observed: 3 attacks per 4 weeks (Singh 2025 page 3 equation `(BLRATE/3)^bHAE`; matches the Section 2.3 simulation baseline attack rate).
- **Source aliases:**
  - `BLRATE` -- Singh 2025 page 3 equation and Table 1 abbreviation ("baseline per-4-week normalized HAE attack rate").
- **Example models:** `Singh_2025_donidalorsen.R` (per-subject baseline attack rate reference 3 attacks/4W; power exponent `e_haerate_bl_emax = 1.03` on Emax of the sigmoidal Emax attack-rate model).
- **Notes:** Specific scope because the variable is HAE-domain-bound. Time-fixed per subject (baseline-only). Conceptually a specific-domain analogue of `ACUTE_MED_DAYS` (baseline migraine acute-medication days per month, migraine E-R models); both encode a baseline symptom / event rate as a power-law scaling covariate on the on-treatment response. If future HAE E-R models use a different screening window or normalisation window (e.g., per-day, per-month), document the per-model window in `covariateData[[HAERATE_BL]]$notes`. Ratified canonically alongside the Singh 2025 donidalorsen exposure-response extraction.

## Drug exposure metrics

### CAV (**canonical for average drug plasma concentration over a dosing interval**)
- **Description:** Average plasma concentration of the modelled drug over a dosing interval (Cav = AUC_tau / tau). Used as the time-varying or per-period exposure metric in exposure-response models that feed individual empirical-Bayes PK predictions from a previously published population PK model into a downstream PD model.
- **Units:** ug/mL (document per-model via `covariateData[[CAV]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used in Emax/EC50 (e.g., `Emax * CAV / (EC50 + CAV)`) or power (e.g., `(CAV / CavMedian)^exponent`) drug-effect terms. Set to 0 for placebo periods.
- **Source aliases:**
  - `CAV`, `Cav`, `CAVG`, `Cav,W` (Svensson 2017 weekly-average bedaquiline plasma concentration; same orientation as the canonical, in mg/L).
  - `METRIC_TASPO_C` -- Li 2015 (per-arm taspoglutide average plasma concentration over weeks 2-4, in pmol/L; the model's `source_name` is "Cavg.2-4w (Li 2015 Section 3.2)"). This descriptive column name maps onto `CAV` via this alias rather than being a separate canonical, consistent with the MBMA usage already documented in this entry's Notes.
- **Example models:** `FiedlerKelly_2020_fremanezumab_em.R`, `FiedlerKelly_2020_fremanezumab_cm.R`, `Schoemaker_2018_levetiracetam.R` (DDMODEL00000239; LEV plasma concentration in mg/L), `Svensson_2017_bedaquiline.R` (weekly-average bedaquiline concentration in mg/L driving an Emax effect on the mycobacterial-load half-life; EC50 = 1.42 mg/L, Emax fixed at -100%; placebo subjects use CAV = 0), `Li_2015_taspoglutide_mbma.R` (MBMA study-arm-level Cavg of taspoglutide between weeks 2 and 4 of QW dosing, in pmol/L; 0 / 59.85 / 119.7 pmol/L for placebo / 10 mg / 20 mg arms; drives an additive Emax response on body-weight change), `Lacy_2018_cabozantinib_tumor.R` (individual predicted daily-average cabozantinib plasma concentration in ng/mL from the upstream Lacy 2018 popPK; drives a saturable Cavg/(EC50 + Cavg) Hill-1 term on a time-attenuating decay rate of tumor SOD; EC50 = 251 ng/mL; per-cohort steady-state Cavg 375 / 750 / 1125 ng/mL for 20 / 40 / 60 mg/day starting doses; dose-hold periods set CAV = 0), `Lacy_2018_cabozantinib_dose_modification.R` (same upstream-popPK-derived Cavg in ng/mL; drives a log-linear effect with coefficient theta_drug = 0.000807 per ng/mL on the active-dose log hazard for repeated dose modifications; dose-hold periods set CAV = 0 and switch to the dose-hold baseline log hazard), `Sano_2023_fesoterodine_mcc.R` (individual 5-HMT average steady-state concentration Cavg,ss in ng/mL from the companion Sano 2023 population PK model, computed as `F * DOSE / (CL/F * tau)` with tau = 24 h; drives an Emax model on maximum cystometric capacity with EC50 = 6.22 ng/mL; set to 0 on the baseline occasion per Sano 2023 Online Resource 8b, which collapses the Emax term so the baseline prediction is exactly BASE), `Yin_2021_pexidartinib.R` (running average pexidartinib plasma concentration up to the current tumor-measurement time, in mg/L, from the upstream Yin 2020 popPK `modellib('Yin_2020_pexidartinib')`; drives a saturable `1 - exp(-kdrug * CAV)` drug-effect term in the longitudinal RECIST tumor-size PD model with kdrug typical value 0.196 (mg/L)^-1; placebo periods carry CAV = 0).
- **Notes:** Specific scope because the value is intrinsically tied to the modelled drug -- there is no shared meaning across drugs or studies. Each model's `covariateData[[CAV]]$notes` should state how the Cav values are derived (e.g., empirical-Bayes from a referenced population PK model) and that the column is set to 0 for placebo periods. The averaging window is also model-specific (per-dosing-interval Cav = AUC_tau / tau in Schoemaker 2018 / Fiedler-Kelly 2020, but weekly-rolling-mean Cav_W in Svensson 2017 -- where the bedaquiline once-daily loading + thrice-weekly maintenance schedule makes "per dosing interval" ambiguous; weeks 2-4 Cavg in Li 2015 carried forward for the entire 8-52 week follow-up); document the averaging convention in each model's `covariateData[[CAV]]$notes`. MBMA usage (Li 2015) treats CAV as a study-arm-level (not individual-level) exposure metric -- the meaning is the same (period-averaged plasma concentration of the modelled drug) so a separate canonical is not warranted.

### DOSE_EMPA_MGD (**canonical for daily empagliflozin dose**)
- **Description:** Patient's own once-daily empagliflozin dose, in mg/day. Per-dose-record covariate; constant within an inter-dose interval and updated when the prescriber alters the daily dose. Set to 0 mg/day for placebo arms.
- **Units:** mg/day
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters into the steady-state AUC computation `AUCss [nM*h] = DOSE_EMPA_MGD * 1e6 / 450.91 / cl` that drives the Emax stimulation of FPG elimination in the indirect-response PD model.
- **Source aliases:**
  - `DOSE` -- used in `Baron_2016_empagliflozin.R` (Baron 2016 Methods; studied doses 1, 5, 10, 25, 50 mg QD per Table S2 with 10 and 25 mg dominating; 1129 / 40.9% and 1269 / 46.0% of patients respectively).
- **Example models:** `Baron_2016_empagliflozin.R` (drives AUCss feeding Gmax * AUCss / (AUC50 + AUCss) -- AUC50 = 703 nM*h).
- **Notes:** Follows the `DOSE_<DRUG>_<UNITS>` auto-approve family (e.g., `DOSE_PHT_MGKGD` for phenytoin). Distinct from `DOSE_PHT_MGKGD` (per-kg phenytoin daily-dose) -- empagliflozin is dosed in flat mg/day (no per-kg adjustment in label). Future once-daily SGLT2-inhibitor extractions should register sibling canonicals (e.g., `DOSE_DAPA_MGD` for dapagliflozin, `DOSE_CANA_MGD` for canagliflozin) rather than reuse this name. Ratified canonically on 2026-06-24 alongside the Baron 2016 empagliflozin extraction.

### DOSE_LOR_MGD (**canonical for daily lorlatinib dose**)
- **Description:** Patient's own total daily lorlatinib dose, in mg/day. Per-dose-record covariate; constant within an inter-dose interval and updated when the prescriber alters the daily dose (e.g., following a dose reduction for tolerability). For q.d. regimens the value equals the single-dose amount (e.g., 100 mg q.d. -> DOSE_LOR_MGD = 100); for b.i.d. regimens the value is the sum across the day (e.g., 75 mg b.i.d. -> DOSE_LOR_MGD = 150). Set to 0 mg/day during off-treatment periods (e.g., planned drug holidays).
- **Units:** mg/day
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a linear centered effect `1 + e_dose_lor_cl * (DOSE_LOR_MGD - 100)` on total lorlatinib clearance (both the single-dose CLI and the steady-state induced CLMX arm), with reference 100 mg/day (the phase II / labelled recommended dose). Effect coefficient observed: `e_dose_lor_cl = 0.001` per mg/day in `Chen_2021_lorlatinib.R` (Chen 2021 Table 4 `theta_TDOSE_on_CL`); 200 mg/day gives a 10% CL increase, 10 mg/day gives a 9% CL reduction relative to the 100 mg/day reference.
- **Source aliases:**
  - `TDOSE` -- used in `Chen_2021_lorlatinib.R` (Chen 2021 Methods "Inclusion of covariates"; total daily lorlatinib dose across the 10-200 mg/day range spanning phase I/II B7461001 escalation cohorts and healthy-participant single-dose studies).
- **Example models:** `Chen_2021_lorlatinib.R` (drives dose-nonlinearity of CL via a linear centered term shared between CLI and CLMX; captures increased auto-induction potency at higher doses).
- **Notes:** Follows the `DOSE_<DRUG>_<UNITS>` auto-approve family (siblings: `DOSE_EMPA_MGD` for empagliflozin, `DOSE_PHT_MGKGD` for phenytoin, `DOSE_CIPARGAMIN_MG` for cipargamin). Lorlatinib is dosed in flat mg/day (no per-kg adjustment in label), matching the `DOSE_EMPA_MGD` pattern. Distinct from the rxode2/nlmixr2 event column `amt` (which carries the administered dose at each dose event); `DOSE_LOR_MGD` is a per-record covariate carrying the current daily-dose LEVEL used by the covariate model, updated at each dose-schedule change. When simulating typical q.d. regimens without dose changes it can be set as a per-subject constant equal to the assigned dose level. Ratified canonically on 2026-07-24 alongside the Chen 2021 lorlatinib extraction.

### DOSE_TPM_MGD (**canonical for daily topiramate dose**)
- **Description:** Patient's total daily topiramate dose, in mg/day. Per-record covariate; constant within a dosing interval and updated when the prescriber alters the daily dose. For a twice-daily regimen the value is the sum across the day (89.2 mg b.i.d. -> `DOSE_TPM_MGD` = 178.4), matching the [[DOSE_LOR_MGD]] convention. Set to 0 mg/day during off-treatment periods.
- **Units:** mg/day
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a power effect `(DOSE_TPM_MGD / 100)^e_dose_tpm_cl` on apparent clearance, normalized at 100 mg/day. Effect coefficient observed: `e_dose_tpm_cl` = 0.193 in `Lee_2024_topiramate.R` (Lee 2024 Table S1 theta9); 200 mg/day raises CL/F by 14.3% and 50 mg/day lowers it by 12.5% relative to the 100 mg/day reference.
- **Source aliases:**
  - `DOSE` -- used in `Lee_2024_topiramate.R` (Lee 2024 Results CL/F equation and Table S1; cohort mean 178.4 +/- 117.9 mg/day).
- **Example models:** `Lee_2024_topiramate.R` (mild positive dose-dependence of topiramate apparent clearance, multiplying the additive enzyme-inducing-comedication clearance sum alongside the [[CRCL]] power term).
- **Notes:** Follows the `DOSE_<DRUG>_<UNITS>` auto-approve family (siblings: [[DOSE_EMPA_MGD]] for empagliflozin, [[DOSE_LOR_MGD]] for lorlatinib, [[DOSE_PHT_MGKGD]] for phenytoin). Topiramate is dosed in flat mg/day for adults (no per-kg adjustment in the adult label), matching the `DOSE_EMPA_MGD` / `DOSE_LOR_MGD` pattern rather than the per-kg `DOSE_PHT_MGKGD` pattern; a future paediatric topiramate extraction dosing in mg/kg/day should register a sibling `DOSE_TPM_MGKGD` rather than reuse this name. Distinct from the rxode2 / nlmixr2 event column `amt`, which carries the amount of each individual administration; `DOSE_TPM_MGD` is a per-record covariate carrying the current daily-dose LEVEL used by the covariate model. Because the covariate is the daily dose while `amt` is the per-administration amount, the two must be kept consistent when simulating: for a b.i.d. regimen `amt` is half of `DOSE_TPM_MGD`. A dose-level covariate on clearance encodes a mild PK nonlinearity as a covariate rather than as a saturable elimination term, so simulations that vary the dose must update this column or the nonlinearity will be silently lost. Ratified canonically on 2026-08-03 alongside the Lee 2024 topiramate extraction.

### DOSE_SEMAGLUTIDE_MG (**canonical for per-subject maintenance semaglutide dose**)
- **Description:** Per-subject maintenance dose of semaglutide, in mg per once-weekly SC injection (the target maintenance dose reached after the dose-escalation phase). Constant per subject in the phase III SUSTAIN maintenance analysis. Values used in the founding example (Carlsson Petri 2018): 0.5 or 1.0 mg once weekly. A binary indicator such as `DOSE_LOW_MAINT = as.integer(DOSE_SEMAGLUTIDE_MG < 0.75)` can be derived inline in `model()` when the source paper tests a binary low-vs-high maintenance-dose contrast.
- **Units:** mg (per once-weekly injection)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with a binary indicator derived from the continuous value (e.g., `theta^DOSE_LOW_MAINT` in Carlsson Petri 2018 with 1.0 mg as the reference).
- **Source aliases:**
  - `DOSE` (per-subject maintenance dose column) -- used in `CarlssonPetri_2018_semaglutide.R` (Carlsson Petri 2018 Methods 'For semaglutide, there are two maintenance doses (0.5 and 1.0 mg), which were included as covariates to assess the dose dependency of semaglutide exposure').
- **Example models:** `CarlssonPetri_2018_semaglutide.R` (binary 0.5-vs-1.0 mg maintenance-dose indicator derived inline in `model()`; CL/F ratio 1.00 per Table S3 confirming dose proportionality; the coefficient is fixed at 1.00 in the model file to preserve fidelity to the published estimate).
- **Notes:** Follows the `DOSE_<DRUG>_<UNITS>` auto-approve family. Distinct from the rxode2/nlmixr2 event column `amt` (which carries the administered dose at dose events; `DOSE_SEMAGLUTIDE_MG` is a per-subject fixed covariate carrying the assigned MAINTENANCE dose target, not the per-record actual dose which varies during the dose-escalation phase). Future once-weekly-semaglutide extractions with the same 0.5-vs-1.0 mg contrast (or an extension to 2.0 mg / oral 3-14 mg tablets) can reuse this canonical; other GLP-1 receptor agonists dosed in mg (dulaglutide, tirzepatide) should register sibling canonicals (e.g. `DOSE_DULAGLUTIDE_MG`, `DOSE_TIRZEPATIDE_MG`) rather than overload this name because the numeric coefficient value is drug-specific. Founded alongside the CarlssonPetri_2018_semaglutide extraction.

### DOSE_EFP_MAX_MG (**canonical for per-subject maximum administered efaproxiral dose**)
- **Description:** Per-subject maximum administered single-dose efaproxiral dose, in mg. Time-fixed per subject; defined as the largest single-administration dose the subject received during the trial. Distinct from a per-administration `DOSE` column because the covariate enters the model as a static per-subject scalar (a power-model effect on the RBC:plasma proportionality SLPRBC) rather than a per-dose-record exposure regressor.
- **Units:** mg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a power-model effect `(DOSE_EFP_MAX_MG / 6800)^theta_SLPRBC~MDOS` on the RBC:plasma proportionality SLPRBC. Reference value 6800 mg per Equation 1 of Gastonguay 2005 (close to the median 100 mg/kg x ~72 kg derived from the cohort).
- **Source aliases:**
  - `MDOS` -- used in `Gastonguay_2005_efaproxiral.R` (Gastonguay 2005 Data section; per-subject maximum administered dose value used as a power-model covariate on SLPRBC).
- **Example models:** `Gastonguay_2005_efaproxiral.R` (efaproxiral; theta_SLPRBC~MDOS = -0.125, 95% CI -0.220 to -0.0222, classified NCI = not clinically important).
- **Notes:** Specific scope because the absolute reference value (6800 mg) is tied to the efaproxiral cohort and the per-administration MDOS abstraction is uncommon outside hemoglobin-modifier infusion programs. Follows the `DOSE_<DRUG>_<MODIFIER>_<UNITS>` auto-approve family (e.g., `DOSE_PHT_MGKGD` for phenytoin, `DOSE_EMPA_MGD` for empagliflozin) -- the `_MAX_` token disambiguates from a per-administration `DOSE_EFP_MG` column that a future extraction may need. Ratified canonically on 2026-06-30 alongside the Gastonguay 2005 efaproxiral extraction.

### DOSE_CIPARGAMIN_MG (**canonical for administered cipargamin single-dose amount**)
- **Description:** Administered single oral dose of the spiroindolone antimalarial cipargamin (formerly KAE609), in mg. Time-fixed per subject in the founding single-dose study (each patient receives one dose on day 1); a per-dose-record covariate in principle if the design ever ran a multi-dose regimen. Not a PK covariate -- the amount already appears on the dose record via `amt`. Used inside `model()` as the regressor in the dose-dependent Emax equation `Emax_i = TVEmax * (DOSE_CIPARGAMIN_MG / 10)^COVdose_Emax` (Hien 2017 equation 7 in the supplemental text).
- **Units:** mg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via `(DOSE_CIPARGAMIN_MG / 10)^e_dose_emax` with reference dose 10 mg (the smallest study cohort dose in Hien 2017).
- **Source aliases:**
  - `DOSE` -- used in `Hien_2017_cipargamin.R` (Hien 2017 Methods 'Study design'; studied doses 10, 15, 20, 21 [one patient administered 21 mg in error], and 30 mg).
- **Example models:** `Hien_2017_cipargamin.R` (drives the dose-dependent typical Emax in the two-population parasite clearance PD model: `emax = emax_typical * (DOSE_CIPARGAMIN_MG / 10)^e_dose_emax` with `e_dose_emax = 0.0463` per Hien 2017 Table 3).
- **Notes:** Follows the `DOSE_<DRUG>_<UNITS>` auto-approve family (siblings: `DOSE_EMPA_MGD`, `DOSE_PHT_MGKGD`). Distinct from those two because cipargamin is a single-dose administration (mg, not mg/day) in the founding study, so the units suffix is `MG`. Sibling canonicals may be registered for other single-dose antimalarial extractions using the same auto-approve pattern. Ratified canonically on 2026-07-08 alongside the Hien 2017 cipargamin extraction.

### DOSE_CABAMIQUINE_MG (**canonical for administered cabamiquine single-dose amount**)
- **Description:** Administered single oral dose of the Plasmodium eEF2 inhibitor cabamiquine (formerly DDD107498 / M5717), in milligrams of **free base**. Time-fixed per subject in the founding studies (each participant receives one dose). Not a PK covariate in the usual sense -- the amount already appears on the dose record via `amt` -- but it is required as a data column because the model uses it as the regressor in an empirical power effect of dose on the apparent central volume, `V2/F = 2363 * (WT/70)^1 * DOSE_CABAMIQUINE_MG^e_dose_vc` with `e_dose_vc = -0.50` (Courlet 2023 Supplementary Material 1). The authors introduced the term to describe greater-than-dose-proportional exposure that they could not explain mechanistically: "it was ultimately necessary to include an empirical covariate effect of dose on V2/F in the model".
- **Units:** mg (free base)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as an **uncentred** power term, `DOSE_CABAMIQUINE_MG^e_dose_vc`, i.e. an implicit reference dose of 1 mg. Courlet 2023 never reports a centering dose for the term; the uncentred reading is the only one compatible with the reported 146-193 h terminal half-life, because centering on a typical study dose would leave V2/F = 2363 L and a steady-state volume above the Vz implied by that half-life. Ratified by operator sidecar `oare_PMC10720512` request 001 question 1 (answer A).
- **Source aliases:**
  - `DOSE` -- used in `Courlet_2023_cabamiquine_pk.R` and `Courlet_2023_cabamiquine.R` (Courlet 2023 Results 'PK/PD data'; 14 dose levels pooled across the two phase I studies).
- **Example models:** `Courlet_2023_cabamiquine_pk.R` and `Courlet_2023_cabamiquine.R` (drives the empirical dose effect on the apparent central volume of the three-compartment transit-absorption / enterohepatic-recycling PK model).
- **Notes:** Follows the `DOSE_<DRUG>_<UNITS>` auto-approve family (siblings: `DOSE_CIPARGAMIN_MG`, `DOSE_EMPA_MGD`, `DOSE_TBPPI_MG`). The **free-base basis is load-bearing**: Courlet 2023 study 1 (IBSM) administered cabamiquine as the succinate salt and study 2 (SpzCh) as free base, with 1 mg succinate salt = 0.797 mg free base (Methods 'Data set'), so the 150 / 400 / 800 mg salt IBSM cohorts are 119.6 / 318.8 / 637.6 mg free base. Populate this column -- and the dose-record `amt` -- on the free-base scale. The vignette shows that the free-base reading reproduces the published per-dose recrudescence rates (all three predictions inside the observed Clopper-Pearson 95% CIs) whereas the salt reading does not. Distinct from `DOSE_CIPARGAMIN_MG` (a different antimalarial, and a covariate on Emax rather than on volume). Ratified canonically alongside the Courlet 2023 cabamiquine extraction.

### DOSE_TBPPI_MG (**canonical for administered tebipenem pivoxil hydrobromide dose level**)
- **Description:** Administered dose of the oral carbapenem pro-drug tebipenem pivoxil hydrobromide (TBP-PI-HBr) carried on each record, in milligrams of pro-drug (not of the active moiety tebipenem). Time-fixed per subject in fixed-dose designs and time-varying in crossover designs where the same subject receives more than one dose level.
- **Units:** mg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as a normalized linear term `(DOSE_TBPPI_MG / 1200)` inside a gated covariate effect, where 1200 mg is the high dose of the study SPR994-104 crossover.
- **Source aliases:**
  - `DOSEMG` -- the column name printed in Ganesan 2023 Eq. 6.
- **Example models:** `Ganesan_2023_tebipenem.R` (dose effect on the absorption rate constant, gated to a single study: `ka * (1 + e_dose_ka * (DOSE_TBPPI_MG / 1200) * STUDY_SPR994_104)` with `e_dose_ka = -0.478`).
- **Notes:** A drug-specific member of the `DOSE_<drug>_<units>` family, preferred here over the general `DOSE` canonical for two reasons. First, the dose recorded is pro-drug mass, so every apparent PK parameter estimated against it is conditioned on the pro-drug-to-active-moiety mass ratio and on the (unmeasured, presumed near-complete) conversion fraction -- a drug-specific caveat worth carrying in the column name. Second, rxode2's event-table translator (`etTrans()`) consumes a column literally named `DOSE` and does not expose it to `model()` as a covariate, so a model that reads the dose level as a covariate must use a distinguishable name or supply it through `params`.

### DOSE_GHI_MLKG (**canonical for administered guhong injection volume dose per kg body weight**)
- **Description:** Volume dose of guhong injection (GHI), a Chinese herbal compound preparation of N-acetyl-L-glutamine plus an aqueous safflower (*Carthamus tinctorius* L.) extract, administered as a single intravenous injection and recorded in mL of preparation per kg body weight. The preparation is dosed by volume rather than by the mass of any one constituent, so the volume dose is the natural per-record quantity: the dose of each individual quantified constituent is this column multiplied by that constituent's content in GHI (Chen 2024 Results 3.2: 30.0 mg/mL N-acetyl-L-glutamine, 1 mg/mL hydroxysafflor yellow A, 12.9 ug/mL chlorogenic acid, 78.3 ug/mL p-coumaric acid, 11.7 ug/mL rutin, 3.4 ug/mL hyperoside, 65.7 ug/mL kaempferol-3-O-rutinoside, 8.7 ug/mL kaempferol-3-O-glucoside).
- **Units:** mL/kg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as a dose-group selector, not as a normalized continuous term. Chen 2024 fitted an independent two-compartment disposition model and an independent set of sigmoid-Emax parameters in each of its three dose groups (GHI-L 2.5 mL/kg, GHI-M 5 mL/kg, GHI-H 10 mL/kg) rather than estimating a dose-covariate function, so models that carry this column select a parameter set by dose level. The `Chen_2024_*_rat.R` family assigns GHI-L below 3.75 mL/kg, GHI-M from 3.75 to below 7.5 mL/kg, and GHI-H at or above 7.5 mL/kg -- the midpoints of the studied levels. Values outside 2.5-10 mL/kg extrapolate the nearest fitted group and are not supported by the source data.
- **Source aliases:**
  - `GHI dose (mL/kg)` -- the wording used in Chen 2024 Section 2.3 ("GHI-administered groups were respectively injected with GHI (2.5, 5, and 10 mL/kg, i.v.)"); the paper prints no data-column name.
- **Example models:** `Chen_2024_chlorogenicAcid_rat.R`, `Chen_2024_hydroxysafflorYellowA_rat.R`, `Chen_2024_hyperoside_rat.R`, `Chen_2024_kaempferol3OGlucoside_rat.R`, `Chen_2024_kaempferol3ORutinoside_rat.R`, `Chen_2024_nAcetylglutamine_rat.R`, `Chen_2024_pCoumaricAcid_rat.R`, `Chen_2024_rutin_rat.R` (all eight select V1, V2, CL1 and Q -- and, where a PK/PD equation was fitted, Emax, ED50 and gamma -- from this column).
- **Notes:** A drug-specific member of the `DOSE_<drug>_<units>` family. The general `DOSE` canonical is unusable here for the mechanical reason documented under `DOSE_TBPPI_MG`: rxode2's `etTrans()` consumes a column literally named `DOSE` and never exposes it to `model()`. Because GHI is a multi-constituent botanical preparation with no single active moiety, the volume dose -- not a mass dose -- is what the study randomized and what indexes the fitted parameter sets; a per-constituent `DOSE_<constituent>_UGKG` column would have to be derived separately for each of the eight analytes and would lose the fact that they are dosed together in fixed proportion. Distinct from the general `DOSE_HIGH` high-dose-cohort indicator, which is binary; this column preserves the actual administered volume so a downstream user can recover each constituent's dose.

### DOSE_UFH_UH (**canonical for concomitant continuous-infusion unfractionated heparin dose rate**)
- **Description:** Patient's concurrent continuous intravenous infusion rate of unfractionated heparin (UFH), in absolute units per hour. Absolute (not weight-normalized) infusion rate is preserved at the column level because the source model parameterizes the UFH-on-clearance effect against a cohort-median absolute rate rather than a per-kg rate. Time-varying per observation as the UFH infusion is titrated. Set to 0 units/h for patients not receiving concurrent UFH.
- **Units:** units/h (absolute infusion rate; document per-model via `covariateData[[DOSE_UFH_UH]]$units` if a different unit is reported).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as an additive linear increment on clearance in the source paper's final model: `CL = CL_base * (WT / 70)^0.75 + e_ufh_cl * (DOSE_UFH_UH / ref)`. Reference values observed: 173 units/h (Moffett 2017 cohort median absolute UFH infusion rate). The additive form (rather than multiplicative) reflects the observed clinical mechanism -- UFH binds to and potentiates antithrombin activity, so higher UFH infusion rates deplete circulating antithrombin at a constant additive rate on top of the baseline weight-scaled clearance.
- **Source aliases:**
  - `UFH` -- used in `Moffett_2017_antithrombin.R` (Moffett 2017 Table 3 covariate; the paper's absolute UFH infusion-rate column in units/h). The cohort mean per-kg UFH dose was 34.1 +/- 22.7 units/kg/h (Moffett 2017 Results and Table 5); the per-record absolute rate is the value that entered the (UFH/173) covariate ratio.
- **Example models:** `Moffett_2017_antithrombin.R` (linear-additive effect on AT clearance: `CL_dL_h = 0.917 * (WT/70)^0.75 + 0.129 * (DOSE_UFH_UH / 173)`; reference 173 units/h from Moffett 2017 Table 3).
- **Notes:** Specific scope until a second antithrombin / anticoagulation-covariate model ratifies the canonical. UFH is a heterogeneous polymeric anticoagulant that acts by potentiating endogenous antithrombin; concurrent UFH therefore accelerates depletion of dosed exogenous antithrombin, motivating its inclusion as a covariate on AT clearance. Future models parameterizing the same covariate on a per-kg basis (units/kg/h) should either add a source alias here with the value transformation `DOSE_UFH_UKGH = DOSE_UFH_UH / WT` documented, or register a sibling canonical `DOSE_UFH_UKGH` if the effect coefficient's meaning changes. Distinct from the AT-related coagulation biomarker `AT_BL_UDL` (baseline antithrombin activity, used as a covariate on VD in the same Moffett 2017 model). Set to 0 for patients not receiving concurrent UFH; the additive covariate term collapses to zero and the clearance reduces to the baseline weight-scaled arm. Ratified canonically on 2026-06-21 alongside the Moffett 2017 antithrombin extraction.

### DOSE_PHT_MGKGD (**canonical for daily phenytoin dose per kg body weight**)
- **Description:** Patient's own total daily dose of phenytoin (mg) divided by current body weight (kg), expressed as mg/kg/d. Per-dose-record covariate; constant within an inter-dose interval and updated when the prescriber alters the daily dose.
- **Units:** mg/kg/d
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a self-dose-rate regressor in the dose-dependent powder bioavailability formula `F_powder = 1 - exp(-theta / DOSE_PHT_MGKGD)`. Has no effect when paired with `FORM_POWDER = 0` (tablet); a non-NA non-zero placeholder must still be supplied.
- **Source aliases:**
  - `Dij` -- used in `Yukawa_1990_phenytoin.R` (paper's per-record daily-dose-per-weight regressor, mg/kg/d, in the powder bioavailability equation 4 of Yukawa 1990).
- **Example models:** `Yukawa_1990_phenytoin.R` (powder bioavailability `F_powder = 1 - exp(-9.92 / DOSE_PHT_MGKGD)`; F approaches 1 below ~2 mg/kg/d and decreases monotonically as the daily dose increases, reflecting the lower wettability of the Aleviatin brand phenytoin powder formulation).
- **Notes:** Specific scope because the value is intrinsically tied to phenytoin (PHT) and the Yukawa 1990 powder-vs-tablet bioavailability contrast. Drug-self-dose covariates for other drugs should register sibling canonicals (e.g., `DOSE_<DRUG>_MGKGD`) rather than reuse this name -- the absolute coefficient (theta_BA2 = 9.92 in Yukawa 1990) is not transferable across drugs. Computed as the total daily dose summed across the 2-3 daily phenytoin doses (mg/d) divided by the patient's body weight at the dose record (kg). Ratified canonically on 2026-05-10 alongside the Yukawa 1990 phenytoin extraction.

### DOSE_VPA_MGKGD (**canonical for daily valproic acid dose per kg body weight**)
- **Description:** Patient's own total daily dose of valproic acid (mg), summed across the 1-3 daily administrations, divided by the current body weight (kg). Per-dose-record covariate; constant within an inter-dose interval and updated when the prescriber alters the daily dose.
- **Units:** mg/kg/d
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a self-dose-rate power regressor on apparent clearance, `cl = CLp/F * (DOSE_VPA_MGKGD / 25)^k`, with 25 mg/kg/d the rounded cohort mean daily dose. Must be strictly positive; a zero or `NA` value makes the power term undefined.
- **Source aliases:**
  - `DD` -- used in `Zhang_2023_valproic_acid_exponent.R` (Zhang 2023 Eq. 7, the "simple exponent model"; the paper's abbreviation list defines `DD` as daily dose in mg/day and `DDW` as daily dose in mg/kg/day, but Eq. 7's reference value of 25 and the reported `CLp/F` of 0.331 L/h against a base-model `CL/F` of 0.311 L/h only reconcile when `DD` is read on the mg/kg/day scale -- see that model's vignette Errata).
- **Example models:** `Zhang_2023_valproic_acid_exponent.R` (power effect on CL/F with exponent 0.658 and reference 25 mg/kg/d; observed range 8.70-57.69 mg/kg/d in a paediatric epilepsy therapeutic-drug-monitoring cohort).
- **Notes:** Specific scope because the value is intrinsically tied to valproic acid and to the saturable-plasma-protein-binding non-linearity that makes valproate clearance rise with dose; the exponent is not transferable across drugs. Sibling canonical of `DOSE_PHT_MGKGD` (same drug-self-daily-dose-per-weight concept for phenytoin) -- register a further `DOSE_<DRUG>_MGKGD` sibling rather than reusing either name. Note the modelling caveat the source authors themselves raise: because the daily dose is the quantity a therapeutic-drug-monitoring model is meant to predict, using it as a clearance covariate is circular, so models built on this covariate reproduce a published comparison rather than recommend a dosing strategy.

### DOSE_VPA_MGD (**canonical for total daily valproic acid dose**)
- **Description:** Patient's own total daily dose of valproic acid (mg), summed across the 1-3 daily administrations and NOT normalised by body weight. Per-dose-record covariate; constant within an inter-dose interval and updated when the prescriber alters the daily dose.
- **Units:** mg/d
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as the driver of a sigmoid-Emax term on apparent clearance, `cl = CLp/F * (1 + Emax * DOSE_VPA_MGD^gamma / (DD50^gamma + DOSE_VPA_MGD^gamma))`. Must be strictly positive.
- **Source aliases:**
  - `DD` -- used in `Zhang_2023_valproic_acid_ddemax.R` (Zhang 2023 Eq. 5, the "dose-dependent maximum effect model").
- **Example models:** `Zhang_2023_valproic_acid_ddemax.R` (sigmoid-Emax effect on CL/F with Emax 2.8, Hill 1.68 and DD50 37.4 mg/d, all fixed from Ding 2015; observed range 60-1250 mg/d, median 480, in a paediatric epilepsy therapeutic-drug-monitoring cohort).
- **Notes:** The mass-scale sibling of `DOSE_VPA_MGKGD` (same drug, same self-daily-dose concept, per-patient rather than per-kilogram). Both are needed because Zhang 2023 uses the per-kilogram scale in its Eq. 7 simple exponent model and the per-patient scale in its Eq. 5 dose-dependent maximum effect model -- do not merge them or convert one into the other inside `model()`, since the reference values (25 mg/kg/d and 37.4 mg/d respectively) are not interconvertible without a weight. The scale for Eq. 5 is an operator-ratified reading (sidecar `oare_PMC10587682` q2): Eq. 5's text defines `DD` in mg/kg/day and Ding 2015 tabulates DD50 = 37.4 against a mg/kg/day daily dose, but only the mg/day reading reproduces Zhang's own reported clearance (0.307 L/h against a base-model 0.311 L/h) and its near-zero MDPE; the mg/kg/day reading gives 0.153 L/h and a typical concentration roughly 2.7-fold the observed median. The conflicting evidence -- Supplementary Figure S5A favours mg/kg/day -- is recorded in that model's vignette Errata. Never name a dose covariate column bare `DOSE`: `rxode2::etTrans()` consumes a column of that name (any casing) before `model()` sees it.
- **Register a further `DOSE_<DRUG>_MGD` sibling** rather than reusing this name for another drug.

### PRED_DOSE (**canonical for concomitant oral prednisolone daily dose**)
- **Description:** Concomitant oral prednisolone (or prednisolone-equivalent glucocorticoid) daily dose. Time-varying across the dosing period as the post-transplant conmed_steroid taper progresses.
- **Units:** mg/day. Document per-model via `covariateData[[PRED_DOSE]]$units` when a paper uses a different unit (mg/kg/day) or a different glucocorticoid (methylprednisolone, dexamethasone, hydrocortisone) -- in the latter case convert to prednisolone-equivalent mg/day before populating the column and record the conversion factor in `covariateData[[PRED_DOSE]]$notes`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- continuous, with 0 mg/day (no prednisolone) the natural reference value. Effect forms in the registered example models: sigmoid-Emax fractional reduction `(1 - Pred_max * PRED_DOSE / (Pred_50 + PRED_DOSE))` on bioavailability (Storset 2014, `Pred_max = 0.67`, `Pred_50 = 35 mg/day`, Hill = 1); threshold-form binary multiplier `(1 + e * (PRED_DOSE >= 20))` on intrinsic clearance (ter Heine 2018, `e = 0.31` for the >= 20 mg/day high-dose contrast). Document the per-model functional form in `covariateData[[PRED_DOSE]]$notes`.
- **Source aliases:**
  - `Prednisolone dose` -- used in `Storset_2014_tacrolimus.R` (mg/day).
  - `Prednisolone dose (total daily dose, mg/day)` -- used in `TerHeine_2018_everolimus.R` (mg/day; collapsed to a binary high-dose indicator at the >= 20 mg/day threshold inside `model()`).
- **Example models:** `Storset_2014_tacrolimus.R` (Emax-style fractional reduction in tacrolimus oral bioavailability via prednisolone-driven induction of intestinal CYP3A / P-glycoprotein; Storset 2014 Methods Equations 4 + 6 with Hill = 1), `TerHeine_2018_everolimus.R` (threshold-form binary high-dose indicator at >= 20 mg/day driving a multiplicative +31% increase in apparent intrinsic clearance for everolimus via prednisolone-driven CYP3A4 induction; ter Heine 2018 Table 2 'Final model').
- **Notes:** Distinct from `PRED_CMAX_FREE` (free prednisolone Cmax co-medication exposure) -- `PRED_CMAX_FREE` is the modelled-from-data peak free concentration, whereas `PRED_DOSE` is the administered daily-dose level supplied directly from the dosing record. Both can coexist in a future model that simultaneously tests dose-driven and exposure-driven effects of prednisolone on a tacrolimus PK parameter. Distinct from `CONMED_STEROID` (binary baseline / concomitant corticosteroid use indicator) and `PRICORT` (binary prior corticosteroid use indicator) -- `PRED_DOSE` carries the daily dose value, not just an on / off flag. Time-varying because tacrolimus / everolimus PK depend on the conmed_steroid dose at the time of each observation; the conmed_steroid taper schedule must be supplied as a per-time-row covariate column. The corresponding methylprednisolone single-dose induction-bolus indicator (Storset 2014 binary covariate, not retained in the final model) would warrant a separate canonical (e.g. `MPRED_BOLUS`) if a future model retains it. Threshold-form binary indicators (e.g. `PRED_DOSE >= 20 mg/day` in ter Heine 2018) are derived inside `model()` from the continuous `PRED_DOSE` column rather than as separate registered canonicals so the underlying continuous dose value remains available for sensitivity analyses. Scope promoted from specific to general on 2026-05-24 with the ter Heine 2018 everolimus extraction (second model ratifying the canonical, this time as a CYP3A4-induction covariate on intrinsic clearance rather than a CYP3A / P-gp-induction covariate on oral bioavailability).

### PRED_CMAX_FREE (**canonical for free prednisolone Cmax co-medication exposure**)
- **Description:** Maximum free (unbound, ultrafiltrable) plasma prednisolone concentration over a co-medication dosing interval, used as a co-medication-exposure covariate on a primary modelled drug (e.g., tacrolimus) when concomitant prednisolone is suspected to alter the primary drug's PK via membrane-permeability or fluid-balance effects. Time-fixed per subject in the source paper (one Cmax value per subject, derived from limited-sampling concentrations).
- **Units:** nmol/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used in centred-deviation form `(1 + e * (PRED_CMAX_FREE - ref))`. Reference value observed: 155.5 nmol/L (Bergmann 2014 study median).
- **Source aliases:**
  - `PredCmax,free` / `PREDCFR` -- used in `Bergmann_2014_tacrolimus.R` (Bergmann 2014 Table 2 footnote; 162 nmol/L median per Table 1, 155.5 nmol/L centring value per Table 2 equation).
- **Example models:** `Bergmann_2014_tacrolimus.R` (linear deviation effect on tacrolimus apparent central volume V1/F: every 1 nmol/L increase from 155.5 nmol/L decreases V1/F by 0.28%).
- **Notes:** Specific scope because the value is intrinsically tied to a particular co-medication (oral prednisolone) and a particular reference population (kidney transplant recipients on a tapering conmed_steroid regimen). Future popPK models that test free prednisolone Cmax as a covariate on a different primary drug should reuse this canonical; if a future model uses a different prednisolone exposure metric (e.g., `PRED_AUC0_12_FREE` for free AUC 0-12 h, or `PRED_CMAX_TOTAL` for total Cmax), register parallel canonicals rather than overload `PRED_CMAX_FREE`. The source-paper Cmax is derived from limited-sampling concentrations at 1 / 2 / 4 hours postdose per Bergmann 2014 Methods (validated against earlier full-profile data from the same cohort). Distinct from `CAV` (average concentration of the modelled drug) and `CP_MGL` (instantaneous concentration of the modelled drug as a time-varying PD driver) -- `PRED_CMAX_FREE` is the maximum concentration of a co-medication, used as a per-subject covariate. Ratified canonically on 2026-05-08 alongside the Bergmann 2014 extraction.

### CU_ASP8232 (**canonical for time-varying unbound ASP8232 plasma concentration input to a PD-only exposure-response model**)
- **Description:** Time-varying unbound (not bound to soluble or membrane-bound VAP-1) plasma concentration of the vascular adhesion protein-1 (VAP-1) inhibitor ASP8232 (Cu), used as the exposure driver for a PD-only exposure-response model whose PK layer is defined in a separate paper. Per-observation covariate; supplied by the user in the event table because the modelling paper (Hoefman 2021, PD/ER) does not embed a PK compartment. The exposure time-course is produced by the companion multi-target TMDD PK-PD paper (Snelder 2021, `doi:10.1007/s10928-020-09717-w`) and consumed by the PD-layer paper (Hoefman 2021, `doi:10.1007/s10928-020-09716-x`).
- **Units:** nM
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- 0 nM for placebo subjects at every record; the paper reports Cu at steady state of 125.58 nM for 40 mg qd oral ASP8232 in a typical DKD subject (Hoefman 2021 Methods Simulations).
- **Source aliases:**
  - `Cu` -- Hoefman 2021 Methods Sampling-and-measurements subsection and Table 1 caption: `Cu model-predicted unbound ASP8232 plasma concentration in nM`; identical orientation, no value transformation.
- **Example models:** `Hoefman_2021_asp8232.R` (drives three drug-effect terms: (a) acute additive eGFR CysC decline `theta_4 * Cu`; (b) sigmoid Imax albuminuria-lowering on AER via `logCu = log(1 + 1000 * Cu)`, `theta_9 * logCu^theta_24 / (theta_23^theta_24 + logCu^theta_24)` with `theta_24` = Hill = 10 FIXED; (c) proportional Emax creatinine-transporter-inhibition on sCr `theta_13 * Cu / (theta_31 + Cu)` with `theta_31` = 52.9 nM FIXED from a separate multi-trial PopPK model per Discussion / "data on file").
- **Notes:** Specific scope because the value is intrinsically tied to a particular drug (ASP8232) and a particular upstream PK model; the concept "time-varying unbound driver-drug concentration supplied as external data because the modelling paper does not embed a PK layer" is not currently generalizable across drugs. Future PD-only exposure-response models for other drugs that inherit their PK from a companion paper should register sibling canonicals (e.g., `CU_<DRUG>`) rather than reuse this name. The `log(1 + 1000 * Cu)` linearization stated in Table 1's caption is a nM-to-per-unit unit-shift plus a `log(1 + x)` transform for numerical stability at Cu = 0 -- the `1 + ...` is what allows the transform to remain finite in the placebo condition. Distinct from `CAV` (period-averaged concentration of the modelled drug from an in-file PK model) and `PRED_CMAX_FREE` (co-medication free concentration): `CU_ASP8232` is the concentration of the modelled drug, but supplied externally rather than computed in the model file. Ratified canonically on 2026-07-08 alongside the Hoefman 2021 ASP8232 PD extraction.

### AUC_CARBO (**canonical for per-cycle average AUC of carboplatin**)
- **Description:** Per-cycle average AUC of carboplatin used as the time-varying drug-exposure covariate driving cytotoxic tumour-death rates in tumour-size-dynamics models of platinum-based chemotherapy. The value is held step-wise constant over each chemotherapy cycle and resets at the start of the next cycle.
- **Units:** carboplatin AUC units (typically `mg*min/mL`); document per-model via `covariateData[[AUC_CARBO]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- set to 0 in cycles where carboplatin is not administered (e.g., post-discontinuation or non-platinum arms).
- **Source aliases:**
  - `CB` (NONMEM `$INPUT` column in DDMODEL00000217 / DDMODEL00000218) -- used in `Zecchin_2016_tumorovarian.R` and `Zecchin_2016_survival.R`. The DDMORE bundles ship the simulated datasets with the column re-labelled `AUC0`; downstream consumers should map `AUC0` -> `AUC_CARBO`.
- **Example models:** `Zecchin_2016_tumorovarian.R` (Zecchin 2016 SLD model for advanced ovarian cancer, DDMODEL00000217), `Zecchin_2016_survival.R` (Zecchin 2016 OS model, DDMODEL00000218; the OS model integrates the same SLD ODE inline, with the prior IPP-fit subject-level KG/KD0/KD1/IBASE supplied via the dataset).
- **Notes:** Specific scope because the column meaning is tied to a particular cytotoxic agent (carboplatin) and a particular per-cycle averaging convention. Reusing the same column for another platinum analogue (cisplatin, oxaliplatin) is not appropriate -- register a sibling canonical (`AUC_CISPLATIN`, `AUC_OXALIPLATIN`) when needed. The Zecchin 2016 SLD and OS models use the value directly in the death-rate term `kd0 * AUC_CARBO * tumorSize`, with an internal `/1000` numerical scaling carried verbatim from the source `$DES` block.

### AUC_BAST_FW (**canonical for first-week AUC in the BAST PTTE 2017 teaching dataset**)
- **Description:** Area under the plasma concentration-time curve over the first week of treatment for the unspecified hypothetical drug used in the BAST PTTE 2017 teaching guiding-document (DDMODEL00000243). Per-subject, time-fixed (a single early-exposure summary value carried as a baseline covariate). Used to drive the Event 2 Gompertz hazard via centred-deviation form `exp(coef * (AUC_BAST_FW - 3065.5))`.
- **Units:** ug*h/L (per BAST PTTE guiding document Section  2.2.2)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used in centred-deviation form. Reference value observed: 3065.5 ug*h/L (BAST PTTE 2017 simulated cohort median; runEV2_105 base hazard).
- **Source aliases:**
  - `AUC` -- verbatim NM-TRAN `$INPUT` column name in DDMODEL00000243's executable .mod files and in the bundle's `Simulated_event_data.csv`. Renamed to `AUC_BAST_FW` in the canonical register so that future models using a generic `AUC` column with different semantics will not silently collide.
- **Example models:** `NA_NA_tte_gompertz_ev2.R` (BAST PTTE 2017 / DDMODEL00000243 Event 2 hazard model; centred at 3065.5 ug*h/L, coefficient 3.09e-4 on the NONMEM rescaled scale, equivalent to `exp(3.09e-4 * (AUC_BAST_FW - 3065.5))` on the hazard).
- **Notes:** Specific scope because the value is intrinsically tied to an unspecified hypothetical drug and a fictional simulated population in the BAST PTTE 2017 guiding document -- the column has no shared meaning across drugs or studies. Future models should register a sibling canonical (e.g., `AUC_<DRUG>`) with explicit drug semantics rather than overload this name. The BAST guiding document Section  2.2.2 defines this as "AUC of drug treatment given within the first week (ug*h/L)."

### AUC_GEM (**canonical for per-cycle average AUC of gemcitabine**)
- **Description:** Per-cycle average AUC of gemcitabine (sum of parent and active metabolite exposure, per Zecchin 2016 Methods) used as the time-varying drug-exposure covariate driving cytotoxic tumour-death rates in tumour-size-dynamics models of gemcitabine-containing chemotherapy.
- **Units:** gemcitabine AUC units (typically `mg*h/L` or the paper's `mol*day / 10^6 cells` scaling for the parent-plus-active-metabolite composite); document per-model via `covariateData[[AUC_GEM]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- set to 0 in cycles where gemcitabine is not administered (e.g., carboplatin-monotherapy arms).
- **Source aliases:**
  - `G` (NONMEM `$INPUT` column in DDMODEL00000217 / DDMODEL00000218) -- used in `Zecchin_2016_tumorovarian.R` and `Zecchin_2016_survival.R`. The DDMORE bundles ship the simulated datasets with the column re-labelled `AUC1`; downstream consumers should map `AUC1` -> `AUC_GEM`.
- **Example models:** `Zecchin_2016_tumorovarian.R` (Zecchin 2016 SLD model for advanced ovarian cancer, DDMODEL00000217), `Zecchin_2016_survival.R` (Zecchin 2016 OS model, DDMODEL00000218).
- **Notes:** Specific scope. The Zecchin 2016 SLD and OS models use the value directly in the death-rate term `kd1 * AUC_GEM * tumorSize`, with an internal `/100` numerical scaling carried verbatim from the source `$DES` block.

### AUC_GCV (**canonical for per-q12h-interval AUC of ganciclovir**)
- **Description:** Time-varying ganciclovir AUC over a q12h dosing interval (AUC_0-12), used as the drug-exposure input to indirect-response viral-turnover PK/PD models of cytomegalovirus (CMV) viral load decline under (val)ganciclovir treatment. The Koloskoff 2025 source computes individual AUC_0-12 from an upstream popPK model (Franck 2021) and feeds it to the PD model as a Monolix "varying input"; the PD model itself does not integrate a PK ODE, so AUC_GCV is supplied to nlmixr2 as a time-varying data column.
- **Units:** `mg*h/L` (document per-model via `covariateData[[AUC_GCV]]$units` if a different exposure unit is reported).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- set to 0 in pre-treatment / off-treatment records so the drug-stimulation term `Emax * AUC_GCV / (EC50 + AUC_GCV)` vanishes and the viral load returns to the `kin / kout` steady-state baseline.
- **Source aliases:**
  - `AUC_0-12` -- the printed variable name in Koloskoff 2025 (Methods Section 2.3, Eq. 1, and Table 1). Q24h dosing intervals are entered as `AUC_0-24 / 2` so all data live in the q12h framework (Koloskoff 2025 Methods Section 2.1).
- **Example models:** `Koloskoff_2025_ganciclovir.R` (Koloskoff 2025 indirect viral turnover model for CMV viral load in pediatric SOT / HSCT recipients; AUC_GCV enters the ODE via `kout * (1 + Emax * AUC_GCV / (EC50 + AUC_GCV)) * viralLoad`).
- **Notes:** Specific scope -- the column meaning is tied to ganciclovir as the drug and to a q12h interval-averaging convention. Sibling drug-specific AUC canonicals (`AUC_CARBO`, `AUC_GEM`, `AUC_BAST_FW`, `AUC_PAZO`) follow the same `AUC_<DRUG>` naming pattern; a future PK/PD model that uses a different exposure metric for ganciclovir (e.g., trough concentration, instantaneous concentration) should register a parallel canonical rather than overload `AUC_GCV`. Koloskoff 2025 Monte Carlo simulations are reported under AUC_0-24 (Tables 3 and 4) assuming AUC_0-24 = 2 x AUC_0-12 at steady state; nlmixr2 simulations should set AUC_GCV to the q12h-interval value (i.e., AUC_0-24 / 2).

### AUC_LCM (**canonical for daily AUC of lacosamide at steady state**)
- **Description:** Daily area under the plasma concentration-time curve of lacosamide (LCM) at steady state (mg*h/L), used as the drug-exposure covariate on the seizure hazard in time-to-seizure models of LCM anti-epileptic therapy. Dose-step-varying (the value tracks the patient's currently assigned LCM target dose level: 100 -> 200 -> 400 -> 600 mg/day). Set to 0 for subjects not on lacosamide so the centred-deviation covariate contribution is gated by a treatment-arm indicator (`CONMED_LCM`) inside the model rather than by the covariate value itself.
- **Units:** `mg*h/L` (document per-model via `covariateData[[AUC_LCM]]$units` if a different exposure unit is reported).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via centred-deviation form `slope * (AUC_LCM - AUC_ref)`. Reference value observed: 104 mg*h/L (Lindauer 2017 SP0993 typical daily AUC at the first LCM target dose 200 mg/day; Lindauer 2017 Table 3 note c). Approximate scaling at higher doses: ~208 mg*h/L at 400 mg/day, ~312 mg*h/L at 600 mg/day.
- **Source aliases:**
  - `AUC_LCM` -- printed name in Lindauer 2017 Table 3 (rows `AUC_LCM * k1` and `AUC_LCM * k2`); the paper computes per-subject daily AUC from an empirical-Bayes clearance estimate of a previously published lacosamide popPK model (Lindauer 2017 Section 2.3).
- **Example models:** `Lindauer_2017_lacosamide_seizure.R` (centred-deviation form on both first-seizure and subsequent-seizure Weibull scale parameters: slopes -0.00917 (1st) and -0.00751 (2nd+); gated by `CONMED_LCM = 1`).
- **Notes:** Specific scope; lacosamide-specific. Follows the `AUC_<DRUG>` sibling family (`AUC_CARBO`, `AUC_GEM`, `AUC_BAST_FW`, `AUC_PAZO`, `AUC_GCV`, `AUC_RTV`). Downstream users should compute the per-subject daily AUC from an on-disk lacosamide popPK model (e.g., a Cawello 2013 / 2015 popPK) or from the actual LCM dose regimen if a compartmental model is being coupled to the TTE hazard. Ratified canonically on 2026-07-03 alongside the Lindauer 2017 lacosamide time-to-seizure extraction.

### AUC_CBZ (**canonical for daily AUC of carbamazepine at steady state**)
- **Description:** Daily area under the plasma concentration-time curve of carbamazepine (CBZ; typically as controlled-release CBZ-CR) at steady state (mg*h/L), used as the drug-exposure covariate on the seizure hazard in time-to-seizure models of CBZ anti-epileptic therapy. Dose-step-varying (the value tracks the patient's currently assigned CBZ-CR target dose level: 200 -> 400 -> 800 -> 1200 mg/day in the Lindauer 2017 SP0993 design). Set to 0 for subjects not on carbamazepine so the centred-deviation covariate contribution is gated by a treatment-arm indicator (`CONMED_LCM` in Lindauer 2017; equivalent to `1 - CONMED_CBZ` for that design) inside the model rather than by the covariate value itself.
- **Units:** `mg*h/L` (document per-model via `covariateData[[AUC_CBZ]]$units` if a different exposure unit is reported).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via centred-deviation form `slope * (AUC_CBZ - AUC_ref)`. Reference value observed: 132 mg*h/L (Lindauer 2017 SP0993 typical daily AUC at the first CBZ-CR target dose 400 mg/day for a 70-kg patient; Lindauer 2017 Section 3.4 first paragraph).
- **Source aliases:**
  - `AUC_CBZ` -- printed name in Lindauer 2017 Table 3 (rows `AUC_CBZ * k1` and `AUC_CBZ * k2`); the paper computes per-subject daily AUC from an empirical-Bayes clearance estimate of a previously published carbamazepine popPK model (Lindauer 2017 Section 2.3).
- **Example models:** `Lindauer_2017_lacosamide_seizure.R` (centred-deviation form on both first-seizure and subsequent-seizure Weibull scale parameters: slopes -0.00658 (1st) and -0.0153 (2nd+); gated by `CONMED_LCM = 0` -- i.e., the CBZ-CR arm).
- **Notes:** Specific scope; carbamazepine-specific. Distinct from `CONMED_CBZ` -- `CONMED_CBZ` is the binary carbamazepine coadministration indicator (used in models where the effect of CBZ enters as a categorical on / off shift, e.g., Schoemaker 2017 brivaracetam), while `AUC_CBZ` carries the magnitude of CBZ exposure and is used when the effect scales with dose. Follows the `AUC_<DRUG>` sibling family. Ratified canonically on 2026-07-03 alongside the Lindauer 2017 lacosamide time-to-seizure extraction.

### AUC_PAZO (**canonical for per-period mean AUC of pazopanib**)
- **Description:** Per-period (per-dose-group in preclinical xenograft studies; per-subject mean dose-adjusted in clinical studies) mean AUC of pazopanib used as the drug-exposure covariate driving the antiangiogenic and cytotoxic effect rates in semi-mechanistic tumour-growth / angiogenesis-inhibition (TGI) models of pazopanib in renal-cell carcinoma. Time-varying step-wise (held constant within a treatment period and resetting when dose level changes or treatment ends).
- **Units:** `ug*h/mL` (`= mg*h/L`). Document per-model via `covariateData[[AUC_PAZO]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via power form `a = a0 * AUC_PAZO^e_auc_pazo_a` and `c = c0 * AUC_PAZO^e_auc_pazo_c`. Set to 0 in periods where pazopanib is not administered; the model rate terms reduce to baseline drug-off (a, c -> 0 when AUC_PAZO -> 0) under that convention.
- **Source aliases:**
  - `AUC` -- used in `Ouerdani_2015_pazopanib_mouse.R` and `Ouerdani_2015_pazopanib.R` (Ouerdani 2015 Methods Equations 2-3; preclinical values 220.2, 656.8, 1140.8 ug*h/mL for the 10, 30, 100 mg/kg mouse dose groups; clinical mean 771.6 ug*h/mL for 800 mg QD pazopanib in RCC patients, with per-subject values 629.4-802.4 ug*h/mL derived from an Emax fit to mean AUCs at the patient's dose history).
- **Example models:** `Ouerdani_2015_pazopanib_mouse.R` (preclinical TGI in CAKI-2 xenograft mice; AUC enters as `c = c0 * AUC_PAZO^0.332` only -- the cytotoxic exponent `e_auc_pazo_a` was fixed to 0 because the in-mouse cytotoxic effect did not vary with exposure across the 10-100 mg/kg range), `Ouerdani_2015_pazopanib.R` (clinical TGI in RCC patients; AUC enters as both `a = a0 * AUC_PAZO^0.125` and `c = c0 * AUC_PAZO^0.142`).
- **Notes:** Specific scope because the column meaning is tied to a particular drug (pazopanib) and the power-form parameterisation that the Ouerdani 2015 model uses. Reusing the name for a different tyrosine-kinase inhibitor is not appropriate -- register a sibling canonical (`AUC_SORAF` for sorafenib, `AUC_SUNI` for sunitinib, etc.) when needed. The Ouerdani 2015 paper reports the preclinical AUCs in `ug*h/mL` from a separate preclinical PK study (cited as the FDA Pharmacology Review for pazopanib NDA 022465); the clinical AUCs come from an Emax fit (Equation derived from Methods) to pooled mean AUCs at varying daily doses (5 mg to 2000 mg) across five prior pazopanib trials. Ratified canonically on 2026-05-12 alongside the Ouerdani 2015 pazopanib mouse and clinical extractions.

### AUC_RTV (**canonical for ritonavir AUC over the 0-24 h dosing interval**)
- **Description:** Per-subject (time-fixed within an evaluated regimen) ritonavir AUC over the 0-24 h once-daily dosing interval, used as a co-medication exposure covariate driving boosted-protease-inhibitor (atazanavir, lopinavir, darunavir, etc.) clearance in popPK models that account for ritonavir's CYP3A4-inhibition effect. In Dickinson 2009 the value is computed by non-compartmental methods on the observed ritonavir concentration-time profile (WinNonlin 5.2) and feeds the atazanavir CL/F power form `cl = exp(lcl) * (AUC_RTV / 7.52)^e_aucrtv_cl`.
- **Units:** `mg*h/L` (document per-model via `covariateData[[AUC_RTV]]$units` if a different exposure unit is reported).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via centred power form. Reference value observed: 7.52 mg*h/L (Dickinson 2009 cohort median across HIV-infected and healthy volunteers; healthy 7.36, HIV 7.59 per Table 1).
- **Source aliases:**
  - `RTVAUC` -- printed name in Dickinson 2009 Table 1 / Table 3 (the paper writes `RTVAUC0-24` with the dosing-interval subscript; the column name is registered without the subscript to fit standard data-column character constraints).
- **Example models:** `Dickinson_2009_atazanavir.R` (atazanavir CL/F power-function dependence on RTVAUC0-24, centred at 7.52 mg*h/L with exponent -0.8).
- **Notes:** Specific scope because the column meaning is tied to ritonavir as the booster drug and to the 0-24 h once-daily dosing-interval AUC convention. Sibling drug-specific AUC canonicals (`AUC_CARBO`, `AUC_GEM`, `AUC_BAST_FW`, `AUC_PAZO`, `AUC_GCV`) follow the same `AUC_<DRUG>` naming pattern. A future PK model that uses a different ritonavir exposure metric (trough concentration, q12h-interval AUC for BID ritonavir regimens) should register a parallel canonical rather than overload `AUC_RTV`. For simulation users without observed ritonavir AUC, the Dickinson 2009 cohort median 7.52 mg*h/L reproduces typical-value behaviour (the centring point of the covariate effect).

### AUC_VERUB (**canonical for verubecestat AUC over the 24 h dosing interval at steady state**)
- **Description:** Time-varying verubecestat plasma AUC over the once-daily 24 h dosing interval at steady state, used as the driver of the inhibitory Emax sigmoid on the amyloid plaque formation rate Kin in the van Maanen 2025 amyloid plaque turnover model (paper Eq 4: `Inh_verub = Imax * AUC_VERUB / (AUC_VERUB + AUC50)`). The van Maanen 2025 analysis derives individual AUC_VERUB from the upstream Dockendorf 2022 verubecestat population PK model (cited in Table S1).
- **Units:** `uM*h` (micromolar * hour). Must be in the same units as the model's AUC50 parameter (uM*h) so the sigmoid AUC/(AUC + AUC50) is dimensionless. Document per-model via `covariateData[[AUC_VERUB]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via Emax-sigmoid form `Imax * AUC / (AUC + AUC50)`. Set to 0 in periods where verubecestat is not administered so the inhibition term vanishes and Kin returns to its typical (undrugged) value. Typical AUC values reverse-computed from the paper's Table S5 stable-plaque predictions and AUC50 = 0.392 uM*h: 40 mg daily -> approximately 4.4 uM*h (91.8% inhibition); 11.9 mg -> approximately 1.44 uM*h (78.7%); 2.9 mg -> approximately 0.35 uM*h (47.2%); 1.7 mg -> approximately 0.21 uM*h (34.5%).
- **Source aliases:**
  - `AUC_verub` -- printed name in van Maanen 2025 Eq 4 and Figure 1.
- **Example models:** `vanMaanen_2025_amyloid.R` (verubecestat inhibition of plaque formation via Imax = 1 fixed and AUC50 = 0.392 uM*h).
- **Notes:** Specific scope because the value is intrinsically tied to verubecestat (BACE1 inhibitor) and the once-daily 24 h dosing-interval AUC convention. Sibling drug-specific AUC canonicals (`AUC_CARBO`, `AUC_GEM`, `AUC_BAST_FW`, `AUC_PAZO`, `AUC_GCV`, `AUC_LCM`, `AUC_CBZ`, `AUC_RTV`, `AUC_EMPA`, `AUC_ADU`, `AUC_DON`, `AUC_GAN`, `AUC_LEC`) follow the same `AUC_<DRUG>` naming pattern. Ratified canonically on 2026-07-24 alongside the van Maanen 2025 amyloid plaque turnover extraction.

### AUC_ADU (**canonical for aducanumab serum AUC over the 4-week dosing interval at steady state**)
- **Description:** Time-varying aducanumab serum AUC over the 4-week Q4W dosing interval at steady state, used as the driver of the linear stimulation term on the amyloid plaque elimination rate Kout in the van Maanen 2025 amyloid plaque turnover model (paper Eq 6: `Stim_ADU = slope_adu * (AUC_ADU / MW_ADU)`). The van Maanen 2025 analysis derives aducanumab AUC from the upstream Sevigny 2016 phase 1b popPK study (Table S1) with linear dose scaling where the target dose differs from the source PK study dose.
- **Units:** `mg*day/L` (milligrams * day per litre). Must be in mg*day/L (not mg*h/mL, not ug*h/mL) so that `AUC_ADU / MW_ADU_g_per_mol` evaluates in mmol*day/L = mM*day and the paper's slope units (mM^-1 * day^-1) multiply to a dimensionless stimulation term. Conversion from mg*h/L: divide by 24. Document per-model via `covariateData[[AUC_ADU]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via linear form `slope_adu * AUC_ADU / MW_ADU`. Set to 0 in periods where aducanumab is not administered so the stimulation term vanishes and Kout returns to its typical (undrugged) value. Reference regimen (Table S3): EMERGE/ENGAGE high-dose titration -> target 10 mg/kg IV Q4W, giving a typical steady-state 4-week AUC of approximately 1944 mg*day/L for a 70 kg subject at CL approximately 0.36 L/day.
- **Source aliases:**
  - `AUC_mAb` (for aducanumab) -- printed name in van Maanen 2025 Eq 6 and Figure 1.
- **Example models:** `vanMaanen_2025_amyloid.R` (aducanumab stimulation of plaque elimination via slope_adu = 719 mM^-1 day^-1 and MW_ADU = 145912 g/mol).
- **Notes:** Specific scope. Sibling anti-A-beta mAb AUC canonicals `AUC_DON` (donanemab), `AUC_GAN` (gantenerumab), `AUC_LEC` (lecanemab) follow the same convention and are consumed by the same van Maanen 2025 model. Ratified canonically on 2026-07-24 alongside the van Maanen 2025 amyloid plaque turnover extraction.

### AUC_DON (**canonical for donanemab serum AUC over the 4-week dosing interval at steady state**)
- **Description:** Time-varying donanemab serum AUC over the 4-week Q4W dosing interval at steady state, used as the driver of the linear stimulation term on the amyloid plaque elimination rate Kout in the van Maanen 2025 amyloid plaque turnover model (paper Eq 6: `Stim_DON = slope_don * (AUC_DON / MW_DON)`). The van Maanen 2025 analysis derives donanemab AUC from the Lowe 2021 phase 1b SAD/MAD popPK data (Table S1).
- **Units:** `mg*day/L`. Same unit convention as `AUC_ADU` (see notes there). Document per-model via `covariateData[[AUC_DON]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- set to 0 in periods where donanemab is not administered. Reference regimen (Table S3): TRAILBLAZER-ALZ 1/2/4 titration to 1400 mg IV Q4W after 3 loading doses of 10 mg/kg IV Q4W.
- **Source aliases:**
  - `AUC_mAb` (for donanemab) -- printed name in van Maanen 2025 Eq 6 and Figure 1.
- **Example models:** `vanMaanen_2025_amyloid.R` (donanemab stimulation of plaque elimination via slope_don = 1120 mM^-1 day^-1 and MW_DON = 145087 g/mol).
- **Notes:** Specific scope. Sibling anti-A-beta mAb AUC canonicals `AUC_ADU`, `AUC_GAN`, `AUC_LEC` follow the same convention. Ratified canonically on 2026-07-24 alongside the van Maanen 2025 extraction.

### AUC_GAN (**canonical for gantenerumab serum AUC over the 4-week dosing interval at steady state**)
- **Description:** Time-varying gantenerumab serum AUC over the 4-week dosing interval at steady state (delivered as SC Q4W or IV titration in the source trials, expressed here as a 4-week interval-AUC to match the paper's Kout stimulation term), used as the driver of the linear stimulation term on the amyloid plaque elimination rate Kout in the van Maanen 2025 amyloid plaque turnover model (paper Eq 6: `Stim_GAN = slope_gan * (AUC_GAN / MW_GAN)`). The van Maanen 2025 analysis derives gantenerumab AUC from the Portron 2020 phase 1 single active dose PK data (Table S1).
- **Units:** `mg*day/L`. Same unit convention as `AUC_ADU`. Document per-model via `covariateData[[AUC_GAN]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- set to 0 in periods where gantenerumab is not administered. Reference regimen (Table S3): GRADUATE 1/2 titration to 1020 mg every 4 weeks (delivered as 510 mg SC Q2W) after a 9-month step-up sequence of 120 -> 255 -> 510 -> 1020 mg per 4 weeks.
- **Source aliases:**
  - `AUC_mAb` (for gantenerumab) -- printed name in van Maanen 2025 Eq 6 and Figure 1.
- **Example models:** `vanMaanen_2025_amyloid.R` (gantenerumab stimulation of plaque elimination via slope_gan = 397 mM^-1 day^-1 and MW_GAN = 146300 g/mol).
- **Notes:** Specific scope. Sibling anti-A-beta mAb AUC canonicals `AUC_ADU`, `AUC_DON`, `AUC_LEC` follow the same convention. Ratified canonically on 2026-07-24 alongside the van Maanen 2025 extraction.

### AUC_LEC (**canonical for lecanemab serum AUC over the 4-week dosing interval at steady state**)
- **Description:** Time-varying lecanemab serum AUC over the 4-week dosing interval at steady state (delivered as IV Q2W in Clarity AD, expressed here as the equivalent 4-week interval-AUC = 2 * Q2W-interval AUC), used as the driver of the linear stimulation term on the amyloid plaque elimination rate Kout in the van Maanen 2025 amyloid plaque turnover model (paper Eq 6: `Stim_LEC = slope_lec * (AUC_LEC / MW_LEC)`). The van Maanen 2025 analysis derives lecanemab AUC from the Logovinsky 2016 phase 1 SAD/MAD PK data (Table S1).
- **Units:** `mg*day/L`. Same unit convention as `AUC_ADU`. Document per-model via `covariateData[[AUC_LEC]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- set to 0 in periods where lecanemab is not administered. Reference regimen (Table S3): Clarity AD 10 mg/kg IV Q2W (equivalent to approximately 20 mg/kg per 4 weeks).
- **Source aliases:**
  - `AUC_mAb` (for lecanemab) -- printed name in van Maanen 2025 Eq 6 and Figure 1.
- **Example models:** `vanMaanen_2025_amyloid.R` (lecanemab stimulation of plaque elimination via slope_lec = 606 mM^-1 day^-1 and MW_LEC = 150000 g/mol).
- **Notes:** Specific scope. Sibling anti-A-beta mAb AUC canonicals `AUC_ADU`, `AUC_DON`, `AUC_GAN` follow the same convention. Ratified canonically on 2026-07-24 alongside the van Maanen 2025 extraction.

### AUC_IBRU (**canonical for daily AUC(0-24) of ibrutinib**)
- **Description:** Daily 0-24 h area under the plasma concentration-time curve of ibrutinib, used as the systemic-exposure driver in ibrutinib exposure-response models. Dose-step-varying: the value tracks the patient's currently assigned daily ibrutinib dose level (420, 280 or 140 mg/day in the de-escalation schedules simulated by Ibrahim 2023; 420 and 840 mg/day in the PCYC-1102 study itself) and drops to 0 during treatment interruptions. Ibrahim 2023 selected AUC(0-24) rather than a concentration because ibrutinib's half-life is short (4-13 h) and daily AUC has been proposed as the surrogate exposure metric for its therapeutic drug monitoring.
- **Units:** `h*ng/mL` (document per-model via `covariateData[[AUC_IBRU]]$units` if a different exposure unit is reported).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters saturable Emax / Imax forms of the shape `AUC_IBRU / (AUC50 + AUC_IBRU)` rather than a centred-deviation or power form. Reference (half-maximal) values observed: 34.1 h*ng/mL for inhibition of phosphorylated-Btk production (Ibrahim 2023 Table 1, `IAUC50,pBTK`); 91.7 h*ng/mL for stimulation of systolic blood pressure and 63.1 h*ng/mL for diastolic blood pressure (Ibrahim 2023 Table 2, `AUC50,sBP` / `AUC50,dBP`).
- **Source aliases:**
  - `DAILYAUC` -- printed name in the Ibrahim 2023 nlmixr control streams (Supporting Information S4, `auc <- DAILYAUC` in run100 / run200 / run300).
- **Example models:** `Ibrahim_2023_ibrutinib_leukocyte_spd.R` (Imax inhibition of pBtk production, `IAUC50 = 34.1`), `Ibrahim_2023_ibrutinib_sbp.R` (Emax stimulation of sBP production, `AUC50 = 91.7`), `Ibrahim_2023_ibrutinib_dbp.R` (Emax stimulation of dBP production, `AUC50 = 63.1`).
- **Notes:** Specific scope; ibrutinib-specific. Follows the `AUC_<DRUG>` sibling family (`AUC_CARBO`, `AUC_GEM`, `AUC_LCM`, `AUC_CBZ`, `AUC_PAZO`, `AUC_GCV`, `AUC_RTV`, `AUC_EMPA`). As with `AUC_LCM`, downstream users must compute the per-subject daily AUC from an ibrutinib popPK model rather than from this register -- Ibrahim 2023 integrated individual post hoc profiles of the two-compartment ibrutinib popPK model of Marostica et al. (Cancer Chemother Pharmacol. 2015;75(1):111-121), which is not currently part of nlmixr2lib. Because all three Ibrahim 2023 models use the *same* exposure column with *different* half-maximal constants, the ratio of those constants is itself interpretable: `AUC50,sBP` and `AUC50,dBP` are about three and two times `IAUC50,pBTK`, which is the quantitative basis for the paper's finding that dose de-escalation sheds hypertension risk faster than it sheds efficacy. Ratified canonically on 2026-07-30 alongside the Ibrahim 2023 ibrutinib extraction.

### AUC_AMPH (**canonical for per-24-h-interval AUC of amphenmulin in an in-vitro dynamic model**)
- **Description:** Amphenmulin area under the concentration-time curve over the current 24 h dosing interval (AUC_0-24h), measured in the medium of an in-vitro dynamic (dilution) apparatus rather than in plasma, used as the drug-exposure input to antibacterial PK/PD-index models. Wang 2024 samples the reaction chamber of a peristaltic-pump-driven three-necked-flask model by HPLC-MS/MS and derives the per-interval value non-compartmentally in Phoenix; because that source publishes no structural PK model for the apparatus, the PD model consumes AUC_AMPH directly as a piecewise-constant time-varying covariate (one value per 24 h interval) rather than integrating a PK ODE. The model forms the PK/PD index AUC24h/MIC by dividing this covariate by the challenge strain's MIC.
- **Units:** `h*ug/mL` (equivalently `mg*h/L`). Document per-model via `covariateData[[AUC_AMPH]]$units` if a different exposure unit is reported.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- set to 0 for untreated control records so the sigmoid term `(E0 - Emax) * (AUC_AMPH/MIC)^N / (EC50^N + (AUC_AMPH/MIC)^N)` vanishes and the predicted 24 h change in mycoplasma count reduces to the control value `E0`.
- **Source aliases:**
  - `AUC24h` / `AUC 24 h` -- printed names in Wang 2024 Materials and Methods (Integration and modeling of pharmacokinetics/pharmacodynamics, defining the AUC24h/MIC index) and Table 3. Wang 2024 Results (In vitro pharmacokinetics and the effects on M. gallisepticum) reports the per-interval ranges across the six regimens: 0.34-6.19 h*ug/mL over 0-24 h, 0.57-7.69 h*ug/mL over 24-48 h and 0.62-8.23 h*ug/mL over 48-72 h.
- **Example models:** `Wang_2024_amphenmulin_pkpd_index.R` (Wang 2024 inhibitory sigmoid Emax PK/PD integration against *Mycoplasma gallisepticum* strain S6 in an in-vitro dynamic model; `AUC_AMPH / mic` drives the per-interval signed change in log10 CFU/mL).
- **Notes:** Specific scope -- amphenmulin-specific, and tied to a 24 h interval-AUC convention. Member of the `AUC_<DRUG>` family; `AUC_TILM` is the closest structural analogue, being likewise a per-24-h-interval antibacterial exposure handed to a sigmoid PK/PD-index model in place of a PK ODE, and originating from the same laboratory (Ding H, South China Agricultural University). Distinct from the `CONC_<DRUG>_<UNITS>` family, which carries an instantaneous in-vitro concentration driving a mechanism-based kill-rate model: the sibling model `Wang_2024_amphenmulin_killrate.R` needs no such covariate because it integrates the apparatus PK as an ODE state. A future amphenmulin model using the Cmax/MIC metric, which Wang 2024 Table 3 also reports, should register a parallel canonical rather than overload `AUC_AMPH`. Ratified canonically alongside the Wang 2024 amphenmulin extraction.

### AUC_LEN (**canonical for lenvatinib steady-state daily AUC**)
- **Description:** Lenvatinib area under the plasma concentration-time curve over the once-daily 24 h dosing interval at steady state, used as the drug-exposure driver of PK/PD models in which lenvatinib exposure acts on serum biomarker turnover or on tumor growth inhibition. Time-varying step-wise: held constant between assessments and updated when the dose level changes (or when treatment is interrupted or stopped). Majid 2024 derives it inside the upstream population PK run as `AUC = 1000 * F1 * DGRP / CL` -- 1000 x relative bioavailability x current daily dose (mg) divided by the individual apparent clearance (L/h) -- and carries it into the two downstream PK/PD runs as a data column. Two closely related conventions occur in that paper and are pooled onto this single canonical: the value at the time of the biomarker assessment (`LENAUC`, biomarker model) and the average over the interval between two tumor assessments computed from the average dose in that interval (`LEVAVAUC`, tumor model); record which one applies via `covariateData[[AUC_LEN]]$notes`.
- **Units:** `ng*h/mL` (equivalently `ug*h/L`). Must be in the same units as the model's EC50 so the Emax term is dimensionless. Note that the Majid 2024 tumor control stream (Text S3) rescales the column to `ug*h/mL` with `AUC = LEVAVAUC/1000` and divides its carried EC50 values by 1000 to match; the register keeps the untransformed `ng*h/mL` scale so the column is interchangeable across the biomarker and tumor models. Document per-model via `covariateData[[AUC_LEN]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via Emax / sigmoid-Emax forms `Emax * AUC^gamma / (EC50^gamma + AUC^gamma)` (biomarker turnover) and `Emax * AUC / (AUC + EC50 * exp(lambda*t))` (tumor growth inhibition). Set to 0 for placebo subjects and for off-treatment periods; both forms then vanish exactly, leaving the disease-progression / pure-growth dynamics. Reference values observed: the common biomarker EC50 is 930 ng*h/mL and the tumor EC50 is 1420 ng*h/mL (Majid 2024 Tables 2 and 3); records above 10 000 ng*h/mL were excluded from the biomarker fit (Text S2 `$DATA IGNORE`).
- **Source aliases:**
  - `LENAUC` (Majid 2024 Text S2 biomarker control stream; lenvatinib AUC at the time of the biomarker assessment).
  - `LEVAVAUC` (Majid 2024 Text S3 tumor control stream; lenvatinib average AUC based on the average dose between two tumor assessments).
  - `AUC` / `DNAUC` (Majid 2024 Text S1 population PK `$TABLE` output columns; `DNAUC` is the dose-normalised form `AUC * 24 / DGRP` and is NOT this canonical).
- **Example models:** `Majid_2024_lenvatinib_biomarkers.R` (drives the sigmoid Emax effect on Kout for VEGF and FGF-23 and on Kin for Tie-2 and Ang-2, with a common EC50 of 930 ng*h/mL), `Majid_2024_lenvatinib_tumor.R` (drives both the tumor-shrinkage Emax term with EC50 1420 ng*h/mL and the re-integrated Tie-2 / Ang-2 turnover sub-models), `Majid_2024_lenvatinib_tumor_asdeposited.R` (same, but rescales the column internally to ug*h/mL as the deposited control stream does, which is what exposes the stream's unscaled Tie-2 EC50).
- **Notes:** Specific scope because the column meaning is tied to lenvatinib and to the once-daily steady-state 24 h AUC convention. Member of the `AUC_<DRUG>` family (`AUC_CARBO`, `AUC_GEM`, `AUC_GCV`, `AUC_PAZO`, `AUC_RTV`, `AUC_VERUB`, `AUC_ADU`, `AUC_DON`, `AUC_GAN`, `AUC_LEC`, `AUC_IBRU`, `AUC_LCM`, `AUC_CBZ`, `AUC_AMPH`); `AUC_PAZO` is the closest structural analogue, being likewise a per-period mean AUC of a tyrosine-kinase inhibitor driving a tumour-growth / angiogenesis model in place of a PK ODE, and the `AUC_PAZO` entry explicitly directs that a different TKI be given its own sibling canonical rather than reusing that name. A future lenvatinib model using a different exposure metric (Cmax, Ctrough, or a cumulative rather than per-interval AUC) should register a parallel canonical rather than overload `AUC_LEN`. Ratified canonically alongside the Majid 2024 lenvatinib PK/PD extraction.

### PLAQUE_BL (**canonical for per-subject baseline amyloid plaque burden (initial-condition use)**)
- **Description:** Per-subject pre-treatment amyloid plaque burden on the Centiloid scale, used as the initial condition for a plaque state variable in indirect-response amyloid plaque turnover models. Parallel to `HGB_BL` (per-subject baseline haemoglobin as an initial condition for haemoglobin turnover models): `PLAQUE_BL` is a static per-subject baseline that supplies the plaque state's initial value at t = 0 so the trajectory is anchored to the observed baseline before any drug effect starts perturbing it. van Maanen 2025 Supplement Section 'Further details on the exposure-response model' specifies this pattern: 'Individual participant "observed" baseline amyloid plaque burden (before the first dose) was an initial condition at time 0. No additional interindividual variability was included.'
- **Units:** CL (Centiloid; 0 = amyloid-negative, 100 = typical AD plaque burden per Klunk 2015 Centiloid consensus). Document per-model via `covariateData[[PLAQUE_BL]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- subject-level baseline supplied as a covariate column. Reference values observed: 70-104 CL across the van Maanen 2025 modelling cohorts (Table S2); 71.2 CL median in the APECS individual analysis dataset. Amyloid-positivity threshold used in the van Maanen 2025 ADNI external validation was > 24 CL.
- **Source aliases:**
  - `Baseline_Plaque` -- printed name in van Maanen 2025 Eq 5 (initial condition line `Plaque_{t=0} = Baseline_Plaque`).
- **Example models:** `vanMaanen_2025_amyloid.R` (van Maanen 2025 indirect-response plaque turnover model; sets `plaque(0) <- PLAQUE_BL` per subject).
- **Notes:** Specific scope because the initial-condition idiom is paper-defined (van Maanen 2025 specifies per-subject baseline rather than estimating a typical plaque baseline). Promote to `general` if a second paper ratifies the same baseline-plaque-as-initial-condition pattern (e.g., an amyloid-plaque or biomarker turnover model in an independent Alzheimer's disease programme). Ratified canonically on 2026-07-24 alongside the van Maanen 2025 amyloid plaque turnover extraction.

### CLI (**canonical for individual posthoc clearance from an upstream popPK fit**)
- **Description:** Subject-specific empirical-Bayes (posthoc) total plasma clearance from a separately published population PK model that the current PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in PD-only models that derive a per-cycle exposure metric (e.g., AUC = DOSE / CLI) without instantiating a PK ODE.
- **Units:** L/h (document per-model via `covariateData[[CLI]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- appears directly in derived-exposure expressions; not a covariate effect coefficient.
- **Source aliases:**
  - `CL` -- used in the Hansson 2013 sunitinib biomarker / TGI / fatigue / OS PD-model family (DDMODEL00000197 and siblings, including DDMODEL00000222 and the paper-derived `Hansson_2013_sunitinib_OS.R`) and in `Schindler_2016_sunitinib.R` (DDMODEL00000221) as the posthoc CL column from the paper's upstream 2-compartment popPK fit.
- **Example models:** `Hansson_2013a_sunitinib.R` (DDMODEL00000197; typical-value reference 32.819 L/h, drawn from the bundle's simulated dataset for subject 1 -- broadly consistent with Houk et al. 2010 typical sunitinib CL), `Hansson_2013b_sunitinib.R` (DDMODEL00000198; tumor growth inhibition variant; same per-subject `CL` column as Hansson 2013a/c), `Hansson_2013c_sunitinib.R` (DDMODEL00000222; uses a per-record `CL` column with subject-specific values 30-43 L/h in the bundle's three-subject simulated dataset), `Hansson_2013_sunitinib_OS.R` (parametric overall-survival Weibull TTE from Hansson 2013 e84 paper text; same per-subject `CL` covariate fed in alongside DOSE for the per-cycle exposure summary auc = DOSE / CLI), `Schindler_2016_sunitinib.R` (DDMODEL00000221; per-subject post-hoc CL fed in as the `CL` column, vignette uses 50 L/h literature-typical sunitinib CL/F per Houk 2010), `Hansson_2013_sunitinib_myelosuppression.R` (Hansson 2013 e85 paper-direct extraction; uses the same upstream Houk 2009 sunitinib popPK as the source of `CL` / `CLI` per Methods), `Hansson_2013_sunitinib_dbp.R` (Hansson 2013 e85; indirect-response dBP sub-model), `Hansson_2013_sunitinib_hfs.R` (Hansson 2013 e85; hand-foot syndrome Markov + PO sub-model paralleling Hansson_2013c fatigue), `Schindler_2017_sunitinib_fatigue.R` (Schindler 2017 mCTMM fatigue model; consumes the same per-subject upstream sunitinib CL as the Hansson 2013 family), `Schindler_2017_sunitinib_hfs.R` (Schindler 2017 mCTMM HFS model; consumes the same per-subject upstream sunitinib CL as the Hansson 2013 family).
- **Notes:** Specific scope because the values are intrinsically tied to a specific upstream popPK fit (sunitinib in this case; another model adopting CLI would carry its own upstream-PK lineage). Renamed from the source's `CL` column because `cl` is the canonical nlmixr2 PK parameter name (a parameter, not a data column). Each model's `covariateData[[CLI]]$notes` should cite the upstream popPK source (paper or DDMORE ID) and explain how to populate the column for new simulations (typically: simulate the upstream popPK first to obtain individual CL, or set every subject to the typical-value CL for typical-trajectory simulations). Distinct from `DOSE` (current administered dose level) -- the two columns jointly carry a per-cycle drug-exposure summary (`AUC = DOSE / CLI`) for PD-only models that consume posthoc PK from an upstream popPK fit instead of instantiating their own PK ODE.

### CL_INDIV (**canonical for per-subject empirical-Bayes drug clearance**)
- **Description:** Individual point estimate of the modelled drug's clearance, supplied per-subject as a fixed data column. Used in sequential PK->PD models where the PK structure has been fixed from a previously-published population PK analysis and the per-subject empirical-Bayes (POSTHOC) clearances are passed through to drive the PD layer rather than being re-estimated alongside the PD parameters.
- **Units:** L/h (document per-model via `covariateData[[CL_INDIV]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used directly inside `model()` as `CL_INDIV` in place of an estimated `cl <- exp(lcl + etalcl)`.
- **Source aliases:** `CLI` -- used in `Friberg_2002_paclitaxel.R` (NM-TRAN data column for per-subject paclitaxel CL).
- **Example models:** `Friberg_2002_paclitaxel.R`, `Wahlby_2004_paclitaxel_myelosuppression.R` (per-subject paclitaxel CL EBE supplied as a covariate column following the Friberg 2002 convention; the Wahlby 2004 PD model layers BIL and DBIL covariate effects on top of the Friberg-Karlsson chain).
- **Notes:** Specific scope because the value is intrinsically tied to the modelled drug -- there is no shared meaning across drugs. Each model's `covariateData[[CL_INDIV]]$notes` should state which upstream popPK source the EBE values come from (e.g., Henningsson 2001 paclitaxel popPK, fixed in the DDMORE encoding) and whether placebo periods are present. Companion volumes are registered as `VC_INDIV` and `VP_INDIV`.

### CMAX_M1 (**canonical for maximum drug plasma concentration during month 1**)
- **Description:** Empirical-Bayes maximum plasma concentration (Cmax) reached during the first month (or the first cycle) of dosing for the modelled drug, used as a per-subject early-exposure covariate in PD / safety models that condition later-time outcomes on early exposure. Continuous, time-invariant per subject (set once from the month-1 / cycle-1 PK simulation).
- **Units:** ng/mL (document per-model via `covariateData[[CMAX_M1]]$units` if a different concentration unit is reported).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as an additive logit shift `(CMAX_M1 - CMAX_M1_REF) * theta` (Girard 2012) or as a power scaling depending on the source. Reference value observed: 0 ng/mL (Girard 2012 sets `MED17 = 0` as the centering reference).
- **Source aliases:**
  - `CMAXM1` -- used in `Girard_2012_pimasertib.R`.
- **Example models:** `Girard_2012_pimasertib.R` (additive logit shift on the cumulative-logit AE-score model: `theta_cmaxm1 * CMAXM1`).
- **Notes:** Specific scope because the value depends on the upstream drug-specific population-PK model used to derive the empirical-Bayes Cmax. Sibling of the existing `CAV` (average dosing-interval concentration); both are derived exposure metrics fed into downstream PD / safety models. Document the upstream PK model in `covariateData[[CMAX_M1]]$notes` for any future user.

### VC_INDIV (**canonical for per-subject empirical-Bayes central volume of distribution**)
- **Description:** Individual point estimate of the modelled drug's central volume of distribution, supplied per-subject as a fixed data column. Companion to `CL_INDIV` in sequential PK->PD encodings.
- **Units:** L (document per-model via `covariateData[[VC_INDIV]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used directly inside `model()` as `VC_INDIV` in place of an estimated `vc <- exp(lvc + etalvc)`.
- **Source aliases:** `V1I` -- used in `Friberg_2002_paclitaxel.R` (NM-TRAN data column for per-subject paclitaxel V1).
- **Example models:** `Friberg_2002_paclitaxel.R`, `Wahlby_2004_paclitaxel_myelosuppression.R` (per-subject paclitaxel V1 EBE following the Friberg 2002 convention).
- **Notes:** See `CL_INDIV` notes for the broader convention.

### VP_INDIV (**canonical for per-subject empirical-Bayes peripheral volume of distribution**)
- **Description:** Individual point estimate of the modelled drug's first peripheral volume of distribution, supplied per-subject as a fixed data column. Companion to `CL_INDIV` and `VC_INDIV` in sequential PK->PD encodings.
- **Units:** L (document per-model via `covariateData[[VP_INDIV]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used directly inside `model()` as `VP_INDIV` in place of an estimated `vp <- exp(lvp + etalvp)`.
- **Source aliases:** `V2I` -- used in `Friberg_2002_paclitaxel.R` (NM-TRAN data column for per-subject paclitaxel V2).
- **Example models:** `Friberg_2002_paclitaxel.R`, `Wahlby_2004_paclitaxel_myelosuppression.R` (per-subject paclitaxel V2 EBE following the Friberg 2002 convention).
- **Notes:** See `CL_INDIV` notes for the broader convention. For models requiring a second peripheral compartment, register `VP2_INDIV` (and add a follow-on entry to this register) when a second model legitimately needs it.

### BAS_SVEGFR3 (**canonical for individual posthoc baseline soluble VEGFR-3 concentration from an upstream PD fit**)
- **Description:** Subject-specific empirical-Bayes (posthoc) baseline plasma sVEGFR-3 (soluble vascular endothelial growth factor receptor 3) concentration from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in fatigue / adverse-event PD models that consume the upstream biomarker dynamics as data covariates without instantiating the biomarker ODE.
- **Units:** pg/mL (document per-model via `covariateData[[BAS_SVEGFR3]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- appears directly inside `model()` as the initial condition `svegfr3(0) <- BAS_SVEGFR3` and inside the relative-change driver `bm = (svegfr3 - BAS_SVEGFR3) / BAS_SVEGFR3`.
- **Source aliases:**
  - `BAS3` -- used in `Hansson_2013b_sunitinib.R` (DDMODEL00000198), `Hansson_2013c_sunitinib.R` (DDMODEL00000222), and `Hansson_2013_sunitinib_OS.R` as the posthoc sVEGFR-3 baseline column from the paper's upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
- **Example models:** `Hansson_2013b_sunitinib.R` (DDMODEL00000198; tumor growth inhibition with sVEGFR-3 driven shrinkage), `Hansson_2013c_sunitinib.R` (DDMODEL00000222; bundle's three-subject simulated dataset reports BAS_SVEGFR3 values 42554-57365 pg/mL), `Hansson_2013_sunitinib_OS.R` (parametric overall-survival Weibull TTE from Hansson 2013 e84 paper text; sVEGFR-3 dynamics simulated inline using the upstream biomarker-PD per-subject parameters), `Hansson_2013_sunitinib_myelosuppression.R` (Hansson 2013 e85 paper-direct extraction; simulates sVEGFR-3 turnover in-model to drive the Emax effect on neutrophil proliferation), `Hansson_2013_sunitinib_hfs.R` (Hansson 2013 e85; hand-foot syndrome Markov + PO sub-model driven by sVEGFR-3 relative change through an effect compartment), `Schindler_2017_sunitinib_fatigue.R` (Schindler 2017 mCTMM fatigue model; sVEGFR-3 baseline consumed as the upstream Hansson 2013a per-subject BAS_SVEGFR3 posthoc), `Schindler_2017_sunitinib_hfs.R` (Schindler 2017 mCTMM HFS model; sVEGFR-3 baseline consumed as the upstream Hansson 2013a per-subject BAS_SVEGFR3 posthoc).
- **Notes:** Specific scope because the value is intrinsically tied to a specific upstream biomarker model (sVEGFR-3 indirect response under sunitinib in this case). The downstream fatigue model only consumes individual posthoc baseline / MRT / EC50 of the upstream biomarker; it does not re-fit them. Each model's `covariateData[[BAS_SVEGFR3]]$notes` should cite the upstream biomarker-PD source (paper or DDMORE ID) and explain how to populate the column for new simulations (typically: simulate from the upstream biomarker model to obtain individual posthoc baselines, or set every subject to the typical-value baseline for typical-trajectory simulations).

### MRT_SVEGFR3 (**canonical for individual posthoc mean residence time of soluble VEGFR-3 from an upstream PD fit**)
- **Description:** Subject-specific empirical-Bayes (posthoc) mean residence time of plasma sVEGFR-3 from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in fatigue / adverse-event PD models that consume the upstream biomarker dynamics as data covariates without instantiating the biomarker ODE; appears as `kout3 = 1 / MRT_SVEGFR3` inside `model()`.
- **Units:** h (hours) -- document per-model via `covariateData[[MRT_SVEGFR3]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- appears directly in derived rate-constant expressions; not a covariate effect coefficient.
- **Source aliases:**
  - `MRT3` -- used in `Hansson_2013b_sunitinib.R` (DDMODEL00000198), `Hansson_2013c_sunitinib.R` (DDMODEL00000222), and `Hansson_2013_sunitinib_OS.R` as the posthoc sVEGFR-3 MRT column from the paper's upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
- **Example models:** `Hansson_2013b_sunitinib.R` (DDMODEL00000198), `Hansson_2013c_sunitinib.R` (DDMODEL00000222; bundle's three-subject simulated dataset reports MRT_SVEGFR3 values 313-408 h, broadly consistent with the Hansson 2013a typical sVEGFR-3 MRT of 401 h), `Hansson_2013_sunitinib_OS.R` (parametric overall-survival Weibull TTE; sVEGFR-3 dynamics simulated inline using the upstream biomarker-PD per-subject MRT), `Hansson_2013_sunitinib_myelosuppression.R` (Hansson 2013 e85 paper-direct extraction), `Hansson_2013_sunitinib_hfs.R` (Hansson 2013 e85 paper-direct extraction), `Schindler_2017_sunitinib_fatigue.R` (Schindler 2017 mCTMM fatigue; sVEGFR-3 MRT consumed as the upstream Hansson 2013a per-subject MRT_SVEGFR3 posthoc), `Schindler_2017_sunitinib_hfs.R` (Schindler 2017 mCTMM HFS; sVEGFR-3 MRT consumed as the upstream Hansson 2013a per-subject MRT_SVEGFR3 posthoc).
- **Notes:** Specific scope; same upstream-biomarker dependency rationale as `BAS_SVEGFR3`. The downstream fatigue model consumes the upstream MRT directly without re-fitting it.

### EC50_SVEGFR3 (**canonical for individual posthoc drug-effect EC50 on soluble VEGFR-3 from an upstream PD fit**)
- **Description:** Subject-specific empirical-Bayes (posthoc) half-maximum-effect concentration of the modelled drug on the production of sVEGFR-3 (or, depending on the upstream model parameterization, on the analogous Imax inhibition pathway) from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in fatigue / adverse-event PD models that consume the upstream biomarker dynamics as data covariates without instantiating the biomarker ODE.
- **Units:** mg*h/L (when the per-cycle drug-exposure summary is `auc = DOSE / CLI` in mg*h/L, EC50_SVEGFR3 carries the same units; document per-model via `covariateData[[EC50_SVEGFR3]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- appears directly inside `model()` in the simple-Imax drug-effect term `eff3 = auc / (EC50_SVEGFR3 + auc)`.
- **Source aliases:**
  - `EC53` -- used in `Hansson_2013b_sunitinib.R` (DDMODEL00000198), `Hansson_2013c_sunitinib.R` (DDMODEL00000222), and `Hansson_2013_sunitinib_OS.R` as the posthoc sVEGFR-3 EC50 column from the paper's upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
- **Example models:** `Hansson_2013b_sunitinib.R` (DDMODEL00000198), `Hansson_2013c_sunitinib.R` (DDMODEL00000222; bundle's three-subject simulated dataset reports EC50_SVEGFR3 values 1.0-2.8 mg*h/L, consistent with the Hansson 2013a typical sVEGFR-3 IC50 typical value of 1.0 mg*h/L), `Hansson_2013_sunitinib_OS.R` (parametric overall-survival Weibull TTE; sVEGFR-3 dynamics simulated inline using the upstream biomarker-PD per-subject EC50), `Hansson_2013_sunitinib_myelosuppression.R` (Hansson 2013 e85 paper-direct extraction), `Hansson_2013_sunitinib_hfs.R` (Hansson 2013 e85 paper-direct extraction), `Schindler_2017_sunitinib_fatigue.R` (Schindler 2017 mCTMM fatigue; sVEGFR-3 EC50 consumed as the upstream Hansson 2013a per-subject EC50_SVEGFR3 posthoc), `Schindler_2017_sunitinib_hfs.R` (Schindler 2017 mCTMM HFS; sVEGFR-3 EC50 consumed as the upstream Hansson 2013a per-subject EC50_SVEGFR3 posthoc).
- **Notes:** Specific scope; same upstream-biomarker dependency rationale as `BAS_SVEGFR3`. The downstream fatigue model consumes the upstream EC50 directly without re-fitting it.

### BAS_SKIT (**canonical for individual posthoc baseline soluble KIT concentration from an upstream PD fit**)
- **Description:** Subject-specific empirical-Bayes (posthoc) baseline plasma sKIT (soluble stem cell factor receptor) concentration from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in tumor-growth-inhibition / fatigue / adverse-event PD models that consume the upstream sKIT dynamics as data covariates without instantiating the biomarker ODE for sKIT in isolation.
- **Units:** pg/mL (document per-model via `covariateData[[BAS_SKIT]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- appears directly inside `model()` as the initial condition for treated and placebo sKIT compartments and inside the relative-change driver `(skit_pla - skit_drug) / skit_pla` (or the analogous BAS_SKIT-denominated form when only one sKIT compartment is simulated).
- **Source aliases:**
  - `SBAS` -- used in `Hansson_2013b_sunitinib.R` (DDMODEL00000198) as the posthoc sKIT baseline column from the paper's upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
- **Example models:** `Hansson_2013b_sunitinib.R` (DDMODEL00000198; the Hansson 2013 e84 paper Table 2 reports a typical sKIT baseline of 39200 pg/mL with ~50% CV, matching `Hansson_2013a_sunitinib`'s typical value).
- **Notes:** Specific scope because the value is intrinsically tied to a specific upstream biomarker model (sKIT indirect response under sunitinib in this case). The downstream tumor-growth-inhibition model only consumes individual posthoc baseline / MRT / EC50 / DP-slope of the upstream biomarker; it does not re-fit them. Each model's `covariateData[[BAS_SKIT]]$notes` should cite the upstream biomarker-PD source (paper or DDMORE ID) and explain how to populate the column for new simulations (typically: simulate from the upstream biomarker model to obtain individual posthoc baselines, or set every subject to the typical-value baseline for typical-trajectory simulations). Sister covariates: `MRT_SKIT`, `EC50_SKIT`, `SLOPE_SKIT` (companions for the same upstream biomarker fit).

### MRT_SKIT (**canonical for individual posthoc mean residence time of soluble KIT from an upstream PD fit**)
- **Description:** Subject-specific empirical-Bayes (posthoc) mean residence time of plasma sKIT from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in tumor-growth-inhibition / fatigue / adverse-event PD models that consume the upstream sKIT dynamics as data covariates without instantiating the biomarker ODE for sKIT in isolation; appears as `kout_skit = 1 / MRT_SKIT` inside `model()`.
- **Units:** h (hours) -- document per-model via `covariateData[[MRT_SKIT]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- appears directly in derived rate-constant expressions; not a covariate effect coefficient.
- **Source aliases:**
  - `SMRT` -- used in `Hansson_2013b_sunitinib.R` (DDMODEL00000198) as the posthoc sKIT MRT column from the paper's upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
- **Example models:** `Hansson_2013b_sunitinib.R` (DDMODEL00000198; the Hansson 2013 e84 paper Table 2 reports a typical sKIT MRT of 101 days = 2424 h, matching `Hansson_2013a_sunitinib`'s typical value of 2430 h).
- **Notes:** Specific scope; same upstream-biomarker dependency rationale as `BAS_SKIT`. The downstream tumor-growth-inhibition model consumes the upstream MRT directly without re-fitting it.

### EC50_SKIT (**canonical for individual posthoc drug-effect EC50 on soluble KIT from an upstream PD fit**)
- **Description:** Subject-specific empirical-Bayes (posthoc) half-maximum-effect concentration of the modelled drug on the production of sKIT (or, depending on the upstream model parameterization, on the analogous Imax inhibition pathway) from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in tumor-growth-inhibition / fatigue / adverse-event PD models that consume the upstream sKIT dynamics as data covariates without instantiating the biomarker ODE for sKIT in isolation.
- **Units:** mg*h/L (when the per-cycle drug-exposure summary is `auc = DOSE / CLI` in mg*h/L, EC50_SKIT carries the same units; document per-model via `covariateData[[EC50_SKIT]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- appears directly inside `model()` in the simple-Imax drug-effect term `eff_skit = auc / (EC50_SKIT + auc)`.
- **Source aliases:**
  - `SEC5` -- used in `Hansson_2013b_sunitinib.R` (DDMODEL00000198) as the posthoc sKIT EC50 column from the paper's upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
- **Example models:** `Hansson_2013b_sunitinib.R` (DDMODEL00000198; the Hansson 2013 e84 paper Table 2 reports a typical (common across all four biomarkers) IC50 of 1.0 mg*h/L, matching `Hansson_2013a_sunitinib`'s shared typical value).
- **Notes:** Specific scope; same upstream-biomarker dependency rationale as `BAS_SKIT`. The downstream tumor-growth-inhibition model consumes the upstream EC50 directly without re-fitting it.

### SLOPE_SKIT (**canonical for individual posthoc linear disease-progression slope on soluble KIT from an upstream PD fit**)
- **Description:** Subject-specific empirical-Bayes (posthoc) linear disease-progression slope on the placebo / untreated sKIT compartment from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in tumor-growth-inhibition models that need to simulate the placebo-arm sKIT trajectory as a comparator for the drug-arm sKIT compartment; appears as `dps_skit = BAS_SKIT * (1 + SLOPE_SKIT * t)` and `kin_skit = dps_skit * kout_skit` inside `model()`.
- **Units:** 1/h (document per-model via `covariateData[[SLOPE_SKIT]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- appears directly in the placebo-arm Kin expression for sKIT; not a covariate effect coefficient on a structural rate.
- **Source aliases:**
  - `SLO` -- used in `Hansson_2013b_sunitinib.R` (DDMODEL00000198) as the posthoc sKIT linear disease-progression slope column from the paper's upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
- **Example models:** `Hansson_2013b_sunitinib.R` (DDMODEL00000198; the Hansson 2013 e84 paper Table 2 reports a typical disease-progression slope of 0.0261/month shared between VEGF and sKIT, which equals approximately 3.5e-5/h, matching `Hansson_2013a_sunitinib`'s typical value).
- **Notes:** Specific scope; same upstream-biomarker dependency rationale as `BAS_SKIT`. Sign convention follows the upstream biomarker fit -- positive slope means the placebo / natural-history sKIT trajectory drifts upward over time (capturing disease progression).

### CSS_RBV (**canonical for individual posthoc ribavirin steady-state plasma concentration from an upstream PK fit**)
- **Description:** Subject-specific empirical-Bayes (posthoc) modal estimate of the ribavirin (RBV) steady-state plasma trough concentration from a separately fitted population PK model (e.g., the Laouenan 2015 upstream RBV popPK fit) that the current downstream PD model treats as a fixed input. Used together with `K_RBV` inside `model()` to reconstruct the individual ribavirin concentration time-course analytically as `riba(t) = CSS_RBV * (1 - exp(-K_RBV * t))`.
- **Units:** ng/mL (document per-model via `covariateData[[CSS_RBV]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- appears directly inside `model()` in the analytical RBV-concentration expression `riba = CSS_RBV * (1 - exp(-K_RBV * t))` that drives the inhibition term `riba / (riba + ec50)`.
- **Source aliases:**
  - `css_mode` (modal posterior estimate of individual ribavirin Css from the upstream popPK fit, in ng/mL) -- used in `Laouenan_2015_ribavirin.R` (DDMODEL00000285). Rename `css_mode` -> `CSS_RBV` before passing the dataset to `rxSolve`.
- **Example models:** `Laouenan_2015_ribavirin.R` (DDMODEL00000285; the bundle's `Simulated_Laouenant_2015_CPTPSP_hb_RBV.txt` carries `css_mode` values 2,400-4,000 ng/mL in the 15-subject ANRS-CO20-CUPIC cohort).
- **Notes:** Specific scope because the value is intrinsically tied to a specific upstream popPK fit (ribavirin in HCV-cirrhotic patients on triple therapy in this case) and to the analytical `Css*(1-exp(-k*t))` parameterization; another drug or another popPK parameterization would carry its own canonical. Each model's `covariateData[[CSS_RBV]]$notes` should cite the upstream popPK source (paper or DDMORE ID) and explain how to populate the column for new simulations (typically: simulate the upstream popPK first to obtain individual Css and approach-rate, or set every subject to the typical Css for typical-trajectory simulations). Companion column: `K_RBV`. Distinct from `CAV` (dosing-interval-averaged concentration used in Emax / EC50 PD models with a single per-period exposure number) -- `CSS_RBV` carries the asymptotic steady-state value paired with the approach-rate constant, supporting the full time-course reconstruction.

### K_RBV (**canonical for individual posthoc ribavirin approach-to-steady-state rate constant from an upstream PK fit**)
- **Description:** Subject-specific empirical-Bayes (posthoc) modal estimate of the first-order rate constant governing the exponential approach of ribavirin plasma trough concentrations to steady state, from the same upstream popPK fit that supplies `CSS_RBV`. Used together with `CSS_RBV` inside `model()` to reconstruct the individual ribavirin concentration time-course analytically as `riba(t) = CSS_RBV * (1 - exp(-K_RBV * t))`.
- **Units:** 1/day (document per-model via `covariateData[[K_RBV]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- appears directly inside the analytical RBV-concentration expression alongside `CSS_RBV`.
- **Source aliases:**
  - `k_mode` (modal posterior estimate of individual ribavirin approach-to-Css rate constant from the upstream popPK fit, in 1/day) -- used in `Laouenan_2015_ribavirin.R` (DDMODEL00000285). Rename `k_mode` -> `K_RBV` before passing the dataset to `rxSolve`.
- **Example models:** `Laouenan_2015_ribavirin.R` (DDMODEL00000285; the bundle's `Simulated_Laouenant_2015_CPTPSP_hb_RBV.txt` carries `k_mode` values 0.013-0.47 day^-1 across the 15-subject cohort, corresponding to approach-to-Css half-lives of 1.5-55 days).
- **Notes:** Specific scope; same upstream-PK-dependency rationale as `CSS_RBV`. Distinct from any structural `kel` PK parameter -- `K_RBV` is an *apparent* approach-to-Css rate from a lumped exponential parameterization of the trough time-course, not the elimination-rate constant of a one-compartment IV model (the lumped form absorbs absorption, distribution, and elimination into a single first-order rate). Companion column: `CSS_RBV`.

### CSS_DFO (**canonical for deferoxamine average steady-state plasma concentration from an upstream PK fit**)
- **Description:** Subject-specific (or time-varying) average steady-state plasma concentration of deferoxamine (DFO; iron chelator). In Bellanti 2015 it is generated externally by a literature-derived two-compartment, zero-order-absorption (8-h SC infusion), first-order-elimination PK model (CL/F 19.3 L/h, Q/F 17.6 L/h, V/F 77.4 L, Vp/F 238 L at adult 70-kg reference; allometric exponents 0.75 on clearances and 1.00 on volumes) and fed into the downstream ferritin disease model as the drug-exposure driver in the linear concentration-effect term DFO = SLP * CSS_DFO on the ferritin degradation rate. Treated as time-varying so that drug holidays / partial compliance are captured by setting `CSS_DFO = 0` (or a reduced value) over the affected interval -- the paper's compliance-corrected effective concentration TCss_AV = SCss_AV * (1 - CMPL) collapses to a CSS_DFO scaling in this implementation.
- **Units:** ug/mL (equivalently mg/L; the reporting convention used in Bellanti 2015 Fig 1 and Fig 3). Document per-model via `covariateData[[CSS_DFO]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters directly into the drug-effect expression `DFO = SLP * CSS_DFO`, where SLP has units of `1/(ug/mL)`. Set to 0 to disable the chelation effect (drug holidays, untreated baseline disease-progression simulations). Reference values observed: ~3.5 ug/mL for 30 mg/kg/day, ~5.5 ug/mL for 45 mg/kg/day, ~7.5 ug/mL for 60 mg/kg/day at 45 kg body weight on the 5-days-per-week 8-h SC infusion schedule (Bellanti 2015 Fig 3).
- **Source aliases:**
  - `SCssAV` (Bellanti 2015 paper symbol; "simulated steady-state concentration, average") -- the per-subject population-PRED value before compliance correction.
  - `TCssAV` (Bellanti 2015 paper symbol; "true steady-state concentration, average") -- the post-compliance value `SCssAV * (1 - CMPL)`; collapse into a single time-varying `CSS_DFO` column for nlmixr2lib by precomputing the (1 - CMPL) reduction in the input data.
- **Example models:** `Bellanti_2015_deferoxamine.R` (ug/mL; time-varying input on the linear `DFO = SLP * CSS_DFO` ferritin-degradation effect; 27 transfusion-dependent beta-thalassaemia major paediatric / adolescent patients on 20-60 mg/kg/day DFO 5 days per week).
- **Notes:** Specific scope because the column is intrinsically tied to deferoxamine PK and to the linear `slope * CssAV` effect form used in this paper. Sibling drug-specific Css canonical: `CSS_RBV` (ribavirin). The companion `K_RBV` approach-to-Css rate constant is **not** needed here because Bellanti 2015 treats CssAV as a population-typical steady-state value rather than reconstructing the rise-to-Css trajectory. For new simulations: a user supplies `CSS_DFO` directly (either a constant typical-Css value, or a time-varying column that switches to 0 during drug holidays); the vignette walks through computing CssAV analytically from a desired dose schedule via `CssAV = (dose_per_week * F) / (CL_i * 168 h)` with `CL_i = 19.3 * (WT/70)^0.75 L/h` for allometric scaling to paediatric / adolescent body weights. Ratified canonically on 2026-05-22 alongside the Bellanti 2015 extraction.

### CP_MGL (**canonical for instantaneous drug plasma concentration as a time-varying PD driver**)
- **Description:** Instantaneous (per-event-record) plasma concentration of the modeled drug, supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only myelosuppression / toxicity / response models that consume an upstream PK trajectory as input -- typically because the source analysis was done as a sequential PK-then-PD fit, with the PK model fixed from a previously published popPK analysis (e.g., a Bruno-style docetaxel popPK feeding a Friberg / Kloft myelosuppression PD model).
- **Units:** mg/L (= ug/mL; the two labels are numerically equivalent and the canonical reporting convention for cytotoxic-chemotherapy concentrations in Kloft 2006 / Friberg 2002 family analyses). Document per-model via `covariateData[[CP_MGL]]$units` if a different unit is used.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- typically enters as a linear term in the drug-effect expression (e.g., `drug = SL * CP_MGL`, where `SL` has units of `1/(mg/L)`). Set to 0 for placebo periods or any time outside the drug-exposure window. Reference values observed: docetaxel typical Cmax ~3 mg/L after a 100 mg/m^2 1-hour IV infusion (Kloft 2006 / Netterberg 2017 simulated dataset).
- **Source aliases:**
  - `CP` (Kloft 2006 / Netterberg 2017 NM-TRAN $INPUT convention for "predicted drug concentration"; values in mg/L) -- used in `Netterberg_2017_docetaxel.R`.
- **Example models:** `Netterberg_2017_docetaxel.R` (linear drug effect on the proliferation rate of the Friberg myelosuppression chain: `(1 - SL * CP_MGL)` with `SL = 19.27 (mg/L)^-1` after Kloft 2006's `THETA(3)/808*1000` MW-808 conversion; CP_MGL supplied per event row from an upstream docetaxel popPK simulation), `Puisset_2007_docetaxel.R` (linear drug effect `Slope * CP_MGL` on the proliferating compartment of the Friberg myelosuppression chain; the upstream docetaxel PK in Puisset 2007 was Baille 1997 / Bruno 1996 individual Bayesian posthoc profiles supplied externally and not encoded in the model file).
- **Notes:** Specific scope because the covariate's mechanistic meaning is bound to the modeled drug and the source paper's chosen PK input (e.g., a Bruno 1996/1998 docetaxel popPK trajectory for Netterberg 2017). Distinct from `CAV` (dosing-interval-averaged exposure used in Emax / EC50 PD models) -- `CP_MGL` is the instantaneous concentration, sampled at every PD event time. When a future paper requires the same time-varying-PK-as-PD-input pattern for a different drug, register a drug-specific canonical (e.g., `CP_PACL_MGL` for paclitaxel) rather than overloading this name; `CP_MGL` retains the implicit "drug = the modeled drug under the PD analysis" semantics. When a paper supplies the time-varying PK as separate per-subject empirical-Bayes PK parameters (e.g., `CL_INDIV`, `VC_INDIV`, `VP_INDIV`), use those columns in a coupled PK-PD ODE model (see `Friberg_2002_paclitaxel.R`) rather than reducing to `CP_MGL`. The choice between PK-as-covariate (this canonical) and PK-as-EBE-parameters depends on whether the source paper's NM-TRAN dataset shipped Cp directly or shipped the upstream individual PK parameters.

### CP_OXA_MGL (**canonical for instantaneous oxaliplatin plasma concentration as a time-varying PD driver**)
- **Description:** Instantaneous plasma concentration of oxaliplatin supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only platelet-dynamics / myelosuppression models that consume an upstream oxaliplatin popPK trajectory as input.
- **Units:** mg/L (= ug/mL; oxaliplatin MW = 397.29 g/mol, so 1 mg/L = 2.52 umol/L).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a power-function term in the platelet-dynamics drug-effect expression `E_drug = alpha * CP_OXA_MGL^beta`. Set to 0 outside the drug-exposure window (e.g., pre-HIO or CRS-alone patients). Reference values observed: hyperthermic intraperitoneal oxaliplatin (HIO) administered at 200 mg/L initial peritoneal concentration for 30 min gives typical plasma Cmax ~1 mg/L with half-life ~14 h (Perez-Ruixo 2013 Cancer Chemother Pharmacol 71:693-704).
- **Source aliases:**
  - `Cp` (Perez-Ruixo 2015 The AAPS Journal Methods and Eq. 8 symbol for the oxaliplatin plasma concentration driving E_drug) -- used in `PerezRuixo_2015_oxaliplatin_platelet_dynamics.R`.
- **Example models:** `PerezRuixo_2015_oxaliplatin_platelet_dynamics.R` (drug effect `E_drug = alpha * CP_OXA_MGL^beta` on the megakaryocyte-progenitor proliferation rate ktr, with alpha = 0.881 L/mg and beta = 2.63 per Perez-Ruixo 2015 Table I; CP_OXA_MGL supplied per event row from an upstream oxaliplatin popPK simulation not packaged in nlmixr2lib at extraction time -- the vignette shows a placeholder monoexponential Cp trajectory tuned to typical HIO Cmax ~1 mg/L and half-life ~14 h).
- **Notes:** Specific scope; oxaliplatin-specific. The drug-specific naming follows the `CP_<drug>_<units>` precedent from `CP_MGL` (general docetaxel-family form), `CP_OXY_NGML`, `CP_FBX_NGML`, `CP_LSN_NGML`, `CP_MORPH_NGML`, `CP_RIF_UM`, `CP_GDC_UM`, `CP_REM_NGML`, and `CP_GLASDEGIB_NGML`. The paper uses `Cp` as the paper-symbol; renamed to `CP_OXA_MGL` in the register per the naming pattern. The natural input source is a coupled oxaliplatin popPK simulation (Perez-Ruixo 2013 Cancer Chemother Pharmacol; not on disk at extraction time); users can either digitise Cp trajectories from Figure 5 of Perez-Ruixo 2015 or simulate a placeholder monoexponential decline with typical HIO parameters. Ratified canonically on 2026-07-10 alongside the Perez-Ruixo 2015 platelet-dynamics extraction.

### CP_OXY_NGML (**canonical for instantaneous oxypurinol plasma concentration as a time-varying PD driver**)
- **Description:** Instantaneous plasma concentration of oxypurinol (the active metabolite of allopurinol; xanthine oxidase inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in semi-mechanistic uric-acid disposition models that take XOI exposure as input to the production-inhibition equation.
- **Units:** ng/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a Hill term in the production-inhibition expression `1 - Rmax * CP_OXY_NGML / (CP_OXY_NGML + p50)`. Set to 0 outside the drug-exposure window or for non-XOI scenarios. Reference values observed: mean daily concentration on 300 mg/day allopurinol is approximately 10,000 ng/mL (Aksenov 2018, Eq. 13).
- **Source aliases:**
  - `[P]_PIN` (oxypurinol) -- the symbol used in Aksenov 2018 Eq. 9 for the production-inhibitor concentration when the inhibitor is oxypurinol.
- **Example models:** `Aksenov_2018_uricAcid.R` (Hill-type production inhibition with `rmax_oxy = 0.84` and `p50_oxy = 14000 ng/mL` per Aksenov 2018 Table 1).
- **Notes:** Specific scope because the canonical name is bound to oxypurinol; allopurinol's PK is conventionally summarized via the active metabolite oxypurinol (Day et al. 2007). Distinct from `CP_FBX_NGML` (febuxostat) and `CP_LSN_NGML` (lesinurad). When the source paper supplies an upstream popPK for oxypurinol (e.g., Wright et al. 2013, Anzai & Endou 2012), the user simulates that PK to populate this column; otherwise a steady-state value can be used. Ratified canonically on 2026-05-08 alongside the Aksenov 2018 extraction.

### CP_FBX_NGML (**canonical for instantaneous febuxostat plasma concentration as a time-varying PD driver**)
- **Description:** Instantaneous plasma concentration of febuxostat (xanthine oxidase inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in semi-mechanistic uric-acid disposition models that take XOI exposure as input to the production-inhibition equation.
- **Units:** ng/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a Hill term in the production-inhibition expression `1 - Rmax * CP_FBX_NGML / (CP_FBX_NGML + p50)`. Set to 0 outside the drug-exposure window or for non-XOI scenarios. Reference values observed: mean daily concentration on 40 mg/day febuxostat is approximately 1000-2000 ng/mL (Aksenov 2018, Bhattaram & Gobburu 2017 regulatory review).
- **Source aliases:**
  - `[P]_PIN` (febuxostat) -- the symbol used in Aksenov 2018 Eq. 9 for the production-inhibitor concentration when the inhibitor is febuxostat.
- **Example models:** `Aksenov_2018_uricAcid.R` (Hill-type production inhibition with `rmax_fbx = 1` (fixed) and `p50_fbx = 120 ng/mL` for hyperuricemic subjects (or 87 ng/mL for normouricemic subjects) per Aksenov 2018 Table 1).
- **Notes:** Specific scope; febuxostat-specific. The `p50` parameter differs between hyperuricemic and normouricemic populations in Aksenov 2018; `Rmax` is fixed at 1 per Bhattaram & Gobburu 2017. Distinct from `CP_OXY_NGML` (oxypurinol) and `CP_LSN_NGML` (lesinurad). Ratified canonically on 2026-05-08 alongside the Aksenov 2018 extraction.

### CP_LSN_NGML (**canonical for instantaneous lesinurad plasma concentration as a time-varying PD driver**)
- **Description:** Instantaneous plasma concentration of lesinurad (uricosuric URAT1 inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in semi-mechanistic uric-acid disposition models that take uricosuric exposure as input to the fractional-excretion-increase equation.
- **Units:** ng/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a Hill term in the fractional-excretion expression `FE = FE0 + Fmax * CP_LSN_NGML / (CP_LSN_NGML + p50)`. Set to 0 outside the drug-exposure window. Reference values observed: peak plasma concentration after single dose 200 mg lesinurad is approximately 6,000-9,000 ng/mL (Fleischmann et al. 2014; Shen et al. 2015).
- **Source aliases:**
  - `[P]_RIN` (lesinurad) -- the symbol used in Aksenov 2018 Eq. 10 for the reabsorption-inhibitor concentration when the inhibitor is lesinurad.
- **Example models:** `Aksenov_2018_uricAcid.R` (Hill-type increase in fractional excretion with `fmax_lsn = 0.56` (fixed) and `p50_lsn = 23000 ng/mL` for hyperuricemic subjects (or 11000 ng/mL for normouricemic subjects) per Aksenov 2018 Table 1).
- **Notes:** Specific scope; lesinurad-specific. The `p50` parameter differs between hyperuricemic and normouricemic populations in Aksenov 2018; `Fmax` was fixed during estimation. Distinct from `CP_OXY_NGML` (oxypurinol) and `CP_FBX_NGML` (febuxostat). Ratified canonically on 2026-05-08 alongside the Aksenov 2018 extraction.

### CP_MORPH_NGML (**canonical for instantaneous morphine plasma concentration as a time-varying PD driver**)
- **Description:** Instantaneous plasma concentration of morphine supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only IRT / latent-pain models that take morphine exposure as an external input to a concentration-effect equation.
- **Units:** ng/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters linearly into the latent-pain equation `pain = pain_state - e_morph_pain * CP_MORPH_NGML + e_time_pain * time` (Valitalo 2017). Reference values observed: most individual predicted concentrations in Valitalo 2017 were within 0-60 ng/mL (Figure 2a); the IRT linear morphine-effect slope is 0.0091 (ng/mL)^-1, so a 20 ng/mL morphine exposure reduces the latent pain by ~0.18 latent-variable units.
- **Source aliases:**
  - `CP` (Valitalo 2017 NM-TRAN $INPUT convention for "morphine plasma concentration"; values in ng/mL) -- used in `Valitalo_2017_morphine.R`.
- **Example models:** `Valitalo_2017_morphine.R` (linear morphine concentration-effect on the IRT latent pain variable; CP_MORPH_NGML supplied per event row from an upstream morphine popPK simulation, typically `Knibbe_2009_morphine.R`).
- **Notes:** Specific scope; morphine-specific. The drug-specific naming follows the existing `CP_OXY_NGML` / `CP_FBX_NGML` / `CP_LSN_NGML` precedent established with Aksenov 2018. Distinct from the broader `CP_MGL` (mg/L PD-driver convention used in Netterberg 2017 docetaxel myelosuppression and similar) because the IRT PD models in this family use ng/mL natively. When a future morphine PD analysis uses mg/L, the conversion is `CP_MORPH_NGML = CP_MORPH_MGL * 1000`. Ratified canonically alongside the Valitalo 2017 morphine extraction (DDMODEL00000247).

### CP_BPN_NGML (**canonical for instantaneous buprenorphine plasma concentration as a time-varying PD driver**)
- **Description:** Instantaneous plasma concentration of buprenorphine (BPN) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only opioid-blockade models that take buprenorphine exposure as an external input to an Imax concentration-effect equation for abuse-liability and withdrawal endpoints.
- **Units:** ng/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as the Hill term of an Imax inhibition expression, e.g. `1 - imax * CP_BPN_NGML^hill / (ic50^hill + CP_BPN_NGML^hill)` (Walsh 2024). Reference values observed: the observed BPN plasma concentration range across the Walsh 2024 phase 2 study was 0.636-12.3 ng/mL, and the paper's own COWS application simulation swept 0-10 ng/mL. Set to 0 for the pre-treatment (unblocked) condition, which returns the model to its baseline.
- **Source aliases:**
  - `Cp` (Walsh 2024 notation, "the time-matched BPN concentration"; values in ng/mL) -- used in `Walsh_2024_buprenorphine_drugLiking.R`, `Walsh_2024_buprenorphine_desireToUse.R` and `Walsh_2024_buprenorphine_cows.R`.
- **Example models:** `Walsh_2024_buprenorphine_drugLiking.R` (Imax blockade of period-corrected drug liking Emax VAS after an intramuscular hydromorphone 18 mg challenge; IC50 0.075 ng/mL), `Walsh_2024_buprenorphine_desireToUse.R` (Imax suppression of desire to use VAS with an exponential onset delay; IC50 0.0129 ng/mL), `Walsh_2024_buprenorphine_cows.R` (bounded-integer Imax suppression of the COWS total score; IC90 0.109 ng/mL).
- **Notes:** Specific scope; buprenorphine-specific. The drug-specific naming follows the established `CP_<drug>_<units>` precedent (`CP_OXY_NGML`, `CP_FBX_NGML`, `CP_LSN_NGML`, `CP_MORPH_NGML`, `CP_RIF_UM`, `CP_REM_NGML`, `CP_GLASDEGIB_NGML`). Walsh 2024 sampled one BPN plasma concentration per challenge day, approximately 60 min before each hydromorphone administration, and time-matched it to the PD score; the analysis is therefore driven by daily concentrations rather than a dense profile. The upstream CAM2038 (subcutaneous long-acting buprenorphine depot) popPK model is NOT reported in Walsh 2024 and no buprenorphine popPK model exists in the nlmixr2lib registry, so downstream users must supply the concentration trajectory externally -- either from their own popPK source or by digitising Supplementary Figure S2 of Walsh 2024. Ratified canonically on 2026-08-05 alongside the Walsh 2024 CAM2038 extraction.

### CP_EIDD_NGML (**canonical for instantaneous EIDD-1931 plasma concentration as a time-varying antiviral PD driver**)
- **Description:** Instantaneous plasma concentration of EIDD-1931, the pharmacologically active nucleoside-analogue metabolite of the antiviral prodrug molnupiravir, supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in within-host viral-dynamics / QSP models that consume an external molnupiravir exposure profile as the driver of an Imax inhibition of viable-virus production.
- **Units:** ng/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the Imax inhibition function `1 - Imax * CP_EIDD_NGML^nhill / (IC50_av^nhill + CP_EIDD_NGML^nhill)`; set to 0 for placebo arms, for neutralizing-antibody-only arms, and for any time outside the drug-exposure window. Reference values observed: Rao 2023 Supplementary Figure 16 (digitized from Painter et al. 2021, doi:10.1128/AAC.02428-20) shows single-dose peaks of roughly 300 ng/mL (50 mg) to 3000 ng/mL (800 mg) within 1-2 h, falling below ~100 ng/mL by 12 h; the fitted antiviral IC50 is 1086 ng/mL.
- **Source aliases:**
  - `Cav` / `Cantiviral` (Rao 2023 Supplementary Methods and `src/covid19_dxdt.m`, where the concentration enters as an `interp1()` lookup on the digitized profile) -- used in `Rao_2023_covid19_qsp.R`.
- **Example models:** `Rao_2023_covid19_qsp.R` (Imax inhibition of viable-virus production from infected cells in the COVID-19 QSP model; the digitized 0-12 h profiles for the 50, 100, 200, 300, 400, 600 and 800 mg single doses ship with the authors' code release as `data/molnupiravir_PK.csv`).
- **Notes:** Specific scope; bound to EIDD-1931 and to the plasma (rather than intracellular) compartment. Rao 2023 states explicitly that the intracellular active triphosphate was not modelled and that the IC50 was optimized against the reported *plasma* EIDD-1931 concentration, so this canonical must not be reused for an intracellular-metabolite driver. Named for the active metabolite rather than the prodrug because that is the moiety whose concentration drives the effect; a future model that drives an effect from molnupiravir parent concentrations should register a separate `CP_MOLNUPIRAVIR_NGML`. Follows the drug-specific `CP_<DRUG>_NGML` precedent established with Aksenov 2018 (`CP_OXY_NGML`, `CP_FBX_NGML`, `CP_LSN_NGML`) and Valitalo 2017 (`CP_MORPH_NGML`).

### CP_RIF_UM (**canonical for instantaneous rifampicin plasma concentration as a time-varying OATP1B-perpetrator covariate**)
- **Description:** Instantaneous plasma concentration of rifampicin supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used as the perpetrator-drug input to a competitive OATP1B inhibition term in DDI popPK models for OATP1B substrates and endogenous OATP1B biomarkers (e.g., coproporphyrin I, rosuvastatin). Set to 0 outside the rifampicin co-administration window so the inhibition term collapses to the baseline form.
- **Units:** umol/L (= uM; rifampicin MW = 822.94 g/mol so 1 umol/L = 0.823 mg/L = 823 ng/mL).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as the denominator of a Michaelis-Menten-style competitive-inhibition term `cl_b_eff = cl_b / (1 + CP_RIF_UM / ki)` where ki is the OATP1B inhibition constant in umol/L. Reference peak observed: a single 600 mg oral rifampicin dose in the Barnett 2018 cohort produces a typical Cmax of approximately 29 umol/L in the modellib('Barnett_2018_rifampicin') typical-value simulation.
- **Source aliases:**
  - `CRIF` (Barnett 2018 Eq. 4 and the analogous RSV inhibition equation; values reported in umol/L).
- **Example models:** `Barnett_2018_coproporphyrin_I.R` (drives competitive OATP1B inhibition of biliary CPI clearance: `cl_b_eff = cl_b / (1 + CP_RIF_UM / ki)` with ki = 1.15 umol/L total / 0.13 umol/L unbound), `Barnett_2018_rosuvastatin.R` (analogous form with ki = 2.23 umol/L total / 0.25 umol/L unbound), `Yoshida_2018_coproporphyrin_I_rifampin.R` (drives competitive OATP1B inhibition of the hepatic component of CPI clearance in Yoshida 2018's one-compartment fNH-parameterised model: `kdeg_eff = kdeg * (fnh + (1 - fnh) / (1 + CP_RIF_UM / kiu))` with kiu = 0.0203 umol/L unbound).
- **Notes:** Specific scope; rifampicin-specific. The drug-specific naming follows the established `CP_<drug>_<units>` precedent (`CP_OXY_NGML`, `CP_FBX_NGML`, `CP_LSN_NGML`, `CP_MORPH_NGML`). The natural input source is a coupled rifampicin popPK simulation; in the Barnett 2018 extraction package, users typically simulate `modellib('Barnett_2018_rifampicin')` first (which returns rifampicin Cc in umol/L after the in-model MW conversion) and feed its central-compartment output as the CP_RIF_UM column on the CPI or RSV event table. Distinct from the binary indicator `CONMED_RIF` (which captures period-level effects like the V1 / V2 / Q binary covariate shifts and does not carry magnitude information). Ratified canonically on 2026-05-26 alongside the Barnett 2018 CPI / RSV extractions. The Yoshida 2018 rifampin-CPI extraction (2026-05-30) used the Simcyp v16r1 default single-dose rifampin model output for portal-vein unbound concentration; that PBPK profile is not reproducible from on-disk sources, and the paper itself documents a ~5x sensitivity of the estimated Ki,u to the choice of perpetrator-PK model, so downstream users must supply their own CP_RIF_UM profile and treat the resulting CPI excursion as conditional on that choice.

### CP_GDC_UM (**canonical for instantaneous GDC-0810 portal-vein unbound concentration as a time-varying OATP1B-perpetrator covariate**)
- **Description:** Instantaneous unbound portal-vein concentration of GDC-0810 (an orally bioavailable selective estrogen receptor downregulator and weak OATP1B inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PBPK model. Used as the perpetrator-drug input to a competitive OATP1B inhibition term in DDI models for OATP1B substrates and endogenous OATP1B biomarkers (coproporphyrin I). Set to 0 outside the GDC-0810 co-administration window so the inhibition term collapses to the baseline (no-inhibition) form.
- **Units:** umol/L (= uM)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as the denominator of a Michaelis-Menten-style competitive-inhibition term `kdeg_eff = kdeg * (fnh + (1 - fnh) / (1 + CP_GDC_UM / kiu))` where kiu is the OATP1B unbound inhibition constant in umol/L (Yoshida 2018 estimated kiu = 0.00174 umol/L for GDC-0810).
- **Source aliases:**
  - `CGDC` (Yoshida 2018 NM-TRAN convention for the per-record GDC-0810 portal-vein unbound concentration; values in umol/L). The Yoshida 2018 paper itself reports values from the in-house Y. Chen et al. PBPK model (referenced as personal communication; not on disk).
- **Example models:** `Yoshida_2018_coproporphyrin_I_GDC0810.R` (drives competitive OATP1B inhibition of the hepatic component of CPI clearance in Yoshida 2018's one-compartment fNH-parameterised model).
- **Notes:** Specific scope; GDC-0810-specific. The drug-specific naming follows the established `CP_<drug>_<units>` precedent (`CP_OXY_NGML`, `CP_FBX_NGML`, `CP_LSN_NGML`, `CP_MORPH_NGML`, `CP_RIF_UM`). The natural input source in Yoshida 2018 was an in-house PBPK model for GDC-0810 (Y. Chen et al., personal communication); that source is not on disk and no GDC-0810 PK model exists in the nlmixr2lib registry, so downstream users must supply CP_GDC_UM externally. The paper notes (Discussion) that observed GDC-0810 plasma AUC IIV was approximately 20%, so the IIV reported on Ki,u (30.1% CV) partially includes per-subject variability in portal-vein exposure rather than purely the intrinsic inhibition-constant variability. Ratified canonically on 2026-05-30 alongside the Yoshida 2018 GDC-0810-CPI extraction.

### STIM_QUININE_MM (**canonical for applied quinine HCl dihydrate stimulus concentration in a brief-access taste aversion experiment**)
- **Description:** Applied sipper-tube concentration of quinine HCl dihydrate (mM) directly presented to the rat during an 8-second brief-access taste aversion (BATA) trial. This is the experimental stimulus level that contacts the taste receptors in solution -- not a systemic plasma concentration of an absorbed drug. Drives the sigmoid-Emax effect on the logistic mixing probability in Sheng 2016's two-generalized-Poisson mixture model for bimodal lick-count data.
- **Units:** mM
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a Hill term in the logistic-Emax expression `E = E0 + Emax * STIM_QUININE_MM^c / (RIC50^c + STIM_QUININE_MM^c)` (Sheng 2016, Methods, "Model for the drug effect"). Set to 0 for the water (control) presentation. Reference values observed: Sheng 2016 presented seven concentrations 0 / 0.01 / 0.03 / 0.1 / 0.3 / 1 / 3 mM (Table 1); the final-model RIC50 is 0.0423 mM.
- **Source aliases:**
  - `QUININE` (Sheng 2016 NM-TRAN $INPUT column name for the per-record applied stimulus concentration; values in mM).
- **Example models:** `Sheng_2016_quinine_rat.R` (sigmoid Emax on the logistic mixing probability between a low-count generalized-Poisson distribution and a right-truncated high-count generalized-Poisson distribution; STIM_QUININE_MM is the only model covariate).
- **Notes:** Specific scope because the canonical name is bound to quinine HCl dihydrate as the bitter stimulus. Distinct from `CAV` (systemic average drug plasma concentration over a dosing interval) and the `CP_*` family (instantaneous plasma concentration as time-varying PD driver) -- STIM_QUININE_MM is the applied stimulus concentration in solution that contacts the taste receptors directly, with no PK absorption / distribution involved. Future BATA-style extractions with a different bitter compound (caffeine, denatonium, etc.) should register a parallel canonical (e.g. `STIM_CAFFEINE_MM`) rather than overload this name; the `STIM_<drug>_<units>` pattern mirrors the established `CP_<drug>_<units>` precedent. Ratified canonically alongside the Sheng 2016 quinine BATA extraction.

### STIM_ARTESUNATE_NM (**canonical for applied in-vitro artesunate concentration driving a P. falciparum hypoxanthine-uptake-inhibition model**)
- **Description:** Applied artesunate concentration in the in vitro hypoxanthine-uptake-inhibition assay well (nM). Per-record covariate (the applied well concentration that contacts the parasite culture directly; the model has no PK and no time evolution). Drives the sigmoid Emax inhibition of normalised hypoxanthine uptake by clinical Plasmodium falciparum isolates.
- **Units:** nM
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the sigmoid Emax inhibition expression as the concentration argument; set to 0 for the drug-free control well. Reference values observed: Simpson 2013 used a doubling-dilution series 0.044 to 87.0 nM plus a drug-free control well; the WT-reference (Genotype 1) population EC50 is 2.3 nM (Table 3).
- **Source aliases:**
  - `C` -- used in `Simpson_2013_artesunate.R` (per-record applied well concentration in nM).
- **Example models:** `Simpson_2013_artesunate.R` (the only model covariate that varies per record; drives the sigmoid Emax inhibition with the pfmdr1 genotype effects entering on EC50).
- **Notes:** Specific scope because the canonical name is bound to artesunate as the applied antimalarial stimulus. Member of the in-vitro applied-drug-concentration `STIM_<drug>_<units>` family (siblings `STIM_CHLOROQUINE_NM`, `STIM_LUMEFANTRINE_NM`, `STIM_MEFLOQUINE_NM`, and the bitter-stimulus `STIM_QUININE_MM`) -- the applied stimulus / well concentration that contacts the target directly, distinct from `CAV` (systemic average plasma concentration), the `CP_<drug>` plasma-PD-driver family, and the `CONC_<drug>_MGL` in-vitro antibacterial family (which is reported in mg/L). Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

### STIM_CHLOROQUINE_NM (**canonical for applied in-vitro chloroquine concentration driving a P. falciparum hypoxanthine-uptake-inhibition model**)
- **Description:** Applied chloroquine concentration in the in vitro hypoxanthine-uptake-inhibition assay well (nM). Per-record covariate (the applied well concentration that contacts the parasite culture directly; the model has no PK and no time evolution). Drives the sigmoid Emax inhibition of normalised hypoxanthine uptake by clinical Plasmodium falciparum isolates.
- **Units:** nM
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the sigmoid Emax inhibition expression as the concentration argument; set to 0 for the drug-free control well. Reference values observed: Simpson 2013 used a doubling-dilution series 10.02 to 10255.9 nM plus a drug-free control well; the WT-reference (Genotype 1) population EC50 is 242 nM (Table 3).
- **Source aliases:**
  - `C` -- used in `Simpson_2013_chloroquine.R` (per-record applied well concentration in nM).
- **Example models:** `Simpson_2013_chloroquine.R` (the only model covariate that varies per record; drives the sigmoid Emax inhibition with the pfmdr1 genotype effects entering on EC50).
- **Notes:** Specific scope because the canonical name is bound to chloroquine as the applied antimalarial stimulus. Member of the in-vitro applied-drug-concentration `STIM_<drug>_<units>` family (siblings `STIM_ARTESUNATE_NM`, `STIM_LUMEFANTRINE_NM`, `STIM_MEFLOQUINE_NM`, and the bitter-stimulus `STIM_QUININE_MM`) -- the applied stimulus / well concentration that contacts the target directly, distinct from `CAV`, the `CP_<drug>` family, and the `CONC_<drug>_MGL` in-vitro antibacterial family. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

### STIM_LUMEFANTRINE_NM (**canonical for applied in-vitro lumefantrine concentration driving a P. falciparum hypoxanthine-uptake-inhibition model**)
- **Description:** Applied lumefantrine concentration in the in vitro hypoxanthine-uptake-inhibition assay well (nM). Per-record covariate (the applied well concentration that contacts the parasite culture directly; the model has no PK and no time evolution). Drives the sigmoid Emax inhibition of normalised hypoxanthine uptake by clinical Plasmodium falciparum isolates.
- **Units:** nM
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the sigmoid Emax inhibition expression as the concentration argument; set to 0 for the drug-free control well. Reference values observed: Simpson 2013 used a doubling-dilution series 2.40 to 235.8 nM plus a drug-free control well; the WT-reference (Genotype 1) population EC50 is 35.7 nM (Table 3).
- **Source aliases:**
  - `C` -- used in `Simpson_2013_lumefantrine.R` (per-record applied well concentration in nM).
- **Example models:** `Simpson_2013_lumefantrine.R` (the only model covariate that varies per record; drives the sigmoid Emax inhibition with the pfmdr1 genotype effects entering on EC50).
- **Notes:** Specific scope because the canonical name is bound to lumefantrine as the applied antimalarial stimulus. Member of the in-vitro applied-drug-concentration `STIM_<drug>_<units>` family (siblings `STIM_ARTESUNATE_NM`, `STIM_CHLOROQUINE_NM`, `STIM_MEFLOQUINE_NM`, and the bitter-stimulus `STIM_QUININE_MM`) -- the applied stimulus / well concentration that contacts the target directly, distinct from `CAV`, the `CP_<drug>` family, and the `CONC_<drug>_MGL` in-vitro antibacterial family. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

### STIM_MEFLOQUINE_NM (**canonical for applied in-vitro mefloquine concentration driving a P. falciparum hypoxanthine-uptake-inhibition model**)
- **Description:** Applied mefloquine concentration in the in vitro hypoxanthine-uptake-inhibition assay well (nM). Per-record covariate (the applied well concentration that contacts the parasite culture directly; the model has no PK and no time evolution). Drives the sigmoid Emax inhibition of normalised hypoxanthine uptake by clinical Plasmodium falciparum isolates.
- **Units:** nM
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the sigmoid Emax inhibition expression as the concentration argument; set to 0 for the drug-free control well. Reference values observed: Simpson 2013 used a doubling-dilution series 1.62 to 1646.6 nM plus a drug-free control well; the WT-reference (Genotype 1) population EC50 is 53.0 nM (Table 3).
- **Source aliases:**
  - `C` -- used in `Simpson_2013_mefloquine.R` (per-record applied well concentration in nM).
- **Example models:** `Simpson_2013_mefloquine.R` (the only model covariate that varies per record; drives the sigmoid Emax inhibition with the pfmdr1 genotype effects entering on EC50).
- **Notes:** Specific scope because the canonical name is bound to mefloquine as the applied antimalarial stimulus. Member of the in-vitro applied-drug-concentration `STIM_<drug>_<units>` family (siblings `STIM_ARTESUNATE_NM`, `STIM_CHLOROQUINE_NM`, `STIM_LUMEFANTRINE_NM`, and the bitter-stimulus `STIM_QUININE_MM`) -- the applied stimulus / well concentration that contacts the target directly, distinct from `CAV`, the `CP_<drug>` family, and the `CONC_<drug>_MGL` in-vitro antibacterial family. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

### ETSEVO (**canonical for end-tidal sevoflurane concentration in the breathing circuit**)
- **Description:** End-tidal sevoflurane concentration (vol %) recorded continuously by an anesthesia gas monitor during sevoflurane general anesthesia. Distinct from a systemic plasma concentration -- ETSEVO is the alveolar / breathing-circuit concentration in vol %, used directly as the PD driver in sigmoid-Emax models of consciousness / recovery endpoints. Per-record covariate (per observation epoch); decreases during emergence from approximately the maintenance MAC value (e.g. 1 MAC, 1.5-1.7 vol % in school-aged children with 50% N2O) to 0.
- **Units:** vol % (volume percent in the breathing circuit; equivalent to fractional inspired/expired sevoflurane x 100)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters directly as the concentration argument C in a sigmoid Emax probability expression `P(ROC) = C50^gamma / (C50^gamma + C^gamma)` (Shin 2014 Methods). Reference values observed: typical C50 for return of consciousness 0.37 vol % (mentally intact) and 0.19 vol % (mentally disabled) in Shin 2014 Table 2; literature MAC-awake for sevoflurane is 0.6-0.78 vol % in healthy adults / children.
- **Source aliases:**
  - `DOSE` -- used in `Shin_2014_sevoflurane.R` (Shin 2014 Appendix 1 $INPUT column name; the NONMEM column is labelled DOSE but holds the per-record end-tidal concentration, not an administered dose -- the model uses it directly in `PROB = 1 - DOSE**GAM/(CE50**GAM + DOSE**GAM)`).
- **Example models:** `Shin_2014_sevoflurane.R` (drives the sigmoid-Emax probability of return of consciousness in pediatric dental-surgery patients during emergence from sevoflurane / N2O general anesthesia).
- **Notes:** Specific scope because the canonical name is bound to sevoflurane as the volatile anesthetic. Future emergence-from-anesthesia PD extractions for a different volatile (isoflurane, desflurane) should register a parallel canonical (e.g. `ETISO`, `ETDES`) rather than overload this name; the `ET<agent>` pattern parallels the `CP_<drug>_<units>` precedent for IV PD drivers. Distinct from `CP_*` (systemic plasma concentration), `CAV` (steady-state average plasma exposure), and `STIM_*` (applied non-systemic stimulus concentration): ETSEVO is the alveolar / circuit concentration that equilibrates with brain tissue during inhalation anesthesia, sampled at the exhalation peak.

### CEFFECT (**canonical for time-varying effect-site (biophase) concentration of an IV anesthetic / sedative supplied as a PD driver**)
- **Description:** Instantaneous (per-event-record) effect-site (biophase) concentration of an IV anesthetic, sedative, or other agent whose pharmacodynamics are commonly modelled against the effect-site Ce rather than plasma Cp. Supplied directly as a time-varying covariate column rather than computed from a coupled PK + effect-site model inside the consuming PD model file. Used in sigmoid Emax / probability-of-event PD-only analyses (e.g., probability of recovery of consciousness, probability of LOC, BIS-response models) where the source paper fixed the upstream PK + effect-site from an external target-controlled-infusion (TCI) controller (Schnider 1998 / 1999 for propofol; Minto 1997 for remifentanil; etc.) and fit only the downstream PD parameters. The PD model consumes CEFFECT directly in `model()` (typically `conc <- CEFFECT` followed by a sigmoid Emax of `conc`); the user is responsible for generating the CEFFECT trajectory upstream (TCI-controller simulation, a registered propofol PK model chained with an external Keo effect-site link, or observed plasma concentrations equilibrated with an effect-site rate constant).
- **Units:** drug-specific; document the actual unit per-model via `covariateData[[CEFFECT]]$units`. Reference values observed: propofol effect-site Ce in `Koo_2012_propofol.R` is reported in `ug/mL`.
- **Type:** continuous
- **Scope:** general (the abstract concept "effect-site PD driver" applies to any drug whose PD is fit on Ce; the drug-specific identity is conveyed by the surrounding model file, not by an embedded drug suffix on the column name). Drug-specific override aliases are documented per-model in `covariateData[[CEFFECT]]$notes` when the source paper uses a different column name.
- **Reference category:** n/a -- enters directly as the concentration argument C in a sigmoid Emax expression such as `P(ROC) = C50^lambda / (C50^lambda + C^lambda)`. Set to 0 for the drug-free reference (e.g., before infusion start or after full washout). Reference values observed: propofol effect-site Ce range approximately 4.4 +/- 1.1 ug/mL at LOC, 3.2 +/- 1.0 ug/mL at end of surgery, and 1.1 +/- 0.3 ug/mL at ROC in Koo 2012's adult population (Results).
- **Source aliases:**
  - `Ce` (Koo 2012 Methods; the per-record propofol effect-site concentration predicted by the Schnider TCI controller, ug/mL). Not stored as a single named NONMEM column in the source $TABLE -- the TCI controller computes it and feeds the NONMEM dataset directly.
- **Example models:** `Koo_2012_propofol.R` (drives the sigmoid Emax probability of return of consciousness in adult eye / ENT patients during emergence from propofol-remifentanil TCI anesthesia; CEFFECT carries propofol effect-site Ce in ug/mL).
- **Notes:** General-scope canonical for the abstract "effect-site PD driver" concept; the per-model `units` field tells the user which drug and which scale the trajectory must use. Distinct from `CP_*` (systemic plasma concentration, mass-balance from the modeled-drug central compartment), `CAV` (dosing-interval-averaged steady-state plasma exposure), `STIM_*` (applied in-vitro / sipper-tube stimulus concentration), `ETSEVO` / `ETISO` / `ETDES` (alveolar end-tidal volatile-anesthetic concentration in vol %), and `L_OPIOID_pM` / `L_ANTAGONIST_pM` (Mann 2022 multi-ligand competitive-binding effect-site slots in pM, where the SLOT identity is the abstraction rather than the drug identity). The CEFFECT canonical is the operator-ratified auto-approve name for the "effect-site PD driver" family per the SKILL.md policy precheck (`effect-site PD driver -> CEFFECT`). Future PD extractions that consume an effect-site Ce as a PD driver should reuse this canonical with the per-model `units` / `notes` recording the drug and the scale; if a future extraction requires simultaneous occupation of TWO separate effect-site slots (a competitive / interaction model akin to Mann 2022's two ligand slots), register sibling canonicals (e.g., `CEFFECT_AGONIST`, `CEFFECT_ANTAGONIST`) rather than overloading CEFFECT with both. Ratified canonically on 2026-06-27 alongside the Koo 2012 propofol-ROC extraction (task `frompeople-670`).

### FPG (**canonical for baseline fasting plasma glucose**)
- **Description:** Fasting plasma glucose concentration at baseline (or time-varying baseline-style observation; document per-model). Distinct from `GLU` (time-varying plasma glucose regressor input for mechanistic glucose-kinetics models).
- **Units:** mmol/L (or mg/dL -- 1 mmol/L glucose is approximately 18.02 mg/dL). Document per-model via `covariateData[[FPG]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with linear-deviation form `1 + theta * (FPG - ref)`. Reference values observed: 8.90 mmol/L (Retlich 2015 popPK/PD linagliptin median fasting glucose at baseline).
- **Source aliases:** none known.
- **Example models:** `Retlich_2015_linagliptin.R` (mmol/L, reference 8.90; linear-deviation effect on baseline DPP-4 activity BSL with coefficient 1.46 % per mmol/L deviation).
- **Notes:** Glycemic-control covariate (baseline FPG); routinely reported alongside HbA1c in T2DM populations. Distinct from `GLU` which is a time-varying within-subject glucose regressor for mechanistic glucose-kinetics models (Bizzotto 2016). Ratified canonically alongside the Retlich 2015 linagliptin extraction.

### DPP4_BL_RFU (**canonical for baseline plasma dipeptidyl peptidase-4 activity in relative fluorescence units**)
- **Description:** Baseline plasma dipeptidyl peptidase-4 (DPP-4) enzymatic activity, measured by relative fluorescence units (RFU). DPP-4 is the pharmacological target of the gliptin (DPP-4-inhibitor) drug class; baseline activity correlates with circulating DPP-4 protein concentration in the central compartment and serves as a covariate on the central-compartment binding-site concentration in TMDD models for gliptins.
- **Units:** RFU (assay-specific; document the assay in `covariateData[[DPP4_BL_RFU]]$notes` since RFU values are not directly comparable across assays).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with linear-deviation form `1 + theta * (DPP4_BL_RFU - ref)`. Reference values observed: 12,497 RFU (Retlich 2015 popPK linagliptin median baseline), 11,600 RFU (Retlich 2015 popPK/PD linagliptin median baseline, applied to the individual-predicted BSL_i parameter on EC50).
- **Source aliases:** none known.
- **Example models:** `Retlich_2015_linagliptin.R` (RFU, reference 12,497; linear-deviation effect on the central-compartment binding-site concentration Bmax,C with coefficient 0.00332 % per RFU deviation -- captures the inter-individual correlation between baseline DPP-4 protein concentration and the apparent saturable-binding amplitude).
- **Notes:** Specific scope because DPP-4-activity values in RFU are assay-specific and not directly transferable between studies / instruments. Future gliptin extractions reporting DPP-4 activity in the same assay can reuse this canonical; extractions in absolute enzymatic-rate units (pmol AMC per minute) or normalised units should register a sibling canonical (e.g., `DPP4_BL_PMOL_MIN`). Ratified canonically alongside the Retlich 2015 linagliptin extraction.

### GLU (**canonical for plasma glucose time-course regressor**)
- **Description:** Plasma glucose concentration as a time-varying *regressor* input that drives a mechanistic glucose-kinetics model. Not a covariate that modifies a parameter; the model integrates `GLU` directly through a smoothing filter into a site-of-action glucose variable.
- **Units:** mmol/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as a time-varying regressor. The model declares `linear(GLU)` so rxode2 linearly interpolates `GLU` between dataset rows.
- **Source aliases:**
  - `iglu` (glucose at the current row time) -- used in the DDMORE bundle's `Simulated_glucoseKinetics.csv` for `DDMODEL00000227`. Rename `iglu` -> `GLU` before passing to `rxSolve`.
- **Example models:** `Bizzotto_2016_glucose.R` (driving regressor for the glucose-at-site-of-action delay), `Lu_2014_sglt_qsp.R` (drives the rate of glucose entry into PCT1 by glomerular filtration in the SGLT renal-glucose-reabsorption QSP model: filtered glucose load = GFR * GLU mmol/h), `Bosch_2025_glp1ra_hba1c.R` (drives the IGRH HbA1c sub-model: time-varying average daily glucose -> glycation flux KG*GLU_mgdl and glucose-dependent RBC life span (GLU_mgdl/149)^gamma_ls; internal conversion mmol/L -> mg/dL via factor 18.016).
- **Notes:** Specific scope because `GLU` is meaningful only for glucose-kinetics or glucose-PD models that take plasma glucose as an exogenous regressor. The DDMORE bundle's hand-rolled piecewise-linear interpolation (`GL = (t-T1)/(TOBS-T1)*(GLU-GLU1)+GLU1` with bracketing columns `iglu / glun / td / tn`) is replaced in nlmixr2 by `linear(GLU)` declared in `model()`; the bracketing columns are not required.

### INS (**canonical for plasma insulin time-course regressor**)
- **Description:** Plasma insulin concentration as a time-varying *regressor* input that drives a mechanistic glucose-kinetics model. Not a covariate that modifies a parameter; the model integrates `INS` directly through a smoothing filter into a site-of-action insulin variable.
- **Units:** pmol/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as a time-varying regressor. The model declares `linear(INS)` so rxode2 linearly interpolates `INS` between dataset rows.
- **Source aliases:**
  - `iins` (insulin at the current row time) -- used in the DDMORE bundle's `Simulated_glucoseKinetics.csv` for `DDMODEL00000227`. Rename `iins` -> `INS` before passing to `rxSolve`.
  - `INSU` -- used in the DDMORE bundle's `Simulated_ddmoremockdata2.txt` for `DDMODEL00000228`. Rename `INSU` -> `INS` before passing to `rxSolve`.
- **Example models:** `Bizzotto_2016_glucose.R` (driving regressor for the insulin-at-site-of-action delay), `NA_NA_paracetamol.R` (DDMODEL00000228 OGTT model: drives the insulin-on-glucose-elimination first-order effect compartment via `kie * (INS / 6.945 - effect_ins)`), `Denti_2010_glucoseMinimal.R` (Bergman minimal-model insulin forcing function entering the insulin-action ODE as `p2 * si * (INS - INS_BL)`; units pmol/L; supplied via `linear(INS)`).
- **Notes:** Specific scope because `INS` is meaningful only for glucose-kinetics or insulin-PD models that take plasma insulin as an exogenous regressor. For drugs that *modify* circulating insulin as a downstream effect, use a different mechanism-specific name. The DDMORE bundle's hand-rolled piecewise-linear interpolation (`I = (t-T1)/(TOBS-T1)*(INS-INS1)+INS1` with bracketing columns `iins / insn / td / tn`) is replaced in nlmixr2 by `linear(INS)` declared in `model()`; the bracketing columns are not required.

### INS_BL (**canonical for baseline (fasting) plasma insulin concentration**)
- **Description:** Baseline (fasting) plasma insulin concentration, time-fixed per subject. Used as the per-subject anchor for steady-state insulin-driven processes (e.g., initial condition of an insulin-on-elimination effect compartment) and as the per-subject baseline-state insulin input to a baseline-glucose-production rate calculation.
- **Units:** pmol/L (or uU/mL; document per-model via `covariateData[[INS_BL]]$units`). The example model rescales via `INS_BL / 6.945` to convert pmol/L to uU/mL.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a.
- **Source aliases:**
  - `BASI` (baseline insulin) -- used in the DDMORE bundle's `Simulated_ddmoremockdata2.txt` for `DDMODEL00000228`. Rename `BASI` -> `INS_BL` before passing to `rxSolve`.
- **Example models:** `NA_NA_paracetamol.R` (DDMODEL00000228 OGTT model: initialises the insulin-on-elimination effect compartment `effect_ins(0) = INS_BL / 6.945` and feeds the steady-state baseline-glucose-production rate `gpro = gss * (kg + kgi * INS_BL / 6.945) * vg * 180 / 1000`), `Hong_2013_glucose_insulin_HGC.R` (anchors the dynamic-state `insulin(0) = ICss * VI` initial condition in mU plus the baseline insulin secretion `ICss * CLI`, no rescaling), `Hong_2013_glucose_insulin_MTT.R` (same anchor as the HGC companion), `Denti_2010_glucoseMinimal.R` (Bergman minimal-model basal-insulin anchor; covariate effect on `lsi` (-0.0282 per pmol/L) and `lp2` (-0.0150 per pmol/L), and Ib in the insulin-action ODE `p2 * si * (INS - INS_BL)`; units pmol/L despite the source paper's apparent 'pmol/ml' table label, which is documented as an apparent typo).
- **Notes:** Distinct from `INS` (time-varying regressor); `INS_BL` is a per-subject baseline-state anchor used in initial conditions and steady-state derived quantities, not the dynamic regressor itself. Specific scope because the conversion factor (1/6.945) and the rescaled-units interpretation are paper-specific; future extractions that report baseline insulin in mIU/L or pmol/L directly without rescaling can ratify the same canonical and document the per-model units / conversion in `covariateData[[INS_BL]]$units` / `notes`. Companion concept to `FPG` (baseline fasting plasma glucose).

### CINH (**canonical for plasma SGLT-inhibitor concentration time-course regressor**)
- **Description:** Plasma SGLT-inhibitor (e.g., dapagliflozin, canagliflozin) concentration as a time-varying *regressor* input that drives the rate of unbound-drug entry into the proximal tubule via glomerular filtration in renal-glucose-reabsorption QSP models. Not a covariate that modifies a parameter; the model integrates `CINH` directly through `linear(CINH)` and multiplies by the unbound fraction `fup` inside `model()`.
- **Units:** nmol/L (matching the units of the inhibitor's affinity constants Ki1 and Ki2 in the SGLT MM kinetics).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as a time-varying regressor. The model declares `linear(CINH)` so rxode2 linearly interpolates `CINH` between dataset rows.
- **Source aliases:** none known.
- **Example models:** `Lu_2014_sglt_qsp.R` (drives the rate of unbound inhibitor entering PCT1 by glomerular filtration: filtered drug load = GFR * fup * CINH nmol/h; set CINH = 0 for baseline / no-inhibitor simulations).
- **Notes:** Specific scope because `CINH` is meaningful only for renal-glucose-reabsorption or other SGLT-mediated models that take plasma SGLT-inhibitor exposure as an exogenous regressor. The clinical reporting unit is ng/mL; convert by `1 ng/mL = (1000 / MW_drug) nmol/L` (dapagliflozin MW = 409 g/mol so 1 ng/mL = 2.44 nmol/L; canagliflozin MW = 454 g/mol so 1 ng/mL = 2.20 nmol/L). The Lu 2014 paper feeds the model an interpolated dapagliflozin observed mean profile (DeFronzo et al. 2013) or a fitted two-compartment canagliflozin PK profile (Devineni et al. 2013) -- the SGLT-mechanism model itself does not include an internal PK sub-model for the inhibitor. Companion concept to `GLU` (the plasma-glucose regressor that drives the same filtration arm).

### IGE (**canonical for serum total immunoglobulin E concentration**)
- **Description:** Baseline serum total immunoglobulin E concentration (free IgE plus, in patients on anti-IgE therapy, omalizumab-IgE complex). For anti-IgE monoclonal antibodies (omalizumab, ligelizumab) IgE is the pharmacologic target; baseline IgE sets the magnitude of the target sink and modifies free-IgE clearance and the rate of IgE production in mechanism-based binding/turnover models.
- **Units:** ng/mL (typical clinical-PK convention). Pretreatment values reported in `IU/mL` are converted via `1 IU/mL = 2.42 ng/mL` (Hayashi 2007 Methods). Document per-model via `covariateData[[IGE]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(IGE / ref)^exponent`. Reference value observed: 482.4 ng/mL (Hayashi 2007 Japanese atopic-asthma cohort).
- **Source aliases:**
  - `IgE0` (baseline IgE concentration) -- used in `Hayashi_2007_omalizumab.R`.
- **Example models:** `Hayashi_2007_omalizumab.R` (ng/mL, reference 482.4; power exponents -0.281 on apparent CL of free IgE and +0.657 on apparent IgE production rate; also used as the initial value for the total-IgE state at t = 0).
- **Notes:** General scope because baseline serum total IgE is a routine clinical-laboratory measurement, not a target tied to one drug. In mechanism-based anti-IgE binding/turnover models the in-model IgE state is a separate dynamic variable (`X_TE`, in nmol or nmol/L) -- `IGE` is the per-subject baseline column used for covariate scaling and (when applicable) state initialization, not the dynamic state itself. For models that use the alternative reporting unit `IU/mL`, multiply by 2.42 before applying the canonical-units (ng/mL) reference value, or document the per-model unit choice in `covariateData[[IGE]]$units` so downstream tooling can interpret the values correctly. Distinct from `IGE_FREE` (the time-varying serum **free** IgE concentration used as the PD-driver input in PD-only IgE-FEV1 models).

### IGE_FREE (**canonical for time-varying serum free immunoglobulin E concentration as PD-driver input**)
- **Description:** Time-varying serum free immunoglobulin E concentration (the unbound fraction not in drug-IgE complex) used as the exogenous PD-driver input in PD-only IgE-FEV1 (and other anti-IgE biomarker) models that consume free IgE as an externally-supplied trajectory rather than computing it from a coupled PK / binding ODE. Per-event-record (one value per PD observation time). Cross-reference: distinct from `IGE` (per-subject baseline total IgE used for power scaling in mechanism-based binding / turnover popPK models like Hayashi 2007). For sequential PK-PD use, this column is populated from an upstream omalizumab popPK/IgE-binding model (e.g. `modellib("Hayashi_2007_omalizumab")` emits a `freeIgE` observable in ng/mL); for standalone fits the column is interpolated from observed free-IgE assays.
- **Units:** ng/mL (matches the Zhu 2023 paper's reported values and the Hayashi 2007 `freeIgE` output convention). Document per-model via `covariateData[[IGE_FREE]]$units`.
- **Type:** continuous
- **Scope:** general (anti-IgE PD modeling -- omalizumab today, plausibly ligelizumab or other anti-IgE agents in future).
- **Reference category:** n/a -- enters directly into the inhibition Hill term `imax * IGE_FREE^hill / (ec50^hill + IGE_FREE^hill)`. Set to the subject's baseline total IgE at pretreatment / placebo records (per Zhu 2023 Methods: free IgE equals total IgE before omalizumab treatment) so the inhibition term evaluates at the subject's natural free-IgE level. Reference values observed: 16-960 ng/mL range across placebo + omalizumab observations in pediatric Study IA05 (Zhu 2023 Results paragraph).
- **Source aliases:**
  - `C_IgE,i(t)` (Zhu 2023 paper symbol in the structural-model equation, PDF page 2). The NM-TRAN dataset column name is not disclosed in the paper text.
- **Example models:** `Zhu_2023_omalizumab_pediatric.R` (founding example; ng/mL, per-event-record free IgE driving the IDR-Type-IV FEV1 percent predicted response with `imax = 0.0717`, `ec50 = 39.4 ng/mL`, `hill = 9` fixed).
- **Notes:** Distinct from `IGE` (baseline total IgE for power-scaling). The free-IgE-vs-total-IgE distinction is load-bearing for anti-IgE drugs: free IgE is the pharmacologically active fraction and the relevant PD driver, while total IgE is the assay-reported sum of free + drug-bound IgE that does NOT decline under omalizumab treatment (Zhu 2023 Methods 'Data Used in the Population Pediatric IgE-FEV1 Model'). Sequential PK-PD compatibility: `Hayashi_2007_omalizumab.R` emits a `freeIgE` observable in ng/mL; downstream users simulate that model and populate the `IGE_FREE` column in the PD dataset from the simulated trajectory. For placebo subjects (no anti-IgE drug), the paper used the subject's average total IgE level across the steroid-stable period as the `IGE_FREE` input, because free IgE equals total IgE in the absence of anti-IgE drug (Zhu 2023 Methods).

### ESAD (**canonical for prior erythropoiesis-stimulating-agent (ESA) dose at baseline**)
- **Description:** Subject's recorded prior erythropoiesis-stimulating-agent (ESA) dose at study baseline, in epoetin-equivalent activity units per week. Used by ESA-switch population PK/PD models for hemoglobin response to a new ESA when prior endogenous-erythropoietin levels were not measured -- the prior-ESA dose acts as a surrogate for the residual hematopoietic stimulation present at randomization. Time-fixed per subject (records the patient's stable maintenance dose immediately before the new-ESA start).
- **Units:** units/week (epoetin-equivalent activity units per week). Document per-model via `covariateData[[ESAD]]$units` when the source paper uses a different per-time unit (units/month, units/day) or converts darbepoetin / methoxy-polyethylene-glycol epoetin beta doses to epoetin equivalents.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with the paper-specific log-scale slope on baseline hemoglobin (HgbBL). Reference values observed: 7996 units/week in `Naik_2013_peginesatide.R` (Naik 2013 eq 16; paper-reported population median). The covariate effect is gated by an ESADF indicator (1 if ESAD > 0, else 0) so that subjects with missing or unrecorded prior ESA dose (encoded as ESAD = 0) carry no covariate adjustment to HgbBL, matching Naik 2013's "no effect of ESAD was incorporated for subjects whose ESAD dose information was not available."
- **Source aliases:**
  - `ESAD` -- used in `Naik_2013_peginesatide.R` (Naik 2013 paper notation; prior epoetin alfa / darbepoetin alfa weekly dose in units/week for CKD hemodialysis subjects enrolling on peginesatide).
- **Example models:** `Naik_2013_peginesatide.R` (Naik 2013 eq 16; exponential covariate on the baseline-hemoglobin parameter: `hgbbl = exp(lhgbbl + etalhgbbl + e_esad_lhgbbl * (ESAD - 7996) * ESADF)` with `e_esad_lhgbbl = -4.49e-7 1/(units/week)`; effect is small in magnitude but retained as the only PD-side statistically significant covariate per backward-elimination at P < 0.005).
- **Notes:** Specific scope because the values are intrinsically tied to ESA-switch popPK/PD studies and the units/week reporting convention is paper-specific (different ESAs have different specific activities; epoetin-equivalent conversion factors must be documented per-model). The ESADF indicator (built inline in the model() block as `(ESAD > 0)`) handles the "no prior ESA dose available" data-quality case used by Naik 2013; future papers that distinguish prior-ESA-naive from prior-ESA-treated-with-unrecorded-dose may register a parallel canonical (e.g., `ESA_NAIVE`). Ratified canonically on 2026-05-22 alongside the Naik 2013 peginesatide extraction.

### DOSE_IND (**canonical for per-arm once-daily inhaled indacaterol dose**)
- **Description:** Per-study-arm once-daily inhaled indacaterol dose (ug/day; 0 for placebo arms). Study-arm-level (not individual-level) drug-exposure covariate used as the dose regressor in a model-based meta-analysis (MBMA) of bronchodilator dose-response.
- **Units:** ug/day
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as the dose regressor in the MBMA (Emax-type) bronchodilator dose-response; set to 0 for placebo arms.
- **Source aliases:**
  - `DOSE_IND` -- used in `Renard_2011_indacaterol.R` (per-arm indacaterol dose, ug/day; Renard 2011 Table 1).
- **Example models:** `Renard_2011_indacaterol.R` (per-arm once-daily inhaled indacaterol dose driving the MBMA dose-response; dose range 18.75-600 ug/day across 11 trials, with the six discrete reported doses 18.75, 37.5, 75, 150, 300, and 600 ug).
- **Notes:** Specific scope because the value is intrinsically tied to indacaterol and the Renard 2011 MBMA dose-response. Drug-specific dose canonical paralleling `DOSE_PHT_MGKGD` (phenytoin); future MBMA / dose-response models for other drugs should register a sibling `DOSE_<DRUG>` canonical rather than overloading this name. Ratified canonically on 2026-05-27 alongside the Renard 2011 indacaterol extraction.

### CELLS_INTACT (**canonical for the intact-versus-lysed cell-preparation flag in an in-vitro target-binding assay**)
- **Description:** Binary flag recording which cell preparation an in-vitro target-binding observation came from: `1` = intact (whole) bacterial cells, in which the outer membrane is present and drug must penetrate it to reach a periplasmic or intracellular target; `0` = lysed cells, i.e. isolated target-containing membrane fractions, in which the outer-membrane barrier has been removed and drug is applied in vast excess. The flag is a property of the sample preparation, not of the bioanalytical measurement method -- it is therefore distinct from the `ASSAY_<METHOD>` bioanalytical-method family.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** `0` = lysed cells (isolated membranes). Structural, not a coefficient multiplier: the flag gates both the outer-membrane influx term (`rate_influx = CELLS_INTACT * rate_influx_scaled * CONC_<DRUG>_MGL`) and the periplasmic initial condition (`periplasm(0) = (1 - CELLS_INTACT) * n_peri_lysed`).
- **Source aliases:**
  - `INTACT` -- used in the Lopez-Arguello 2023 S-ADAPT-TRAN estimation code (Fig. S8 line 66, `IF (INTACT.EQ.1) THEN`).
- **Example models:** `LopezArguello_2023_<drug>_qsp.R`.
- **Notes:** General scope because the intact-versus-lysed contrast is a standard design in Gram-negative target-site-penetration work and is not specific to beta-lactams or to PBPs; a new model using the same design should reuse this name rather than register a sibling. Because the flag changes model structure rather than scaling a parameter, it has no associated `e_<cov>_<param>` covariate-effect coefficient. Ratified canonically on 2026-07-29 (operator sidecar `oare_PMC10269149` request-001 q1, answer A) alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_DOR_MGL (**canonical for static in-vitro doripenem concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) doripenem concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_DOR_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_doripenem_qsp.R` (static 2 mg/L, 2x the MIC of 1 mg/L; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to doripenem and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_MEM_MGL (**canonical for static in-vitro meropenem concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) meropenem concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_MEM_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_meropenem_qsp.R` (static 1 mg/L, 2x the MIC of 0.5 mg/L; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to meropenem and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_ETP_MGL (**canonical for static in-vitro ertapenem concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) ertapenem concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_ETP_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_ertapenem_qsp.R` (static 8 mg/L, 2x the MIC of 4 mg/L; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to ertapenem and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_CAZ_MGL (**canonical for static in-vitro ceftazidime concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) ceftazidime concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_CAZ_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_ceftazidime_qsp.R` (static 2 mg/L, 2x the MIC of 1 mg/L; Lopez-Arguello 2023 Table 1), `Kroemer_2024_ceftazidime_avibactam_fosfomycin_tkc.R` (static time-kill concentrations 0.002-128 mg/L against a clinical MDR *Escherichia coli*; Kroemer 2024 Fig. 1).
- **Notes:** Specific scope because the value is bound to ceftazidime and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_FEP_MGL (**canonical for static in-vitro cefepime concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) cefepime concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_FEP_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_cefepime_qsp.R` (static 2 mg/L, 2x the MIC of 1 mg/L; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to cefepime and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_FOF_MGL (**canonical for static in-vitro fosfomycin concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) fosfomycin concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- 0 mg/L is the drug-free growth control.
- **Source aliases:** `FOF` (Kroemer 2024).
- **Example models:** `Kroemer_2024_ceftazidime_avibactam_fosfomycin_tkc.R` (static time-kill concentrations 2-16 mg/L against a clinical MDR *Escherichia coli*; Kroemer 2024 Fig. 1).
- **Notes:** Specific scope because the value is bound to fosfomycin and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_CAZ_MGL`, `CONC_AVI_MGL`, `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Fosfomycin susceptibility testing and time-kill media are supplemented with 25 mg/L glucose-6-phosphate per EUCAST recommendations; the covariate carries the fosfomycin concentration only, not the glucose-6-phosphate. The dynamic sibling model `Kroemer_2024_ceftazidime_avibactam_fosfomycin_hfim.R` carries fosfomycin as a dosable ODE state (`conc_fof`) instead, because the hollow fiber concentrations are time-varying.

### CONC_FOX_MGL (**canonical for static in-vitro cefoxitin concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) cefoxitin concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_FOX_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_cefoxitin_qsp.R` (static 2,048 mg/L, 2x the MIC of 1,024 mg/L; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to cefoxitin and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_ATM_MGL (**canonical for static in-vitro aztreonam concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) aztreonam concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_ATM_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_aztreonam_qsp.R` (static 8 mg/L, 2x the MIC of 4 mg/L; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to aztreonam and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_PIP_MGL (**canonical for static in-vitro piperacillin concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) piperacillin concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_PIP_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_piperacillin_qsp.R` (static 8 mg/L, 2x the MIC of 4 mg/L; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to piperacillin and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_CAR_MGL (**canonical for static in-vitro carbenicillin concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) carbenicillin concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_CAR_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_carbenicillin_qsp.R` (static 96 mg/L, 2x the MIC of 48 mg/L; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to carbenicillin and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_TIC_MGL (**canonical for static in-vitro ticarcillin concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) ticarcillin concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_TIC_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_ticarcillin_qsp.R` (static 48 mg/L, 2x the MIC of 24 mg/L; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to ticarcillin and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_AVI_MGL (**canonical for static in-vitro avibactam concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) avibactam concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_AVI_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_avibactam_qsp.R` (static 4 mg/L, a fixed concentration within the clinically relevant range; MIC not determined for beta-lactamase inhibitors; Lopez-Arguello 2023 Table 1), `Kroemer_2024_ceftazidime_avibactam_fosfomycin_tkc.R` (static time-kill concentrations up to ~64 mg/L against a clinical MDR *Escherichia coli*; Kroemer 2024 Fig. 1).
- **Notes:** Specific scope because the value is bound to avibactam and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_REL_MGL (**canonical for static in-vitro relebactam concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) relebactam concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_REL_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_relebactam_qsp.R` (static 4 mg/L, a fixed concentration within the clinically relevant range; MIC not determined for beta-lactamase inhibitors; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to relebactam and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_SUL_MGL (**canonical for static in-vitro sulbactam concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) sulbactam concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_SUL_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_sulbactam_qsp.R` (static 4 mg/L, a fixed concentration within the clinically relevant range; MIC not determined for beta-lactamase inhibitors; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to sulbactam and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_TZB_MGL (**canonical for static in-vitro tazobactam concentration driving a receptor-binding or antibacterial PD model**)
- **Description:** Static (time-invariant) tazobactam concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_TZB_MGL). The lysed-cell arm of that assay sets `CELLS_INTACT = 0`, which zeroes the influx term, so the covariate has no effect there.
- **Source aliases:** none standardized (Lopez-Arguello 2023 writes `C_drug` in Eq 1 and `CDRUG` in the Fig. S8 estimation code).
- **Example models:** `LopezArguello_2023_tazobactam_qsp.R` (static 4 mg/L, a fixed concentration within the clinically relevant range; MIC not determined for beta-lactamase inhibitors; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to tazobactam and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

### CONC_RIF_MGL (**canonical for static in-vitro rifampicin concentration driving an antibacterial PD model**)
- **Description:** Static (time-invariant) rifampicin concentration in the growth medium of an in-vitro antibacterial time-kill experiment, supplied as an exogenous covariate that drives the bacterial-kill PD effect. Distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<DRUG>` plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the sigmoidal kill function; set to 0 for regimens without rifampicin.
- **Source aliases:**
  - `CRIF` -- used in `Clewe_2018_TB_MTP_GPDI_invitro.R` (Clewe 2018 Materials and methods; static rifampicin concentration).
- **Example models:** `Clewe_2018_TB_MTP_GPDI_invitro.R` (static rifampicin concentration driving the multistate-TB kill effects; tested concentrations 0.002, 0.008, 0.03, 0.125, 0.5, 8 mg/L per Figure 1).
- **Notes:** Specific scope because the value is bound to rifampicin and the in-vitro experimental design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`) -- use this family for exogenous static or time-varying drug concentrations in in-vitro time-kill / hollow-fiber PD models, as distinct from the `CP_<DRUG>` plasma-concentration PD-driver family. Ratified canonically on 2026-05-27 alongside the Clewe 2018 extraction.

### CONC_INH_MGL (**canonical for static in-vitro isoniazid concentration driving an antibacterial PD model**)
- **Description:** Static (time-invariant) isoniazid concentration in the growth medium of an in-vitro antibacterial time-kill experiment, supplied as an exogenous covariate that drives the bacterial-kill PD effect and the adaptive-resistance transition. Applied experimental concentration in the in-vitro matrix; distinct from `Cc` and the `CP_<DRUG>` plasma-PD-driver family.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- drives the kill effects on the fast- and slow-growing sub-states and the adaptive-resistance AR_on/AR_off transition rate; set to 0 for regimens without isoniazid.
- **Source aliases:**
  - `CINH` -- used in `Clewe_2018_TB_MTP_GPDI_invitro.R` (Clewe 2018 Materials and methods; static isoniazid concentration).
- **Example models:** `Clewe_2018_TB_MTP_GPDI_invitro.R` (static isoniazid concentration driving the kill effects on the F and S sub-states and the adaptive-resistance transition `kon * CONC_INH_MGL`; tested concentrations 0.01, 0.039, 0.156, 0.625, 2.5, 10, 40 mg/L per Figure 1).
- **Notes:** Specific scope because the value is bound to isoniazid and the in-vitro experimental design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-05-27 alongside the Clewe 2018 extraction.

### CONC_EMB_MGL (**canonical for static in-vitro ethambutol concentration driving an antibacterial PD model**)
- **Description:** Static (time-invariant) ethambutol concentration in the growth medium of an in-vitro antibacterial time-kill experiment, supplied as an exogenous covariate that drives the bacterial-kill PD effect. Applied experimental concentration in the in-vitro matrix; distinct from `Cc` and the `CP_<DRUG>` plasma-PD-driver family.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the kill function; set to 0 for regimens without ethambutol.
- **Source aliases:**
  - `CEMB` -- used in `Clewe_2018_TB_MTP_GPDI_invitro.R` (Clewe 2018 Materials and methods; static ethambutol concentration).
- **Example models:** `Clewe_2018_TB_MTP_GPDI_invitro.R` (static ethambutol concentration driving the multistate-TB kill effects; tested concentrations 0.0078, 0.031, 0.125, 0.5, 2, 8, 32 mg/L per Figure 1).
- **Notes:** Specific scope because the value is bound to ethambutol and the in-vitro experimental design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_IPM_MGL`, `CONC_TOB_MGL`). Ratified canonically on 2026-05-27 alongside the Clewe 2018 extraction.

### CONC_BAI_UM (**canonical for static in-vitro baicalein concentration driving an inflammatory-mediator PD model**)
- **Description:** Static (time-invariant) baicalein concentration in the cell-culture medium of an in-vitro LPS-stimulated-macrophage experiment, supplied as an exogenous covariate that drives the anti-inflammatory PD effect. Applied experimental concentration in the in-vitro matrix; distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<drug>` plasma-PD-driver family.
- **Units:** uM
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the log-linear inhibition term `f(Bai) = alpha * log(CONC_BAI_UM + 1)` (the +1 shift encodes the control with CONC_BAI_UM = 0 -> f = 0 without an undefined ln(0)); set to 0 for the control well. Reference values observed: Xiang 2018 tested 0 (control), 10, 20, and 40 uM (Materials and Methods; Figures 3-4).
- **Source aliases:** none -- the source uses `C_Bai` (Xiang 2018 Eq 2) in prose; the model column is the canonical `CONC_BAI_UM`.
- **Example models:** `Xiang_2018_baicalein.R` (time-invariant baicalein concentration driving the log-linear inhibition of LPS-stimulated TNF-alpha production in RAW264.7 macrophages, propagating downstream to IL-6, iNOS, and NO).
- **Notes:** Specific scope because the value is bound to baicalein and the in-vitro experimental design. Member of the in-vitro applied-drug-concentration `CONC_<drug>_<units>` family; here the unit is uM (sibling concentration covariates such as `CONC_RIF_MGL` are reported in mg/L, so the `<units>` suffix is load-bearing). Distinct from the `STIM_<drug>_<units>` antimalarial-well family and the `CP_<drug>` plasma-PD-driver family. Ratified canonically alongside the Xiang 2018 baicalein extraction.

### CONC_OXA_UM (**canonical for static in-vitro oxaliplatin concentration driving a tumour-organoid cytotoxicity PD model**)
- **Description:** Static (time-invariant) oxaliplatin concentration applied to the culture medium of a patient-derived tumour-organoid (PDTO) drug-sensitivity assay, supplied as an exogenous covariate that drives the sigmoidal Emax killing term. Applied experimental concentration in the in-vitro matrix; distinct from a state-derived plasma concentration (`Cc`) and from the `CP_<drug>` plasma-PD-driver family.
- **Units:** uM
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the Hill killing term `Emax * C^hill / (EC50^hill + C^hill)`; set to 0 for the vehicle-control organoid. Zhu 2023 does not tabulate the tested concentration grid; Figure 2A spans roughly 1-1000 umol/L, bracketing the estimated EC50 range of 246-622 umol/L (Table 2).
- **Source aliases:** none -- the source writes `C` in Equation (2); the model column is the canonical `CONC_OXA_UM`.
- **Example models:** `Zhu_2023_oxaliplatin_organoid.R` (96 h static oxaliplatin exposure of colorectal-cancer PDTOs; cell viability read as the treated-to-vehicle-control organoid volume ratio).
- **Notes:** Specific scope because the value is bound to oxaliplatin and to the in-vitro organoid assay design. Member of the in-vitro applied-drug-concentration `CONC_<drug>_<units>` family; the unit suffix is load-bearing because sibling entries such as `CONC_RIF_MGL` are reported in mg/L while the organoid assays are reported in umol/L. Ratified canonically alongside the Zhu 2023 extraction.

### CONC_SN38_UM (**canonical for static in-vitro SN-38 concentration driving a tumour-organoid cytotoxicity PD model**)
- **Description:** Static (time-invariant) SN-38 concentration applied to the culture medium of a patient-derived tumour-organoid (PDTO) drug-sensitivity assay, supplied as an exogenous covariate that drives the sigmoidal Emax killing term. SN-38 is the active metabolite of irinotecan. Applied experimental concentration in the in-vitro matrix; distinct from a state-derived plasma concentration (`Cc_sn38`) and from the `CP_<drug>` plasma-PD-driver family.
- **Units:** uM
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the Hill killing term `Emax * C^hill / (EC50^hill + C^hill)`; set to 0 for the vehicle-control organoid. Zhu 2023 does not tabulate the tested concentration grid; Figure 2B spans roughly 0.01-100 umol/L, bracketing the estimated EC50 range of 4.17-15.5 umol/L (Table 2).
- **Source aliases:** none -- the source writes `C` in Equation (2); the model column is the canonical `CONC_SN38_UM`.
- **Example models:** `Zhu_2023_sn38_organoid.R` (96 h static SN-38 exposure of colorectal-cancer PDTOs; cell viability read as the treated-to-vehicle-control organoid volume ratio).
- **Notes:** Specific scope because the value is bound to SN-38 and to the in-vitro organoid assay design. Member of the in-vitro applied-drug-concentration `CONC_<drug>_<units>` family; `SN38` matches the registered metabolite suffix `sn38` used for the paired compartment and parameter names. Ratified canonically alongside the Zhu 2023 extraction.

### CONC_IPM_MGL (**canonical for in-vitro imipenem concentration driving an antibacterial PD or receptor-binding model**)
- **Description:** Unbound imipenem concentration applied to an in-vitro bacterial system, supplied externally as an exogenous covariate. Applied experimental concentration in the in-vitro matrix; distinct from `Cc` and the `CP_<DRUG>` plasma-PD-driver family. Used both time-varying (hollow-fiber infection model growth medium, driving the bacterial-kill PD effect) and static (60-min whole-cell penicillin-binding-protein binding assay, scaling the rate of net influx and PBP access).
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the sigmoidal (Hill) imipenem kill function; set to 0 for tobramycin-monotherapy or control arms.
- **Source aliases:** none standardized (the model column uses the canonical name directly; Landersdorfer 2018 labels it "imipenem concentration" in the Methods and Fig. 1 legend).
- **Example models:** `Landersdorfer_2018_imipenem_tobramycin.R` (externally-supplied time-varying unbound imipenem concentration driving the Hill kill function; the HFIM used continuous infusion targeting the 5th-percentile 7.6, median 13.4, and 95th-percentile 23.3 mg/L unbound concentrations from imipenem 4 g/day continuous infusion in critically ill patients), `LopezArguello_2023_imipenem_qsp.R` (static 2 mg/L = 2x the MIC of 1 mg/L, scaling the rate of net influx and PBP access in the whole-cell PBP-binding QSP model; Lopez-Arguello 2023 Table 1).
- **Notes:** Specific scope because the value is bound to imipenem and to an in-vitro assay design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_TOB_MGL`, and the fourteen `CONC_<DRUG>_MGL` entries ratified with the Lopez-Arguello 2023 extraction). Renamed from the model's earlier bare `Cipm` column on 2026-05-27 for consistency with the `CONC_<DRUG>_MGL` family. Ratified canonically on 2026-05-27 alongside the Landersdorfer 2018 extraction; scope broadened on 2026-07-29 to cover the static in-vitro receptor-binding use.

### CONC_TOB_MGL (**canonical for time-varying in-vitro tobramycin concentration driving an antibacterial PD model**)
- **Description:** Time-varying unbound tobramycin concentration in the hollow-fiber infection model (HFIM) growth medium, supplied externally as an exogenous covariate that drives the bacterial-kill PD effect and the mechanistic-synergy switch on the imipenem KC50. Applied experimental concentration in the in-vitro matrix; distinct from `Cc` and the `CP_<DRUG>` plasma-PD-driver family.
- **Units:** mg/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the Emax tobramycin kill function and gates the discrete imipenem-KC50 synergy reduction at the `tob_cut = 1.15 mg/L` threshold; set to 0 for imipenem-monotherapy or control arms.
- **Source aliases:** none standardized (the model column uses the canonical name directly; Landersdorfer 2018 labels it "tobramycin concentration" in the Methods and Fig. S1 reference).
- **Example models:** `Landersdorfer_2018_imipenem_tobramycin.R` (externally-supplied time-varying unbound tobramycin concentration driving the Emax kill function and the 70-fold imipenem-KC50 synergy reduction against population 3 when CONC_TOB_MGL >= 1.15 mg/L; HFIM simulated the two-compartment unbound profile of 7 mg/kg q24h 0.5-h infusions).
- **Notes:** Specific scope because the value is bound to tobramycin and the in-vitro HFIM design. Member of the in-vitro applied-drug-concentration `CONC_<DRUG>_MGL` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_EMB_MGL`, `CONC_IPM_MGL`). Renamed from the model's earlier bare `Ctob` column on 2026-05-27 for consistency with the `CONC_<DRUG>_MGL` family. Ratified canonically on 2026-05-27 alongside the Landersdorfer 2018 extraction.

### CONC_VORI_NGML (**canonical for in-vivo voriconazole whole-blood concentration driving a CYP3A drug-drug-interaction term**)
- **Description:** Voriconazole whole-blood concentration supplied externally as a (time-varying) covariate that drives a reversible CYP3A-inhibition term inside a victim-drug PBPK model. Distinct from the victim drug's own `Cc`: this is the perpetrator's concentration, provided as data rather than simulated, for models whose perpetrator sub-model is not reproducible from the source. Set to 0 for a victim-drug-alone arm, which collapses the inhibition ratio to 1 (no inhibition).
- **Units:** ng/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the reversible-inhibition ratio `DDIRE = 1 / (1 + CONC_VORI_NGML * fub_vori / KI)`; 0 gives DDIRE = 1.
- **Source aliases:**
  - `Cb,vor` -- symbol used in Pei 2023 supplement Eq 1 ("Cb,vor = whole blood concentration of voriconazole").
- **Example models:** `Pei_2023_tacrolimus_pbpk.R` (reversible CYP3A inhibition applied to the CYP3A-mediated fraction of hepatic tacrolimus clearance, with `fub_vori = 0.42` and `KI = 8.70 ng/mL` from Pei 2023 Table S6; Pei 2023 supplement Eqs 1-2).
- **Notes:** Member of the applied-drug-concentration `CONC_<DRUG>_<UNITS>` family (siblings `CONC_RIF_MGL`, `CONC_INH_MGL`, `CONC_TOB_MGL`, ...), extended here from the in-vitro applied-concentration case to an in-vivo perpetrator concentration; the unit token is `NGML` rather than `MGL` because the founding source reports voriconazole in ng/mL and the inhibition constant KI is tabulated on the same scale. The column exists because Pei 2023 generated Cb,vor from a companion voriconazole PBPK model whose tissue-to-plasma partition coefficients, absorption rate and absorbed fraction are not tabulated anywhere in the paper or supplement (Table S6 gives only MW, pKa, LogP, fup, BPR, KI and CLint); exposing the perpetrator concentration as data encodes the published inhibition equation faithfully without substituting distribution parameters from another paper, which the PBPK / QSP sourcing rule forbids. Distinct from the binary [[CONMED_VORICONAZOLE]], which is the right column when a popPK model estimates an on/off coefficient rather than a concentration-driven inhibition term. Ratified 2026-08-05 alongside the Pei 2023 tacrolimus PBPK extraction.

### CONMED_RTV_AUC (**canonical for ritonavir AUC over the 0-24 h dosing interval**)
- **Description:** Per-subject (time-fixed within an evaluated regimen) ritonavir AUC over the 0-24 h once-daily dosing interval, used as a co-medication exposure covariate driving boosted-protease-inhibitor (atazanavir, lopinavir, darunavir, etc.) clearance in popPK models that account for ritonavir's CYP3A4-inhibition effect. In Dickinson 2009 the value is computed by non-compartmental methods on the observed ritonavir concentration-time profile (WinNonlin 5.2) and feeds the atazanavir CL/F power form `cl = exp(lcl) * (CONMED_RTV_AUC / 7.52)^e_aucrtv_cl`.
- **Units:** `mg*h/L` (document per-model via `covariateData[[CONMED_RTV_AUC]]$units` if a different exposure unit is reported).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via centred power form. Reference value observed: 7.52 mg*h/L (Dickinson 2009 cohort median across HIV-infected and healthy volunteers; healthy 7.36, HIV 7.59 per Table 1).
- **Source aliases:**
  - `RTVAUC` -- printed name in Dickinson 2009 Table 1 / Table 3 (the paper writes `RTVAUC0-24` with the dosing-interval subscript; the column name is registered without the subscript to fit standard data-column character constraints).
- **Example models:** `Dickinson_2009_atazanavir.R` (atazanavir CL/F power-function dependence on RTVAUC0-24, centred at 7.52 mg*h/L with exponent -0.8).
- **Notes:** Specific scope because the column meaning is tied to ritonavir as the booster drug and to the 0-24 h once-daily dosing-interval AUC convention. A future PK model that uses a different ritonavir exposure metric (trough concentration, q12h-interval AUC for BID ritonavir regimens) should register a parallel canonical rather than overload `CONMED_RTV_AUC`. For simulation users without observed ritonavir AUC, the Dickinson 2009 cohort median 7.52 mg*h/L reproduces typical-value behaviour (the centring point of the covariate effect). Distinct from `CONMED_RTV` (binary boost-status flag used in Colombo 2006); the two canonicals coexist for the binary-indicator and continuous-exposure parameterisations of the same co-medication.


### DOSE_RTV_MGKG (**canonical for concomitant ritonavir per-administration dose per kg body weight**)
- **Description:** Concomitant ritonavir dose given per administration in mg per kg of body weight (mg/kg). Per-dose-record covariate carried on the lopinavir (or other RTV-boosted antiretroviral) dose row so the parent drug's bioavailability or clearance equation can read the co-administered ritonavir dose at the moment of dosing. Time-varying across cohort / regimen changes; constant within an inter-dose interval at steady state. Distinct from the per-administration mg dose of ritonavir on the RTV dose-event row -- this covariate is the value mg-divided-by-kg, available on every record so the bioavailability formula can be evaluated without back-computing dose from the event table.
- **Units:** mg/kg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a linear-shift regressor in the LPV bioavailability formula `F_LPV = 1 - RIF * CONMED_RIF + SLP * (DOSE_RTV_MGKG - DOSE_RTV_STD)` of Zhang 2012 Equation 4, with `DOSE_RTV_STD = 3 mg/kg` (the median ritonavir dose in the no-rifampicin standard-LPV/r arm). The standard 4:1 LPV/r without rifampicin reference uses `DOSE_RTV_MGKG = 3` so the linear-shift term vanishes; super-boosted 1:1 LPV/r at median LPV 14 mg/kg gives `DOSE_RTV_MGKG = 14`; double-dose 4:1 LPV/r at median LPV 23 mg/kg gives `DOSE_RTV_MGKG = 5.75`.
- **Source aliases:**
  - `DoseRTV` -- used in `Zhang_2012_lopinavir_ritonavir.R` (paper Equation 4 regressor; in the paper's NONMEM dataset this is the per-dose individual ritonavir mg/kg carried as a per-dose covariate column rather than a derived state).
- **Example models:** `Zhang_2012_lopinavir_ritonavir.R` (linear-shift on LPV bioavailability: `F_LPV = 1 - 0.832 * CONMED_RIF + 0.021 * (DOSE_RTV_MGKG - 3)`; the linear approximation is valid only inside the cohort-tested ritonavir-dose range of 2.9-14 mg/kg per Zhang 2012 Discussion paragraph 3).
- **Notes:** Specific scope because the absolute slope coefficient (0.021 / mg/kg in Zhang 2012) and the reference dose (3 mg/kg) are intrinsically tied to the lopinavir-ritonavir-rifampicin pediatric integrated model. Drug-self-dose covariates for other drugs should register sibling canonicals (e.g., `DOSE_<DRUG>_MGKG` for per-administration or `DOSE_<DRUG>_MGKGD` for daily) rather than reuse this name. Carrying ritonavir dose as an explicit covariate rather than reading it from the RTV dose-event amount avoids tightly coupling the LPV bioavailability formula to the rxode2 event-table structure and lets a downstream simulation user supply a synthetic per-administration ritonavir dose without constructing matching event records. Ratified canonically on 2026-06-10 alongside the Zhang 2012 lopinavir-ritonavir extraction.


### DOSE_RIV_MGKG (**canonical for per-administration rivaroxaban dose per kg body weight**)
- **Description:** Per-administration rivaroxaban dose in mg per kg of body weight (mg/kg). Per-dose-record covariate carried on the rivaroxaban dose row so the dose-dependent relative oral bioavailability function can read the per-event mg/kg value at the moment of dosing. Time-varying across study phase / regimen changes (single dose, b.i.d., t.i.d.) and weight-band re-titrations; constant within an inter-dose interval at steady state.
- **Units:** mg/kg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters non-linearly as the input to the exponential decay function `tf1 = f1min + (f1max - f1min) * exp(-log(2)/d50 * DOSE_RIV_MGKG)` of Willmann 2021 supplement S2 NONMEM $PK block, with `f1max = 1.25`, `f1min = 0.59`, `d50 = 14.4/82.48 = 0.1746` mg/kg, anchored to F1 = 1.0 at `DOSE_RIV_MGKG = 10/82.48 = 0.1213` mg/kg (the median adult dose per the integrated adult popPK analysis in Willmann 2021 reference 19). Reference values observed: 0.1213 mg/kg (adult-anchor F1 = 1.0); 0.30 mg/kg (F1 = 0.791); 0.50 mg/kg (F1 = 0.681) -- Willmann 2021 Results p. 1199. Bodyweight-normalised single rivaroxaban doses ranged 0.1-0.5 mg/kg across the EINSTEIN-Jr pediatric program (Willmann 2021 Results p. 1199 and Figure 1).
- **Source aliases:**
  - `DW` (= `DOSE/WGHT` in supplement S2 $PK block) -- used in `Willmann_2021_rivaroxaban.R` (supplement S2 NONMEM $PK block `DW = DOSE/WGHT ; TF1 = F1MIN + (F1MAX - F1MIN) * EXP(-LOG(2)/D50 * DW)`).
- **Example models:** `Willmann_2021_rivaroxaban.R` (input to the exponential-decay relative-bioavailability function for the pediatric popPK model of rivaroxaban; `tf1 = 0.59 + 0.66 * exp(-log(2)/0.1746 * DOSE_RIV_MGKG)` with `f1 = tf1 * exp(etalfdepot)` and `f(depot) <- f1`).
- **Notes:** Specific scope because the absolute constants `f1max`, `f1min`, and `d50` are intrinsically tied to the dose-dependency function carried over from the integrated adult popPK analysis in Willmann 2021 reference 19 and re-anchored to F1 = 1.0 at 10 mg/82.48 kg for the pediatric extrapolation. Carrying rivaroxaban dose per kg as an explicit covariate column (rather than back-computing from the rxode2 event-table amt and a body-weight column) lets the bioavailability formula be evaluated on every record including non-dose observation records. Mirrors the sibling drug-self-dose canonicals `DOSE_PHT_MGKGD` (Yukawa 1990 daily phenytoin dose per kg) and `DOSE_RTV_MGKG` (Zhang 2012 per-administration ritonavir dose per kg); pattern `DOSE_<DRUG>_MGKG` (per-administration) vs `DOSE_<DRUG>_MGKGD` (daily). Ratified canonically alongside the Willmann 2021 rivaroxaban pediatric popPK extraction.

### DOSE_GLN_GKG (**canonical for per-administration L-glutamine dose per kg body weight**)
- **Description:** Per-administration oral L-glutamine dose in grams per kg of body weight (g/kg). Carried on every record (not only dose rows) so a dose-dependent covariate term can be evaluated at observation times as well. Time-varying across dose-escalation periods; constant within a dose level.
- **Units:** g/kg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a power term normalised to the lowest studied dose level, `(DOSE_GLN_GKG / 0.1)^theta`. Sadaf 2024 studied 0.1, 0.3, and 0.6 g/kg, with 0.1 g/kg as the normalisation reference.
- **Source aliases:**
  - `DOSE per kg` -- used in `Sadaf_2024_glutamine.R` (Sadaf 2024 Table 2 `Vi = theta3 * (WT/70) * (DOSE per kg/0.1)^theta4`).
- **Example models:** `Sadaf_2024_glutamine.R` (power effect on apparent central volume: `vc = 52.3 * (WT/70)^1 * (DOSE_GLN_GKG/0.1)^0.27`, Sadaf 2024 Table 2 theta4 = 0.27).
- **Notes:** Specific scope until a second L-glutamine model ratifies the canonical. Member of the `DOSE_<DRUG>_<UNITS>` drug-self-dose family alongside `DOSE_RIV_MGKG`, `DOSE_RTV_MGKG`, `DOSE_PHT_MGKGD`, and `DOSE_VPA_MGKGD`; the `GKG` unit token is used rather than `MGKG` because oral amino-acid supplementation is dosed in grams per kg, and silently rescaling to mg/kg would make the published normalisation reference (0.1) read as 100. Distinct from `GLN_BL`, the *endogenous* baseline glutamine concentration, which is the other Sadaf 2024 covariate. Ratified canonically alongside the Sadaf 2024 L-glutamine sickle-cell-disease extraction.


### CP_PRB_MGL (**canonical for instantaneous probenecid plasma concentration as a time-varying renal-tubular-perpetrator covariate**)
- **Description:** Instantaneous plasma concentration of probenecid supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used as the perpetrator-drug input to a competitive inhibition term on saturable (Michaelis-Menten) renal tubular secretion of a co-administered substrate in DDI popPK models. Set to 0 outside the probenecid co-administration window so the apparent-Km inhibition term collapses to the baseline form `km_apparent = km`.
- **Units:** mg/L (probenecid MW = 285.34 g/mol; 1 mg/L = 3.505 umol/L).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as the multiplier on the saturable-secretion apparent-Km term `km_apparent = km * (1 + CP_PRB_MGL / ki)` where `ki` is the probenecid competitive inhibition constant at the renal tubular transporter in mg/L. Reference peak observed: the Landersdorfer 2009 8-dose probenecid regimen (total 4.5 g over 70 h) produces typical plasma probenecid concentrations in the range ~30 to 60 mg/L during the gemifloxacin sampling window (Landersdorfer 2009 Fig. 1C).
- **Source aliases:**
  - `[P]` (Landersdorfer 2009 Methods / Table 1, "plasma probenecid concentration" symbol entering the competitive-inhibition formula `km_apparent = km * (1 + [P] / kic)`).
- **Example models:** `Landersdorfer_2009_gemifloxacin.R` (drives competitive inhibition of the renal-tubular-secretion arm of gemifloxacin clearance: `cl_tubular_secretion = vmax * Cc / (km * (1 + CP_PRB_MGL / ki) + Cc)` with ki = 69.3 mg/L; gemifloxacin filtration clearance (fu * GFR ~ 2 L/h, fixed) and non-renal clearance are unaffected by this term but the static-treatment-arm effect on non-renal clearance is encoded separately via `CONMED_PROBENECID`).
- **Notes:** Specific scope; probenecid-specific. The drug-specific naming follows the established `CP_<drug>_<units>` precedent (`CP_OXY_NGML`, `CP_FBX_NGML`, `CP_LSN_NGML`, `CP_MORPH_NGML`, `CP_RIF_UM`). Distinct from the binary indicator `CONMED_PROBENECID` (which captures treatment-arm-level static effects -- e.g., on absorption or non-renal clearance -- and does not carry plasma-concentration magnitude information). The Landersdorfer 2009 paper does NOT report a probenecid PK sub-model parameter table on disk (the Methods describe the structural form -- 1-compartment + lag + parallel first-order + mixed-order elimination -- but no parameter table), so users simulating the with-probenecid arm must supply `CP_PRB_MGL` from an external probenecid popPK source (e.g., literature digitisation of Landersdorfer 2009 Fig. 1C, or an unrelated published probenecid popPK model). Ratified canonically on 2026-06-07 alongside the Landersdorfer 2009 gemifloxacin extraction.


### CONC_AGONIST_M (**canonical for applied ex-vivo agonist concentration in the Grzesk 2016 vascular-reactivity sigmoidal Emax CRC model**)
- **Description:** Applied concentration (mol/L) of the vasoactive agonist whose identity is selected by the companion `AGONIST_CODE` covariate, supplied as a per-record exogenous covariate to the static sigmoidal Emax concentration-response model of perfusion pressure in the isolated, perfused male Wistar rat tail artery. Polymorphic in agonist identity: a single CONC_AGONIST_M column carries phenylephrine concentrations on phenylephrine records, arg-vasopressin concentrations on arg-vasopressin records, mastoparan-7 concentrations on mastoparan-7 records, and Bay K8644 concentrations on Bay K8644 records, with the per-agonist EC50 / Emax pair selected inside `model()` by `AGONIST_CODE`. The model has no PK and no time dynamics; CONC_AGONIST_M is the dose-response x-axis.
- **Units:** M (mol/L)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters the sigmoidal Emax expression `PP = emax * CONC_AGONIST_M^hill / (ec50^hill + CONC_AGONIST_M^hill)`; CONC_AGONIST_M = 0 yields PP = 0 (no agonist contraction).
- **Source aliases:** none -- Grzesk 2016 Table I reports per-agonist EC50 in M/L directly; the model column is the canonical `CONC_AGONIST_M`.
- **Example models:** `Grzesk_2016_m3M3FBS.R` (Grzesk 2016 Table I CRC data: per-agonist EC50 values 7.50e-8 M (PHE), 1.84e-8 M (AVP), 4.48e-8 M (mastoparan-7), 1.96e-6 M (Bay K8644); m-3M3FBS-shifted EC50 values 6.45e-8, 1.42e-8, 2.55e-8, 2.05e-6 M respectively).
- **Notes:** Specific scope because the polymorphic agonist-identity-selected-by-companion-column semantics are tied to the Grzesk 2016 four-agonist CRC design. Distinct from the `STIM_<drug>_<units>` and `CONC_<DRUG>_<UNITS>` in-vitro-drug-concentration families (where the drug identity is fixed in the canonical name and the unit suffix is mandatory) because Grzesk 2016 deliberately tests four mechanistically distinct vasoactive agonists in a shared experimental scaffold with a shared concentration axis -- baking the agonist identity into the canonical name would force four parallel columns rather than the paper's one-column-plus-selector encoding. M (mol/L) chosen as the unit because Grzesk 2016 Table I reports EC50 values in mol/L and the four agonists span 1e-8 to 1e-6 M (a uniform molar axis is the only practical choice); the `_M` suffix preserves the `CONC_<...>_<UNITS>` family's unit-in-name discipline. Future ex-vivo / in-vitro CRC papers from the Grzesk-group series (Biomed Rep 2 / 2014; Mol Med Report 5 / 2012; Exp Ther Med 4 / 2012; etc.) that test multiple agonists on the same vascular-reactivity scaffold should reuse this canonical with an extended `AGONIST_CODE` mapping. Ratified canonically alongside the Grzesk 2016 extraction.


### CP_REM_NGML (**canonical for instantaneous remikiren plasma concentration as a time-varying PD driver**)
- **Description:** Instantaneous plasma concentration of remikiren (Ro 42-5892; orally active renin inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only Hill / Emax models that take remikiren exposure as an external input to a renin-inhibition concentration-effect equation.
- **Units:** ng/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a Hill / Emax term in the inhibition-of-angiotensin-I-production-rate expression `APR = Imax * CP_REM_NGML^hill / (IC50^hill + CP_REM_NGML^hill)` (Weber 1993). Set to 0 outside the drug-exposure window (the inhibition term then collapses to 0). Reference values observed: mean Cmax was 4-6 ng/mL after 200 mg oral, 23-27 ng/mL after 300 mg oral, 47-83 ng/mL after 600-800 mg oral, with substantial intersubject variability (Weber 1993 Table 1).
- **Source aliases:**
  - `Cp` (Weber 1993 Methods 'Concentration-effect modelling' Hill equation; values in ng/mL) -- the observed plasma remikiren concentration measured by HPLC with fluorescence or coulometric electrochemical detection or by a protein-binding assay (Weber 1993 Methods, 'Remikiren concentrations' section).
- **Example models:** `Weber_1993_remikiren.R` (PD-only Hill / Emax model for inhibition of the angiotensin I production rate in hypertensive patients; CP_REM_NGML is the observed or simulated plasma concentration of remikiren supplied per event row).
- **Notes:** Specific scope; remikiren-specific. The drug-specific naming follows the established `CP_<drug>_<units>` precedent (`CP_OXY_NGML`, `CP_FBX_NGML`, `CP_LSN_NGML`, `CP_MORPH_NGML`, `CP_RIF_UM`). Weber 1993 fit the PD model directly to observed plasma concentrations because no structural population PK model was estimated -- the paper characterised remikiren PK with model-independent NCA only. Users wishing to drive the PD model from a simulated PK source must supply their own concentration trajectory (the published single-dose Weber-group papers from the same series report compartmental PK in healthy volunteers but are not on disk in nlmixr2lib). Ratified canonically on 2026-06-10 alongside the Weber 1993 remikiren PD extraction.


### CP_RITUXIMAB_UGML (**canonical for instantaneous rituximab plasma concentration as a time-varying PD driver**)
- **Description:** Instantaneous plasma concentration of rituximab (chimeric anti-CD20 monoclonal antibody), or of one of its biosimilars, supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only exposure-response models that take rituximab exposure as an external input to a concentration-effect equation (e.g., Emax on DAS28 change from baseline).
- **Units:** ug/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as an Emax-style concentration-effect term `fC = Emax * CP_RITUXIMAB_UGML / (CP_RITUXIMAB_UGML + ec50)`. Set to 0 outside the drug-exposure window (the concentration-effect term then collapses to 0). Reference values observed: typical steady-state Cmax after the two-dose loading course (1000 mg IV on days 1 and 15) reaches the low hundreds of ug/mL and washes out to <1 ug/mL by week 24 (Williams 2016 Supplemental Figure S1 VPC).
- **Source aliases:**
  - `CONC` -- Williams 2016 popPK/PD analysis; the paper's popPK model produced individual predicted concentrations `Cij` that were passed to the DAS28cfb PD model as an input variable.
- **Example models:** `Williams_2016_rituximab_das28cfb.R` (PD-only Emax-style exposure-response model for the DAS28 change from baseline; the source rituximab popPK model was two-compartment with baseline-BSA and sex covariates on CL and Vc, but Williams 2016 does not tabulate the popPK parameter estimates, so users must supply their own concentration trajectory).
- **Notes:** Specific scope; rituximab-specific. Same covariate is used for the biosimilar candidate PF-05280586 in the Williams 2016 trial (PF-05280586 was shown to be PK-similar to reference rituximab). Drug-product distinction is captured by the separate `TRT` covariate rather than by separate concentration columns. Ratified canonically on 2026-07-09 alongside the Williams 2016 biosimilar-rituximab DAS28cfb extraction.


### CP_GLASDEGIB_NGML (**canonical for instantaneous glasdegib plasma concentration as a time-varying PD driver**)
- **Description:** Instantaneous plasma concentration of glasdegib (PF-04449913; orally active Hedgehog pathway / Smoothened inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only linear exposure-response models that take glasdegib exposure as an external input to a concentration-effect equation (e.g., concentration-QTc).
- **Units:** ng/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a linear-slope term in the concentration-QTc expression `QTcF = e0 + slope * (CP_GLASDEGIB_NGML / 1000)` (Fostvedt 2021; the in-model `/ 1000` converts the canonical ng/mL covariate to the paper's reported microgram-per-mL slope scaling). Set to 0 outside the drug-exposure window (the concentration-slope term then collapses to 0). Reference values observed: geometric-mean steady-state Cmax was 1137 ng/mL at 100 mg QD (therapeutic dose) and 2445 ng/mL at 200 mg QD (a 2-fold supratherapeutic exposure used in Fostvedt 2021's bootstrap predictions; see Fostvedt 2021 Methods 2.3 and Table 4).
- **Source aliases:**
  - `CONC` (Fostvedt 2021 Methods 2.2 and 2.3 'concentration-QTc modelling' equations; values in ng/mL but model fit with the slope reported per microgram-per-mL after a `CONC / 1000` rescaling internal to the estimation -- "The estimates are reported on the microgram scale as the scaling helped the estimation procedure", Fostvedt 2021 Table 3 footnote).
- **Example models:** `Fostvedt_2021_glasdegib_QTcF.R`, `Fostvedt_2021_glasdegib_QTcS.R` (PD-only linear mixed-effects E-R model for QTcF / QTcS prolongation in patients with cancer; CP_GLASDEGIB_NGML is the observed or simulated plasma concentration supplied per event row).
- **Notes:** Specific scope; glasdegib-specific. The drug-specific naming follows the established `CP_<drug>_<units>` precedent (`CP_OXY_NGML`, `CP_FBX_NGML`, `CP_LSN_NGML`, `CP_MORPH_NGML`, `CP_RIF_UM`, `CP_REM_NGML`). Fostvedt 2021 fit the PD model directly to observed plasma concentrations because no structural population PK model is published in the source paper -- the geometric-mean Cmax values used for the parametric-bootstrap QTcF predictions (Fostvedt 2021 Section 3.4; cited to reference [15] of the paper) come from a separate Pfizer phase 2 study in patients with hematologic malignancies, not from a parameterised popPK model included in this paper. Users wishing to drive the PD model from a simulated PK source must supply their own concentration trajectory; no glasdegib popPK model exists in the nlmixr2lib registry. Ratified canonically on 2026-06-24 alongside the Fostvedt 2021 glasdegib QTc extraction.

### CP_RACSOTALOL_UGML (**canonical for instantaneous rac-sotalol plasma concentration as a time-varying PD driver**)
- **Description:** Instantaneous plasma concentration of rac-sotalol (racemic 4-[1-hydroxy-2-(propan-2-ylamino)ethyl]-N-methyl-benzene-sulfonamide; class III antiarrhythmic with non-selective beta-adrenoceptor blocking activity) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only linear exposure-response models that take rac-sotalol exposure as an external input to a concentration-DeltaQTc equation.
- **Units:** ug/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters as a linear-slope term in the concentration-DeltaQTc expression `DeltaQTcI = e0 + slope * CP_RACSOTALOL_UGML + ...` (Darpo 2014 Table 1 and Figure 6 captions; the slope is reported in ms per ug/mL, so no in-model unit rescaling is needed). Set to 0 outside the drug-exposure window (the concentration-slope term then collapses to 0). Reference values observed: Tmax 2.8 h (men) / 2.9 h (women); Cmax 1.4 ug/mL (men, range 0.9-1.9) / 1.8 ug/mL (women, range 1.1-2.8); AUC_inf 14.6 (men) / 17.1 (women) ug/mL*h after a single 160 mg oral dose (Darpo 2014 Results 'Rac-sotalol pharmacokinetic profile' and Figure 1).
- **Source aliases:**
  - `rac-sotalol plasma concentration` (Darpo 2014 Methods 'Study outline' and Table 1; values in ug/mL throughout, as quantitated by a validated HPLC assay with LLOQ 10 ng/mL and inter-day CV < 8.4%).
- **Example models:** `Darpo_2014_racSotalol_QTcI.R`, `Darpo_2014_racSotalol_QTcF.R` (PD-only linear mixed-effects E-R model for DeltaQTcI / DeltaQTcF in healthy adults after single 160 mg oral rac-sotalol; CP_RACSOTALOL_UGML is the observed or simulated plasma concentration supplied per event row).
- **Notes:** Specific scope; rac-sotalol-specific. The drug-specific naming follows the established `CP_<drug>_<units>` precedent (`CP_OXY_NGML`, `CP_FBX_NGML`, `CP_LSN_NGML`, `CP_MORPH_NGML`, `CP_RIF_UM`, `CP_REM_NGML`, `CP_GLASDEGIB_NGML`). The "rac" prefix is preserved from the source paper because Darpo 2014 consistently uses "rac-sotalol" to distinguish the dosed racemate (Betapace) from d-sotalol (the dextro-isomer studied separately in the SWORD trial cited in the Darpo Discussion). Darpo 2014 fit the PD model directly to observed plasma concentrations because no structural population PK model was estimated -- the paper characterised rac-sotalol PK with NCA only (descriptive Cmax / Tmax / AUC). Users wishing to drive the PD model from a simulated PK source must supply their own concentration trajectory; no rac-sotalol popPK model exists in the nlmixr2lib registry. Ratified canonically on 2026-06-30 alongside the Darpo 2014 rac-sotalol concentration-QTc extraction.


### DOSE_OBILTOXAXIMAB_MGKG (**canonical for administered obiltoxaximab dose level in mg/kg**)
- **Description:** Per-subject obiltoxaximab dose level, in mg/kg of body weight, carried as a covariate so a dose-response model can consume it directly. Constant per subject in single-dose trigger-to-treat designs. Set to 0 for placebo subjects, which reduces a dose-response term to its untreated baseline.
- **Units:** mg/kg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- drives the Emax dose-response on the logit cure fraction, `Emax * DOSE_OBILTOXAXIMAB_MGKG / (ED50 + DOSE_OBILTOXAXIMAB_MGKG)`, with ED50 = 1.64 mg/kg in the founding example.
- **Source aliases:**
  - `DOSE` -- used in `Nagy_2017_obiltoxaximab_survival.R` (Nagy 2017 Results, "Animal survival modeling"; dose levels 0 / 1 / 4 / 8 / 16 / 32 mg/kg i.v. across rabbit Study 2 and macaque Studies 2-5 per Table 1).
- **Example models:** `Nagy_2017_obiltoxaximab_survival.R` (Weibull cure-rate survival model; the paper reports ED50 = 1.64 mg/kg (95% CI 0.515-5.22) and ED90 = 14.8 mg/kg (95% CI 4.6-47.0) from this term, and selects 16 mg/kg as the efficacious dose because it exceeds the ED90).
- **Notes:** Follows the `DOSE_<DRUG>_<UNITS>` auto-approve family (siblings: `DOSE_EMPA_MGD`, `DOSE_LOR_MGD`, `DOSE_SEMAGLUTIDE_MG`, `DOSE_PHT_MGKGD`). The `_MGKG` token records that obiltoxaximab is dosed per kilogram of body weight rather than as a flat dose, which is load-bearing here: the whole animal-to-human translation argument rests on the same 16 mg/kg per-kg dose producing comparable Cmax and superior AUC in humans versus animals. Distinct from the rxode2/nlmixr2 event column `amt` -- this is a per-subject covariate carrying the assigned dose LEVEL for a model that has no PK compartment, whereas `amt` carries an administered amount at a dose event. The companion population PK model `Nagy_2017_obiltoxaximab.R` does not use this covariate; it takes the dose through the ordinary event table in mg. Ratified canonically alongside the Nagy 2017 obiltoxaximab extraction.


## Count / Markov-feedback PD covariates

These columns are specific to count / Markov / time-to-event PD models that
fit per-record event counts (e.g., daily or monthly seizure counts) with
optional dependence on the previous-period count. Register names retain
their source-paper conventions where those names are unambiguous and
readable.

### TRT_PHASE (**canonical for double-blind active-treatment-phase indicator**)
- **Description:** 1 = the record falls within the active double-blind treatment phase (drug + placebo effects are switched on); 0 = baseline / run-in / off-treatment (drug + placebo effects are zeroed).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (baseline / off-treatment).
- **Source aliases:**
  - `Q2` -- used in the Schoemaker 2018 LEV / BRV pediatric extrapolation (DDMODEL00000239) as the treatment-phase gating multiplier on the combined placebo + drug-effect log-rate term (`LE = LS0 + Q2 * LTRTE`).
- **Example models:** `Schoemaker_2018_levetiracetam.R` (DDMODEL00000239).
- **Notes:** New canonical because the source name `Q2` collides with the canonical PK parameter `q2` (inter-compartmental clearance to peripheral2) -- `q2` could not be used as a covariate column without confusing source-trace lookups. Useful for any phase-gated PD model where placebo and drug effects are constrained to a specific study period; analogous to a "treatment-on" indicator.

### PDV (**canonical for previous-period observed count (Markov-feedback state)**)
- **Description:** Previous-period observed count, supplied per-record as a covariate input to capture Markov dependence on the previous count. For daily-count datasets PDV is the observation on the immediately preceding day; for monthly (or other aggregate) records PDV is the observed count from the immediately preceding interval. The biological identity of the count is paper-specific (seizures in Ahn 2010 / Schoemaker 2018, contrast-enhancing MS lesions in Velez de Mendizabal 2013); the canonical concept is "the immediately preceding observed count, used as a Markov-state covariate".
- **Units:** (count, non-negative integer)
- **Type:** count
- **Scope:** specific
- **Reference category:** n/a -- typically used as `<param> * PDV / (ES50 + PDV)` (Markov-Hill) or as a linear coefficient `<param> * PDV` in additive-mean Markov models (Velez de Mendizabal 2013 equation 5).
- **Source aliases:** `PDV` -- used in the Schoemaker 2018 LEV/BRV pediatric extrapolation (DDMODEL00000239), in the Ahn 2010 pregabalin Markov seizure-count model that the Schoemaker 2018 publication cites as the precursor structure, and in the Velez de Mendizabal 2013 MS CEL-count NB nested MAK2 model.
- **Example models:** `Schoemaker_2018_levetiracetam.R` (DDMODEL00000239), `VelezdeMendizabal_2013_multipleSclerosis.R` (additive-mean Markov form, equation 5).
- **Notes:** Specific scope because the column's interpretation (a Markov feedback state) is intrinsic to count-likelihood Markov-feedback PD models. nlmixr2 / rxode2 cannot natively express a Markov dependence of an observation on the immediately preceding observation as a model state -- supplying PDV as a per-record data column is the operator-approved (sidecar response-001 Q2) way to preserve the published structure. For records where the Markov term should not contribute (e.g., monthly counts in a mixed daily / monthly cohort, or the first monthly observation per subject) two conventions exist: (a) the sentinel value -99, used in `Schoemaker_2018_levetiracetam.R` where the model gates the Markov contribution on `CHILD = 1` so the sentinel is multiplied by zero and is harmless; (b) the natural zero value PDV = 0, used in `VelezdeMendizabal_2013_multipleSclerosis.R` for the first per-subject observation, which makes the Markov contribution exactly zero without any indicator gating. Pick whichever convention matches the source paper's encoding for the model at hand. The companion second-order Markov-state covariate is [[PPDV]] (the observed count two periods prior); see the PPDV entry for the second-order form.

### PPDV (**canonical for second-prior-period observed count (second-order Markov-feedback state)**)
- **Description:** Observed count from two periods prior, supplied per-record as a covariate input to capture a second-order Markov dependence on the count two intervals before the current one. For monthly-count datasets PPDV is the observed count from two monthly MRIs ago; for daily-count datasets it would be the observation from two days prior if a second-order term were used. Companion to the first-order [[PDV]] state.
- **Units:** (count, non-negative integer)
- **Type:** count
- **Scope:** specific
- **Reference category:** n/a -- typically used as a linear coefficient `<param> * PPDV` in additive-mean Markov-count models (Velez de Mendizabal 2013 equation 5).
- **Source aliases:** `PPDV` -- used in the Velez de Mendizabal 2013 MS CEL-count NB nested MAK2 model (paper notation "PPDV" = "previous-previous DV", paralleling PDV = "previous DV").
- **Example models:** `VelezdeMendizabal_2013_multipleSclerosis.R` (equation 5 second-order Markov term; theta_PPDV = 0.150, materially smaller than theta_PDV = 0.447 per the source paper's decreasing-pattern observation across Markov orders).
- **Notes:** Specific scope because the column's interpretation (a second-order Markov feedback state) is intrinsic to count-likelihood Markov-feedback PD models. Set PPDV = 0 at the first two per-subject observations (no second-prior interval) so the second-order Markov contribution is exactly zero; this matches the convention adopted in `VelezdeMendizabal_2013_multipleSclerosis.R`. The Velez de Mendizabal 2013 paper also explored a third-order Markov term [[PPPDV]] -- "previous-previous-previous DV", coefficient theta_PPPDV -- but the third-order fit improvement was not statistically significant, and the third-order column is not registered until / unless a future model retains it.

### NDAYS (**canonical for number of days in the count-record interval**)
- **Description:** Number of days in the interval over which the observed seizure count was tabulated. Multiplies the per-day rate to give the expected count for the record (e.g., `LAMB = exp(LE) * NDAYS`).
- **Units:** days
- **Type:** count
- **Scope:** general
- **Reference category:** n/a -- appears as a multiplier on a per-day rate.
- **Source aliases:** `NDAYS` -- used in the Schoemaker 2018 LEV/BRV pediatric extrapolation (DDMODEL00000239).
- **Example models:** `Schoemaker_2018_levetiracetam.R` (DDMODEL00000239).
- **Notes:** General scope because the count-interval-length concept is shared across any count or rate-based PD model that mixes record granularity (e.g., daily and monthly counts in the same dataset). For pure-daily or pure-monthly cohorts the column is constant; including it as a covariate keeps the model usable in mixed-granularity simulations.

### MOMENT (**canonical for endotracheal suctioning procedural state**)
- **Description:** Procedural state at the time of the pain / distress assessment in invasive-ventilation studies, used to select between separate pre-procedure / intra-procedure / post-procedure baseline parameters. Coded 1 = before suctioning, 2 = during suctioning, 3 = after suctioning.
- **Units:** (categorical, 3 levels)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** 1 (before suctioning).
- **Source aliases:** `MOMENT` -- used in the Valitalo 2017 IRT morphine PD model (DDMODEL00000247).
- **Example models:** `Valitalo_2017_morphine.R` (DDMODEL00000247; selects between the three baseline pain typical values `presuct` / `suct` / `aftsuct` and the matching 3x3 correlated etas).
- **Notes:** Specific scope because the column's meaning is tied to a particular study procedure (endotracheal suctioning during mechanical ventilation in neonates). The Simons 2003 cohort that Valitalo 2017 re-analysed scheduled pain assessments around suctioning events, so MOMENT changes within-subject at each scheduled assessment. Ratified canonically alongside the Valitalo 2017 morphine extraction.

### ITEM (**canonical for pain-assessment item identifier in IRT graded-response models**)
- **Description:** Identifier of the specific pain-assessment item being scored at each observation row, used to dispatch between the IRT discrimination / difficulty parameter sets in a graded-response model. Valitalo 2017 coding: 1 = COMFORT-B alertness; 2 = COMFORT-B calmness/agitation; 3 = COMFORT-B respiratory response; 5 = COMFORT-B body movement; 7 = COMFORT-B facial tension; 12 = VAS (cm, range 0-10); 25 = PIPP brow bulge; 26 = PIPP eye squeeze; 27 = PIPP nasolabial furrow; 28 = NIPS total.
- **Units:** (categorical, 9-10 levels depending on cohort)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** n/a -- selects per-item parameter sets rather than acting as a reference contrast.
- **Source aliases:** `ITEM` -- used in the Valitalo 2017 IRT morphine PD model (DDMODEL00000247).
- **Example models:** `Valitalo_2017_morphine.R` (DDMODEL00000247; switches the IRT graded-response discrimination / difficulty parameters per row).
- **Notes:** Specific scope because the integer-to-item mapping is tied to the Valitalo 2017 NM-TRAN dataset's coding. Other IRT graded-response models in the library (when they are added) may use different integer codings and should register their own canonical (e.g., `ITEM_<study>`) when the codings collide. The COMFORT-B "muscle tension" item is omitted from the Valitalo 2017 coding because it could not be assessed from video recordings.

### OBSTYPE (**canonical for VAS observer type**)
- **Description:** Observer type for visual-analogue-scale pain assessments: 1 = investigator (video-based), 2 = bedside nurse. Used to select between observer-specific VAS difficulty / discrimination THETAs and between observer-specific residual-error SDs.
- **Units:** (categorical, 2 levels)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** 1 (video investigator).
- **Source aliases:** `OBSTYPE` -- used in the Valitalo 2017 IRT morphine PD model (DDMODEL00000247).
- **Example models:** `Valitalo_2017_morphine.R` (DDMODEL00000247; selects between `diff_vas_video` vs `diff_vas_bedside`, `discr_vas_video` vs `discr_vas_bedside`, and `addSd_vas_video` vs `addSd_vas_bedside`).
- **Notes:** Specific scope because the binary observer-coding is tied to the Valitalo 2017 NM-TRAN dataset. Future IRT models with a different observer split (e.g., parent / nurse / physician) should register their own canonical rather than overloading this 2-level coding.

## Race / ethnicity

**Canonical pattern: `RACE_<GROUP>`.** Use one indicator per race/ethnicity group the source models. Reference category is the implicit 0 = all other groups; document explicitly which groups are in the reference. When the source uses composite groups (e.g., "Black or Other"), name them accordingly (`RACE_BLACK_OTHER`) and list the components in `notes`. The base `RACE_<GROUP>` indicators are scope: general; composite groupings are scope: specific because the grouping is tied to the study's analysis plan.

### RACE_BLACK (**canonical for Black / African American race indicator**)
- **Description:** 1 = Black / African American, 0 = other.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (document the actual reference groups used).
- **Source aliases:**
  - `BLACK` -- used in `Hu_2026_clesrovimab.R`, `Robbie_2012_palivizumab.R`.
- **Example models:** `Zhu_2017_lebrikizumab.R` (canonical form), `Robbie_2012_palivizumab.R`, `Wada_2023_sparsentan.R` (log-additive effect on the apparent central volume: `exp(0.309 * RACE_BLACK)`, i.e. 36% higher Vc/F than the White reference; paired with `RACE_ASIAN` so both = 0 selects White).

### RACE_WHITE (**canonical for White race indicator**)
- **Description:** 1 = White, 0 = non-White.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-White; complement composition depends on the source paper, typically pooling Black/African American, Asian, American Indian/Alaska Native, Native Hawaiian/Pacific Islander, Other, Not reported, Unknown). Some papers (e.g., Hu 2014) instead use the Caucasian (RACE_WHITE = 1) subgroup as the typical-value reference; the column encoding is unchanged but the model implements the effect on `(1 - RACE_WHITE)`.
- **Source aliases:**
  - `RACE` (with values `1 = White / 0 = non-White`) -- used in `Lin_2024_casirivimab.R`. Source column name `RACE` is generic; the canonical name is intentionally explicit because some other models use `RACE` for a different dichotomy.
  - `RACE` (Caucasian-vs-non-Caucasian dichotomy as named in Hu 2014 Table 2) -- used in `Hu_2014_bapineuzumab.R`. Same canonical column name and 1 = White / 0 = non-White encoding; the typical-value reference is the Caucasian subgroup, so the model implements the 15% non-Caucasian effect on `(1 - RACE_WHITE)`.
- **Example models:** `Lin_2024_casirivimab.R` (multiplicative fractional change on CL relative to non-White reference), `Hu_2014_bapineuzumab.R` (multiplicative 15% increase in CL for non-Caucasian relative to Caucasian reference).
- **Notes:** Used by papers that dichotomize race as White vs. non-White rather than decomposing into separate group indicators. Sign and reference-category interpretation are inverted relative to `RACE_BLACK` / `RACE_ASIAN` / etc.; do NOT combine `RACE_WHITE` with the decomposed indicators in the same model. The model's typical-value reference category (which subgroup gets the unmodified `lcl` / `lvc`) varies between papers -- Lin 2024 uses non-White as the reference, Hu 2014 uses Caucasian (White) as the reference; both share the same canonical column encoding.

### RACE_BLACK_OTH (**canonical for composite Black/Other group**)
- **Description:** 1 = Black/African American or Other race, 0 = other groups.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = White or Native Hawaiian/Pacific Islander (Clegg 2024 grouping).
- **Source aliases:** `BLACK_OTH` -- used in `Clegg_2024_nirsevimab.R`.
- **Example models:** `Clegg_2024_nirsevimab.R`.
- **Notes:** Kept distinct from `RACE_BLACK` because the composite is not interchangeable.

### RACE_BLACK_HISPANIC (**canonical for composite Black / Hispanic American group**)
- **Description:** 1 = subject self-identifies as Black / African American or as Hispanic American; 0 = White or Asian. Paper-specific composite developed by Thoueille 2023, which first fitted a separate tenofovir clearance per ethnic group (rich model) and then pooled the levels into this two-way split, retained because the reduced grouping cost only dOFV = +2 relative to the full ethnicity model (P > 0.05).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = White or Asian (Thoueille 2023 grouping; the paper's model-building cohort contained only White 71%, Black 23%, Hispanic American 4% and Asian 2%, with no "Other" level, so the indicator and its reference exactly partition the cohort).
- **Source aliases:**
  - Paper's ethnicity levels "Black" + "Hispanic American" -- decompose into `RACE_BLACK_HISPANIC = as.integer(ETHNICITY %in% c("Black", "Hispanic American"))`. Thoueille 2023 Table 2 and Table S3 label the coefficient row "theta_Black or Hispano American" (the papers's spelling of "Hispanic").
- **Example models:** `Thoueille_2023_tenofovir_full.R`, `Thoueille_2023_tenofovir_alafenamide.R` (multiplicative fractional effect on apparent tenofovir clearance: `(1 + e_race_black_hispanic_cl * RACE_BLACK_HISPANIC)` with `e_race_black_hispanic_cl` = 0.119 and 0.122 respectively, i.e. ~12% higher CL/F than the White / Asian reference).
- **Notes:** Operator-ratified sidecar 2026-07-29 (oare_PMC10232258 request-001 Q2, option A). Mutually exclusive with the decomposed constituents [[RACE_BLACK]] and [[RACE_HISPANIC]] -- a model uses either this composite or the two separate indicators, never both, because the composite carries a single pooled coefficient that the constituents would double-count. Distinct from [[RACE_BLACK_OTH]] (Black or Other, reference White / Native Hawaiian-Pacific Islander) and, importantly, from [[RACE_NONBLACK_NONWHITE]], which places Black in the *reference* group and therefore carries the opposite sign convention. Specific scope: the grouping is paper-specific and reflects the empirical clustering of Thoueille 2023's ethnicity covariate step rather than any biological homogeneity across the two constituent populations; future papers that split Black from Hispanic, or that add an "Other" level, should register their own composite rather than overload this one.

### RACE_ASIAN (**canonical for Asian race indicator**)
- **Description:** 1 = Asian, 0 = other.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Source aliases:** `ASIAN` -- used in `Hu_2026_clesrovimab.R`, `Robbie_2012_palivizumab.R`, `Fau_2020_isatuximab.R`. `RAAS` (race-Asian-vs-other indicator as named in Bajaj 2017 Table 1) -- used in `Bajaj_2017_nivolumab.R`. `RACEN` (race-numeric indicator as named in Lu 2019 / Shi 2020 NONMEM control stream; ASIAN = 1 if RACEN == 1) -- used in `Lu_2019_polatuzumab.R`.
- **Example models:** `Zhu_2017_lebrikizumab.R` (canonical form), `Robbie_2012_palivizumab.R`, `Bajaj_2017_nivolumab.R`, `Fau_2020_isatuximab.R`, `Lu_2019_polatuzumab.R` (multiplicative factor `e_asian_vc = 0.929` on acMMAE Vc, i.e., 7.1% lower V1 in Asian patients; verbatim Shi 2020 (PMID 32770353) ethnicity-sensitivity re-quote of the Lu 2019 popPK Asian-race covariate), `Wada_2023_sparsentan.R` (log-additive effect on the apparent central volume: `exp(0.265 * RACE_ASIAN)`, i.e. 30% higher Vc/F than the White reference; paired with `RACE_BLACK` so both = 0 selects White).

### RACE_ASIAN_AMIND_OTH (**canonical for composite Asian / American Indian / Other group**)
- **Description:** 1 = Asian, American Indian / Alaska Native, or Other race; 0 = White or Black. Composite indicator that pools the smaller-N race groups in a population dominated by White and Black subjects, with White + Black serving as the reference category. Distinct from `RACE_ASIAN_AMIND_MULTI` (Clegg 2024 grouping that includes Multiracial; pooled against a different reference), from `RACE_ASIAN_OTH` (within-Asian-population sub-indicator), and from `RACE_BLACK_OTH` (different composite).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = White or Black (the larger-N pooled group used as the reference in the source paper).
- **Source aliases:** none formally; Frey 2013's NONMEM control stream uses the inline race classification rather than a separate named column.
- **Example models:** `Frey_2013_tocilizumab.R` (multiplicative fractional effect on the DAS28 first-order loss rate Kout: `Kout * (1 - 0.25 * RACE_ASIAN_AMIND_OTH)` -- Kout is 25% lower in the Asian/AmInd/Other composite group relative to the White+Black reference).
- **Notes:** Specific scope because the composite grouping is defined by the source paper's analysis plan rather than by a uniform external standard. Do not combine with the decomposed `RACE_ASIAN`, `RACE_OTHER`, etc. indicators in the same model; the composite indicator is mutually exclusive with the decomposition. Ratified canonically on 2026-04-29 in support of the Frey 2013 tocilizumab DAS28 PKPD model. Frey 2013 uses TWO distinct race covariates: this `RACE_ASIAN_AMIND_OTH` indicator on Kout (DAS28-PD-side; pools Asian + AmInd + Other vs White+Black) AND the within-Asian `RACE_ASIAN_OTH` indicator on CL (PK-side; isolates the "Other Asian" subgroup within the Asian-only cohort).

### RACE_ASIAN_AMIND_MULTI (**canonical for composite group**)
- **Description:** 1 = Asian, American Indian / Alaskan Native, or Multiple races, 0 = other.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = White, Black / African American, Native Hawaiian / Pacific Islander, or Other (Clegg 2024 grouping).
- **Source aliases:** `ASIAN_AMIND_MULTI` -- used in `Clegg_2024_nirsevimab.R`.
- **Example models:** `Clegg_2024_nirsevimab.R`.
- **Notes:** Clegg 2024 applies this covariate to both CL and V2 with different coefficients.

### RACE_ASIAN_OTH (**canonical for Asian-other composite race indicator**)
- **Description:** 1 = subject self-identifies as Asian-other (Asian heritage outside the locally-dominant Asian subgroup, e.g. non-Chinese in a Chinese-dominant cohort, or "Other Asian" as a study-defined catch-all category). 0 = otherwise. Reference category is the cohort's dominant race grouping (typically Chinese or White, depending on the study).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0. Document the dominant subgroup explicitly in `covariateData[[RACE_ASIAN_OTH]]$notes` for every model that uses this covariate.
- **Source aliases:** none.
- **Example models:** `Brown_2017_osimertinib.R` (paper's "Asian (not Japanese or Chinese)" composite indicator with linear effect on apparent clearance of the AZ5104 metabolite; reference category Caucasian).
- **Notes:** Distinct from `RACE_ASIAN_AMIND_MULTI` (a 4-way composite of Asian + American Indian + Multiple Races), `RACE_ASIAN_AMIND_OTH` (a 3-way Asian + AmInd + Other composite against a White+Black reference, used in Frey 2013), and `RACE_BLACK_OTH` (different composite). `RACE_ASIAN_OTH` is a within-Asian-population sub-indicator, not a multi-race composite. Operator decision (2026-04-28): kept separate from `RACE_ASIAN` because the paper's "Other Asian" category is its own grouping, not an alias of "Asian (any)". Brown 2017 uses Caucasian (not Chinese) as the dominant reference.

### RACE_ASIAN_NORTHEAST (**canonical for North East Asian composite race indicator**)
- **Description:** 1 = North East Asian heritage (worldwide Chinese, Japanese, or Korean), 0 = non-North East Asian. Composite indicator analogous to `RACE_ASIAN` but specifically restricted to the East Asian subgroup most-relevant to ICH E5 ethnic-sensitivity / Asian-region bridging analyses.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any non-North East Asian race, including South / Southeast Asian, White, Black, etc.).
- **Source aliases:**
  - `RACE_NEAS` -- prior canonical name (pre-2026-06-19 readability standardization).
  - `RAC4` -- used in `Zhou_2021_belimumab.R` (Zhou 2021 Table 2 footnote d).
- **Example models:** `Zhou_2021_belimumab.R` (multiplicative factor 1.07 on V1).
- **Notes:** Distinct from the broader `RACE_ASIAN` (which can include South / Southeast Asian populations) because Zhou 2021 specifically tested whether Chinese/Japanese/Korean patients had different PK from the rest of the dataset; the analysis explicitly compared `RAC4` (North East Asian) against alternative race definitions and chose `RAC4` by AIC. Renamed from `RACE_NEAS` to `RACE_ASIAN_NORTHEAST` on 2026-06-19 per the canonical-register standardization audit (operator decision: spell out "Northeast" rather than the opaque `NEAS` abbreviation; consistent with `RACE_<region>` rather than `RACE_<abbreviation>`).

### RACE_MULTI (**canonical for multiracial indicator**)
- **Description:** 1 = multiracial, 0 = other.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Source aliases:** `MULTIRACIAL` -- used in `Hu_2026_clesrovimab.R`.
- **Example models:** `Hu_2026_clesrovimab.R`.

### RACE_OTHER (**canonical for race-category 'Other' indicator**)
- **Description:** 1 = race category "Other," 0 = not.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Source aliases:**
  - `OTHER` -- used in `Robbie_2012_palivizumab.R`.
- **Example models:** `Zhu_2017_lebrikizumab.R`, `Robbie_2012_palivizumab.R`.

### RACE_NONBLACK_NONWHITE (**canonical for the non-Black non-White composite race indicator**)
- **Description:** 1 = race is anything other than White or Black (composite that pools Asian + Hispanic + American Indian + Other or analogous smaller-N groups from a multi-level race classification), 0 = White or Black. Composite indicator used by papers that collapse a multi-level race column into a three-level effective structure with White as the reference, Black as a separate indicator, and "non-Black ethnic origin" as the composite.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = White or Black (the larger-N pooled reference group in the source paper). Pair with `RACE_BLACK` on the same model so the two indicators are mutually exclusive and the implicit White reference category is reproduced cleanly.
- **Source aliases:** none formally; Fisher 2008's NONMEM control stream uses the 6-level inline race classification rather than a separate composite column.
- **Example models:** `Fisher_2008_fosamprenavir.R` (multiplicative CL/F factor 1.06 vs the White reference; the composite pools Race = 3 (Asian), 4 (Hispanic), 5 (American Indian), 6 (Other) into a single indicator alongside the separate `RACE_BLACK` indicator).
- **Notes:** Specific scope because the composite grouping is paper-defined and is mutually exclusive with the decomposed `RACE_ASIAN`, `RACE_HISPANIC`, `RACE_AMIND`, `RACE_OTHER` indicators in the same model (do not combine the composite indicator with its constituent decompositions). Distinct from `RACE_ASIAN_AMIND_OTH` (Frey 2013 grouping; explicitly excludes Hispanic) and from `RACE_BLACK_OTH` (Clegg 2024 grouping; includes Black in the composite). Ratified canonically alongside the Fisher 2008 fosamprenavir extraction.

### RACE_HISPANIC (**canonical for Hispanic / Latino ethnicity indicator**)
- **Description:** 1 = Hispanic / Latino, 0 = non-Hispanic. Used by papers that report Hispanic as a separate category alongside Black, Asian, and Other rather than as a distinct ethnicity dimension.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-Hispanic; document the paper-specific reference race composition per-model).
- **Source aliases:**
  - `HISPANIC` -- used in `Robbie_2012_palivizumab.R`.
  - `Hispanic ethnicity indicator` -- used in `Overgaard_2016_liraglutide.R` (Overgaard 2016 Fig. 2 / Table S1 "Cov. Hispanic"; the paper reports race and ethnicity as separate dimensions but the column encoding, 1 = Hispanic vs 0 = non-Hispanic, is identical to the canonical form -- per-model `notes` field records the ethnicity-vs-race interpretation).
- **Example models:** `Robbie_2012_palivizumab.R` (fractional effect on CL; additional effect on Vc), `Overgaard_2016_liraglutide.R` (log-scale multiplicative effect on CL/F, coefficient +0.08 per Table S1).
- **Notes:** In the US Office-of-Management-and-Budget (OMB) classification Hispanic is an ethnicity rather than a race, but clinical PK analyses frequently treat it as one of the race indicators. When a paper treats Hispanic as a race, use this column; otherwise encode ethnicity separately. Register-wise, this follows the `RACE_<GROUP>` indicator-decomposition pattern. Overgaard 2016 explicitly reports race and ethnicity as separate covariate dimensions but the numerical Hispanic-vs-non-Hispanic encoding matches this canonical -- the per-model `covariateData[[RACE_HISPANIC]]$notes` records the "reported as ethnicity" interpretation.

### RACE_JAPANESE (**canonical for Japanese-heritage race indicator**)
- **Description:** 1 = Japanese heritage, 0 = non-Japanese. Used when Japanese subjects form a distinct subgroup in the study design (e.g., ICH E5 bridging analyses or studies with a dedicated Japanese healthy-volunteer cohort).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-Japanese).
- **Source aliases:**
  - `JAPANESE_HV` -- used in `Wang_2017_benralizumab.R` (Japanese healthy-volunteer cohort indicator; the healthy-volunteer vs. asthma-patient distinction is captured separately, not in this covariate).
- **Example models:** `Wade_2015_certolizumab.R` (multiplicative fractional effect on V/F; Wade 2015 breaks Japanese [RACE.EQ.8] out separately from RACE_ASIAN), `Wang_2017_benralizumab.R` (multiplicative factor 1.34 on Vc).
- **Notes:** Distinct from `RACE_NEAS` (North East Asian composite, includes Chinese, Japanese, and Korean) and from `RACE_ASIAN`. Use `RACE_JAPANESE` only when the source paper breaks out Japanese heritage as its own indicator; do not aggregate with other Asian groups when the paper keeps them separate. Ratified canonically on 2026-04-26.

### RACE_CHINESE (**canonical for Chinese-heritage race indicator**)
- **Description:** 1 = Chinese heritage, 0 = non-Chinese. Used when Chinese subjects form a distinct subgroup alongside Japanese, Asian-other, and other race categories (e.g., multiregional oncology trials enrolling Chinese, Japanese, and other Asian cohorts as separate strata).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-Chinese; document the paper-specific reference race composition per-model).
- **Source aliases:** none formally; source NONMEM control streams typically use ad-hoc names (e.g., `CHINESE`, `RACE.EQ.X`).
- **Example models:** `Brown_2017_osimertinib.R` (linear additive effect `(1 + 0.17 * RACE_CHINESE)` on apparent clearance of the AZ5104 metabolite; reference category Caucasian).
- **Notes:** Distinct from `RACE_NEAS` (North East Asian composite, includes Chinese, Japanese, and Korean) and from `RACE_ASIAN`. Use `RACE_CHINESE` only when the source paper breaks out Chinese heritage as its own indicator alongside `RACE_JAPANESE` and `RACE_ASIAN_OTH`; do not aggregate with other Asian groups when the paper keeps them separate. Parallels the established `RACE_JAPANESE` entry. Ratified canonically on 2026-05-09.

### RACE_PAPUAN (**canonical for Papuan / indigenous Melanesian Indonesian heritage race indicator**)
- **Description:** 1 = Papuan (indigenous Melanesian heritage native to the Indonesian provinces of Papua / West Papua, the western half of the island of New Guinea), 0 = non-Papuan (typically Indonesian residents of mainland-Indonesian Austronesian origin, e.g., Javanese / Sumatran transmigrants enrolled at Papua-based clinical sites; possibly also any non-Indonesian subjects in mixed cohorts). Used as a binary race / ethnicity indicator in clinical pharmacology studies conducted at sites in Indonesian Papua where the local population is a mixture of indigenous Melanesian Papuans and mainland-Indonesian transmigrants, and where PK differences across the two groups are clinically relevant.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-Papuan).
- **Source aliases:**
  - Paper's binary `Papuan` indicator paired with the `f` multiplicative covariate term in Brussee 2016 Table 2 -- used in `Brussee_2016_arginine.R` (values 1 = Papuan, 0 = non-Papuan; same orientation as the canonical).
- **Example models:** `Brussee_2016_arginine.R` (multiplicative factor `f = 1.9` on L-arginine CL: `CL_i = CL * (WT/60)^k1 * f^RACE_PAPUAN` -- Papuan patients have ~1.9x higher arginine clearance than the non-Papuan reference).
- **Notes:** Distinct from all existing `RACE_<GROUP>` indicators. Papuans are an indigenous Melanesian / Oceanic population genetically and culturally distinct from `RACE_ASIAN` (mainland-Indonesian transmigrants are typically Austronesian Asian by international population-genetics classification). The Brussee 2016 cohort was enrolled exclusively at the Mitra Masyarakat Hospital in Timika, Papua, so a paired `REGION_INDONESIA` indicator is not needed for that model -- future models that mix Papuan and non-Papuan Indonesian sites may need such a region indicator separately. Ratified canonically on 2026-06-09 (general scope; the Brussee 2016 L-arginine PKPD model is the second example following Yeo 2008 arginine, which also used the same indigenous Papuan vs non-Papuan stratification).

### RACE_IND_CHI_TWN (**canonical for Indian/Chinese/Taiwanese composite race indicator (Schmid 2017 nintedanib)**)
- **Description:** 1 = subject self-identifies as Indian (South Asian), Chinese, or Taiwanese; 0 = otherwise (Caucasian, Black, Korean, or other Asian). Paper-specific composite developed by Schmid 2017 in pooling South Asian Indian and East Asian Chinese/Taiwanese subgroups together as a single nintedanib-bioavailability (F1) effect category against a Caucasian/Black/other-Asian reference. The grouping reflects the empirical clustering of the F1 covariate analysis (Schmid 2017 Table 3 ethnic-origin row); it does not imply biological homogeneity across the three constituent populations.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = Caucasian, Black, Korean, or other Asian (paired with `RACE_KOREAN` to capture the Korean subgroup separately).
- **Source aliases:**
  - Paper's ethnic-origin categorical levels "Indian / Chinese / Taiwanese origin" -- decompose into `RACE_IND_CHI_TWN = as.integer(ETHNIC %in% c("Indian", "Chinese", "Taiwanese"))`.
- **Example models:** `Schmid_2017_nintedanib.R` (multiplicative effect 1.33 on nintedanib relative bioavailability F1; reference category Caucasian / Black / other Asian).
- **Notes:** Operator-ratified sidecar 2026-06-21 (request-002 Q1, option B). Specific scope: the composite grouping is paper-specific to Schmid 2017 and is unlikely to recur unchanged in other popPK extractions; future papers that distinguish South Asian from East Asian (or that lump Korean with Chinese) should register their own composite rather than overload this one. The constituent subgroups Chinese / Indian / Taiwanese remain available as individual indicators (`RACE_CHINESE` is canonical; `RACE_INDIAN` was added alongside this entry; `RACE_TAIWANESE` is not yet canonical but can be added when a future paper requires it).

### RACE_KOREAN (**canonical for Korean-heritage race indicator**)
- **Description:** 1 = Korean heritage (subject self-identifies as Korean or is enrolled at a Korean-population stratum of a multiregional trial), 0 = non-Korean. Used when Korean subjects form a distinct subgroup whose PK differs from the Caucasian / Black / non-Korean Asian reference (e.g., Schmid 2017 nintedanib showed Korean patients have ~22% lower F1 than the Caucasian reference).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-Korean; document the paper-specific reference race composition per-model in the covariate's notes).
- **Source aliases:** none formally; source NONMEM control streams typically use ad-hoc names (e.g., `KOREAN`, `RACE.EQ.K`).
- **Example models:** `Schmid_2017_nintedanib.R` (multiplicative effect 0.781 on nintedanib relative bioavailability F1; reference category Caucasian / Black / other Asian), `vandenBerg_2021_finerenone.R` (multiplicative effect `1.29^RACE_KOREAN` on Vc/F; 2.4% of the FIDELIO-DKD cohort were Korean, and the paper Discussion notes the effect "may be a spurious finding based on limited data").
- **Notes:** Operator-ratified sidecar 2026-06-21 (request-002 Q1, option B). Parallels the established `RACE_CHINESE` / `RACE_JAPANESE` entries -- individual East Asian nationality indicators when the source paper distinguishes them as distinct subgroups rather than pooling under `RACE_ASIAN_NORTHEAST`. Korean is the third NE-Asian individual nationality canonical alongside Chinese and Japanese; together they exhaust the `RACE_ASIAN_NORTHEAST` composite (Chinese + Japanese + Korean) so a model that uses all three individual indicators can omit `RACE_ASIAN_NORTHEAST` without information loss. Distinct from `RACE_ASIAN_NORTHEAST` (composite) and from `RACE_ASIAN_OTH` (within-Asian-population sub-indicator).

### RACE_INDIAN (**canonical for Indian (South Asian) race indicator**)
- **Description:** 1 = South Asian Indian heritage (subject self-identifies as Indian; refers to nationals/descendants of India and not to American Indian / Native American groups), 0 = non-Indian. Used when Indian subjects form a distinct subgroup whose PK differs from the Caucasian / Black / non-Indian Asian reference (e.g., Schmid 2017 nintedanib showed Indian patients have ~90% higher BIBF 1202 F2 than the Caucasian / Black reference).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-Indian; document the paper-specific reference race composition per-model in the covariate's notes).
- **Source aliases:** none formally; source NONMEM control streams typically use ad-hoc names (e.g., `INDIAN`, `RACE.EQ.I`).
- **Example models:** `Schmid_2017_nintedanib.R` (multiplicative effect 1.90 on BIBF 1202 relative bioavailability F2; paired with a non-Indian-Asian indicator at 1.20; reference category Caucasian / Black).
- **Notes:** Added 2026-06-27 alongside `RACE_IND_CHI_TWN` and `RACE_KOREAN` for the Schmid 2017 nintedanib extraction (request-002 Q1, option B). Parallels `RACE_CHINESE` / `RACE_JAPANESE` / `RACE_KOREAN` -- individual South / East Asian nationality indicators when the source paper distinguishes them as distinct subgroups. **DISTINCT** from `RACE_ASIAN_AMIND_OTH` and `RACE_ASIAN_AMIND_MULTI`, both of which use "AmInd" / "AMIND" to mean American Indian / Alaska Native (a North American indigenous population), not South Asian Indian. This is a recurring source of confusion in race-canonical lookups; reviewers should verify the meaning whenever a paper mentions "Indian" patients in a popPK cohort.


## Geographic / enrollment-country indicators

Geographical study-site region indicators. Distinct from race / ethnicity (`RACE_*`), which describe subject ancestry; these describe the geographical location of the clinical trial site that enrolled the subject. Used in multi-regional studies (typically those including bridging analyses for Japan or East Asia) to capture region-specific clinical-practice or unmeasured-environment effects on PK that remain after accounting for body weight, race, and laboratory covariates. Encoded as a set of mutually exclusive binary indicators with US as the implicit reference category (all indicators = 0). When a paper groups some non-US regions with US (e.g., Hong 2025 groups US and Japan as the DXd CL reference), the model code uses only the indicators that distinguish the non-reference groups; the data column for the grouped region (e.g., `REGION_JAPAN`) is still recorded so the same dataset can serve other parameters that do separate that group.

### REGION_JAPAN (**canonical for Japan study-site / enrollment-country indicator**)
- **Description:** 1 = study site in Japan (or patient enrolled in Japan, depending on source paper's reporting), 0 = study site / enrollment country outside Japan. Geographical Japan indicator.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Japan sites / enrollment; specific reference set varies per model -- e.g., Hong 2025 Dato-DXd CL uses "any non-Japan region", whereas Hong 2025 DXd CL groups Japan with US into the reference).
- **Source aliases:**
  - `COUNTRY_JPN` -- retired canonical; used in `Yin_2021_trastuzumabDeruxtecan.R` as an enrollment-country (not study-site-region) indicator. Some papers report country-of-enrollment rather than site region; both map to `REGION_JAPAN` when the binary contrast is Japan vs. non-Japan.
- **Example models:** `Hong_2025_datopotamab.R` (multiplicative effect 1 + (-0.219) = 0.781 on Dato-DXd linear clearance), `Yin_2021_trastuzumabDeruxtecan.R` (multiplicative effect 0.903 on CL_intact and 0.738 on V2_intact when REGION_JAPAN = 1; Yin 2021 retained Japan enrollment-country over Japanese race because the two were highly confounded, correlation -0.81).
- **Notes:** Distinct from `RACE_JAPANESE` (subject ancestry). A subject of Japanese ancestry enrolled at a US site has `RACE_JAPANESE = 1` but `REGION_JAPAN = 0`. Some papers (e.g., Yin 2021) report enrollment country rather than study-site region; both are encoded as `REGION_JAPAN` when the binary contrast is Japan vs. non-Japan. Paired with `REGION_EUROPE` and `REGION_ROW` in multi-regional studies (e.g., Hong 2025); `REGION_JAPAN = 0` for a US-only cohort.

### REGION_EUROPE (**canonical for Europe study-site indicator**)
- **Description:** 1 = study site in Europe, 0 = study site outside Europe. Geographical study-site region.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Europe study sites; specific reference set varies per model).
- **Source aliases:** none yet; canonical name preferred.
- **Example models:** `Hong_2025_datopotamab.R` (multiplicative effect 1 + 0.240 = 1.240 on DXd clearance versus US/Japan reference), `Naik_2016_vortioxetine.R` (additive intercept-shift form: TVCL_EU = 39 L/hr is the typical CL/F when `REGION_EUROPE = 1`, versus USA reference TVCL = 51 L/hr).
- **Notes:** Pair with `REGION_JAPAN` and `REGION_ROW` to encode multi-regional study membership; subjects with all three indicators = 0 are in the "US" reference group.

### REGION_ROW (**canonical for Rest-of-World study-site indicator**)
- **Description:** 1 = study site in Rest of World (i.e., not US, Japan, or Europe), 0 = study site in US / Japan / Europe. Residual-region indicator for multi-regional studies.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (US / Japan / Europe study sites; specific reference set varies per model).
- **Source aliases:**
  - `REGION_RoW` -- mixed-case variant in some source publications (e.g., Naik 2016).
- **Example models:** `Hong_2025_datopotamab.R` (multiplicative effect 1 + 0.196 = 1.196 on DXd clearance versus US/Japan reference), `Naik_2016_vortioxetine.R` (additive intercept-shift form: TVCL_RoW = 38 L/hr is the typical CL/F when `REGION_ROW = 1`, versus USA reference TVCL = 51 L/hr; the RoW group for Naik 2016 spans study sites in Canada, Australia, and Asia).
- **Notes:** "Rest of the World" composition is paper-specific (e.g., Hong 2025 = study sites outside US, Japan, and Europe; Naik 2016 = Canada, Australia, and Asia). Document the subject set in `covariateData[[REGION_ROW]]$notes` per model.

### REGION_FRANCE (**canonical for France study-site / enrollment-country indicator**)
- **Description:** 1 = study site in France (or French-population enrollment), 0 = otherwise. Country-level study-site indicator used when a multi-regional trial reports a France-vs-other contrast in popPK / PD parameters (cultural / behavioural-response differences, regional dosing-practice differences, etc.).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-France study sites; in Holford 1992 tacrine the reference cohort is the US protocol 970-01).
- **Source aliases:**
  - `PROT == 4` -- protocol-number alias used in Holford 1992 (the France cohort is protocol 970-04, encoded as `PROT = 4`; the US cohort is protocol 970-01, encoded as `PROT = 1`). Derive `REGION_FRANCE = as.integer(PROT == 4)`.
- **Example models:** `Holford_1992_tacrine.R` (multiplicative scale factors on baseline ADAS-cog `(1 + e_region_france_s0 * REGION_FRANCE)` with `e_region_france_s0 = 0.08`; on placebo potency `(1 + e_region_france_betap * REGION_FRANCE)` with `e_region_france_betap = 0.76`; on placebo elimination half-time `(1 + e_region_france_t12elp * REGION_FRANCE)` with `e_region_france_t12elp = 1.78` -- so the France cohort has 8 percent higher baseline ADAS-cog, 76 percent larger placebo response, and 2.78x longer placebo wash-out half-time relative to the US cohort).
- **Notes:** Distinct from `RACE_FRENCH` (subject ancestry, no canonical at present). Holford 1992 introduces this as a behavioural-response covariate rather than a PK exposure covariate; the underlying mechanism is hypothesised cultural / clinical-trial-conduct differences, not pharmacology. Pair with `REGION_JAPAN`, `REGION_EUROPE`, `REGION_ROW` etc. when the same model needs to encode multiple geographic contrasts. Specific scope until a second model ratifies the name; at that point promote to `general`. Ratified canonically on 2026-05-23 alongside the Holford 1992 tacrine extraction.

### REGION_MOZAMBIQUE (**canonical for Mozambique study-site / enrollment-country indicator**)
- **Description:** 1 = study site in Mozambique, 0 = otherwise. Country-level study-site indicator used in multi-country sub-Saharan African trials of intermittent preventive treatment of malaria in pregnancy (IPTp).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Mozambique sites; specific reference set varies per model -- in de Kock 2017 the reference is Mali).
- **Source aliases:** none yet; canonical name preferred.
- **Example models:** `deKock_2017_sulfadoxinePyrimethamine.R` (multiplicative -20.2% effect on apparent pyrimethamine clearance; +57.6% scaling on observed pyrimethamine concentrations; +21.2% scaling on observed sulfadoxine concentrations; the on-observation scalings capture residual site-specific differences in apparent bioavailability or dried-blood-spot sample handling).
- **Notes:** Specific scope because the canonical models a single Mozambique vs non-Mozambique contrast within a specific multi-site trial; the reference set is paper-specific (e.g., Mali in de Kock 2017). Pairs with `REGION_SUDAN` and `REGION_ZAMBIA` for a 4-country sub-Saharan African IPTp trial design with Mali as the implicit reference. Ratified canonically on 2026-05-18.

### REGION_SUDAN (**canonical for Sudan study-site / enrollment-country indicator**)
- **Description:** 1 = study site in Sudan, 0 = otherwise. Country-level study-site indicator used in multi-country sub-Saharan African trials.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Sudan sites; specific reference set varies per model -- in de Kock 2017 the reference is Mali).
- **Source aliases:** none yet; canonical name preferred.
- **Example models:** `deKock_2017_sulfadoxinePyrimethamine.R` (+15.5% scaling on observed sulfadoxine concentrations; +33.2% scaling on observed pyrimethamine concentrations).
- **Notes:** Specific scope; pairs with `REGION_MOZAMBIQUE` and `REGION_ZAMBIA`. Ratified canonically on 2026-05-18.

### REGION_ZAMBIA (**canonical for Zambia study-site / enrollment-country indicator**)
- **Description:** 1 = study site in Zambia, 0 = otherwise. Country-level study-site indicator used in multi-country sub-Saharan African trials.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Zambia sites; specific reference set varies per model -- in de Kock 2017 the reference is Mali).
- **Source aliases:** none yet; canonical name preferred.
- **Example models:** `deKock_2017_sulfadoxinePyrimethamine.R` (-24.8% scaling on observed sulfadoxine concentrations; -5.4% scaling on observed pyrimethamine concentrations).
- **Notes:** Specific scope; pairs with `REGION_MOZAMBIQUE` and `REGION_SUDAN`. Ratified canonically on 2026-05-18.

### REGION_TANZANIA (**canonical for Tanzania study-site / enrollment-country indicator**)
- **Description:** 1 = study site in Tanzania, 0 = otherwise. Country-level study-site indicator used in multi-country sub-Saharan African trials. In Pillay-Fuentes Lorente 2024 the contrast is Tanzania (Pemba Island) versus Cote d'Ivoire, and the paper refers to it as the "study population" covariate rather than by country name.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Tanzania sites; the specific reference set varies per model -- in Pillay-Fuentes Lorente 2024 the reference is Cote d'Ivoire).
- **Source aliases:** `Country`, `study population`.
- **Example models:** `PillayFuentesLorente_2024_albendazole.R` (exponential categorical effect on both apparent metabolite clearances: exp(0.56) = 1.75, i.e. 75% higher albendazole sulfoxide CL/F, and exp(0.38) = 1.46, i.e. 46% higher albendazole sulfone CL/F, in the Tanzanian study population).
- **Notes:** Specific scope, following the same pattern as `REGION_MOZAMBIQUE` / `REGION_SUDAN` / `REGION_ZAMBIA`: the canonical models a single Tanzania vs non-Tanzania contrast within one multi-site trial, and the comparator set is paper-specific. Note that a country indicator in this family is a proxy for whatever differs between the study populations (in Pillay-Fuentes Lorente 2024 the authors attribute it to drug-metabolising-enzyme polymorphisms, host-parasite interaction, nutritional status and gut microbiome differences), not to geography as such.

### REGION_POLAND (**canonical for Poland EPPICC enrollment-country indicator**)
- **Description:** 1 = subject enrolled at the Polish EPPICC cohort site (Medical University Warsaw / Regional Hospital of Infectious Disease), 0 = otherwise. Country-level enrollment-country indicator used in the multi-country European Pregnancy and Paediatric HIV Cohort Collaboration (EPPICC) HIV/HCV coinfection study.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Poland EPPICC sites; specific reference set varies per model -- in Majekodunmi 2017 the reference is Ukraine).
- **Source aliases:** none yet; canonical name preferred.
- **Example models:** `Majekodunmi_2017_HIV_HCV_CD4_recovery.R` (additive +0.44 shift on pre-ART CD4 z-score intercept; Ukraine reference).
- **Notes:** Specific scope; pairs with `REGION_RUSSIA`, `REGION_SWITZERLAND`, `REGION_UK`, `REGION_SPAIN`, `REGION_GERMANY`, `REGION_ITALY` for the EPPICC 8-country pediatric HIV cohort with Ukraine as the implicit reference. Distinct from a Polish race/ethnicity indicator. Ratified canonically on 2026-05-22.

### REGION_RUSSIA (**canonical for Russia EPPICC enrollment-country indicator**)
- **Description:** 1 = subject enrolled at the Russian EPPICC cohort site (Republican Hospital of Infectious Diseases, St Petersburg), 0 = otherwise. Country-level enrollment-country indicator used in the EPPICC HIV/HCV coinfection study.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Russia EPPICC sites; specific reference set varies per model -- in Majekodunmi 2017 the reference is Ukraine).
- **Source aliases:** none yet; canonical name preferred.
- **Example models:** `Majekodunmi_2017_HIV_HCV_CD4_recovery.R` (additive +0.69 shift on pre-ART CD4 z-score intercept; Ukraine reference).
- **Notes:** Specific scope; pairs with `REGION_POLAND`, `REGION_SWITZERLAND`, `REGION_UK`, `REGION_SPAIN`, `REGION_GERMANY`, `REGION_ITALY` for the EPPICC 8-country pediatric HIV cohort with Ukraine as the implicit reference. Ratified canonically on 2026-05-22.

### REGION_SWITZERLAND (**canonical for Switzerland EPPICC enrollment-country indicator**)
- **Description:** 1 = subject enrolled at the Swiss Mother and Child HIV Cohort Study (MoCHiV), 0 = otherwise. Country-level enrollment-country indicator used in the EPPICC HIV/HCV coinfection study.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Switzerland EPPICC sites; specific reference set varies per model -- in Majekodunmi 2017 the reference is Ukraine).
- **Source aliases:** none yet; canonical name preferred.
- **Example models:** `Majekodunmi_2017_HIV_HCV_CD4_recovery.R` (additive +0.02 shift on pre-ART CD4 z-score intercept; Ukraine reference).
- **Notes:** Specific scope; pairs with the other EPPICC REGION indicators. Ratified canonically on 2026-05-22.

### REGION_UK (**canonical for United Kingdom EPPICC enrollment-country indicator**)
- **Description:** 1 = subject enrolled in the UK Collaborative HIV Paediatric Study (CHIPS), 0 = otherwise. Country-level enrollment-country indicator used in the EPPICC HIV/HCV coinfection study.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-UK EPPICC sites; specific reference set varies per model -- in Majekodunmi 2017 the reference is Ukraine).
- **Source aliases:** none yet; canonical name preferred.
- **Example models:** `Majekodunmi_2017_HIV_HCV_CD4_recovery.R` (additive -17.5 shift on pre-ART CD4 z-score intercept with Ukraine reference; magnitude implausibly large for a z-score effect and anchored on a UK cohort of only 2 subjects -- reproduced verbatim per the published table and flagged in the model file and vignette as a small-sample artifact).
- **Notes:** Specific scope; pairs with the other EPPICC REGION indicators. Distinct from any Britain/England/Scotland-specific indicator. Ratified canonically on 2026-05-22.

### REGION_SPAIN (**canonical for Spain EPPICC enrollment-country indicator**)
- **Description:** 1 = subject enrolled at the Spanish Paediatric HIV Network (CoRISpe; Madrid and Barcelona), 0 = otherwise. Country-level enrollment-country indicator used in the EPPICC HIV/HCV coinfection study.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Spain EPPICC sites; specific reference set varies per model -- in Majekodunmi 2017 the reference is Ukraine).
- **Source aliases:** none yet; canonical name preferred.
- **Example models:** `Majekodunmi_2017_HIV_HCV_CD4_recovery.R` (additive +2.89 shift on pre-ART CD4 z-score intercept; Ukraine reference).
- **Notes:** Specific scope; pairs with the other EPPICC REGION indicators. Ratified canonically on 2026-05-22.

### REGION_GERMANY (**canonical for Germany EPPICC enrollment-country indicator**)
- **Description:** 1 = subject enrolled in the German Competence Network on HIV-infected Children, 0 = otherwise. Country-level enrollment-country indicator used in the EPPICC HIV/HCV coinfection study.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Germany EPPICC sites; specific reference set varies per model -- in Majekodunmi 2017 the reference is Ukraine).
- **Source aliases:** none yet; canonical name preferred.
- **Example models:** `Majekodunmi_2017_HIV_HCV_CD4_recovery.R` (additive +0.34 shift on pre-ART CD4 z-score intercept; Ukraine reference).
- **Notes:** Specific scope; pairs with the other EPPICC REGION indicators. Ratified canonically on 2026-05-22.

### REGION_ITALY (**canonical for Italy EPPICC enrollment-country indicator**)
- **Description:** 1 = subject enrolled in the Italian Register for HIV-infection in Children, 0 = otherwise. Country-level enrollment-country indicator used in the EPPICC HIV/HCV coinfection study.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Italy EPPICC sites; specific reference set varies per model -- in Majekodunmi 2017 the reference is Ukraine).
- **Source aliases:** none yet; canonical name preferred.
- **Example models:** `Majekodunmi_2017_HIV_HCV_CD4_recovery.R` (additive -3.63 shift on pre-ART CD4 z-score intercept; Ukraine reference; small-sample subgroup with n = 2).
- **Notes:** Specific scope; pairs with the other EPPICC REGION indicators. Ratified canonically on 2026-05-22.

### REGION_EASTASIA (**canonical for East Asian region-of-origin study-site indicator**)
- **Description:** 1 = subject enrolled at a study site in the East Asian region, 0 = enrolled elsewhere. Multi-country regional grouping (broader than a single-country `REGION_<COUNTRY>` indicator, narrower than a Rest-of-World `REGION_ROW` bucket). The exact country membership is protocol-specific and must be documented in per-model `covariateData[[REGION_EASTASIA]]$notes`. Distinct from the `RACE_ASIAN*` family: `REGION_EASTASIA` records where a subject was enrolled, not self-reported race; source papers routinely test the two separately and can find one significant and the other not.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-East-Asian region).
- **Source aliases:**
  - `EASIAFL` -- East Asian flag; used in `Cammarata_2024_sulbactam_durlobactam.R` (Table 1 rows `CLEASIAFL1` and `V1EASIAFL1`).
  - `EAST_ASIA`, `REGION = East Asia` -- variant spellings of the same regional grouping.
- **Example models:** `Cammarata_2024_sulbactam_durlobactam.R` (China, Taiwan, or South Korea; proportional shifts of -0.199 on durlobactam total CL and -0.263 on durlobactam Vc, with no sulbactam region effect; 45 of 373 pooled subjects, 12.1%).
- **Notes:** Scope `specific`, matching every other member of the `REGION_*` family. Sibling to the single-country `REGION_JAPAN` / `REGION_FRANCE` / ... entries and to `REGION_EUROPE` / `REGION_ROW`. When a source paper reports BOTH a region effect and a race effect, register the race arm separately under the `RACE_*` family and keep the two columns distinct -- Cammarata 2024 explicitly found race and country of origin non-significant while East Asian region was significant. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

## Pediatric comorbidities

### DIS_CLD_PREM (**canonical for chronic lung disease of prematurity**)
- **Description:** 1 = chronic lung disease of prematurity (bronchopulmonary dysplasia, BPD), 0 = no CLD. Time-fixed per subject (diagnosis at study entry).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no CLD of prematurity).
- **Source aliases:**
  - `CLD_PREM` -- prior canonical name used in `Robbie_2012_palivizumab.R` prior to the 2026-06-19 DIS_ prefix standardization.
  - `CLD` -- used in `Robbie_2012_palivizumab.R`.
  - `BPD` -- bronchopulmonary-dysplasia shorthand.
- **Example models:** `Robbie_2012_palivizumab.R` (fractional +20% effect on CL).
- **Notes:** Standard pediatric / neonatology comorbidity flag; ties to palivizumab's label population (high-risk preterm infants) and may re-appear in future pediatric mAb PK analyses (RSV, parenteral nutrition, etc.). Renamed from `CLD_PREM` to `DIS_CLD_PREM` on 2026-06-19 per the canonical-register standardization audit (operator decision to apply the `DIS_<concept>` prefix uniformly to disease-state indicators).

## Comorbidities

### DIS_DIAB (**canonical for diabetes-mellitus comorbidity indicator**)
- **Description:** 1 = patient has diabetes mellitus comorbidity, 0 = no diabetes comorbidity. Time-fixed at study entry per subject. Type 1 vs Type 2 are pooled at the column level; record the per-model Type-2-specific detail (if any) in the per-model `covariateData[[DIS_DIAB]]$source_name` / `notes` fields rather than maintaining a parallel canonical.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no diabetes comorbidity).
- **Source aliases:**
  - `DIAB` -- prior canonical name (pre-2026-06-19 DIS_ prefix standardization) used in `Chen_2022_guselkumab.R`, `Yao_2018_guselkumab.R`, `Overgaard_2019_semaglutide.R`.
  - `T2DM` -- prior parallel canonical (merged into `DIS_DIAB` on 2026-06-19) used in `NA_NA_paracetamol.R` (DDMODEL00000228), `Lu_2014_sglt_qsp.R`, `Sharma_2018_naltrexone_bupropion.R`.
  - `Diabetes` -- used in `Sherer_2012_AAA.R` (Sherer 2012 Methods page 2 symbol "Diabetes").
  - `T2D` -- used in `Guiastrennec_2016_gastric_emptying.R` (matched-cohort flag, 1 = T2D patient vs 0 = matched nondiabetic control).
  - `ZDF` -- used in `Han_2018_methionineMetabolismCycle.R` (Zucker Diabetic Fatty rat as T2DM animal model; ZDF/Gmi fa/fa coded as DIS_DIAB = 1 vs ZDF/Gmi fa/? non-diabetic littermate control coded as DIS_DIAB = 0).
- **Example models:**
  - `Chen_2022_guselkumab.R` (multiplicative effect on CL/F: 1.15^DIS_DIAB, +15% in patients with diabetes).
  - `Yao_2018_guselkumab.R` (multiplicative effect on CL/F: 1.12^DIS_DIAB, +12% in patients with diabetes).
  - `Overgaard_2019_semaglutide.R` (multiplicative effects 1.12 on CL and 0.544 on ka per Overgaard 2019 Table 4; T2D-vs-normoglycaemic stratification).
  - `Sherer_2012_AAA.R` (additive shift on the first derivative of AAA growth rate with size beta2: `e_diab_b2 = -0.32/year` for diabetics; cohort prevalence 14%).
  - `Sharma_2018_naltrexone_bupropion.R` (Type-2 diabetes mellitus stratifier on Emax, kout, kpro, baseline BW; from Sharma 2018 study 6, NB-304).
  - `Lu_2014_sglt_qsp.R` (multiplicative +17.6% shift on the typical-value Vmax2 of SGLT2 in the renal-glucose-reabsorption QSP model: Vmax2_DIS_DIAB = 110 mmol/h vs Vmax2_healthy = 93.5 mmol/h per Lu 2014 Table 2 calibration).
  - `NA_NA_paracetamol.R` (DDMODEL00000228).
  - `Guiastrennec_2016_gastric_emptying.R` (multiplicative -81.1% depression of POTcarbC, the carbohydrate potency on CCK release; all other parameters are common across cohorts).
  - `Han_2018_methionineMetabolismCycle.R` (preclinical rat MMC model; multiplicative-on-log-scale T2DM effects on five rate constants K_SH (+16 %), K_HM (-92 %), K_HC (-95 %), K_HP (-86 %), K_PH (-99 %) derived from Han 2018 Table 1 ZDF/control ratios).
- **Notes:** Captures pre-existing diabetes mellitus as a comorbidity in non-diabetes-primary indications (e.g., psoriatic arthritis, psoriasis, vascular disease) AND as a primary stratifier in diabetes-population studies (semaglutide, SGLT-QSP, naltrexone-bupropion T2DM substudies). Distinct from a primary disease-state indicator like `DIS_UC`. Type 1 vs Type 2 mellitus are pooled onto the single `DIS_DIAB` column; if a paper distinguishes them, record "T2DM" or "Type 2" in the per-model `covariateData[[DIS_DIAB]]$source_name` notes, and if a future paper genuinely needs a binary Type-2-vs-Type-1 split (e.g., a popPK/PD analysis stratifying by HbA1c level), register `DIS_DIAB_TYPE2` at that point. Diabetic patients tend to have higher inflammation and altered IgG turnover, which can manifest as modest changes in monoclonal-antibody clearance. In vascular populations (Sherer 2012) diabetes is associated with slower AAA growth, possibly via aberrant monocyte-matrix interactions (Golledge 2008 mechanism cited in Sherer 2012 Discussion). Renamed from `DIAB` to `DIS_DIAB` and merged the prior `T2DM` canonical into it on 2026-06-19 per the canonical-register standardization audit (operator decision: apply the `DIS_<concept>` prefix uniformly to disease-state indicators; pool T1/T2 at the column level and record the Type-2 detail per-model rather than maintaining a parallel T2DM canonical). Overgaard 2016 (weight-management liraglutide) reports T2DM as one level of a three-level "baseline glycaemic status" covariate (normoglycaemic reference, prediabetic, T2DM); the T2DM level maps to `DIS_DIAB = 1` and the prediabetic level uses the sibling canonical [[DIS_PREDIAB]].

### DIS_PREDIAB (**canonical for prediabetes / impaired glucose regulation indicator**)
- **Description:** 1 = subject has prediabetes / impaired glucose regulation at baseline (e.g., impaired fasting glucose 5.6-6.9 mmol/L, impaired glucose tolerance, or HbA1c 5.7-6.4 % per ADA criteria), 0 = subject is not prediabetic (typically normoglycaemic; T2DM subjects are usually stratified onto the sibling canonical `DIS_DIAB` in the same model). Time-fixed at study entry per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (not prediabetic; typically normoglycaemic, but paper-defined).
- **Source aliases:**
  - `Prediabetes` -- used in `Overgaard_2016_liraglutide.R` (Overgaard 2016 Fig. 2 / Table S1 "Cov. prediabetes"; one of the three levels of a "baseline glycaemic status" covariate alongside normoglycaemic reference and T2DM).
- **Example models:** `Overgaard_2016_liraglutide.R` (multiplicative log-scale effect on CL/F, coefficient +0.00 per Table S1; effect is negligible per the paper's own analysis).
- **Notes:** Sibling to `DIS_DIAB` in the disease-state family. Used when a source paper stratifies a diabetes / obesity cohort into normoglycaemic vs prediabetic vs T2DM strata and reports covariate effects on each stratum separately. Only ratified when both `DIS_PREDIAB` and `DIS_DIAB` are needed to reproduce the paper's three-level glycaemic decomposition -- when a paper only reports a single "any dysglycaemia" indicator, use `DIS_DIAB` alone (or the appropriate primary-disease-state canonical). ADA prediabetes criteria: FPG 100-125 mg/dL (5.6-6.9 mmol/L), 2-h PG 140-199 mg/dL (7.8-11.0 mmol/L) on 75 g OGTT, or HbA1c 5.7-6.4 % (39-46 mmol/mol). Ratified canonically on 2026-07-10 alongside the Overgaard 2016 weight-management-liraglutide extraction.

### DIS_HYPERT (**canonical for hypertension comorbidity / medical-history indicator**)
- **Description:** 1 = patient has a history of (or current) hypertension as a comorbidity; 0 = no hypertension. Time-fixed at study entry per subject (medical-history flag rather than time-varying blood-pressure measurement).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no hypertension comorbidity).
- **Source aliases:**
  - `HYPERT` -- prior canonical name used in `Girard_2012_pimasertib.R` prior to the 2026-06-19 DIS_ prefix standardization.
  - `MHHY` (medical history of hypertension) -- used in `Girard_2012_pimasertib.R`.
- **Example models:** `Girard_2012_pimasertib.R` (additive shift on the cumulative-logit AE-score model: `theta_mhhy * DIS_HYPERT`; +0.539 logit units in patients with prior hypertension).
- **Notes:** Companion to `DIS_DIAB` (diabetes-mellitus comorbidity); both are baseline binary medical-history flags collected from clinical-history forms. Captures any prior or current hypertension diagnosis, regardless of treatment status; if a future model needs to separate treated vs untreated hypertension, register a refinement (`DIS_HYPERT_TREATED`). Renamed from `HYPERT` to `DIS_HYPERT` on 2026-06-19 per the canonical-register standardization audit (operator decision to apply the `DIS_<concept>` prefix uniformly to disease-state indicators).

### DIS_HYPERLIP (**canonical for hyperlipidemia / hypercholesterolemia diagnosis indicator**)
- **Description:** 1 = participant carries a diagnosis of hyperlipidemia (equivalently hypercholesterolemia / dyslipidemia) at study entry; 0 = no hyperlipidemia diagnosis (typically a healthy-volunteer or normolipidemic comparator arm pooled into the same analysis). Time-fixed per subject. Captures the generic acquired / polygenic lipid disorder that defines the target population of lipid-modifying therapies, as distinct from the monogenic familial forms carried by `DIS_HEFH` and `DIS_HOFH`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no hyperlipidemia diagnosis).
- **Source aliases:**
  - `Hyperlipidemia` -- Jadhav 2023 Table 2 covariate row name.
  - `HLD`, `HCHOL`, `DYSLIP` -- common clinical-dataset abbreviations for the same diagnosis flag.
- **Example models:** `Jadhav_2023_bempedoicAcid.R`, `Jadhav_2023_bempedoicAcid_ldlc.R` (proportional shift `CL/F * (1 + (-0.0945) * DIS_HYPERLIP)`, i.e. 9.5% lower apparent bempedoic acid clearance in participants with hyperlipidemia relative to the pooled healthy / non-hyperlipidemic reference; Jadhav 2023 Table 2).
- **Notes:** Not derivable from `DIS_HEALTHY` by complement, and not mutually exclusive with `DIS_DIAB`: the Jadhav 2023 popPK dataset contained 49 participants with diabetes alone plus 310 with hyperlipidemia and diabetes (Table 1 footnote c), so all three flags can be jointly set. Distinct from `DIS_HEFH` / `DIS_HOFH`, which identify the heterozygous and homozygous familial-hypercholesterolemia subsets; a HeFH patient normally also carries `DIS_HYPERLIP = 1`, so record the per-model convention in `covariateData[[DIS_HYPERLIP]]$notes` whenever both appear. Also distinct from the continuous lipid-panel canonicals (`LDLC`, `HDLC`), which carry the measured concentration rather than the diagnosis. Ratified canonically on 2026-07-27 alongside the Jadhav 2023 bempedoic acid extraction (sidecar request 001, operator answer A).

## Surgical history / disease state

### POD (**canonical for post-operative day**)
- **Description:** Days elapsed since the qualifying surgical event (e.g., solid-organ transplantation, major resection). Time-varying within subject; integer- or fractional-day valued; rises monotonically from 0 at the day of surgery. Captures time-since-surgery effects on PK that are not explained by other covariates -- e.g., post-transplant clearance of immunosuppressants typically declines toward a steady value over the first weeks-to-months as graft function, fluid status, hematocrit, and corticosteroid taper stabilise.
- **Units:** days
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- typically used in centred-deviation form `(1 + e_pod_param * (POD - ref_pod))`, sometimes with an upper cap (e.g., values > 180 fixed to 180 days when the residual time-varying effect plateaus).
- **Source aliases:**
  - `POD` -- used in `Bergmann_2014_tacrolimus.R` (capped at 180 days; centred at 22.7 days, the dataset mean).
- **Example models:** `Bergmann_2014_tacrolimus.R` (linear deviation from POD = 22.7 days on tacrolimus CL/F; coefficient -0.0021 per day implies a 0.21% per-day decrease in apparent oral clearance with a 180-day plateau).
- **Notes:** Time-varying within subject; the per-row value is the integer day count from the date of surgery to the observation date. For solid-organ-transplant cohorts, `POD` is the conventional NONMEM `$INPUT` column name. When the source paper reports a different name (`DPT` for "days post-transplant", `TX_DAY`, `T_POSTOP`), record the alias here. Distinct from `TIME` (rxode2 time clock) and from `OCC` (integer-valued occasion / period indicator for IOV). When a paper uses `POD` jointly with an IOV occasion column, both can coexist in the dataset: `POD` enters the typical-value covariate equation, `OCC` multiplexes the IOV etas. The 180-day cap in Bergmann 2014 is data-driven (most observations are within the first 90 days post-transplant, so the linear effect is identifiable only over that window) -- document any per-model cap in `covariateData[[POD]]$notes`. Ratified canonically on 2026-05-08 alongside the Bergmann 2014 extraction.

### TTD (**canonical for time to death**)
- **Description:** Days remaining from the observation time to the patient's recorded time of death (TTD >= 0; falls to 0 on the day of death). Time-varying per subject. Available only retrospectively in observational palliative-care / end-of-life cohorts; not usable as a prospective predictor.
- **Units:** days
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used in a first-order exponential decay form on a structural PK parameter. The Franken 2015 morphine model parameterises `CL(TTD) = CL_pop - theta_D * exp(-theta_rate * TTD)`, so the decay term vanishes as TTD -> infinity (asymptotic CL far from death) and reaches its peak drop `theta_D` as TTD -> 0 (day of death).
- **Source aliases:**
  - `TTD` -- used in `Franken_2015_morphine.R` (Franken 2015 NONMEM column for time-to-death in days; paper Eq. 3 and Table 2).
- **Example models:** `Franken_2015_morphine.R` (Franken 2015 Clin Pharmacokinet; first-order exponential decay term on morphine clearance with theta_D = 17.6 L/h and theta_rate = 0.13 /day; CL drops from 47.5 to 29.9 L/h as TTD goes from infinity to 0).
- **Notes:** Specific scope because the covariate's semantics depend on a palliative-care / observational-cohort study design where time of death is known by retrospective abstraction. The Franken 2015 model interprets the TTD-dependent CL change as a composite of physiological end-of-life processes (e.g., reduced hepatic blood flow, cachexia) that are not captured by standard blood-chemistry covariates. For prospective simulation of a virtual cohort without a known time of death, set TTD to a large value (e.g., > 50 days) to recover the asymptotic CL_pop. Distinct from the canonical `POD` (post-operative day, monotonically increasing from a surgery date) -- TTD counts down to death rather than up from a procedure. Ratified canonically on 2026-05-16 alongside the Franken 2015 morphine extraction.

### POSTTX_DAY1 (**canonical for first-24-hours-post-transplant indicator**)
- **Description:** Binary indicator for the first 24 hours post-transplant. 1 = the observation falls within the first 24 hours (day 1) post-transplant; 0 = otherwise. Time-varying per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any observation outside the first 24 hours post-transplant).
- **Source aliases:**
  - `first day post-transplant` -- used in `Storset_2014_tacrolimus.R`.
- **Example models:** `Storset_2014_tacrolimus.R` (multiplicative ~2.68-fold increase in oral bioavailability on day 1: `fdepot *= 2.68^POSTTX_DAY1`; Storset 2014 Table 2 final theory-based model retains the day-1 factor with subject-level IIV of 57% CV on the day-1 multiplier).
- **Notes:** Distinct from the continuous `POD` (post-operative day) canonical above -- `POSTTX_DAY1` is a derived binary indicator (operationally `POSTTX_DAY1 = as.integer(POD < 1)` when both columns are present in the dataset). The two coexist in the same model when the source paper uses POD-based continuous effects on some parameters and a separate binary day-1 effect on others (Storset 2014 retains the binary day-1 factor explicitly because the day-1 oral bioavailability is ~2.68-fold higher than the rest of the post-transplant period and is not well captured by a continuous POD effect). Storset 2014 Discussion attributes the day-1 oral-bioavailability spike to candidate mechanisms including methylprednisolone-bolus inhibition of intestinal CYP3A / P-glycoprotein, surgery-related inflammation, anaesthesia / opioid effects on gut motility, and reduced food intake -- but no single mechanism was identifiable in the data. The 2.68-fold factor was retained because it produced a 209-point OFV decrease and was crucial for predicting concentrations measured on the first post-transplant day. In Storset 2014 the day-1 effect carries its own subject-level eta (BSV 57% CV on the day-1 factor); only subjects with day-1 observations contribute to that eta. Ratified canonically on 2026-05-08.

### PFA (**canonical for perioperative intra-operative fluid administration volume**)
- **Description:** Total volume of intra-operative fluid administered during a surgical procedure -- saline / albumin / fresh-frozen-plasma / blood / platelet replacement summed across the entire procedure. Time-fixed per subject (a single per-subject scalar capturing the full intra-operative perfusion load). Used in solid-organ-transplant and other major-surgery popPK models where the volume expansion from peri-operative fluid resuscitation is large enough to perturb post-operative body composition and apparent volume of distribution beyond what pre-operative body weight alone explains.
- **Units:** mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used algebraically. The Oualha 2018 paediatric-LT model uses PFA additively with pre-operative body weight to define a transient post-operative body-weight curve: `BW(t) = (BWPREOP + PFA/1000) * (1 - (1 - fbw) * t^hill_bw / (tbw50^hill_bw + t^hill_bw))`, with the `/1000` conversion applying the 1 mL ~ 1 g fluid-density-1 convention so PFA in mL maps onto a kg increment of body weight. Future surgical / critical-care popPK papers may use PFA differently (additive on Vc, multiplicative on CL); the per-paper parameterisation should be recorded in `covariateData[[PFA]]$notes`.
- **Source aliases:**
  - `PFA` -- used in `Oualha_2018_enoxaparin.R` (Oualha 2018 BJCP; total intra-operative fluid volume in mL summed over the entire liver-transplant procedure; cohort median 2634 mL, range 1008-6520 mL).
- **Example models:** `Oualha_2018_enoxaparin.R` (Oualha 2018 paediatric liver-transplant cohort; PFA in mL adds to BWPREOP after conversion to L, defining a transient post-operative body-weight curve BW(t) that drives the time-varying allometric scaling of V; the Hill / fBW / tBW50 parameters of the BW(t) curve are jointly estimated with the enoxaparin PK).
- **Notes:** Specific scope until a second surgical / critical-care popPK model ratifies the name; at that point promote to `general`. The 1 mL ~ 1 g (density-1) convention is conventional and adequate for resuscitation fluids (crystalloids and blood products); a more precise mass-balance accounting could account for the slightly higher density of packed RBCs (~1.08 g/mL) or 5% albumin (~1.02 g/mL), but the difference is well within the IIV captured by `lfbw` / `ltbw50`. Distinct from `URINE_FLOW` (instantaneous urine flow rate, time-varying within an observation interval) and from `WT` (the pre- or post-operative body weight scalar). Founding example: Oualha 2018 enoxaparin (paediatric liver transplantation, intra-operative fluid resuscitation typical of major paediatric surgery). When a future model needs a separate accounting of crystalloid vs colloid vs blood-product volumes, register sibling canonicals (`PFA_CRYST`, `PFA_COLLOID`, `PFA_BLOOD_PROD`) rather than overloading this entry.

### TX_LIVER (**canonical for liver (hepatic) transplant indicator**)
- **Description:** Binary indicator for liver / hepatic-graft transplant recipients in a pooled solid-organ-transplant cohort. 1 = liver transplant; 0 = non-liver solid-organ transplant (kidney, heart, or lung). Time-fixed per subject (assigned at transplantation date).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-liver solid-organ graft -- typically kidney as the most common non-liver comparator in pooled solid-organ-transplant analyses).
- **Source aliases:**
  - `Hepatic trans_CL` -- used in `Nanga_2019_tacrolimus_metaanalysis.R` (Table 3 covariate-effect label; encoded as a multiplicative power coefficient theta^TX_LIVER on CL/F).
- **Example models:** `Nanga_2019_tacrolimus_metaanalysis.R` (multiplicative effect on apparent oral clearance: `cl_typ *= 0.38^TX_LIVER`, so liver-graft recipients have CL/F 62% lower than non-liver recipients at the same body weight and post-transplant day; Nanga 2019 Table 3 'Hepatic trans_CL' = 0.38), `Perrottet_2009_ganciclovir.R` (Perrottet 2009 pools lung and liver under a single CL slope; the model applies a shared multiplicative effect via `(TX_LUNG + TX_LIVER)`: pooled lung-or-liver `theta_lung/liver = 1.17` vs `theta_kidney = 1.68`, so lung-or-liver recipients have a CL slope 30% lower than kidney recipients at the same GFR and sex; Perrottet 2009 Table 3).
- **Notes:** Specific scope because the reference complement (non-liver solid-organ graft) is paper-defined -- in Nanga 2019 the complement is exclusively kidney-transplant patients (Table 5: 201 liver, 80 kidney). Future pooled solid-organ-transplant popPK models that distinguish liver from non-liver patients should reuse this canonical; if a future model decomposes the non-liver class into separate kidney / heart / lung indicators, register sibling canonicals (`TX_KIDNEY`, `TX_HEART`, `TX_LUNG`) rather than overloading this entry. If instead a model contrasts transplant recipients against NON-transplant patients, that is `TX_ANY`, not this entry -- `TX_LIVER` = 0 does not distinguish a non-liver graft recipient from a subject who was never transplanted. Distinct from `POSTTX_DAY1` (first-24-hour-post-transplant indicator, time-varying) and from `POD` (continuous post-transplant day); all three can coexist when a model parameterises both organ type and time-after-transplantation effects. Ratified canonically on 2026-05-18 alongside the Nanga 2019 tacrolimus meta-analysis extraction.

### DONOR_DECEASED (**canonical for deceased-vs-living donor indicator (solid-organ transplant)**)
- **Description:** Binary indicator for the source of the transplanted solid-organ graft. 1 = recipient received the graft from a deceased (post-mortal / cadaveric) donor; 0 = recipient received the graft from a living donor. Time-fixed per subject (assigned at transplantation date). Donor source is a graft-quality / preservation-status proxy: deceased-donor grafts typically experience longer cold-ischaemia times, more variable preservation conditions, and a higher rate of slow / delayed graft function than living-donor grafts; in solid-organ-transplant popPK studies these differences can translate into a measurable shift in apparent oral clearance of drugs that undergo hepatic / intestinal CYP3A metabolism (the mechanism is not fully understood and is hypothesised to involve recipient catabolic state, free-fraction shifts, or other unmeasured covariates).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (living-donor graft -- the more common subgroup in most paediatric and adult kidney-transplant cohorts).
- **Source aliases:**
  - `donor` / `Donor` -- common NONMEM `$INPUT` form (used in `Andrews_2017_tacrolimus.R`; Andrews 2017 codes the column as a binary living-vs-deceased indicator).
- **Example models:** `Andrews_2017_tacrolimus.R` (multiplicative effect on apparent oral clearance: living-donor recipients have CL/F 26% lower than deceased-donor recipients of the same body weight, CYP3A5 genotype, eGFR, and hematocrit; Andrews 2017 Table 2 final-model coefficient: `theta_donor_living = 0.74` for the living-donor cohort relative to the deceased-donor reference, equivalent to deceased-donor recipients having ~35% higher CL/F than living-donor recipients as reported in Section 3.4).
- **Notes:** General scope because donor source is a universally applicable solid-organ-transplant cohort attribute, captured in transplant-registry data for kidney, liver, heart, lung, and pancreas recipients. The reference category is `0 = living donor` (not `0 = deceased donor`) to match the "1 = the perturbed / index condition" convention used elsewhere in the register (e.g., `RRT_HEMODIAL_STATUS`, `POSTTX_DAY1`, `TX_LIVER`); the index condition is "received a deceased-donor graft" because it carries the more variable graft quality and is the condition that perturbs clearance upward in published kidney-tacrolimus models. Source papers that encode the column as "1 = living donor" (e.g., Andrews 2017 itself parameterises the equation with a multiplier on living-donor recipients) should still record the column under the canonical orientation: set `DONOR_DECEASED = 1 - source_living_indicator` and document the value inversion in `covariateData[[DONOR_DECEASED]]$notes`. Distinct from the donor-genotype canonical `CYP3A5_EXPR_DONOR`: donor-source (deceased vs living) is a logistic / graft-procurement covariate, while donor-genotype is a pharmacogenetic covariate; both can coexist in the same dataset when the source paper genotypes both recipients and donors. Ratified canonically on 2026-05-25 alongside the Andrews 2017 tacrolimus extraction.

### PRIOR_GAST (**canonical for prior gastrectomy**)
- **Description:** Prior (partial or total) gastrectomy indicator, 1 = prior gastrectomy, 0 = no prior gastrectomy. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no prior gastrectomy).
- **Source aliases:**
  - `GAST` -- used in `Yamada_2025_zolbetuximab.R`.
- **Example models:** `Yamada_2025_zolbetuximab.R` (fractional effects on CLss, CLT, V1).
- **Notes:** Renamed from `GAST` on 2026-04-20 to follow the `PRIOR_TNF` / `PRICORT` naming pattern for prior-treatment and surgical-history indicators. Applicable to any PK model where gastrointestinal anatomy affects absorption, first-pass, or protein turnover; not inherently oncology-specific. No distinction between partial vs total gastrectomy unless the source paper separates them.

### PRIOR_SPLEN (**canonical for prior splenectomy**)
- **Description:** Prior splenectomy indicator, 1 = prior splenectomy (spleen surgically removed before or concurrent with the modelled treatment period), 0 = spleen intact. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (spleen intact).
- **Source aliases:**
  - `splenectomy` -- used in `PerezRuixo_2015_oxaliplatin_platelet_dynamics.R` (Perez-Ruixo 2015 Table I: subject-level binary covariate on the platelet random-destruction rate constant `ks`).
- **Example models:** `PerezRuixo_2015_oxaliplatin_platelet_dynamics.R` (multiplicative effect on `ks`: `ks_i = ks * Phi^PRIOR_SPLEN` with `Phi = 0.475`, i.e., splenectomized patients have a 47.5% reduction in the random platelet destruction rate constant, prolonging platelet lifespan from 3.23 days in patients with an intact spleen to 7.78 days in splenectomized patients; Perez-Ruixo 2015 The AAPS Journal Table I and Discussion).
- **Notes:** Follows the `PRIOR_GAST` / `PRIOR_TNF` / `PRICORT` naming pattern for prior surgical-history and prior-treatment indicators (short surgical stem, all uppercase, binary 0/1). General scope because the mechanism (loss of splenic platelet sequestration / phagocytic clearance) is applicable across therapeutic areas -- Petrov 2024 romiplostim also identified splenectomy as a subgroup with distinct platelet PK/PD though encoded it as a subpopulation split rather than a data-driven column. Distinct from `INTRAOP` (active-surgery indicator, time-varying) and from `POD` (post-operative day, monotonically increasing continuous). Ratified canonically on 2026-07-10 alongside the Perez-Ruixo 2015 oxaliplatin platelet-dynamics extraction.

### TX_HEART (**canonical for heart (cardiac) transplant indicator**)
- **Description:** Binary indicator for heart / cardiac-graft transplant recipients in a pooled solid-organ-transplant cohort. 1 = heart transplant; 0 = non-heart solid-organ transplant (kidney, lung, or liver). Time-fixed per subject (assigned at transplantation date).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-heart solid-organ graft -- typically kidney as the most common non-heart comparator in pooled solid-organ-transplant analyses).
- **Source aliases:**
  - `graft_type` -- pooled categorical column with levels K / H / Lu / Li; decompose into binary `TX_HEART = as.integer(graft_type == "H")` and document the decomposition in `covariateData[[TX_HEART]]$notes`. Used in `Perrottet_2009_ganciclovir.R`.
- **Example models:** `Perrottet_2009_ganciclovir.R` (multiplicative effect on the CL-vs-GFR slope: `theta_heart = 0.86` vs `theta_kidney = 1.68`, so heart-transplant recipients have a CL slope 49% lower than kidney recipients at the same GFR and sex; Perrottet 2009 Table 3).
- **Notes:** Specific scope because the reference complement is paper-defined; sibling to `TX_LIVER`, `TX_LUNG`, and the implicit kidney-reference cohort. Distinct from `TX_ANY`, whose reference category is a non-transplant patient rather than a different solid-organ graft. Ratified canonically on 2026-06-08 alongside the Perrottet 2009 ganciclovir extraction.


### TX_LUNG (**canonical for lung (pulmonary) transplant indicator**)
- **Description:** Binary indicator for lung / pulmonary-graft transplant recipients in a pooled solid-organ-transplant cohort. 1 = lung transplant; 0 = non-lung solid-organ transplant (kidney, heart, or liver). Time-fixed per subject (assigned at transplantation date).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-lung solid-organ graft -- typically kidney as the most common non-lung comparator in pooled solid-organ-transplant analyses).
- **Source aliases:**
  - `graft_type` -- pooled categorical column with levels K / H / Lu / Li; decompose into binary `TX_LUNG = as.integer(graft_type == "Lu")` and document the decomposition in `covariateData[[TX_LUNG]]$notes`. Used in `Perrottet_2009_ganciclovir.R`.
- **Example models:** `Perrottet_2009_ganciclovir.R` (Perrottet 2009 pools lung and liver under a single CL slope; the model applies a shared multiplicative effect via `(TX_LUNG + TX_LIVER)`: pooled lung-or-liver `theta_lung/liver = 1.17` vs `theta_kidney = 1.68`, so lung-or-liver recipients have a CL slope 30% lower than kidney recipients at the same GFR and sex; Perrottet 2009 Table 3).
- **Notes:** Specific scope because the reference complement is paper-defined; sibling to `TX_LIVER`, `TX_HEART`, and the implicit kidney-reference cohort. Distinct from `TX_ANY`, whose reference category is a non-transplant patient rather than a different solid-organ graft. Future models that estimate distinct lung-vs-liver effects (rather than pooling) should keep this canonical for lung and continue to use `TX_LIVER` for liver. Ratified canonically on 2026-06-08 alongside the Perrottet 2009 ganciclovir extraction.

### TX_ANY (**canonical for any-solid-organ-transplant indicator against a non-transplant reference**)
- **Description:** Binary indicator that the subject is the recipient of any solid-organ graft, contrasted against a **non-transplant** patient. 1 = recipient of any solid-organ transplant (kidney, liver, heart, lung, pancreas, or a combination); 0 = non-transplant patient. Time-fixed per subject (assigned at transplantation date). Used when a population PK analysis pools transplant recipients with patients who have never been transplanted and tests the transplant-vs-non-transplant contrast as a PK covariate -- typically because transplant recipients differ from the rest of the cohort in renal function, immunosuppressive co-medication, and general physiological reserve in ways the measured covariates do not fully capture.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-transplant patient; the complement group is defined per-model -- in Yuen 1995 it is the CMV-retinitis and CMV-urine-shedding patients who make up 48 of the 53 subjects).
- **Source aliases:**
  - `T` -- single-letter column name used in `Yuen_1995_ganciclovir.R` (Yang 2023 Table 3 footnote: "T: T = 0 for non-transplant patients and 0.76 for transplant patients"; the source folds the coefficient into the column's coded value, so store the canonical column as a plain 0 / 1 indicator and carry 0.76 as a named `ini()` parameter).
  - `transplant (yes/no)` -- the covariate label used in Yang 2023 Table 4 for the same column.
- **Example models:** `Yuen_1995_ganciclovir.R` (fractional reduction of the renal-elimination term of clearance: `cl = exp(lcl) + e_wt_crcl_cl * WT * CRCL/100 * (1 - 0.76 * TX_ANY) * (1 - 0.41 * DIS_CMV_RETINITIS)`, i.e. a transplant recipient's weight-and-creatinine-clearance-driven clearance term is 76% lower than a non-transplant patient's at the same weight and creatinine clearance; Yang 2023 Table 3 and its footnote).
- **Notes:** Distinct in kind from the organ-specific canonicals `TX_LIVER` / `TX_HEART` / `TX_LUNG`, which partition **within** a transplant cohort (their reference category is a different solid-organ graft, not the absence of a transplant). `TX_ANY` and the organ-specific siblings can coexist in one dataset -- `TX_ANY = 1` holds for every subject with any `TX_*` indicator set -- but `TX_ANY` must **NOT** be inferred from the organ-specific indicators when the cohort contains non-transplant patients, because a subject with `TX_LIVER = 0` may be either a non-liver graft recipient or a non-transplant patient. Also distinct from `DIS_CMV` (transplant-recipient-with-CMV vs non-CMV subject: that canonical conflates transplant status with CMV infection and its reference group is a healthy volunteer or phase I participant, not a non-transplant patient) and from `DONOR_DECEASED` (graft source among transplant recipients). Scope: specific, because the reference complement is paper-defined. Registered as `TX_ANY` rather than `TX_SOT` per operator decision (sidecar request 001, question 2, 2026-07-30); if a future paper contrasts hematopoietic cell transplant recipients against solid-organ recipients, register a sibling `TX_HCT` and record in this entry's notes whether that paper's `TX_ANY` includes HCT. Ratified canonically alongside the Yuen 1995 ganciclovir extraction.


### INTRAOP (**canonical for intra-operative period indicator (active surgery)**)
- **Description:** 1 = the observation time falls within the intra-operative period (active surgery, between the start of the surgical procedure and skin closure / end of surgery); 0 = pre- or post-operative. Time-varying within subject. The exact boundaries of the intra-operative window are paper-specific (e.g., "from end of induction to skin closure", "from the post-loading-dose PK sample to end of surgery", "from incision to last suture") -- document the per-model boundaries in `covariateData[[INTRAOP]]$notes`. Used as a categorical covariate to capture composite physiological changes during active surgery (hemodynamic effects of general anaesthesia, blood loss, fluid resuscitation, surgical stress, temperature management) that are jointly unidentifiable individually in typical perioperative PK datasets.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (pre- or post-operative; i.e., not during active surgery).
- **Source aliases:**
  - (paper-prose) -- used in `Stricker_2013_aminocaproicAcid.R` (Stricker 2013 Methods "Full covariate model" and Table 4: the intra-operative period is defined as "the time immediately after the post-loading dose PK sample through the end of the surgery"; CL and V1 are estimated separately for pre/postoperative vs intraoperative periods, with multiplicative shifts of 0.89 on CL and 0.80 on V1 during the intra-operative window).
- **Example models:** `Stricker_2013_aminocaproicAcid.R` (multiplicative effect on CL and V1: `cl = cl_postop * 0.89^INTRAOP`, `vc = vc_postop * 0.80^INTRAOP`; reference category is pre/postoperative).
- **Notes:** Distinct from `POD` (post-operative day -- continuous days since surgery, rising monotonically from 0) and `POSTTX_DAY1` (binary first-24-hours-post-transplant indicator). INTRAOP captures the active-surgery window itself, whereas POD and POSTTX_DAY1 capture the post-surgical recovery time course. The three canonicals can coexist in a single dataset that pools perioperative and post-discharge PK samples. The intra-operative effect is typically interpreted as a composite of multiple confounded physiological perturbations and is not directly attributable to any single mechanism (e.g., Stricker 2013 Discussion: "the model results may be simply reflecting the net effect of a possible increased CL due to blood loss and decreased CL due to other confounding intra-operative factors"). Pairs with a future `INTRAOP_BL` (per-subject calculated intra-operative blood loss) canonical when a source paper reports a continuous blood-loss column alongside or instead of the binary indicator -- that is a separate canonical to be proposed when needed.

### SURG_SEV_MAJOR (**canonical for major (vs minor) surgical procedure severity indicator**)
- **Description:** 1 = the surgical procedure is classified as major or high-risk according to a paper-specific severity taxonomy (e.g., major orthopedic replacement, major abdominal / thoracic / vascular surgery); 0 = the procedure is classified as minor (e.g., central-line placement, small ENT / ophthalmic procedures, dental extraction, biopsy). Time-fixed per surgical procedure (a subject undergoing multiple surgeries in the study window may have different SURG_SEV_MAJOR values on each occasion). Distinct from `INTRAOP` (a time-varying within-surgery window indicator) -- SURG_SEV_MAJOR is a per-procedure severity classification that persists across the perioperative period. Captures composite physiological differences between major and minor surgery (larger blood loss / fluid resuscitation, longer duration, higher acute-phase response, more consumption of coagulation factors) that can shift factor concentrate PK in bleeding disorders.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (minor surgical procedure).
- **Source aliases:**
  - `severity of surgical procedure` (paper-prose 0/1 indicator, 1 = major) -- used in `Hazendonk_2016_factor_viii.R` (Hazendonk 2016 Table 5: `0.93 ^ severity` on CL, so major-surgery cases show 7% lower typical FVIII clearance than minor-surgery cases; classification per Koshy et al. 1995 with major and high-risk collapsed together).
- **Example models:** `Hazendonk_2016_factor_viii.R` (multiplicative effect on CL: `(1 + (-0.07) * SURG_SEV_MAJOR)` -- equivalent to `0.93 ^ SURG_SEV_MAJOR` when binary; reference category 0 = minor surgery).
- **Notes:** General scope because major-vs-minor surgery severity classification recurs across perioperative popPK models for hemostatic factor concentrates and other drugs where surgical stress affects clearance. The exact severity taxonomy is paper-specific -- Hazendonk 2016 follows Koshy et al. 1995 (a hemophilia-specific major / minor / high-risk classification, with major and high-risk collapsed into `SURG_SEV_MAJOR = 1`), while other surgical popPK models may use ASA physical-status class, RACHS-1 (cardiac only), or ad-hoc paper-specific criteria. Record the source-paper severity taxonomy in `covariateData[[SURG_SEV_MAJOR]]$notes`. Distinct from `INTRAOP` (time-varying within-surgery window indicator, applicable to any procedure regardless of severity), from `RACHS1` (ordinal cardiac-surgery-specific risk score, cardiac only), and from `MAJORSURG_HISTORY` (would be a lifetime prior-major-surgery indicator, not yet ratified). Sign of the effect on CL is paper-specific and physiologically ambiguous: major surgery can plausibly increase FVIII clearance through blood loss and consumption OR decrease it through VWF-mediated stabilization from the surgical stress response (Hazendonk 2016 Discussion attributes the 7% decrease to confounding by age -- older patients underwent more major procedures). Ratified canonically on 2026-07-11 alongside the Hazendonk 2016 perioperative FVIII extraction.


### T_CPB (**canonical for total cardiopulmonary bypass duration during the most recent cardiac surgery**)
- **Description:** Total time the patient spent on cardiopulmonary bypass (CPB) during their most recent cardiac-surgery operation. Time-fixed per subject (the duration of the bypass run is a single scalar set at the close of surgery). Captures composite physiological / pharmacological effects of CPB on post-operative drug disposition (hemodilution from priming volume, systemic inflammatory response, transient hepatic / renal hypoperfusion).
- **Units:** minutes
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- typically enters as a power-centred effect `(T_CPB / ref)^exponent`. Reference value is paper-specific (cohort median total CPB time): Su 2016 uses 60 minutes (overall cohort median, Table 3).
- **Source aliases:**
  - `TBYP` -- used in `Su_2016_dexmedetomidine.R` (Su 2016 NONMEM column for total bypass time in minutes; Methods 'Full Covariate Model' equation and Table 4 footnote).
- **Example models:** `Su_2016_dexmedetomidine.R` (power effect on CL: `(T_CPB / 60)^(-0.31)`; negative exponent so longer bypass time reduces post-operative CL).
- **Notes:** General scope because CPB time is a routine clinical covariate in any popPK study of patients undergoing open heart surgery. Distinct from `T_POST_ECMO` (time AFTER decannulation from ECMO -- T_CPB captures the DURATION of the bypass procedure during surgery, not time since the end of it; T_POST_ECMO is time-varying within subject whereas T_CPB is time-fixed). Distinct from `POD` (post-operative day) and `INTRAOP` (binary intra-operative indicator). Per the T_<event> canonical family, the `T_` prefix denotes a procedure-related time covariate; for T_CPB the "event" is the bypass run itself and the column carries the duration of that run. Future paediatric / adult cardiac-surgery popPK extractions that retain CPB time should reuse this canonical. Ratified canonically on 2026-06-28 alongside the Su 2016 dexmedetomidine extraction.


### ICSHUNT_R2L (**canonical for right-to-left intracardiac shunt indicator**)
- **Description:** Binary indicator that the patient's cardiac anatomy produces a right-to-left intracardiac shunt with pulmonary blood flow to systemic blood flow ratio Qp:Qs < 1. 1 = right-to-left shunt present (e.g., single-ventricle physiology after stage 2 palliation -- Glenn or hemi-Fontan); 0 = no right-to-left shunt (Qp:Qs >= 1). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no right-to-left shunt; Qp:Qs >= 1).
- **Source aliases:**
  - `intracardiac shunt` -- used in `Su_2016_dexmedetomidine.R` (Su 2016 Methods 'Full Covariate Model' definition; encoded in the source dataset as a binary indicator and entered in the CL equation as a multiplicative factor when shunt = 1).
- **Example models:** `Su_2016_dexmedetomidine.R` (multiplicative effect on CL: CL is multiplied by 1.24 when ICSHUNT_R2L = 1; Su 2016 Methods hypothesise the mechanism is shunt-induced reduction in first-pass lung extraction combined with increased hepatic perfusion for a drug with relatively high hepatic extraction ratio).
- **Notes:** General scope because intracardiac shunting is a defining anatomical feature in any congenital-heart-disease popPK cohort and is likely to recur in future paediatric / adult cardiac-surgery popPK studies. The Qp:Qs < 1 threshold is the standard clinical definition for right-to-left shunting. Distinct from `TX_HEART` (heart-transplant indicator), `RACHS1` (paediatric cardiac surgery risk category), and `INTRAOP` (binary intra-operative indicator). Future extractions that report a continuous Qp:Qs ratio could either (a) reuse ICSHUNT_R2L as a binary derived from the continuous ratio (Qp:Qs < 1) or (b) register a sibling continuous canonical `QP_QS`. Ratified canonically on 2026-06-28 alongside the Su 2016 dexmedetomidine extraction.


## Disease state (cross-population indicators)

### DIS_CMV (**canonical for transplant-recipient-with-cytomegalovirus-infection disease-state indicator**)
- **Description:** 1 = hematopoietic cell transplant (HCT) or solid organ transplant (SOT) recipient with cytomegalovirus (CMV) infection/disease; 0 = non-CMV subject (healthy volunteer, or a phase I participant such as a renal- or hepatic-impairment cohort member, pooled into the same analysis as the reference group). Time-fixed per subject. Used when a population PK model pools healthy volunteers / phase I participants with transplant recipients who have CMV infection and tests the patient-vs-healthy contrast as a PK covariate.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-CMV subject; the complement group is defined per-model -- typically healthy volunteers plus the phase I special-population cohorts).
- **Source aliases:**
  - `HSCMV` -- NONMEM control-stream column name used in `Sun_2023_maribavir.R`.
- **Example models:** `Sun_2023_maribavir.R` (log-scale additive shift on CL/F: `exp(e_dis_cmv_cl * DIS_CMV)` with `e_dis_cmv_cl = -0.280346`, i.e. CL/F is 0.756x lower in transplant recipients with CMV than in the healthy 70-kg reference subject).
- **Notes:** Distinct from the organ-specific transplant indicators `TX_LIVER` / `TX_HEART` / `TX_LUNG`, which record which organ was transplanted rather than the presence of CMV infection, and from `DIS_HEALTHY`, which is the complementary healthy-cohort indicator. `DIS_CMV` is preferred over encoding this contrast as `DIS_HEALTHY = 1 - DIS_CMV` because the source's structural reference subject is explicitly the non-CMV individual (Sun 2023 Table S2: "The reference population is a 70-kg individual without CMV administered a 800 mg maribavir dose"), so keeping the patient group as the indicator preserves the published meaning of the reference THETA. Also distinct from `AUC_GCV`, which supplies ganciclovir exposure to CMV viral-load PD models rather than flagging CMV disease state. Also distinct from `DIS_CMV_RETINITIS`, which is a within-CMV-positive disease-presentation split (retinitis vs no retinitis) and is orthogonal to this entry: both indicators can appear in one dataset, and a `DIS_CMV_RETINITIS` cohort carries `DIS_CMV = 1` throughout. Scope: specific; promote to general if a second paper pools transplant recipients with CMV against a non-CMV reference with the same semantics. Registered alongside the Sun 2023 maribavir extraction.
### DIS_CMV_RETINITIS (**canonical for CMV-retinitis indicator within a CMV-positive cohort**)
- **Description:** Binary indicator that a CMV-positive subject has CMV retinitis, i.e. symptomatic end-organ CMV disease of the retina, as opposed to CMV positivity without retinitis. 1 = CMV retinitis; 0 = CMV-positive without retinitis (in the founding study, asymptomatic CMV urine shedding). Time-fixed per subject. Used when a population PK analysis contrasts two CMV **presentations** inside a CMV-infected cohort, rather than contrasting CMV-infected subjects against CMV-negative ones.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (CMV-positive without retinitis; the complement group is defined per-model -- in Yuen 1995 it is the 17 patients shedding CMV in urine).
- **Source aliases:**
  - `CMV` -- column name used in `Yuen_1995_ganciclovir.R` (Yang 2023 Table 3 footnote: "CMV: CMV = 0 for CMV-shedding patients and 0.41 for patients with CMV retinitis"; the source folds the coefficient into the column's coded value, so store the canonical column as a plain 0 / 1 indicator and carry 0.41 as a named `ini()` parameter).
  - `CMV-shedding or CMV retinitis` -- the covariate label used in Yang 2023 Table 4 for the same column.
- **Example models:** `Yuen_1995_ganciclovir.R` (fractional reduction of the renal-elimination term of clearance: `cl = exp(lcl) + e_wt_crcl_cl * WT * CRCL/100 * (1 - 0.76 * TX_ANY) * (1 - 0.41 * DIS_CMV_RETINITIS)`, i.e. a retinitis patient's weight-and-creatinine-clearance-driven clearance term is 41% lower than an asymptomatic urine-shedding patient's at the same weight and creatinine clearance; Yang 2023 Table 3 and its footnote).
- **Notes:** This is a **within-CMV-positive** contrast and is therefore ORTHOGONAL to `DIS_CMV`, which encodes transplant-recipient-with-CMV-infection versus a non-CMV reference (healthy volunteer or phase I participant). Both can appear in one dataset, and every subject in a `DIS_CMV_RETINITIS` cohort -- retinitis and non-retinitis alike -- would carry `DIS_CMV = 1`; reusing `DIS_CMV` for the retinitis split would overload it with incompatible semantics and would change the meaning of the published reference THETA in `Sun_2023_maribavir.R`. Registered as the retinitis-specific `DIS_CMV_RETINITIS` rather than a broader end-organ-disease canonical per operator decision (sidecar request 001, question 3, 2026-07-30), because retinitis was the only end-organ site present in the founding cohort. If a future paper contrasts a different symptomatic CMV site (colitis, pneumonitis, oesophagitis) against asymptomatic CMV positivity, register a sibling canonical on the same pattern (e.g. `DIS_CMV_COLITIS`) rather than widening this entry; if a paper pools several sites into a single "end-organ disease" indicator, that is the point at which a `DIS_CMV_ORGAN` parent becomes worth registering. Scope: specific, because the reference complement is paper-defined. Ratified canonically alongside the Yuen 1995 ganciclovir extraction.


### DIS_COVID19 (**canonical for COVID-19 patient disease-state indicator**)
- **Description:** 1 = participant with coronavirus disease 2019 (COVID-19; laboratory-confirmed SARS-CoV-2 infection) enrolled as a patient in the source analysis; 0 = non-COVID-19 subject (healthy volunteer, or a phase I special-population participant such as a renal- or hepatic-impairment cohort member, pooled into the same analysis as the reference group). Time-fixed per subject. Used when a population PK / PD model pools phase I participants with COVID-19 patients and tests the patient-vs-non-patient contrast as a covariate.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-COVID-19 subject; the complement group is defined per-model -- typically healthy volunteers plus the phase I special-population cohorts).
- **Source aliases:**
  - `PTST` (patient status) -- NONMEM control-stream column name used in `Chan_2023_nirmatrelvir.R` (Chan 2023 Data S1 `$PK`: `CLPTST = 1; IF(PTST.EQ.1) CLPTST = 1+THETA(17)`).
- **Example models:** `Chan_2023_nirmatrelvir.R` (fractional multiplier on nirmatrelvir CL: `1 + e_dis_covid19_cl * DIS_COVID19` with `e_dis_covid19_cl = -0.341`, i.e. CL is 34.1% lower in adults with COVID-19 than in the phase I reference participant at the same weight and renal function).
- **Notes:** Distinct from `DIS_HEALTHY`, and `DIS_COVID19 = 1 - DIS_HEALTHY` is NOT a valid re-expression here: the reference group in Chan 2023 contains renal- and hepatic-impairment cohorts who are neither healthy nor COVID-19 patients, so the two indicators are not complements. Same structural pattern as `DIS_CMV` (transplant-recipient-with-CMV vs pooled phase I reference), which is the closest precedent in the `DIS_` family. Also distinct from `SARS_VLOAD` (baseline SARS-CoV-2 viral load) and `SARS_SEROPOS` (baseline serostatus), which quantify infection within a COVID-19-only cohort rather than flagging cohort membership. Chan 2023 Discussion cautions that the COVID-19-on-CL effect and the 150-mg-tablet formulation effect on F1 are likely partially confounded because the 150-mg tablet was evaluated only in a single single-dose healthy-participant study. Scope: specific; promote to general if a second paper pools COVID-19 patients with a non-COVID-19 reference under the same semantics. Registered alongside the Chan 2023 nirmatrelvir extraction.

### DIS_UC (**canonical for ulcerative colitis disease-state indicator**)
- **Description:** 1 = ulcerative colitis patient, 0 = non-UC (e.g., healthy volunteer or non-IBD indication). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-UC subject; the complement group is defined per-model -- typically healthy volunteers and/or patients with another indication such as asthma).
- **Source aliases:**
  - `UC` -- used in `Hua_2015_anrukinzumab.R`.
- **Example models:** `Hua_2015_anrukinzumab.R` (multiplicative fractional increase in CL, +72.8%, on top of weight and albumin effects), `Wojciechowski_2023_ritlecitinib_updated.R` (two linear fractional effects from Wojciechowski 2023 Table 2: `CL/F *= (1 - 0.560 * DIS_UC)` and `F *= (1 - 0.224 * DIS_UC)`; the CL/F reference category is the healthy-participant cohort while the F reference category is healthy participants plus the RA, alopecia areata and vitiligo cohorts, and the UC cohort also contributes to the pooled inflammatory-disease group that scales the IIV and proportional-residual-error magnitudes).
- **Notes:** Used when a population PK model pools UC patients with a non-UC reference population (e.g., Hua 2015: healthy volunteers + asthma patients + UC patients) and UC disease status is tested as a PK covariate. Distinct from `DISEXT_EP` / `DISEXT_OTHER`, which operate *within* a UC-only cohort (disease extension). Start as scope: specific; promote to general if a second paper pools UC with a non-UC reference.

### DIS_DUOD_ULCER (**canonical for duodenal-ulcer disease-state indicator**)
- **Description:** 1 = duodenal-ulcer patient (peptic ulcer disease of the duodenum, endoscopically confirmed), 0 = non-duodenal-ulcer reference (e.g., healthy volunteer, or other non-peptic-ulcer indication pooled in the source analysis). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-duodenal-ulcer subject; the complement group is paper-defined -- for Yu 2024 the reference is the pooled healthy-subject cohort across 4 phase I trials).
- **Source aliases:**
  - `Disease status` -- Yu 2024 paper narrative and Table 3 footnote (1 = duodenal ulcer, 0 = healthy).
- **Example models:** `Yu_2024_ilaprazole.R` (exponential effects `exp(0.290 * DIS_DUOD_ULCER)` on CL and `exp(0.356 * DIS_DUOD_ULCER)` on Vp of the 2-compartment IV ilaprazole model; reference category is the pooled healthy-subject cohort from CTR20132848 / CTR20140147 / CTR20150686 / CTR20150685, and the phase IIa duodenal-ulcer cohort CTR20132846 required an ulcer diameter <= 15 mm with no combined ulcer bleeding; Yu 2024 Table 3 and Eqs. 7-9).
- **Notes:** Distinct from `ENDO_ULCER` (IBD mucosal-ulcer activity indicator scored *within* an IBD cohort at baseline ileocolonoscopy) because `DIS_DUOD_ULCER` is a peptic-duodenal-ulcer-patient vs non-patient cohort indicator, following the `DIS_UC` / `DIS_PSORIASIS` / `DIS_HAE` pattern of specific-disease-vs-non-disease pooled-cohort covariates. Distinct from `DIS_UC` and any future IBD indicators because peptic ulcer disease is a non-IBD upper-GI condition. Also distinct from `DIS_HEALTHY`: that canonical carries a healthy-vs-pooled-patient contrast with 0 = patient, whereas a paper enrolling a single named disease cohort alongside healthy participants is encoded with the disease-specific indicator so the source coefficients and typical values transfer without a sign flip or re-baselining. The shorter forms `DIS_DU` (collides with other DU acronyms), `DIS_PUD`, and `DIS_PEPTIC_ULCER` (both broader than the duodenal-only cohort Yu 2024 enrolled) were considered and rejected. Scope: specific because the complement reference category is paper-defined; promote to general if a second paper pools duodenal-ulcer patients with a non-peptic-ulcer reference. Ratified canonically on 2026-07-28 alongside the Yu 2024 ilaprazole extraction.

### DIS_SASTHMA (**canonical for moderate-to-severe asthma disease-state indicator**)
- **Description:** 1 = moderate-to-severe asthma patient, 0 = not (e.g., healthy volunteer, mild-to-moderate asthma, or other indication). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-moderate-to-severe-asthma subject; the complement group is defined per-model).
- **Source aliases:**
  - `sAsthma` -- used in `Hua_2015_anrukinzumab.R`.
- **Example models:** `Hua_2015_anrukinzumab.R` (multiplicative fractional change in SC bioavailability, -30.9%).
- **Notes:** The moderate-to-severe vs. mild-to-moderate asthma cutoff is protocol-defined; Hua 2015 uses FEV1 55-80% and ACQ-5 >= 2 for "moderate to severe" (study 4) versus FEV1 > 70% and ACQ-5 <= 1 for "mild to moderate" (study 1). Scope: specific because the severity threshold is tied to a particular analysis plan; future asthma-severity indicators with different thresholds should register as separate canonicals.

### DIS_PJIA (**canonical for polyarticular juvenile idiopathic arthritis disease-state indicator**)
- **Description:** 1 = polyarticular juvenile idiopathic arthritis (pJIA) patient, 0 = non-pJIA (e.g., adult rheumatoid arthritis or other indication). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-pJIA subject; the complement group is defined per-model -- typically adult RA in pooled abatacept analyses).
- **Source aliases:**
  - `JIA` -- used in `Gandhi_2021_abatacept.R` and `Zhong_2026_abatacept.R`.
- **Example models:** `Gandhi_2021_abatacept.R` (additive coefficient on logit-F: pJIA patients have markedly higher SC bioavailability than RA reference), `Zhong_2026_abatacept.R` (additive coefficient +3.08 on logit-F transferred verbatim from a previous internal JIA PPK model that matches Gandhi 2021's published value).
- **Notes:** Used when a population PK model pools pJIA patients with a non-pJIA reference population (e.g., Gandhi 2021: pooled adult RA + pediatric pJIA; Zhong 2026: pooled adult RA + pediatric pJIA + adult/pediatric HM) and pJIA disease/age status is tested as a PK covariate (typically on bioavailability rather than CL). Distinct from `CHILD` and `ADOLESCENT`, which are pure age-band indicators independent of indication. Scope: specific; promote to general if a third paper pools pJIA with a non-pJIA reference and the reference category remains adult RA.

### DIS_CANCER (**canonical for advanced-solid-tumor / oncology cohort indicator**)
- **Description:** 1 = patient with an advanced or metastatic solid tumor (the oncology cohort in a pooled multi-indication PK/PD analysis), 0 = non-oncology subject (healthy volunteer or non-oncology disease cohort pooled in the source analysis). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-oncology subject; the complement group is paper-defined -- typically the union of healthy volunteers and a non-oncology disease cohort such as cGVHD pooled in the source analysis).
- **Source aliases:** none; source NONMEM / Monolix control streams typically derive the indicator from a `POP` or `STUDY` categorical alongside `DIS_HEALTHY`.
- **Example models:** `Yang_2024_axatilimab.R` (multiplicative effect on baseline NCMC: `BL_NCMC x exp(1.22 x DIS_CANCER + 0.618 x DIS_HEALTHY)`; reference category cGVHD when both indicators are 0), `Bonate_2004_apomine.R` (log-additive shifts on baseline CL/F and Vc/F for the advanced-solid-tumor cohort vs the healthy adult-male reference: `e_cancer_cl = log(10.2 / 40.7) = -1.384` and `e_cancer_vc = log(7.11 / 12.3) = -0.548`; reference category 0 = healthy adult male volunteer), `Silva_2024_apx3330.R` (log-additive shift on apparent oral clearance for the advanced-solid-tumor cohort vs the healthy Japanese male volunteer reference: `e_cancer_cl = 0.409`, Silva 2024 Table 2 'Combined data' row `beta SubjectSource,CL`, applied as `CL/F x exp(0.409 x DIS_CANCER)` per the paper's Eq. 2; reference category 0 = healthy Japanese male volunteer. The source paper names the covariate 'subject source' and chose it over serum albumin deliberately -- it stands in for the whole bundle of between-cohort differences (disease state, age, ethnicity and albumin), which is exactly the paper-defined-complement semantic that makes this entry `specific`).
- **Notes:** Used together with `DIS_HEALTHY` to decompose a three-level "participant population" categorical (cGVHD reference, advanced solid tumor, healthy volunteer) into two orthogonal binary indicators (Yang 2024 form), or as a single binary stratifier between an oncology cohort and a healthy-volunteer reference (Bonate 2004 and Silva 2024 form). Scope: specific because the disease-pooling reference category is paper-defined (Yang 2024 reference is patients with cGVHD; Bonate 2004 reference is healthy adult males; Silva 2024 reference is healthy Japanese male volunteers). A "study source" / "data source" categorical that separates an oncology cohort from a healthy-volunteer cohort pooled in the same analysis maps to `DIS_CANCER` rather than to a new `STUDY_*` indicator, because the modelled contrast is the disease cohort rather than a specific trial protocol. Ratified canonically on 2026-04-28; extended to the Bonate 2004 apomine extraction on 2026-06-04.

### DIS_CANCER_PED (**canonical for pediatric oncology cohort indicator**)
- **Description:** 1 = pediatric patient receiving cancer-directed therapy (any malignancy, including hematologic cancers such as leukemia and lymphoma as well as solid tumors / blastomas), 0 = pediatric patient admitted for a non-oncology indication (e.g. infection, surgery, transplant). Time-fixed per subject. Distinct from `DIS_CANCER`, which is restricted to advanced/metastatic solid tumors in adults; `DIS_CANCER_PED` is the pediatric variant in the `DIS_CANCER*` family and explicitly covers leukemia-dominant pediatric cohorts.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-oncology pediatric patient; the complement group is paper-defined -- in Llanos-Paez 2020 the complement is pediatric patients admitted for various non-oncology indications, with appendicitis and kidney disease / urinary tract infection the most common).
- **Source aliases:**
  - `ONCOLOGY` -- Llanos-Paez 2020 NONMEM column with the same orientation (1 = oncology, 0 = nononcology); maps directly to `DIS_CANCER_PED`.
- **Example models:** `Llanos-Paez_2020_gentamicin.R` (multiplicative cohort shifts on V1 (-0.154) and Q (-0.321) relative to the nononcology baseline; CL has no oncology effect), `deAlwis_1998_ondansetron.R` (cohort indicator used together with `DIS_HEALTHY` and `AGE` thresholds to switch the per-subject proportional-residual-error magnitude across five paper-defined sub-populations; DIS_CANCER_PED = 1 routes to the paediatric-chemotherapy stratum (Table 1 group *4, propSd 0.178), DIS_CANCER_PED = 0 paired with DIS_HEALTHY = 0 routes to the paediatric-general-anaesthesia stratum (Table 1 group *5, propSd 0.145); reference complement is the paediatric general-anaesthesia cohort (study 4) plus all non-paediatric subjects).
- **Notes:** Use `DIS_CANCER_PED` rather than `DIS_CANCER` whenever the source paper's "oncology" cohort includes hematologic malignancies (leukemia / lymphoma) or pediatric blastomas, because `DIS_CANCER` is canonically restricted to advanced/metastatic solid tumors. Reference-category complement is paper-defined (Llanos-Paez 2020 complement is the pooled pediatric non-oncology admissions cohort; de Alwis 1998 complement is the paediatric general-anaesthesia sub-cohort plus all non-paediatric subjects). Scope: specific because the complement is paper-defined. Covariate-effect parameters drop the `DIS_` prefix per the existing `DIS_CANCER` -> `e_cancer_*` convention (Yang 2024); use `e_cancer_ped_<param>`.

### DIS_HEALTHY (**canonical for healthy-participant cohort indicator**)
- **Description:** 1 = healthy participant (no diagnosis), 0 = patient (any diagnosis represented in the pooled cohort). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (patient subject; the complement group is the union of disease cohorts pooled in the source analysis).
- **Source aliases:** none known; healthy-participant indicators in source NONMEM control streams typically use ad-hoc names (e.g., `HV`, `HEALTHY`, `DIS_HV`).
- **Example models:** `Nikanjam_2019_siltuximab.R` (multiplicative effects: 0.77 on CL, 0.83 on Vss; reference category is the pooled non-healthy oncology cohort), `Okada_2025_rocatinlimab.R` (multiplicative shift `1 - 0.532` on Vmax when 1; reference complement is the pooled atopic-dermatitis + ulcerative-colitis + plaque-psoriasis patient cohort), `Yang_2024_axatilimab.R` (multiplicative effect on baseline NCMC: `BL_NCMC x exp(1.22 x DIS_CANCER + 0.618 x DIS_HEALTHY)`; reference category cGVHD), `Goel_2016_Sonidegib.R` (multiplicative power-form effect on CL/F: `2.96^DIS_HEALTHY`; reference category is the pooled cancer-patient cohort across X2101 / X1101 / A2201), `Brown_2017_osimertinib.R` (linear factor `(1 + 0.44 x DIS_HEALTHY)` on apparent osimertinib clearance and `(1 + 1.25 x DIS_HEALTHY)` on apparent AZ5104 clearance; reference category is the pooled NSCLC cohort across AURA / AURA2), `Lu_2015_vismodegib.R` (additive-on-log-scale shift on ka via `exp(0.671 * DIS_HEALTHY)` and on F via `exp(0.881 * DIS_HEALTHY)` gated on the Phase I formulation indicator; reference category is the pooled cancer-patient cohort across SHH3925g / SHH4610g / SHH4476g), `Gupta_2016_lenvatinib.R` (multiplicative power-form effect on CL/F: `1.15^DIS_HEALTHY`; reference category is the pooled solid-tumor / thyroid-cancer patient cohort across 15 phase 1-3 studies; healthy subjects show +15 percent CL/F vs cancer patients), `Bienczak_2025_ligelizumab.R` (log-additive `exp(-0.087 * DIS_HEALTHY)` on apparent ligelizumab CL/F; reference category is the pooled chronic-spontaneous-urticaria patient cohort across C2201 / C2202 / C2302 / C2303), `Lu_2015_tacrolimus.R` (multiplicative factor `1 / 0.562 = 1.78` on CL/F at DIS_HEALTHY = 1; reference category is the adult Chinese orthotopic liver-transplant recipient cohort; healthy-volunteer CL/F is 1.78x patient CL/F at ALT = 0. Paper Eq. 10 uses the reverse-coded `SubPop` indicator -- the model file re-expresses it as DIS_HEALTHY = 1 - SubPop), `Li_2017_CC292.R` (binary stratifier on the proportional residual error magnitude: HNP cohort uses propSd = sqrt(0.234) and patient cohort uses propSd = sqrt(0.659); reference category 0 is the pooled relapsed/refractory B-cell-malignancy patient cohort from AVL-292-003; source column `HNP`), `Yoneyama_2017_emicizumab.R` (exponential effects `exp(-0.232 * DIS_HEALTHY)` on CL/F and `exp(-0.175 * DIS_HEALTHY)` on Vd/F; reference category is the pooled Japanese male adult/adolescent severe-hemophilia-A patient cohort across the Japanese MAD phase I and phase I/II extension; paper Eq. 2 uses the reverse-coded `PATIENT` indicator and the model file re-expresses it as DIS_HEALTHY = 1 - PATIENT, shifting the structural typicals to the patient state), `Kleideiter_2017_cebranopadol.R` (multiplicative effect on bioavailability: `f_disease *= 0.837` for healthy volunteers relative to the LBP/OA chronic-pain reference; paired with `DIS_DPN` and `DIS_BUNIONECTOMY` to form the four-level disease-status stratification), `Kleideiter_2018_cebranopadol.R` (multiplicative power-form effect on bioavailability F: factor 0.837, i.e. about -16% F for healthy adults vs the pooled nociceptive-pain (LBP and OA) reference cohort; sibling indicators `DIS_DPN` and `DIS_BUN` carry the diabetic-polyneuropathy and bunionectomy effects in the same four-level disease-status encoding; Kleideiter 2018 Table 13), `Taubert_2018_finafloxacin.R` (log-additive effects on the canonical lcl_renal + lcl_nonren decomposition: `exp(+0.985 * DIS_HEALTHY)` on CL_renal and `exp(+0.068 * DIS_HEALTHY)` on CL_nonren; reference category is the cUTI patient cohort (Trial III), with healthy effects rederived from paper Table 3 CL_total = 20.9 L/h * patient effect (1 - 0.29) and FER1 = 0.40 / FER2 = 0.21; source column `PATIENT` in the paper, re-expressed as DIS_HEALTHY = 1 - PATIENT), `Goggin_2004_emfilermin.R` (log-additive `exp(+0.4325 * DIS_HEALTHY)` on apparent CL/F, where +0.4325 = -log(0.649); the patient reference category is the IVF-ET premenopausal cohort with recurrent implantation failure (Study 3, n = 39) and the model file shifts lcl to the IVF-ET state lcl = log(57 * 0.649) = log(37.0) so the +0.4325 shift restores the healthy-postmenopausal-women-typical 57 L/h at DIS_HEALTHY = 1; source column `TYPE` in the paper, re-expressed as DIS_HEALTHY = 1 - TYPE), `Klunder_2017_upadacitinib.R` (paired healthy/RA structural means `lcl_h` / `lcl_ra` and `lvc_h` / `lvc_ra` gated by `DIS_HEALTHY`, with cohort-specific log-normal IIV on CL/F and Vc/F; reference category 0 is the adult RA cohort. The CL/F means encode the paper's verbatim Table 3 contrast `lcl_ra = log(39.7 * 0.76)`; Vc/F means are identical because Klunder 2017 reports no disease-state effect on typical Vc/F, only on its ISV), `Bulitta_2010_ceftazidime.R` (log-additive effects on CL and on V1 / V2 / V3 of the 3-compartment ceftazidime IV model: `exp(log(1 / 1.17) * DIS_HEALTHY)` on CL and `exp(log(1 / 1.01) * DIS_HEALTHY)` shared across V1, V2, and V3; reference category 0 is the cystic-fibrosis patient cohort, and Bulitta 2010 Table 3 reports FCYFCL = 1.17 and FCYFVSS = 1.01 with the healthy-volunteer cohort as the paper's structural reference; the model file re-expresses the paper's CF-vs-HV scale factors onto the canonical DIS_HEALTHY orientation so the typical-value parameters equal the Table 3 CF column), `Desai_2016_isavuconazole.R` (multiplicative fractional effect on peripheral volume V_p: `1 + e_dis_healthy_vp * DIS_HEALTHY` with e_dis_healthy_vp = -0.3765, so healthy V_p baseline is about 38% lower than the patient reference at BMI 24.80 kg/m^2; reference category is the pooled SECURE-trial invasive-aspergillosis / other-filamentous-fungi patient cohort; source column `SP` (1 = patient, 0 = healthy) re-expressed as DIS_HEALTHY = 1 - SP; assignment of Table 5 theta_4 = 417 L to patients and theta_11 = 260 L to healthy subjects was inferred from the Discussion typical-value report V_p ~390 L (patients) / ~292 L (healthy)), `deAlwis_1998_ondansetron.R` (cohort indicator used together with `DIS_CANCER_PED` and `AGE` thresholds to switch the per-subject proportional-residual-error magnitude across five paper-defined sub-populations: DIS_HEALTHY = 1 routes the subject into one of three volunteer strata selected by AGE (young < 45 y -> propSd 0.125, elderly 45-75 y -> propSd 0.133, aged >= 75 y -> propSd 0.169), DIS_HEALTHY = 0 routes the subject into one of two paediatric strata selected by DIS_CANCER_PED (chemotherapy -> propSd 0.178, anaesthesia -> propSd 0.145); reference complement under DIS_HEALTHY = 0 is the pooled paediatric-patient cohort from studies 3 and 4), `Iida_2008_nicorandil.R` (multiplicative log-additive effects on apparent CL, V1, Q, V2 of the 2-compartment IV nicorandil model: `exp(log(1 / 1.94) * DIS_HEALTHY)` on CL, `exp(log(1 / 1.39) * DIS_HEALTHY)` on V1, `exp(log(1 / 0.519) * DIS_HEALTHY)` on Q, `exp(log(1 / 4.06) * DIS_HEALTHY)` on V2; reference category 0 is the acute heart failure (AHF) patient cohort, and Iida 2008 Table 2 reports POP_CL, POP_V1, POP_Q, POP_V2 with multiplicative FCL = 1.94, FV1 = 1.39, FQ = 0.519, FV2 = 4.06 carrying the AHF-vs-healthy contrast with the healthy-volunteer cohort as the paper's structural reference; the model file re-expresses the paper's HV-vs-AHF scale factors onto the canonical DIS_HEALTHY orientation so the typical-value parameters equal the AHF cohort), `Shoji_2011_pregabalin.R` (binary stratifier on the combined proportional + additive residual error magnitudes: healthy cohort uses propSd = 0.220 and addSd = 0.0239 ug/mL; patient cohort uses propSd = 0.285 and addSd = 0.236 ug/mL; reference complement under DIS_HEALTHY = 0 is the pooled post-herpetic neuralgia (PT01-PT04) and diabetic peripheral neuropathy (PT05) patient cohort; Shoji 2011 Table 3 final model rows for residual variability), `Farrell_2013_conestatAlfa.R` (cohort indicator gating the endogenous-baseline structural mean and IIV between healthy volunteers and patients with hereditary angioedema: DIS_HEALTHY = 1 selects `lrbase_hv = log(0.901)` with `etalrbase_hv` (12.7% CV) and DIS_HEALTHY = 0 selects `lrbase_hae = log(0.176)` with `etalrbase_hae` (54.4% CV); reference complement is the pooled adolescent / adult HAE-patient cohort from Studies C1 1101-01 / 1202-01 / 1203-01 / 1205-01 / 1304-01), `PerezRuixo_2006_tipifarnib.R` (ratio-form multiplicative effects on five structural parameters: CL ratio 1.21, V2 ratio 0.55, Q4 ratio 8.83, V4 ratio 2.66, and Ka ratio 2.31 applied as `ratio^DIS_HEALTHY`; reference category is the pooled adult-cancer cohort across 14 phase 1, 2, and 3 studies; healthy subjects show +21 percent CL and 2.31-fold higher Ka than cancer patients, while V2 is 0.55-fold smaller), `Yin_2020_pexidartinib.R` (multiplicative effect on CL/F: 1.26x when DIS_HEALTHY = 1 corresponding to a 21 percent lower steady-state AUC0-24 in healthy subjects vs patients per Yin 2020 Results; the residual-error proportional SD also switches by DIS_HEALTHY: 0.297 for patients vs 0.196 for healthy subjects; reference category 0 is the pooled TGCT / other solid-tumour patient cohort from Studies PLX108-01 and ENLIVEN), `Melhem_2018_g_csf.R` (log-additive shifts on the granulopoiesis parameters KINT and STM2: KINT_HV / KINT_PT = 0.197 / 0.113 = 1.743 log-multiplier and STM2_HV / STM2_PT = 5.21 / 3.89 = 1.339 log-multiplier applied on the healthy-volunteer arm; reference category 0 is the adult cancer-patient-on-chemotherapy cohort in the primary Dataset A reference model; healthy-volunteer subjects show approximately 74 percent higher receptor internalisation rate and 34 percent higher maximum stimulation of bone-marrow transit compared with cancer patients on chemotherapy), `Chen_2023_nemonoxacin.R` (multiplicative power-form effect on the peripheral volume of distribution Vp: `e_dis_healthy_vp^DIS_HEALTHY` with `e_dis_healthy_vp = 1 / 1.23 = 0.813`; reference category 0 is the pooled Chinese community-acquired-pneumonia cohort from the phase II (TG-873870-C-3) and phase III (TG-873870-C-4) nemonoxacin trials, and the healthy stratum is the phase I (TG-873870-C-1) cohort; Chen 2023 Eq. 8 prints `V3 = 28 x (BW/70) x 1.23^DisStat` against the reverse-coded `DisStat` indicator (0 = healthy, 1 = CAP patient), so the model file re-expresses it as DIS_HEALTHY = 1 - DisStat and shifts `lvp` to the patient state `log(28 x 1.23) = log(34.4)` so DIS_HEALTHY = 1 restores the printed healthy typical 28 L), `Galluppi_2021_ulotaront.R` (log-additive shift `exp(-log(0.809) * DIS_HEALTHY) = exp(+0.2119 * DIS_HEALTHY)` on apparent CL/F; reference category 0 is the pooled adult-schizophrenia-patient cohort across seven phase I studies plus the phase II acute + 6-month open-label extension studies, with the structural typical `lcl = log(32.5 * 0.809) = log(26.29)` shifted from the paper's HV-typical 32.5 L/h to the patient state so exp(+0.2119) restores 32.5 L/h at DIS_HEALTHY = 1; source column `PATIENT` in the paper, re-expressed as DIS_HEALTHY = 1 - PATIENT), `Mitra_2026_ziftomenib.R` (multiplicative effects on parent ziftomenib CL/F (2.59x when DIS_HEALTHY = 1, log-scale THETA(12) = 0.950; reference category is R/R AML patients), on FM (0.348x, logit-scale THETA(17) = -1.62), on KO-739 Vc (0.197x, log-scale THETA(21) = -1.62), and on KO-516 Vc (0.154x, log-scale THETA(22) = -1.87); source paper column 'HV'), `Marathe_2023_belzutifan.R` (binary stratifier on the proportional residual error magnitude only, with no effect on any structural or random-effect PK parameter: healthy participants use propSd = 0.26 (RESHV) and patients use propSd = 0.29 (RES PAT) per Marathe 2023 Table 2; reference complement under DIS_HEALTHY = 0 is the pooled advanced-RCC / other-advanced-solid-tumor / VHL-RCC patient cohort from Studies 1 and 4. The source control stream switches on `STUDY` (`PROP = THETA(8) IF (STUDY.EQ.1.OR.STUDY.EQ.4)`), and because Studies 1 and 4 are exactly the two patient studies the switch is re-expressed on the canonical DIS_HEALTHY orientation. Note that disease status was separately screened as a structural covariate on CL/F and V2/F and was NOT retained, so this model carries a residual-error-only disease effect), `Lee_2023_tripegfilgrastim.R` (the paper's "study population" covariate on the tripegfilgrastim PDMDD parameters VD/F and KD: Lee 2023 Table 2 reports both typical values directly rather than the exponential coefficients, so the model file encodes `lvd = log(12.7)` / `e_hv_vd = log(4.7 / 12.7)` and `lkd = log(16.2)` / `e_hv_kd = log(42.2 / 16.2)`; reference category 0 is the Korean pediatric solid-tumor cohort receiving chemotherapy, matching the orientation of the upstream `Melhem_2018_g_csf.R` framework this model inherits. In this analysis the indicator is fully confounded with age stratum and chemotherapy exposure -- every healthy participant is an adult aged 20-38 and every patient is pediatric aged 6-17 and on chemotherapy -- which the paper acknowledges as its principal limitation), `Ganesan_2023_tebipenem.R` (cohort indicator gating (a) three multiplicative structural effects from Ganesan 2023 Eq. 4-6 -- `Vc/F * (1 - 0.290 * DIS_HEALTHY)`, `Vp/F * (1 - 0.245 * DIS_HEALTHY)`, `Ka * (1 + 0.368 * DIS_HEALTHY)` -- and (b) which of two cohort-specific CL/F IIV variances applies (0.0614, 24.8 %CV for the 99 healthy phase 1 subjects vs 0.328, 57.2 %CV for the 647 infected ADAPT-PO patients), hosted on paired `lcl_healthy` / `lcl_patient` anchors fixed at 0 because the source reports no infection-status effect on the typical CL/F; reference category 0 is the pooled cUTI / acute-pyelonephritis patient cohort, and the paper's `(1 - Infected)` term is re-expressed as DIS_HEALTHY), `Zhang_2023_brazikumab.R` (linear fractional effect on CL: `1 - 0.362 * DIS_HEALTHY`, i.e. healthy participants clear brazikumab 36.2 percent more slowly than patients at the same baseline albumin; reference category 0 is the pooled mild-to-severe / moderate-to-severe Crohn's disease cohort across the phase Ib NCT01258205 and phase IIa NCT01714726 studies, and Zhang 2023 attributes the faster patient clearance to faecal protein loss through the inflamed gut wall; source column `GRP` coded 1 = CD patient / 2 = healthy, re-expressed as DIS_HEALTHY = GRP - 1), `Zhang_2023_brazikumab_il22.R`, `Zhang_2023_brazikumab_crp.R` (same CL effect carried forward as a `fixed()` parameter into the two biomarker-driven CDAI indirect-response PK/PD variants; every subject in their phase IIa efficacy cohort has DIS_HEALTHY = 0, so the term is inert there and is retained only to keep the PK layer identical to the parent popPK model), `Cleary_2023_risdiplam.R` (linear fractional effect on apparent clearance: `CL/F x (1 + 0.524 * DIS_HEALTHY)`, i.e. healthy adults clear risdiplam 1.524-fold faster than patients at the same weight and age; reference category 0 is the pooled spinal-muscular-atrophy type 1 / 2 / 3 cohort across NCT03032172 / NCT02908685 / NCT02913482, and all 61 healthy participants are adults from NCT02633709 and NCT03988907. Cleary 2023 Table 2 prints the effect only as "Healthy subjects on CL/F | Factor | 0.524" and never states the functional form, so the linear encoding is an inferred assumption rather than a sourced one: the literal multiplicative reading would place healthy adults 48% below patients, contradicting ESM Fig. S4 (post-hoc CL/F 5.60 vs 3.52 L/h), the Discussion's "approximately 30% lower" and the Mech-PPK Table 3 adult intrinsic-clearance ratios of 1.374 (CYP3A) and 1.375 (FMO3)), `AitOudhia_2024_sotatercept.R` (binary stratifier on the log-scale (`lnorm`) residual error magnitude only, with no effect on any structural or random-effect PK parameter: healthy participants use expSd = sqrt(0.0570) = 0.239 and patients use expSd = sqrt(0.0357) = 0.189 per Ait-Oudhia 2024 Table 2 rows "Residual variability in HV / in PAH (log units)"; reference complement under DIS_HEALTHY = 0 is the pooled pulmonary-arterial-hypertension cohort from PULSAR, SPECTRA, and STELLAR, and the healthy stratum is the two phase 1 post-menopausal-women studies (SAD and MAD). As in `Marathe_2023_belzutifan.R`, disease status was separately screened as a structural covariate, entered during forward selection, and was NOT retained during backward elimination, so this model carries a residual-error-only disease effect).
- **Notes:** Used when a population PK model pools healthy participants with patients across heterogeneous indications and the healthy-vs-patient contrast is retained as a covariate. Scope: specific because the complement reference category is paper-defined (Nikanjam 2019 reference is "all non-healthy, non-Castleman, non-SMM tumor types"; Okada 2025 reference is the pooled AD+UC+psoriasis patient cohort; Yang 2024 reference is patients with cGVHD; Goel 2016 reference is the pooled cancer-patient cohort with advanced solid tumors or BCC; Brown 2017 reference is the pooled advanced NSCLC cohort; Lu 2015 vismodegib reference is the pooled cancer-patient cohort with advanced solid tumors / metastatic or locally-advanced BCC; Lu 2015 tacrolimus reference is the adult Chinese liver-transplant recipient cohort). The retired canonical name `DIS_HV` (healthy-volunteer) was renamed on 2026-05-11 because "volunteer" terminology is discouraged for clinical-trial participants. Ratified canonically on 2026-04-24.

### DIS_DPN (**canonical for diabetic polyneuropathy disease-state indicator**)
- **Description:** 1 = patient with painful diabetic peripheral polyneuropathy (DPN; type 1 or 2 diabetes mellitus with clinically documented painful peripheral neuropathy), 0 = non-DPN subject (e.g., chronic low back pain, osteoarthritis, post-bunionectomy acute pain, or healthy volunteer). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-DPN subject; the complement reference is paper-defined -- for the Kleideiter cebranopadol models the typical-value reference is the pooled nociceptive-pain (low back pain + osteoarthritis) cohort, with the sibling canonicals `DIS_HEALTHY` and `DIS_BUNIONECTOMY` carrying the healthy-volunteer and bunionectomy effects respectively).
- **Source aliases:**
  - `DIS` -- Kleideiter 2017 (paper Table 13 categorical disease status decomposed to binary `DIS_DPN`).
- **Example models:** `Kleideiter_2017_cebranopadol.R` (multiplicative effect on bioavailability: `f_disease *= 1.132` for DPN patients relative to the LBP/OA nociceptive-pain reference; the value 1.132 reflects the 2018 erratum correction in which Table 13 rows 27-28 for bunionectomy and DPN were swapped), `Kleideiter_2018_cebranopadol.R` (DPN-vs-LBP/OA bioavailability ratio 1.132 applied as `ratio^DIS_DPN`; one level of the four-level disease-status stratification {LBP/OA reference, healthy, DPN, bunionectomy}, Kleideiter 2018 Table 13 erratum-corrected).
- **Notes:** Used when a population PK model pools DPN patients with a non-DPN reference cohort (typically the chronic-pain `LBP/OA` reference plus other strata) and DPN disease status is retained as a covariate. Pairs with `DIS_HEALTHY` and `DIS_BUNIONECTOMY` in the Kleideiter four-level disease-status stratification (LBP/OA reference, healthy, DPN, bunionectomy). Distinct from the existing `DIS_DIAB` canonical (binary type-1-or-type-2 diabetes-mellitus comorbidity indicator; pre-2026-06-19 names `DIAB` and `T2DM` were merged into `DIS_DIAB`) because `DIS_DPN` flags the specific painful-polyneuropathy complication of diabetes used as a chronic-pain clinical-trial enrollment category (cebranopadol phase IIa trials 10 and 12; phase II trial 14). Distinct from the `DIS_HEALTHY` canonical because both indicators may be 0 simultaneously in a pooled chronic-pain analysis where DIS_HEALTHY = 0 means 'patient' but `DIS_DPN = 0` may still mean 'non-DPN patient' (e.g., LBP / OA or bunionectomy patients). Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-05-25 alongside the Kleideiter 2017 cebranopadol extraction.

### DIS_BUNIONECTOMY (**canonical for post-bunionectomy acute-pain cohort indicator**)
- **Description:** 1 = patient with moderate-to-severe acute pain following primary unilateral first-metatarsal bunionectomy surgery, 0 = non-bunionectomy subject (e.g., chronic low back pain, osteoarthritis, painful diabetic polyneuropathy, or healthy volunteer). Time-fixed per subject within the postoperative analgesic-dosing window.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-bunionectomy subject; the complement reference is paper-defined -- for the Kleideiter cebranopadol models the typical-value reference is the pooled nociceptive-pain (low back pain + osteoarthritis) cohort, with the sibling canonicals `DIS_HEALTHY` and `DIS_DPN` carrying the healthy-volunteer and DPN effects respectively).
- **Source aliases:**
  - `DIS` -- Kleideiter 2017 (paper Table 13 categorical disease status decomposed to binary `DIS_BUNIONECTOMY`).
- **Example models:** `Kleideiter_2017_cebranopadol.R` (multiplicative effect on bioavailability: `f_disease *= 1.801` for bunionectomy patients relative to the LBP/OA nociceptive-pain reference; the value 1.801 reflects the 2018 erratum correction in which Table 13 rows 27-28 for bunionectomy and DPN were swapped; in the corrected Table 14 bunionectomy patients had 80% higher exposure than the reference, the largest disease-status effect in the analysis), `Kleideiter_2018_cebranopadol.R` (bunionectomy-vs-LBP/OA bioavailability ratio 1.801 applied as `ratio^DIS_BUNIONECTOMY`; one level of the four-level disease-status stratification {LBP/OA reference, healthy, DPN, bunionectomy}, Kleideiter 2018 Table 13 erratum-corrected).
- **Notes:** Bunionectomy patients are an acute-pain clinical-trial enrollment category distinct from the chronic-pain LBP / OA / DPN categories in the same analysis cohort. Use this canonical when a pooled analgesic / opioid population PK analysis retains a post-bunionectomy cohort as a covariate group; future bunionectomy-anchored analgesic models should extend the example list and document the complement reference. Pairs with `DIS_HEALTHY` and `DIS_DPN` in the Kleideiter four-level disease-status stratification. Distinct from `POD` (continuous post-operative day) and `POSTTX_DAY1` (first-24h-post-transplant indicator) -- those describe surgical-recovery time windows rather than the bunionectomy-cohort enrollment label itself. The shorter form `DIS_BUN` is deliberately avoided to prevent confusion with the common BUN (blood urea nitrogen) laboratory abbreviation. Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-05-25 alongside the Kleideiter 2017 cebranopadol extraction.

### DIS_CASTLEMAN (**canonical for Castleman's disease indicator**)
- **Description:** 1 = Castleman's disease (multicentric or unicentric), 0 = not Castleman's disease. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Castleman subject; the complement group is the union of healthy volunteers and other indications pooled in the source analysis).
- **Source aliases:** none known; source NONMEM control streams typically use ad-hoc names (e.g., `CD`, `CASTLEMAN`).
- **Example models:** `Nikanjam_2019_siltuximab.R` (multiplicative +24% effect on CL; no Vss effect).
- **Notes:** Castleman's disease is a lymphoproliferative disorder strongly associated with elevated IL-6 levels; it is the only FDA-approved indication for siltuximab. Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-04-24.

### DIS_HOFH (**canonical for homozygous familial hypercholesterolemia patient indicator**)
- **Description:** 1 = patient with homozygous familial hypercholesterolemia (HoFH), 0 = non-HoFH subject (typically healthy volunteer or another reference cohort pooled in the source analysis). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-HoFH subject; the complement group is paper-defined -- for Pu 2021 the reference is the pooled healthy-volunteer cohort).
- **Source aliases:**
  - `DISTYPN` -- used in `Pu_2021_evinacumab.R` (Pu 2021 NM-TRAN $INPUT column for HoFH-vs-HV disease type, 1 = HoFH).
- **Example models:** `Pu_2021_evinacumab.R` (multiplicative `exp(theta * DIS_HOFH)` factor on Vmax with theta = -0.289, i.e. HoFH patients show ~25% lower target-mediated Vmax than the HV reference; biologically consistent with the LDLR-pathway disruption in HoFH altering ANGPTL3 catabolic kinetics).
- **Notes:** Used when a population PK model pools HoFH patients with healthy volunteers (or another non-HoFH cohort) and HoFH disease status is retained as a covariate. Distinct from a heterozygous-FH (HeFH) indicator because HoFH patients have markedly higher baseline LDL-C (untreated levels often > 500 mg/dL) and a more pronounced response to LDLR-independent therapies. Scope: specific because the reference category is paper-defined.

### DIS_HAE (**canonical for hereditary angioedema patient indicator**)
- **Description:** 1 = patient with hereditary angioedema (HAE-C1INH-Type1, HAE-C1INH-Type2, or HAE-nC1INH), 0 = healthy volunteer (or other non-HAE reference cohort pooled in the source analysis). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-HAE subject; the complement group is paper-defined -- for Diep 2026 the reference is the pooled healthy-volunteer cohort from NCT03263507 and ISIS 721744-CS9).
- **Source aliases:** paper narrative "patient with HAE" / "healthy volunteer" subgroup labels driving the Diep 2026 disease-status covariate effects on Vc/F, Q/F, baseline PKK, and IC50.
- **Example models:** `Diep_2026_donidalorsen.R` (linear `(1 + theta * DIS_HAE)` multiplicative effects on apparent central volume Vc/F (theta = +0.426, +42.6%), apparent intercompartmental clearance Q/F (theta = -0.261, -26.1%), baseline plasma prekallikrein BL (theta = -0.132, -13.2%), and donidalorsen IC50 on PKK production (theta = +0.770, +77.0%) for patients with HAE vs healthy volunteers).
- **Notes:** Used when a population PK/PD model pools HAE patients with healthy volunteers and HAE disease status is retained as a covariate. The three molecular HAE subtypes (HAE-C1INH-Type1, HAE-C1INH-Type2, HAE-nC1INH) are pooled in this indicator following the Diep 2026 analysis; if a future paper resolves subtype-specific covariate effects, separate canonical indicators (e.g., `DIS_HAE_C1INH_T1`) can be added without conflicting with this pooled indicator. Scope: specific because the complement reference category is paper-defined.

### DIS_PBC (**canonical for primary biliary cirrhosis disease-state indicator**)
- **Description:** 1 = patient with primary biliary cirrhosis (PBC), an autoimmune destruction of intrahepatic bile ducts causing chronic cholestasis; 0 = non-PBC subject (healthy reference cohort or other non-PBC reference). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-PBC subject; complement reference is paper-defined -- for Zuo 2016 the reference is the pooled healthy-adult cohort from Xiang 2011 / Dilger 2012 / Hess 2004).
- **Source aliases:** paper narrative "patient with PBC" / "healthy" subgroup labels in Zuo 2016.
- **Example models:** `Zuo_2016_UDCA.R` (multiplicative scaling on liver-to-biliary rate constants when DIS_PBC = 1: K_LB,0 scaled by 0.10 -- 90% reduction; K_LB,1 scaled by 0.30 -- 70% reduction; K_LB,2 scaled by 0.10 -- 90% reduction; reproduces the Zuo 2016 Figure 3 PBC simulation).
- **Notes:** Used when a systems / popPK model adapts a healthy-state structural model to a PBC population via fixed disease-state scaling on hepatic-excretion rate constants. Scope: specific because the structural adaptation form (which K parameters are scaled, by how much) is paper-defined; future PBC extractions that re-estimate or alter the scaling pattern can extend the example-models list. Distinct from `DIS_HEPATIMP` (hepatic-impairment severity categorical), `DBIL` (direct bilirubin biomarker), and `ALP` (cholestasis biomarker), which describe pathophysiology rather than the disease label itself.

### DIS_DMD (**canonical for Duchenne muscular dystrophy patient indicator**)
- **Description:** 1 = patient with Duchenne muscular dystrophy (DMD), 0 = non-DMD subject (healthy volunteer or other reference cohort). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-DMD subject; the complement group is the reference cohort the source analysis pools alongside the DMD population -- typically healthy adult volunteers).
- **Source aliases:** `SPOP` (Wojciechowski 2022 study-population indicator with the same orientation: 1 = DMD pediatric patient, 0 = healthy adult volunteer).
- **Example models:** `Wojciechowski_2022_domagrozumab.R` (additive `1 + theta` shift on baseline myostatin and on the joint kdeg/kint axis; theta_BASE = -0.641, theta_kdegkint = -0.900), `Hajjar_2018_DMD_6MWT.R` (gates the latent-disease coefficients ALPHA and BETA -- both zero for DIS_DMD = 0 -- and scales the 6MWT production rate KIN by the multiplier KCOV = 0.63 for DMD subjects; reference cohort is the Henricson 2012 healthy-boy controls).
- **Notes:** Used when a population PK/PD model pools DMD patients with a non-DMD reference population and DMD disease status is retained as a covariate. Scope: specific because the reference category is paper-defined. Ratified canonically on 2026-04-26.

### DIS_SMM (**canonical for smoldering multiple myeloma indicator**)
- **Description:** 1 = smoldering (asymptomatic) multiple myeloma, 0 = not smoldering MM. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-SMM subject; the complement group is the union of healthy volunteers and other indications pooled in the source analysis).
- **Source aliases:** none known; source NONMEM control streams typically use ad-hoc names (e.g., `SMM`, `SMOLDMM`).
- **Example models:** `Nikanjam_2019_siltuximab.R` (multiplicative -23% effect on Vss; no CL effect).
- **Notes:** Smoldering multiple myeloma is an asymptomatic plasma-cell disorder distinct from active multiple myeloma; pooled with the Nikanjam 2019 cohort that also included MGUS, multiple myeloma, RCC, ovarian, and other tumor types. Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-04-24.

### DIS_MM (**canonical for active multiple myeloma disease indicator**)
- **Description:** 1 = active (non-smoldering) multiple myeloma, 0 = other hematologic malignancy or reference group. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-MM subject; the complement group is defined per-model -- in Ogasawara 2020 the reference is the union of MDS, AML, and non-Hodgkin lymphoma cohorts).
- **Source aliases:**
  - `MM` -- prior canonical name (pre-2026-06-19 DIS_ prefix standardization) and source-paper column in `Ogasawara_2020_durvalumab.R`. The short `MM` token also collides with the SI unit millimolar (used in `STIM_*_MM` in-vitro stimulus concentrations and `RIC50` in mM) and with the melanoma abbreviation in some oncology datasets (e.g., `TUMTP == "MM"` in Aoyama 2012 where MM means malignant melanoma); the `DIS_MM` form disambiguates.
- **Example models:** `Ogasawara_2020_durvalumab.R` (multiplicative factor 0.820 on Vc per Ogasawara 2020 Table 3 footnote c; active (non-smoldering) multiple myeloma cohort from studies MEDI4736-MM-002 and -MM-005).
- **Notes:** Renamed from `MM` to `DIS_MM` on 2026-06-19 per the canonical-register standardization audit (operator decision to apply the `DIS_<concept>` prefix uniformly to disease-state indicators; the bare `MM` token clashed with the millimolar SI unit and with the TUMTP "MM = malignant melanoma" usage). Distinct from `DIS_SMM` (smoldering multiple myeloma) and `MM_NIGG` (the immunoglobulin-subtype stratifier within multiple myeloma cohorts).

### DIS_EMD (**canonical for extramedullary disease indicator**)
- **Description:** 1 = extramedullary disease (plasmacytoma or plasma-cell infiltration outside the bone marrow) present at baseline / screening, 0 = disease confined to the bone marrow. Time-fixed per subject. An aggressive-phenotype and high-tumour-burden marker in multiple myeloma and related plasma-cell malignancies; prevalence is roughly 15-20% at initial diagnosis and higher in relapsed/refractory populations.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (medullary / bone-marrow-confined disease).
- **Source aliases:**
  - `MEDFL` -- used in `Collins_2023_belantamab_mprotein.R`; the same column name appears in the related GSK belantamab mafodotin exposure-response analyses.
  - `EMD`, `EMD_BL`, `EXTRAMED` -- plausible alternative NONMEM `$INPUT` forms.
- **Example models:** `Collins_2023_belantamab_mprotein.R` (multiplicative factor 0.108 on the effect-compartment rate constant KEO of the serum M-protein tumour-growth-inhibition model when `DIS_EMD = 1`, per Collins 2023 Table 1; 95% CI 0.0617-0.187).
- **Notes:** Distinct from `DIS_MM` (active vs smoldering multiple-myeloma disease status) and `DIS_SMM` (smoldering multiple myeloma), and complementary to both: a patient with active multiple myeloma may or may not have extramedullary disease. Also distinct from the ulcerative-colitis disease-extent canonicals `DISEXT_EP` / `DISEXT_OTHER`, which describe the anatomical extent of inflammatory bowel disease rather than a plasma-cell malignancy outside the marrow. Covariate-effect parameters drop the `DIS_` prefix per the `DIS_CANCER` -> `e_cancer_*` convention; use `e_emd_<param>`. Scope: specific because the concept is mechanistically bound to plasma-cell malignancies. Ratified canonically on 2026-07-27 alongside the Collins 2023 belantamab mafodotin M-protein extraction.

### DIS_PNH (**canonical for paroxysmal nocturnal hemoglobinuria indicator**)
- **Description:** 1 = paroxysmal nocturnal hemoglobinuria (PNH) patient, 0 = non-PNH subject (healthy volunteer or another indication pooled in the source analysis). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-PNH subject; the complement group is paper-defined -- for Lin 2024 it pools healthy adult volunteers and CHAPLE disease patients, while for Lee 2024 it is the healthy phase I subjects only).
- **Source aliases:** none known; source NONMEM control streams typically use ad-hoc names (e.g., `PNH`, `DPNH`); Lee 2024 calls the column "subject group (healthy subjects versus PNH patients)".
- **Example models:** `Lin_2024_pozelimab.R` (additive-fractional +34.07% effect on Vc; no CL or Vp effect; reference category pools healthy volunteers and CHAPLE patients), `Lee_2024_eculizumab.R` (separately estimated typical values per subject group on Vc, on the terminal-complement-activity baseline E0 and on Imax, encoded as log-scale shifts `e_pnh_vc`, `e_pnh_rbase_tca`, `e_pnh_imax_tca`; reference category is the healthy phase I cohort).
- **Notes:** Paroxysmal nocturnal hemoglobinuria is a rare hematological disease characterized by uncontrolled complement activation on red blood cells; treated with C5-targeted complement inhibitors (eculizumab, ravulizumab, pozelimab). Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-04-27.

### DIS_MDS_AML (**canonical for MDS or AML disease-type indicator**)
- **Description:** 1 = patient with myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML), 0 = other hematologic malignancy or reference group. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-MDS/AML subjects; the complement group is defined per-model -- typically multiple myeloma and non-Hodgkin lymphoma in Ogasawara 2020).
- **Source aliases:**
  - `MDSAML` -- prior canonical name (pre-2026-06-19 DIS_ prefix standardization) and combined indicator used directly in source analyses; `MDSAML` is the column name in `Ogasawara_2020_durvalumab.R`.
- **Example models:** `Ogasawara_2020_durvalumab.R` (multiplicative factor 1.26 on CL; reference group is the union of MM and NHL subjects), `vanIersel_2018_posaconazole.R` (multiplicative effect on relative bioavailability F1: `fdepot *= (1 + (-0.165) * DIS_MDS_AML)` -- 16.5% lower F1 in AML/MDS patients relative to the non-AML/MDS reference (healthy volunteers and HSCT recipients pooled); van Iersel 2018 Table 2 final-model 'AML/MDS on F1' = -0.165).
- **Notes:** Use `DIS_MDS_AML` as a combined MDS+AML indicator when the source paper collapses the two diagnoses into one covariate. If a future paper separates MDS and AML as distinct indicators, register `DIS_MDS` and `DIS_AML` separately. Scope: specific because the reference category is paper-defined. Ratified canonically on 2026-04-26. Renamed from `MDSAML` to `DIS_MDS_AML` on 2026-06-19 per the canonical-register standardization audit (operator decision to apply the `DIS_<concept>` prefix uniformly to disease-state indicators and to insert the underscore so the combined indicator matches the sibling `DIS_MDS` / `DIS_AML` shape).

### DIS_AML (**canonical for acute myeloid leukemia disease-state indicator**)
- **Description:** 1 = patient with acute myeloid leukemia (AML), 0 = non-AML subject (the complement group in a pooled multi-indication PK analysis). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-AML subject; the complement group is paper-defined -- for Xu 2023 the reference is patients with advanced solid tumors, alongside the parallel `DIS_MDS` and `DIS_CMML` indicators that decompose the hematologic-malignancy cohort).
- **Source aliases:**
  - `DISEASE_abb == "AML"` -- used in `Xu_2023_MBG453.R` (the Monolix supplement Appendix S2 encodes disease as the categorical column `DISEASE_abb` with categories `{AML, CMML, MDS, Solid_Tumor}` and reference `Solid_Tumor`; the canonical column carries the binary `as.integer(DISEASE_abb == "AML")`).
- **Example models:** `Xu_2023_MBG453.R` (exponential effect on CL: `exp(-0.0146 * DIS_AML)`; not statistically significant in the full covariate model but retained because Xu 2023 used the full-covariate-model approach).
- **Notes:** Use `DIS_AML` (rather than the combined `DIS_MDS_AML`) when the source paper separates AML from MDS as distinct indicators. Scope: specific because the disease-pooling reference category is paper-defined.

### DIS_MDS (**canonical for myelodysplastic syndrome disease-state indicator**)
- **Description:** 1 = patient with myelodysplastic syndrome (MDS), 0 = non-MDS subject (the complement group in a pooled multi-indication PK analysis). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-MDS subject; the complement group is paper-defined -- for Xu 2023 the reference is patients with advanced solid tumors, alongside the parallel `DIS_AML` and `DIS_CMML` indicators).
- **Source aliases:**
  - `DISEASE_abb == "MDS"` -- used in `Xu_2023_MBG453.R` (Monolix supplement Appendix S2 categorical column; reference category `Solid_Tumor`).
- **Example models:** `Xu_2023_MBG453.R` (exponential effect on CL: `exp(-0.149 * DIS_MDS)`; statistically significant, p = 0.021 -- patients with MDS have ~14% lower CL than the solid-tumor reference).
- **Notes:** Use `DIS_MDS` (rather than the combined `DIS_MDS_AML`) when the source paper separates MDS from AML as distinct indicators. Scope: specific because the disease-pooling reference category is paper-defined.

### DIS_CMML (**canonical for chronic myelomonocytic leukemia disease-state indicator**)
- **Description:** 1 = patient with chronic myelomonocytic leukemia (CMML), 0 = non-CMML subject (the complement group in a pooled multi-indication PK analysis). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-CMML subject; the complement group is paper-defined -- for Xu 2023 the reference is patients with advanced solid tumors, alongside the parallel `DIS_AML` and `DIS_MDS` indicators that decompose the hematologic-malignancy cohort).
- **Source aliases:**
  - `DISEASE_abb == "CMML"` -- used in `Xu_2023_MBG453.R` (Monolix supplement Appendix S2 categorical column; reference category `Solid_Tumor`).
- **Example models:** `Xu_2023_MBG453.R` (exponential effect on CL: `exp(-0.0411 * DIS_CMML)`; not statistically significant in the full covariate model but retained because Xu 2023 used the full-covariate-model approach).
- **Notes:** CMML is a clonal myeloid malignancy with overlapping features of MDS and myeloproliferative neoplasms. Scope: specific because the disease-pooling reference category is paper-defined.

### DIS_BCPALL (**canonical for B-cell precursor acute lymphoblastic leukemia disease-state indicator**)
- **Description:** 1 = B-cell precursor acute lymphoblastic leukemia (BCP-ALL), 0 = B-cell non-Hodgkin's lymphoma (NHL) or other non-BCP-ALL indication pooled in the source analysis. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-BCP-ALL; in the Wu 2024 cohort this is the adult B-cell NHL stratum).
- **Source aliases:**
  - `ALL` -- used in `Wu_2024_inotuzumab.R` (Wu 2024 calls it the "ALL effect" and notes it bundles disease type with the corresponding bioanalytical assay difference).
- **Example models:** `Wu_2024_inotuzumab.R` (additive fractional-change effects on CL1 (-0.767) and CL2 (-0.362), and gates the BLSTABL and AGE effects on kdes; for kdes itself a -0.924 fractional change for BCP-ALL).
- **Notes:** Used when a population PK model pools BCP-ALL patients with a non-BCP-ALL reference (e.g., Wu 2024: pooled adult B-cell NHL + adult BCP-ALL + pediatric BCP-ALL). Scope: specific because the complement reference category is paper-defined (Wu 2024 reference is pooled adult B-cell NHL). The "ALL effect" theta in Wu 2024 conflates two physiologically distinct sources of variation -- B-cell tumor type (NHL vs ALL surface CD22 burden) and bioanalytical method (ELISA for adult NHL vs HPLC-MS for ALL) -- and cannot be split with the available data; document this confounding when comparing across populations. Ratified canonically on 2026-04-26.

### DIS_ALL (**canonical for acute lymphoblastic leukemia disease-state indicator**)
- **Description:** 1 = patient with acute lymphoblastic leukemia (ALL) of any lineage, 0 = any other indication pooled in the source analysis. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-ALL; the complement group is paper-defined -- for Schreib 2024 the reference is every other hematopoietic-stem-cell-transplantation indication in the cohort, i.e. AML, chronic granulomatous disease, HLH/XLP, hemoglobinopathies, primary immunodeficiencies, metabolic diseases, neuroblastoma, and others).
- **Source aliases:**
  - `ALL` -- used in `Schreib_2024_busulfan.R` (Table 3 covariate `theta_k5`, effect of ALL on `ln(k)`; the source dataset carries a nine-level `diagnosis group` factor and the canonical column is `as.integer(DiaGroup == "ALL")`).
- **Example models:** `Schreib_2024_busulfan.R` (exponential effect on the elimination rate constant: `exp(-0.210 * DIS_ALL)`, about a 19% lower `k` and `CL`; 13 of 124 patients, 10%).
- **Notes:** Lineage-agnostic ALL indicator. Distinct from `DIS_BCPALL`, which is specifically B-cell *precursor* ALL against a paper-defined reference of B-cell non-Hodgkin lymphoma or other non-BCP-ALL indication; `DIS_BCPALL` additionally conflates disease type with a bioanalytical-assay difference in its founding model and is not interchangeable with `DIS_ALL`. Use `DIS_ALL` when the source pools all ALL lineages against a general non-ALL reference. Scope: specific because the complement reference category is paper-defined. Ratified canonically on 2026-08-05 (task `oare_PMC11154452` sidecar question q3, answer A) alongside the Schreib 2024 busulfan extraction.

### DIS_HLHXLP (**canonical for hemophagocytic lymphohistiocytosis / X-linked lymphoproliferative disease indicator**)
- **Description:** 1 = patient with hemophagocytic lymphohistiocytosis (HLH) or X-linked lymphoproliferative disease (XLP), 0 = any other indication pooled in the source analysis. Time-fixed per subject. The two diagnoses are pooled into one indicator because the source analyses that use it treat them as a single disease group.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (neither HLH nor XLP; the complement group is paper-defined -- for Schreib 2024 the reference is every other hematopoietic-stem-cell-transplantation indication in the cohort).
- **Source aliases:**
  - `HLH/XLP` -- used in `Schreib_2024_busulfan.R` (Table 3 covariate `theta_dk2`; the source dataset carries a nine-level `diagnosis group` factor and the canonical column is `as.integer(DiaGroup == "HLH/XLP")`).
- **Example models:** `Schreib_2024_busulfan.R` (additive effect of -0.145 on `kel_exp_famp`, the fractional amplitude of the decline in the elimination rate constant over a course of therapy, taking it from -0.167 to -0.312, i.e. from a 17% to a 31% fall in `k` and `CL`; 14 of 124 patients, 11%).
- **Notes:** Registered as a single pooled token without an internal underscore so that covariate-effect parameter names stay unambiguous (`e_hlhxlp_kel_exp_famp`). Splitting into separate `DIS_HLH` and `DIS_XLP` columns was considered and declined: the founding source pools the two into one 14-patient diagnosis group and estimates a single coefficient, so a split cannot be sourced from it. Register `DIS_HLH` / `DIS_XLP` separately only if a future source estimates them apart. Scope: specific because the complement reference category is paper-defined. Ratified canonically on 2026-08-05 (task `oare_PMC11154452` sidecar question q3, answer A) alongside the Schreib 2024 busulfan extraction.

### DIS_BCELLNHL (**canonical for B-cell non-Hodgkin lymphoma disease-state indicator**)
- **Description:** 1 = patient with B-cell non-Hodgkin lymphoma (B-cell NHL), 0 = non-B-cell-NHL subject (the complement group in a pooled multi-indication PK analysis of hematologic malignancies). Time-fixed per subject. Used when a population PK model treats B-cell NHL as its own indicator alongside a sibling CLL/SLL reference and other lymphoma / leukemia strata.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-B-cell-NHL; the complement group is paper-defined -- for Kemal 2026 the reference is chronic lymphocytic leukemia / small lymphocytic lymphoma (CLL/SLL), alongside the parallel `DIS_WM` and `DIS_OTHER_HEME` indicators that decompose the hematologic-malignancy cohort).
- **Source aliases:**
  - `INDIC == 1` -- used in `Kemal_2026_nemtabrutinib.R` (Kemal 2026 NONMEM control stream in the supplement encodes disease as the categorical column `INDIC` with categories `{B-cell NHL, CLL/SLL, WM, Other, MZL, FL, MCL, RT}` and reference `CLL/SLL`; the canonical column carries the binary `as.integer(INDIC == 1)`).
- **Example models:** `Kemal_2026_nemtabrutinib.R` (fractional-change effects on CL/F (-0.166) and Vc/F (0.00224); reference category CLL/SLL).
- **Notes:** Distinct from `DIS_BCPALL` (B-cell precursor acute lymphoblastic leukemia). Use `DIS_BCELLNHL` when the source paper treats B-cell NHL as one indication category in a pooled analysis of chronic B-cell malignancies (CLL/SLL, WM, DLBCL, follicular, mantle-cell, marginal-zone) and the model estimates a disease-specific effect on PK. Scope: specific because the complement reference category is paper-defined.

### DIS_WM (**canonical for Waldenstrom's macroglobulinemia disease-state indicator**)
- **Description:** 1 = patient with Waldenstrom's macroglobulinemia (WM), 0 = non-WM subject. Time-fixed per subject. WM is a rare indolent B-cell lymphoma / IgM-secreting lymphoplasmacytic disorder that is sometimes pooled with other B-cell malignancies in oncology popPK analyses.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-WM; the complement group is paper-defined -- for Kemal 2026 the reference is CLL/SLL, alongside the parallel `DIS_BCELLNHL` and `DIS_OTHER_HEME` indicators).
- **Source aliases:**
  - `INDIC == 3` -- used in `Kemal_2026_nemtabrutinib.R` (Kemal 2026 NONMEM control stream in the supplement; reference category CLL/SLL).
- **Example models:** `Kemal_2026_nemtabrutinib.R` (fractional-change effects on CL/F (0.0718) and Vc/F (0.152); reference category CLL/SLL).
- **Notes:** Scope: specific because the complement reference category is paper-defined.

### DIS_OTHER_HEME (**canonical for pooled 'other hematologic malignancy' indicator**)
- **Description:** 1 = patient with a hematologic malignancy other than the explicitly-modeled sibling indications (typically pools some subset of MZL, FL, MCL, Richter's transformation, or unclassified 'other'), 0 = one of the explicitly-modeled indications or the reference disease. Time-fixed per subject. The exact composition of the pool is paper-specific and must be documented in per-model `covariateData[[DIS_OTHER_HEME]]$notes`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (subject falls into one of the sibling indication indicators or the reference; the reference disease is paper-defined -- for Kemal 2026 the reference is CLL/SLL).
- **Source aliases:**
  - `INDIC IN (4, 5, 6, 7, 8)` -- used in `Kemal_2026_nemtabrutinib.R` (Kemal 2026 NONMEM control stream pools 'Other', marginal zone lymphoma (MZL), follicular lymphoma (FL), mantle cell lymphoma (MCL), and Richter's transformation (RT) into a single indicator).
- **Example models:** `Kemal_2026_nemtabrutinib.R` (fractional-change effects on CL/F (-0.0244) and Vc/F (-0.0200); reference category CLL/SLL).
- **Notes:** Different-composition pools do not share coefficients; every model that uses `DIS_OTHER_HEME` must document the exact set of pooled diseases in `covariateData[[DIS_OTHER_HEME]]$notes` so downstream users know what the coefficient represents. When a paper analyzes MZL, FL, MCL, RT, or a specific 'other' indication as its own indicator, register a sibling per-disease canonical (e.g. `DIS_MZL`, `DIS_FL`, `DIS_MCL`, `DIS_RT`) rather than reusing `DIS_OTHER_HEME`. Scope: specific because the pool composition is paper-defined.

### DIS_SAD (**canonical for secondary antibody deficiency indicator**)
- **Description:** 1 = secondary antibody deficiency (SAD) patient (hypogammaglobulinaemia from external causes such as B-cell-depleting therapy, haematological malignancy, or other immunosuppression), 0 = primary immunodeficiency (PID) patient. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (PID patient; the complement category is the genetic / inborn-error-of-immunity primary immunodeficiency cohort pooled with SAD in the source analysis).
- **Source aliases:** none known; source NONMEM control streams typically use ad-hoc names (e.g., `SAD`, `IMD`, `DIS`).
- **Example models:** `Cheng_2026_immunoglobulin.R` (multiplicative `theta^DIS_SAD` factors on CL (0.542) and on baseline IgG (CBAS, 0.541); reference category PID).
- **Notes:** Used when a population PK model pools PID and SAD pediatric or adult patients receiving immunoglobulin replacement therapy (IgRT) and tests SAD-vs-PID as a covariate. Distinct from the disease-state indicators that pool oncology / autoimmune indications: `DIS_SAD` specifically partitions hypogammaglobulinaemia by its underlying mechanism (genetic vs. acquired). Scope: specific because the SAD cohort composition is paper-defined (in Cheng 2026, 75% post-rituximab and 25% post-CAR-T cell therapy). Ratified canonically on 2026-04-28.

### DIS_AD (**canonical for Alzheimer's disease patient indicator**)
- **Description:** 1 = participant with Alzheimer's disease (clinical AD diagnosis), 0 = non-AD subject (typically healthy volunteer pooled in the source analysis). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-AD subject; the complement group is paper-defined -- for Perez-Ruixo 2025 the reference is the pooled healthy-volunteer cohort).
- **Source aliases:** none known; source NONMEM control streams typically use ad-hoc names (e.g., `AD`, `STATUS`, `DISGRP`).
- **Example models:** `PerezRuixo_2025_posdinemab.R` (acts on baseline free p217+tau in CSF, R0; healthy R0 = 0.793 pmol/L vs AD R0 = 5.995 pmol/L, a 656% relative increase, no PK-parameter effects).
- **Notes:** Used when a population PK/PD model pools healthy volunteers with Alzheimer's disease patients and the AD-vs-HV contrast is retained as a covariate on a target-related parameter (e.g., baseline p-tau, baseline p217+tau). Scope: specific because the complement reference category is paper-defined. Ratified canonically on 2026-04-28.

### DIS_COPD (**canonical for chronic obstructive pulmonary disease patient indicator**)
- **Description:** 1 = patient with chronic obstructive pulmonary disease (clinical COPD diagnosis, typically moderate-to-severe per GOLD criteria), 0 = non-COPD subject (typically healthy volunteer pooled in the source analysis). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-COPD subject; the complement group is paper-defined -- for Lahu 2010 the reference is the pooled phase I healthy-volunteer cohort).
- **Source aliases:**
  - `COPD` -- used in `Lahu_2010_roflumilast.R` (paper text covariate symbol in equation 6 and 7).
- **Example models:** `Lahu_2010_roflumilast.R` (linear additive effects on roflumilast parent CL (-39.4%) and V1 (+184%) and on roflumilast N-oxide CL (-7.9%) and Vd (-21.4%); reference category 0 = pooled phase I healthy volunteers, 1 = pooled phase II/III moderate-to-severe COPD patient), `Facius_2018_roflumilast.R` (linear additive phase II-III patient effects on KA (-73.3%), parent CL (-55.2%), N-oxide CL (-24.4%), and N-oxide central V3 (-20.7%) on the joint Lahu 2010 base model re-estimated on the OPTIMIZE + REACT phase III COPD dataset; reference category 0 = the implicit phase I healthy-volunteer cohort that backed the upstream Lahu 2010 fixed structural parameters), `Yang_2013_losmapimod.R` (mutually-exclusive proportional residual variance switch: sigma^2_prop = 0.061 for non-COPD subjects vs sigma^2_prop,COPD = 0.268 for COPD subjects, reproducing the NONMEM IF (COPD.EQ.1) EPS(2) ELSE EPS(1) $ERROR pattern; reference category 0 = pooled healthy volunteers plus rheumatoid arthritis patients).
- **Notes:** Used when a population PK/PD model pools healthy volunteers (with or without additional non-COPD patient cohorts such as rheumatoid arthritis in Yang 2013) with COPD patients and the COPD-vs-non-COPD contrast is retained either as a covariate on structural PK parameters or as a switch on residual-error magnitude. Scope: specific because the complement reference category and the COPD-severity inclusion criteria are paper-defined.

### DIS_OBESE_MORBID (**canonical for morbidly obese cohort indicator**)
- **Description:** 1 = morbidly obese patient (BMI > 40 kg/m^2 in the canonical definition; typical pooled-analysis enrollment criterion is bariatric-surgery patients), 0 = non-obese subject (typically healthy volunteer pooled in the source analysis). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-obese subject; the complement group is paper-defined -- for de Hoogd 2017 the reference is the pooled healthy-volunteer cohort from Sarton 2000 and Romberg 2004).
- **Source aliases:** none known; source NONMEM control streams typically use ad-hoc names (e.g., `OBESE`, `MO`, `COHORT`).
- **Example models:** `deHoogd_2017_morphine.R` (selects per-cohort proportional residual error magnitudes for each of three observed species -- morphine, M3G, M6G -- after a pooled-cohort fit of 20 morbidly obese surgical patients and 20 healthy volunteers).
- **Notes:** Used when a population PK or PK/PD model pools morbidly obese patients with a non-obese reference population (typically healthy volunteers) and the cohort indicator selects per-cohort parameter values (residual error magnitudes, study-specific bioavailability, or similar). Distinct from `BMI` (which is the continuous body-mass-index covariate used for parameter scaling) -- `DIS_OBESE_MORBID` is the binary cohort-membership flag and does not encode a specific BMI threshold for general use; the threshold is paper-defined. Scope: specific because the complement reference category is paper-defined. Ratified canonically on 2026-05-11.

### HSCT_URD_7OF8 (**canonical for hematopoietic stem cell transplant from a 7-of-8 HLA-matched unrelated donor**)
- **Description:** 1 = patient received an allogeneic hematopoietic stem cell transplant (HSCT) from an unrelated donor (URD) HLA-matched at 7 of 8 alleles (single-allele mismatch), 0 = otherwise (the union of patients not in this transplant cohort, including non-HSCT patients and HSCT recipients matched at all 8 alleles). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not in the 7-of-8-matched HSCT cohort; the complement group is paper-defined -- for Zhong 2026 it pools the RA + pJIA studies and the 8-of-8-matched HSCT cohort, with the latter encoded by the parallel `HSCT_URD_8OF8` indicator).
- **Source aliases:**
  - `COHORT7` -- used in `Zhong_2026_abatacept.R` (Zhong 2026 NM-TRAN indicator for ABA2 study Cohort 7/8).
- **Example models:** `Zhong_2026_abatacept.R` (exponential coefficient -0.326 on CL; the single-allele-mismatch HSCT cohort exhibits ~28% lower abatacept clearance than the reference complement).
- **Notes:** Used together with `HSCT_URD_8OF8` to decompose a three-level "transplant cohort" categorical (non-HSCT-cohort / 7-of-8 / 8-of-8) into two orthogonal binary indicators. The 7-of-8 cohort represents a higher GvHD-risk population because of the single-allele HLA mismatch. Scope: specific because the reference complement (the union of non-transplant disease cohorts pooled in the source analysis) is paper-defined. Ratified canonically on 2026-04-29.

### HSCT_URD_8OF8 (**canonical for hematopoietic stem cell transplant from an 8-of-8 HLA-matched unrelated donor**)
- **Description:** 1 = patient received an allogeneic hematopoietic stem cell transplant (HSCT) from an unrelated donor (URD) HLA-matched at all 8 alleles (full match), 0 = otherwise (the union of patients not in this transplant cohort, including non-HSCT patients and HSCT recipients matched at 7 of 8 alleles). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not in the 8-of-8-matched HSCT cohort; the complement group is paper-defined -- for Zhong 2026 it pools the RA + pJIA studies and the 7-of-8-matched HSCT cohort, with the latter encoded by the parallel `HSCT_URD_7OF8` indicator).
- **Source aliases:**
  - `COHORT8` -- used in `Zhong_2026_abatacept.R` (Zhong 2026 NM-TRAN indicator for ABA2 study Cohort 8/8).
- **Example models:** `Zhong_2026_abatacept.R` (exponential coefficient -0.0934 on CL and +0.257 on VC; the fully-HLA-matched HSCT cohort exhibits a small CL decrease and a larger VC increase relative to the reference complement).
- **Notes:** Used together with `HSCT_URD_7OF8` to decompose a three-level "transplant cohort" categorical (non-HSCT-cohort / 7-of-8 / 8-of-8) into two orthogonal binary indicators. The 8-of-8 cohort is the lower-risk HLA-matching configuration. Scope: specific because the reference complement (the union of non-transplant disease cohorts pooled in the source analysis) is paper-defined. Ratified canonically on 2026-04-29.

### DIS_PSORIASIS (**canonical for plaque psoriasis disease-state indicator**)
- **Description:** 1 = plaque psoriasis patient, 0 = non-psoriasis subject (e.g., atopic dermatitis, ulcerative colitis, or healthy volunteer). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-psoriasis subject; the complement group is paper-defined -- the union of other disease cohorts pooled in the source analysis).
- **Source aliases:** none known; source NONMEM control streams typically use a categorical `DIS` indicator (e.g., Okada 2025: `DIS=1` for psoriasis, `DIS=0` for healthy, `DIS=2` for UC, `DIS=3` for AD), decomposed into a binary `DIS_PSORIASIS` indicator at ingestion.
- **Example models:** `Okada_2025_rocatinlimab.R` (multiplicative shift `1 - 0.372` on linear CL when 1; reference complement is the pooled atopic dermatitis + ulcerative colitis + healthy-volunteer cohort).
- **Notes:** Used when a population PK model pools plaque-psoriasis patients with a non-psoriasis reference population and psoriasis disease status is retained as a covariate. Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-04-27.

### DIS_RA (**canonical for rheumatoid arthritis disease-state indicator**)
- **Description:** 1 = adult rheumatoid arthritis patient, 0 = non-RA subject (e.g., healthy volunteer, Crohn's disease, systemic lupus erythematosus, or other indication). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-RA subject; the complement group is paper-defined -- the union of other cohorts pooled in the source analysis).
- **Source aliases:** none known; source NONMEM control streams typically derive the indicator from a `POPULATION` / `STUDY` categorical alongside other `DIS_*` indicators.
- **Example models:** `Li_2018_PF04236921.R` (log-shift `e_ra_cl = log(0.00588 / 0.00546) = 0.0741` on linear CL, log-shifts on baseline CRP / IC50 / logit-Imax; one of three orthogonal indicators (DIS_RA / DIS_CD / DIS_SLE) decomposing the four-level HV / RA / CD / SLE cohort with HV as the reference category), `Wojciechowski_2023_ritlecitinib_base.R` and `Wojciechowski_2023_ritlecitinib_updated.R` (linear fractional effect on apparent clearance `CL/F *= (1 + e_ra_cl * DIS_RA)` with `e_ra_cl = -0.439` in the base model and `-0.496` in the updated model; reference category is the healthy-participant cohort, and the RA cohort also contributes to the pooled inflammatory-disease group that scales the IIV and proportional-residual-error magnitudes; one of four orthogonal indicators (DIS_RA / DIS_UC / DIS_ALOPECIA_AREATA / DIS_VITILIGO) decomposing the source's multi-level `PTST` patient-type column).
- **Notes:** Use when a population PK model pools adult RA patients with a non-RA reference population and RA disease status is retained as a covariate distinct from CD / SLE / other indications. Distinct from `DIS_PJIA` (polyarticular juvenile idiopathic arthritis, a pediatric-cohort sibling indicator). Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-06-01 alongside the Li 2018 PF-04236921 extraction.

### DIS_CD (**canonical for Crohn's disease state indicator (multi-indication pooled analyses)**)
- **Description:** 1 = Crohn's disease patient, 0 = non-CD subject (e.g., healthy volunteer, rheumatoid arthritis, systemic lupus erythematosus, or other indication). Time-fixed per subject. Distinct from `IBD_CD`, which is a pooled-UC+CD discriminator with UC as the reference category; `DIS_CD` is used when the complement group is a heterogeneous non-IBD cohort rather than UC specifically.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-CD subject; the complement group is paper-defined -- the union of other cohorts pooled in the source analysis).
- **Source aliases:** none known; source NONMEM control streams typically derive the indicator from a `POPULATION` / `STUDY` categorical alongside other `DIS_*` indicators.
- **Example models:** `Li_2018_PF04236921.R` (log-shift `e_cd_cl = log(0.00946 / 0.00546) = 0.5499` on linear CL -- a 73 percent higher typical CL in CD vs the HV reference, consistent with the paper's reported 60 percent higher CL in CD when other covariates are held at reference; also log-shifts on baseline CRP, IC50, and logit-Imax).
- **Notes:** Use when a population PK model pools CD patients with a non-IBD reference population and Crohn's disease status is retained as a covariate. When the analysis pools CD with UC only (and tests CD-vs-UC as a discriminator), use `IBD_CD` instead. Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-06-01 alongside the Li 2018 PF-04236921 extraction.

### DIS_SLE (**canonical for systemic lupus erythematosus disease-state indicator**)
- **Description:** 1 = systemic lupus erythematosus patient, 0 = non-SLE subject (e.g., healthy volunteer, rheumatoid arthritis, Crohn's disease, or other indication). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-SLE subject; the complement group is paper-defined -- the union of other cohorts pooled in the source analysis).
- **Source aliases:** none known; source NONMEM control streams typically derive the indicator from a `POPULATION` / `STUDY` categorical alongside other `DIS_*` indicators.
- **Example models:** `Li_2018_PF04236921.R` (log-shift `e_sle_cl = log(0.00643 / 0.00546) = 0.1632` on linear CL, log-shifts on baseline CRP / IC50 / logit-Imax, and a Hill coefficient effect `e_sle_gamma = log(1.55) = 0.4383` shifting gamma from 1 in the HV/RA/CD reference to 1.55 in SLE).
- **Notes:** Use when a population PK model pools SLE patients with a non-SLE reference population and SLE disease status is retained as a covariate. Future SLE-anchored extractions (e.g., anifrolumab, belimumab) that include SLE as a covariate against a non-SLE comparator should extend the example list. Distinct from `BGENE21` / `BGENE21_HIGH` (continuous / binary IFN-21-gene scores within an SLE cohort) -- those operate within an SLE-only population whereas `DIS_SLE` is the across-cohort disease-state flag. Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-06-01 alongside the Li 2018 PF-04236921 extraction.

### DIS_INFECT_CSSSI_SEV (**canonical for complicated skin and skin-structure infection severity indicator**)
- **Description:** 1 = severe complicated skin and skin-structure infection (cSSSI), 0 = not severe cSSSI. Within-cohort severity indicator: stratifies severity inside an already-defined cSSSI / acute bacterial skin and skin-structure infection (ABSSSI) population, not a disease-vs-non-disease cohort indicator. Time-fixed per subject in the source analysis. The exact clinical criteria that classify a patient as severe vs not severe are protocol-defined within the trial dataset and may differ across antimicrobial development programs; per-model `covariateData[[DIS_INFECT_CSSSI_SEV]]$notes` should document the underlying definition when the source paper provides one.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not severe cSSSI).
- **Source aliases:**
  - `SOI` (severity-of-infection indicator; same orientation, 1 = severe / 0 = not severe) -- used in `Lodise_2018_iclaprim.R` (ASSIST-1 / ASSIST-2 phase 3 cSSSI trials).
- **Example models:** `Lodise_2018_iclaprim.R` (additive-linear shift on inter-compartmental clearance Q: `q_typ = exp(lq) + e_infect_csssi_sev_q * DIS_INFECT_CSSSI_SEV` with `e_infect_csssi_sev_q = +13.5 L/h`, so severe-cSSSI patients have Q rise from 1.85 L/h to 15.35 L/h relative to non-severe patients).
- **Notes:** Specific scope because the "severe cSSSI" definition is protocol-defined; future antimicrobial popPK papers that test a severity-of-infection contrast in a different infection class (pneumonia, HABP/VABP, bloodstream infection, bone and joint infection) should register sibling canonicals (e.g., `DIS_INFECT_PNEUM_SEV`, `DIS_HABP_SEV`) rather than overloading this entry. Distinct from `DIS_SASTHMA` and other disease-state indicators (which contrast a disease cohort with a non-disease reference) -- `DIS_INFECT_CSSSI_SEV` operates *within* an already-cSSSI cohort. The covariate-effect parameter naming drops the `DIS_` prefix per the existing `DIS_CANCER` -> `e_cancer_*` / `DIS_CANCER_PED` -> `e_cancer_ped_*` convention; here that gives `e_infect_csssi_sev_<param>`. Ratified canonically on 2026-05-30 alongside the Lodise 2018 iclaprim extraction.

### DIS_HABP (**canonical for hospital-acquired bacterial pneumonia infection-type indicator**)
- **Description:** 1 = the subject's index infection is hospital-acquired bacterial pneumonia (HABP), 0 = otherwise. Infection-TYPE indicator (which infection the subject has), not a within-class severity indicator. Member of the mutually exclusive `DIS_<infection>` cohort-indicator set used by antimicrobial popPK analyses that pool healthy volunteers with several infected cohorts; the shared reference category (all indicators 0) is normally the uninfected healthy-volunteer stratum, but the reference must be documented per model because some analyses use one infection type as reference instead.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no HABP; in `Cammarata_2024_sulbactam_durlobactam.R` the shared all-zero reference is the uninfected Phase 1 subject).
- **Source aliases:**
  - `INFTYPN = 1` -- numeric infection-type flag level; used in `Cammarata_2024_sulbactam_durlobactam.R` (Table 1 rows `CLINFTYPN1`, `V3INFTYPN1`, and the merged `V1INFTYPN1&2`).
- **Example models:** `Cammarata_2024_sulbactam_durlobactam.R` (proportional shifts of -0.424 on sulbactam total CL and +0.836 on sulbactam Vc; durlobactam Vc uses a merged HABP-or-VABP coefficient of +1.52 applied to `DIS_HABP + DIS_VABP`).
- **Notes:** Plain `DIS_<disease>` cohort indicator in the same shape as `DIS_HEALTHY` / `DIS_CANCER` / `DIS_MDS` / `DIS_AML`. Sibling to `DIS_VABP`, `DIS_CUTI`, `DIS_BACTEREMIA`, and `DIS_AP`. Scope is `specific` until a second model ratifies each, per the operator decision recorded in the Cammarata 2024 extraction sidecar (`agcand_13067668` request-001 q1, answered A on 2026-07-27); the alternative `DIS_INFECT_<TYPE>` prefix suggested by the `DIS_INFECT_CSSSI_SEV` Notes was offered as option B and not chosen, because `DIS_INFECT_CSSSI_SEV` is a severity-WITHIN-cohort indicator while these are type-of-infection COHORT indicators. Covariate-effect naming drops the `DIS_` prefix: `e_habp_<param>`, or `e_habp_vabp_<param>` when a model shares one coefficient across the two pneumonia types. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

### DIS_VABP (**canonical for ventilator-associated bacterial pneumonia infection-type indicator**)
- **Description:** 1 = the subject's index infection is ventilator-associated bacterial pneumonia (VABP), 0 = otherwise. Member of the mutually exclusive `DIS_<infection>` cohort-indicator set. See `DIS_HABP` for the shared-reference-category discipline.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no VABP; shared all-zero reference documented per model).
- **Source aliases:**
  - `INFTYPN = 2` -- numeric infection-type flag level; used in `Cammarata_2024_sulbactam_durlobactam.R` (Table 1 rows `CLINFTYPN2`, `V3INFTYPN2`, and the merged `V1INFTYPN1&2`).
- **Example models:** `Cammarata_2024_sulbactam_durlobactam.R` (proportional shifts of -0.298 on sulbactam total CL and +1.43 on sulbactam Vc; shares the merged +1.52 HABP-or-VABP coefficient on durlobactam Vc because the separate HABP and VABP durlobactam terms 'did not yield significantly different effects on PK from one another').
- **Notes:** Scope `specific` until a second model ratifies it; sibling to `DIS_HABP`. When a source paper merges the HABP and VABP arms into one coefficient, keep the two covariate COLUMNS distinct and apply the shared coefficient to their sum inside `model()` -- do not collapse to a single `DIS_HABP_VABP` column, because sibling analyses (and the same paper's other analyte) may separate them again. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

### DIS_CUTI (**canonical for complicated urinary tract infection infection-type indicator**)
- **Description:** 1 = the subject's index infection is a complicated urinary tract infection (cUTI), 0 = otherwise. Member of the mutually exclusive `DIS_<infection>` cohort-indicator set. Papers that enroll a combined "cUTI including acute pyelonephritis" cohort but report separate model coefficients for the two should pair this column with `DIS_AP`; papers that report a single pooled coefficient should use this column alone and say so in the per-model notes.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no cUTI; shared all-zero reference documented per model).
- **Source aliases:**
  - `INFTYPN = 3` -- numeric infection-type flag level; used in `Cammarata_2024_sulbactam_durlobactam.R` (Table 1 rows `V1INFTYPN3`, `CLINFTYPN3`, `V3INFTYPN3`).
- **Example models:** `Cammarata_2024_sulbactam_durlobactam.R` (proportional shifts of +0.343 on durlobactam Vc, -0.157 on sulbactam total CL, and +0.17 on sulbactam Vc; cUTI subjects came from the Phase 2 study CS2514-2017-0003, in which acute pyelonephritis was reported as a separate infection-type level).
- **Notes:** Scope `specific` until a second model ratifies it; sibling to `DIS_HABP` / `DIS_VABP` / `DIS_BACTEREMIA` / `DIS_AP`. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

### DIS_BACTEREMIA (**canonical for bacteremia / bloodstream-infection infection-type indicator**)
- **Description:** 1 = the subject's index infection is bacteremia (bloodstream infection), 0 = otherwise. Member of the mutually exclusive `DIS_<infection>` cohort-indicator set. Whether bacteremia secondary to another focus counts as bacteremia or as the primary focus is protocol-specific and must be documented per model.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no bacteremia; shared all-zero reference documented per model).
- **Source aliases:**
  - `INFTYPN = 4` -- numeric infection-type flag level; used in `Cammarata_2024_sulbactam_durlobactam.R` (Table 1 rows `V1INFTYPN4`, `CLINFTYPN4`, `V3INFTYPN4`).
- **Example models:** `Cammarata_2024_sulbactam_durlobactam.R` (proportional shifts of +3.32 on durlobactam Vc -- the largest single covariate effect in the model -- plus -0.444 on sulbactam total CL and +1.85 on sulbactam Vc; bacteremia due to *Acinetobacter baumannii-calcoaceticus* complex was one of the Phase 3 enrollment categories).
- **Notes:** Scope `specific` until a second model ratifies it; sibling to `DIS_HABP` / `DIS_VABP` / `DIS_CUTI` / `DIS_AP`. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

### DIS_AP (**canonical for acute pyelonephritis infection-type indicator**)
- **Description:** 1 = the subject's index infection is acute pyelonephritis (AP), 0 = otherwise. Member of the mutually exclusive `DIS_<infection>` cohort-indicator set. Used when a source paper reports AP as a model level distinct from `DIS_CUTI`, which is common because AP trials enroll under a combined "cUTI including AP" protocol but the two present with materially different renal and distributional physiology.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no AP; shared all-zero reference documented per model).
- **Source aliases:**
  - `INFTYPN = 5` -- numeric infection-type flag level; used in `Cammarata_2024_sulbactam_durlobactam.R` (Table 1 rows `CLINFTYPN5`, `V3INFTYPN5`; the table footnote defines 'AP, acute pyelonephritis').
- **Example models:** `Cammarata_2024_sulbactam_durlobactam.R` (proportional shifts of -0.382 on sulbactam total CL and -0.704 on sulbactam Vc; durlobactam carries no AP term because no `V1INFTYPN5` row appears in the durlobactam half of Table 1).
- **Notes:** Scope `specific` until a second model ratifies it; sibling to `DIS_CUTI`. A model may legitimately carry an AP coefficient for one analyte and not the other -- do not synthesise a missing coefficient by borrowing the cUTI value. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

### CARRAGEENAN (**canonical for intraplantar-carrageenan inflammatory-challenge indicator**)
- **Description:** Binary indicator for intraplantar injection of carrageenan suspension as an experimental inflammatory / hyperalgesic challenge. 1 = subject received an intraplantar carrageenan injection at the start of the experiment (the carrageenan-induced peripheral inflammation / thermal-hyperalgesia paradigm); 0 = subject received an intraplantar saline injection (sham control). Time-fixed per subject within an experiment.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (saline-injected sham animal; no induced inflammation).
- **Source aliases:**
  - `CARRAGEENAN` -- used in `VasquezBahena_2009_lumiracoxib_rat.R` (1 = groups II-IX, 100 uL of 1% carrageenan suspension into the right hind paw; 0 = group I, 100 uL of 0.9% saline solution).
- **Example models:** `VasquezBahena_2009_lumiracoxib_rat.R` (switches the COX-2 synthesis-rate model: CARRAGEENAN = 0 selects the constant saline synthesis rate `ks_cox2_saline` and CARRAGEENAN = 1 selects the time-variant gamma function `ks_cox2(t) = A * t^alpha * exp(-beta * t)` driving the carrageenan-induced inflammation profile).
- **Notes:** The carrageenan-induced peripheral inflammation / hyperalgesia model (Winter 1962; Hargreaves 1988 thermal-hyperalgesia variant) is one of the most widely used preclinical assays for screening anti-inflammatory and analgesic drugs in rodents, so the canonical name is reusable for future preclinical extractions. Scope: specific until a second model ratifies the binary-switch semantics on a different PD framework. The Hargreaves-test thermal-hyperalgesia readout (`LT` paw withdrawal latency, seconds) is the typical observable when this indicator is in use, but other readouts (paw oedema, mechanical-allodynia von Frey threshold) are equally valid. Distinct from disease-state indicators (`DIS_*`) because the inflammatory state is experimentally induced at a defined time, not a chronic patient condition.

### MENT_DISABLED (**canonical for severe mental-disability indicator (paper-defined diagnosis)**)
- **Description:** 1 = subject diagnosed with severe mental disability by the pediatrics department per the source-paper clinical criteria; 0 = mentally intact (ASA class 1, normal mental ability). Time-fixed per subject. Subject-level indicator; the underlying definition is broad (severe developmental / cognitive impairment with impairment of daily-living activities irrespective of primary neurologic cause, per Shin 2014 Results) rather than a single diagnostic category.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (mentally intact; ASA class 1).
- **Source aliases:**
  - `MEN` -- used in `Shin_2014_sevoflurane.R` (Shin 2014 Appendix 1 NONMEM `$INPUT` column; same orientation: MEN = 1 mentally disabled, MEN = 0 mentally intact).
- **Example models:** `Shin_2014_sevoflurane.R` (stratifies both C50 and the Hill coefficient of the sigmoid-Emax probability of return of consciousness during emergence from sevoflurane anesthesia: typical-value C50 0.37 vol % vs 0.19 vol % and gamma 16.4 vs 4.53 for intact vs disabled).
- **Notes:** Specific scope because the "severe mental disability" diagnostic criterion is paper-defined (Shin 2014 enrolled pediatric patients diagnosed with severe mental disability by the pediatrics department, without specifying a structured instrument); per-model `covariateData[[MENT_DISABLED]]$notes` should document the diagnostic basis when the source paper provides one. Distinct from `DIS_*` disease-state indicators (which name a specific disease) and from cognitive-score covariates (`SCORE_ADAS_COG`, `SCORE_MMSE`, `SCORE_CDR_SOB`, `SCORE_FAQ`) which are continuous measurements; MENT_DISABLED is a binary developmental / cognitive-impairment cohort indicator. Future emergence-from-anesthesia or perioperative PK/PD models that use a similar binary cognitive-impairment indicator may extend this entry; promote to general scope once a second model ratifies the name with the same diagnostic criterion family. Ratified canonically alongside the Shin 2014 sevoflurane emergence-PD extraction.

### DIS_CF (**canonical for cystic-fibrosis disease-state indicator**)
- **Description:** 1 = cystic fibrosis patient, 0 = healthy participant (or non-CF reference cohort pooled in the source analysis). Time-fixed per subject. Distinct from the `Harun_2019_cysticFibrosis.R` model which is single-cohort (all CF) and therefore carries no `DIS_CF` column; `DIS_CF` is the canonical for popPK / PD analyses that pool a CF patient cohort with a non-CF reference (typically healthy volunteers) and retain the CF-vs-non-CF contrast as a covariate.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-CF subject; the complement is paper-defined -- for Bulitta 2007 the reference is the pooled adult healthy-volunteer cohort).
- **Source aliases:** none known; source NONMEM control streams typically encode the cohort indicator with ad-hoc names (e.g., `CF`, `CYS`, `PATIENT`, or via a `STUDY` / `GROUP` categorical).
- **Example models:** `Bulitta_2007_piperacillin.R` (multiplicative power-form effects on volumes via `fcyf_vss^DIS_CF` with `fcyf_vss = 0.926`, i.e. about a 7.4% reduction in V1 and V2 for CF patients vs healthy adults of the same lean body mass; CL carries the boundary-estimated `fcyf_cl^DIS_CF` with `fcyf_cl = 1.00`, retained from the published model for traceability though it has no numerical effect at the published point estimate; reference category is the pooled adult healthy-volunteer cohort), `Bulitta_2011_cefpirome.R` (log-scale effects on the canonical lcl_renal + lcl_nonren decomposition and on the central / shallow-peripheral / deep-peripheral volumes: `e_cf_cl_renal = log(1.07) = +0.06766` on CL_R, `e_cf_cl_nonren = log(1.13) = +0.12222` on CL_NR, and `e_cf_vc_vp_vp2 = log(0.98) = -0.02020` applied uniformly to V1, V2, V3; typical values anchored to the HV column of paper Table 3 with FCYF effects from paper Table 4 LBM-allometric row).
- **Notes:** Used when a population PK / PD analysis pools CF patients with a non-CF reference (typically healthy volunteers) and the CF-vs-non-CF contrast is retained as a covariate. Scope: specific until a second model ratifies the canonical; promote to general at that point. Pair with `LBM` rather than `WT` as the size descriptor when the source paper found LBM to reduce unexplained BSV more than total body weight, which is common in CF cohorts whose body composition differs substantially from healthy adults. Ratified canonically on 2026-06-07 alongside the Bulitta 2007 piperacillin extraction.


### DIS_SEPSIS (**canonical for active-sepsis co-condition indicator**)
- **Description:** 1 = active sepsis / septic syndrome at the start of (or during) the modeled PK interval, 0 = no sepsis. Time-fixed per subject for the typical popPK use-case (Han 2013), although time-varying use is permitted; document per-model in `covariateData[[DIS_SEPSIS]]$notes` whether the indicator is fixed at PK-sampling onset or updated dynamically over the observation period. Used as a binary co-condition indicator on PK parameters (clearance, volume) when a study population pools septic and non-septic subjects, typically in ICU / burn-ICU / critically ill cohorts.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no sepsis).
- **Source aliases:**
  - `SEPS` -- used in `Han_2013_fluconazole.R` (Han 2013 Methods / Table 1 covariate with values 0 = no sepsis, 1 = sepsis; same orientation as the canonical).
- **Example models:** `Han_2013_fluconazole.R` (additive shift on the non-RRT CL arm: `CL_norrt = theta1 + (CLCR/120)*theta4 + TBI*theta7 + SEPS*theta9` with `theta9 = -0.369 L/h` -- septic patients have about 0.37 L/h lower CL in the non-RRT arm), `Jeon_2014_piperacillin.R` (additive shift on central volume V1: `TVV1 = exp(lvc) + e_sepsis_vc * DIS_SEPSIS` with `e_sepsis_vc = 14.8 L` -- septic patients have V1 = 25.3 + 14.8 = 40.1 L versus 25.3 L in non-septic patients, consistent with capillary leakage and interstitial edema in sepsis).
- **Notes:** Distinct from chronic disease-state indicators (e.g., `DIS_UC`, `DIS_HAE`) -- sepsis is an acute / transient co-condition rather than a chronic indication label. Distinct from `CRP` / `IL6` / `SAPS_II`, which are biomarker / severity-score columns that may co-vary with clinical sepsis but are conceptually distinct from a binary clinical-sepsis diagnostic flag. Ratified canonically on 2026-06-09 alongside the Han 2013 fluconazole extraction.


### DIS_INFECT_ACTIVE (**canonical for active clinical infection episode indicator**)
- **Description:** 1 = the record falls within an active clinical infection episode; 0 = no active infection. Time-varying per record (an acute, transient episode), in contrast to chronic disease-state indicators. The clinical criterion is paper-specific and MUST be recorded in `covariateData[[DIS_INFECT_ACTIVE]]$notes` -- e.g. Kloos 2021 used "fever (>38 degrees Celsius) and hospital admission or prescription of antibiotics". Used as a multiplicative or additive shift on clearance (and occasionally volume) when acute infection / inflammation alters drug elimination, e.g. via activation of the mononuclear phagocyte system.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no active infection).
- **Source aliases:**
  - `INFECTION` -- used in `Kloos_2021_pegasparaginase.R` (Kloos 2021 Table 2 / Table 3 covariate; same orientation as the canonical).
- **Example models:** `Kloos_2021_pegasparaginase.R` (multiplicative on CL: `1.38^DIS_INFECT_ACTIVE`, i.e. 38 percent higher PEGasparaginase clearance during an infection, attributed by the authors to activation of the mononuclear phagocyte system that clears PEGylated asparaginase).
- **Notes:** Distinct from [[DIS_SEPSIS]], which denotes a specific septic syndrome -- `DIS_INFECT_ACTIVE` is the broader "any active clinical infection episode" flag and is typically time-varying where `DIS_SEPSIS` is often time-fixed at PK-sampling onset. Distinct from `DIS_INFECT_CSSSI_SEV` (an infection-severity grade in a specific-scope cSSSI indication). Distinct from its constituent measurements `BODYTEMP`, `CRP`, and the `CONMED_<INN>` antibiotic columns, which are biomarker / comedication columns that may co-vary with a clinical infection flag but are conceptually separate. Ratified canonically alongside the Kloos 2021 PEGasparaginase extraction (sidecar request-001 / response-001, question q2, option A).


### DIS_EDEMA (**canonical for clinical edema presence indicator**)
- **Description:** 1 = clinical edema present (typical clinical definition: puffy face and/or pitting peripheral edema), 0 = no clinical edema. Time-fixed per subject in the typical popPK use-case (Han 2013), although time-varying use is permitted; document per-model in `covariateData[[DIS_EDEMA]]$notes` whether the indicator is fixed at PK-sampling onset or updated dynamically. Used as a binary co-condition indicator on PK volume (most commonly) when third-space / extravascular fluid expansion is expected to alter drug distribution.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no clinical edema).
- **Source aliases:**
  - `EDEM` -- used in `Han_2013_fluconazole.R` (Han 2013 Methods / Table 1 covariate with values 0 = no edema, 1 = clinical edema by the paper's puffy-face and leg-pitting criterion; same orientation as the canonical).
- **Example models:** `Han_2013_fluconazole.R` (additive shift on V: `V = (WT/65)*theta5 + EDEM*theta6 + TBI*theta8` with `theta6 = 13.6 L` -- edematous patients have about 13.6 L larger V).
- **Notes:** Conceptually distinct from `DIS_HAE` (hereditary angioedema disease-state indicator) -- `DIS_EDEMA` is a generic clinical-sign flag (e.g., burn-related capillary leak, congestive heart failure, hypoalbuminemia) rather than a disease classification. Distinct from a continuous fluid-balance / weight-gain column. Ratified canonically on 2026-06-09 alongside the Han 2013 fluconazole extraction.


### DIS_BURN_RECENT (**canonical for recent-postburn-injury hypermetabolic-phase indicator**)
- **Description:** 1 = within the paper-defined recent-postburn window (i.e., the acute hypermetabolic phase shortly after burn injury), 0 = beyond the window. Time-varying within a subject is permitted as time since injury crosses the cutoff; in Han 2013 the cutoff is 30 days postburn (the hypermetabolic response is maximized between days 7-17 and substantially resolved by day 30; the binary recoding captures patients whose physiology is dominated by the hypermetabolic period). The exact recency threshold is paper-specific -- document the threshold in each model's `covariateData[[DIS_BURN_RECENT]]$notes`. Used as a piecewise covariate on PK parameters (CL and / or V) that change during the acute hypermetabolic phase.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (beyond the recent-postburn window; i.e., resolved hypermetabolic phase).
- **Source aliases:**
  - `TBI` -- used in `Han_2013_fluconazole.R` (Han 2013 Methods / Tables 2 and 3 covariate with values 1 = within 30 days of burn injury, 0 = at least 30 days postburn; same orientation as the canonical, with the recency threshold = 30 days documented per-model). NOTE: `TBI` in this register is a recoded binary covariate; it does NOT denote traumatic brain injury (the common medical abbreviation) and is NOT a continuous time-from-burn-injury value.
- **Example models:** `Han_2013_fluconazole.R` (recency threshold = 30 days; additive shifts on both CL and V: `CL_norrt += TBI*theta7` with `theta7 = 0.504 L/h` and `V += TBI*theta8` with `theta8 = 9.61 L` -- recent-postburn patients have about 0.5 L/h higher CL and 9.6 L larger V relative to the resolved-phase reference).
- **Notes:** Generalised binary recoding rather than a continuous time-from-burn-injury column. A future paper that uses a continuous days-from-burn-injury covariate (analogous to `TPP` for time-postpartum) would warrant its own canonical (suggested name: `POSTBURN_DAYS`); `DIS_BURN_RECENT` is reserved for binary phase indicators. The paper's source-column name `TBI` is preserved as a documented alias even though it collides with the medical abbreviation for traumatic brain injury -- this is the author's chosen column name and the canonical avoids the collision. Ratified canonically on 2026-06-09 alongside the Han 2013 fluconazole extraction.


### DIS_ARDS (**canonical for acute respiratory distress syndrome indicator**)
- **Description:** 1 = acute respiratory distress syndrome (ARDS) present at the start of (or during) the modeled PK interval, 0 = no ARDS. Diagnostic standard is paper-specific (Berlin 2012 criteria, the older American-European Consensus Conference / AECC criteria, or a clinician-adjudicated diagnosis on the chart) -- document per-model in `covariateData[[DIS_ARDS]]$notes` which definition was used. Used as a binary co-condition indicator on PK parameters (most often clearance and / or volume) when ARDS is hypothesized to alter drug disposition through pulmonary inflammation, increased pulmonary reactive oxygen species, ventilator-induced lung injury, or altered fluid balance and capillary leak.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no ARDS).
- **Source aliases:**
  - `ARDS` -- used in `Taubert_2016_linezolid.R` (Taubert 2016 patient-group-1 baseline indicator; ARDS = 1 multiplies CL by 1.82, encoded inside `model()` as a log-additive shift `e_dis_ards_cl = log(1.82)` per Taubert 2016 page 5256 covariate-model equation).
- **Example models:** `Taubert_2016_linezolid.R` (multiplicative factor on CL: typical-value CL is multiplied by 1.82 in ARDS patients, equivalent to a ~82% increase in linezolid clearance; the proposed mechanism is non-enzymatic oxidation of linezolid by elevated pulmonary reactive oxygen species in ARDS lungs per Taubert 2016 Discussion).
- **Notes:** Distinct from `DIS_SEPSIS` (acute septic syndrome co-condition) and from `DIS_CKD` / `DIS_HEPATIC` chronic organ-failure indicators -- ARDS is an acute pulmonary syndrome that may coexist with sepsis but has its own pathophysiology and is captured separately when both are tested as covariates. General scope because ARDS has a stable internationally-standardised clinical definition (Berlin criteria) and is universally applicable as a critically-ill PK covariate. Time-varying within a subject is permitted as the syndrome resolves or worsens. Ratified canonically on 2026-06-30 alongside the Taubert 2016 linezolid extraction.


### DIS_PERIT (**canonical for peritonitis indicator**)
- **Description:** 1 = peritonitis (infection / inflammation of the peritoneum, including primary spontaneous bacterial peritonitis, secondary peritonitis from a perforated viscus or anastomotic leak, and tertiary / persistent peritonitis) present at the start of (or during) the modeled PK interval, 0 = no peritonitis. Used as a binary co-condition indicator on PK parameters when peritoneal inflammation, intra-abdominal fluid sequestration, third-space loss, or altered hepatic synthetic function is hypothesized to change drug disposition.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no peritonitis).
- **Source aliases:**
  - `Peritonitis` -- used in `Taubert_2016_linezolid.R` (Taubert 2016 patient-group-1 baseline indicator; peritonitis = 1 multiplies Vc by 1.53, encoded inside `model()` as a log-additive shift `e_dis_perit_vc = log(1.53)` per Taubert 2016 page 5256 covariate-model equation).
- **Example models:** `Taubert_2016_linezolid.R` (multiplicative factor on Vc: typical-value central volume is multiplied by 1.53 in peritonitis patients, equivalent to a ~53% larger central volume of distribution, attributed in Taubert 2016 Discussion to third-space fluid accumulation in the peritoneal cavity).
- **Notes:** Distinct from `PERIT_DIAL` (chronic peritoneal-dialysis modality indicator) -- `DIS_PERIT` is an acute infection / inflammation of the peritoneum, while `PERIT_DIAL` is a chronic therapeutic dialysis modality; the two have entirely different pathophysiologies and PK implications. Future papers that retain peritonitis as a covariate may extend `Example models`. General scope because peritonitis has a clear universal clinical definition and any future critically-ill PK model with the same encoding may reuse the canonical. Ratified canonically on 2026-06-30 alongside the Taubert 2016 linezolid extraction.


### DIS_SCOL_IDIO (**canonical for idiopathic-scoliosis aetiology indicator**)
- **Description:** 1 = subject has idiopathic scoliosis (no underlying systemic / syndromic cause; classic adolescent idiopathic scoliosis or idiopathic kyphoscoliosis), 0 = otherwise (subject is in either the non-idiopathic-scoliosis cohort or the reference complement cohort). Time-fixed per subject. Used as a binary cohort indicator in popPK / popPD models that pool an adolescent scoliosis population with another paediatric surgical reference cohort and the scoliosis-aetiology effect on PK is retained as a covariate.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not idiopathic-scoliosis; the complement reference is paper-defined -- in Stricker 2015 the implicit reference cohort is the pooled infant craniofacial-reconstruction cohort from the earlier Stricker 2013 BJA paper).
- **Source aliases:**
  - paper narrative `idiopathic scoliosis` / `idiopathic kyphoscoliosis` diagnosis labels in Stricker 2015 Table 1, decomposed from a 3-level diagnosis categorical {craniofacial, idiopathic scoliosis, non-idiopathic scoliosis} into binary DIS_SCOL_IDIO with value 1 for the idiopathic-scoliosis cohort.
- **Example models:** `Stricker_2015_aminocaproic_acid.R` (multiplicative power-form effect on CL: `cl *= 1.10^DIS_SCOL_IDIO`; idiopathic-scoliosis adolescents have ~10% higher CL than the craniofacial-cohort reference at the same body weight and post-natal age, after allometric scaling; Stricker 2015 Table 5 'Impact of idiopathic spines' = 1.10; effect retained for magnitude / precision reporting even though the authors note it is not clinically meaningful).
- **Notes:** Specific scope because the reference complement (infant craniofacial reconstruction cohort) is paper-defined. Pairs with `DIS_SCOL_NONIDIO` in the Stricker 2015 three-level surgical-cohort stratification {craniofacial reference, idiopathic scoliosis, non-idiopathic scoliosis}; both = 0 selects the craniofacial reference. Distinct from `ADOLESCENT` (pure age-cohort indicator independent of aetiology) and from `CHILD` (broader paediatric age-cohort indicator). Ratified canonically on 2026-06-12 alongside the Stricker 2015 epsilon-aminocaproic acid extraction.


### DIS_SCOL_NONIDIO (**canonical for non-idiopathic / syndromic scoliosis aetiology indicator**)
- **Description:** 1 = subject has non-idiopathic / syndromic scoliosis (cerebral palsy, Marfan syndrome, Ehlers-Danlos syndrome, neurofibromatosis type 1, spina bifida, congenital neuromuscular scoliosis, syringomyelia, 22q deletion, cortical dysgenesis, and similar systemic / syndromic aetiologies), 0 = otherwise. Time-fixed per subject. Used as a binary cohort indicator in popPK / popPD models that pool a syndromic-scoliosis paediatric population with another paediatric surgical reference cohort and the scoliosis-aetiology effect on PK is retained as a covariate.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not non-idiopathic-scoliosis; the complement reference is paper-defined -- in Stricker 2015 the implicit reference cohort is the pooled infant craniofacial-reconstruction cohort from the earlier Stricker 2013 BJA paper).
- **Source aliases:**
  - paper narrative diagnosis labels in Stricker 2015 Table 1 (cerebral palsy, Marfan, Ehlers-Danlos, neurofibromatosis, spina bifida, congenital neuromuscular scoliosis, syringomyelia, 22q deletion, cortical dysgenesis), decomposed from the 3-level diagnosis categorical {craniofacial, idiopathic scoliosis, non-idiopathic scoliosis} into binary DIS_SCOL_NONIDIO with value 1 for any of the syndromic-scoliosis aetiologies.
- **Example models:** `Stricker_2015_aminocaproic_acid.R` (multiplicative power-form effect on CL: `cl *= 0.97^DIS_SCOL_NONIDIO`; non-idiopathic-scoliosis adolescents have ~3% lower CL than the craniofacial-cohort reference at the same body weight and post-natal age, after allometric scaling; Stricker 2015 Table 5 'Impact of non-idiopathic spines' = 0.97; the authors describe this effect as not clinically meaningful but retain it in the final model for precision estimation).
- **Notes:** Specific scope because the reference complement (infant craniofacial reconstruction cohort) is paper-defined and because the non-idiopathic class pools many aetiologically heterogeneous syndromes (cerebral palsy, connective-tissue disorders, neuromuscular disorders, neurocutaneous disorders, etc.) that future papers may want to resolve into separate sub-cohort indicators. Pairs with `DIS_SCOL_IDIO` in the Stricker 2015 three-level surgical-cohort stratification {craniofacial reference, idiopathic scoliosis, non-idiopathic scoliosis}; both = 0 selects the craniofacial reference. Ratified canonically on 2026-06-12 alongside the Stricker 2015 epsilon-aminocaproic acid extraction.


### DIS_CHF (**canonical for compensated congestive heart failure indicator**)
- **Description:** 1 = subject has compensated congestive heart failure (chronic CHF stabilised on diuretics, cardiac glycosides, or other background therapy; NYHA class typically not stratified at this level), 0 = no diagnosed CHF. Time-fixed per subject in popPK / popPK-PD analyses that include CHF as a baseline disease-state covariate.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no CHF).
- **Source aliases:**
  - `chf` -- used in `Thomson_1989_lisinopril.R` (Thomson 1989 Table 2 cardiac-failure indicator; treated as a multiplicative factor 0.645 raised to `chf` on CL/F so the unaffected reference group keeps factor 1).
- **Example models:** `Thomson_1989_lisinopril.R` (multiplicative power-form effect on CL/F: `e_chf_cl^DIS_CHF` with `e_chf_cl = 0.645`; ~35% lower apparent clearance with compensated cardiac failure).
- **Notes:** Use this canonical when the source paper tests a binary cardiac-failure indicator (compensated, stable, on background therapy) as a structural covariate on PK parameters. Distinct from concomitant-medication indicators such as `CONMED_SPIRON` (which captures the diuretic / aldosterone-antagonist effect typically given alongside CHF therapy) and from haemodynamic / autonomic covariates such as `HR` and the blood-pressure outputs. For papers that decompose CHF severity into NYHA classes, prefer a per-class encoding (e.g., `DIS_CHF_NYHA3`, `DIS_CHF_NYHA4`) when the strata are retained in the final model. The covariate-effect parameter form is `e_chf_<param>` (drops the `DIS_` prefix per the existing `DIS_CANCER -> e_cancer_<param>` precedent). Ratified canonically on 2026-06-10 alongside the Thomson 1989 lisinopril extraction.


## Epilepsy baseline seizure-severity indicators

Baseline seizure-severity indicators derived from a pre-trial seizure count (typically the number of seizures in the 3 months before study entry, decomposed by the source paper into severity bins). The count itself is decomposed into a set of mutually-exclusive binary indicators per the standing operator policy for count covariates ("count covariate -> decomposed binary indicators, not a single integer count"). Members are jointly restricted to at most one being = 1 for any subject; the reference category is 2-6 seizures in the previous 3 months (all indicators = 0 selects the reference).

### NSP3M_LT2 (**canonical for baseline seizure count fewer than 2 in the previous 3 months**)
- **Description:** Binary indicator that the subject reported fewer than 2 seizures in the 3 months before trial start (baseline period). 1 = <2 baseline seizures (the low-severity bin), 0 = otherwise. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (2-6 baseline seizures per the Lindauer 2017 NSP3M decomposition; the reference bin). Subjects in the 7-50 or >50 bins have `NSP3M_LT2 = 0` and are identified by `NSP3M_7_50 = 1` or `NSP3M_GT50 = 1` instead.
- **Source aliases:**
  - `NSP3M` (categorical, the <2 bin) -- used in `Lindauer_2017_lacosamide_seizure.R` (Lindauer 2017 Section 2.5 splits NSP3M into four categories: <2, 2-6, 7-50, >50, with 2-6 as reference; the low-severity bin is registered here as `NSP3M_LT2`).
- **Example models:** `Lindauer_2017_lacosamide_seizure.R` (log-hazard shifts on both first-seizure and subsequent-seizure Weibull scale: e_nsp3m_lt2_1st = -1.12 (first event), e_nsp3m_lt2_2nd = -1.37 (subsequent events); the associated hazard ratios vs the 2-6 reference are 0.58 and 0.38 per Lindauer 2017 Table 4).
- **Notes:** Specific scope because the concept is tied to the pre-trial-seizure-count decomposition used in the SP0993 / N01061 monotherapy epilepsy trials (Lindauer 2017 references Abrantes et al. for the two-Weibull-sub-model approach with NSP3M-derived binary categories). Sibling canonicals `NSP3M_7_50` and `NSP3M_GT50` complete the 4-level decomposition. Data assemblers must enforce mutual exclusivity: at most one of {`NSP3M_LT2`, `NSP3M_7_50`, `NSP3M_GT50`} is 1 for each subject; if all three are 0 the subject belongs to the reference 2-6 bin. The covariate-effect parameter form is `e_nsp3m_lt2_<param>` (drops `_LT2` structure into the coefficient name). Ratified canonically on 2026-07-03 alongside the Lindauer 2017 lacosamide time-to-seizure extraction.

### NSP3M_7_50 (**canonical for baseline seizure count 7 to 50 in the previous 3 months**)
- **Description:** Binary indicator that the subject reported 7 to 50 seizures in the 3 months before trial start (baseline period). 1 = 7-50 baseline seizures (the moderate-high-severity bin), 0 = otherwise. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (2-6 baseline seizures per the Lindauer 2017 NSP3M decomposition; the reference bin).
- **Source aliases:**
  - `NSP3M` (categorical, the 7-50 bin) -- used in `Lindauer_2017_lacosamide_seizure.R`.
- **Example models:** `Lindauer_2017_lacosamide_seizure.R` (log-hazard shifts on both first-seizure and subsequent-seizure Weibull scale: e_nsp3m_7_50_1st = +1.94 (first event), e_nsp3m_7_50_2nd = +1.36 (subsequent events); the associated hazard ratios vs the 2-6 reference are 2.60 and 2.63 per Lindauer 2017 Table 4).
- **Notes:** Specific scope. Sibling of `NSP3M_LT2` and `NSP3M_GT50` in the 4-level NSP3M decomposition; see `NSP3M_LT2` notes for the mutual-exclusivity rule and covariate-effect naming. The Lindauer 2017 Table 4 hazard ratio of 2.60 (90% CI 2.02-3.31) for the first-seizure hazard was highlighted in the paper Abstract as the primary NSP3M-severity finding. Ratified canonically on 2026-07-03 alongside the Lindauer 2017 lacosamide time-to-seizure extraction.

### NSP3M_GT50 (**canonical for baseline seizure count greater than 50 in the previous 3 months**)
- **Description:** Binary indicator that the subject reported more than 50 seizures in the 3 months before trial start (baseline period). 1 = >50 baseline seizures (the highest-severity bin), 0 = otherwise. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (2-6 baseline seizures per the Lindauer 2017 NSP3M decomposition; the reference bin).
- **Source aliases:**
  - `NSP3M` (categorical, the >50 bin) -- used in `Lindauer_2017_lacosamide_seizure.R`.
- **Example models:** `Lindauer_2017_lacosamide_seizure.R` (log-hazard shifts on both first-seizure and subsequent-seizure Weibull scale: e_nsp3m_gt50_1st = +3.30 (first event), e_nsp3m_gt50_2nd = +2.53 (subsequent events); the associated hazard ratios vs the 2-6 reference are 5.09 and 6.09 per Lindauer 2017 Table 4).
- **Notes:** Specific scope. Sibling of `NSP3M_LT2` and `NSP3M_7_50` in the 4-level NSP3M decomposition; see `NSP3M_LT2` notes for the mutual-exclusivity rule and covariate-effect naming. Ratified canonically on 2026-07-03 alongside the Lindauer 2017 lacosamide time-to-seizure extraction.


## Pulmonary / lung-disease biomarkers

### FEV1 (**canonical for forced expiratory volume in 1 second**)
- **Description:** Baseline forced expiratory volume in 1 second (FEV1) reported as an absolute volume in litres. Pulmonary-function spirometry endpoint; reflects large-airway airflow obstruction and is a standard covariate in chronic obstructive pulmonary disease, alpha-1 antitrypsin deficiency, asthma, and cystic-fibrosis disease-progression analyses. Distinct from FEV1 percent-predicted (the % predicted value standardises absolute FEV1 by reference equations from sex / age / height / ethnicity); use this canonical only for the absolute-litre value supplied as a covariate column.
- **Units:** L (absolute volume; document the exhalation-effort standard the source paper cites in `covariateData[[FEV1]]$notes` if non-default -- ATS / ERS post-bronchodilator is the typical convention).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with linear-deviation forms `(theta + e_fev1_param * (FEV1 - ref))` or power forms `(FEV1 / ref)^exponent`. Reference values observed: 1.6 L (Tortorici 2017, population median across the RAPID-RCT/RAPID-OLE A1-PI augmentation cohort).
- **Source aliases:** none yet; canonical name preferred. Source papers typically use the abbreviation `FEV1` directly.
- **Example models:** `Tortorici_2017_a1pi.R` (linear-deviation effect on the lung-density decline rate: `theta5 * (FEV1 - 1.6)` with `theta5 = +0.56 (g/L/year per L FEV1)`; lower-FEV1 patients have steeper natural decline rates independent of A1-PI exposure).
- **Notes:** Distinct from FEV1 percent-predicted (which is a derived ratio with the reference-equation denominator built in; FEV1% is the outcome variable in `Harun_2019_cysticFibrosis.R` rather than a covariate column). Use `FEV1` only when the source paper supplies the absolute-volume value; if the source supplies a percent-predicted value as a covariate, the canonical for that surface is `FEV1_PCTPRED`. Scope: specific until a second model ratifies the absolute-litre semantics; promote to general at that point. Ratified canonically on 2026-05-09 alongside the Tortorici 2017 extraction.

### FEV1_PCTPRED (**canonical for baseline FEV1 as percent of the predicted value**)
- **Description:** Baseline forced expiratory volume in 1 second expressed as a percent of the sex / age / height / ethnicity reference-equation predicted value (FEV1% predicted). The percent-predicted scaling normalises the absolute-litre FEV1 measurement against a healthy-reference standard so a single covariate value is comparable across patient ages and body sizes. Standard pulmonary-function covariate in cystic-fibrosis disease-severity and inhaled-therapy popPK analyses. Time-fixed at study entry / baseline.
- **Units:** % predicted (numeric percentage, e.g. 62.1 for the Ting 2014 population median; not a fraction 0.621).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power-form effects `(FEV1_PCTPRED / ref)^exponent` or linear-deviation forms `(1 + e * (FEV1_PCTPRED - ref))`. Reference values observed: 62.1 % (Ting 2014, population median across the combined three-study cystic-fibrosis cohort).
- **Source aliases:**
  - `FEV1% predicted` -- used in `Ting_2014_tobramycin_inhaled.R` (paper Table 1 / Table 2). Free-text label, not a typical NMTRAN column header; downstream data sets should use the canonical column name `FEV1_PCTPRED`.
- **Example models:** `Ting_2014_tobramycin_inhaled.R` (power-form effect on apparent central volume of distribution: `(FEV1_PCTPRED / 62.1)^-0.303`; lower lung-function patients have larger apparent Vd/F, consistent with the paper's hypothesis that worsening lung disease increases central-airway aerosol deposition).
- **Notes:** Distinct from absolute-litre `FEV1` (canonical for the unstandardised volume) and from `FEV1` as an outcome variable in disease-progression models (e.g. `Harun_2019_cysticFibrosis.R`, where percent-predicted FEV1 is the dependent variable rather than a covariate). The reference equation used to derive the percent-predicted value is paper-specific; document any non-default reference standard (Hankinson 1999 NHANES III, GLI 2012, Wang 1993, etc.) in `covariateData[[FEV1_PCTPRED]]$notes` per model. Scope: specific until a second model ratifies the percent-predicted semantics; promote to general at that point. Ratified canonically on 2026-05-12 alongside the Ting 2014 extraction.

### A1PI (**canonical for serum alpha-1 proteinase inhibitor concentration**)
- **Description:** Baseline serum alpha-1 proteinase inhibitor (A1-PI; also known as alpha-1 antitrypsin, AAT) concentration. Used in alpha-1 antitrypsin deficiency (AATD) augmentation-therapy modelling as a per-subject pre-treatment exposure covariate (the subject's endogenous A1-PI level at study entry, before any augmentation infusions). Time-fixed per subject. Distinct from a time-course of A1-PI used as a state variable -- when the source paper carries A1-PI as the dynamic dependent variable (the augmentation model's PD output), use `Cc` rather than `A1PI`.
- **Units:** umol/L (typical SI-convention reporting; also reported as mg/dL in US-convention papers -- document the unit used in each model via `covariateData[[A1PI]]$units`). Conversion: 1 umol/L A1-PI ~= 5.2 mg/dL (using MW ~52 kDa). The 11 umol/L "putative protective threshold" used clinically corresponds to ~57 mg/dL.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power-form effects `(A1PI / ref)^exponent` on the post-treatment exposure intercept and slope. Reference values observed: 5.5 umol/L (Tortorici 2017, approximate median pre-treatment A1-PI among RAPID-RCT placebo-randomised patients, used as the normalisation denominator for power-form covariate effects).
- **Source aliases:** `Cbase` -- used in Tortorici 2017's published equation 6 to denote the baseline pre-treatment A1-PI value; the column name in the modelled dataset would be `A1PI`.
- **Example models:** `Tortorici_2017_a1pi.R` (two power-form effects: `(A1PI/5.5)^theta5` with `theta5 = +0.73` on the placebo-arm post-treatment exposure intercept, and `(A1PI/5.5)^theta4` with `theta4 = -0.12` on the dose-rate slope; together they encode the modest dose-exposure dependence on each subject's endogenous A1-PI level).
- **Notes:** AATD enrolment criteria typically restrict A1PI to <= 11 umol/L (severe deficiency); reference / heterozygous PI*MZ phenotypes have higher levels. Document the source paper's AATD-genotype enrolment criteria in `covariateData[[A1PI]]$notes` per model. Specific scope because the canonical is tied to AATD augmentation-therapy modelling; future PK / PD analyses of A1-PI (or AAT) in non-AATD contexts (acute-phase response, smoking-induced inflammation) should ratify general scope at that time. Ratified canonically on 2026-05-09 alongside the Tortorici 2017 extraction.

## Cystic fibrosis lung-disease indicators

### AIR_TRAP_5Y (**canonical for severe air trapping on chest HRCT scan at age 5 years**)
- **Description:** 1 = subject had a non-zero "air trapping" component score on the validated Brody-II chest high-resolution computed tomography (HRCT) scan performed at age 5 years; 0 = air trapping component score of 0 (absent). Time-fixed per subject (the indicator captures the single end-of-study HRCT performed at age 5 in the Australasian Cystic Fibrosis Bronchoalveolar Lavage (ACFBAL) study). The Brody-II scoring system reports air trapping as a percentage of maximum possible HRCT score; the binary "present vs absent" dichotomy follows the same convention as Rosenow 2015 (Am J Respir Crit Care Med 191:1158-65).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no air trapping detected on the age-5 HRCT scan).
- **Source aliases:**
  - `ATS5C` -- used in `Harun_2019_cysticFibrosis.R` (Harun 2019 NMTRAN `$INPUT` column, "presence of air trapping at age 5"; values 0 = absent, 1 = present).
- **Example models:** `Harun_2019_cysticFibrosis.R` (linear-deviation effect on baseline FEV1% predicted at age 5: `(1 + e_at_baseline * AIR_TRAP_5Y)` with coefficient -0.0417, i.e., subjects with severe air trapping at age 5 have a baseline FEV1% predicted approximately 4.17% lower than those without).
- **Notes:** Specific scope because the indicator is tied to the ACFBAL study's standardised HRCT-at-age-5 protocol and the Brody-II scoring rubric; future paediatric CF lung-disease studies that score HRCT at a different age or use a different scoring system should register a separate canonical (`AIR_TRAP_8Y`, `AIR_TRAP_PRAGMA`, etc.) rather than overload this name. Ratified canonically on 2026-05-08 alongside the Harun 2019 extraction.

### HOSPRA (**canonical for hospitalisation due to a pulmonary exacerbation**)
- **Description:** Time-varying binary indicator of inpatient hospitalisation for management of a pulmonary exacerbation at the time of the FEV1% predicted measurement: 1 = subject is hospitalised because of a pulmonary exacerbation when the spirometry value is recorded, 0 = not hospitalised at that visit. Used as a per-visit covariate in disease-progression models of FEV1% decline in cystic fibrosis where pulmonary exacerbations are tracked from the Australian Cystic Fibrosis Data Registry (ACFDR) inpatient records.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not hospitalised for a pulmonary exacerbation at the time of the FEV1% measurement).
- **Source aliases:**
  - `HOSPRA` -- used in `Harun_2019_cysticFibrosis.R` (Harun 2019 NMTRAN `$INPUT` column, "hospitalisation at the time of FEV1% predicted measurement; 0=no, 1=yes").
- **Example models:** `Harun_2019_cysticFibrosis.R` (linear-deviation effects on the disease-progression maximum drop and on the half-effect age: `(1 + e_hpe_dmax * HOSPRA)` with coefficient -0.22 on the maximum FEV1% drop and `(1 + e_hpe_t50max * HOSPRA)` with coefficient -0.235 on the age at which 50% of the maximum drop occurs; hospitalised visits accelerate both the magnitude and the onset of FEV1% decline).
- **Notes:** Specific scope because the canonical encoding pools all pulmonary exacerbation-driven hospitalisations into a single binary regardless of severity, duration, or treatment intensity; future CF / chronic-respiratory-disease studies that need to distinguish exacerbation severity (e.g., requiring intravenous antibiotics vs oral) should register a finer-grained canonical. Ratified canonically on 2026-05-08 alongside the Harun 2019 extraction.

## Infectious disease

### LNPC (**canonical for log-transformed admission Plasmodium parasitaemia**)
- **Description:** Natural logarithm of the asexual Plasmodium parasite count (parasites per microlitre of blood) at study admission. Time-fixed per subject (one value per subject, captured at enrolment).
- **Units:** log(parasites/uL)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with linear-deviation forms `(1 + e * (LNPC - ref))`. Reference values observed: 5.88 log(parasites/uL) (Birgersson 2019, population median in the pooled pregnant + non-pregnant Burkina Faso cohort).
- **Source aliases:** none formally; companion column `PARA` (raw asexual parasite count per microlitre) is provided alongside `LNPC` in the Birgersson 2019 NONMEM dataset but the model uses `LNPC = log(PARA)` as the active covariate.
- **Example models:** `Birgersson_2019_artesunate.R` (linear-deviation effect on relative bioavailability `F1`: `F1LNPC = 1 + e_lnpc_f * (LNPC - 5.88)`; positive coefficient `e_lnpc_f = +0.138` per unit increase in log-parasite-count, reflecting increased oral artesunate bioavailability with higher parasite burden).
- **Notes:** Disease-severity covariate specific to malaria PK models. Higher parasitaemia is a marker of more severe acute malaria infection and has been associated in the source publication with altered oral bioavailability of artesunate (presumably via gut-mucosal / first-pass effects of the febrile parasitised state). Scope: specific because the canonical reference value (5.88) is the Birgersson 2019 cohort median; future malaria-in-pregnancy or malaria-in-children PK models may legitimately reuse `LNPC` but should document their own cohort-specific reference value in `covariateData[[LNPC]]$notes`. Distinct from `PARA` (raw parasitaemia in parasites/uL), which is the companion canonical for models that apply the log transform inside `model()` rather than pre-computing it at dataset-assembly time, and that use a time-varying (last-observation-carried-forward) parasitaemia trajectory rather than the admission-only fixed value. Ratified canonically on 2026-05-07.

### PARA (**canonical for raw Plasmodium parasitaemia (parasites/uL)**)
- **Description:** Asexual Plasmodium parasite count per microlitre of blood. May be admission-only (time-fixed) or time-varying with serial counts across follow-up (last-observation-carried-forward is the typical NONMEM idiom in this lab's malaria models). Document time-varying vs time-fixed mode in `covariateData[[PARA]]$notes` per model.
- **Units:** parasites/uL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with log-transformed linear forms `(1 + e * log10(PARA))` or `(1 + e * (log10(PARA) - ref))` when the source paper applies the log transform inside the model. Reference values observed: implicit log10(PARA) = 0 (i.e. PARA = 1 parasite/uL) in Kloprogge 2014 (the linear form anchors F at the typical population estimate when PARA = 1).
- **Source aliases:**
  - `PARA` -- used in `Kloprogge_2014_quinine.R` (raw parasitaemia in parasites/uL, time-varying via last-observation-carried-forward across the 7-day quinine treatment course), in `Kloprogge_2018_lumefantrine.R` (admission-only / time-fixed parasitaemia, centered at the model-building geometric-mean 15,800 parasites/uL = log10 4.2), in `Tarning_2012_dihydroartemisinin.R` (admission-only / time-fixed parasitaemia, centered at the typical-patient log10(PARA) = 3.98 reported in the Table 4 footnote of Tarning 2012 AAC), and in `Hien_2017_cipargamin.R` (admission-only / time-fixed parasitaemia in parasites/uL, used to seed the initial-condition split of the two-population parasite clearance PD model into sensitive and refractory subpools).
- **Example models:** `Kloprogge_2014_quinine.R` (linear effect on relative bioavailability with the log10 transform applied inside `model()`: `F1PARA = 1 + e_para_f * log10(max(PARA, 1))` with `e_para_f = +0.389` per log10 parasitaemia; gated by `max(PARA, 1)` so that PARA values below 1 parasite/uL (effectively zero / below detection) collapse to no covariate effect, matching the source paper's "effect only during the acute phase when parasitaemia was above the limit of detection" wording).
  - `Kloprogge_2014_quinine.R` -- linear effect on relative bioavailability with the log10 transform applied inside `model()`: `F1PARA = 1 + e_para_f * log10(max(PARA, 1))` with `e_para_f = +0.389` per log10 parasitaemia; gated by `max(PARA, 1)` so that PARA values below 1 parasite/uL (effectively zero / below detection) collapse to no covariate effect, matching the source paper's "effect only during the acute phase when parasitaemia was above the limit of detection" wording.
  - `Kloprogge_2018_lumefantrine.R` -- exponential effect on relative bioavailability with the log10 transform applied inside `model()`, centered on log10(15,800) = 4.2: `F1PARA = exp(e_lnpc_f * (log10(max(PARA, 1)) - 4.2))` with `e_lnpc_f = -0.643` per log10 parasitaemia (higher pre-treatment parasitaemia is associated with lower relative bioavailability, consistent with reduced visceral blood flow in more severe disease). Same `max(PARA, 1)` gating convention as Kloprogge 2014.
  - `Tarning_2012_dihydroartemisinin.R` -- linear-deviation effect on relative bioavailability with the log10 transform applied inside `model()`, centered on log10(PARA) = 3.98 (the pooled-cohort median in the Thai-Myanmar-border dihydroartemisinin-piperaquine combination trial): `fpara = 1 + e_para_f * (log10(max(PARA, 1)) - 3.98)` with `e_para_f = +0.278` per log10 unit (Results: '27.8% linear increase per unit logarithmic parasitemia'). Higher pre-treatment parasitaemia is associated with higher relative bioavailability of dihydroartemisinin (Tarning 2012 Discussion: '...consistent with a disease-related decrease in first-pass metabolism possibly compounded by reduced hepatic blood flow...'). Same `max(PARA, 1)` gating convention as the Kloprogge models; admission-only / time-fixed.
  - `Hien_2017_cipargamin.R` -- admission-only / time-fixed parasitaemia in parasites/uL (enrolment inclusion criterion 5,000-50,000 asexual parasites/uL per Hien 2017 Methods 'Patients'). Not used as a bioavailability covariate; instead PARA seeds the parasite ODE compartments at simulation start: `parasite_sensitive(0) = fsen * PARA` and `parasite_refractory(0) = (1 - fsen) * PARA`. The ODE states carry the same parasites/uL units as PARA (no total-burden conversion applied inside the model), matching the Fig 3 individually-predicted-clearance-profile reporting scale.
- **Notes:** Companion to `LNPC` -- both canonicals describe the same biological quantity (asexual Plasmodium parasite count) but differ in the transform location: `LNPC` is pre-transformed to natural log at dataset-assembly time and used as `(LNPC - ref)`, whereas `PARA` is the raw count with the log transform applied inside `model()`. Use `PARA` when the source paper reports the covariate coefficient on the log10 scale (per log10 parasitaemia) and / or when parasitaemia is time-varying. Use `LNPC` when the source dataset pre-computes the natural log at admission. Scope: specific because the gating-at-1 convention and the log10-inside-model() idiom reflect the Mahidol-Oxford malaria popPK lab's specific implementation; a future malaria PK model that uses a different gating threshold (e.g., LOQ-aware, or log base-e inside the model) or a different reference value should document its own convention in `covariateData[[PARA]]$notes`. Ratified canonically on 2026-05-21 alongside the Kloprogge 2014 quinine extraction; extended to Kloprogge 2018 lumefantrine on 2026-05-22 (same log10-inside-model() idiom, distinct centering value: 4.2 = log10(15,800) for Kloprogge 2018 vs 0 = log10(1) for Kloprogge 2014); extended to Tarning 2012 dihydroartemisinin on 2026-06-01 (same log10-inside-model() idiom with the source paper's typical-patient centering value 3.98 = log10(pooled-cohort-median ~ 9,550 parasites/uL)).

### SARS_VLOAD (**canonical for SARS-CoV-2 baseline viral load**)
- **Description:** Baseline (pre-treatment) SARS-CoV-2 viral load measured from nasopharyngeal swab by RT-qPCR, reported as log10 RNA copies/mL.
- **Units:** log10 copies/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(SARS_VLOAD / ref)^exponent`. Reference values observed: 6.4 log10 copies/mL (Lin 2024, median in pooled COVID-19 cohort).
- **Source aliases:**
  - `VIRAL` -- used in `Lin_2024_casirivimab.R`.
- **Example models:** `Lin_2024_casirivimab.R` (small negative exponent -0.0075 on CL).
- **Notes:** SARS-CoV-2-specific. For non-infected subjects, the value is encoded as 0 in the source dataset (below assay detection); the population-PK exponent is small enough that this 0 is absorbed by the reference shift. Register a parallel canonical for any future paper that uses a different infection (e.g., RSV, influenza).

### HEV_VLOAD (**canonical for hepatitis E virus baseline viral load**)
- **Description:** Pre-treatment (per-subject anchor-time) hepatitis E virus (HEV) RNA concentration in plasma, reported by RT-qPCR. Used as a static per-subject covariate that supplies the initial condition for a virion-state ODE in target-cell-limited HEV viral-dynamics models (`virus(0) <- HEV_VLOAD`) and that feeds the steady-state derivation of the infection-rate and virion-production-rate constants (`beta = kloss * rho / HEV_VLOAD`, `p = elim * HEV_VLOAD / rho`) so that all three viral compartments (healthy hepatocytes, infected hepatocytes, virions) start at pre-treatment steady state.
- **Units:** IU/mL (international units per millilitre; the WHO-standardized HEV-RNA reporting unit since the 2011 first WHO international standard for HEV RNA, NIBSC code 6329/10).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- subject-level baseline supplied as a covariate column. Reference values observed: 1.886e6 IU/mL (Mulder 2025 chronic-HEV SOT cohort median; range 527-1.68e8 IU/mL).
- **Source aliases:**
  - `VLBASE` -- used in `Mulder_2025_ribavirin.R`.
- **Example models:** `Mulder_2025_ribavirin.R` (IU/mL; initial condition for the `virus` compartment; used in the baseline steady-state derivations for the infection-rate `beta` and virion-production rate `p`; cohort median 1,886,058 IU/mL).
- **Notes:** HEV-specific (parallel to `SARS_VLOAD` for SARS-CoV-2 and to viral-load canonicals for HCV / HBV / HIV that should be registered separately when first encountered). The IU/mL reporting convention is the WHO-standardized one for HEV; older HEV literature may report viral load in genomic copies/mL with a study-specific copies-to-IU conversion factor (roughly 1 IU ~ 0.3-2.5 copies depending on assay). The Mulder 2025 dataset is chronically infected subjects only (no zero baselines); papers that pool infected and non-infected subjects should encode the non-infected value as the assay LOD and document the convention in `covariateData[[HEV_VLOAD]]$notes`. Specific scope; promote to `general` only if a second paper ratifies the same HEV-RNA-baseline-as-initial-condition pattern.

### SARS_SEROPOS (**canonical for SARS-CoV-2 baseline serostatus positive**)
- **Description:** 1 = SARS-CoV-2 spike or nucleocapsid antibody positive at baseline (prior infection or prior vaccination), 0 = seronegative or other / unknown.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (seronegative; "Other" / unknown serostatus is typically pooled into the reference per the source paper's analysis plan).
- **Source aliases:**
  - `SERPOS` -- used in `Lin_2024_casirivimab.R`.
- **Example models:** `Lin_2024_casirivimab.R` (multiplicative fractional change on CL).
- **Notes:** SARS-CoV-2-specific. The exact assay (anti-spike vs anti-nucleocapsid; vendor) varies by study; document per-model in `covariateData[[SARS_SEROPOS]]$notes`.

### OXYSUP_LOW (**canonical for low-flow supplemental oxygen indicator**)
- **Description:** 1 = subject is receiving low-flow supplemental oxygen at baseline (e.g., nasal cannula, simple face mask), 0 = no supplemental oxygen at baseline.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no supplemental oxygen at baseline; the high-flow / mechanical-ventilation categories are encoded by the parallel `OXYSUP_HIGH` indicator).
- **Source aliases:**
  - `OXYSTAT1` -- used in `Lin_2024_casirivimab.R`.
- **Example models:** `Lin_2024_casirivimab.R` (multiplicative fractional change on CL; +10.6%).
- **Notes:** Decomposed indicator from a 4-level ordered categorical (no oxygen / low-flow / high-flow / mechanical ventilation). Use with the parallel `OXYSUP_HIGH` indicator. Register a separate `OXYSUP_VENT` canonical if a future analysis splits mechanical ventilation from high-flow oxygen.

### OXYSUP_HIGH (**canonical for high-flow supplemental oxygen indicator**)
- **Description:** 1 = subject is receiving high-flow supplemental oxygen at baseline (high-flow nasal cannula, non-rebreather mask, non-invasive positive-pressure ventilation, OR mechanical ventilation pooled into the high-flow category), 0 = otherwise (no supplemental oxygen or low-flow).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no supplemental oxygen at baseline; document whether the source paper pooled mechanical ventilation into this indicator or treated it separately).
- **Source aliases:**
  - `OXYSTAT2` -- used in `Lin_2024_casirivimab.R`.
- **Example models:** `Lin_2024_casirivimab.R` (multiplicative fractional change on CL; +38.0%).
- **Notes:** Companion indicator to `OXYSUP_LOW`. In Lin 2024 the rare mechanical-ventilation cases were pooled into the high-flow indicator (n = 24 across the 7598-subject dataset).

### HIV_POS (**canonical for HIV-positive comorbidity indicator**)
- **Description:** 1 = HIV-1 antibody positive at study entry, 0 = HIV-negative. Time-fixed per subject. Used as a binary comorbidity indicator on PK parameters (typically bioavailability or clearance) when a study population pools HIV-positive and HIV-negative subjects on a non-HIV primary indication (tuberculosis treatment, hepatitis treatment, etc.).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (HIV-negative).
- **Source aliases:**
  - `HIV` -- used in `Jonsson_2011_ethambutol.R` (DDMODEL00000220 NMTRAN `$INPUT` column with values 0 = HIV negative, 1 = HIV positive; same orientation as the canonical).
- **Example models:** `Jonsson_2011_ethambutol.R` (multiplicative `1 + e_hiv_pos_f * HIV_POS` shift on bioavailability; HIV-positive patients exhibit a 15.5% reduction in ethambutol bioavailability versus HIV-negative reference), `Jonsson_2011_ethambutol_ddmore.R` (multiplicative `1 + e_hiv_pos_f * HIV_POS` shift on bioavailability; HIV-positive patients exhibit a 15.4% reduction in ethambutol bioavailability versus HIV-negative reference).
- **Notes:** Parallels the `_POS` suffix convention used by `ADA_POS`, `SARS_SEROPOS`, and other serostatus / antibody-positivity indicators. Distinct from a primary disease-state indicator like `DIS_HIV` (not yet registered) -- `HIV_POS` is a comorbidity flag in non-HIV-primary indications where HIV-vs-non-HIV is tested as a PK covariate. Ratified canonically on 2026-05-06.

### TB_POS (**canonical for active tuberculosis co-infection indicator**)
- **Description:** 1 = active tuberculosis (typically pulmonary) at study entry, 0 = no active TB. Time-fixed per subject. Used as a binary comorbidity indicator on PK or PD parameters (typically albumin secretion, hepatic clearance, bioavailability, or disease-progression rates) when a study population pools TB-positive and TB-negative subjects on a non-TB primary indication (HIV ART, hepatitis treatment, malnutrition, etc.) or when a TB-cohort study pools TB subjects against a separately enrolled non-TB comparator group.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no active TB).
- **Source aliases:**
  - `TB` -- used in `Bisaso_2014_albumin.R` (paper text and Table 1 stratification column with values 0 = HIV only, 1 = HIV + TB co-infection; same orientation as the canonical).
- **Example models:** `Bisaso_2014_albumin.R` (multiplicative additive shift on baseline albumin secretion rate Q0: `Q0 = exp(lq0) * (1 + e_tb_pos_q0 * TB_POS)` with `e_tb_pos_q0 = -0.308`; TB-positive subjects have ~30.8% lower Q0 than the HIV-only reference -- equivalent to the paper text's "44.2% lower" framing relative to the TB-HIV cohort).
- **Notes:** Parallels the `_POS` suffix convention used by `HIV_POS`, `ADA_POS`, `SARS_SEROPOS`, and other serostatus / disease-state indicators. Distinct from any TB-treatment-regimen indicator (e.g. `CONMED_RIF_LPVR4` for concomitant rifampicin) -- `TB_POS` is the active-disease flag; the medication exposure is a separate concept. In Bisaso 2014 all 158 TB-positive subjects were also on rifampicin-based anti-TB therapy, so the two are confounded in that single cohort; the canonical preserves the conceptual distinction for future studies that decouple them. Ratified canonically on 2026-05-20 alongside the Bisaso 2014 albumin extraction.

### HCV_POS (**canonical for HCV coinfection / hepatitis C virus positive indicator**)
- **Description:** 1 = chronic hepatitis C virus (HCV) coinfection at study entry (HCV antibody positive and/or >= 2 positive HCV RNA detections on separate visits at least 3 months apart, with subjects of known spontaneous HCV clearance excluded), 0 = HCV uninfected / HCV cleared. Time-fixed per subject. Used as a binary comorbidity / coinfection indicator on PD parameters (e.g., immune-reconstitution recovery rates) or PK parameters when a study pools HCV-coinfected and HCV-uninfected subjects on a non-HCV primary indication (HIV ART, transplant medicine, IBD biologics, etc.).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (HCV uninfected).
- **Source aliases:**
  - `HCV` -- used in `Majekodunmi_2017_HIV_HCV_CD4_recovery.R` (paper Methods 'Definitions' and Table 2 covariate 'C:Coinf' for HIV/HCV coinfected vs HIV monoinfected; same orientation as the canonical).
- **Example models:** `Majekodunmi_2017_HIV_HCV_CD4_recovery.R` (multiplicative fractional reduction on the CD4 z-score recovery-rate constant c: `c = (c_pop + etac) * (1 + e_hcv_pos_c * HCV_POS)` with `e_hcv_pos_c = -0.77`; HCV-coinfected children recover at 23% of the HIV-monoinfected rate -- 0.357 /year versus 1.55 /year typical).
- **Notes:** Parallels the `_POS` suffix convention used by `HIV_POS`, `TB_POS`, `ADA_POS`, `SARS_SEROPOS`, and other serostatus / disease-state indicators. Distinct from any anti-HCV treatment-regimen indicator (e.g., pegylated interferon + ribavirin in Majekodunmi 2017's coinfected subset of 10 children) and from any HCV-genotype indicator (1/2/3/4 distribution reported in Majekodunmi 2017 Table 1 but not used as a covariate). Distinct from a primary disease-state indicator like `DIS_HCV` (not yet registered) -- `HCV_POS` is the coinfection / comorbidity flag in non-HCV-primary indications. Ratified canonically on 2026-05-22 alongside the Majekodunmi 2017 CD4 recovery extraction.

### EARLY_ART (**canonical for early-vs-delayed antiretroviral-treatment-initiation arm indicator**)
- **Description:** Trial randomization-arm indicator: 1 = subject was randomized to initiate antiretroviral treatment (ART) early (within the first 14 days of admission, before nutritional recovery), 0 = subject was randomized to delayed ART initiation (after nutritional recovery, > 14 days from admission). Time-fixed per subject within the trial. The indicator captures the early-vs-delayed-ART contrast tested in the Archary 2019 / MATCH (Malnutrition and ART Timing in Children with HIV) trial in severely malnourished HIV-infected children; the early-ART arm exhibits ~31% higher abacavir bioavailability than the delayed-ART arm.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (delayed ART initiation; F = 1 typical-value reference).
- **Source aliases:**
  - `EARLY` -- Archary 2019 source description ("randomized to early ART"); same orientation as the canonical (1 = early arm).
- **Example models:** `Archary_2019_abacavir.R` (multiplicative additive shift on bioavailability F: `f(depot) <- (1 + e_earlyart_f * EARLY_ART) * exp(etalfdepot)` with `e_earlyart_f = 0.31`; the early-arm IIV on F is 21.4%, the delayed-arm IIV on F is fixed at 0 in the source -- see model-file vignette Errata).
- **Notes:** Specific scope because the early/delayed cutoff (14 days from admission) and the underlying nutritional-rehabilitation context are tied to the MATCH-trial design; future studies that test a similar early-vs-delayed contrast with a different time cutoff or non-nutritional context should register a new canonical (e.g., `EARLY_ART_28D`). Distinct from `TRT_PHASE` (which gates active-vs-baseline study-phase contributions on a per-record basis) and from `DAY14` (which is a within-subject time-since-treatment-initiation landmark, not an enrolment arm). Ratified canonically on 2026-05-08.

### DIS_TB_XDR (**canonical for (pre-)XDR vs MDR tuberculosis drug-resistance stratum indicator**)
- **Description:** 1 = subject's *Mycobacterium tuberculosis* isolate is classified as pre-extensively-drug-resistant (pre-XDR; resistant to isoniazid + rifampicin plus a second-line fluoroquinolone OR an injectable, but not both) or extensively-drug-resistant (XDR; resistant to isoniazid + rifampicin plus a second-line fluoroquinolone AND an injectable); 0 = multidrug-resistant (MDR; resistant to isoniazid + rifampicin only), drug-susceptible, or unclassified TB. Time-fixed per subject (one drug-susceptibility profile per subject from baseline sputum culture). The dichotomisation pools pre-XDR with XDR into the "1" stratum because Svensson 2017 found these two categories share the same slower-clearance bacterial-load phenotype (28.1% longer half-life of mycobacterial load decline relative to MDR).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (MDR / susceptible / missing-treated-as-MDR; the typical-value reference). Per Svensson 2017 Results paragraph 4, the 19% of subjects with missing TB-type information were assigned to the reference (MDR) group after testing that the missing group did not differ significantly from MDR.
- **Source aliases:** none formally; the Svensson 2017 source describes the variable in prose as the "(pre-)XDR" stratum, operationally encoded as 1 when TB type = pre-XDR or XDR and 0 otherwise (MDR + susceptible + missing).
- **Example models:** `Svensson_2017_bedaquiline.R` (multiplicative `(1 + e_xdr_hl * DIS_TB_XDR)` shift on the half-life of mycobacterial load decline; pre-XDR/XDR patients have a 28.1% longer MBL half-life than MDR/susceptible patients, translating to 2-4 weeks longer median time-to-sputum-culture-conversion per Svensson 2017 Table 3).
- **Notes:** Specific scope because the dichotomisation is tied to the Svensson 2017 multidrug-resistant tuberculosis cohort (TMC207-C208 Phase IIb registration trial) and the pre-XDR + XDR pooling convention may not transfer to studies that report a different TB-resistance categorisation (e.g., separate XDR vs pre-XDR stratification, or rifampicin-monoresistant TB as a distinct category). Future TB drug-resistance models should consider sibling canonicals (e.g., `DIS_TB_XDR_STRICT` for XDR-only, `DIS_TB_RIFRES` for rifampicin-monoresistant) rather than overloading this name; the `DIS_TB_XDR_STRICT` sibling anticipated here was subsequently ratified for `Lin_2024_TB_multistate.R`, which places pre-XDR in the reference group. Distinct from `HIV_POS` (a comorbidity indicator on TB-coinfected patients, not a drug-resistance stratum). Ratified canonically on 2026-05-21 alongside the Svensson 2017 bedaquiline extraction.

### DIS_TB_XDR_STRICT (**canonical for XDR-only tuberculosis drug-resistance stratum indicator**)
- **Description:** 1 = subject's *Mycobacterium tuberculosis* isolate is classified as extensively-drug-resistant (XDR; resistant to isoniazid + rifampicin plus a second-line fluoroquinolone AND a second-line injectable); 0 = every other resistance stratum, including pre-XDR (resistant to isoniazid + rifampicin plus a fluoroquinolone OR an injectable, but not both), MDR, drug-susceptible and unclassified TB. Time-fixed per subject (one drug-susceptibility profile per subject from baseline sputum culture). Pre-2021 WHO drug-resistance definitions.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-XDR: pre-XDR + MDR + drug-susceptible + missing-treated-as-MDR).
- **Source aliases:**
  - `XDR` (Lin 2024 NONMEM control-stream variable, derived as `XDR = 0; IF(TBTYPE.EQ.4) XDR = 1` from a four-level `TBTYPE` column) -- used in `Lin_2024_TB_multistate.R`.
- **Example models:** `Lin_2024_TB_multistate.R` (log-hazard effect on the surge amplitude of the sputum-culture-conversion hazard, `SA12 = exp(lsa12 + ... + e_xdr_sa12 * DIS_TB_XDR_STRICT)` with `e_xdr_sa12 = -0.622792`; `exp(-0.622792) = 0.536` reproduces the paper's reported "lambda12 decreased by 46% (95% CI, 24%-62%)" for XDR patients).
- **Notes:** The *strict* sibling of `DIS_TB_XDR`, and the two are NOT interchangeable: `DIS_TB_XDR` pools pre-XDR with XDR into the "1" stratum, whereas `DIS_TB_XDR_STRICT` places pre-XDR in the reference group. Choosing the wrong one misclassifies the pre-XDR patients, who are a substantial fraction of a typical drug-resistant TB cohort (95 of 402, 28%, in Lin 2024 Table 1). Read the source paper's own contrast wording before picking: Lin 2024 states the effect for XDR-TB patients "compared with those with non-XDR-TB", while Svensson 2017 reports a pooled "(pre-)XDR" effect. Specific scope because the dichotomisation depends on which resistance categories the source study enumerated. Distinct from `HIV_POS` (a comorbidity indicator on TB-coinfected patients, not a drug-resistance stratum). The sibling name was anticipated in the `DIS_TB_XDR` Notes at its 2026-05-21 ratification and is realised here for the Lin 2024 multistate extraction; patients with a missing resistance profile (~16% in Lin 2024) were assigned to the MDR reference group after the source tested that they did not differ significantly from MDR.

### TTP_MGIT_BASE (**canonical for baseline mean time-to-positivity in mycobacterial growth indicator tube**)
- **Description:** Subject-specific baseline (pre-treatment) mean time-to-positivity (TTP) in the mycobacterial growth indicator tube (MGIT) liquid-culture system, expressed in days. Computed in the source paper as the mean of three replicate spot-sputum-sample TTP values collected the day before the start of treatment. The MGIT system automatically records the time in hours from inoculation to a positive signal indicating presence of *M. tuberculosis* (cap at 42 days = negative); the per-subject baseline TTP is then used as a covariate driving the model's starting mycobacterial load (a longer baseline TTP corresponds to fewer viable bacteria in the inoculum and a lower starting MBL). Time-fixed per subject in the Svensson 2017 source.
- **Units:** days
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used in power form `(TTP_MGIT_BASE / ref)^exponent`. Reference value observed: 6.8 days (Svensson 2017 Table 1 cohort median, n = 191).
- **Source aliases:**
  - `mTTP0` (Svensson 2017 paper symbol, also written `mTTP_{0,i}`) -- used in `Svensson_2017_bedaquiline.R`.
- **Example models:** `Svensson_2017_bedaquiline.R` (power-form covariate on the starting mycobacterial load `mbl0_i = exp(lmbl0) * (TTP_MGIT_BASE / 6.8)^e_ttp_mbl0` with `e_ttp_mbl0 = -3.69`; a four-fold longer median TSCC is predicted in patients with the lowest baseline TTP versus the highest, per Svensson 2017 Discussion paragraph 3), `Lin_2024_TB_multistate.R` (exponential-deviation covariate shared between the peak time and the surge width of the sputum-culture-conversion hazard, `PT12 = exp(lpt12 - e_ttp_pt12sw12 * (TTP_MGIT_BASE - 9.06944)/7)` and `SW12 = exp(lsw12 + e_ttp_pt12sw12 * (TTP_MGIT_BASE - 9.06944)/7)` with `e_ttp_pt12sw12 = 0.442668`; a longer baseline TTP moves the conversion peak earlier and widens the surge; cohort-specific reference 9.06944 days for the pooled C208 + C209 cohort, n = 402, and the divisor 7 converts the day deviation to weeks so the coefficient is per week of baseline TTP).
- **Notes:** Specific scope because the cohort-median reference value (6.8 days) is tied to the Svensson 2017 cohort (TMC207-C208 Phase IIb registration trial); future MGIT-based mycobacterial-load models should reuse this canonical but document their own cohort-specific reference in `covariateData[[TTP_MGIT_BASE]]$notes` (as `Lin_2024_TB_multistate.R` does for its 9.06944-day pooled-cohort median). Source columns are frequently recorded in HOURS by the MGIT instrument (Lin 2024's `MTTP` column is centered at 217.6667 h = 9.06944 days); always convert to days when populating this canonical. Distinct from `LNPC` (log-transformed admission Plasmodium parasitaemia, a different pathogen-quantification method for malaria) and from `SARS_VLOAD` (SARS-CoV-2 RT-qPCR log10 copies/mL, a different infectious-disease baseline biomarker). MGIT samples without a positive signal within 42 days are classified as negative in the source paper; for the per-subject baseline mean, only the positive triplicate values are averaged (a subject with all-negative baseline samples has missing TTP_MGIT_BASE). Ratified canonically on 2026-05-21 alongside the Svensson 2017 bedaquiline extraction.

### MBL_HL_WK2 (**canonical for model-derived half-life of mycobacterial-load decline through week 2**)
- **Description:** Per-subject half-life of the decline of the model-derived mycobacterial load (MBL) over the first 2 weeks of anti-tuberculosis treatment, expressed in weeks. A *model-derived* covariate: it is not measured directly but is a secondary metric read out of an upstream longitudinal mycobacterial-load PK-PD model, which integrates baseline bacterial burden, drug-resistance profile, prior anti-TB treatment and individual drug exposure over time into a single early-bactericidal-activity summary. A shorter half-life means faster bacterial clearance. Time-fixed per subject in the models that use it: the source computes the metric prospectively from data up to week 2 only (to avoid immortal-time bias) and carries the week-2 value forward thereafter, so a single scalar per subject reproduces the model exactly.
- **Units:** week
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as an exponential deviation `exp(beta * (MBL_HL_WK2 - ref))`. Reference value observed: 0.69443 weeks (Lin 2024 cohort median, hardcoded in the source NONMEM control stream and confirmed by Lin 2024 Figure 3, whose reference individual has "0.69 weeks of half-life of bacterial clearance at Week 2"). Lin 2024 Figure 6 reports the 5th and 95th percentiles as 0.33 weeks (high clearance) and 1.1 weeks (low clearance).
- **Source aliases:**
  - `HL2` (Lin 2024 paper symbol and data column; the subscript denotes "up to week 2") -- used in `Lin_2024_TB_multistate.R`.
- **Example models:** `Lin_2024_TB_multistate.R` (enters BOTH surge parameters of the sputum-culture-conversion hazard through a single shared coefficient of opposite sign, `SA12 = exp(lsa12 + e_hl2_sa12pt12 * (MBL_HL_WK2 - 0.69443) + ...)` and `PT12 = exp(lpt12 - ... - e_hl2_sa12pt12 * (MBL_HL_WK2 - 0.69443))` with `e_hl2_sa12pt12 = -0.686145`; because the coefficient is negative, a shorter half-life simultaneously raises the surge amplitude and moves the peak earlier).
- **Notes:** Must be strictly positive. The intended source is the half-life of the mycobacterial-load state of `modellib('Svensson_2017_bedaquiline')`, whose `hl_i` is already in weeks -- no conversion is needed; supplying it from any other longitudinal bacterial-load model is acceptable provided the half-life is expressed in weeks and evaluated at week 2. Paired with `MBL_END`, the end-of-treatment load from the same upstream model; the two share the `MBL_` prefix precisely because they are different readouts of one underlying mycobacterial-load trajectory, and the family extends naturally (`MBL_BASE`, `MBL_HL_WK8`, ...). Distinct from `TTP_MGIT_BASE`, which is a directly measured *baseline* culture-growth time rather than a model-derived *rate* of on-treatment decline; the two are complementary and Lin 2024 retains both. Specific scope because the cohort-median reference value is tied to the pooled TMC207-C208 + TMC207-C209 cohort; models reusing this canonical should document their own reference in `covariateData[[MBL_HL_WK2]]$notes`.

### MBL_END (**canonical for model-derived mycobacterial load at the end of treatment**)
- **Description:** Per-subject model-derived mycobacterial load (MBL) at the end of the investigational anti-tuberculosis treatment period, on the natural (untransformed) scale, in number of bacteria per sample inoculum. A *model-derived* covariate read out of an upstream longitudinal mycobacterial-load PK-PD model; the source describes it as "a composite of disease severity at baseline and on-treatment response throughout the whole treatment period", because the upstream model integrates baseline burden, resistance profile and the full longitudinal individual drug exposure. Time-fixed per subject.
- **Units:** n bacteria per sample inoculum
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as a natural-log deviation `exp(beta * (log(MBL_END) - log(ref)))`. Reference value observed: 5.5726e-05 bacteria per inoculum (Lin 2024 cohort median, hardcoded in the source NONMEM control stream as `LOG(0.000055726)`); `log10(5.5726e-05) = -4.25`, matching the "-4.3 log10(MMBLend)" reference individual of Lin 2024 Figure 3.
- **Source aliases:**
  - `MMBLend` (Lin 2024 paper symbol and data column; "model-derived Mycobacterial Load at end of treatment") -- used in `Lin_2024_TB_multistate.R`.
- **Example models:** `Lin_2024_TB_multistate.R` (log-linear effect on the recurrence hazard, gated to act only after week 26: `lambda23 = exp(llambda23 + e_sexf_lambda23 * SEXF + e_mblend_lambda23 * (t > 26) * (log(MBL_END) - log(5.5726e-05)))` with `e_mblend_lambda23 = 0.0371081`).
- **Notes:** **Store on the NATURAL scale and let `model()` take the log** -- the coefficient is per unit of *natural* log, so a per-log10-unit hazard ratio is `exp(beta * ln(10))` (for Lin 2024, `exp(0.0371081 * ln(10)) = 1.089`). Confusing the natural-log and log10 conventions is the obvious failure mode here because the source paper's figures are labelled in `log10(MMBLend)` while the control stream uses `LOG()` (natural log). Must be **strictly positive**: `log(0)` is undefined and would propagate `NaN` through the entire ODE solve even at times before the gate opens, since `0 * -Inf` is `NaN`. Units match the mycobacterial-load state of `modellib('Svensson_2017_bedaquiline')` (n bacteria per sample inoculum, typical starting `MBL_0 = 2.14e3`), so that model's `mbl` state at the end of treatment can be supplied directly. Paired with `MBL_HL_WK2` (see that entry for the shared `MBL_` family rationale). Specific scope because both the cohort-median reference and the "end of treatment" timepoint (24 weeks in Lin 2024) are study-design-dependent; models reusing this canonical should document their own reference value and end-of-treatment definition in `covariateData[[MBL_END]]$notes`.

### CD19_ABS (**canonical for absolute CD19+ B-lymphocyte count**)
- **Description:** Absolute peripheral-blood CD19+ B-lymphocyte count. Baseline or time-varying; document the time resolution per model via `covariateData[[CD19_ABS]]$notes`. Used as a continuous target-antigen-burden / B-cell-mediated-clearance biomarker in anti-CD20 (rituximab, ofatumumab, obinutuzumab) and other B-cell-targeted antibody popPK models, where the therapeutic mAb elimination increases with the size of the on-target B-cell pool via target-mediated drug disposition.
- **Units:** cells/mm^3 (equivalently cells/uL; the two labels are numerically equivalent).
- **Type:** continuous
- **Scope:** general -- the absolute CD19+ B-cell count is the standard flow-cytometry biomarker of circulating B-cell burden and is expected to reuse across anti-CD20 / anti-CD79b / anti-CD22 popPK extractions where the source paper carries the count as an exogenous exposure covariate rather than as a modelled PD state.
- **Reference category:** n/a -- enters as a centred power term `(CD19_ABS / ref)^exponent` against a population reference. Document the form and reference value in each model's `covariateData[[CD19_ABS]]$notes`.
- **Source aliases:**
  - `CD19` -- bare-name source-paper column; common in clinical-PK papers (Lioger 2017 prints "CD19+ count" throughout Methods, Results, and Table 2).
  - `CD19_COUNT` -- explicit-suffix variant.
- **Example models:** `Lioger_2017_rituximab.R` (time-varying pre-course CD19+ count as a covariate on the elimination rate constant `k10`; power exponent 0.035, reference 100 cells/mm^3; sign is positive because higher CD19+ counts correlate with faster rituximab elimination via target-mediated drug disposition).
- **Notes:** Distinct from `BLBCELL` (baseline-only CD19+ B-cell count used as a *baseline* covariate in ofatumumab / polatuzumab vedotin PK-PD models, where the count is measured prior to first dose only) -- `CD19_ABS` is the general canonical for CD19+ counts that are carried as time-varying exogenous covariates measured at multiple visits. `BLBCELL`'s notes ("Distinct from a *time-varying* B cell count, which is the PD response variable rather than a covariate") reflected the earlier register where the only registered anti-CD20 CD19+ use case was baseline; `CD19_ABS` fills the exogenous time-varying covariate slot (analogous to how `CD4_ABS` covers both baseline and time-varying CD4+ counts). Distinct from a *modelled* time-varying B-cell state, which is a PD response variable and would be encoded as an ODE state, not a covariate column. Ratified canonically on 2026-07-09 alongside the Lioger 2017 rituximab extraction.

### CD4_ABS (**canonical for absolute CD4+ T-lymphocyte count**)
- **Description:** Absolute peripheral-blood CD4+ T-lymphocyte count. Baseline or time-varying; document the time resolution per model via `covariateData[[CD4_ABS]]$notes`. Used as a continuous disease-severity / immunosuppression-depth biomarker in HIV / AIDS popPK models and as a covariate on clearance for drugs whose disposition is altered by advanced HIV immunosuppression.
- **Units:** cells/mm^3 (equivalently cells/uL; the two labels are numerically equivalent).
- **Type:** continuous
- **Scope:** general -- the absolute CD4 count is a textbook HIV-severity covariate that future popPK extractions in HIV / AIDS cohorts are expected to reuse.
- **Reference category:** n/a -- enters either as a raw additive linear slope (McLachlan 1996, `CL = exp(lcl) + e_crcl_cl * CRCL + e_cd4_abs_cl * CD4_ABS`; coefficient in L/h per cell/mm^3) or as a centred linear / power term `(CD4_ABS / ref)^exponent` against a population reference (typical reference 50 or 200 cells/mm^3 in HIV cohorts). Document the form and reference value in each model's `covariateData[[CD4_ABS]]$notes`.
- **Source aliases:**
  - `CD4` -- bare-name source-paper column; common in clinical-PK papers (McLachlan 1996 prints "CD4 cell count" in the abstract and "CD4+ T-lymphocyte count" in the Methods).
  - `CD4_COUNT` -- explicit-suffix variant.
  - `CD4_T_COUNT` -- verbatim "CD4+ T-lymphocyte count" form used in some clinical-PK papers.
- **Example models:** `McLachlan_1996_fluconazole.R` (raw additive linear slope on the CL covariate model: 0.00068 L/h per cell/mm^3; cohort mean 69 cells/mm^3 with 97 of 109 covariate-evaluable subjects below 200 cells/mm^3 per Table 3; entered as `e_cd4_abs_cl * CD4_ABS` in the additive intercept-plus-slopes CL regression alongside `e_crcl_cl * CRCL`).
- **Notes:** Distinct from the CD4 z-score family (e.g., the age-standardised z-score modelled by `Majekodunmi_2017_HIV_HCV_CD4_recovery.R`), which is a paediatric-recovery standardised metric on the natural-z-score scale and is not directly interchangeable with the absolute count. Distinct from CD4 percentage (CD4_PCT, the proportion of total lymphocytes that are CD4+; commonly reported in paediatric HIV studies and not yet registered) -- register CD4_PCT separately if a future paper retains it. Distinct from `HIV_POS` (the binary serostatus indicator), which captures HIV-positive-vs-negative rather than the depth of immunosuppression within an HIV-positive cohort. The two can be paired in a single model when an HIV-positive subset is further stratified by CD4 count, but most HIV-popPK papers focus on either the comorbidity flag (mixed HIV+/HIV- cohort) or the absolute count (HIV+-only cohort), not both. Ratified canonically on 2026-06-10 alongside the McLachlan 1996 fluconazole extraction.


### DIS_ANTHRAX (**canonical for active inhalational-anthrax infection indicator**)
- **Description:** 1 = the subject has active inhalational anthrax (in the founding example, following aerosol challenge with a target 200 LD50 of *Bacillus anthracis* Ames-strain spores); 0 = healthy / unexposed. Time-fixed per subject in challenge-model designs, where an animal is allocated to either the challenged or the unexposed arm for the whole study. Distinguishes the infected cohort from the healthy cohort when a population PK model is fit simultaneously to both in order to estimate a disease effect on disposition.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general -- a disease-state indicator in the established `DIS_<condition>` family; anthrax medical countermeasures developed under the FDA Animal Rule (obiltoxaximab, raxibacumab, anthrax immune globulin) are a recurring extraction class.
- **Reference category:** 0 (healthy / unexposed).
- **Source aliases:**
  - `INFECTED` -- used in `Nagy_2017_obiltoxaximab.R` (Nagy 2017 Results: "TMDD in infected NZW rabbits and cynomolgus macaques was approximated via parallel nonlinear elimination for infected animals only").
- **Example models:** `Nagy_2017_obiltoxaximab.R` (switches on a parallel Michaelis-Menten elimination arm approximating protective-antigen target-mediated drug disposition: `vmax <- DIS_ANTHRAX * vmaxRef * (WT / wtRefVmax)^e_wt_cl`).
- **Notes:** In anthrax monoclonal-antibody models the biological content of this indicator is the presence of the drug's binding target: protective antigen (PA) circulates only during active infection, so target-mediated disposition exists only when `DIS_ANTHRAX = 1`. That is why the founding paper's *human* population PK model -- fit to healthy volunteers only -- has no fitted nonlinear arm, and why setting `DIS_ANTHRAX = 1` for a human subject invokes a component transferred from the macaque rather than one estimated in humans. Distinct from `DIS_INFECT_CSSSI_SEV` (a severity grade for complicated skin and skin-structure infection) and from the pathogen-load covariate `BACT_PTT_LOG10CFU`, which quantifies *how advanced* the infection is rather than whether it is present; the two are complementary and a model may carry both. Ratified canonically alongside the Nagy 2017 obiltoxaximab extraction.

### BACT_PTT_LOG10CFU (**canonical for prior-to-treatment quantitative bacteremia on the log10 CFU/mL scale**)
- **Description:** Quantitative blood bacterial burden measured immediately prior to the start of treatment ("prior-to-treatment", PTT), expressed as log10 colony-forming units per mL of blood. Per-subject scalar captured at the treatment-trigger time point. Serves as the disease-severity covariate in trigger-to-treat infection models, where treatment is initiated on a physiological trigger rather than at a fixed time and the bacterial burden at that moment is the dominant predictor of outcome.
- **Units:** log10 CFU/mL
- **Type:** continuous
- **Scope:** general -- prior-to-treatment bacteremia is the standard severity covariate across bacterial-challenge medical-countermeasure programmes, not specific to anthrax.
- **Reference category:** n/a -- enters on its natural log10 scale. In the founding example it appears twice in the same model: as an exponentiated penalty on the cure fraction, `logit(psurv) = theta0 - (theta1 * BACT_PTT_LOG10CFU)^theta2 + Emax * dose / (ED50 + dose)`, and as a log-linear effect on the Weibull death rate, `log(lambda) = lambda0 + lambda1 * BACT_PTT_LOG10CFU`. A value of 0 denotes no detectable PTT bacteremia and makes the penalty term vanish.
- **Source aliases:**
  - `PTT` -- used in `Nagy_2017_obiltoxaximab_survival.R` (Nagy 2017 Results, "Animal survival modeling"; the paper writes the covariate as "log10(PTT bacteremia)" and "PTT quantitative bacteremia").
- **Example models:** `Nagy_2017_obiltoxaximab_survival.R` (Weibull cure-rate survival model; Supplementary Figure S2 stratifies the observed and predicted dose-response into quartiles of this covariate -- [BLQ, 3.02], [3.03, 3.95], [3.96, 4.87], (4.87, 8.56] log10 CFU)..
- **Notes:** Values below the assay's limit of quantification are reported by the founding paper as "BLQ" and pooled into the lowest quartile; a model consuming this covariate must decide how to impute BLQ records and document the choice in `covariateData[[BACT_PTT_LOG10CFU]]$notes` (the founding vignette uses the quartile midpoint when reproducing the published figure). The `_PTT` token marks the measurement *timing* (at treatment trigger), which matters because bacteremia rises steeply during untreated infection -- a post-treatment or peak bacteremia value is a different covariate and should be registered separately (e.g. `BACT_PEAK_LOG10CFU`) rather than folded into this name. The `_LOG10CFU` token marks the scale explicitly so that a raw-CFU column is never silently substituted; register `BACT_PTT_CFU` if a paper models the untransformed count. Distinct from `LNPC` (natural-log Plasmodium parasitaemia per microlitre), which is the malaria analogue on a different log base, a different organism and a different volume unit. Ratified canonically alongside the Nagy 2017 obiltoxaximab extraction.


## Oncology

### RCFB1MAX (**canonical for week-1 maximum across-lesion relative change from baseline in SUVmax**)
- **Description:** Per-subject scalar predictor entering the overall-survival hazard in the Schindler 2016 sunitinib joint SUVmax / SLD / OS-TTE / dropout model. Defined as the maximum (across the up-to-five tracked target lesions) of `(SUVmax(t = 168 h) - SUVmax(0)) / SUVmax(0)`, i.e., the most negative (largest reduction) relative change in `[18F]FDG-PET` standardized uptake value at one week of sunitinib therapy. The OS Weibull hazard is `lambh * alphh * t^(alphh - 1) * exp(theta_pred * RCFB1MAX)` with `alphh = 1` (degenerates to constant baseline hazard).
- **Units:** unitless (relative change; typically negative for responders).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- entered directly into the OS hazard exponent. Sign convention: more negative `RCFB1MAX` (greater week-1 SUVmax suppression) reduces the OS hazard (Schindler 2016 reports a positive theta_pred = 5.36, so `exp(5.36 * RCFB1MAX)` is < 1 for negative RCFB1MAX).
- **Source aliases:**
  - `RCFB1MAX` (Schindler 2016 NONMEM `$ERROR` block intermediate; "max relative change in SUVmax from baseline at week 1 across lesions"). Computed inline in the source `.mod` from the on-the-fly SUVmax states at TIME = 168 h and reused in subsequent records.
- **Example models:** `Schindler_2016_sunitinib.R` (DDMODEL00000221).
- **Notes:** Specific scope because the metric is tied to the Schindler 2016 GIST-on-sunitinib joint biomarker / OS analysis. In the source NONMEM `.mod` `RCFB1MAX` is a record-loop state, not a true subject-level covariate -- it is captured at FLAG = 1 / TIME = 168 h from the running SUVmax compartment values and reused on every subsequent record. nlmixr2 / rxode2 do not have an idiomatic equivalent of NONMEM's record-loop persistent state, so the model file consumes `RCFB1MAX` as a per-subject input covariate; reproducing the source's behavior requires a two-stage simulation (run the SUVmax + SLD ODEs first, compute `RCFB1MAX` per subject from the t = 168 h SUVmax values, then run the OS / dropout TTE arms with `RCFB1MAX` bound). The vignette virtual cohort follows this pattern.

### PFS_EVENT (**canonical for progression-free-survival event indicator observed during follow-up**)
- **Description:** Binary indicator of a documented disease-progression event observed during the trial follow-up window (1 = the patient experienced a progression event during follow-up per the study's response-assessment criteria; 0 = no progression event observed through end of follow-up). Time-fixed per subject in a post-hoc analysis (the indicator is assigned once follow-up is complete). Used by Rozman 2017 as a stratifying covariate on the target-mediated CL decay-rate parameter kdes of rituximab -- patients who later experienced progression had 82.2% lower kdes (slower decay of the time-varying CL arm), interpreted mechanistically as sustained CD20 target burden in poor responders. Standard oncology-trial terminology derived from progression-free-survival endpoints (PFS event = first documented disease progression or death from any cause during follow-up).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no progression event observed during follow-up).
- **Source aliases:**
  - Rozman 2017 uses the prose descriptor "disease progression" / "with disease progression" / "no disease progression" throughout Methods / Results / Table 2; the paper does not disclose the NONMEM `$INPUT` column name. The canonical `PFS_EVENT` follows the widely used oncology-trial convention.
- **Example models:** `Rozman_2017_rituximab.R` (multiplicative effect on the time-varying CL decay-rate `kdes`: `kdes = exp(lkdes + e_pfs_event_kdes * PFS_EVENT + etalkdes)` with e_pfs_event_kdes = log(1 - 0.822) = log(0.178), reproducing the paper's 82.2% reduction from 0.143/day in non-progressors to 0.0254/day in progressors per Rozman 2017 Table 2).
- **Notes:** This is an outcome-derived covariate: the indicator is assigned from longitudinal response-assessment data (typically CT / PET imaging plus clinical follow-up per the study's response criteria) and then used post-hoc as a stratifying covariate on a PK parameter. That is unusual but is a legitimate published modeling choice; models using `PFS_EVENT` should document the post-hoc nature and the associated selection-bias caveat in the vignette Assumptions and deviations section. Time-fixed by construction. If a downstream paper treats progression as a time-varying Markov state (progression-free at time t vs. progressed by time t), register a distinct time-varying canonical rather than reusing `PFS_EVENT`. Distinct from `LMET` (baseline liver metastases -- a baseline burden indicator, not a follow-up outcome) and from `TUMSZ` / `TUM_SLD` (baseline continuous tumor-size measures).

### TUMSZ (**canonical for baseline tumor size**)
- **Description:** Baseline tumor size. For solid tumors, the sum of diameters of target lesions per RECIST; for classical Hodgkin lymphoma and lymphoma generally, the sum of products of perpendicular diameters (SPPD) or the sum of linear diameters of target lesions, depending on the source paper.
- **Units:** mm (for linear-diameter constructs); mm^2 (for SPPD constructs; record the per-model convention in `covariateData[[TUMSZ]]$units` and `notes`).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(TUMSZ / ref)^exponent` for continuous effects, or with a paper-specific threshold for categorical-stratum indicators (e.g., Gibiansky 2014 splits BSIZ at 1750 mm^2 into low- vs high-burden strata). Reference values observed: 41 mm (Zhou 2025); 63 mm (Budha 2023); 90 mm (Lu 2014, source reference 9 cm converted to mm); 1750 mm^2 threshold (Gibiansky 2014, SPPD).
- **Source aliases:**
  - `LDIAM` (Zhou 2025; pediatric lymphoma "linear diameter" of target lesions in mm).
  - `TMBD` (originally in cm; `TUMSZ_mm = TMBD_cm * 10`) -- used in `Lu_2014_trastuzumabemtansine.R`.
  - `BSIZ` (Gibiansky 2014; baseline tumor size as the sum of products of perpendicular diameters of target lesions, mm^2; used as the categorical indicator `(BSIZ <= 1750)` in the obinutuzumab popPK model rather than as a continuous power covariate).
- **Example models:** `Budha_2023_tislelizumab.R` (reference 63 mm), `Lu_2014_trastuzumabemtansine.R` (reference 90 mm; source column TMBD in cm, values converted to mm on ingestion), `Zhou_2025_brentuximab.R` (reference 41 mm; source column LDIAM is the sum of linear diameters of target lesions; effect on ADC clearance only), `Gibiansky_2014_obinutuzumab.R` (SPPD in mm^2; used as a categorical indicator `(TUMSZ <= 1750)` on the time-dependent clearance decay rate kdes, not as a continuous power covariate), `Hansson_2013b_sunitinib.R` (DDMODEL00000198; observed baseline tumor SLD used as the per-subject IC of the tumor-size ODE via the IPP-style proportional baseline-residual construction `tumor(0) = TUMSZ * (1 + etaibase * propSd)`; the source .mod reads OBASE from DV at TIME=0/FLAG=4, but nlmixr2 / rxode2 cannot replicate the in-record assignment idiom and consumes the observed baseline as a covariate instead).
- **Notes:** Promoted to scope: general on 2026-04-20 as a conventional oncology baseline-tumor-size measure (RECIST for solid tumors, SPPD or sum-of-linear-diameters for lymphomas). The SPPD vs sum-of-diameters vs sum-of-linear-diameters convention is pooled onto a single column; document the per-model mixture where relevant. When the source paper reports tumor size in cm, convert to mm (the canonical unit) on data ingestion and scale the per-model reference accordingly so `(TUMSZ / ref)^exp` is numerically invariant. For SPPD constructs the natural unit is mm^2 (a product of two perpendicular diameters in mm); record that in the per-model `covariateData[[TUMSZ]]$units` field and do NOT cross-mix mm and mm^2 within a single ingest. When a source paper specifically reports the RECIST 1.1 "sum of longest diameters" of target lesions, use the more specific `TUM_SLD` canonical instead -- `TUMSZ` remains the pooled-tumor-burden register.

### TUM_SLD (**canonical for sum of longest diameters of target lesions**)
- **Description:** Baseline sum of longest diameters of target lesions per RECIST 1.1. More specific than the pooled `TUMSZ` canonical; use `TUM_SLD` when the source paper explicitly reports "sum of longest diameters" (or "sum of lesions") as the tumor-burden metric, distinct from the pooled "sum of diameters / SPPD / sum of linear diameters" mixture covered by `TUMSZ`.
- **Units:** mm
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(TUM_SLD / ref)^exponent`. Reference values observed: 70.0 mm (de Vries Schultink 2020 zenocutuzumab population median).
- **Source aliases:**
  - `SoL` / "sum of lesions" (de Vries Schultink 2020 zenocutuzumab) -- same construct, mm.
- **Example models:** `deVriesSchultink_2020_zenocutuzumab.R` (reference 70.0 mm; power exponent 0.447 on Vmax of the parallel non-linear / Michaelis-Menten clearance).
- **Notes:** Distinct from `TUMSZ` (pooled tumor-size canonical covering RECIST sum-of-diameters / SPPD / sum-of-linear-diameters); `TUM_SLD` is the precise RECIST 1.1 sum-of-longest-diameters metric. Ratified canonically on 2026-04-29 alongside the pilot bispecific extraction (de Vries Schultink 2020 zenocutuzumab). When the source paper reports tumor burden in cm, convert to mm (the canonical unit) on data ingestion and scale the per-model reference accordingly so `(TUM_SLD / ref)^exp` is numerically invariant. Also used as the per-subject initial-condition input for tumour-growth / angiogenesis-inhibition (TGI) ODE models where the source paper sets the SLD state at time zero from the observed baseline SLD rather than estimating a typical-value baseline (e.g., `Ouerdani_2015_pazopanib.R` uses `tumorSize(0) <- TUM_SLD`).

### TUM_VOL (**canonical for volumetric tumor burden**)
- **Description:** Tumor burden expressed as a volume, pooling every measurement modality that yields one. Two established sources: (a) baseline tumor volume measured by handheld caliper in subcutaneous-xenograft preclinical studies and similar small-animal models, computed from longest length and orthogonal width via `volume = (length * width^2) / 2` (the standard ellipsoid-approximation formula used in xenograft pharmacology); and (b) clinical total tumor volume delineated on a tomographic image (CT, or the CT component of a SPECT/CT or PET/CT acquisition) and summed over all lesions. Used both as a covariate stratifier (size at randomisation, or tumor burden as a covariate on an uptake parameter) and as the per-subject initial-condition input for TGI ODE models where the source paper sets the tumor-volume state at time zero from the observed measurement rather than estimating a typical-value baseline. Usually a per-subject baseline held constant, but may be time-varying when the source re-measures it per treatment cycle.
- **Units:** `mm^3`
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- typically used directly as the ODE initial state (`tumorSize(0) <- TUM_VOL`); not a covariate effect coefficient.
- **Source aliases:**
  - `P0` (Ouerdani 2015 mouse model symbol for the observed initial tumour volume; mm^3 in the CAKI-2 xenograft cohort; range 100-250 mm^3 at randomisation per the paper's preclinical Methods).
  - `total tumor volume` (Siebinga 2023 clinical CT-delineated volume summed over all PSMA-positive lesions; reported in mL, so multiply by 1000 to reach the canonical mm^3).
- **Example models:** `Ouerdani_2015_pazopanib_mouse.R` (Ouerdani 2015 preclinical TGI in CAKI-2 xenograft mice; per-subject `TUM_VOL` initialises the `tumorSize` state and is held constant per individual across the 24-day dosing window), `Siebinga_2023_lu177psma617.R` (clinical CT-delineated total tumor volume as a power covariate on the tumor uptake rate constant, `(TUM_VOL / 1730)^0.705`; re-measured before each treatment cycle).
- **Notes:** General scope because tumor-volume measurements have a shared meaning across papers regardless of drug, cell line, or measurement modality. Carried in mm^3 regardless of the unit the source reports (clinical papers usually report mL; 1 mL = 1000 mm^3), so convert on data ingestion and scale any per-model reference accordingly to keep `(TUM_VOL / ref)^exp` numerically invariant. Use `TUM_SLD` for clinical RECIST sum-of-longest-diameters (a length, not a volume) and `TUMSZ` for the pooled "baseline tumor burden as a covariate on PK" use case where the source does not resolve to a volume. Ratified canonically on 2026-05-12 alongside the Ouerdani 2015 pazopanib mouse extraction, initially scoped to caliper-measured preclinical xenograft volumes; scope promoted on 2026-07-30 to any volumetric tumor-burden measurement (operator-ratified alongside the Siebinga 2023 extraction, which supplies the founding clinical volumetric example). The promotion follows the `CRCL` precedent, where creatinine-based and tracer-measured GFR are pooled onto one canonical because they play the same operational role.

### CTDNA (**canonical for baseline circulating tumor DNA burden**)
- **Description:** Baseline (pre-treatment, cycle 1 day 1) circulating tumor DNA burden in plasma, quantified by next-generation sequencing as the average number of mutant tumor molecules per millilitre of plasma (MMPM). ctDNA is shed into the circulation when tumor cells die by apoptosis or necrosis, so MMPM acts as a liquid-biopsy surrogate for total tumor burden that is independent of the RECIST target-lesion selection captured by `TUM_SLD`.
- **Units:** MMPM (mutant molecules per mL of plasma)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used as the per-subject regressor / ODE initial-condition input rather than as a covariate effect coefficient. Models that fit ctDNA on the base-10 logarithmic scale (the common convention, because raw MMPM spans several orders of magnitude) derive the state initial condition inside `model()` as `rbase_ctdna <- log10(CTDNA)`; the register stores the untransformed MMPM value so the transformation is visible at the call site.
- **Source aliases:**
  - `y0` (Ribba 2022 Eq. 1 symbol for the observed baseline used as a Monolix regressor).
  - `ctDNA0` (Ribba 2022 Eq. 2 symbol for the same quantity in the joint ctDNA / SLD model).
- **Example models:** `Ribba_2022_ctdna.R` (Stein bi-exponential on log10 ctDNA; `growth_ctdna(0) <- log10(CTDNA)`), `Ribba_2022_ctdna_sld_joint.R` (joint ctDNA / SLD model; same initial-condition use alongside `TUM_SLD`).
- **Notes:** Ratified canonically on 2026-07-28 alongside the Ribba 2022 ctDNA extraction, the first ctDNA-modality model in the library. Deliberately NOT pooled with variant-allele-frequency (VAF) or ctDNA-tumor-fraction (cTF) readouts: MMPM is an absolute concentration of mutant molecules whereas VAF and cTF are dimensionless ratios of mutant to wild-type (or aneuploidy-derived) signal, so the two are not interconvertible without the wild-type denominator. A future VAF / cTF canonical should be registered separately (e.g. `CTDNA_VAF`) rather than aliased onto `CTDNA`. The assay platform matters for cross-study pooling -- Ribba 2022 used the Roche AVENIO panel for the MMPM cohorts (Weber 2021 and OAK) and the FMI panel for the cTF cohort (IMspire170) -- so record the panel in the per-model `covariateData[[CTDNA]]$notes`.

### TUMTP_HODGKIN_CLASSICAL (**canonical for classical Hodgkin lymphoma tumor-type indicator**)
<!-- AUDIT 2026-06-19: renamed from `TUMTP_CHL` to `TUMTP_HODGKIN_CLASSICAL` per the canonical-register standardization audit. The prior name `TUMTP_CHL` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** 1 = classical Hodgkin lymphoma (cHL) or Hodgkin lymphoma generally, 0 = other tumor types.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (e.g., NSCLC, EC, HCC, UC, GC, CRC, NPC, OC, "Other" solid tumors in the Budha 2023 cohort; systemic anaplastic large-cell lymphoma in the Zhou 2025 pediatric cohort).
- **Source aliases:**
  - `TUMTP_CHL` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `TUMTP` (categorical column with levels like `cHL`, `GC`, ...) -- decompose into `TUMTP_HODGKIN_CLASSICAL = as.integer(TUMTP == "cHL")`.
  - `DIS` (Zhou 2025; integer code with `DIS == 1` flagging HL) -- decompose into `TUMTP_HODGKIN_CLASSICAL = as.integer(DIS == 1)`. Zhou 2025 calls the complement "non-HL"; in the Zhou 2025 cohort the non-HL group is exclusively sALCL.
- **Example models:** `Budha_2023_tislelizumab.R`, `Zhou_2025_brentuximab.R` (effects on ADC Q2, MMAE central volume VM, and the ADC->MMAE conversion-decay rate ALFM; the Zhou 2025 paper anchors typical-value parameters to HL patients so the model uses `(1 - TUMTP_HODGKIN_CLASSICAL)` as the on-effect indicator with reference category 1 = HL).
- **Notes:** Paired with `TUMTP_GASTRIC` in Budha 2023; a patient can have at most one of the indicators set to 1 (the remaining tumor types collapse into the reference 0 group). The reference category is the off-encoded value (0) by definition; when a source paper anchors typical-value parameters to the HL group rather than the non-HL group (as Zhou 2025 does), encode the effect as `coef^(1 - TUMTP_HODGKIN_CLASSICAL)` so the canonical column meaning (1 = cHL/HL) is preserved while the paper's reference (HL) still receives multiplier 1.

### HER2_ECD (**canonical for HER2 shed extracellular domain concentration**)
- **Description:** Baseline serum concentration of the shed extracellular domain of human epidermal growth factor receptor 2 (HER2). Serves as a soluble-antigen biomarker of HER2-mediated target-mediated drug disposition for HER2-directed mAbs / ADCs.
- **Units:** ng/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(HER2_ECD / ref)^exponent`. Reference 25 ng/mL used in Lu 2014; reference 8.23 ng/mL (population median) used in Bruno 2005, with a 200 ng/mL plateau cap.
- **Source aliases:**
  - `ECD` -- used in `Lu_2014_trastuzumabemtansine.R` and `Bruno_2005_trastuzumab.R`.
- **Example models:** `Lu_2014_trastuzumabemtansine.R` (reference 25 ng/mL; exponent 0.035 on CL), `Bruno_2005_trastuzumab.R` (reference 8.23 ng/mL; power exponents 0.041 on CL and 0.105 on V; HER2_ECD capped at 200 ng/mL inside model() to reflect the Bruno 2005 plateau observation), `deVriesSchultink_2018_trastuzumab_LVEF.R` (inherits the Bruno 2005 trastuzumab popPK as a deterministic forcing function with the same 8.23 ng/mL reference and 200 ng/mL plateau cap; PK IIV is omitted per the source paper's 'fixed effect parameters' clause, so HER2_ECD only enters via the typical-value PK covariate equations).
- **Notes:** Scoped specific because the covariate is meaningful only for HER2-targeted agents; if a non-HER2 paper uses a shed-antigen analog for a different target, register a target-specific canonical (e.g., `EGFR_ECD`) rather than reusing this one. Disambiguated from the covariate-columns register by the explicit `HER2_` prefix.

### TRAST_BL (**canonical for baseline trastuzumab concentration from prior therapy**)
- **Description:** Baseline serum concentration of residual unconjugated trastuzumab remaining from prior trastuzumab-containing therapy, measured at the start of a subsequent anti-HER2 treatment (e.g., trastuzumab emtansine). Encodes the magnitude of residual HER2-site competition from a previous trastuzumab exposure.
- **Units:** ug/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as a linear covariate on log(CL) via `exp(coef * TRAST_BL)`. TRAST_BL = 0 corresponds to no detectable residual trastuzumab (reference condition used in Lu 2014).
- **Source aliases:**
  - `TBL` -- used in `Lu_2014_trastuzumabemtansine.R`.
- **Example models:** `Lu_2014_trastuzumabemtansine.R` (linear-on-log coefficient -0.002 per ug/mL on CL).
- **Notes:** Scoped specific because the covariate is meaningful only for drugs that compete with trastuzumab at the HER2 binding site. Expect values clustered at 0 for trastuzumab-naive patients; Lu 2014 observed 0 at the 5th percentile and 54 ug/mL at the 95th percentile.

### TROPONIN_T_MAX (**canonical for peak post-anthracycline high-sensitive serum troponin T concentration**)
- **Description:** Peak (maximum) high-sensitive serum troponin T concentration observed per subject during or after the anthracycline-containing portion of an adjuvant or neoadjuvant chemotherapy regimen, in ng/L. Used as a covariate on subsequent trastuzumab-induced LVEF decline (the peak captures the cumulative anthracycline-driven myocyte damage that sensitises the heart to subsequent HER2-directed therapy).
- **Units:** ng/L
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- median-centered power covariate in de Vries Schultink 2018: `EC50_i = EC50_pop * (TROPONIN_T_MAX / 18)^(-1.16)`, where 18 ng/L is the cohort-median TROPONIN_T_MAX. A TROPONIN_T_MAX value at the cohort median leaves EC50 unchanged from its population value.
- **Source aliases:**
  - `TRPmax` -- used in `deVriesSchultink_2018_trastuzumab_LVEF.R`.
- **Example models:** `deVriesSchultink_2018_trastuzumab_LVEF.R` (power covariate effect on EC50 with median-centered normalisation, exponent -1.16, explaining 15.1% of inter-individual variability in EC50; per de Vries Schultink 2018 Table 2 and the EC50 covariate equation in Results).
- **Notes:** Derived per-subject from the upstream K-PD anthracycline-troponin T model output (see `deVriesSchultink_2018_anthracycline_troponinT.R`). Treated as a time-fixed baseline covariate for the LVEF model: even though the troponin T peak occurs during anthracycline treatment, by the time the LVEF observations start (typically a few weeks after the last anthracycline dose), the peak is a determined historical scalar per subject. Specific scope until a second cardiotoxicity / cardiac-biomarker model legitimately reuses this indicator. Values are positive; sub-LLOQ peak values are imputed at LLOQ/2 (= 1.5 ng/L for the Roche Modular E hs-TnT assay used in de Vries Schultink 2018).

### TUMTP_GASTRIC (**canonical for gastric-cancer tumor-type indicator**)
<!-- AUDIT 2026-06-19: renamed from `TUMTP_GC` to `TUMTP_GASTRIC` per the canonical-register standardization audit. The prior name `TUMTP_GC` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** 1 = gastric cancer (GC) or adenocarcinoma of the gastroesophageal junction (GEJ), 0 = other tumor types.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (same reference group as `TUMTP_HODGKIN_CLASSICAL`).
- **Source aliases:**
  - `TUMTP_GC` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `TUMTP` (categorical column) -- decompose into `TUMTP_GASTRIC = as.integer(TUMTP == "GC")`.
  - `TTYPE` (Quartino 2019; categorical column with levels `MBC`, `EBC`, `HV`, `AGC`, `Others`) -- decompose into `TUMTP_GASTRIC = as.integer(TTYPE == "AGC")`.
  - `TTYPE4` (Wang 2024; level 4 of a five-level tumor-type factor labelled "GCGEJ" in the source) -- decompose into `TUMTP_GASTRIC = as.integer(TTYPE4 == 1)`.
- **Example models:** `Budha_2023_tislelizumab.R`, `Quartino_2019_trastuzumab.R` (advanced gastric cancer; per-group typical-value switch on linear CL and Vc rather than an exponential multiplier), `Wang_2024_sugemalimab.R` (gastric + GEJ adenocarcinoma pooled; exponential coefficient log(1.13) on CL and log(1.14) on Vc).
- **Notes:** Follows the `RACE_<GROUP>` indicator-decomposition pattern. New oncology tumor types should be added as additional `TUMTP_<GROUP>` entries so the reference set stays explicit. "Advanced gastric cancer" (AGC), "gastric cancer" (GC), and "GC or adenocarcinoma of the gastroesophageal junction" (GCGEJ) are pooled onto a single `TUMTP_GASTRIC` indicator; document the per-paper stage-of-disease and GEJ-inclusion detail in `covariateData[[TUMTP_GASTRIC]]$notes`. ESCC (squamous histology) is captured by the separate `TUMTP_ESCC` indicator and is not pooled here.

### TUMTP_OTHER (**canonical for 'other tumor types' residual indicator**)
<!-- AUDIT 2026-06-19: renamed from `TUMTP_OTH` to `TUMTP_OTHER` per the canonical-register standardization audit. The prior name `TUMTP_OTH` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** 1 = heterogeneous "other" tumor-type pool (typically NSCLC plus miscellaneous solid tumors such as prostate, ovarian, and colorectal), 0 = one of the named tumor-type groups in the same analysis.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = named tumor-type groups defined per-paper (e.g., MBC, EBC, HV, AGC in Quartino 2019). The complement of all `TUMTP_<GROUP>` indicators defined in the same model.
- **Source aliases:**
  - `TUMTP_OTH` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `TTYPE` (Quartino 2019) -- decompose into `TUMTP_OTHER = as.integer(TTYPE == "Others")`.
  - `PAT2` (Sathe 2024, Sathe 2025) -- integer-coded tumor type column with levels 1 (mTNBC), 2 (mUC or HR+/HER2- mBC), 4 (Other epithelial); the source NONMEM control stream collapses PAT2 = 1 and PAT2 = 2 into the reference (no effect) and applies the deviation only when PAT2 = 4. Decompose into `TUMTP_OTHER = as.integer(PAT2 == 4)`.
- **Example models:** `Quartino_2019_trastuzumab.R` (per-group typical-value switch on linear CL; NSCLC plus a small residual group of prostate, ovarian, and other histologies), `Sathe_2024_sacituzumab.R` (multiplicative effect on tAB CL: -13.4% when TUMTP_OTHER = 1; "Other" pool = small-cell and non-small-cell lung cancer, colorectal cancer, esophageal cancer, pancreatic ductal adenocarcinoma, etc., n = 184; reference = pooled mTNBC + mUC + HR+/HER2- mBC, n = 345), `Sathe_2025_sacituzumab.R` (updated 3-study pooled analysis of the same drug adding TROPiCS-02 HR+/HER2- mBC data to Sathe 2024; multiplicative effect on tAB CL: -11.2% when TUMTP_OTHER = 1; "Other" pool composition and n = 184 unchanged from Sathe 2024; reference = pooled mTNBC + mUC + HR+/HER2- mBC, n = 605), `Lacy_2018_cabozantinib.R` (multiplicative fractional effect on CL/F = +0.178 and on Vc/F = -0.186 for "other malignancies" relative to the healthy-volunteer reference; n = 40 / 1534 = 2.6% of the cohort, all from Study XL184-001 mixed-malignancy first-in-human cohort), `Wang_2024_sugemalimab.R` (multiplicative effect `exp(e_oth_cl * TUMTP_OTHER)` on CL and `exp(e_oth_vc * TUMTP_OTHER)` on Vc, with the "Other" pool = the residual tumor-type bucket complementing TUMTP_LYMPH / TUMTP_GASTRIC / TUMTP_ESCC in the same model), `Gastonguay_2005_efaproxiral.R` (categorical power-model multiplier on SLPp50 = 0.854 for "other" cancer type vs lung-cancer reference; CATP level 4; cancer types not in {lung, breast, GBM} pooled into "other", n = 72 / 451 = 16.0% of cohort).
- **Notes:** Scope: specific because the set of histologies collapsed into "Others" is defined by the analysis plan of the source paper; two papers' `TUMTP_OTHER` columns are not interchangeable. Document the exact per-paper composition (e.g., "NSCLC + prostate + ovarian + other, n = 107 in Quartino 2019"; "small-cell + non-small-cell lung + CRC + esophageal + pancreatic ductal adenocarcinoma, n = 184 in Sathe 2024 and Sathe 2025 (same PAT2 = 4 pool carried across the two analyses)"; "advanced mixed malignancies enrolled in the FIH Study XL184-001, n = 40 in Lacy 2018"; "all non-{lung, breast, GBM} cancer types in the radiation-therapy cohort, n = 72 in Gastonguay 2005") in `covariateData[[TUMTP_OTHER]]$notes`. A given subject can have at most one of the `TUMTP_<GROUP>` indicators (including `TUMTP_OTHER`) set to 1; all-zero means the reference group.

### SPDL1 (**canonical for soluble PD-L1 concentration**)
- **Description:** Baseline (or time-varying) serum concentration of soluble programmed death-ligand 1 (sPD-L1). Serves as a circulating biomarker of target burden and immune activation for anti-PD-1/PD-L1 antibodies.
- **Units:** pg/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(SPDL1 / ref)^exponent`. Reference value observed: 173.8 pg/mL (study-population median in Ogasawara 2020).
- **Source aliases:** none; `SPDL1` is the standard abbreviation used directly in source analyses.
- **Example models:** `Ogasawara_2020_durvalumab.R` (power effect on CL, exponent 0.0617, reference 173.8 pg/mL; time-varying; values below LOD imputed as LOD/2 = 33.55 pg/mL), `Quartino_2019_trastuzumab.R` (per-group typical-value switch on linear CL; NSCLC plus a small residual group of prostate, ovarian, and other histologies), `Wang_2024_sugemalimab.R` (heterogeneous solid-tumor residual group of n = 174; exponential coefficient log(0.885) on CL and log(0.926) on Vc; NSCLC is the reference group, not part of `TUMTP_OTHER`).
- **Notes:** Scope: specific because sPD-L1 is meaningful only for drugs targeting the PD-1/PD-L1 pathway. For other checkpoint biomarkers (e.g., soluble CTLA-4, soluble LAG-3) register new dedicated canonicals rather than reusing this one. Ratified canonically on 2026-04-26.
  - `TTYPE3` (Wang 2024; level 3 of a five-level tumor-type factor labelled "Other" in the source) -- decompose into `TUMTP_OTHER = as.integer(TTYPE3 == 1)`.
- **Example models:** `Quartino_2019_trastuzumab.R` (per-group typical-value switch on linear CL; NSCLC plus a small residual group of prostate, ovarian, and other histologies), `Wang_2024_sugemalimab.R` (heterogeneous solid-tumor residual group of n = 174; exponential coefficient log(0.885) on CL and log(0.926) on Vc; NSCLC is the reference group, not part of `TUMTP_OTHER`), `Ogasawara_2020_durvalumab.R` (power effect on CL, exponent 0.0617, reference 173.8 pg/mL; time-varying; values below LOD imputed as LOD/2 = 33.55 pg/mL).
- **Notes:** Scope: specific because the set of histologies collapsed into "Others" is defined by the analysis plan of the source paper; two papers' `TUMTP_OTHER` columns are not interchangeable. Document the exact per-paper composition (e.g., "NSCLC + prostate + ovarian + other, n = 107 in Quartino 2019"; "miscellaneous solid tumors excluding NSCLC, lymphoma, GCGEJ, and ESCC, n = 174 in Wang 2024") in `covariateData[[TUMTP_OTHER]]$notes`. A given subject can have at most one of the `TUMTP_<GROUP>` indicators (including `TUMTP_OTHER`) set to 1; all-zero means the reference group.

### MCPROT (**canonical for serum monoclonal (M) protein concentration**)
- **Description:** Serum monoclonal (M) protein concentration. Multiple-myeloma plasma-cell-burden marker secreted by the tumor clone; elevated MCPROT reflects higher tumor burden and (for IgG-secreting MM) competes with therapeutic IgG mAbs for FcRn-mediated salvage and target-mediated elimination. Typically time-varying -- measured at multiple visits over the treatment course and supplied at every PK observation time via linear interpolation between measurements.
- **Units:** g/dL (US-convention; equivalent to 10 g/L SI). Document the unit used in each model via `covariateData[[MCPROT]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as a continuous, log-linear effect on the Vmax of target-mediated elimination via `exp(theta * MCPROT)` (i.e., MCPROT enters un-log-transformed). Reference values observed: 0 g/dL (Ide 2020 Vmax,REF reference) and 2.0 g/dL (Ide 2020 figure-1 reference patient).
- **Source aliases:**
  - `TMCPROT` (time-varying serum M-protein concentration) -- used in `Ide_2020_elotuzumab.R`. NONMEM column with imputation sentinel `-99` for missing observations, replaced by population median 2.1 g/dL via `IF(TMCPROT.EQ.-99) TMCPROT = 2.1`.
- **Example models:** `Ide_2020_elotuzumab.R` (g/dL, time-varying; entered un-log-transformed as `exp(0.277 * MCPROT)` on Vmax of the Michaelis-Menten target-mediated elimination from the central compartment), `Collins_2023_belantamab_mprotein.R` (g/L, time-fixed baseline; seeds the initial condition of the modelled M-protein `tumor` state and is binarized as `(MCPROT < 20)` to carry a multiplicative factor 1.41 on the kill rate constant KD).
- **Notes:** Specific scope because the column is mechanistically meaningful only for plasma-cell-targeting therapies in multiple myeloma (e.g., elotuzumab anti-SLAMF7, daratumumab anti-CD38, isatuximab anti-CD38, belantamab anti-BCMA, and BCMA-bispecifics / CAR-T). MCPROT decreases with treatment response; the time-varying form is the only correct way to capture the diminishing target-mediated-elimination component as the tumor regresses. In NONMEM datasets MCPROT is supplied at each event-row time, with linear interpolation between observations and last-observation-carried-forward beyond the last sample (Ide 2020 Methods). Distinct from `MM_NIGG` (which is the immunoglobulin subtype, an MM-disease stratifier that is time-fixed), `SBCMA` (soluble BCMA, a different MM tumor-burden biomarker for BCMA-targeting drugs), and `B2M` (beta-2-microglobulin, a renal-function-and-MM-disease-burden marker). The 1 g/dL = 10 g/L conversion lets future SI-convention papers register the same canonical with their own unit string.

### PDL1_TUM (**canonical for tumor PD-L1 expression (tumor proportion score)**)
- **Description:** Baseline tumor PD-L1 expression measured by immunohistochemistry, reported as Tumor Proportion Score (TPS) -- the percent of viable tumor cells with complete or partial membrane staining at any intensity. Used as a prognostic and predictive covariate for anti-PD-1 / anti-PD-L1 therapies. Distinct from `SPDL1` (soluble serum PD-L1 in pg/mL); `PDL1_TUM` is a tissue biomarker, `SPDL1` is a circulating biomarker.
- **Units:** percent (0-100)
- **Type:** continuous
- **Scope:** general (oncology)
- **Reference category:** n/a -- enters typically as `exp(theta * PDL1_TUM)` (uncentred multiplicative) or `1 + theta * (PDL1_TUM - ref)` (centred linear deviation). Reference values observed: 15 (Struemper 2025 population median across PD-1 inhibitor arms); 70 (Struemper 2025 INTR@PID imputed value when the 22C3 assay was unavailable).
- **Source aliases:** `PD-L1`, `PDL1`, `TPS`, `PDL1_TPS` -- paper notation varies; map to canonical `PDL1_TUM` with no value transformation.
- **Example models:** `Struemper_2025_tumorsize_OS_nsclc.R` (exponential effect on the tumor shrinkage rate `ks` of a Stein bi-exponential TGI model; coefficient 0.00902 per percent; applied only to PD-1 inhibitor-containing treatment arms via a derived `has_pd1` indicator that gates the effect when `TRT` is one of the eight pembrolizumab- or dostarlimab-containing categories).
- **Notes:** The PD-L1 IHC antibody clone (22C3, 73-10, SP142, SP263, etc.) determines cross-study comparability; document the assay per-model in `covariateData[[PDL1_TUM]]$notes`. Tumor Proportion Score (TPS) is the percent of TUMOR cells stained; Combined Positive Score (CPS) and Immune Cell (IC) score are distinct percent metrics that should be registered separately if a paper relies on them. PDL1_TUM is a tissue-level biomarker that may coexist in a model with the serum-level `SPDL1`. Scope: general because PDL1 IHC is a widely reused oncology covariate. Founding example: Struemper 2025 (`exp(PDL1_TUM * 0.00902)` on `ks`, paper Table 3 footnote b).

### LMET (**canonical for baseline presence of liver metastases**)
- **Description:** Binary indicator of radiologically documented liver metastases at baseline, 1 = liver metastases present, 0 = no liver metastases.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no liver metastases at baseline).
- **Source aliases:** none; `LMET` is the common NONMEM / clinical-PK abbreviation used directly by the source papers.
- **Example models:** `Quartino_2019_trastuzumab.R` (exponential effect on linear CL; +16.4% typical CL when LMET = 1, per Quartino 2019 Table 1 theta12 = 0.152).
- **Notes:** Liver metastases are associated with hepatic protein-synthesis impairment and altered IgG catabolism, making `LMET` a commonly tested covariate in oncology mAb population PK analyses. Scope: general so future oncology papers can reuse the canonical column. Time-fixed baseline indicator; if a source paper treats it as time-varying (progression during treatment), document in `covariateData[[LMET]]$notes`.

### MET_GE4 (**canonical for baseline number of metastatic sites >= 4 indicator**)
- **Description:** Binary indicator dichotomising the count of baseline metastatic sites at 4, 1 = patient has four or more documented metastatic sites at baseline, 0 = patient has zero to three metastatic sites at baseline. Time-fixed per subject. Treated as a surrogate for tumor burden in oncology mAb popPK analyses.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (fewer than four metastatic sites at baseline).
- **Source aliases:**
  - `MET` -- used in `Bruno_2005_trastuzumab.R` (Bruno 2005 Methods: "MET=1 if number of metastatic sites=4 or greater; otherwise MET=0"). When a source paper supplies the raw integer count column (`NMET`, `N_METS`, etc.) rather than the pre-binarised indicator, derive `MET_GE4 = as.integer(NMET >= 4)`.
- **Example models:** `Bruno_2005_trastuzumab.R` (multiplicative effect on linear CL: typical CL multiplied by `(1 + 0.221 * MET_GE4)`, i.e. +22.1% CL in MET_GE4 = 1 patients; reference MET_GE4 = 0 per Bruno 2005 Table 3).
- **Notes:** The >= 4 split is the dichotomisation used by Bruno 2005 to capture "patients with four or more metastatic sites" as a high-tumor-burden subgroup. Scope: general so future oncology popPK papers using the same dichotomisation can reuse this canonical column. If a future paper uses a different split (e.g. >= 2 or >= 5), register a separate `MET_GEN` canonical rather than overloading this entry. A `MET_GE4 = 1` patient may also have `LMET = 1`; the two columns are not mutually exclusive (liver-only metastases would have `LMET = 1`, `MET_GE4 = 0`).

### MET_GE3 (**canonical for baseline number of metastatic sites >= 3 indicator**)
- **Description:** Binary indicator dichotomising the count of baseline metastatic sites at 3, 1 = patient has three or more documented metastatic sites at baseline, 0 = patient has zero to two metastatic sites at baseline. Time-fixed per subject. Treated as a surrogate for tumor burden in oncology popPK / TGD analyses that centre the source-paper continuous integer count at 3.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (fewer than three metastatic sites at baseline).
- **Source aliases:**
  - `NumMet` (raw integer count 0-12; used in `Terranova_2022_TGD_OS_gastric.R`). When a source paper supplies the raw integer count column, derive `MET_GE3 = as.integer(NumMet >= 3)`.
- **Example models:** `Terranova_2022_TGD_OS_gastric.R` (multiplicative-additive effect on Gompertzian baseline tumor size: `BASE = exp(ltvBase + etalBase) * exp(0.143 * MET_GE3)` -- single-step binarisation of the paper's continuous per-metastasis coefficient `exp(0.143 * (NumMet - 3))`; deviation from paper's continuous form documented in vignette Errata per the count-covariate policy).
- **Notes:** Sibling of `MET_GE4` (Bruno 2005) and `NTARGET_GE3` (Struemper 2025). The >= 3 split matches Terranova 2022's centering value of 3 (avelumab-arm median NumMet). Auto-approved under the count-covariate-decomposed-to-binary policy on 2026-07-24 (agcand_13066655). If a future paper uses a different split (e.g. >= 2 or >= 5), register a separate sibling (`MET_GE2`, `MET_GE5`) rather than overloading this entry. A `MET_GE3 = 1` patient may also have `LMET = 1` (liver metastasis) or `PERIT_CARC = 1` (peritoneal carcinomatosis); the three columns are not mutually exclusive.

### PERIT_CARC (**canonical for peritoneal carcinomatosis indicator**)
- **Description:** Binary indicator of radiologically or surgically documented peritoneal carcinomatosis (diffuse peritoneal-surface metastatic spread) at baseline / re-baseline in a solid-tumor oncology cohort, 1 = peritoneal carcinomatosis present, 0 = absent. Time-fixed per subject at the assessed baseline. Distinct from `PERIT_DIAL` (peritoneal-dialysis treatment-status indicator; entirely different clinical concept) and from `DIS_PERIT` (peritonitis, an infectious inflammation of the peritoneum).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no peritoneal carcinomatosis at baseline).
- **Source aliases:**
  - `Peritoneal carcinomatosis` -- used in `Terranova_2022_TGD_OS_gastric.R` (Terranova 2022 Table S2 categorical covariate on log-median OS, coded 1/0).
- **Example models:** `Terranova_2022_TGD_OS_gastric.R` (linear-additive effect on log-median OS: `log_median_OS += -0.181 * PERIT_CARC`; peritoneal carcinomatosis shortens median OS by ~16.6% relative to patients without it, one of the covariates flagged as meaningful in the paper's +/-15% posterior-median threshold).
- **Notes:** Specific scope because peritoneal carcinomatosis is a gastrointestinal-cancer-associated metastasis pattern (colon, gastric, ovarian, appendiceal); future GI-oncology or ovarian-cancer popPK models retaining this covariate should extend the Example-models list rather than register a sibling. Ratified canonically on 2026-07-24 alongside the Terranova 2022 avelumab JAVELIN Gastric 100 extraction (agcand_13066655 sidecar request-001 q1=A). Sibling to `LMET` (liver metastasis) and `MET_GE3` / `MET_GE4` (aggregate metastatic-site count binarised at different thresholds); all four indicators can coexist for a patient with widespread advanced disease.

### RESP_SD (**canonical for RECIST re-baseline response = stable disease indicator**)
- **Description:** Binary indicator, 1 = the patient's RECIST 1.1 best response at re-baseline (typically the end of induction chemotherapy or the pre-maintenance assessment) is stable disease (SD); 0 = any other response category (CR, PR, PD, NED, NE, or non-CR/non-PD). Time-fixed at the re-baseline assessment. Used as one leaf of a decomposed RECIST-response categorical covariate under the count-covariate-decomposed-to-binary policy.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (RECIST response is anything other than SD -- typically responder [CR/PR], non-CR/non-PD, PD, or NE).
- **Source aliases:**
  - `Re-baseline stable disease vs other` -- used in `Terranova_2022_TGD_OS_gastric.R` (Terranova 2022 Table S2 categorical covariate; also enters TGD BASE equation).
  - `RESSD` -- source NONMEM column name derived from the RECIST assessment.
- **Example models:** `Terranova_2022_TGD_OS_gastric.R` (multiplicative effect on Gompertzian baseline tumor size `BASE = ... * (1 + 0.644 * RESP_SD)` -- SD patients have +64% larger baseline tumor size than the responder+PD+NE reference at re-baseline; and additive linear effect on log-median OS: `log_median_OS += -0.0919 * RESP_SD`).
- **Notes:** One of three parallel RECIST-response indicators (`RESP_SD`, `RESP_NONPDCR`, `RESP_RESPONDER`) that decompose the RECIST 1.1 response categorical into binary flags per the count-covariate-decomposed-to-binary policy. The three flags share the same source RECIST-response column and different subsets are used by different sub-models within the same paper (Terranova 2022 TGD uses `RESP_SD` + `RESP_NONPDCR` with implicit reference = responder/PD/NE; Terranova 2022 OS TTE uses `RESP_SD` + `RESP_RESPONDER` with implicit reference = non-responder-non-SD). Ratified canonically on 2026-07-24 alongside the Terranova 2022 avelumab JAVELIN Gastric 100 extraction (agcand_13066655 sidecar request-001 q1=A).

### RESP_NONPDCR (**canonical for RECIST re-baseline response = neither CR nor PR indicator**)
- **Description:** Binary indicator, 1 = the patient's RECIST 1.1 best response at re-baseline is NOT complete response (CR) and NOT partial response (PR) -- i.e., the patient is a non-responder by RECIST criteria (any of stable disease [SD], non-CR/non-PD, no evidence of disease [NED], progressive disease [PD], or not evaluable [NE]); 0 = responder (CR or PR). Time-fixed at the re-baseline assessment. Complement of `RESP_RESPONDER` from a different reference-category framing; both are needed because different sub-models within the same analysis use different reference categories.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (responder: CR or PR at re-baseline).
- **Source aliases:**
  - `RES_nonPR/nonCR` -- Terranova 2022 Supplementary Methods notation; equivalent to "response at re-baseline was not complete or partial response" per the paper's definition. Coded 1/0.
- **Example models:** `Terranova_2022_TGD_OS_gastric.R` (multiplicative effect on Gompertzian baseline tumor size: `BASE = ... * (1 - 0.0769 * RESP_NONPDCR)` -- non-responders have -7.69% smaller baseline tumor size than responders at re-baseline; used in the TGD sub-model only).
- **Notes:** Sibling of `RESP_SD` and `RESP_RESPONDER`. The name is deliberately explicit about "not CR / not PR" rather than the shorter `RESP_NONRESP` because the source paper's coding is defined on the exact CR/PR complement rather than an arbitrary "responder / non-responder" binarisation of a broader ordinal. Ratified canonically on 2026-07-24 alongside the Terranova 2022 avelumab JAVELIN Gastric 100 extraction (agcand_13066655 sidecar request-001 q1=A).

### RESP_RESPONDER (**canonical for RECIST re-baseline response = responder (CR or PR) indicator**)
- **Description:** Binary indicator, 1 = the patient's RECIST 1.1 best response at re-baseline is either complete response (CR) or partial response (PR) -- i.e., the patient is a responder by RECIST criteria; 0 = non-responder (any of SD, non-CR/non-PD, NED, PD, NE). Time-fixed at the re-baseline assessment. Complement of `RESP_NONPDCR` from a different reference-category framing; both are needed because different sub-models within the same analysis use different reference categories.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-responder at re-baseline; typically the pooled reference alongside a separate `RESP_SD` flag).
- **Source aliases:**
  - `Re-baseline responder vs other` -- Terranova 2022 Table S2 OS TTE covariate label; coded 1/0.
- **Example models:** `Terranova_2022_TGD_OS_gastric.R` (additive linear effect on log-median OS: `log_median_OS += 0.146 * RESP_RESPONDER`; responder patients have ~15.7% longer median OS than non-responder-non-SD reference).
- **Notes:** Sibling of `RESP_SD` and `RESP_NONPDCR`. Explicitly `RESPONDER` rather than `CRPR` because responder-vs-other is the clinically meaningful contrast used by the source paper's OS sub-model. Ratified canonically on 2026-07-24 alongside the Terranova 2022 avelumab JAVELIN Gastric 100 extraction (agcand_13066655 sidecar request-001 q1=A).

### NTARGET_GE3 (**canonical for baseline number of target lesions >= 3 indicator**)
- **Description:** Binary indicator dichotomising the count of target lesions at baseline at 3, 1 = three or more target lesions at baseline (per RECIST 1.1), 0 = one or two target lesions at baseline. Time-fixed per subject. Distinct from `TUM_SLD` (continuous sum of longest diameters, mm), which captures tumor burden magnitude; `NTARGET_GE3` captures lesion multiplicity / spread.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general (oncology)
- **Reference category:** 0 (one or two target lesions at baseline).
- **Source aliases:**
  - `NTARGET` -- raw integer count of target lesions per RECIST 1.1 (paper notation in Struemper 2025). When a source paper supplies the integer count column, derive `NTARGET_GE3 = as.integer(NTARGET >= 3)`.
- **Example models:** `Struemper_2025_tumorsize_OS_nsclc.R` (multiplicative effect on the typical-value baseline tumor size `TVTSb`: `TVTSb * (1 + 0.288 * NTARGET_GE3)`; +28.8% TVTSb for patients with three or more target lesions vs one or two).
- **Notes:** The >= 3 split aligns with the Struemper 2025 NSCLC analysis equation centring (NTARGET = 3 reference) even though the population median is 2 lesions (paper Table S3); the binarisation preserves the qualitative effect direction (more lesions => larger baseline tumor) while avoiding the linear extrapolation past the data range (paper data: NTARGET 1-8) that the source paper's linear form implies. If a future oncology paper uses a different split (e.g. >= 2 or >= 5), register a separate `NTARGET_GEN` canonical rather than overloading this entry. Scope: general so future oncology popPK / popPD papers using the same dichotomisation can reuse this column. Founding example: Struemper 2025.

### JOINT_SMALL (**canonical for small-joint tenosynovial giant cell tumor (TGCT) involvement indicator**)
- **Description:** 1 = the primary TGCT tumor is located in a small joint (e.g., hand / wrist / foot / ankle), 0 = the tumor is located in a large joint (e.g., knee / hip / shoulder / elbow; reference category). Time-fixed per subject; the anatomic joint-size classification is investigator-assigned per patient in TGCT trials. Small-joint TGCT is associated with lower baseline tumor size and greater response to CSF1R inhibition.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (large joint; the reference joint-size category in Yin 2021 Figure 2 caption reference patient).
- **Source aliases:**
  - `JOINT_SIZE` (with values `small` / `large`) -- decompose into `JOINT_SMALL = as.integer(JOINT_SIZE == "small")` (Yin 2021 codes joint size as a binary categorical stratifier without publishing the raw column name).
- **Example models:** `Yin_2021_pexidartinib.R` (multiplicative effects `exp(theta4)^JOINT_SMALL` on baseline Y0 (0.581), `exp(theta10)^JOINT_SMALL` on kdrug (2.14), and `exp(theta14)^JOINT_SMALL` on konset (1.82) in the longitudinal RECIST tumor-size model; small-joint TGCT reduces baseline tumor size and increases drug-effect rate constant kdrug relative to large-joint reference).
- **Notes:** Specific scope because the small-vs-large joint-size stratifier is tied to musculoskeletal-oncology (TGCT) analyses; future TGCT popPK / popPD papers using the same dichotomisation should extend this entry's example list rather than register a new canonical. The paper does not enumerate which anatomic joints are pooled as `small` vs `large`; this is an investigator-assigned per-patient classification. Ratified canonically on 2026-07-24 alongside the Yin 2021 pexidartinib TGCT exposure-response extraction. Distinct from `SWOL_28JOINT` (rheumatoid-arthritis 28-joint swollen-joint count from the DAS28 composite; integer 0-28) and from `NTARGET_GE3` (RECIST target-lesion count binarised at 3).

### TUMEXT_UPPER (**canonical for upper-extremity tenosynovial giant cell tumor (TGCT) location indicator**)
- **Description:** 1 = the primary TGCT tumor is located in an upper extremity (e.g., shoulder / elbow / wrist / hand), 0 = the tumor is located in a lower extremity (e.g., hip / knee / ankle / foot; reference category). Time-fixed per subject; the anatomic extremity classification is investigator-assigned per patient in TGCT trials. TGCT is overwhelmingly lower-extremity (~90% in the Yin 2021 cohort).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (lower extremity; the reference extremity category in Yin 2021 Figure 2 caption reference patient).
- **Source aliases:**
  - `EXTREMITY` (with values `upper` / `lower`) -- decompose into `TUMEXT_UPPER = as.integer(EXTREMITY == "upper")` (Yin 2021 codes tumor extremity as a binary categorical stratifier without publishing the raw column name).
- **Example models:** `Yin_2021_pexidartinib.R` (multiplicative effects `exp(theta3)^TUMEXT_UPPER` on baseline Y0 (0.692), `exp(theta9)^TUMEXT_UPPER` on kdrug (0.527), and `exp(theta13)^TUMEXT_UPPER` on konset (7.82) in the longitudinal RECIST tumor-size model; upper-extremity TGCT has wide-CI non-significant effects on kdrug and konset in this cohort due to small upper-extremity cell counts, but the covariate is retained in the paper's final model).
- **Notes:** Specific scope because the upper-vs-lower extremity stratifier is tied to musculoskeletal-oncology (TGCT) analyses; future TGCT popPK / popPD papers using the same dichotomisation should extend this entry's example list rather than register a new canonical. Ratified canonically on 2026-07-24 alongside the Yin 2021 pexidartinib TGCT exposure-response extraction. Distinct from a hypothetical `TUMEXT_LOWER` canonical (would be the polar complement); the register uses the paper-published direction (`upper` as the non-reference indicator) to match Yin 2021 Table S2 sign convention. Complementary to `JOINT_SMALL` (small-vs-large joint-size stratifier); the two indicators describe orthogonal anatomic-location features of TGCT and may coexist in the same model.

### ECOG_GE1 (**canonical for Eastern Cooperative Oncology Group performance-status indicator, >= 1**)
- **Description:** 1 if baseline Eastern Cooperative Oncology Group (ECOG) performance status is greater than or equal to 1, 0 if ECOG = 0. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (ECOG performance status = 0, i.e., fully active / asymptomatic).
- **Source aliases:**
  - `PS` / `BPS` -- used in `Bajaj_2017_nivolumab.R` (BPS = "baseline performance status"; the one study using Karnofsky Performance Status was mapped to ECOG via Oken 1982 before binarization) and `Zhang_2019_nivolumab.R` (paper's binary collapse PS=0 vs. PS>0).
  - `ECOG_1` -- alternative explicit form; equivalent to `ECOG_GE1` when ECOG only takes values 0, 1, 2 in the analysis dataset (the typical oncology case).
  - `ECOG_PS_GT0` -- retired name used in earlier register drafts; semantically identical (`>= 1` equals `> 0` for integer ECOG scores).
  - `ECOG101` (categorical 0/1/2 score with thresholding `IF(ECOG101.GT.0.5)`) -- used in `Ide_2020_elotuzumab.R`. Decompose: `ECOG_GE1 = as.integer(ECOG101 >= 1)`.
- **Example models:** `Bajaj_2017_nivolumab.R` (exponential effect on CL with coefficient 0.172), `Zhang_2019_nivolumab.R` (exponential effect exp(0.181) on baseline CL; additive effect -0.138 on the time-varying-CL Emax parameter), `Ide_2020_elotuzumab.R` (multiplicative effect on CL = 1.03; paired with `ECOG_GE2` for separate ECOG=1 vs ECOG>=2 effects), `Netterberg_2017_docetaxel.R` (multiplicative effect on baseline ANC of the Friberg myelosuppression chain: `BACOV *= (1 + theta * ECOG_GE1)` with theta = 0.130; source column `PERF` with ordinal ECOG 0/1/2 values, binarized via `ECOG_GE1 = as.integer(PERF >= 1)` per Kloft 2006).
- **Notes:** Oncology papers conventionally report ECOG as an integer (0-5) but binarize at >= 1 because ECOG >= 2 is rare in trial cohorts. When a source paper provides the ordinal ECOG score separately, derive `ECOG_GE1 = as.integer(ECOG >= 1)`. Zhang 2019 uses `ECOG_GE1` on both baseline CL and the time-varying Emax parameter (unlike Bajaj 2017, which uses it on CL only); document the structural role in each model's `covariateData[[ECOG_GE1]]$notes`. When a paper retains separate effects for ECOG = 1 vs ECOG >= 2 (Ide 2020), pair this column with `ECOG_GE2` and supply both indicators in the event dataset.

### ECOG_GE2 (**canonical for Eastern Cooperative Oncology Group performance-status indicator, >= 2**)
- **Description:** 1 if baseline Eastern Cooperative Oncology Group (ECOG) performance status is greater than or equal to 2, 0 if ECOG <= 1. Time-fixed per subject. Used in models that retain separate effects for ECOG = 1 vs ECOG >= 2 by pairing this column with `ECOG_GE1`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (ECOG performance status <= 1; in models that pair `ECOG_GE1` and `ECOG_GE2`, both indicators = 0 corresponds to ECOG = 0 and (`ECOG_GE1` = 1, `ECOG_GE2` = 0) corresponds to ECOG = 1).
- **Source aliases:**
  - `ECOG101` (categorical 0/1/2 score with thresholding `IF(ECOG101.GT.1.5)`) -- used in `Ide_2020_elotuzumab.R`. Decompose: `ECOG_GE2 = as.integer(ECOG101 >= 2)`.
- **Example models:** `Ide_2020_elotuzumab.R` (multiplicative effect on CL = 1.15; paired with `ECOG_GE1` to retain separate ECOG = 1 vs ECOG >= 2 effects).
- **Notes:** Parallels `ECOG_GE1`. Use only when the source paper reports a separate effect for ECOG >= 2 in addition to ECOG_GE1; otherwise `ECOG_GE1` alone is sufficient. The paired (`ECOG_GE1`, `ECOG_GE2`) decomposition reproduces a three-level (`ECOG = 0`, `ECOG = 1`, `ECOG >= 2`) ordinal effect with two binaries.

### TUMTP_SCLC (**canonical for small-cell-lung-cancer tumor-type indicator**)
- **Description:** 1 = small cell lung cancer (SCLC), 0 = other tumor types.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (e.g., melanoma, NSCLC, RCC, HCC, CRC in the Sanghavi 2020 cohort; reference category is melanoma).
- **Source aliases:**
  - `TUMTP` (categorical column with levels including `melanoma`, `NSCLC`, `SCLC`, `CRC`, `HCC`, `RCC`) -- decompose into `TUMTP_SCLC = as.integer(TUMTP == "SCLC")`.
- **Example models:** `Sanghavi_2020_ipilimumab.R` (exponential coefficient -0.124 on CL).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` decomposition pattern. SCLC is the only retained tumor-type indicator in the Sanghavi 2020 final model after backward elimination; the other tumor types collapse into the reference (melanoma) group.

### TUMTP_NSCLC (**canonical for non-small-cell-lung-cancer tumor-type indicator**)
- **Description:** 1 = non-small cell lung cancer (NSCLC), 0 = other tumor types.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 = all other tumor types (in Ahamadi 2017 the implicit reference is melanoma, with the rare "other" cancer type pooled into the reference).
- **Source aliases:**
  - `TUMTP` (categorical column with levels including `melanoma`, `NSCLC`, `other`) -- decompose into `TUMTP_NSCLC = as.integer(TUMTP == "NSCLC")`.
- **Example models:** `Ahamadi_2017_pembrolizumab.R` (proportional change of +14.5% on CL for NSCLC patients relative to melanoma; the "other" cancer type cohort -- 1.01% of the population -- is pooled into the melanoma reference per the paper's model description), `Aoyama_2012_sepantronium.R`.
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` decomposition pattern. Scope: general because NSCLC is a high-frequency tumor-type contrast (with melanoma or "other" reference) across PD-1 / PD-L1 / chemotherapy popPK analyses, and is likely to recur in future extractions. Ratified canonically on 2026-05-17 alongside the Ahamadi 2017 pembrolizumab extraction.

### TUMTP_NSCLC_NONADENO (**canonical for non-adenocarcinoma-histology NSCLC sub-indicator**)
- **Description:** 1 = NSCLC of a histology OTHER than adenocarcinoma (squamous-cell carcinoma, large-cell carcinoma, or other non-adenocarcinoma NSCLC subtype); 0 = adenocarcinoma NSCLC, NSCLC of unknown histology, or a non-NSCLC diagnosis. Within-NSCLC histology sub-indicator paired with `TUMTP_NSCLC` (which distinguishes NSCLC patients overall from other tumor types).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (adenocarcinoma NSCLC, unknown-histology NSCLC, or non-NSCLC subject; in particular IPF / non-cancer subjects in a mixed cohort are assigned 0 because they have no NSCLC histology).
- **Source aliases:**
  - Schmid 2017 categorical column with levels "NSCLC-adenocarcinoma" / "NSCLC-no adenocarcinoma" / "IPF or NSCLC of unknown histology" -- decompose into `TUMTP_NSCLC_NONADENO = as.integer(NSCLC_histology == "non-adenocarcinoma")`.
- **Example models:** `Schmid_2017_nintedanib.R` (multiplicative effect 1.36 on BIBF 1202 ka2 absorption rate; reference category adenocarcinoma NSCLC pooled with IPF and unknown-histology NSCLC).
- **Notes:** Follows the `TUMTP_<type>` decomposition pattern (auto-approved family). Within-NSCLC histology distinctions arise when a popPK / PD model in NSCLC differentiates adenocarcinoma from squamous-cell carcinoma; the paired adenocarcinoma reference can be made explicit by adding a `TUMTP_NSCLC_ADENO` sibling in a future extraction if needed. Ratified canonically on 2026-06-27 alongside the Schmid 2017 nintedanib extraction.

### TUMTP_HRPC (**canonical for hormone-refractory prostate cancer tumor-type indicator**)
- **Description:** 1 = hormone-refractory prostate cancer (HRPC), 0 = other tumor types. The historical term HRPC has since been displaced by CRPC (castration-resistant prostate cancer); the two terms refer to the same clinical entity. `TUMTP_HRPC` is the canonical column for both wordings (no separate `TUMTP_CRPC` is registered; if a future paper distinguishes hormone-naive metastatic prostate cancer from CRPC, register a more specific canonical at that time).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 = all other tumor types (per source paper; in Aoyama 2012 the implicit reference is NSCLC when paired with TUMTP_MEL = 0).
- **Source aliases:**
  - `TUMTP` (categorical column with levels including `HRPC`, `NSCLC`, `MM`) -- decompose into `TUMTP_HRPC = as.integer(TUMTP == "HRPC")`.
  - `STUDY` / `CANCER_TYPE` factor columns where one level is `HRPC` or `CRPC` -- decompose identically.
- **Example models:** `Aoyama_2012_sepantronium.R` (proportional change of -4.5% on CL for HRPC patients relative to the NSCLC reference; ratio THETA_HRPC = 0.955 in the paper's power form), `Lacy_2018_cabozantinib.R` (multiplicative fractional effect on CL/F = -0.009 and on Vc/F = -0.241 for CRPC patients relative to the healthy-volunteer reference; Lacy 2018 enrolled 823 CRPC patients across Studies XL184-203, XL184-306, and XL184-307, the largest cancer-type cohort in the integrated popPK analysis).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` / `TUMTP_NSCLC` decomposition pattern. Scope: general because prostate-cancer cohorts (under either HRPC or CRPC nomenclature) recur across small-molecule and targeted-therapy popPK analyses. The "hormone-refractory" wording reflects the 2010s convention; modern papers using CRPC map onto the same canonical. Ratified canonically on 2026-05-20 alongside the Aoyama 2012 sepantronium extraction.

### TUMTP_MTC (**canonical for medullary thyroid carcinoma tumor-type indicator**)
- **Description:** 1 = medullary thyroid carcinoma (MTC), 0 = other tumor types. Time-fixed per subject. MTC is a distinct C-cell-derived thyroid malignancy (not papillary / follicular / anaplastic thyroid cancer); cabozantinib and vandetanib are the principal MTC-approved tyrosine-kinase inhibitors.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 = all other tumor types (per source paper; in Lacy 2018 the implicit reference is healthy volunteer when paired with all other TUMTP_* indicators = 0).
- **Source aliases:**
  - `POP` (Lacy 2018 NONMEM categorical population indicator with levels HV / RCC / CRPC / MTC / GB / Other) -- decompose into `TUMTP_MTC = as.integer(POP == "MTC")`.
- **Example models:** `Lacy_2018_cabozantinib.R` (multiplicative fractional effect on CL/F = +0.928 and on Vc/F = -0.07 for MTC patients relative to the healthy-volunteer reference; the +93% CL/F increase in MTC is the load-bearing finding of Lacy 2018 and explains why the MTC capsule label dose is 140 mg/day while the RCC tablet label dose is only 60 mg/day).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` / `TUMTP_NSCLC` decomposition pattern. Scope: general because MTC is the approved indication for Cometriq (cabozantinib capsule) and Caprelsa (vandetanib), and future TKI popPK papers in MTC populations are likely to recur. Ratified canonically on 2026-05-25 alongside the Lacy 2018 cabozantinib extraction.

### TUMTP_RCC (**canonical for renal cell carcinoma tumor-type indicator**)
- **Description:** 1 = renal cell carcinoma (RCC), 0 = other tumor types. Time-fixed per subject. Includes both clear-cell and non-clear-cell histologies pooled at the cohort level; if a future paper distinguishes clear-cell from non-clear-cell, register a finer-grained canonical at that time.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 = all other tumor types (per source paper; in Lacy 2018 the implicit reference is healthy volunteer when paired with all other TUMTP_* indicators = 0).
- **Source aliases:**
  - `POP` (Lacy 2018 NONMEM categorical population indicator with levels HV / RCC / CRPC / MTC / GB / Other) -- decompose into `TUMTP_RCC = as.integer(POP == "RCC")`.
- **Example models:** `Lacy_2018_cabozantinib.R` (multiplicative fractional effect on CL/F = -0.129 and on Vc/F = -0.63 for RCC patients relative to the healthy-volunteer reference; Lacy 2018 enrolled 282 RCC patients in Study XL184-308 dosed at 60 mg/day cabozantinib tablet).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` / `TUMTP_NSCLC` decomposition pattern. Scope: general because RCC cohorts recur frequently across TKI, anti-PD-1, and anti-VEGF popPK analyses. Ratified canonically on 2026-05-25 alongside the Lacy 2018 cabozantinib extraction.

### TUMTP_DTC (**canonical for differentiated thyroid cancer tumor-type indicator**)
- **Description:** 1 = differentiated thyroid cancer (DTC, i.e. papillary or follicular thyroid carcinoma including their variants; in most modelling cohorts specifically radioiodine-refractory DTC, RR-DTC), 0 = other tumor types or healthy subject. Time-fixed per subject. Distinct from `TUMTP_MTC` (medullary thyroid carcinoma), which arises from parafollicular C cells rather than the follicular epithelium and is a separate disease entity; do not pool the two.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 = all other tumor types (per source paper; in Majid 2024 the reference stratum is cancer patients with a solid tumor other than DTC, RCC or HCC, obtained by setting `TUMTP_DTC`, `TUMTP_RCC`, `TUMTP_HCC` and `DIS_HEALTHY` all to 0).
- **Source aliases:**
  - `DTC` (Majid 2024 Text S1 NONMEM flag derived from the study number: `IF (STUD.EQ.303.OR.STUD.EQ.211.OR.STUD.EQ.201) DTC=1`).
  - `TUMTYP` (Majid 2024 Text S1 categorical column with levels 1 = DTC, 2 = RCC, 3 = HCC, 4 = other solid tumor) -- decompose into `TUMTP_DTC = as.integer(TUMTYP == 1)`.
  - `TUM` / `POP` / `CANCER_TYPE` factor columns where one level is `DTC`, `RR-DTC`, `papillary` or `follicular` thyroid cancer -- decompose identically.
- **Example models:** `Majid_2024_lenvatinib.R` (multiplicative power-form effect on CL/F: `0.951^TUMTP_DTC`, i.e. -4.9 percent relative to the other-solid-tumor reference; 542 of 1921 pooled subjects were DTC).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` / `TUMTP_NSCLC` / `TUMTP_RCC` decomposition pattern. Scope: general because DTC cohorts recur across multikinase-inhibitor popPK analyses (lenvatinib, sorafenib, cabozantinib, vandetanib) and thyroid-cancer PK/PD work. `Gupta_2016_lenvatinib.R` records its DTC cohort only in `population$disease_state` because that model has no DTC covariate effect; that is not an omission of this column. Ratified canonically alongside the Majid 2024 lenvatinib PK/PD extraction.

### TUMTP_HCC (**canonical for hepatocellular carcinoma tumor-type indicator**)
- **Description:** 1 = hepatocellular carcinoma (HCC), 0 = other tumor types or healthy subject. Time-fixed per subject. Note that an HCC cohort usually also carries altered hepatic-function laboratory covariates (albumin, alkaline phosphatase, bilirubin, Child-Pugh class); when a model fits both this indicator and continuous liver-function markers, the indicator captures the residual tumor-type effect after those markers are accounted for.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 = all other tumor types (per source paper; in Majid 2024 the reference stratum is cancer patients with a solid tumor other than DTC, RCC or HCC).
- **Source aliases:**
  - `HCC` (Majid 2024 Text S1 NONMEM flag derived from the study number: `IF (STUD.EQ.202.OR.STUD.EQ.304) HCC=1`).
  - `TUMTYP` (Majid 2024 Text S1 categorical column with levels 1 = DTC, 2 = RCC, 3 = HCC, 4 = other solid tumor) -- decompose into `TUMTP_HCC = as.integer(TUMTYP == 3)`.
  - `POP` / `CANCER_TYPE` factor columns where one level is `HCC` or `hepatocellular` -- decompose identically.
- **Example models:** `Majid_2024_lenvatinib.R` (multiplicative power-form effect on CL/F: `0.862^TUMTP_HCC`, i.e. -13.8 percent relative to the other-solid-tumor reference; 534 of 1921 pooled subjects were HCC).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` / `TUMTP_NSCLC` / `TUMTP_RCC` decomposition pattern. Scope: general because HCC cohorts recur across TKI, anti-PD-(L)1 and anti-VEGF popPK analyses. Distinct from the `HEPIMP_*` (NCI ODWG hepatic-impairment) family, which classifies hepatic function by bilirubin and AST regardless of tumor type; an HCC patient may have normal hepatic function and a non-HCC patient may be hepatically impaired, so the two families are not substitutes. Ratified canonically alongside the Majid 2024 lenvatinib PK/PD extraction.

### TUMTP_MEL (**canonical for melanoma tumor-type indicator**)
- **Description:** 1 = melanoma (any anatomic site; in advanced-solid-tumor cohorts typically unresectable stage III or IV cutaneous melanoma), 0 = other tumor types.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 = all other tumor types (per source paper; in Aoyama 2012 the implicit reference is NSCLC when paired with TUMTP_HRPC = 0).
- **Source aliases:**
  - `TUMTP` (categorical column with levels including `MM`, `NSCLC`, `HRPC`) -- decompose into `TUMTP_MEL = as.integer(TUMTP == "MM")`. NOTE: in Aoyama 2012 the abbreviation `MM` denotes malignant (unresectable) melanoma, NOT multiple myeloma; do not confuse with the canonical `MM` register entry (which is specifically active multiple myeloma).
  - `STUDY` / `CANCER_TYPE` factor columns where one level is `melanoma` or `MM` (melanoma sense) -- decompose identically.
- **Example models:** `Aoyama_2012_sepantronium.R` (proportional change of +24% on CL for melanoma patients relative to the NSCLC reference; ratio THETA_MM = 1.24 in the paper's power form).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` / `TUMTP_NSCLC` decomposition pattern. The canonical name uses `MEL` (not `MM`) to disambiguate from the existing `MM` register entry for multiple myeloma. Scope: general because melanoma cohorts recur across PD-1 / PD-L1 / BRAF-inhibitor / small-molecule popPK analyses. Ratified canonically on 2026-05-20 alongside the Aoyama 2012 sepantronium extraction.

### TUMTP_LYMPH (**canonical for lymphoma (pooled) tumor-type indicator**)
- **Description:** 1 = lymphoma (heterogeneous lymphoma pool spanning multiple lymphoma histologies -- e.g., classical Hodgkin lymphoma combined with extranodal NK/T-cell lymphoma; or any-histology lymphoma pooled with solid-tumor and leukemia cohorts), 0 = solid tumor or other tumor type.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 = non-lymphoma tumor type (per source paper; e.g., NSCLC, GC/GEJ, ESCC, "Other" solid tumors in the Wang 2024 cohort, with NSCLC as the implicit reference when paired with the other Wang 2024 `TUMTP_*` indicators; or leukemia as the implicit reference in the Akbar 2025 cohort).
- **Source aliases:**
  - `TTYPE1` (Wang 2024) -- decompose into `TUMTP_LYMPH = as.integer(TTYPE1 == 1)`. The Wang 2024 source paper uses a multi-level `TTYPE` factor with levels 1 = lymphoma, 2 = lung cancer (reference), 3 = other, 4 = GCGEJ, 5 = ESCC.
  - Categorical column "type of cancer" with level "Lymphoma" (Akbar 2025) -- decompose into `TUMTP_LYMPH = as.integer(cancer_type == "Lymphoma")`.
- **Example models:** `Wang_2024_sugemalimab.R` (exponential coefficient log(0.877) on baseline CL and log(0.879) on Vc), `Akbar_2025_voriconazole.R` (additive-fractional +1.91% effect on CL relative to leukemia reference; 95% CI spans zero).
- **Notes:** Distinct from `TUMTP_HODGKIN_CLASSICAL` (which is specifically classical Hodgkin lymphoma). Wang 2024 pools two lymphoma histologies (extranodal NK/T-cell lymphoma from CS1001-201 / NCT03595657 and classical Hodgkin lymphoma from CS1001-202 / NCT03505996) into a single lymphoma indicator; the indicator therefore captures a generic "hematologic-vs-solid-tumor" contrast rather than a histology-specific effect. Akbar 2025 uses a single "Lymphoma" category alongside leukemia, sarcoma, breast cancer, myeloma, and glioma in a heterogeneous-cancer TDM cohort. When a future paper studies a single lymphoma histology distinct from cHL, register a more specific canonical (e.g., `TUMTP_ENKTL`, `TUMTP_NHL`) rather than overloading this one. Document the per-paper histology composition in `covariateData[[TUMTP_LYMPH]]$notes`. Promoted from `Scope: specific` to `Scope: general` on 2026-05-09 alongside the Akbar 2025 voriconazole extraction so that any heterogeneous-cancer-cohort PK analysis can use this canonical name without scope-violation.

### TUMTP_BREAST (**canonical for breast-cancer tumor-type indicator**)
<!-- AUDIT 2026-06-19: renamed from `TUMTP_BC` to `TUMTP_BREAST` per the canonical-register standardization audit. The prior name `TUMTP_BC` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** 1 = breast cancer (any histology / receptor status), 0 = other tumor types.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 = all other tumor types (per source paper; in Lu 2022 the implicit reference is NSCLC, with colorectal cancer pooled into the reference because its CL effect was found insignificant relative to NSCLC).
- **Source aliases:**
  - `TUMTP_BC` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `TUMTP` (categorical column with levels including `BC`, `NSCLC`, `CRC`) -- decompose into `TUMTP_BREAST = as.integer(TUMTP == "BC")`.
- **Example models:** `Lu_2022_patritumab.R` (multiplicative fractional effect 0.811 on CLlin of DXd-conjugated antibody for breast-cancer patients vs the NSCLC reference; CRC effect was tested and found insignificant so CRC is pooled into the reference).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` decomposition pattern. Registers the breast-cancer arm of an oncology-cohort tumor-type contrast; pair with sister `TUMTP_<GROUP>` indicators (e.g., `TUMTP_NSCLC`, `TUMTP_CRC`) when a future paper retains separate effects for additional tumor types beyond the implicit reference. Ratified canonically on 2026-04-28.

### TUMTP_ESCC (**canonical for oesophageal-squamous-cell-carcinoma tumor-type indicator**)
- **Description:** 1 = oesophageal squamous cell carcinoma (ESCC), 0 = other tumor types.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (per source paper; in Wang 2024, the implicit reference is NSCLC when all the other `TUMTP_*` indicators are also 0).
- **Source aliases:**
  - `TTYPE5` (Wang 2024) -- decompose into `TUMTP_ESCC = as.integer(TTYPE5 == 1)`.
- **Example models:** `Wang_2024_sugemalimab.R` (exponential coefficient log(0.99) on baseline CL and log(1.08) on Vc).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` decomposition pattern. Distinct from gastroesophageal-junction adenocarcinoma (which is captured by the broader `TUMTP_GASTRIC` indicator that pools GC and GEJ adenocarcinomas) -- ESCC is a squamous-cell histology, not adenocarcinoma. Document the per-paper histology composition in `covariateData[[TUMTP_ESCC]]$notes`.

### TUMTP_PCALCL (**canonical for primary cutaneous anaplastic large-cell lymphoma indicator**)
- **Description:** 1 = primary cutaneous anaplastic large-cell lymphoma (pcALCL), 0 = other tumor types.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (in Suri 2018, the non-pcALCL reference comprises Hodgkin lymphoma, systemic ALCL, mycosis fungoides, and other CD30+ hematologic malignancies pooled together).
- **Source aliases:**
  - `PCALCL` -- used in `Suri_2018_brentuximab.R`. Suri 2018 reports the effect as a power-form multiplier `cl_adc *= 0.728^TUMTP_PCALCL` (pcALCL CL ~27% lower than non-pcALCL).
- **Example models:** `Suri_2018_brentuximab.R` (effect on ADC clearance only).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` decomposition pattern. Distinct from `TUMTP_LYMPH` (heterogeneous lymphoma pool) and `TUMTP_HODGKIN_CLASSICAL` (classical Hodgkin lymphoma). pcALCL is one of two histologies pooled into the broader CTCL category in Suri 2018 (alongside mycosis fungoides); the model singles out pcALCL because Suri 2018 backward elimination retained pcALCL as a separate effect on ADC clearance after exploring the broader CTCL contrast. Ratified canonically on 2026-04-28.

### TUMTP_SARC (**canonical for sarcoma tumor-type indicator**)
- **Description:** 1 = sarcoma (any histology -- soft-tissue or bone sarcoma pooled), 0 = other tumor types.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (per source paper; in Akbar 2025 the implicit reference is leukemia when paired with the other Akbar `TUMTP_*` indicators all = 0).
- **Source aliases:**
  - Categorical column "type of cancer" with level "Sarcoma" -- decompose into `TUMTP_SARC = as.integer(cancer_type == "Sarcoma")`. Used in `Akbar_2025_voriconazole.R`.
- **Example models:** `Akbar_2025_voriconazole.R` (additive-fractional +18.5% effect on CL relative to leukemia reference).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` decomposition pattern. Akbar 2025 pools soft-tissue and bone sarcoma histologies into a single sarcoma category. Scope: specific because the reference category (leukemia in Akbar 2025) is paper-defined. Ratified canonically on 2026-05-09.

### TUMTP_MYELO (**canonical for multiple myeloma tumor-type indicator (used in heterogeneous-cancer pooled cohorts)**)
- **Description:** 1 = multiple myeloma, 0 = other tumor types.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (per source paper; in Akbar 2025 the implicit reference is leukemia when paired with the other Akbar `TUMTP_*` indicators all = 0).
- **Source aliases:**
  - Categorical column "type of cancer" with level "Myeloma" -- decompose into `TUMTP_MYELO = as.integer(cancer_type == "Myeloma")`. Used in `Akbar_2025_voriconazole.R`.
- **Example models:** `Akbar_2025_voriconazole.R` (additive-fractional -2.33% effect on CL relative to leukemia reference; the 95% CI spans zero).
- **Notes:** Distinct from the stub `MM` entry (which is reserved for multiple-myeloma-as-primary-indication PK studies; the `MM` definition lacks a complete schema and predates the TUMTP_* convention) and from `DIS_SMM` (smoldering multiple myeloma, an asymptomatic plasma-cell disorder). Use `TUMTP_MYELO` when the source paper pools multiple myeloma alongside other tumor types in a heterogeneous oncology cohort and treats `cancer type` as a many-level categorical covariate. Scope: specific because the reference category (leukemia in Akbar 2025) is paper-defined. Ratified canonically on 2026-05-09.

### TUMTP_GLIO (**canonical for glioma tumor-type indicator**)
- **Description:** 1 = glioma (any grade / histology -- e.g., glioblastoma, anaplastic astrocytoma, oligodendroglioma pooled), 0 = other tumor types.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (per source paper; in Akbar 2025 the implicit reference is leukemia when paired with the other Akbar `TUMTP_*` indicators all = 0; in Lacy 2018 the reference is healthy volunteer when paired with the other `TUMTP_*` indicators = 0).
- **Source aliases:**
  - Categorical column "type of cancer" with level "Glioma" -- decompose into `TUMTP_GLIO = as.integer(cancer_type == "Glioma")`. Used in `Akbar_2025_voriconazole.R`.
  - `POP` (Lacy 2018 NONMEM categorical population indicator) -- decompose into `TUMTP_GLIO = as.integer(POP == "GB")` (Lacy 2018 enrolled glioblastoma multiforme patients in Study XL184-201 dosed at 140 mg/day cabozantinib capsule).
- **Example models:** `Akbar_2025_voriconazole.R` (additive-fractional +8.81% effect on CL relative to leukemia reference; the 95% CI spans zero), `Lacy_2018_cabozantinib.R` (multiplicative fractional effect on CL/F = +0.216 and on Vc/F = -0.569 for GB patients relative to the healthy-volunteer reference; n = 39 GB patients in Study XL184-201), `Gastonguay_2005_efaproxiral.R` (categorical power-model multiplier on SLPp50 = 1.28 for cranial glioblastoma multiforme vs lung-cancer reference; CATP level 3; n = 72 GBM patients / 451 = 16.0% of radiation-therapy cohort).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` decomposition pattern. Akbar 2025 reports a single "glioma" category without further subdivision by histology or grade. Scope: specific because the reference category (leukemia in Akbar 2025; healthy volunteer in Lacy 2018; lung cancer in Gastonguay 2005) is paper-defined. Ratified canonically on 2026-05-09.

### TUMTP_CNS_PRIM (**canonical for primary CNS tumour umbrella indicator**)
- **Description:** 1 = primary central nervous system (CNS) tumour (an umbrella pooling glioma, medulloblastoma, ependymoma, brainstem glioma, atypical teratoid / rhabdoid tumour, and other tumours arising within the brain or spinal cord), 0 = non-primary-CNS tumour (per-source-paper reference cohort; e.g. sarcomas in Han 2015). Distinct from `TUMTP_GLIO`, which is glioma-only; use `TUMTP_CNS_PRIM` when the source paper's binary tumour-type contrast pools glioma with non-glioma primary CNS histologies. The `PRIM` suffix distinguishes primary CNS tumours from metastatic CNS involvement (which is classified by primary site of origin under the other `TUMTP_*` entries). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = non-primary-CNS tumour (paper-defined; for Han 2015 the complement cohort is the pooled paediatric sarcomas: refractory sarcomas AVF2771s + newly-diagnosed osteosarcoma AVF4117s + metastatic soft-tissue sarcoma BO20924).
- **Source aliases:**
  - Study-based indicator "Primary CNS tumour" as defined by paper narrative (Han 2015 Table 1 splits the paediatric cohort into two clinical groups: primary-CNS-tumour patients in AVF3842s and sarcoma patients in AVF2771s + AVF4117s + BO20924, with n = 76 in each group of the model-building population).
- **Example models:** `Han_2015_bevacizumab.R` (multiplicative fractional effect: `CL x 0.725^TUMTP_CNS_PRIM` and `V1 x 0.854^TUMTP_CNS_PRIM` relative to the paediatric-sarcoma reference; Han 2015 Table 2 rows 'Primary CNS tumour on CL' = 0.725 and 'Primary CNS tumour on V1' = 0.854 with RSE 4.3% and 3.7% respectively).
- **Notes:** Follows the `TUMTP_CHL` / `TUMTP_GC` / `TUMTP_SCLC` decomposition pattern. Use this canonical when the source paper reports a single umbrella indicator for primary CNS tumours as a group rather than resolving individual histologies (glioma / medulloblastoma / ependymoma). When a future paper resolves specific primary-CNS-tumour histologies separately, register the finer-grained canonical(s) alongside; the umbrella indicator can coexist with the specific ones because the pooling is a paper-level analytic decision. Scope: specific because the reference category is paper-defined. Ratified canonically on 2026-06-20 alongside the Han 2015 bevacizumab paediatric-popPK extraction.

### TUMTP_LEUK (**canonical for leukemia tumor-type indicator**)
- **Description:** 1 = leukemia (any subtype -- AML / ALL / CLL / CML pooled), 0 = other tumor types. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (per source paper). In Akbar 2025 leukemia is the implicit reference (column not used directly), but the canonical name is registered so that future papers that retain leukemia as a non-reference contrast can use it.
- **Source aliases:**
  - Categorical column "type of cancer" with level "Leukemia" -- decompose into `TUMTP_LEUK = as.integer(cancer_type == "Leukemia")`. Implicit reference category in `Akbar_2025_voriconazole.R` (so the model file does not consume this column directly; it is registered for future heterogeneous-cancer-cohort analyses).
- **Example models:** `Akbar_2025_voriconazole.R`.
- **Notes:** Distinct from the more specific `DIS_AML`, `DIS_BCPALL`, `DIS_CMML`, `DIS_MDS_AML` entries -- those are for leukemia-only or leukemia-vs-leukemia contrasts; `TUMTP_LEUK` is for heterogeneous-cancer pooled cohorts where leukemia is one of several tumor types and the analysis treats `cancer type` as a many-level categorical. Akbar 2025 had leukemia as 56.8% of the cohort and used it as the reference category. Scope: specific because the reference category in any source paper is paper-defined. Ratified canonically on 2026-05-09.

### TUMTP_BCL (**canonical for B-cell lymphoma (pooled residual) tumor-type indicator**)
- **Description:** 1 = B-cell lymphoma (BCL), 0 = other tumor types. Time-fixed per subject. In Gibiansky 2014 the BCL category is a pooled residual indolent-B-cell-lymphoma group that includes follicular lymphoma (FL was the primary indication in GAUDI; the four-level DIS column in the NONMEM control stream splits B-cell histologies into CLL = 1, BCL = 2 (residual indolent B-cell-lymphoma pool including FL), DLBCL = 3, MCL = 4).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (in Gibiansky 2014 the implicit reference is CLL when paired with `TUMTP_DLBCL` and `TUMTP_MCL` all = 0; the residual indolent-B-cell-lymphoma pool in this paper is dominated by follicular lymphoma).
- **Source aliases:**
  - `DIS` (Gibiansky 2014; integer code with `DIS == 2` flagging BCL) -- decompose into `TUMTP_BCL = as.integer(DIS == 2)`.
- **Example models:** `Gibiansky_2014_obinutuzumab.R` (effect on time-dependent clearance decay rate kdes via the composite `(TUMTP_BCL + TUMTP_DLBCL + TUMTP_MCL)` (any-NHL effect; ratio 2.08) and on both time-dependent CL_T and steady-state CL_inf via the composite `(TUMTP_BCL + TUMTP_DLBCL)` (shared BCL/DLBCL effect; ratio 0.834 in the reverse direction, i.e., 16.6% lower CL than CLL)).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` decomposition pattern. Distinct from `TUMTP_LYMPH` (a broader heterogeneous lymphoma pool that lumps cHL with NHL histologies) -- `TUMTP_BCL` is specifically B-cell lymphoma and pairs with sibling `TUMTP_DLBCL` and `TUMTP_MCL` for histology-specific contrasts within the NHL family. When a future paper studies follicular lymphoma in isolation (rather than pooled into BCL), register a more specific canonical (e.g., `TUMTP_FL`) rather than overloading this one. Ratified canonically on 2026-05-11.

### TUMTP_DLBCL (**canonical for diffuse large B-cell lymphoma indicator**)
- **Description:** 1 = diffuse large B-cell lymphoma (DLBCL), 0 = other tumor types. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (in Gibiansky 2014 the implicit reference is CLL when paired with `TUMTP_BCL` and `TUMTP_MCL` all = 0).
- **Source aliases:**
  - `DIS` (Gibiansky 2014; integer code with `DIS == 3` flagging DLBCL) -- decompose into `TUMTP_DLBCL = as.integer(DIS == 3)`.
- **Example models:** `Gibiansky_2014_obinutuzumab.R` (effect on kdes via the any-NHL composite indicator; effect on CL_T and CL_inf via the shared BCL/DLBCL composite indicator), `Lu_2017_polatuzumab_neuropathy.R` (multiplicative effect on the grade >= 2 peripheral neuropathy hazard via `exp(theta_DLBCL * TUMTP_DLBCL)` with `theta_DLBCL = -0.0697`, SE 0.365; the median hazard ratio of DLBCL vs FL is 0.931 per Figure 3).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` decomposition pattern. Distinct from `TUMTP_LYMPH` (broader lymphoma pool) and `TUMTP_PCALCL` (primary cutaneous anaplastic large-cell lymphoma; a CD30+ T-cell-lineage entity unrelated to DLBCL). DLBCL is the most common high-grade B-cell-NHL subtype; the Gibiansky 2014 cohort had only 30 DLBCL patients (4.4%), so a single estimated effect on CL is shared with BCL (a much larger pooled group; see `TUMTP_BCL` notes). Ratified canonically on 2026-05-11.

### TUMTP_MCL (**canonical for mantle cell lymphoma indicator**)
- **Description:** 1 = mantle cell lymphoma (MCL), 0 = other tumor types. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (in Gibiansky 2014 the implicit reference is CLL when paired with `TUMTP_BCL` and `TUMTP_DLBCL` all = 0).
- **Source aliases:**
  - `DIS` (Gibiansky 2014; integer code with `DIS == 4` flagging MCL) -- decompose into `TUMTP_MCL = as.integer(DIS == 4)`.
- **Example models:** `Gibiansky_2014_obinutuzumab.R` (effect on kdes via the any-NHL composite indicator; separate effect on CL_T and CL_inf via the standalone MCL indicator (ratio 1.75, i.e., 75% higher CL than CLL)).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` decomposition pattern. Distinct from `TUMTP_LYMPH` (broader lymphoma pool). The Gibiansky 2014 cohort had only 20 MCL patients (2.9%); the paper reports the highest obinutuzumab CL among the four B-cell-malignancy histologies for MCL, consistent with the highest CD20 expression density on MCL B-cells relative to the other histologies. Ratified canonically on 2026-05-11.

### TUMTP_FL (**canonical for follicular lymphoma indicator**)
- **Description:** 1 = follicular lymphoma (FL), 0 = other tumor types. Time-fixed per subject. FL is the most common indolent non-Hodgkin lymphoma (NHL) subtype, derived from germinal-center B cells and characterized by the t(14;18) BCL2-IGH translocation in most cases.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = all other tumor types (in Lu 2017 the implicit reference is the union of DLBCL and other non-FL NHL when paired with `TUMTP_DLBCL = 0` and `TUMTP_OTHER_NHL = 0`; the Lu 2017 covariate model uses FL itself as the reference category, so the indicator does not appear explicitly in `model()` -- both `TUMTP_DLBCL` and `TUMTP_OTHER_NHL` are 0 for FL patients).
- **Source aliases:**
  - Categorical "tumor histology" column with level `FL` (Lu 2017) -- decompose into `TUMTP_FL = as.integer(tumor_histology == "FL")`.
- **Example models:** `Lu_2017_polatuzumab_neuropathy.R` (FL is the implicit reference category; the model file declares `TUMTP_FL` in `covariatesDataExcluded` to record FL as the reference without referencing it in `model()`).
- **Notes:** Follows the `TUMTP_HODGKIN_CLASSICAL` / `TUMTP_GASTRIC` / `TUMTP_SCLC` decomposition pattern. Distinct from `TUMTP_BCL` (a broader pooled-residual indolent-B-cell-lymphoma indicator in Gibiansky 2014 that lumps FL with other indolent histologies); use `TUMTP_FL` when the source paper names follicular lymphoma in isolation. Ratified canonically on 2026-06-24 alongside the Lu 2017 polatuzumab vedotin TTE extraction.

### TUMTP_OTHER_NHL (**canonical for 'other non-FL non-DLBCL NHL' tumor-histology indicator**)
- **Description:** 1 = a non-follicular-lymphoma (non-FL) non-diffuse-large-B-cell-lymphoma (non-DLBCL) NHL histology (e.g., mantle cell lymphoma, marginal-zone lymphoma, small-lymphocytic lymphoma, transformed FL, chronic lymphocytic leukemia, T-cell NHL, etc., pooled together in the paper's residual histology category), 0 = FL or DLBCL or any non-NHL tumor. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (FL or DLBCL or any non-NHL tumor; in Lu 2017 the implicit reference is FL when paired with `TUMTP_DLBCL = 0`).
- **Source aliases:**
  - Categorical "tumor histology" column with level not in {`FL`, `DLBCL`} (Lu 2017 "others" category) -- decompose into `TUMTP_OTHER_NHL = as.integer(!tumor_histology %in% c("FL", "DLBCL"))`.
- **Example models:** `Lu_2017_polatuzumab_neuropathy.R` (multiplicative effect on hazard of grade >= 2 peripheral neuropathy via the `exp(theta_otherNonFL * TUMTP_OTHER_NHL)` proportional-hazard term with `theta_otherNonFL = 0.688`, SE 0.758; the median hazard ratio of "other non-FL" vs FL is 1.98 per Figure 3 with very wide uncertainty given the small "others" sub-cohort).
- **Notes:** Captures the residual NHL-histology bucket in papers that decompose tumor histology into a three-level categorical (FL reference, DLBCL, otherNonFL). Distinct from `TUMTP_BCL` (Gibiansky 2014 four-level B-cell-lymphoma decomposition: CLL, BCL, DLBCL, MCL -- where BCL is the indolent-B-cell-lymphoma residual including FL). When a future paper retains a different pool of non-FL non-DLBCL histologies, document the per-paper histology composition in `covariateData[[TUMTP_OTHER_NHL]]$notes` rather than overloading the canonical. Ratified canonically on 2026-06-24 alongside the Lu 2017 polatuzumab vedotin TTE extraction.

### LINE_1L (**canonical for first-line-therapy indicator**)
- **Description:** 1 = first-line therapy (1L) / treatment-naive, 0 = second-line or greater (2L+) / relapsed-or-refractory.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (2L+, second-line or greater / relapsed-refractory).
- **Source aliases:**
  - `LINE` (categorical column with levels `1L`, `2L`, `3L+`, ...) -- decompose into `LINE_1L = as.integer(LINE == "1L")`.
  - `RRFN` (relapsed/refractory flag; treatment-naive corresponds to RRFN == 0) -- used in `Lu_2019_polatuzumab.R`. Decompose: `LINE_1L = as.integer(RRFN == 0)`.
- **Example models:** `Sanghavi_2020_ipilimumab.R` (exponential coefficient -0.0949 on CL), `Lu_2019_polatuzumab.R` (multiplicative effects on V1 = 1.20, kdes = 3.38, CL_T = 3.53, FRAC_NS = 0.756; the same pooled-trial NHL cohort mixes 415 R/R and 45 first-line patients).
- **Notes:** Promoted to scope: general on 2026-04-26 after Lu 2019 polatuzumab vedotin ratified the same 1L vs 2L+ binarization that Sanghavi 2020 ipilimumab introduced. The two papers use different indicator semantics (Sanghavi reports the effect as `exp(-0.0949 * LINE_1L)` and Lu reports `theta^LINE_1L` with theta < or > 1 depending on the parameter); both reduce to the same canonical 0/1 column. If a future paper requires finer resolution (separate effects for 2L vs 3L+), add a parallel `LINE_2L` canonical rather than overloading this one.

### NIVO_1Q3W (**canonical for nivolumab 1 mg/kg every 3 weeks co-administration indicator**)
- **Description:** 1 = ipilimumab co-administered with nivolumab 1 mg/kg every 3 weeks; 0 = otherwise (monotherapy or any other nivolumab regimen).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no nivolumab or any non-1Q3W nivolumab regimen).
- **Source aliases:**
  - `NIVO_REGIMEN` (categorical column with levels `none`, `0.3 mg/kg Q3W`, `1 mg/kg Q2W`, `1 mg/kg Q3W`, `3 mg/kg Q2W`, `3 mg/kg Q3W`) -- decompose into `NIVO_1Q3W = as.integer(NIVO_REGIMEN == "1 mg/kg Q3W")`.
- **Example models:** `Sanghavi_2020_ipilimumab.R` (exponential coefficient 0.0950 on ipilimumab CL).
- **Notes:** Paired with `NIVO_3Q2W` in the Sanghavi 2020 final model; both decomposed indicators are 0 for ipilimumab monotherapy. Other nivolumab regimens (0.3 mg/kg Q3W, 1 mg/kg Q2W, 3 mg/kg Q3W) were tested but not retained in the final model and collapse into the reference 0 group.

### NIVO_3Q2W (**canonical for nivolumab 3 mg/kg every 2 weeks co-administration indicator**)
- **Description:** 1 = ipilimumab co-administered with nivolumab 3 mg/kg every 2 weeks; 0 = otherwise (monotherapy or any other nivolumab regimen).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no nivolumab or any non-3Q2W nivolumab regimen).
- **Source aliases:**
  - `NIVO_REGIMEN` (categorical column) -- decompose into `NIVO_3Q2W = as.integer(NIVO_REGIMEN == "3 mg/kg Q2W")`.
- **Example models:** `Sanghavi_2020_ipilimumab.R` (exponential coefficient 0.191 on ipilimumab CL).
- **Notes:** Paired with `NIVO_1Q3W`; same reference grouping convention.

### COMBO_NIVO (**canonical for any-regimen nivolumab combination-therapy indicator**)
- **Description:** 1 = ipilimumab co-administered with any nivolumab regimen, 0 = ipilimumab monotherapy.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (ipilimumab monotherapy).
- **Source aliases:**
  - `COMBO` -- used in `Sanghavi_2020_ipilimumab.R`. Equivalently derivable from `NIVO_REGIMEN` as `COMBO_NIVO = as.integer(NIVO_REGIMEN != "none")`.
- **Example models:** `Sanghavi_2020_ipilimumab.R` (additive effect -0.202 on the Emax parameter of the time-varying CL function).
- **Notes:** Distinct from the per-regimen `NIVO_1Q3W` / `NIVO_3Q2W` indicators on baseline CL: `COMBO_NIVO` aggregates across all nivolumab regimens and acts on the time-varying-CL Emax parameter, whereas the per-regimen indicators act on baseline (time-zero) CL.

### BLSTABL (**canonical for baseline absolute blast counts in peripheral blood**)
- **Description:** Baseline absolute count of blasts (immature lymphoid/myeloid precursor cells) circulating in peripheral blood. Time-fixed baseline value.
- **Units:** 10^9 counts/L (equivalently 10^9 counts; reported as "x 10^9 counts" in Wu 2024 Table 2).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(BLSTABL / <ref>)^exponent`. Reference value observed: 0.352 x 10^9 counts (Wu 2024 Table 3, BCP-ALL median).
- **Source aliases:**
  - `BLSTABL` -- used in `Wu_2024_inotuzumab.R`.
- **Example models:** `Wu_2024_inotuzumab.R` (power exponent -0.0484 on kdes for BCP-ALL patients only; the effect is gated off for B-cell NHL by multiplying the exponent by `DIS_BCPALL`).
- **Notes:** Distinct from blasts in bone marrow (different specimen) and from `BLSTPB` (percentage of blasts in peripheral blood, used by the predecessor Garrett 2019 adult model). Not applicable for B-cell NHL patients in pooled BCP-ALL + NHL analyses (Wu 2024 retains the effect only in BCP-ALL patients via the DIS_BCPALL gate). When supplying BLSTABL for an NHL subject, set the value to the BCP-ALL reference (0.352) so the gated power term evaluates to 1 numerically. Scope: specific because the covariate is most meaningful in B-cell-leukemia population PK analyses; promote to general if a second paper retains it.

### COMBO_RG (**canonical for anti-CD20 (rituximab or obinutuzumab) combination-therapy indicator**)
- **Description:** 1 = polatuzumab vedotin co-administered with rituximab OR obinutuzumab, 0 = single-agent polatuzumab vedotin (or any other regimen lacking an anti-CD20 partner). Time-fixed per subject in the source paper's analysis cohort.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (single-agent polatuzumab vedotin or no anti-CD20 partner).
- **Source aliases:**
  - `COMBO` (categorical: 0 = single agent, 1 = + rituximab, 2 = + obinutuzumab) -- used in `Lu_2019_polatuzumab.R`. Decompose: `COMBO_RG = as.integer(COMBO == 1 | COMBO == 2)`. The Lu 2019 NONMEM separately defines `RTX = as.integer(COMBO == 1)` and `GA101 = as.integer(COMBO == 2)` and applies effects as `theta^(RTX + GA101)`; because RTX and GA101 are mutually exclusive, RTX + GA101 takes values {0, 1} and the effect collapses to `theta^COMBO_RG`.
- **Example models:** `Lu_2019_polatuzumab.R` (multiplicative effects on CL_INF = 0.844, kdes = 0.932, FRAC_NS = 0.709).
- **Notes:** Rituximab and obinutuzumab both bind CD20 on B cells (rituximab is a Type I anti-CD20 mAb, obinutuzumab a glycoengineered Type II), so co-administration is hypothesized to alter polatuzumab vedotin disposition through depletion of CD79b+ target B cells. The Lu 2019 final model fits a single combined effect rather than separate rituximab- and obinutuzumab-specific effects. Scope: specific because the relevant combination partners (CD20-directed mAbs) are tied to NHL pathway; if a future paper distinguishes rituximab from obinutuzumab combinations, register `COMBO_R` and `COMBO_G` separately rather than overloading this canonical.

### COMBO_DURVA (**canonical for durvalumab combination-therapy indicator**)
- **Description:** 1 = the analyzed therapeutic mAb is co-administered with durvalumab (anti-PD-L1 IgG1), 0 = monotherapy.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (monotherapy).
- **Source aliases:**
  - `COMB` -- used in `Hwang_2022_tremelimumab.R` ($INPUT NM-TRAN data item; control-stream switch `IF(COMB.EQ.0)` selects monotherapy parameters and `IF(COMB.EQ.1)` selects combination-therapy parameters).
- **Example models:** `Hwang_2022_tremelimumab.R` (selects between monotherapy and combination-with-durvalumab values of the time-varying-CL Tmax and lambda parameters).
- **Notes:** Parallels `COMBO_NIVO` but for durvalumab rather than nivolumab co-administration. Acts on the time-varying-CL component (Tmax and lambda); baseline CL is shared between monotherapy and combination groups in Hwang 2022.

### COMBO_LEN_DEX (**canonical for lenalidomide plus dexamethasone combination-therapy indicator**)
- **Description:** 1 = the analyzed therapeutic mAb (or other agent under PK study) is co-administered with the lenalidomide + low-dose-dexamethasone (Ld) backbone, 0 = monotherapy or any non-Ld regimen. Lenalidomide is an immunomodulatory imide (IMiD) that activates natural killer cells; dexamethasone is an immunosuppressant glucocorticoid. The Ld backbone is a standard combination partner in multiple-myeloma and other hematologic-malignancy regimens.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (monotherapy or any non-Ld regimen). When a paper reports the reference patient as "with Ld coadministration" (as Ide 2020 does), the model still stores the canonical 0/1 column and applies the effect as `exp(theta * (COMBO_LEN_DEX - 1))` so that COMBO_LEN_DEX = 1 yields factor 1 (paper's reference) and COMBO_LEN_DEX = 0 activates the effect.
- **Source aliases:**
  - `LENDEX` (1 = with Ld, 0 = without Ld; in Ide 2020 derived from `STUDY != 204011` because study 204011 was the Ld-free elotuzumab-monotherapy cohort) -- used in `Ide_2020_elotuzumab.R`.
  - `COMBO_LD` (**retired** canonical name; renamed to `COMBO_LEN_DEX` on 2026-04-27 for clarity).
- **Example models:** `Ide_2020_elotuzumab.R` (multiplicative effects: CLLd = 0.74 on nonspecific CL, encoded as `exp(log(0.74) * (COMBO_LEN_DEX - 1))`; KINTLd = 10.1 on the second-order target-mediated elimination rate from the peripheral compartment, encoded as `exp(log(10.1) * (COMBO_LEN_DEX - 1))`).
- **Notes:** Specific scope because the canonical's mechanistic relevance is hematologic-malignancy-domain-bound (multiple myeloma and related plasma-cell or B-cell disorders). Distinct from `COMBO_BELAMAF` (which pools Ld with bortezomib-dex and pomalidomide-dex into a single broader "any-combination" belantamab indicator); `COMBO_LEN_DEX` is the per-backbone Ld-only flag. If a future paper distinguishes "Ld-only" from a broader "any-IMiD-plus-dex" backbone with separate effects, register a parallel canonical (e.g., `COMBO_PD` for pomalidomide-dex, `COMBO_VD` for bortezomib-dex). Sign of the exponential coefficient is paper-dependent: Ide 2020 reports `CLLd = 0.74` so the Ld-coadministration arm has 26% lower nonspecific CL than the Ld-free arm, but `KINTLd = 10.1` so the Ld arm has 10x higher second-order target-mediated elimination -- both are mechanistically interpretable (dexamethasone suppresses non-specific catabolic clearance; lenalidomide-activated NK cells increase target-cell-binding-mediated elimination).

### COMBO_BELAMAF (**canonical for any-combination belantamab mafodotin therapy indicator**)
- **Description:** 1 = belantamab mafodotin administered as part of a combination regimen (with bortezomib + dexamethasone, lenalidomide + dexamethasone, or pomalidomide + dexamethasone) in the relapsed/refractory multiple myeloma setting; 0 = belantamab mafodotin monotherapy.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (belantamab mafodotin monotherapy).
- **Source aliases:**
  - `COMBO` (when the source dataset uses a generic combination flag for belantamab mafodotin pooled regimens) -- used in `Papathanasiou_2025_belantamab.R`.
- **Example models:** `Papathanasiou_2025_belantamab.R` (multiplicative factor theta = 1.44 on the Imax parameter of the time-varying CL function -- combination therapy increases the steady-state CL reduction from 33.2 % to 44.0 %).
- **Notes:** Pools the three combination backbones tested in DREAMM-6 / DREAMM-7 / DREAMM-8 (Bor-Dex, Len-Dex, Pom-Dex) into a single binary because Papathanasiou 2025 reports no meaningful per-backbone difference in cycle-1 ADC exposure. If a future paper tests per-backbone combination effects, register dedicated indicators (`COMBO_BELAMAF_BORDEX`, etc.) rather than overloading this aggregate.

### KG (**canonical for subject-specific tumour-growth rate constant from a prior IPP fit**)
- **Description:** Empirical-Bayes posterior estimate of the subject-specific tumour-size first-order growth rate constant carried over from an upstream tumour-size-dynamics population model. Supplied per subject in the dataset and used directly inside a downstream model (e.g., overall-survival hazard) that integrates the tumour-size ODE inline conditional on each subject's growth/death rates.
- **Units:** internal scaled rate; the Zecchin 2016 OS model carries the source convention `KG / 1000 * tumorSize` in the SLD ODE so the column units are `(1/day) * 1000` as published in the source NONMEM run. Document per-model via `covariateData[[KG]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used directly inside the SLD ODE (see `Zecchin_2016_survival.R`).
- **Source aliases:**
  - `KG` (NONMEM `$INPUT` column in DDMODEL00000218; identical column shipped in the bundle's Simulated_OS.csv).
- **Example models:** `Zecchin_2016_survival.R` (Zecchin 2016 OS model, DDMODEL00000218).
- **Notes:** Specific scope because the column is the empirical-Bayes output of a particular upstream fit (`modellib('Zecchin_2016_tumorovarian')` / DDMODEL00000217). When this OS model is used standalone, the user must supply `KG` per subject -- typically by first fitting the SLD model and extracting the per-subject empirical-Bayes posterior. The internal `/1000` scaling is preserved verbatim from the source `$DES` block to maintain numerical equivalence with the published estimates.

### KD0 (**canonical for subject-specific carboplatin-related tumour-death rate constant from a prior IPP fit**)
- **Description:** Empirical-Bayes posterior estimate of the subject-specific carboplatin-driven tumour-size death rate constant carried over from an upstream tumour-size-dynamics population model. Pairs with the time-varying `AUC_CARBO` covariate inside the SLD ODE term `KD0 * AUC_CARBO * tumorSize`.
- **Units:** internal scaled rate; the Zecchin 2016 OS model carries the source convention `KD0 / 1000 * AUC_CARBO * tumorSize` so the column units are `(1/day per AUC unit) * 1000` as published in the source NONMEM run. Document per-model via `covariateData[[KD0]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used directly inside the SLD ODE (see `Zecchin_2016_survival.R`).
- **Source aliases:**
  - `KD0` (NONMEM `$INPUT` column in DDMODEL00000218).
- **Example models:** `Zecchin_2016_survival.R` (Zecchin 2016 OS model, DDMODEL00000218).
- **Notes:** Specific scope because the column is tied to a specific drug (carboplatin) and a specific upstream IPP fit. The internal `/1000` scaling is preserved verbatim from the source `$DES` block.

### KD1 (**canonical for subject-specific gemcitabine-related tumour-death rate constant from a prior IPP fit**)
- **Description:** Empirical-Bayes posterior estimate of the subject-specific gemcitabine-driven tumour-size death rate constant carried over from an upstream tumour-size-dynamics population model. Pairs with the time-varying `AUC_GEM` covariate inside the SLD ODE term `KD1 * AUC_GEM * tumorSize`.
- **Units:** internal scaled rate; the Zecchin 2016 OS model carries the source convention `KD1 / 100 * AUC_GEM * tumorSize` so the column units are `(1/day per AUC unit) * 100` as published in the source NONMEM run. Document per-model via `covariateData[[KD1]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used directly inside the SLD ODE (see `Zecchin_2016_survival.R`).
- **Source aliases:**
  - `KD1` (NONMEM `$INPUT` column in DDMODEL00000218).
- **Example models:** `Zecchin_2016_survival.R` (Zecchin 2016 OS model, DDMODEL00000218).
- **Notes:** Specific scope because the column is tied to a specific drug (gemcitabine) and a specific upstream IPP fit. The internal `/100` scaling is preserved verbatim from the source `$DES` block.

### IBASE (**canonical for subject-specific baseline tumour-size estimate from a prior IPP fit**)
- **Description:** Empirical-Bayes posterior estimate of the subject-specific baseline sum-of-longest-diameters (SLD) tumour size carried over from an upstream tumour-size-dynamics population model. Used both to set the SLD ODE initial state (`tumorSize(0) <- IBASE * 1000` in the Zecchin 2016 model: source convention multiplies by 1000 to convert the internal value to mm) and to scale the time-varying tumour-size ratio (`mmbas <- IBASE * 1000`) inside the OS hazard.
- **Units:** internal scaled length; the Zecchin 2016 OS model carries the source convention `IBASE * 1000 = mm` so the column itself is in metres (1 m = 1000 mm). Document per-model via `covariateData[[IBASE]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as the SLD ODE initial condition and as the denominator in `(tumorSize - mmbas) / mmbas`.
- **Source aliases:**
  - `IBASE` (NONMEM `$INPUT` column in DDMODEL00000218; identical column shipped in the bundle's Simulated_OS.csv with values typically in the 0.04-0.50 m range).
- **Example models:** `Zecchin_2016_survival.R` (Zecchin 2016 OS model, DDMODEL00000218).
- **Notes:** Distinct from the canonical `TUM_SLD` column. `TUM_SLD` carries the *measured* baseline tumour size in mm (used to compute the time-fixed `NSLD0 = TUM_SLD / 70` covariate term in the Zecchin 2016 OS hazard), whereas `IBASE` carries the empirical-Bayes *fitted* baseline from the upstream SLD model (used to initialise the integrated SLD trajectory and to define the time-varying TSR(t) reference). The two are correlated but not equal because the upstream IPP fit smooths measurement noise away from the observed SLD0. Specific scope because the column is the empirical-Bayes output of a specific upstream model fit and the internal `*1000` unit-conversion is tied to the source NONMEM coding convention.

### NEW_LESION (**canonical for time-varying new-lesion appearance indicator**)
- **Description:** Time-varying binary indicator of whether a new (non-target) RECIST lesion has appeared since enrolment. 1 = new lesion present at the current observation time; 0 = no new lesion as of the current observation time. Once `NEW_LESION` flips to 1 it stays 1 for subsequent observation times in that subject (a step-function flag, not a transient pulse).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no new lesion appeared as of the current time).
- **Source aliases:**
  - `NWLS` -- prior canonical name (pre-2026-06-19 readability standardization) and NONMEM `$INPUT` column in DDMODEL00000218; the bundle's Simulated_OS.csv re-labels the same column `NWLSCOV`. Downstream consumers should map `NWLSCOV` -> `NEW_LESION`.
- **Example models:** `Zecchin_2016_survival.R` (Zecchin 2016 OS model, DDMODEL00000218; multiplicative effect on the Weibull hazard via `exp(e_nwls_haz * NEW_LESION)` with `e_nwls_haz = 1.23` per Output_real_OS.lst FINAL TH5 / Table 2 of Zecchin 2016).
- **Notes:** Specific scope because the column encodes a per-paper RECIST-style binary that is supplied by the dataset; it is not a generic "any new lesion" indicator the user can populate from routine clinical data without an explicit lesion-appearance imaging schedule. The Zecchin 2016 OS model uses `NEW_LESION` directly (no time-gating), which is faithful to the simulated dataset shipped in the DDMORE bundle. The source `Output_real_OS.lst` (the listing on the original real dataset) gates the indicator with an additional `TNWLS` (lesion-appearance-time) column not shipped in the bundle's simulated dataset; the two encodings are functionally equivalent when the dataset's `NEW_LESION` column is constructed as a 0/1 step that flips at the lesion-appearance time. The bundle's simulated dataset uses the simpler step-function form, and that is the form the nlmixr2lib model expects. Renamed from `NWLS` to `NEW_LESION` on 2026-06-19 per the canonical-register standardization audit (operator decision: readability over abbreviation, since `NWLS` is opaque and not a universally-recognized clinical-trial term; no DIS_ prefix because this is a RECIST-imaging event indicator rather than a disease-state indicator).

### MM_NIGG (**canonical for non-IgG multiple myeloma immunoglobulin-type indicator**)
- **Description:** 1 = patient with non-IgG-secreting multiple myeloma (e.g., IgA, IgD, IgE, IgM, light-chain-only / Bence Jones, or non-secretory MM), 0 = patient with IgG-secreting multiple myeloma.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (IgG MM).
- **Source aliases:**
  - `Ig_type` -- used in `Fau_2020_isatuximab.R`. Values 0 / 1 with the same orientation as the canonical (1 = non-IgG MM).
- **Example models:** `Fau_2020_isatuximab.R` (exponential effect on the steady-state linear CL CLinf with coefficient -0.751, and on the time-varying-CL half-time KCL with coefficient -0.931), `Xu_2020_daratumumab.R` (additive shift `(1 + 0.806 * (1 - MM_NIGG))` on linear CL -- Xu 2020 parameterises with non-IgG MM as reference, so an IgG MM patient receives an 80.6% higher linear CL than a non-IgG MM patient; canonical column semantics 1 = non-IgG / 0 = IgG are preserved).
- **Notes:** Within-disease (multiple-myeloma) immunoglobulin-subtype stratifier. The mechanistic rationale (Fau 2020) is that endogenous IgG monoclonal protein in IgG-MM patients competes with the therapeutic IgG mAb for FcRn-mediated salvage, raising the therapeutic mAb's catabolic clearance; non-IgG-MM patients lack that competition and exhibit lower therapeutic-mAb clearance. Distinct from the disease-state indicators (`DIS_SMM` = smoldering MM); applies only after a multiple-myeloma diagnosis is established. Scope: specific because the comparison is a within-MM stratifier rather than a cross-population indicator. Reference category at the model level (which value of MM_NIGG corresponds to TVCL = base) varies between papers: Fau 2020 anchors to 0 (IgG MM) and Xu 2020 anchors to 1 (non-IgG MM); the canonical column orientation (1 = non-IgG) is fixed across papers and the per-model `covariateData[[MM_NIGG]]$reference_category` field records which anchor each model uses.

### TUM_TP53_MUT (**canonical for tumour TP53 / p53 mutation indicator**)
- **Description:** Binary indicator of tumour-cell TP53 mutational status as assessed in the source paper. 1 = TP53 mutant tumour (typically a missense mutation detected by sequencing, or p53 protein overexpression by immunohistochemistry used as a surrogate for TP53 missense mutation per Gillet et al. J Neurooncol 2014); 0 = TP53 wild-type tumour. Time-fixed per subject (a somatic tumour-genotype call made at diagnosis, not a germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (TP53 wild-type).
- **Source aliases:**
  - "p53 mutation" / "p53 mutated" (Mazzocco 2015, paper text; p53 overexpression by IHC used as a surrogate for TP53 missense mutation).
- **Example models:** `Mazzocco_2015_temozolomide.R` (exponential effect on the temozolomide tumour-cell-death rate constant `gamma`: `gamma = gamma0 * exp(beta_p53 * TUM_TP53_MUT)` with `beta_p53 = log(0.143 / 0.254) = -0.574`; TP53-mutant LGG tumours are 44% less sensitive to TMZ than TP53-wild-type tumours).
- **Notes:** Specific scope because the column encodes a somatic tumour-genotype call (mechanism = altered DNA-damage response in tumour cells) rather than a germline pharmacogenomic variant. Distinct from the `SNP_<GENE>_<RSID>` family, which encodes inherited host germline genotypes affecting drug PK; `TUM_TP53_MUT` encodes a tumour-cell mutation and only makes mechanistic sense for drugs whose effect depends on a functional p53 pathway in the tumour. The Mazzocco 2015 cohort assayed p53 status by IHC overexpression; future extractions that use direct TP53 sequencing should still record their values under this canonical and document the assay method in `covariateData[[TUM_TP53_MUT]]$notes`. Ratified canonically on 2026-05-17 alongside the Mazzocco 2015 temozolomide extraction.

### TUM_1P19Q_CODEL (**canonical for tumour 1p/19q chromosomal codeletion indicator**)
- **Description:** Binary indicator of tumour-cell 1p/19q chromosomal codeletion status. 1 = tumour carries the combined loss of the short arm of chromosome 1 (1p) and the long arm of chromosome 19 (19q); 0 = non-codeleted tumour (intact 1p and/or 19q). Time-fixed per subject (a somatic tumour-genotype call made at diagnosis, typically by fluorescence in-situ hybridisation, comparative genomic hybridisation, or loss-of-heterozygosity assay).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-codeleted; intact 1p and/or 19q).
- **Source aliases:**
  - "1p/19q codeletion" / "1p/19q codeleted" (Mazzocco 2015, paper text).
- **Example models:** `Mazzocco_2015_temozolomide.R` (exponential effect on the damaged-quiescent-to-proliferative repair rate constant `kQpP`: `kQpP = kQpP0 * exp(beta_1p19q * TUM_1P19Q_CODEL)` with `beta_1p19q = log(0.00807 / 0.00947) = -0.160`; 1p/19q-codeleted LGG tumours have 15% lower kQpP than non-codeleted tumours, consistent with longer reported duration of response in codeleted patients).
- **Notes:** Specific scope because the column encodes a brain-tumour-specific somatic chromosomal alteration with mechanistic relevance to DNA-repair capacity in glioma cells; it is not a generic "any tumour-chromosomal-alteration" indicator. The 1p/19q codeletion is a defining molecular feature of oligodendrogliomas (per the 2016 WHO classification of CNS tumours) and is mutually exclusive with TP53 missense mutation in the Mazzocco 2015 cohort (Ricard 2007, ref 12 of Mazzocco 2015). Ratified canonically on 2026-05-17 alongside the Mazzocco 2015 temozolomide extraction.

### TUM_IGHV_MUT (**canonical for immunoglobulin heavy-chain variable region (IGHV) mutational status**)
- **Description:** Binary indicator of the somatic hypermutation status of the immunoglobulin heavy-chain variable region (IGHV) gene rearrangement in a mature B-cell clone, most commonly assayed in chronic lymphocytic leukaemia (CLL). 1 = IGHV-mutated (the clone carries >= 2% divergence from the closest germline IGHV sequence); 0 = IGHV-unmutated (< 2% divergence). Time-fixed per subject -- IGHV status is fixed at the point of clonal transformation and does not change with therapy. Note the prognostic polarity: unlike most mutation indicators, the MUTATED state is the FAVOURABLE one in CLL (IGHV-mutated disease has an indolent course, IGHV-unmutated disease is aggressive), so a positive coefficient on this column usually corresponds to a *lower* disease burden.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (IGHV-unmutated).
- **Source aliases:**
  - `IMPIGVHMS` (Ibrahim 2023 nlmixr control stream, Supporting Information S4 run100: `igvh <- IMPIGVHMS`) -- the `IMP` prefix records that missing categorical covariates (n <= 6) were mode-imputed per Ibrahim 2023 Appendix S1 section 2. Note the source column transposes the `VH` / `HV` letters relative to the standard `IGHV` abbreviation.
  - "IGHV mutational status" / "IGVH-mutated" / "IGVH-unmutated" (Ibrahim 2023 paper text).
- **Example models:** `Ibrahim_2023_ibrutinib_leukocyte_spd.R` (selects the baseline-SPD typical value: `spdbase = spdbase_unm * (1 - TUM_IGHV_MUT) + spdbase_m * TUM_IGHV_MUT`, giving 48.9 cm^2 for unmutated vs 19.5 cm^2 for mutated -- a 2.5-fold higher baseline lymph-node burden in IGHV-unmutated patients).
- **Notes:** Specific scope; a somatic B-cell-clone genotype call rather than a host germline variant, so it belongs to the `TUM_<MARKER>` family (`TUM_TP53_MUT`, `TUM_1P19Q_CODEL`) and NOT to the `SNP_<GENE>_RS<rsid>` germline-pharmacogenomics family. Encoded as an effect on a disease-burden parameter rather than on a PK parameter, which is the typical use: IGHV status is a disease-biology marker, not a metabolism marker. When a source paper reports the covariate with the opposite polarity (an "IGHV-unmutated = 1" column, which some CLL papers use because unmutated is the high-risk group), convert on ingestion with `TUM_IGHV_MUT = 1 - <source column>` and record the transformation in `covariateData[[TUM_IGHV_MUT]]$notes`. Ratified canonically on 2026-07-30 alongside the Ibrahim 2023 ibrutinib extraction.

### TUM_17P_DEL (**canonical for deletion(17p) chromosomal abnormality indicator**)
- **Description:** Binary indicator of the deletion(17p) chromosomal abnormality in a tumour / leukaemic clone. 1 = del(17p) present, 0 = absent. The deleted region carries the *TP53* locus, so del(17p) is functionally a TP53-loss marker and defines the highest-risk cytogenetic subgroup in chronic lymphocytic leukaemia (10-year overall survival 29%; Ibrahim 2023 Discussion). Time-fixed per subject; a baseline cytogenetic call, typically by fluorescence in-situ hybridisation (FISH).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no del(17p)).
- **Source aliases:**
  - `del(17p)` / "deletion (17p)" (Ibrahim 2023 paper text and Table 2 footnote f).
- **Example models:** `Ibrahim_2023_ibrutinib_competing_risk.R` (multiplicative effect on the alive-to-death transition rate of the competing-risk multistate model: `exp(1.42 * TUM_17P_DEL)`, a hazard ratio of 4.14).
- **Notes:** Specific scope; a somatic chromosomal deletion, so it belongs to the `TUM_<MARKER>` family alongside `TUM_1P19Q_CODEL` (the other registered chromosomal-alteration indicator) and NOT to the germline `SNP_<GENE>_RS<rsid>` family. Distinct from `TUM_TP53_MUT`: `TUM_TP53_MUT` records a point mutation / IHC-overexpression call at the TP53 locus, whereas `TUM_17P_DEL` records loss of the whole chromosome arm carrying it. The two are frequently but not always concordant (del(17p) and TP53 mutation can occur independently, and papers that assay both usually report them as separate columns), so do not alias one onto the other -- register a paper's del(17p) column here and its TP53 sequencing / IHC column under `TUM_TP53_MUT`. Ibrahim 2023 also screened deletion(11q) and deletion(13q) as candidate covariates but retained neither in any final model, so no canonicals are registered for them. Ratified canonically on 2026-07-30 alongside the Ibrahim 2023 ibrutinib extraction.

### WHO_PS (**canonical for ordinal-numeric performance-status integer (WHO / ECOG)**)
- **Description:** World Health Organization (WHO) performance-status integer score, identical-by-construction with the Eastern Cooperative Oncology Group (ECOG) PS per the Oken 1982 cross-walk (Am J Clin Oncol 5:649-655). Scale: 0 = fully active / asymptomatic, 1 = ambulatory and able to do light work, 2 = ambulatory and capable of all self-care but unable to work, 3 = bedridden > 50% of waking hours, 4 = completely disabled / bedridden, 5 = dead. Used as a continuous-numeric covariate when the source paper enters the integer score directly into a linear / exponential / power covariate model (e.g. `CL ~ (1 - theta_PS * WHO_PS)` or `CL ~ exp(theta_PS * WHO_PS)`) rather than binarizing into `ECOG_GE1` / `ECOG_GE2`. Time-fixed per subject in the typical baseline-PS application; document time-varying use in per-model `notes`.
- **Units:** (integer score; document the empirical range and reference value in `covariateData[[WHO_PS]]$notes`)
- **Type:** continuous (semantically ordinal but treated as continuous in the covariate model)
- **Scope:** general
- **Reference category:** n/a -- used with linear or power scaling. Reference value observed: `WHO_PS = 0` (Leger 2004 reference patient implicit; the typical CL formula `(1 - 0.12 * WHO_PS)` evaluates to 1 at `WHO_PS = 0`).
- **Source aliases:**
  - `PS` -- WHO performance status integer; used in `Leger_2004_topotecan.R` and historically in Mould 2002 and Gallo 2000 topotecan popPK papers (cited by Leger 2004) using the same linear-ordinal form.
  - `ECOG` -- equivalent ECOG performance status integer.
  - `PERF` -- common NONMEM column header for the ordinal performance-status integer.
- **Example models:** `Leger_2004_topotecan.R` (linear-ordinal effect `(1 - 0.12 * WHO_PS)` on the renal-plus-non-renal CL formula `(theta1 + theta2 * CrCl_Lh) * (1 - e_who_ps_cl * WHO_PS)`; PS distribution in cohort 0 / 1 / 2 / 3 = 79 / 98 / 11 / 2 patients).
- **Notes:** Distinct from `ECOG_GE1` / `ECOG_GE2` (binary indicators used when the source paper collapses the ordinal score). Use `WHO_PS` when the paper retains the integer score in a linear / exponential / power covariate form. If a future paper provides the ordinal integer column but binarizes the effect downstream, derive `ECOG_GE1 = as.integer(WHO_PS >= 1)` and `ECOG_GE2 = as.integer(WHO_PS >= 2)`; do not duplicate the same patient-level integer under two columns. WHO and ECOG PS are operationally identical and cross-walked one-to-one per Oken 1982 (Am J Clin Oncol 5:649-655); the canonical name `WHO_PS` is chosen because the founding example (Leger 2004) names it as WHO performance status. Scope general because ordinal-linear PS is the dominant historical NONMEM covariate-model form for oncology popPK papers and is likely to recur in future extractions.


### GEMOX (**canonical for gemcitabine-then-oxaliplatin sequence indicator**)
- **Description:** 1 = gemcitabine 30-min IV infusion administered first, immediately followed by oxaliplatin 120-min IV infusion on the same study day; 0 = otherwise (gemcitabine alone or oxaliplatin-then-gemcitabine). Time-fixed per cycle.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = gemcitabine monotherapy when paired with `OXGEM = 0`; the combination-vs-monotherapy reference is encoded jointly with `OXGEM`.
- **Source aliases:**
  - `GEMOX` -- used in `Jiang_2008_gemcitabine.R`.
- **Example models:** `Jiang_2008_gemcitabine.R` (multiplicative factor 0.65 on apparent dFdU central volume V_C,dFdU/F when GEMOX = 1; Jiang 2008 page 330 covariate equation: `0.65^GEMOX`).
- **Notes:** Paired with `OXGEM` as the second arm of a 3-level oxaliplatin-coadministration-sequence categorical decomposition (gem-alone / gem-then-ox / ox-then-gem). The two indicators are mutually exclusive: `GEMOX + OXGEM <= 1`. Follows the `NIVO_1Q3W` / `NIVO_3Q2W` decomposition pattern. Scope: specific because the sequence-specific effect is tied to Jiang's gemcitabine + oxaliplatin study design; promote to general if a second paper retains the same pair of indicators.


### OXGEM (**canonical for oxaliplatin-then-gemcitabine sequence indicator**)
- **Description:** 1 = oxaliplatin 120-min IV infusion administered first, immediately followed by gemcitabine 30-min IV infusion on the same study day; 0 = otherwise (gemcitabine alone or gemcitabine-then-oxaliplatin). Time-fixed per cycle.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = gemcitabine monotherapy when paired with `GEMOX = 0`.
- **Source aliases:**
  - `OXGEM` -- used in `Jiang_2008_gemcitabine.R`.
- **Example models:** `Jiang_2008_gemcitabine.R` (multiplicative factor 0.54 on apparent dFdU central volume V_C,dFdU/F when OXGEM = 1; Jiang 2008 page 330 covariate equation: `0.54^OXGEM`).
- **Notes:** Paired with `GEMOX` (see that entry for the joint encoding). The Jiang 2008 cohort distribution is gem-alone n=31, gem-then-ox n=38, ox-then-gem n=25; the larger V_C,dFdU/F reduction observed for ox-then-gem (factor 0.54) vs gem-then-ox (factor 0.65) is hypothesised by the authors to reflect order-dependent tissue-uptake effects of dFdU.


### TUM_CELLS0 (**canonical for subject-specific baseline tumour burden expressed as an absolute cell count**)
- **Description:** Per-subject baseline number of malignant cells in the modelled compartment, in absolute cells (not mm, mm^3, or SLD). Used to initialise a mechanistic tumour-cell ODE state whose currency is cell count rather than a caliper- or imaging-derived size (`tumor(0) <- TUM_CELLS0`). In Minucci 2024 the value is the fitted initial malignant-B-cell burden obtained by trust-region optimisation against the individual CAR T-cell cellular-kinetic trajectory, because the IM19 phase I trial (Ying 2021) reported no direct longitudinal tumour-burden measurement.
- **Units:** cells (count)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used directly as the tumour-cell ODE initial condition.
- **Source aliases:**
  - `N0_tumorCells` -- column in the Minucci 2024 Supplementary Material `Table 1 (1).XLSX`, sheet `params-case-study-final`; used in `Minucci_2024_CART_qsp.R`.
- **Example models:** `Minucci_2024_CART_qsp.R` (13 relapsed/refractory NHL patients; fitted values span 1.0e5 to 1.0e7 cells, with F0109 / F0110 / F0111 / F0123 pinned at the 1e5 lower optimisation bound and F0104 / F0106 / F0107 / F0118 / F0119 at the 1e7 upper bound).
- **Notes:** Deliberately distinct from the imaging/caliper tumour-burden family. `TUMSZ` and `TUM_SLD` carry measured sizes in mm; `TUM_VOL` carries a caliper volume in mm^3; `IBASE` carries an empirical-Bayes *fitted* baseline SLD from an upstream tumour-size model. `TUM_CELLS0` is the cell-count analogue, appropriate for mechanistic / QSP models that track individual malignant cells and their receptor burden, where no length or volume conversion is defined by the source. Same fitted-input class as `KG` / `KD0` / `KD1` / `IBASE` (subject-specific values carried over from a prior fit rather than measured at baseline), hence scope: specific -- a downstream user cannot populate this column from routine clinical data without re-running the source optimisation. Note that several Minucci 2024 values sit exactly on the optimiser's bounds, so they are identifiability-limited rather than precisely estimated; treat the column as a per-subject model input, not as a clinical measurement.

### NDIV (**canonical for the fitted number of divisions per activated cell in an expansion-driven cell-therapy model**)
- **Description:** Per-subject number of cell divisions that an activated (antigen-stimulated) therapeutic cell undergoes before differentiating into the effector phenotype. Drives the expansion amplification factor `2^NDIV` applied to the activated-cell-to-effector flux. Non-integer values are expected and meaningful: the parameter is fitted on a continuous scale and represents a population-average division count, not a per-cell integer.
- **Units:** divisions (dimensionless count, continuous-valued)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- multiplicative expansion parameter.
- **Source aliases:**
  - `ndiv` -- column in the Minucci 2024 Supplementary Material `Table 1 (1).XLSX`, sheet `params-case-study-final` (the sheet's `units` cell reads `# cells`, which is a labelling error: the description column and the paper's Methods 2.3 both define it as a division count); used in `Minucci_2024_CART_qsp.R`.
- **Example models:** `Minucci_2024_CART_qsp.R` (fitted per-patient values 15.88 to 27.70 divisions across the 13 IM19 patients).
- **Notes:** Scope: specific because the value is the output of a per-subject fit to that subject's own cellular-kinetic trajectory, not an independently measurable patient characteristic. Minucci 2024 global sensitivity analysis identifies `NDIV` as the single most influential parameter for CAR T-cell Cmax, contributing more than 80 percent of the variance -- so the column is load-bearing and must not be defaulted. Although the paper classifies it as a drug-product (manufacturing) property, it is carried as a per-subject covariate because CAR T-cell products are manufactured from each patient's own cells. Promote to general only if a second cell-therapy extraction retains an identically-defined division-count input.

### FMEM (**canonical for the fitted effector-to-memory conversion fraction in a cell-therapy model**)
- **Description:** Per-subject fraction of effector therapeutic cells that convert to the long-lived memory phenotype at the point of effector-cell loss; the remaining `1 - FMEM` truly die. Governs the long-term persistence phase of cellular kinetics.
- **Units:** fraction (0 to 1)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- multiplicative branching fraction on the effector-loss flux.
- **Source aliases:**
  - `fmem` -- column in the Minucci 2024 Supplementary Material `Table 1 (1).XLSX`, sheet `params-case-study-final`; used in `Minucci_2024_CART_qsp.R`.
- **Example models:** `Minucci_2024_CART_qsp.R` (fitted per-patient values spanning nine orders of magnitude, 2.91e-10 to 0.1, across the 13 IM19 patients).
- **Notes:** Scope: specific because the value is a per-subject fit output rather than a measured characteristic. Three of the 13 Minucci 2024 patients (F0110, F0111, F0123) sit exactly on the 0.1 upper optimisation bound and three more (F0119, F0125, F0126) are numerically indistinguishable from zero, so the column is identifiability-limited at both ends; the paper interprets the high-`FMEM` patients as those with no clear contraction phase in their cellular kinetics. Distinct from a residual-error or variance term: this is a structural branching fraction supplied per subject as data.

### FCD8TDP (**canonical for the CD8-positive fraction of an infused cell-therapy drug product**)
- **Description:** Fraction of the infused therapeutic cell dose that is CD8-positive; the remaining `1 - FCD8TDP` is CD4-positive. Characterises the composition of the administered product rather than the patient, and is used to split a single total cell dose between the CD8 and CD4 arms of a phenotype-resolved cellular-kinetic model.
- **Units:** fraction (0 to 1)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- dose-splitting fraction.
- **Source aliases:**
  - `fCD8Tdp` -- column in the Minucci 2024 Supplementary Material `Table 1 (1).XLSX`, sheet `params-case-study-final`, derived from the per-patient CD4:CD8 ratio of the infused IM19 product reported in Ying 2021; used in `Minucci_2024_CART_qsp.R`.
- **Example models:** `Minucci_2024_CART_qsp.R` (per-patient values 0.290 to 0.943 across the 13 IM19 patients; drives `amt_CD8 = FCD8TDP * WT * dose_per_kg` and `amt_CD4 = (1 - FCD8TDP) * WT * dose_per_kg` in the event table, and is exposed in `model()` as the `CD8_dose_frac` output).
- **Notes:** A drug-product characterisation covariate, not a patient covariate -- the closest existing analogues are the `FORM_<drug>_<formulation>` formulation indicators, but those are categorical product selectors whereas this is a continuous composition fraction. Scope: specific because autologous cell-therapy product composition is measured per manufacturing run and has no analogue in small-molecule or monoclonal-antibody popPK. Note that Minucci 2024 assumes 100 percent product viability (`fViable = 1`), so `FCD8TDP` alone determines the CD8/CD4 dose split; a source reporting a viability below 1 would need that factor carried separately rather than folded into this column.


## Laboratory / disease-activity

### ALBR (**canonical for serum albumin normalized to the laboratory's upper limit of normal**)
- **Description:** Serum albumin normalized to the laboratory's upper limit of normal (`albumin_observed / ULN_albumin`).
- **Units:** (unitless ratio)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used as a power term `(ALBR / <ref>)^exponent`. Reference 0.78 used in Xu 2019 (corresponds to a median serum albumin of 38 g/L at a typical ULN of ~48.7 g/L).
- **Source aliases:** none.
- **Example models:** `Xu_2019_sarilumab.R`.
- **Notes:** Xu 2019 normalizes to each site's ULN so that values across multiple labs with different reference ranges can be pooled. Scoped specific because the ULN-normalization convention is tied to the Xu 2019 analysis plan; future papers using the same ratio should either add themselves to the example_models list or promote this entry to general.

## Inflammatory-bowel-disease disease-activity covariates

### SCORE_CALPRO (**canonical for fecal calprotectin**)
<!-- AUDIT 2026-06-19: renamed from `CALPRO` to `SCORE_CALPRO` per the canonical-register standardization audit. The prior name `CALPRO` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Fecal calprotectin, a gut-inflammation biomarker (baseline or time-fixed per subject unless a paper explicitly uses a time-varying value).
- **Units:** mg/kg stool (equivalent to ug/g). Document per-model via `covariateData[[SCORE_CALPRO]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(SCORE_CALPRO / ref)^exponent`. Reference 700 mg/kg used in Rosario 2015 (overall population median).
- **Source aliases:**
  - `CALPRO` -- prior canonical name (pre-2026-06-19 SCORE_ family standardization).
- **Example models:** `Rosario_2015_vedolizumab.R` (reference 700 mg/kg; exponent +0.0310 on linear clearance CLL).
- **Notes:** Common IBD severity biomarker (inflammation of the gut epithelium). Assays typically report in ug/g stool; 1 ug/g = 1 mg/kg. Document per-model whether baseline-only or time-varying in `covariateData[[SCORE_CALPRO]]$notes`.

### SCORE_CDAI (**canonical for Crohn's Disease Activity Index**)
<!-- AUDIT 2026-06-19: renamed from `CDAI` to `SCORE_CDAI` per the canonical-register standardization audit. The prior name `CDAI` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Crohn's Disease Activity Index composite score. Higher values indicate more active disease; <150 remission, 150-219 mild, 220-450 moderate, >450 severe. Defined only for patients with a CD diagnosis; set to the reference value (or gate via the indicator) for UC patients.
- **Units:** (score, 0-600)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(SCORE_CDAI / ref)^exponent`. Reference 300 used in Rosario 2015 (typical moderate-CD score).
- **Source aliases:**
  - `CDAI` -- prior canonical name (pre-2026-06-19 SCORE_ family standardization).
- **Example models:** `Rosario_2015_vedolizumab.R` (reference 300; exponent -0.0515 on CLL gated by `IBD_CD` so the effect applies only to CD patients).
- **Notes:** Mutually exclusive with `SCORE_PMAYO` in pooled UC+CD populations: each patient has exactly one disease-activity score (SCORE_CDAI for CD, partial Mayo for UC). Gate via the `IBD_CD` indicator when pooling.

### SCORE_PMAYO (**canonical for partial Mayo score**)
<!-- AUDIT 2026-06-19: renamed from `PMAYO` to `SCORE_PMAYO` per the canonical-register standardization audit. The prior name `PMAYO` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Partial Mayo score for ulcerative colitis (sum of stool-frequency, rectal-bleeding, and physician-global subscores, range 0-9). Higher values indicate more active disease. Defined only for patients with a UC diagnosis; gate via the `IBD_CD` indicator when pooling UC+CD.
- **Units:** (score, 0-9)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(SCORE_PMAYO / ref)^exponent`. Reference 6 used in Rosario 2015 (typical moderate-UC score).
- **Source aliases:**
  - `PMAYO` -- prior canonical name (pre-2026-06-19 SCORE_ family standardization).
- **Example models:** `Rosario_2015_vedolizumab.R` (reference 6; exponent +0.0408 on CLL gated by `(1 - IBD_CD)` so the effect applies only to UC patients).
- **Notes:** The partial Mayo score excludes the endoscopy subscore (the full Mayo score is 0-12). Mutually exclusive with `SCORE_CDAI` in pooled UC+CD populations.

### SCORE_MAYO_E (**canonical for baseline Mayo endoscopic subscore**)
<!-- AUDIT 2026-06-19: renamed from `MAYO_E` to `SCORE_MAYO_E` per the canonical-register standardization audit. The prior name `MAYO_E` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** Mayo endoscopic subscore at baseline for ulcerative colitis, integer 0-3 (0 = normal / inactive disease, 1 = mild, 2 = moderate, 3 = severe). The endoscopic subscore is one of the four components of the full Mayo score (0-12); the partial Mayo score (`SCORE_PMAYO`) excludes it. Time-fixed per subject.
- **Units:** (score, 0-3)
- **Type:** categorical
- **Scope:** general
- **Reference category:** depends on per-model encoding -- papers that include the full 0-3 range typically reference Mayo 0 or 1 (mild), while papers restricted to moderate-to-severe UC (the typical biologic-induction-therapy population) reference Mayo 2 or 3. Document the per-model reference category in `covariateData[[SCORE_MAYO_E]]$reference_category`.
- **Source aliases:**
  - `MAYO_E` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `MPRE` -- used in `Faelens_2021_infliximab.R` (NONMEM column for "Mayo endoscopic score pre-induction"). The Faelens 2021 dataset additionally codes a "missing" sentinel `MPRE = -99`; treat this as out-of-domain when applying the model and document per-model.
- **Example models:** `Faelens_2021_infliximab.R` (categorical effect on KE: separate typical KE for Mayo 1 / Mayo 2 / Mayo 3; reference category Mayo 2).
- **Notes:** Distinct from the full Mayo score (0-12) and the partial Mayo score (`SCORE_PMAYO`, 0-9). The endoscopic subscore alone is the core inclusion criterion in many UC induction-therapy popPK datasets (typically Mayo 2 or 3 = moderate-to-severe disease). Mutually compatible with `SCORE_PMAYO` in datasets that report both.

### SCORE_SLEDAI (**canonical for Systemic Lupus Erythematosus Disease Activity Index**)
- **Description:** Systemic Lupus Erythematosus Disease Activity Index score, a weighted composite of 24 clinical and laboratory features scoring 1-8 points each and summed to a total of 0-105. Higher values indicate more active SLE disease. Time-fixed at baseline in most SLE trials but can be time-varying when measured at follow-up. In model-based meta-analyses of SLE, the *per-arm mean baseline* SLEDAI (rather than an individual score) is the modelled covariate.
- **Units:** (score, 0-105)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used centered on a per-paper reference value (10.5 in Goteti 2024, the across-arm mean baseline SLEDAI across the 81 study arms). Enters via `(1 + beta * (SCORE_SLEDAI - reference))` on latent disease activity.
- **Source aliases:**
  - `SLEDAI` -- printed name in Goteti 2024 Table 2 / Table 3 / equations; same orientation, no transformation.
  - `SLEDAI (mean)` -- Goteti 2024 covariate-summary column header.
- **Example models:** `Goteti_2024_SLE_mbma.R` (per-arm mean baseline SLEDAI; centered at 10.5; multiplicative shifts of +0.208 per unit on latent `mu_ijk` and +0.255 per unit on latent `delta_ijk`).
- **Notes:** SLEDAI has several closely related scoring variants (SLEDAI-2K, SELENA-SLEDAI); the numeric range and interpretation are similar and papers use SLEDAI as an umbrella label. When a paper uses a specific variant, document in `covariateData[[SCORE_SLEDAI]]$notes`. Distinct from disease-specific composite outcomes like BICLA / SRI / LLDAS / CLASI (which are treatment-response composites, not disease-activity scores). Follows the `SCORE_*` family pattern (`SCORE_EASI`, `SCORE_MGADL`, `SCORE_CDAI`, etc.); canonical name is `SCORE_SLEDAI` without a `B` or `BL` prefix to match the `AGE` / `WT` / `ALB` pattern where baseline vs time-varying status is recorded in `notes` rather than the column name. Ratified canonically on 2026-07-24 alongside the Goteti 2024 SLE MBMA extraction.

### ENDO_ULCER (**canonical for endoscopically active luminal disease at baseline**)
- **Description:** 1 = mucosal ulcerations confirmed at baseline ileocolonoscopy / endoscopy (endoscopically active luminal disease), 0 = no mucosal ulcerations at baseline. Time-fixed (assessed at study entry).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no mucosal ulcerations at baseline).
- **Source aliases:** none known.
- **Example models:** `Aguiar_2021_ustekinumab.R` (Aguiar 2021 Table 3; covariate on baseline fecal calprotectin FC0: 213 mg/kg with ulcers vs 102 mg/kg without).
- **Notes:** IBD-specific structural-disease-activity indicator, distinct from the symptom-driven SCORE_CDAI / SCORE_PMAYO and the biomarker-driven SCORE_CALPRO / CRP. Useful as a covariate on baseline biomarkers (FC, CRP) and as a stratifier for simulations of biochemical remission.

## Inflammatory-bowel-disease diagnosis

### IBD_CD (**canonical for Crohn's disease indicator**)
- **Description:** 1 = Crohn's disease (CD) diagnosis, 0 = ulcerative colitis (UC) diagnosis. Used to gate pooled UC+CD population models where some parameters or covariate effects differ by diagnosis.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (ulcerative colitis).
- **Source aliases:**
  - `DIAGNOSIS`, `DX` (categorical `"UC"`/`"CD"`) -- derive `IBD_CD = as.integer(DX == "CD")`.
- **Example models:** `Rosario_2015_vedolizumab.R` (two typical-CLL switch between UC and CD; gates `SCORE_PMAYO` and `SCORE_CDAI` effects; multiplicative +1% effect on Vc).
- **Notes:** Rosario 2015 models separate typical CLL for UC vs CD and gates the partial-Mayo (UC-only) and SCORE_CDAI (CD-only) disease-activity covariates via this indicator.

## Concomitant / prior medication

### CONMED_AD (**canonical for concomitant Alzheimer's-symptomatic medication**)
- **Description:** 1 = subject is on concomitant Alzheimer's-symptomatic medication (typically a cholinesterase inhibitor and/or memantine) at the observation, 0 = not on such medication. Time-fixed in the Conrado 2014 source dataset (one indicator per subject) and treated as such here; future models that need a time-varying form may register a successor canonical or document time-varying use in `covariateData[[CONMED_AD]]$notes`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 1 (on concomitant Alzheimer's-symptomatic medication) in the Conrado 2014 model -- the source paper labels CONMED_AD = 1 as the "most common" category and centres the slope-effect coefficient on this group, so the multiplicative factor on disease-progression slope is `1` for `CONMED_AD = 1` and `1 + e_sl_conmed_ad_off` for `CONMED_AD = 0`. The non-standard "most-common-as-reference" convention is preserved from the source for traceability; future Alzheimer's models that adopt the more common "off-treatment as reference" convention should register a successor canonical (`CONMED_AD_TREATED`) rather than overloading this one.
- **Source aliases:**
  - `COMED2` -- used in `Conrado_2014_alzheimer.R` (DDMORE Foundation Model Repository entry DDMODEL00000290). The `2` suffix in the source distinguishes this binary from upstream `COMED` and `PRIMCOMED` columns (free-text concomitant-medication entries) in the same NONMEM input dataset; the binary `COMED2` flag is what enters the model.
- **Example models:** `Conrado_2014_alzheimer.R` (multiplicative factor on the typical-value disease-progression slope: `slope_factor = 1 + (1 - CONMED_AD) * e_sl_conmed_ad_off` with `e_sl_conmed_ad_off = -0.302`, i.e. ~30% smaller progression slope for the off-treatment reference cohort).
- **Notes:** The source `.mod` does not specify the symptomatic-medication class beyond the binary flag; the Conrado 2014 publication context (CAMD ADAS-Cog disease-progression dataset, 2014) makes cholinesterase-inhibitor / memantine the dominant interpretation. Treating `CONMED_AD = 1` as the reference category is unusual relative to the rest of the `CONMED_*` family (`CONMED_PARA`, `CONMED_NSAID`, `CONMED_AZA`, etc.) which all use 0 = not-on as reference; the inversion is preserved here only because the source paper's coefficient was estimated with the "most common = 1" convention. Ratified canonically on 2026-05-06 alongside the Conrado 2014 DDMORE extraction.

### CONMED_AED (**canonical for any concomitant antiepileptic drug coadministration**)
- **Description:** 1 = subject is on at least one concomitant antiepileptic drug (AED) other than the modelled AED at the PK observation, 0 = on the modelled AED as monotherapy. Time-varying when concurrent AEDs cycle on / off across study occasions, time-fixed when the source paper analyses chronic-maintenance cohorts whose concurrent therapy is unchanged across the analysis window.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (modelled-AED monotherapy).
- **Source aliases:**
  - `CO_AED` -- used in `Yukawa_1990_phenytoin.R` (paper's `CO` indicator inverted: source `CO = 1` if PHT alone, theta_co otherwise; canonical `CONMED_AED = 1 - CO_indicator` so 0 is the PHT-monotherapy reference).
- **Example models:** `Yukawa_1990_phenytoin.R` (multiplicative `^CONMED_AED` factor on Vmax (1.08) and Km (1.32) for chronic phenytoin patients on at least one of phenobarbital, carbamazepine, valproate, primidone, clonazepam, sultiame, ethotoin, ethosuximide, acetazolamide, or diazepam), `Yukawa_2002_clonazepam_pediatric.R` (paired with `CONMED_AED_GE2` to recover the paper's 3-tier drug-interaction factor on clonazepam CL/F).
- **Notes:** Generic concomitant-AED indicator covering the heterogeneous mix of older AEDs (PB, CBZ, VPA, primidone, clonazepam, etc.) studied alongside the modelled drug; the per-paper list of qualifying AEDs must be documented in `covariateData[[CONMED_AED]]$notes`. Distinct from drug-specific concomitant-AED indicators (e.g., a future `CONMED_PB` for concomitant phenobarbital alone) which would warrant separate canonicals when a paper distinguishes effects by AED class. When a paper distinguishes AED-count tiers (monotherapy vs +1 AED vs +>=2 AEDs) rather than the pooled mono-vs-anything contrast, pair `CONMED_AED` with the sibling [[CONMED_AED_GE2]] binary; the >=2-AEDs tier is `CONMED_AED = 1` AND `CONMED_AED_GE2 = 1`, and data assemblers must enforce that `CONMED_AED_GE2 = 1` implies `CONMED_AED = 1`. Follows the `CONMED_*` family pattern (`CONMED_AZA`, `CONMED_NSAID`, etc.). Ratified canonically on 2026-05-10 alongside the Yukawa 1990 phenytoin extraction.

### CONMED_AED_GE2 (**canonical for >=2 concomitant antiepileptic drugs**)
- **Description:** 1 = subject is on at least two concomitant antiepileptic drugs (AEDs) other than the modelled AED at the PK observation, 0 = on at most one concomitant AED (i.e., modelled-AED monotherapy or modelled AED + one concomitant AED). Sibling binary to [[CONMED_AED]] for AED-count-tier covariate models; the two binaries together encode a 3-level tier (monotherapy / +1 / +>=2). Time-varying when concurrent AEDs cycle on / off across study occasions, time-fixed when the source paper analyses chronic-maintenance cohorts whose concurrent therapy is unchanged across the analysis window.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (zero or one concomitant AED). The >=2-tier reference (`CONMED_AED_GE2 = 0` with `CONMED_AED = 1`) is "1 concomitant AED"; the absolute reference (`CONMED_AED_GE2 = 0` with `CONMED_AED = 0`) is "monotherapy".
- **Source aliases:**
  - `CONMED_AED_GE2` -- used in `Yukawa_2002_clonazepam_pediatric.R` (paper's Polytherapy (B) bucket per Table I and Table II groupings: any record with >=2 AEDs alongside clonazepam).
- **Example models:** `Yukawa_2002_clonazepam_pediatric.R` (paired with [[CONMED_AED]] inside `model()` to recover the paper's 3-tier drug-interaction factor: monotherapy DIF = 1, +1 AED DIF = 1.18, +>=2 AEDs DIF = 2.12 * TBW^(-0.119)).
- **Notes:** Specific scope; the binary is only meaningful in papers that distinguish a >=2-AEDs tier from a +1-AED tier. Promote to general scope when a second AED-count-tier paper ratifies the name. Mutual-consistency constraint: `CONMED_AED_GE2 = 1` MUST imply `CONMED_AED = 1`. A record asserting `CONMED_AED_GE2 = 1` and `CONMED_AED = 0` is malformed and the model() body will silently treat it as the >=2 tier (since the indicator decomposition uses `CONMED_AED * (1 - CONMED_AED_GE2)` for the +1 tier and `CONMED_AED_GE2` for the >=2 tier); data assemblers should validate the pair before passing to `rxSolve()`. Distinct from drug-specific `CONMED_<drug>` indicators (`CONMED_CBZ`, `CONMED_VPA`, `CONMED_PB`) which capture a specific AED rather than a count tier; when a future paper distinguishes BOTH the count AND the specific drugs, the two families coexist. Ratified canonically on 2026-06-21 alongside the Yukawa 2002 clonazepam pediatric extraction.

### CONMED_ABX (**canonical for any-other-antibiotic composite coadministration indicator**)
- **Description:** 1 = subject is receiving at least one concomitant systemic antibiotic other than the antibiotic being modelled at the PK observation, 0 = no other concomitant antibiotic. A composite pooled umbrella indicator for anti-infective popPK analyses in which the source paper records only *whether* additional antibacterial therapy was co-administered and never names the individual agents, so no per-INN indicator can be assigned. Time-varying in principle (co-therapy starts and stops during an admission); time-fixed when the source paper captures the flag once at treatment start.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant antibiotic other than the modelled drug).
- **Source aliases:**
  - `COMED` -- used in `Zieck_2023_ceftazidime.R` (Supplementary File S1 `$INPUT` column; enters `$PK` as `FLAG1` via `IF(COMED.EQ.1)FLAG1=1`). Note that `COMED` is a generic NONMEM column name meaning "co-medication"; map it to `CONMED_ABX` only when the source paper defines it specifically as other-antibiotic use, and to the appropriate per-INN or per-class canonical otherwise.
  - `Concomitant other antibiotic use` -- Zieck 2023 Table 2 row label.
- **Example models:** `Zieck_2023_ceftazidime.R` (power-of-coefficient effect on CL: `CL_typ * 1.56^CONMED_ABX`, i.e. +56% clearance when another antibiotic is co-administered; 27/40 patients exposed).
- **Notes:** Distinct from the named-agent indicators `CONMED_MER` (meropenem), `CONMED_GEN` (gentamicin), `CONMED_CIP` (ciprofloxacin) and `CONMED_FUSIDIC` (fusidic acid), which model a specific co-administered antibiotic. Use `CONMED_ABX` only when the source paper itself pools all other antibacterials under a single unnamed binary; use the per-INN indicators when the paper identifies and estimates effects for specific agents. Structurally parallel to [[CONMED_IMMUNOMOD]], the register's other "source paper does not separate the individual agents" composite. An `ABX = 1` flag is frequently confounded with infection severity and with the breadth of empiric coverage, so a positive clearance effect should be read as an empirical marker of a sicker / more heavily co-treated subset rather than as a mechanistic drug-drug interaction -- Zieck 2023's Discussion explicitly makes this caveat about its own 1.56 estimate ("we could not find a physiological explanation for this association ... it cannot be ruled out that the identification of this association is based on coincidence"). Ratified canonically on 2026-07-28 alongside the Zieck 2023 ceftazidime extraction (sidecar `oare_PMC10044023` request-001 q1, operator answered A).

### CONMED_ABI (**canonical for concomitant abiraterone coadministration indicator**)
- **Description:** 1 = subject is coadministered abiraterone acetate (oral CYP17A1 inhibitor used in metastatic castration-resistant prostate cancer; also a clinically relevant CYP2D6 / CYP2C8 inhibitor) at the PK observation, 0 = no abiraterone. Per-subject in Yoshida 2021 because abiraterone coadministration was a study-design factor (every patient in study GO27983 received 1000 mg abiraterone QD with prednisone / prednisolone 5 mg BID; six of 21 patients in JO29655 received abiraterone in stage 2; the other three studies enrolled no abiraterone arms).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no concomitant abiraterone).
- **Source aliases:**
  - `ABIRATER` -- used in `Yoshida_2021_ipatasertib.R` (Yoshida 2021 NONMEM control stream variable name; same per-subject 0/1 orientation).
- **Example models:** `Yoshida_2021_ipatasertib.R` (linear-additive effects: `-18.5%` on apparent ipatasertib parent CL/F when abiraterone is present (Table 3 theta_CLI,Abi = -0.185); on the apparent M1 bioavailability the abiraterone effect is `+61.5%` but ONLY at multiple-dose state (Table 4 theta_FM1,Abi = 0.615 applied as `(1 + 0.615 * MULTI_DOSE * CONMED_ABI)` so that the same single-dose effect was zero per the source NONMEM control stream)).
- **Notes:** Specific scope until a second model legitimately ratifies abiraterone as a covariate; promote to general once that happens. Distinct from `CONMED_AZOLE` (CYP3A4 inhibitor used for fungal indications) and from `CONMED_STEROID` (the prednisone / prednisolone component that is always co-administered with abiraterone in mCRPC); when a model explicitly separates abiraterone vs the steroid backbone, use both covariates. Yoshida 2021 also reports that the precise mechanism of the abiraterone-on-ipatasertib effect is unknown (the standard CYP3A4 / CYP2C8 / CYP2D6 routes were ruled out by in-vitro and DDI studies), so the indicator captures the empirical exposure shift rather than a defined enzyme-inhibition mechanism. Ratified canonically on 2026-05-30 alongside the Yoshida 2021 ipatasertib extraction.

### CONMED_ALTEPLASE (**canonical for concomitant intravenous alteplase (r-tPA) coadministration indicator**)
- **Description:** 1 = subject received concomitant intravenous alteplase (recombinant tissue plasminogen activator, r-tPA; intravenous thrombolysis, IVT) before or concurrent with the index PK measurement window, 0 = no concomitant alteplase. Time-fixed at the index event in stroke / reperfusion contexts where IVT is administered in the emergency window prior to endovascular thrombectomy. Distinct from the broader / class-level "any thrombolytic" indicator (which would also include tenecteplase, reteplase, urokinase, streptokinase); the canonical name is drug-specific because the published popPK literature ties the covariate to alteplase pharmacokinetics directly rather than to the broader tissue-plasminogen-activator class.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant alteplase / IVT).
- **Source aliases:**
  - `IVT` -- intravenous thrombolysis indicator; used in `Vos_2025_iminobiotin.R` (Vos 2025 Supplemental Table S8 uses "IVT" interchangeably with "alteplase" because the Dutch standard-of-care thrombolytic for LVO stroke in 2022-2023 was alteplase exclusively; the same column form `IVT = 0/1` would translate to `CONMED_ALTEPLASE` in any nlmixr2lib reuse).
- **Example models:** `Vos_2025_iminobiotin.R` (multiplicative effect on 2-iminobiotin clearance: `cl *= (1 + e_alteplase_cl * CONMED_ALTEPLASE)` with `e_alteplase_cl = 0.6469`, i.e. concomitant alteplase increases 2-IB CL by ~65% from 9.29 L/h to 15.3 L/h in adult LVO-stroke patients; founding example).
- **Notes:** General scope because alteplase is the dominant thrombolytic agent in acute ischemic stroke worldwide (with tenecteplase emerging as the principal alternative); future popPK models that combine concomitant tenecteplase as a covariate should register a sibling `CONMED_TENECTEPLASE` canonical rather than reusing this one (the two agents differ in plasmin-generation kinetics, fibrin specificity, and elimination half-life and so should not be pooled). The Vos 2025 mechanism for the +65% CL effect on 2-iminobiotin is not firmly established: the paper's Discussion explicitly rules out plasmin-mediated cleavage (2-IB is a small molecule, not a peptide like nerinetide), so the indicator captures an empirical exposure shift rather than a defined biochemical mechanism. Ratified canonically on 2026-06-28 alongside the Vos 2025 2-iminobiotin extraction (sidecar Q3 = B).

### CONMED_AMIO (**canonical for concomitant amiodarone coadministration indicator**)
- **Description:** 1 = subject is coadministered amiodarone (Class III antiarrhythmic; CYP3A4 / CYP2C9 / P-gp inhibitor) during the study, 0 = no concomitant amiodarone. Time-varying when amiodarone start / stop events are captured; the Xia 2024 source treats amiodarone as time-fixed at the analysis baseline.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant amiodarone).
- **Source aliases:**
  - `CM1` -- used in `Xia_2024_warfarin.R` (Xia 2024 Figure 1 / Table 2 covariate-screening variable labelling: `CM1 = 1` indicates combined amiodarone, `0` no combination).
- **Example models:** `Xia_2024_warfarin.R` (piecewise multiplicative effect on warfarin EC50: `ec50 *= (1 + e_amio_ec50 * CONMED_AMIO)` with `e_amio_ec50 = -0.602`, i.e. amiodarone reduces EC50 by ~60% in the Han Chinese cohort).
- **Notes:** Amiodarone is the canonical CYP2C9 / CYP3A4 inhibitor that potentiates warfarin's anticoagulant effect in clinical practice; the Xia 2024 cohort prevalence was 25.7% (Table 1). The per-paper definition (any amiodarone use vs current loading-dose use vs steady-state use) should be documented in `covariateData[[CONMED_AMIO]]$notes`. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin extraction.

### CONMED_AMINO (**canonical for concomitant aminosalicylate therapy**)
- **Description:** 1 = on concomitant aminosalicylate (5-aminosalicylic acid / mesalamine / mesalazine / olsalazine / sulfasalazine etc.) therapy at the PK observation, 0 = not.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant aminosalicylate).
- **Source aliases:** `AMINO` -- used in `Rosario_2015_vedolizumab.R`.
- **Example models:** `Rosario_2015_vedolizumab.R` (power-form on CLL: `CLL * 1.02^CONMED_AMINO`).
- **Notes:** Covers the full aminosalicylate class (5-ASA is the single active moiety shared by most agents); use `CONMED_AMINO` rather than `CONMED_5ASA` unless the source paper explicitly restricts the indicator to 5-ASA monotherapy.

### CONMED_AVD (**canonical for brentuximab vedotin + AVD (adriamycin/doxorubicin, vinblastine, dacarbazine) combination indicator**)
- **Description:** 1 = subject is receiving brentuximab vedotin in combination with the AVD chemotherapy backbone (adriamycin a.k.a. doxorubicin, vinblastine, dacarbazine) for newly diagnosed advanced-stage Hodgkin lymphoma; 0 = otherwise (single-agent brentuximab vedotin). Encodes the A+AVD frontline regimen as a study-design covariate on ADC clearance.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (single-agent brentuximab vedotin -- no AVD coadministration).
- **Source aliases:**
  - `DOX` -- used in `Zhou_2025_brentuximab.R` (the NONMEM dataset uses the doxorubicin-administration flag as the AVD-coadministration indicator because doxorubicin is given on the same days as the other AVD agents in this regimen).
- **Example models:** `Zhou_2025_brentuximab.R` (power-form effect on ADC clearance: `CL * 2.12^CONMED_AVD` -- ADC clearance is ~2.1-fold higher under A+AVD vs single-agent BV).
- **Notes:** Distinct from `CONMED_CHEMO` (which is nivolumab + platinum-based chemotherapy). The A+AVD regimen is the standard chemotherapy backbone for frontline classical Hodgkin lymphoma; promote to general scope if a second BV paper reports the same A+AVD covariate effect with a comparable encoding.

### CONMED_AZA (**canonical for concomitant azathioprine**)
- **Description:** 1 = on concomitant azathioprine at the PK observation, 0 = not on azathioprine.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant azathioprine).
- **Source aliases:** `AZA` -- used in `Rosario_2015_vedolizumab.R`.
- **Example models:** `Rosario_2015_vedolizumab.R` (power-form on CLL: `CLL * 0.998^CONMED_AZA`; effect ~= null).
- **Notes:** Thiopurine immunomodulator used as maintenance therapy in IBD. Standard convention is baseline-use-only, but time-varying use is permitted; document per-model.

### CONMED_AZOLE (**canonical for concomitant azole antifungal therapy (CYP3A4/P-gp inhibitor)**)
- **Description:** 1 = patient coadministered an azole antifungal (itraconazole, voriconazole, fluconazole, ketoconazole, posaconazole, isavuconazole, or another systemic azole) during the observation interval, 0 = no concomitant azole antifungal. Time-varying per subject because azole exposure starts and stops during the observation period.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant azole antifungal).
- **Source aliases:**
  - `AZOLE` -- used in `Kirubakaran_2022_tacrolimus.R`.
- **Example models:** `Kirubakaran_2022_tacrolimus.R` (state-dependent typical CL/F: 21.1 L/h without azole and 4.2 L/h with azole, an 80% reduction; also a state-dependent BSV magnitude on CL/F: 61% CV without azole vs 89.5% CV with azole).
- **Notes:** Azoles are mechanism-based CYP3A4 and P-glycoprotein inhibitors with different inhibitor potencies (itraconazole > voriconazole > fluconazole). The per-model `covariateData[[CONMED_AZOLE]]$notes` must document (1) which azoles are pooled into the indicator, (2) any post-cessation lag (Kirubakaran 2022 carries `CONMED_AZOLE = 1` for one week after azole discontinuation to allow tacrolimus apparent clearance to stabilize given itraconazole's long half-life), and (3) whether the indicator is a baseline-only proxy or a true time-varying flag.

### CONMED_CBZ (**canonical for concomitant carbamazepine coadministration indicator**)
- **Description:** 1 = subject is taking carbamazepine (CBZ) as a concomitant antiepileptic drug at the PK observation, 0 = no concomitant carbamazepine. Carbamazepine is a strong CYP3A4 / UGT / P-gp inducer that increases the apparent clearance of co-administered drugs. Time-varying when carbamazepine starts / stops within the observation window; time-fixed when the source paper analyses chronic-maintenance cohorts whose AED therapy is stable across the analysis window.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant carbamazepine).
- **Source aliases:**
  - `CBZ` -- used in `Schoemaker_2017_brivaracetam.R` (paper covariate `CBZ` for carbamazepine coadministration), `Hashimoto_1994_zonisamide.R` (paper covariate `CBZ` for carbamazepine coadministration on zonisamide Vmax) and `Lee_2024_topiramate.R` (Lee 2024 Table S1 theta4, additive enzyme-induction term on topiramate CL/F).
- **Example models:** `Schoemaker_2017_brivaracetam.R` (multiplicative effect on apparent oral clearance: `cl *= (1 + 0.479 * CONMED_CBZ)`; +47.9% relative to no-CBZ reference, corresponding to ~32% lower brivaracetam exposure, Schoemaker 2017 Table 1), `Hashimoto_1994_zonisamide.R` (multiplicative power-form effect on Vmax of Michaelis-Menten zonisamide elimination: `vmax *= 1.13^CONMED_CBZ`; +13% relative to no-CBZ reference, Hashimoto 1994 Table II theta2), `Lee_2024_topiramate.R` (**additive** effect in absolute units: `cl <- (exp(lcl) + e_cbz_cl * CONMED_CBZ + ...) * ...` with `e_cbz_cl` = 0.703 L/h on a 1.45 L/h monotherapy intercept, i.e. +48% relative to no-CBZ reference, Lee 2024 Table S1 / Discussion).
- **Notes:** Drug-specific CONMED_* indicator anticipated in the [[CONMED_AED]] notes; used when a paper estimates a separate CBZ-induction effect distinct from the pooled EIAED / AED class effect. Distinct from the broader [[CONMED_EIAED]] (any enzyme-inducing AED) and [[CONMED_AED]] (any concomitant AED). When a paper distinguishes individual AEDs separately (Schoemaker 2017: PB, CBZ, VPA each carry their own coefficient; Lee 2024: PHT, CBZ, OXC, PB each carry their own coefficient), use the drug-specific canonicals [[CONMED_PB]], `CONMED_CBZ`, [[CONMED_OXC]], [[CONMED_PHT]], [[CONMED_VPA]] rather than collapsing into the class-level indicator. The functional form of the induction effect is per-paper and not a property of this column: it is multiplicative / fractional in Schoemaker 2017, a power form in Hashimoto 1994, and **additive in absolute L/h** in Lee 2024 -- each model's `model()` body is authoritative, so do not assume a fractional coefficient when reading a `CONMED_CBZ` effect size across models. Hashimoto 1994 (zonisamide) tested phenytoin and valproate coadministration on zonisamide PK but found no significant effect (page 325, "data not shown") and retained only CBZ in the final model, consistent with the AED-specific decomposition rationale.

### CONMED_COBICISTAT (**canonical for concomitant cobicistat (CYP3A4 inhibitor / PK-booster) coadministration indicator**)
- **Description:** 1 = subject is receiving concomitant cobicistat (COBI), a pharmacokinetic enhancer with no intrinsic antiviral activity used to boost co-administered antiretrovirals; 0 = no cobicistat. Cobicistat is a potent mechanism-based CYP3A4 inhibitor and also inhibits P-glycoprotein, BCRP, OATP1B1/1B3 and the renal transporters MATE1 and OCT2, so the indicator flags reduced CYP3A4-mediated clearance and/or increased bioavailability of the boosted co-administered drug. Time-fixed in fixed-dose-combination cohorts; time-varying when boosting starts / stops within the observation window.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no cobicistat coadministration).
- **Source aliases:**
  - `Cobicistat` -- used in `Thoueille_2023_tenofovir_reduced.R` and its sibling models (paper covariate row "theta_Cobicistat", Thoueille 2023 Table 2 / Table S2 / Table S3).
  - `COBI`, `c` -- the standard antiretroviral-literature abbreviations; `c` appears as the trailing boosting token in fixed-dose-combination names (e.g., "elvitegravir/c", "darunavir/c").
- **Example models:** `Thoueille_2023_tenofovir_reduced.R`, `Thoueille_2023_tenofovir_full.R` (additive fractional effect on tenofovir alafenamide relative bioavailability: `f(depot) <- exp(lfdepot + etalfdepot) * (1 + e_cobi_fdepot * CONMED_COBICISTAT)`, with `e_cobi_fdepot` = 1.11 in the reduced model and 1.15 in the full model, i.e. F rises from 1 to ~2.1-2.2).
- **Notes:** Follows the antiretroviral three/four-letter abbreviation convention already used by [[CONMED_RTV]] (ritonavir), [[CONMED_EFV]] (efavirenz), [[CONMED_NVP]] (nevirapine) and [[CONMED_LPV]] (lopinavir). Cobicistat and ritonavir are the two clinically used PK boosters and both are potent CYP3A4 inhibitors, but they are **not** interchangeable columns: ritonavir has intrinsic protease-inhibitor activity and a broader induction / inhibition profile (including CYP2D6 and several UGTs), so a paper that boosts with cobicistat must use this canonical rather than overloading [[CONMED_RTV]]. Per-model `covariateData[[CONMED_COBICISTAT]]$notes` must state whether the indicator is confounded with dose level: in Thoueille 2023 every cobicistat-treated subject received tenofovir alafenamide 10 mg and every other subject 25 mg (cobicistat was forced into the model specifically to absorb that dose difference into relative bioavailability), so the coefficient there is a combined dose-normalization-plus-boosting effect rather than a pure drug-drug-interaction effect. Papers that test cobicistat as a P-glycoprotein perpetrator alongside a separate P-gp-inhibitor class flag should keep the two columns distinct -- Thoueille 2023 deliberately excluded cobicistat from its P-gp-inhibitor covariate analysis to avoid double-counting.

### CONMED_CSA (**canonical for concomitant cyclosporine (CsA) coadministration indicator**)
- **Description:** 1 = subject is receiving cyclosporine (CsA) as the concomitant calcineurin inhibitor at the PK observation; 0 = no concomitant cyclosporine (in mycophenolate mofetil studies the typical alternative comparator is tacrolimus). Cyclosporine inhibits the MRP2 (ABCC2) biliary efflux transporter, which decreases biliary excretion of MPAG into the gut lumen and therefore decreases enterohepatic recirculation (EHC) of mycophenolic acid; cyclosporine also displaces mycophenolic acid from plasma protein binding sites. Time-varying when cyclosporine starts / stops within the observation window; time-fixed when the source paper analyses subjects randomized to one CNI regimen for the entire study window (e.g., de Winter 2009).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant cyclosporine; in MPA studies the reference is typically a tacrolimus-based regimen).
- **Source aliases:**
  - `CsA` -- used in de Winter 2009 (binary indicator on the MPAG-to-gallbladder transport rate constant k57 to encode cyclosporine inhibition of MRP2-mediated biliary efflux); the canonical column is `CONMED_CSA` with the same value semantics.
- **Example models:** `deWinter_2009_mycophenolic_acid.R` (multiplicative power-form effect on the fMPAG-to-gallbladder rate constant k57: `k57 <- exp(lk57) * e_csa_k57^CONMED_CSA` with `e_csa_k57 = 0.002`, so cyclosporine cotreatment reduces k57 from 0.0796 1/h to 0.000159 1/h, suppressing EHC by ~99.8% relative to the tacrolimus reference; Table 2 / Eq. 9 of de Winter 2009).
- **Notes:** Ratified canonically on 2026-05-21 alongside the de Winter 2009 mycophenolic acid extraction. Distinct from a generic concomitant-immunosuppressant indicator: this canonical is specifically cyclosporine vs another CNI / no CNI. In renal-transplant popPK studies that randomise patients between cyclosporine and tacrolimus immunosuppression (the de Winter 2009 design), `CONMED_CSA = 1` and `CONMED_TAC` (if registered later for tacrolimus-as-perpetrator effects) would be mutually exclusive at the patient level. Cyclosporine and tacrolimus differ in their effect on MPA pharmacokinetics: cyclosporine inhibits MRP2 (decreasing EHC of MPAG -> MPA) while tacrolimus does not, so co-administered MPA exposure is typically lower under cyclosporine than under tacrolimus for the same MMF dose. Data assemblers can derive `CONMED_CSA = as.integer(cni_drug == "cyclosporine")` from a raw `CNI_DRUG` text column.

### CONMED_CHEMO (**canonical for anti-PD-(L)1 mAb + chemotherapy combination indicator**)
- **Description:** 1 = subject is receiving an anti-PD-(L)1 monoclonal antibody in combination with platinum-based chemotherapy (gemcitabine + cisplatin, pemetrexed + cisplatin, paclitaxel + carboplatin, or platinum-doublet); 0 = otherwise. Encodes chemotherapy coadministration as a study-design covariate on the antibody's CL.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no chemotherapy coadministration -- monotherapy or, where applicable, a non-chemotherapy combination such as anti-PD-1 + anti-CTLA-4).
- **Source aliases:**
  - `CHEMO` -- used in `Zhang_2019_nivolumab.R`.
  - `MONOTR` -- used in `Kuchimanchi_2024_dostarlimab.R` (the paper's structural-equation indicator for monotherapy; the canonical column carries the inverse value, i.e. `CONMED_CHEMO = 1 - MONOTR`, so the canonical column is 1 when the patient is on combo-chemotherapy).
- **Example models:** `Zhang_2019_nivolumab.R` (exponential effect on baseline CL: `exp(-0.104)` ~= 0.90 fold, i.e. ~9.7% lower CL relative to monotherapy), `Kuchimanchi_2024_dostarlimab.R` (multiplicative effect on baseline CL: `1 - 0.0779` = 0.922, i.e. 7.79% lower CL on dostarlimab + carboplatin/paclitaxel relative to dostarlimab monotherapy).
- **Notes:** Promoted from specific to general scope on 2026-04-27 after the Kuchimanchi 2024 dostarlimab + carboplatin/paclitaxel analysis ratified the same pooling convention (any chemotherapy backbone collapsed into a single binary indicator). The two papers use different functional forms for the effect on CL -- Zhang 2019 uses `exp(theta * CONMED_CHEMO)` (exponential) and Kuchimanchi 2024 uses `(1 + theta * CONMED_CHEMO)` (multiplicative); these are different parameterisations of the same underlying study-design indicator and the canonical column meaning is unchanged. Document the per-model functional form in `covariateData[[CONMED_CHEMO]]$notes`.

### CONMED_DECITABINE (**canonical for concomitant decitabine (hypomethylating-agent) chemotherapy backbone indicator**)
- **Description:** 1 = subject is receiving decitabine as the hypomethylating-agent (HMA) chemotherapy backbone during the study window; 0 = subject is on a different chemotherapy backbone (e.g. low-dose cytarabine) or no HMA at all. Used as a treatment-arm indicator in head-to-head trials that compare a hedgehog-pathway inhibitor (or other targeted agent) layered on a decitabine backbone vs an alternative backbone in older adults with AML or high-risk MDS who are ineligible for intensive chemotherapy.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (subject is not on a decitabine backbone; typically paired with `CONMED_GLASDEGIB = 1` in the only on-disk source (Lin 2020), where the decitabine arm carries both indicators = 1 and the LDAC arms have `CONMED_DECITABINE = 0`).
- **Source aliases:**
  - Derived from the protocol-defined treatment arm in `Lin_2020_glasdegib_decitabine.R` (BRIGHT AML 1003 Phase 1b Arm B: glasdegib + decitabine, n = 7).
- **Example models:** `Lin_2020_glasdegib_decitabine.R` (paired with `CONMED_GLASDEGIB` to identify the three trial arms; multiplicative hazard reduction `1 - 0.618 * (CONMED_GLASDEGIB * CONMED_DECITABINE)` on the exponential overall-survival hazard).
- **Notes:** Specific scope because the only on-disk source is Lin 2020 BRIGHT AML 1003 Phase 1b Arm B (5 AML + 2 MDS, n = 7). The reported CI of the glasdegib + decitabine hazard reduction is wide (-95.0% to -28.6%) due to the small sample. Ratified canonically on 2026-06-24 alongside the Lin 2020 BRIGHT AML 1003 overall-survival extraction. Auto-approved member of the `CONMED_<INN>` family.

### CONMED_DIURETIC (**canonical for concomitant diuretic indicator; class composition is paper-specific**)
- **Description:** 1 = subject is coadministered a diuretic during the study window (or, for per-time-point datasets, at the current time), 0 = no concomitant diuretic. Used in popPK / popPK-PD analyses of renally cleared substrates because diuretic-driven volume contraction and shifts in renal tubular handling can change apparent clearance, and because thiazide and loop diuretics are anti-uricosuric (raise serum urate) and so directly modify urate-related PD endpoints. **The class membership pooled into the indicator is paper-specific and MUST be enumerated in `covariateData[[CONMED_DIURETIC]]$notes` per model** -- the observed range runs from any-class (loop + thiazide + potassium-sparing + osmotic + carbonic-anhydrase, Kleiber 2017) through thiazide + loop + spironolactone (Stocker 2012) to thiazide + loop only, deliberately excluding potassium-sparing agents (Wright 2016). Composite class indicator following the `CONMED_STEROID` / `CONMED_AED` / `CONMED_AZOLE` pattern (pooled class indicator) rather than the `CONMED_<INN>` per-drug pattern (`CONMED_SPIRON`, `CONMED_FUROSEMIDE`, ...).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant diuretic of the paper-specific class set). Effect is typically encoded multiplicatively as `Theta^CONMED_DIURETIC` (Kleiber 2017 Eq 6 / Wright 2016 power form) or as a linear deviation `1 + e_conmed_diuretic_<param> * CONMED_DIURETIC` (Stocker 2012 form).
- **Source aliases:**
  - `DIURETIC` -- used in `Kleiber_2017_clonidine.R` (paper's pooled indicator combining furosemide intermittent + furosemide infusion + spironolactone + bumetanide; source column DIURETIC in the NONMEM dataset).
  - `DIUR` -- used in `Stocker_2012_oxypurinol.R` (Stocker 2012 dataset column; pools thiazide + loop + spironolactone; n = 72 of 155 gouty patients).
  - `diuretic` -- used in `Wright_2016_allopurinol.R` and `Wright_2013_allopurinol.R` (Wright narrative term; pools thiazide + loop diuretics only, excludes spironolactone / amiloride; n = 44 of 133 gouty patients in Wright 2016).
- **Example models:** `Kleiber_2017_clonidine.R` (any-class; multiplicative effect on CL: `diur_cl = 0.659^CONMED_DIURETIC`; per Table 3 / Table 4 CL is reduced by 34.1% when any diuretic is active), `Stocker_2012_oxypurinol.R` (thiazide + loop + potassium-sparing; multiplicative linear-deviation effect on apparent CL/Fm: `cl *= (1 + (-0.294) * CONMED_DIURETIC)`, i.e. -29.4% apparent oxypurinol clearance; Stocker 2012 Table 3 theta7), `Wright_2016_allopurinol.R` (thiazide + loop only, no potassium-sparing; multiplicative power-form effect on both CL/F_oxy and baseline urate U0: `cl *= 0.740^CONMED_DIURETIC` (-26%) and `rbase *= 1.14^CONMED_DIURETIC` (+14%); Wright 2016 Table 3 thetadiuretic and thetaE0_diuretic), `Wright_2013_allopurinol.R` (fractional effect 0.61 on renal CL_oxy via `e_conmed_diuretic_cl_oxy_renal^CONMED_DIURETIC`; Wright 2013 Table 2).
- **Notes:** Composite indicator with paper-specific class membership. Users simulating across models that share this column but differ in class membership MUST populate it according to each paper's own definition -- Wright 2016 deliberately excludes potassium-sparing diuretics because they tend to be uricosuric (lower serum urate) and therefore have an opposite-direction PD effect from the anti-uricosuric thiazide / loop agents, whereas Stocker 2012 and Kleiber 2017 pool them in. Distinct from `CONMED_SPIRON` (spironolactone-only per-drug indicator, used in `Yukawa_1996_digoxin.R`), a member of the `CONMED_<INN>` family; the two canonicals coexist when a paper distinguishes specific drugs. When a paper requires class-resolved encoding (separate thiazide / loop / K-sparing indicators), register sibling canonicals (e.g. `CONMED_THIAZIDE`, `CONMED_LOOP_DIUR`) rather than overloading this one. Ratified canonically alongside the Kleiber 2017 clonidine extraction; the former `CONMED_DIUR` canonical (ratified 2026-06-20 with Stocker 2012, extended 2026-06-30 with Wright 2016) was **merged into this entry on 2026-07-26** per operator directive -- the two names denoted the same concept and the split was an artifact of independent extractions. `CONMED_DIUR` is retired: do not reintroduce it.

### CONMED_DOXORUBICIN (**canonical for concomitant doxorubicin (anthracycline) chemotherapy backbone indicator**)
- **Description:** 1 = subject is receiving (or has received during the relevant observation window) doxorubicin as the anthracycline component of an adjuvant or neoadjuvant chemotherapy regimen; 0 = subject is receiving a different anthracycline (or no anthracycline at all). Used in cardiac-biomarker PD models to differentiate doxorubicin-driven myocardial damage from epirubicin-driven damage (doxorubicin has approximately twice the per-mg cardiotoxic effect of epirubicin at clinically equivalent oncologic exposures).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (subject is not on doxorubicin). When `CONMED_DOXORUBICIN` and `CONMED_EPIRUBICIN` are used together as a pair of complementary indicators (as in de Vries Schultink 2018), both being 0 means no anthracycline.
- **Source aliases:**
  - `ANTH_TYPE = 'doxorubicin'` -- used in `deVriesSchultink_2018_anthracycline_troponinT.R` (the source paper records anthracycline as a categorical with levels {'doxorubicin', 'epirubicin'}; the canonical register splits this into a pair of binary indicators following the `CONMED_<drug>` precedent rather than the categorical `ANTH_TYPE`).
- **Example models:** `deVriesSchultink_2018_anthracycline_troponinT.R` (used jointly with `CONMED_EPIRUBICIN` to switch the K-PD SLOPE parameter for troponin T between doxorubicin (reference) and epirubicin; epirubicin = 0.524-fold of doxorubicin effect, per de Vries Schultink 2018 Table 2).
- **Notes:** Specific scope until a second cardiotoxicity or anthracycline-effect model legitimately reuses this indicator. Distinct from `CONMED_PLDH` (PEGylated liposomal doxorubicin) and from `CONMED_AVD` (the doxorubicin component of the AVD backbone in Hodgkin lymphoma); use those canonicals for product-formulation or regimen-backbone semantics. When the source dataset's anthracycline column is categorical (`ANTH_TYPE`), derive the canonical indicators as `CONMED_DOXORUBICIN = (ANTH_TYPE == 'doxorubicin')`, `CONMED_EPIRUBICIN = (ANTH_TYPE == 'epirubicin')`.

### CONMED_EPIRUBICIN (**canonical for concomitant epirubicin (anthracycline) chemotherapy backbone indicator**)
- **Description:** 1 = subject is receiving (or has received during the relevant observation window) epirubicin as the anthracycline component of an adjuvant or neoadjuvant chemotherapy regimen; 0 = subject is receiving a different anthracycline (or no anthracycline at all). The pairing of `CONMED_EPIRUBICIN` with `CONMED_DOXORUBICIN` captures the cardiotoxicity-relevant choice within the anthracycline class; epirubicin is less cardiotoxic per mg than doxorubicin (lifetime cumulative dose threshold approximately 950 mg/m^2 vs 550 mg/m^2 for doxorubicin).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (subject is not on epirubicin). When `CONMED_DOXORUBICIN` and `CONMED_EPIRUBICIN` are used together (as in de Vries Schultink 2018), both being 0 means no anthracycline.
- **Source aliases:**
  - `ANTH_TYPE = 'epirubicin'` -- used in `deVriesSchultink_2018_anthracycline_troponinT.R`. See the matching entry for `CONMED_DOXORUBICIN`.
- **Example models:** `deVriesSchultink_2018_anthracycline_troponinT.R` (used jointly with `CONMED_DOXORUBICIN` to switch the K-PD SLOPE parameter; epirubicin = 0.524-fold of the doxorubicin reference effect on troponin T).
- **Notes:** Specific scope until a second cardiotoxicity or anthracycline-effect model legitimately reuses this indicator. The two indicators (`CONMED_DOXORUBICIN` and `CONMED_EPIRUBICIN`) are not mutually exclusive in principle (a subject receiving both anthracyclines could have both set to 1), but in practice the de Vries Schultink 2018 cohort assigned exactly one anthracycline per subject.

### CONMED_EFV (**canonical for concomitant efavirenz indicator**)
- **Description:** 1 = subject is receiving efavirenz (EFV)-based antiretroviral therapy as the third agent in a combination ART regimen; 0 = subject is on the comparator regimen specified by the source paper (typically a protease-inhibitor-based regimen such as standard lopinavir/ritonavir 4:1). Efavirenz is a CYP3A and UGT inducer, so the indicator is used to flag PXR-mediated induction of metabolic clearance for co-administered antiretrovirals.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-EFV reference regimen, paper-defined; e.g., LPV/r 4:1 in Tikiso 2021).
- **Source aliases:**
  - `EFV` -- used in `Tikiso_2021_abacavir.R` (the dataset's paper-defined indicator, 1 = on EFV-based ART, 0 = on standard LPV/r 4:1).
- **Example models:** `Tikiso_2021_abacavir.R` (multiplicative effect on apparent oral clearance: `cl *= (1 + 0.120 * CONMED_EFV)`; +12.0% relative to the LPV/r 4:1 reference in HIV-infected children on combination ART), `Svensson_2013_bedaquiline.R` (multiplicative factor on apparent CL_BDQ and CL_M2: `cl_eff = cl_base * 2.07^CONMED_EFV` and on apparent CL_M3: `cl_eff = cl_base * 1.12^CONMED_EFV`; healthy-volunteer DDI study with no-co-medication reference and 1-week onset lag from start of 600 mg once-nightly EFV), `Hoglund_2015_lumefantrine.R` (multiplicative linear-deviation effect on lumefantrine apparent CL/F: `cl *= (1 + 0.726 * CONMED_EFV)`; +72.6% relative to the no-ART reference in HIV-infected Ugandan adults, Hoglund 2015 Table 2 attributing the effect to CYP3A4 induction), `Hoglund_2015_artemether.R` (multiplicative linear-deviation effect on artemether relative bioavailability F: `fdepot_typ *= (1 + (-0.715) * CONMED_EFV)`; -71.5% relative to the no-ART reference, Hoglund 2015 Table 3), `Kay_2020_lumefantrine.R` (TWO independent linear-deviation effects, on lumefantrine apparent CL/F: `cl *= (1 + 0.982 * CONMED_EFV)` (+98.2%, CYP3A4 induction) and on the first-order absorption rate constant: `ka *= (1 + 0.484 * CONMED_EFV)` (+48.4%); relative to the no-ART reference in HIV-infected Ugandan children with malaria (ASTMH 2020 poster 2167 Table 1)), `Kay_2022_lumefantrine.R` (TWO independent linear-deviation effects: on apparent oral lumefantrine CL/F `cl *= (1 + 0.982 * CONMED_EFV)` (+98.2 %, CYP3A4 induction) and on the first-order absorption rate constant `ka *= (1 + 0.484 * CONMED_EFV)` (+48.4 %); HIV-uninfected children are the reference (CONMED_EFV = 0) in a pediatric Ugandan malaria cohort on six-dose Coartem Dispersible, Kay 2022 Table 2 theta_7 and theta_10), `Bukkems_2021_raltegravir.R` (linear additive effect on typical bioavailability F: `f_typ *= (1 + e_efv_fdepot * CONMED_EFV)` with `e_efv_fdepot = -0.167`; efavirenz-associated raltegravir F is 17% lower than the no-efavirenz reference, consistent with efavirenz-mediated UGT1A1 induction of raltegravir glucuronidation, Bukkems 2021 Table 2 'Factor change in F efavirenz co-administration'), `Adeojo_2024_levonorgestrel.R` (TWO independent linear-deviation effects in a meta-analytic fit to pooled trial mean profiles: on systemic clearance `cl *= (1 + 0.7235 * CONMED_EFV)` (CL 5.86 -> 10.1 L/h, +72.4%, CYP3A4 induction) and on oral bioavailability `fdepot *= (1 + (-0.3632) * CONMED_EFV)` (F_oral 0.837 -> 0.533, -36.3%, additionally reflecting gut-wall CYP3A4); reference is no efavirenz and no antiretroviral therapy, and every efavirenz-exposed arm used efavirenz 600 mg orally once daily; coefficients derived from the two absolute values published in Adeojo 2024 Supplementary Table S2).
- **Notes:** Specific scope because the comparator regimen (LPV/r 4:1 in Tikiso 2021) is paper-defined; future ART population-PK models that test EFV-vs-other contrasts should extend the example list when the comparator matches, or register a finer-grained sibling indicator otherwise. The reference category is paper-defined and is NOT always another ART regimen: Hoglund 2015, Kay 2020/2022 and Adeojo 2024 use a no-efavirenz (in Adeojo 2024, no-ART) reference rather than an active comparator, so read each example's stated reference before transferring a coefficient.

### CONMED_EPI (**canonical for concomitant epinephrine (adrenaline) coadministration indicator**)
- **Description:** 1 = subject received epinephrine (INN: epinephrine; alternate INN adrenaline) coadministered with the parent drug during the study window; 0 = subject received the parent drug alone. Used to encode the local vasoconstrictor effect of epinephrine on intraperitoneal (or perineural) drug distribution: alpha-1 vasoconstriction of the peritoneal (or local) vessels reduces splanchnic blood flow and slows the transfer of the parent drug out of the injection compartment, while systemic beta-1 / beta-2 effects (positive inotropic / chronotropic action and peripheral vasodilatation) increase the apparent volume of distribution of the parent drug.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no coadministered epinephrine).
- **Source aliases:**
  - `EPI` -- used in `Royer_2011_cisplatin.R` (Royer 2011 Table 2 covariate on IPCL and V).
- **Example models:** `Royer_2011_cisplatin.R` (intraperitoneal epinephrine in the cisplatin IP bath: multiplicative fractional coefficient -0.531 on intraperitoneal clearance IPCL (53.1% decrease) and +0.805 on central volume V (80.5% increase); the paper dichotomises epinephrine because plasma Pt concentrations were similar across the three epinephrine dose levels of 1, 2, and 3 mg/L in the IP bath).
- **Notes:** Specific scope until a second popPK / PD extraction with coadministered epinephrine reuses this indicator (e.g., a perineural local-anaesthetic + epinephrine PK model). Auto-approved member of the `CONMED_<INN>` family. Ratified canonically on 2026-07-09 alongside the Royer 2011 cisplatin extraction. The per-model `covariateData[[CONMED_EPI]]$notes` field should record the paper's specific epinephrine dosing (route, concentration in the bath / mL, whether dichotomised or dose-response) since the mechanism differs across administration routes.

### CONMED_EIAED (**canonical for concomitant enzyme-inducing antiepileptic drug indicator**)
- **Description:** 1 = subject is taking at least one enzyme-inducing antiepileptic drug (EIAED) such as carbamazepine, phenobarbital, or phenytoin during the study; 0 = no EIAED coadministration. EIAEDs induce hepatic metabolism (CYP3A4/UGT-mediated pathways) and increase the clearance of co-administered antiepileptic drugs and their active metabolites.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no EIAED coadministration).
- **Source aliases:**
  - `MED` (with the value convention inverted: source codes 1 = absence of EIAEDs and 0 = presence; canonical inverts this so 1 = presence and 0 = absence) -- used in `Rodrigues_2017_oxcarbazepine.R`.
- **Example models:** `Rodrigues_2017_oxcarbazepine.R` (exponential effect on MHD apparent clearance: `cl_mhd *= exp(e_eiaed_cl_mhd * (1 - CONMED_EIAED))`; CL_MHD is 29.3% higher with EIAEDs vs without, encoded as +0.257 on the absence-indicator in the source paper).
- **Notes:** Per-model `covariateData[[CONMED_EIAED]]$notes` should list the specific EIAEDs counted as "EIAED = 1" since inclusion criteria vary across antiepileptic-drug studies. Rodrigues 2017 counts carbamazepine, phenobarbital, and phenytoin as EIAEDs; other AEDs in the dataset (vigabatrin, clobazam, valproic acid, clonazepam, lamotrigine, diazepam, ethosuccimide, progabide) are not. The source paper uses an "absence of EIAED" indicator (`MED = 1` if no EIAED, `MED = 0` if EIAED present); the canonical column inverts this so that the 1 group is "on EIAED", matching the convention for other CONMED_* indicators.

### CONMED_ERA (**canonical for concomitant endothelin-receptor-antagonist monotherapy indicator in PAH**)
- **Description:** 1 = patient is on a concomitant endothelin-receptor-antagonist (ERA) PAH therapy (e.g., bosentan, macitentan, ambrisentan) alone -- i.e. on an ERA but NOT also on a phosphodiesterase type 5 inhibitor; 0 = otherwise. Time-fixed per subject for the PK/PD analysis (PAH comedication at baseline, stable dose required before selexipag start).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (PAH-comedication-naive OR on PDE5 inhibitor alone OR on both ERA+PDE5; mutually exclusive with `CONMED_PDE5I` and `CONMED_ERA_PDE5I`).
- **Source aliases:**
  - `PAHCOMED` category 1 ("ERA only") -- decomposed from the categorical `PAHCOMED` source column with levels {naive, ERA, PDE5, ERA+PDE5} in `Krause_2017_selexipag.R`.
- **Example models:** `Krause_2017_selexipag.R` (multiplicative effect on the ACT-333679 elimination rate constant: `km *= (1 + 0.15 * CONMED_ERA)`; +15% relative to the PAH-comedication-naive reference, Krause 2017 Table 1).
- **Notes:** Used together with `CONMED_PDE5I` and `CONMED_ERA_PDE5I` to decompose a four-level PAH-comedication categorical (naive / ERA-only / PDE5-only / ERA-and-PDE5) into three orthogonal mutually-exclusive binary indicators, with the PAH-comedication-naive group as the reference (all three indicators = 0). The four-level categorical encoding preserves the source paper's stratum-specific coefficient set (Krause 2017 reports a separate categorical b-coefficient for each non-reference stratum rather than independent class-level effects). Specific scope because the indicator's semantics are tied to the GRIPHON study's PAH-comedication taxonomy.

### CONMED_ERA_PDE5I (**canonical for concomitant ERA + PDE5-inhibitor combination indicator in PAH**)
- **Description:** 1 = patient is on both a concomitant endothelin-receptor-antagonist (ERA) and a phosphodiesterase type 5 inhibitor (PDE5I) as PAH therapy; 0 = otherwise. Time-fixed per subject for the PK/PD analysis.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (PAH-comedication-naive OR on ERA alone OR on PDE5I alone; mutually exclusive with `CONMED_ERA` and `CONMED_PDE5I`).
- **Source aliases:**
  - `PAHCOMED` category 3 ("ERA and PDE5 inh.") -- decomposed from the categorical `PAHCOMED` source column with levels {naive, ERA, PDE5, ERA+PDE5} in `Krause_2017_selexipag.R`.
- **Example models:** `Krause_2017_selexipag.R` (multiplicative effect on the ACT-333679 elimination rate constant: `km *= (1 + 0.37 * CONMED_ERA_PDE5I)`; +37% relative to the PAH-comedication-naive reference, Krause 2017 Table 1; the combined stratum has its own categorical coefficient distinct from the sum of the ERA-only and PDE5I-only effects).
- **Notes:** Used together with `CONMED_ERA` and `CONMED_PDE5I` to decompose a four-level PAH-comedication categorical (naive / ERA-only / PDE5-only / ERA-and-PDE5) into three orthogonal mutually-exclusive binary indicators. The standalone combined-stratum coefficient (rather than a product of class-level indicator effects) preserves Krause 2017's full categorical-effect parameterisation. Specific scope because the indicator's semantics are tied to the GRIPHON study's PAH-comedication taxonomy.

### CONMED_EZE (**canonical for concomitant ezetimibe coadministration indicator**)
- **Description:** 1 = patient is taking ezetimibe (with or without other lipid-lowering comedication), 0 = not on ezetimibe.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not on ezetimibe).
- **Source aliases:**
  - Derived from an ezetimibe-identifier column in the source.
- **Example models:** `Kuchimanchi_2018_evolocumab.R` (multiplicative effect 1.20 on Vmax: `Vmax * 1.20^CONMED_EZE`; labeled "Statin + ezetimibe exponent" in Kuchimanchi 2018 Table 3 because ~99% of ezetimibe users in the dataset were also on a conmed_statin, so the effect effectively captures combination therapy), `Kuchimanchi_2018_evolocumab_ldlc.R` (same PK-layer effect plus a multiplicative exponent 0.768 on baseline LDL-C in the exposure-response layer, Table 4), `Kakara_2014_atorvastatin.R`, `Kakara_2014_pitavastatin.R`, `Kakara_2014_rosuvastatin.R` (additive +0.109 contribution to the indirect-response Imax inhibition fraction INH on the LDL-C synthesis rate Kin: `INH = Imax * DOSE / (ID50 + DOSE) + 0.109 * CONMED_EZE`; Kakara 2014 Table 2 INH_EZT), `Jadhav_2023_bempedoicAcid.R`, `Jadhav_2023_bempedoicAcid_ldlc.R` (cleanly separated concomitant-ezetimibe indicator: proportional shift `CL/F * (1 + (-0.0934) * CONMED_EZE)` in the PK layer, Jadhav 2023 Table 2, and `Imax * (1 + 0.190 * CONMED_EZE)` in the indirect-response LDL-C layer, Table 3; Jadhav 2023 also carries a separate prior-therapy column [[PRIOR_EZE]], so this indicator is concomitant use only and is not a statin-combination proxy).
- **Notes:** Scope: specific because Kuchimanchi 2018 interprets the ezetimibe indicator as a combination-therapy marker rather than a pure ezetimibe effect. Future popPK/PD models with cleaner ezetimibe separation should add themselves here or register a more specific canonical.

### CONMED_FUSIDIC (**canonical for concomitant fusidic acid coadministration indicator**)
- **Description:** 1 = patient is coadministered oral fusidic acid (typically 500 mg three times daily) alongside the modelled drug during the observation interval; 0 = no concomitant fusidic acid. Fusidic acid is a bacteriostatic protein-synthesis-inhibitor antibiotic used against staphylococcal infections; it is extensively plasma-protein bound (about 97 % to albumin), is orally bioavailable, and has been implicated as a CYP3A4 inhibitor and albumin-binding perpetrator in multiple drug-drug-interaction reports.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no concomitant fusidic acid).
- **Source aliases:**
  - `fusidic acid` -- used in `Marsot_2017_rifampicin.R` (Marsot 2017 Table 1 / Table 2 stratifies typical CL/F and V/F by fusidic acid coadministration; the canonical column `CONMED_FUSIDIC` collapses the two strata into a single binary indicator).
- **Example models:** `Marsot_2017_rifampicin.R` (log-scale multiplicative effects on CL/F and V/F: `cl = exp(lcl + e_conmed_fusidic_cl * CONMED_FUSIDIC + etalcl)` with `e_conmed_fusidic_cl = log(5.1 / 13.7)` recovers Table 2's stratified typical values 13.7 L/h at CONMED_FUSIDIC = 0 and 5.1 L/h at CONMED_FUSIDIC = 1; the analogous encoding on V/F recovers 61.1 L vs 23.8 L).
- **Notes:** Specific scope because the only on-disk source is Marsot 2017 (staphylococcal osteoarticular infections; 10 / 62 patients coadministered fusidic acid at 500 mg three times daily). Auto-approved member of the `CONMED_<INN>` family. Ratified canonically on 2026-07-01 alongside the Marsot 2017 rifampicin extraction. The Marsot 2017 Discussion attributes the interaction to fusidic acid inhibition of CYP3A4 and to displacement of rifampicin from albumin binding, but notes that the mechanism remains uncertain; downstream models that use `CONMED_FUSIDIC` for a different modelled drug should document the mechanism hypothesis in `covariateData[[CONMED_FUSIDIC]]$notes`. When a future paper reports a time-varying fusidic acid coadministration schedule (start / stop within the observation window), the same canonical column can carry the time-varying indicator; the per-model notes should record whether the encoding is time-fixed or time-varying.

### CONMED_GLASDEGIB (**canonical for concomitant glasdegib (smoothened inhibitor) coadministration indicator**)
- **Description:** 1 = subject is in a glasdegib-containing treatment arm (glasdegib added to a chemotherapy backbone such as low-dose cytarabine (LDAC) or decitabine); 0 = subject is in the chemotherapy-backbone-alone comparator arm (no glasdegib). Glasdegib is an oral smoothened (SMO) inhibitor of the Hedgehog signaling pathway, approved in the United States in combination with LDAC for older adults with newly diagnosed AML who are ineligible for intensive chemotherapy.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (LDAC alone or chemotherapy-backbone-alone comparator arm). In `Lin_2020_glasdegib_treatment.R` the reference is the LDAC alone arm (BRIGHT AML 1003 Phase 2). In `Lin_2020_glasdegib_decitabine.R` the reference is also LDAC alone, paired with `CONMED_DECITABINE` to identify the three-arm structure of the exploratory pooled analysis.
- **Source aliases:**
  - Derived from the protocol-defined treatment arm in `Lin_2020_glasdegib_treatment.R` and `Lin_2020_glasdegib_decitabine.R` (BRIGHT AML 1003, NCT01546038).
- **Example models:** `Lin_2020_glasdegib_treatment.R` (binary indicator on the exponential overall-survival hazard; the published equation uses the complementary indicator `LDAC_alone = 1 - CONMED_GLASDEGIB` so the model file derives `ldac_alone <- 1 - CONMED_GLASDEGIB` inside `model()` to preserve the paper's parameter values verbatim), `Lin_2020_glasdegib_decitabine.R` (paired with `CONMED_DECITABINE` in the three-arm exploratory analysis).
- **Notes:** Specific scope because the only on-disk source is the BRIGHT AML 1003 trial (Lin 2020). Auto-approved member of the `CONMED_<INN>` family. Ratified canonically on 2026-06-24 alongside the Lin 2020 BRIGHT AML 1003 overall-survival extraction. The Lin 2020 treatment-response equation uses LDAC_alone as the binary covariate (1 = LDAC alone, 0 = glasdegib + LDAC) with parameter `theta_ldac_alone = 1.376`; the canonical-column form (1 = glasdegib + LDAC) preserves the published value via the in-model `1 - CONMED_GLASDEGIB` mapping rather than refitting the parameter against a re-encoded covariate.

### CONMED_GCSF (**canonical for concomitant granulocyte colony-stimulating factor (G-CSF) treatment indicator**)
- **Description:** 1 = subject received granulocyte colony-stimulating factor (G-CSF; filgrastim, pegfilgrastim, lenograstim, or another G-CSF product) as concomitant medication during the observation period; 0 = did not receive G-CSF. Used in chemotherapy-induced myelosuppression popPK/PD analyses either (a) as an exclusion filter, or (b) as a covariate on Friberg-style bone-marrow lifespan-model parameters (typically MTT, kprol, Slope, and/or Gamma) to capture the effect of prophylactic or reactive G-CSF support on neutrophil recovery kinetics.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant G-CSF treatment).
- **Source aliases:**
  - `G-CSF` -- van Hasselt 2013 paper narrative and Table 2 / Table 3 header notation.
  - `GCSF` -- common NONMEM column-name form used by Friberg-style myelosuppression implementations.
- **Example models:** `vanHasselt_2013_eribulin.R` (proportional / dichotomous effect on MTT with theta = 0.883 -- G-CSF shortens MTT by ~12% -- and on SLOPE with theta = 1.3 -- G-CSF increases the linear drug-effect coefficient by ~30%. Encoded as `mtt = ... * e_gcsf_mtt^CONMED_GCSF` and `slope = ... * e_gcsf_slope^CONMED_GCSF`).
- **Notes:** Time-varying per subject in principle (G-CSF is given as a course over specific days after chemotherapy), but van Hasselt 2013 treats it as a subject-level indicator in the covariate model (subject received G-CSF at any point during the observation period). Per-model `covariateData[[CONMED_GCSF]]$notes` should record whether the paper's encoding is "ever received G-CSF" (subject-level, time-fixed) or per-cycle time-varying. Distinct from `CSF1` (colony-stimulating factor 1 / M-CSF concentration, a target-engagement biomarker in anti-CSF-1R mAb models) -- the two concepts share the abbreviation "CSF" but are otherwise unrelated. Ratified canonically on 2026-07-10 alongside the van Hasselt 2013 eribulin-neutropenia extraction (operator decision in sidecar request 002).

### CONMED_H2RA (**canonical for concomitant H2-receptor-antagonist use**)
- **Description:** 1 = patient on concomitant histamine H2-receptor-antagonist therapy (e.g., ranitidine, famotidine), 0 = no CONMED_H2RA use. Captures another class of gastric-pH-modifying co-medication that may reduce bioavailability of pH-sensitive orally administered drugs.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no CONMED_H2RA use).
- **Source aliases:**
  - `H2` -- used in `Goel_2016_Sonidegib.R` (Goel 2016 dataset; defined as "significant" CONMED_H2RA use, i.e. duration of CONMED_H2RA use >= 80% of the PK assessment phase).
- **Example models:** `Goel_2016_Sonidegib.R` (multiplicative effect on F: `0.996^CONMED_H2RA`, no clinically meaningful effect; reported alongside `CONMED_PPI` for completeness).
- **Notes:** Per-model `covariateData[[CONMED_H2RA]]$notes` must document the operational definition (Goel 2016: >= 80% of PK assessment phase). Distinct from `CONMED_PPI`.

### CONMED_ANTACID (**canonical for concomitant antacid use**)
- **Description:** 1 = patient on concomitant antacid therapy (typically over-the-counter aluminum-, magnesium-, or calcium-based salts such as calcium carbonate, magnesium carbonate, magnesium hydroxide, or aluminum hydroxide), 0 = no CONMED_ANTACID use. Captures the third class of gastric-pH-modifying co-medication alongside `CONMED_PPI` and `CONMED_H2RA`; primarily used to test the impact of transient gastric-pH elevation on the bioavailability of pH-sensitive orally administered drugs.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no CONMED_ANTACID use).
- **Source aliases:**
  - `ANTACIDF` -- used in `Kemal_2026_nemtabrutinib.R` (Kemal 2026 NONMEM control stream in the supplement; time-varying regressor coded 1 = concomitant antacid at the observation, 0 otherwise).
- **Example models:** `Kemal_2026_nemtabrutinib.R` (multiplicative effect on F: `(1 + -0.0360 * CONMED_ANTACID)`, i.e. 3.6% lower F under antacid coadministration; RSE 72.8%, 95% CI includes zero -- retained in the full covariate model).
- **Notes:** Per-model `covariateData[[CONMED_ANTACID]]$notes` should document the operational definition (per-observation time-varying indicator vs subject-level ever-vs-never) and the class of antacids pooled into the `= 1` category, since inclusion criteria vary by study. Distinct from `CONMED_PPI` (proton-pump inhibitors: irreversible H+/K+-ATPase inhibition, sustained gastric-pH elevation) and `CONMED_H2RA` (histamine H2 receptor antagonists: reversible histamine-mediated acid-secretion blockade). Antacids act by direct chemical neutralization of gastric acid rather than by inhibiting acid secretion, giving them a shorter and more localized pH-elevating effect than the other two classes.

### CONMED_PHOSBINDER (**canonical for concomitant phosphate-binding agent use**)
- **Description:** 1 = patient on a concomitant phosphate-binding agent (oral agents that sequester dietary phosphate in the gastrointestinal lumen to lower serum phosphate -- e.g. sevelamer, lanthanum carbonate, calcium acetate, calcium carbonate, sucroferric oxyhydroxide), 0 = no phosphate-binder use. Typically a subject-level ever-vs-never indicator. Two distinct clinical settings generate this covariate: management of hyperphosphatemia in chronic kidney disease, and management of the on-target hyperphosphatemia that is a class effect of fibroblast growth factor receptor (FGFR) inhibitors.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant phosphate-binder use).
- **Source aliases:**
  - `BINDER` -- used in `Gong_2023_pemigatinib.R` (Gong 2023 NONMEM control stream in Appendix S1; `IF(BINDER.EQ.0) CLBINDER = 1` / `IF(BINDER.EQ.1) CLBINDER = (1 + THETA(7))`). Gong 2023 Table 1 footnote b defines it as "phosphate-binding agents".
- **Example models:** `Gong_2023_pemigatinib.R` (multiplicative effect on CL/F: `(1 + -0.155 * CONMED_PHOSBINDER)`, i.e. 15.5% lower apparent clearance with phosphate-binder coadministration; %RSE 14.6, 95% CI -19.9% to -11.1%; the paper states the effect is statistically but not clinically significant, raising simulated Cmax,ss by roughly 6%).
- **Notes:** Per-model `covariateData[[CONMED_PHOSBINDER]]$notes` should document the operational definition (subject-level ever-vs-never vs per-record time-varying) and which agents were pooled into the `= 1` category, since these vary by study. Distinct from `CONMED_ANTACID` even though some compounds appear in both classes (calcium carbonate, calcium acetate, aluminium and magnesium salts): the two canonicals encode different therapeutic intents and different mechanisms of action on the drug of interest. `CONMED_ANTACID` is a gastric-pH-modifying covariate tested alongside `CONMED_PPI` and `CONMED_H2RA` for effects on the absorption of pH-sensitive drugs; `CONMED_PHOSBINDER` is a luminal-chelation covariate whose relevant mechanism is binding of the co-administered drug in the gut, and which in FGFR-inhibitor programmes is also a marker of on-treatment hyperphosphatemia management. When a source paper reports both, register both. Ratified canonically alongside the Gong 2023 pemigatinib extraction.

### CONMED_IFNB1A (**canonical for concomitant interferon beta-1a coadministration indicator**)
- **Description:** 1 = patient coadministered subcutaneous interferon beta-1a (Rebif or equivalent recombinant IFN beta-1a product) during the observation interval, 0 = no concomitant IFN beta-1a. Time-varying per subject because the source studies enrol both monotherapy and IFN beta-1a combination periods.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant IFN beta-1a).
- **Source aliases:**
  - `IFNB1A` -- used in `Savic_2017_cladribine.R`.
- **Example models:** `Savic_2017_cladribine.R` (multiplicative effect on cladribine non-renal clearance: `cl_nonrenal *= (1 + e_ifn_clnr * CONMED_IFNB1A)` with `e_ifn_clnr = 0.21`, i.e. a 21% increase in non-renal CL when coadministered with IFN beta-1a).
- **Notes:** Captured at the dose-record level in Savic 2017 (multiple-dose study 26486 alternated between cladribine alone and cladribine + IFN beta-1a periods). The interaction mechanism is not definitively established in the source paper; Savic 2017 discusses that the observed effect could alternatively be modelled on bioavailability or interpreted as a period-effect / interoccasion variability artefact. Future cladribine + immunomodulator extractions should reuse this canonical when IFN beta-1a is the specific concomitant agent; register a sibling canonical (e.g. `CONMED_IFNB1B`, `CONMED_IFNALPHA`) if a different interferon species is intended.

### CONMED_IFNALPHA (**canonical for concomitant interferon alpha coadministration indicator**)
- **Description:** 1 = patient coadministered systemic interferon alpha (any subtype; recombinant IFN-alpha-2a, IFN-alpha-2b, or pegylated IFN-alpha) during the observation interval, 0 = no concomitant IFN-alpha. Time-fixed when the source paper analyses cohorts whose IFN-alpha exposure is unchanged across the analysis window (per-trial protocol); time-varying when IFN-alpha cycling on/off is recorded at the observation level.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant IFN-alpha).
- **Source aliases:**
  - `IFNa` -- used in `Han_2016_bevacizumab.R` (Han 2016 Table 3 categorical indicator on bevacizumab CL; the 102 IFN-alpha-coadministered patients were enrolled in renal cell carcinoma study BO17705 per Han 2016 Table 1 and Table 2 row 'Concomitant treatment').
- **Example models:** `Han_2016_bevacizumab.R` (multiplicative effect on bevacizumab CL: `cl *= exp(e_ifna_lcl * CONMED_IFNALPHA)` with `e_ifna_lcl = log(0.844) = -0.170`, i.e. CL is 15.6% lower when IFN-alpha is coadministered).
- **Notes:** Sibling canonical to `CONMED_IFNB1A` (interferon beta-1a) under the `CONMED_<INN>` family; the existing CONMED_IFNB1A entry explicitly anticipated this `CONMED_IFNALPHA` name for the alpha-interferon species. Per-paper alpha-subtype detail (IFN-alpha-2a vs -2b vs pegylated) is documented in `covariateData[[CONMED_IFNALPHA]]$notes` where available; pooled here because the source paper (Han 2016) does not separately enumerate subtypes. Distinct from `CONMED_IFNB1A`/`CONMED_IFNB1B` (beta-interferon species). Ratified canonically on 2026-06-25 alongside the Han 2016 bevacizumab extraction.

### CONMED_IMMUNOMOD (**canonical for any-immunomodulator (purine analogue or methotrexate) composite indicator**)
- **Description:** 1 = subject is on at least one concomitant immunomodulator (any of: azathioprine (AZA), 6-mercaptopurine (6-MP), or methotrexate (MTX)) at the PK observation, 0 = no concomitant immunomodulator from this class. A composite pooled indicator used in inflammatory-bowel-disease popPK analyses where the source paper does not separate the individual thiopurine vs MTX effects on therapeutic-mAb (typically anti-TNF) clearance.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant immunomodulator from the AZA / 6-MP / MTX class).
- **Source aliases:**
  - `IMM` -- used in `Frymoyer_2017_infliximab.R` (pooled purine-analogue-or-MTX indicator per Table 1 footnote of Frymoyer 2017: "Concomitant immunomodulation refers to purine-analogue or methotrexate.").
- **Example models:** `Frymoyer_2017_infliximab.R` (power-of-coefficient effect on CL: `CL_typ * 0.863^CONMED_IMMUNOMOD`, i.e. -13.7% when on an immunomodulator).
- **Notes:** Distinct from the per-drug indicators `CONMED_AZA`, `CONMED_MP`, and `CONMED_MTX`, which model the effect of each immunomodulator separately. Use `CONMED_IMMUNOMOD` only when the source paper itself pools the three under a single binary; use the per-drug indicators when the source paper estimates separate effects. Future IBD popPK extractions that pool thiopurines + MTX into a single indicator should reuse this canonical and extend the example list. Ratified canonically on 2026-05-20 alongside the Frymoyer 2017 infliximab pediatric Crohn's disease extraction.

### CONMED_IPI_1Q6W (**canonical for nivolumab + ipilimumab 1 mg/kg q6w combination indicator**)
- **Description:** 1 = subject is receiving nivolumab in combination with ipilimumab 1 mg/kg every 6 weeks (continuous maintenance); 0 = otherwise.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any non-1Q6W regimen -- monotherapy, chemotherapy combination, or another ipilimumab schedule).
- **Source aliases:**
  - `IPI1Q6W` -- used in `Zhang_2019_nivolumab.R`.
- **Example models:** `Zhang_2019_nivolumab.R` (exponential effect on baseline CL: `exp(0.159)` ~= 1.17 fold increase relative to monotherapy).
- **Notes:** Paired with `CONMED_IPI_3Q3W`. See the CONMED_IPI_3Q3W note for how the other ipilimumab schedules collapse into the reference group.

### CONMED_IPI_3Q3W (**canonical for nivolumab + ipilimumab 3 mg/kg q3w combination indicator**)
- **Description:** 1 = subject is receiving nivolumab in combination with ipilimumab 3 mg/kg every 3 weeks (4-dose induction); 0 = otherwise. Encodes the high-intensity ipilimumab combination regimen as a study-design covariate on nivolumab CL.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any non-3Q3W regimen -- monotherapy, chemotherapy combination, or another ipilimumab schedule).
- **Source aliases:**
  - `IPI3Q3W` -- used in `Zhang_2019_nivolumab.R`.
- **Example models:** `Zhang_2019_nivolumab.R` (exponential effect on baseline CL: `exp(0.227)` ~= 1.25 fold increase relative to monotherapy).
- **Notes:** Paired with `CONMED_IPI_1Q6W`; both indicators can coexist in one population, but a single subject has at most one set to 1 in the Zhang 2019 cohort. The remaining ipilimumab schedules (1 mg/kg q3w x 4 induction, 1 mg/kg q12w) had no statistically significant effect on nivolumab CL and were therefore folded into the reference (0) group along with monotherapy, leaving only IPI3Q3W and IPI1Q6W as named non-reference indicators.

### CONMED_IPI_ANY (**canonical for any-ipilimumab-coadministration indicator**)
- **Description:** 1 = subject is receiving nivolumab in combination with any ipilimumab regimen (regardless of dose or schedule); 0 = nivolumab monotherapy or nivolumab + chemotherapy. Encodes the "is there ipilimumab in the regimen" question as a single binary covariate, distinct from the regimen-specific CONMED_IPI_3Q3W and CONMED_IPI_1Q6W indicators above.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no ipilimumab coadministration).
- **Source aliases:**
  - `IPICO` -- used in `Zhang_2019_nivolumab.R`.
- **Example models:** `Zhang_2019_nivolumab.R` (additive effect on the time-varying-CL Emax parameter: Emax += -0.0668 when CONMED_IPI_ANY = 1).
- **Notes:** Logically the union of the regimen-specific indicators (CONMED_IPI_3Q3W, CONMED_IPI_1Q6W, plus the unmodeled 1 mg/kg q3wx4 and 1 mg/kg q12w schedules). Zhang 2019 uses it on the *time-varying* Emax (a different structural parameter from baseline CL), which is why it coexists with the regimen-specific indicators on baseline CL rather than substituting for them.

### CONMED_ITRACONAZOLE (**canonical for concomitant itraconazole (strong CYP3A4 / P-gp inhibitor) coadministration indicator**)
- **Description:** 1 = subject coadministered itraconazole during the observation interval (typically 200 mg once-daily oral or higher), 0 = no concomitant itraconazole. Distinct from the broader [[CONMED_AZOLE]] indicator (any azole antifungal) and from [[CONMED_CYP3A4_INH]] (any CYP3A4 inhibitor) when a paper singles out itraconazole as an index victim / perpetrator; itraconazole is used clinically as a probe strong CYP3A4 + P-glycoprotein inhibitor in dedicated DDI studies. Per-subject time-varying when a DDI study spans on / off itraconazole periods; time-fixed on the on-treatment day of a fixed-sequence DDI arm.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant itraconazole).
- **Source aliases:**
  - `INTR` -- used in `vandenBerg_2021_uprifosbuvir_pbpk.R` (van den Berg 2021 supplement input column INTR = 0 alone / 1 itraconazole, per PN001 Group F and PN010).
- **Example models:** `vandenBerg_2021_uprifosbuvir_pbpk.R` (multiplicative factors on eight gut / hepatic parameters per van den Berg 2021 Table 3: CL_int,G x 0.444, CL_int,H x 0.797, ALAG2 x 0.145, KA2 x 0.499, Ktr1 x 0.166, KM6g x 0.294, FrM6g x 0.174, KelM6g x 0.0290; the combined effect of these factors is a delay in uprifosbuvir Tmax and a large increase in uprifosbuvir plasma exposure without a corresponding rise in M5 / M6, consistent with itraconazole shunting uprifosbuvir away from gut CYP3A4 / P-gp-mediated first-pass metabolism toward hepatic conversion).
- **Notes:** Auto-approved member of the `CONMED_<INN>` family (INN = itraconazole). Distinct from the class-level [[CONMED_AZOLE]] (Kirubakaran 2022 tacrolimus, which pools itraconazole with other systemic azoles as a common CYP3A4 / P-gp inhibitor class effect) because dedicated itraconazole DDI studies -- particularly those where itraconazole is used as the strong probe inhibitor in a Phase 1 mechanistic study -- carry substrate-specific factors on multiple gut and hepatic parameters that would be diluted by pooling with weaker azoles. Future dedicated itraconazole DDI popPK extractions should reuse this canonical and extend the example-models list. Promote to a drug-specific variant if a paper distinguishes multiple itraconazole regimens; keep as the class-neutral indicator otherwise.

### CONMED_VORICONAZOLE (**canonical for concomitant voriconazole (strong CYP3A inhibitor) coadministration indicator**)
- **Description:** 1 = subject coadministered voriconazole during the observation interval (typically 200 mg twice daily oral); 0 = no concomitant voriconazole. Voriconazole is both a reversible and a time-dependent CYP3A inhibitor and is the dominant tacrolimus / calcineurin-inhibitor perpetrator in solid-organ transplant care, where antifungal prophylaxis and treatment are routine. Time-varying when the observation record spans on / off voriconazole periods (the usual case in transplant TDM datasets); time-fixed in a fixed-sequence DDI arm.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant voriconazole).
- **Source aliases:**
  - `Voriconazole` -- used in `Pei_2023_tacrolimus.R` (Pei 2023 Supplement 2.1.1: "Categorical covariates, such as SEX (male = 1 and female = 2) and Voriconazole (co-administration = 1 and none = 0), were included in the analysis using indicator variables").
- **Example models:** `Pei_2023_tacrolimus.R` (exponential effect on apparent oral clearance: `exp(e_vori_cl * CONMED_VORICONAZOLE)` with `e_vori_cl = -0.64`, i.e. a 47% reduction in tacrolimus CL/F on voriconazole; Pei 2023 Table S4).
- **Notes:** Auto-approved member of the `CONMED_<INN>` family (INN = voriconazole). Distinct from the class-level [[CONMED_AZOLE]] (Kirubakaran 2022 tacrolimus, which pools voriconazole with the other systemic azoles) and from [[CONMED_CYP3A4_INH_STRONG]]: register the drug-specific indicator when a paper singles voriconazole out with its own coefficient, because the magnitude of voriconazole's tacrolimus interaction is much larger than the pooled azole class effect (Pei 2023's own PBPK arm predicts a 5.80-fold rise in tacrolimus AUC). Sibling to [[CONMED_ITRACONAZOLE]]. When a source instead supplies the perpetrator's measured concentration rather than an on/off flag, use [[CONC_VORI_NGML]]. Ratified 2026-08-05 alongside the Pei 2023 tacrolimus extraction.

### CONMED_LCM (**canonical for lacosamide (monotherapy or coadministration) indicator**)
- **Description:** 1 = subject is on lacosamide (LCM) at the observation, 0 = not on lacosamide. In an active-controlled monotherapy trial (SP0993) the indicator identifies the LCM arm (the CBZ-CR arm gets 0). In adjunctive-treatment cohorts the same canonical would flag concomitant lacosamide use. Time-varying is permitted when a study spans on / off transitions; time-fixed in monotherapy parallel-group trials.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (not on lacosamide; in monotherapy head-to-head trials this is the comparator arm, e.g., carbamazepine controlled-release in Lindauer 2017 SP0993).
- **Source aliases:**
  - `TYPE` -- used in `Lindauer_2017_lacosamide_dropout.R` and `Lindauer_2017_lacosamide_seizure.R` (Lindauer 2017 SP0993 encoding: TYPE = 1 for the lacosamide arm, 0 for the CBZ-CR arm; same value semantics as the canonical CONMED_LCM column).
- **Example models:** `Lindauer_2017_lacosamide_dropout.R` (multiplicative log-hazard shift on the dropout hazard: `hazard *= exp(-0.138 * CONMED_LCM)`, HR = 0.871 vs the CBZ-CR arm, Lindauer 2017 Table 2 Coeff_TYPE), `Lindauer_2017_lacosamide_seizure.R` (gates the drug-specific AUC covariate contributions -- `AUC_LCM` centred deviation is active only when CONMED_LCM = 1, `AUC_CBZ` centred deviation active only when CONMED_LCM = 0 -- and gates the AGE covariate effect on the first-seizure hazard which is LCM-only per Lindauer 2017 Section 3.4).
- **Notes:** Auto-approved member of the `CONMED_<INN>` family. Follows the `CONMED_*` concomitant-medication pattern (`CONMED_CBZ`, `CONMED_EFV`, `CONMED_AZA`, etc.). Semantically the indicator captures "is the subject taking lacosamide at this record"; in a monotherapy parallel-group trial that equates to arm membership, and in adjunctive or cross-over designs it captures the on / off status. Ratified canonically on 2026-07-03 alongside the Lindauer 2017 lacosamide time-to-seizure / dropout extraction.

### CONMED_LPV (**canonical for concomitant lopinavir co-administration indicator**)
- **Description:** 1 = subject is receiving concomitant lopinavir as part of an antiretroviral regimen at the observation, 0 = not on lopinavir. Captures the LPV-ritonavir DDI on ritonavir apparent oral clearance: when LPV/r is co-administered, ritonavir CL/F is roughly 2.7-fold higher than when ritonavir is given without lopinavir (Kappelhoff 2005 attributes the increase to lopinavir-driven enzyme induction superimposed on ritonavir's own CYP3A4 inhibition). Distinct from the joint rifampicin + super-boosted-LPV/r 4:4 indicator `CONMED_RIF_LPVR4` and from the cholesterol-surrogate use in `Archary_2018_lopinavir.R`; this canonical is the binary "is the subject on lopinavir at all" flag, not a regimen-specific contrast.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant lopinavir).
- **Source aliases:**
  - `LPV` -- used in `Kappelhoff_2005_ritonavir.R` (paper Results equation following Table 2: `CL/F = 10.5 * 2.72^LPV`).
- **Example models:** `Kappelhoff_2005_ritonavir.R` (multiplicative power-form effect on apparent oral ritonavir CL/F: `cl = exp(lcl + etalcl) * e_lpv_cl^CONMED_LPV` with `e_lpv_cl = 2.72`; 36 of 186 subjects on LPV/r in the source cohort), `Hoglund_2015_lumefantrine.R` (TWO independent linear-deviation effects: on lumefantrine apparent CL/F `cl *= (1 + (-0.621) * CONMED_LPV)` (-62.1 %, CYP3A4 inhibition by ritonavir) and on desbutyl-lumefantrine apparent CL/F `cl_desbutlum *= (1 + 3.92 * CONMED_LPV)` (+392 %; mechanism unknown, Hoglund 2015 Discussion); Table 2), `Hoglund_2015_artemether.R` (TWO independent linear-deviation effects: on artemether apparent CL/F `cl *= (1 + 0.328 * CONMED_LPV)` (+32.8 %) and on dihydroartemisinin apparent CL/F `cl_dha *= (1 + 1.43 * CONMED_LPV)` (+143 %; attributed to CYP2B6 / 2C induction by lopinavir, Hoglund 2015 Discussion); Table 3), `Kay_2020_lumefantrine.R` (TWO independent linear-deviation effects, on lumefantrine apparent CL/F: `cl *= (1 + (-0.514) * CONMED_LPV)` (-51.4 %, CYP3A4 inhibition by the ritonavir booster) and on the first-order absorption rate constant: `ka *= (1 + (-0.212) * CONMED_LPV)` (-21.2 %); relative to the no-ART reference in HIV-infected Ugandan children with malaria (ASTMH 2020 poster 2167 Table 1)).
- **Notes:** Follows the `CONMED_*` concomitant-medication pattern (`CONMED_AZA`, `CONMED_RIF`, `CONMED_EFV`, `CONMED_CBZ`, `CONMED_AMIO`, etc.). The Kappelhoff 2005 cohort tested concomitant saquinavir and indinavir on the same parameter via the same univariate procedure; neither was retained, so this canonical captures the only co-PI covariate kept in that paper's final model. Time-fixed within an evaluated regimen in the source cohort (the dataset records baseline LPV/r vs non-LPV/r assignment); a time-varying form is permitted for cohorts with on / off regimen transitions and should be documented in `covariateData[[CONMED_LPV]]$notes`. Ratified canonically alongside the Kappelhoff 2005 ritonavir extraction.

### CONMED_METFORMIN (**canonical for concomitant metformin co-administration indicator**)
- **Description:** 1 = subject is on concomitant metformin during the modelled treatment period, 0 = not. Time-fixed in source datasets where metformin is a study-design "add-on" arm (e.g., Retlich 2015 Study 4 add-on-to-metformin design); permits a time-varying form for cohorts with on/off metformin transitions, document per-model via `covariateData[[CONMED_METFORMIN]]$notes`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant metformin).
- **Source aliases:** none known.
- **Example models:** `Retlich_2015_linagliptin.R` (1 = study 4 add-on-to-metformin cohort; multiplicative effect on linagliptin relative bioavailability F: +69% F for metformin co-administration vs the monotherapy reference; the effect is attributed to a metformin -- linagliptin drug-drug interaction consistent with a separately published DDI study, Graefe-Mody 2009).
- **Notes:** Follows the `CONMED_*` concomitant-medication pattern (AZA / MP / MTX / AMINO / NSAID / PARA / AD / RITUX / AED / CHEMO / EIAED / EFV / AZOLE). Metformin is a widely-co-prescribed first-line T2DM oral antidiabetic; future T2DM-popPK / -DDI extractions should reuse this canonical. Ratified canonically alongside the Retlich 2015 linagliptin extraction.

### CONMED_MP (**canonical for concomitant 6-mercaptopurine**)
- **Description:** 1 = on concomitant 6-mercaptopurine (6-MP), 0 = not.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant 6-MP).
- **Source aliases:** `MP` -- used in `Rosario_2015_vedolizumab.R`.
- **Example models:** `Rosario_2015_vedolizumab.R` (power-form on CLL: `CLL * 1.04^CONMED_MP`).
- **Notes:** Second thiopurine immunomodulator used in IBD maintenance; typically mutually exclusive with `CONMED_AZA` for a given subject.

### CONMED_SULFONYLUREA (**canonical for concomitant sulfonylurea (class) co-administration indicator**)
- **Description:** 1 = subject is on a concomitant sulfonylurea (class indicator pooling glyburide / glibenclamide / glipizide / glimepiride / gliclazide / tolbutamide / glipizide-ER / chlorpropamide and other oral insulin-secretagogue sulfonylureas), 0 = not. Time-fixed in source datasets where the sulfonylurea is a study-design background-therapy arm (e.g., the Baron 2016 metformin + sulfonylurea cohort); permits a time-varying form when a study tracks on / off sulfonylurea transitions.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant sulfonylurea).
- **Source aliases:**
  - `SU` -- used in `Baron_2016_empagliflozin.R` (Baron 2016 Methods and Table S2 'background sulfonylurea' cohort; multiplicative effects on the model-predicted baseline FPG and on Gmax in Table S3).
- **Example models:** `Baron_2016_empagliflozin.R` (multiplicative effect on BFPG: `bfpg_su^CONMED_SULFONYLUREA = 1.01^...`; on Gmax: `gmax_su^CONMED_SULFONYLUREA = 1.27^...` -- sulfonylurea co-treatment increases empagliflozin's maximal FPG-lowering effect, attributed by the authors to possible improvement in beta-cell function).
- **Notes:** Class-level pooling indicator analogous to `CONMED_AZOLE`, `CONMED_NSAID`, `CONMED_STATIN`, `CONMED_AED`, etc. Future T2DM popPK / PD extractions that need to distinguish individual sulfonylureas (e.g., for a DDI sub-analysis on a CYP2C9-metabolized member) should register a per-INN sibling canonical (`CONMED_GLYBURIDE`, `CONMED_GLIPIZIDE`, ...) and keep `CONMED_SULFONYLUREA` for the class-pooled summary. Ratified canonically on 2026-06-24 alongside the Baron 2016 empagliflozin extraction.

### CONMED_PIOGLITAZONE (**canonical for concomitant pioglitazone co-administration indicator**)
- **Description:** 1 = subject is on concomitant pioglitazone (thiazolidinedione (TZD) class oral antidiabetic), 0 = not. Time-fixed in source datasets where pioglitazone is a study-design background-therapy arm; permits a time-varying form when a study tracks on / off transitions.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant pioglitazone).
- **Source aliases:**
  - `PIO` -- used in `Baron_2016_empagliflozin.R` (Baron 2016 Methods and Study 7 background pioglitazone cohort; multiplicative effects on the model-predicted baseline FPG and on Gmax in Table S3).
- **Example models:** `Baron_2016_empagliflozin.R` (multiplicative effect on BFPG: `bfpg_pio^CONMED_PIOGLITAZONE = 0.999^...`; on Gmax: `gmax_pio^CONMED_PIOGLITAZONE = 1.02^...` -- both effects are not statistically significant in the source analysis but are retained per the full-covariate-modelling approach).
- **Notes:** Follows the per-INN `CONMED_<INN>` pattern (CONMED_METFORMIN, CONMED_LPV, CONMED_ATAZANAVIR, CONMED_RIF, CONMED_PROBENECID, etc.). Distinct from a putative future `CONMED_TZD` (class indicator) -- the thiazolidinedione class additionally contains rosiglitazone (largely withdrawn) and lobeglitazone (Korean market); pool a TZD class indicator only if a future paper aggregates them. Ratified canonically on 2026-06-24 alongside the Baron 2016 empagliflozin extraction.

### CONMED_MTX (**canonical for concomitant methotrexate**)
- **Description:** 1 = on concomitant methotrexate, 0 = not.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant methotrexate).
- **Source aliases:** `MTX` -- used in `Rosario_2015_vedolizumab.R`.
- **Example models:** `Rosario_2015_vedolizumab.R` (power-form on CLL: `CLL * 0.983^CONMED_MTX`).
- **Notes:** Immunomodulator used especially in CD maintenance. Generic concomitant-MTX indicator that may also appear in non-IBD models; start as scope: general.

### CONMED_NSAID (**canonical for concomitant NSAID use**)
- **Description:** 1 = on concomitant non-steroidal anti-inflammatory drug (NSAID) therapy at baseline, 0 = not.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant NSAID use; typical patient).
- **Source aliases:**
  - `NSAID` -- used in `Li_2019_abatacept.R`.
- **Example models:** `Li_2019_abatacept.R` (exponential effect on CL: `CL * exp(0.0640 * CONMED_NSAID)`; ~6.6% higher CL, not clinically relevant per Li 2019).
- **Notes:** Baseline-use-only in Li 2019; time-varying use is permitted, document per-model. Follows the `CONMED_*` concomitant-medication pattern established for IBD models (AZA / MP / MTX / AMINO).

### CONMED_NVP (**canonical for concomitant nevirapine indicator**)
- **Description:** 1 = subject is receiving nevirapine (NVP) -- typically as the non-nucleoside reverse-transcriptase inhibitor (NNRTI) third agent in an HIV combination ART regimen -- coadministered with the modelled antiretroviral; 0 = subject is not on nevirapine. Nevirapine is a CYP3A inducer; the indicator is used to flag PXR-mediated induction of metabolic clearance for coadministered CYP3A-cleared antiretrovirals (e.g., the protease inhibitor lopinavir).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no nevirapine; paper-defined ART backbone excluding NVP).
- **Source aliases:**
  - `N` -- in-equation indicator used by Jullien 2006 (final covariate submodel: `CL/F = ... * 1.34^N` with N = 1 if nevirapine combined with lopinavir).
  - `NVP` -- standard 3-letter HIV abbreviation in NONMEM control streams from pediatric-ART cohorts; same orientation, no value transformation.
- **Example models:** `Jullien_2006_lopinavir.R` (exponential effect on CL/F: `cl *= exp(log(1.34) * CONMED_NVP)`; +34% CL/F when nevirapine is coadministered), `Hoglund_2015_lumefantrine.R` (multiplicative linear-deviation effect on lumefantrine relative bioavailability F: `fdepot_typ *= (1 + (-0.248) * CONMED_NVP)`; -24.8% relative to the no-ART reference in HIV-infected Ugandan adults, Hoglund 2015 Table 2), `Hoglund_2015_artemether.R` (two independent linear-deviation effects: on artemether relative bioavailability F `fdepot_typ *= (1 + (-0.663) * CONMED_NVP)` (-66.3 %) and on apparent dihydroartemisinin CL/F `cl_dha *= (1 + (-0.445) * CONMED_NVP)` (-44.5 %); Hoglund 2015 Table 3), `Svensson_2014_bedaquiline_nvp.R` (power-form effects on apparent CL of bedaquiline and its M2 metabolite at full induction: `cl *= 0.915 ^ CONMED_NVP` and `cl_m2 *= 1.05 ^ CONMED_NVP`; Svensson 2014 Supplementary Table S1b 'EFF NVP on BDQ CL = 0.915' and 'EFF NVP on M2 CL = 1.05'), `Kay_2022_lumefantrine.R` (TWO independent linear-deviation effects: on apparent oral lumefantrine CL/F `cl *= (1 + 0.0191 * CONMED_NVP)` (+1.91 %; not statistically significant, 95% CI -0.324 to 0.362) and on the first-order absorption rate constant `ka *= (1 + (-0.0589) * CONMED_NVP)` (-5.89 %; not statistically significant, 95% CI -0.207 to 0.0891); HIV-uninfected children are the reference (CONMED_NVP = 0) in a pediatric Ugandan malaria cohort, Kay 2022 Table 2 theta_9 and theta_12), `Kay_2020_lumefantrine.R` (TWO independent linear-deviation effects, on lumefantrine apparent CL/F: `cl *= (1 + 0.0191 * CONMED_NVP)` (+1.9%) and on the first-order absorption rate constant: `ka *= (1 + (-0.0589) * CONMED_NVP)` (-5.9%); both retained in the final model despite 95% CIs spanning zero (ASTMH 2020 poster 2167 Table 1)).
- **Notes:** Scope promoted from specific to general on 2026-06-27 alongside the Svensson 2014 bedaquiline-NVP extraction (the fourth model ratifying the canonical: Jullien 2006, Hoglund 2015 lumefantrine, Hoglund 2015 artemether, Svensson 2014). Sister indicator to the registered `CONMED_EFV` (efavirenz) entry, applying to the second commonly co-administered NNRTI. Future ART popPK models that test an NVP-vs-comparator contrast should extend the example list when the comparator matches.

### CONMED_PARA (**canonical for concomitant paracetamol (acetaminophen) use**)
- **Description:** 1 = on concomitant paracetamol (acetaminophen) at the observation, 0 = not.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant paracetamol).
- **Source aliases:**
  - `PCM` -- used in `Plan_2012_pain.R` (DDMORE Foundation Model Repository entry DDMODEL00000194).
- **Example models:** `Plan_2012_pain.R` (additive shift on the placebo-effect logit of the typical pain-score lambda: `phl = logit(TVLAM) + 0.364 * CONMED_PARA`; mean pain score ~9% higher on the 0-10 Likert scale during paracetamol use).
- **Notes:** Distinct from `CONMED_NSAID` -- paracetamol is not classed as an NSAID (no anti-inflammatory mechanism, distinct AE profile). Time-varying use is permitted; the daily Likert measurements in Plan 2012 carry PCM as a per-observation flag. Follows the `CONMED_*` concomitant-medication pattern established for IBD models (AZA / MP / MTX / AMINO) and `CONMED_NSAID` (Li 2019). Ratified canonically alongside the Plan 2012 DDMORE extraction.

### CONMED_PB (**canonical for concomitant phenobarbital coadministration indicator**)
- **Description:** 1 = subject is taking phenobarbital (PB) -- including primidone, which is metabolised to phenobarbital and is conventionally pooled with PB -- as a concomitant antiepileptic drug at the PK observation, 0 = no concomitant phenobarbital / primidone. Phenobarbital is a broad-spectrum CYP and UGT inducer that increases the apparent clearance of co-administered drugs. Time-varying when phenobarbital starts / stops within the observation window; time-fixed when the source paper analyses chronic-maintenance cohorts whose AED therapy is stable.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant phenobarbital or primidone).
- **Source aliases:**
  - `PB` -- used in `Schoemaker_2017_brivaracetam.R` (paper covariate `PB` for phenobarbital or primidone coadministration; the source pools primidone with phenobarbital because primidone is metabolised to phenobarbital) and `Lee_2024_topiramate.R` (Lee 2024 Table S1 theta7, additive enzyme-induction term on topiramate CL/F; that paper does not state whether primidone was pooled with phenobarbital).
- **Example models:** `Schoemaker_2017_brivaracetam.R` (multiplicative effect on apparent oral clearance: `cl *= (1 + 0.408 * CONMED_PB)`; +40.8% relative to no-PB reference, corresponding to ~29% lower brivaracetam exposure, Schoemaker 2017 Table 1), `Lee_2024_topiramate.R` (**additive** effect in absolute units: `cl <- (exp(lcl) + e_pb_cl * CONMED_PB + ...) * ...` with `e_pb_cl` = 0.376 L/h on a 1.45 L/h monotherapy intercept, i.e. +26% relative to no-PB reference and the smallest of that model's four enzyme-inducing coefficients, Lee 2024 Table S1 / Discussion).
- **Notes:** Drug-specific CONMED_* indicator anticipated in the [[CONMED_AED]] notes; used when a paper estimates a separate phenobarbital-induction effect distinct from the pooled EIAED / AED class effect. Per-model `covariateData[[CONMED_PB]]$notes` should document whether primidone is pooled with phenobarbital (Schoemaker 2017 pools them because primidone is metabolised to phenobarbital; Lee 2024 is silent on the point). Distinct from the broader [[CONMED_EIAED]] (any enzyme-inducing AED) and [[CONMED_AED]] (any concomitant AED). When a paper distinguishes individual AEDs separately, use the drug-specific canonicals [[CONMED_CBZ]], `CONMED_PB`, [[CONMED_OXC]], [[CONMED_PHT]], [[CONMED_VPA]] rather than collapsing into the class-level indicator. Ratified canonically on 2026-05-20 alongside the Schoemaker 2017 brivaracetam paediatric extraction.

### CONMED_PHT (**canonical for concomitant phenytoin coadministration indicator**)
- **Description:** 1 = subject is taking phenytoin (PHT) as a concomitant antiepileptic drug at the PK observation, 0 = no concomitant phenytoin. Phenytoin is a strong, broad-spectrum CYP (2C9, 2C19, 3A4) and UGT inducer and is generally the most potent enzyme-inducing antiseizure medication in routine use, so it typically carries the largest induction coefficient when a paper decomposes the enzyme-inducing class into individual drugs. Time-varying when phenytoin starts / stops within the observation window; time-fixed when the source paper analyses chronic-maintenance cohorts whose antiseizure therapy is stable across the analysis window.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant phenytoin).
- **Source aliases:**
  - `PHT` -- used in `Lee_2024_topiramate.R` (Lee 2024 Table S1 theta6, additive enzyme-induction term on topiramate CL/F).
- **Example models:** `Lee_2024_topiramate.R` (**additive** effect on apparent oral clearance in absolute units: `cl <- (exp(lcl) + e_pht_cl * CONMED_PHT + ...) * ...` with `e_pht_cl` = 1.02 L/h on a 1.45 L/h monotherapy intercept, i.e. +70% relative to no-PHT reference, Lee 2024 Table S1 / Discussion; the largest of the four enzyme-inducing coefficients in that model).
- **Notes:** Drug-specific CONMED_* indicator anticipated in the [[CONMED_AED]] notes; used when a paper estimates a separate phenytoin-induction effect distinct from the pooled EIAED / AED class effect. Distinct from the broader [[CONMED_EIAED]] (any enzyme-inducing AED) and [[CONMED_AED]] (any concomitant AED). When a paper distinguishes individual AEDs separately, use the drug-specific canonicals [[CONMED_CBZ]], [[CONMED_PB]], [[CONMED_OXC]], `CONMED_PHT` and [[CONMED_VPA]] rather than collapsing into the class-level indicator. Distinct from [[DOSE_PHT_MGKGD]], which carries the phenytoin *daily dose per kg* as a continuous covariate rather than a coadministration indicator -- use this binary canonical when the source paper models only the presence of phenytoin, and `DOSE_PHT_MGKGD` when it models the phenytoin dose level. Hashimoto 1994 (zonisamide) tested phenytoin coadministration but found no significant effect and retained only CBZ in the final model, so that model carries no `CONMED_PHT` column. Ratified canonically on 2026-08-03 alongside the Lee 2024 topiramate extraction.

### CONMED_OXC (**canonical for concomitant oxcarbazepine coadministration indicator**)
- **Description:** 1 = subject is taking oxcarbazepine (OXC) as a concomitant antiepileptic drug at the PK observation, 0 = no concomitant oxcarbazepine. Oxcarbazepine is the 10-keto analogue of carbamazepine and, via its active monohydroxy metabolite, is a weaker and more selective inducer than carbamazepine itself (principally CYP3A4 / UGT, without the potent autoinduction and broad CYP induction of CBZ), so it typically carries a smaller induction coefficient than [[CONMED_CBZ]] when a paper decomposes the enzyme-inducing class into individual drugs. Time-varying when oxcarbazepine starts / stops within the observation window; time-fixed when the source paper analyses chronic-maintenance cohorts whose antiseizure therapy is stable across the analysis window.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant oxcarbazepine).
- **Source aliases:**
  - `OXC` -- used in `Lee_2024_topiramate.R` (Lee 2024 Table S1 theta5, additive enzyme-induction term on topiramate CL/F).
- **Example models:** `Lee_2024_topiramate.R` (**additive** effect on apparent oral clearance in absolute units: `cl <- (exp(lcl) + e_oxc_cl * CONMED_OXC + ...) * ...` with `e_oxc_cl` = 0.419 L/h on a 1.45 L/h monotherapy intercept, i.e. +29% relative to no-OXC reference, Lee 2024 Table S1 / Discussion).
- **Notes:** Drug-specific CONMED_* indicator anticipated in the [[CONMED_AED]] notes; used when a paper estimates a separate oxcarbazepine-induction effect distinct from the pooled EIAED / AED class effect. Must **not** be collapsed into [[CONMED_CBZ]] despite the close structural relationship between the two drugs: oxcarbazepine is a materially weaker inducer, and Lee 2024 estimates the two coefficients separately (OXC 0.419 L/h vs CBZ 0.703 L/h, a 1.7-fold difference), so overloading the carbamazepine column would misattribute the induction magnitude. Distinct from the broader [[CONMED_EIAED]] (any enzyme-inducing AED) and [[CONMED_AED]] (any concomitant AED). When a paper distinguishes individual AEDs separately, use the drug-specific canonicals [[CONMED_CBZ]], [[CONMED_PB]], `CONMED_OXC`, [[CONMED_PHT]] and [[CONMED_VPA]]. Ratified canonically on 2026-08-03 alongside the Lee 2024 topiramate extraction.

### CONMED_PLDH (**canonical for concomitant PEGylated liposomal doxorubicin combination indicator**)
- **Description:** 1 = subject is receiving the modelled drug in combination with PEGylated liposomal doxorubicin (PLDH; Doxil / Caelyx), 0 = otherwise (no concomitant PLDH). Encodes the PLDH co-administration regimen as a study-design covariate on clearance.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no concomitant PEGylated liposomal doxorubicin).
- **Source aliases:**
  - `PLDH` -- used in `Schmitt_2018_vinflunine.R` (Schmitt 2018 NONMEM dataset; 1 = vinflunine + PEGylated liposomal doxorubicin combination cohort).
- **Example models:** `Schmitt_2018_vinflunine.R` (power-form effect on vinflunine clearance: `cl *= 0.865^CONMED_PLDH` -- vinflunine apparent CL is reduced to 86.5% of single-agent value when coadministered with PLDH, Schmitt 2018 Table 2 and explicit CL formula on p.1607).
- **Notes:** PEGylated liposomal doxorubicin (PLDH) is a long-circulating doxorubicin formulation used in oncology combination regimens. The Schmitt 2018 vinflunine analysis is the first paper in this register to use PLDH as a popPK combination covariate; promote to general scope when a second paper ratifies the same encoding. Distinct from `CONMED_AVD` (brentuximab vedotin + AVD chemotherapy backbone, which includes free doxorubicin rather than the PEGylated liposomal formulation) and from `PRIOR_ANTHRACYCLINE_DOSE` (cumulative prior anthracycline exposure, not concomitant). Ratified canonically on 2026-05-25 alongside the Schmitt 2018 vinflunine extraction.

### CONMED_PDE5I (**canonical for concomitant PDE5-inhibitor monotherapy indicator in PAH**)
- **Description:** 1 = patient is on a concomitant phosphodiesterase type 5 inhibitor (PDE5I) PAH therapy (e.g., sildenafil, tadalafil) alone -- i.e. on a PDE5 inhibitor but NOT also on an endothelin-receptor antagonist; 0 = otherwise. Time-fixed per subject for the PK/PD analysis (PAH comedication at baseline, stable dose required before selexipag start).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (PAH-comedication-naive OR on ERA alone OR on both ERA+PDE5; mutually exclusive with `CONMED_ERA` and `CONMED_ERA_PDE5I`).
- **Source aliases:**
  - `PAHCOMED` category 2 ("PDE5 inh.") -- decomposed from the categorical `PAHCOMED` source column with levels {naive, ERA, PDE5, ERA+PDE5} in `Krause_2017_selexipag.R`.
- **Example models:** `Krause_2017_selexipag.R` (multiplicative effect on the ACT-333679 elimination rate constant: `km *= (1 + 0.07 * CONMED_PDE5I)`; +7% relative to the PAH-comedication-naive reference, Krause 2017 Table 1. The PDE5-only coefficient is statistically not significant (p = 0.19) but retained in the final model so the four-level PAH-comedication categorical is preserved end-to-end).
- **Notes:** Used together with `CONMED_ERA` and `CONMED_ERA_PDE5I` to decompose a four-level PAH-comedication categorical (naive / ERA-only / PDE5-only / ERA-and-PDE5) into three orthogonal mutually-exclusive binary indicators. Specific scope because the indicator's semantics are tied to the GRIPHON study's PAH-comedication taxonomy.

### CONMED_PPI (**canonical for concomitant proton-pump inhibitor use**)
- **Description:** 1 = patient on concomitant proton-pump inhibitor (CONMED_PPI) therapy, 0 = no CONMED_PPI use. Captures gastric-pH-elevating co-medication that can reduce the bioavailability of solubility-limited (typically weakly basic) orally administered drugs.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no CONMED_PPI use).
- **Source aliases:**
  - `CONMED_PPI` -- used in `Goel_2016_Sonidegib.R` (Goel 2016 dataset; defined as "significant" CONMED_PPI use, i.e. duration of CONMED_PPI use >= 80% of the PK assessment phase).
  - `PPI` -- source-paper column name; used in `Shu_2024_posaconazole.R` (Shu 2024 dataset; subject-level ever-versus-never indicator, 11 of 62 HSCT patients, with no minimum-duration threshold stated).
- **Example models:** `Goel_2016_Sonidegib.R` (multiplicative effect on F: `0.696^CONMED_PPI`, ~30% lower bioavailability under CONMED_PPI coadministration), `Shu_2024_posaconazole.R` (multiplicative effect on apparent volume: `3.832335^CONMED_PPI` on V/F for posaconazole oral suspension; mechanistically a bioavailability effect that surfaces on V/F because the dataset is oral-only, so F is not separately identifiable -- the same physiology as the Goel 2016 effect on F, relocated by the parameterisation).
- **Notes:** Per-model `covariateData[[CONMED_PPI]]$notes` must document the operational definition (e.g., Goel 2016 requires CONMED_PPI use covering >= 80% of the PK assessment window; other studies may use a simpler ever-vs-never indicator or a per-record time-varying flag). Distinct from `CONMED_H2RA` (H2-receptor antagonist) which acts on gastric pH via a different mechanism. On an apparent-parameter (CL/F, V/F) model fitted to oral-only data the PPI effect can legitimately appear on V/F or CL/F rather than on F; document which parameter carries it, because the sign convention flips (reduced F raises both apparent parameters).

### CONMED_PROBENECID (**canonical for concomitant probenecid co-administration indicator**)
- **Description:** 1 = subject coadministered probenecid (organic-anion transport inhibitor; multidrug resistance protein (MRP) family modulator with documented activity at the blood-brain barrier, blood-CSF barrier, and renal tubular secretion sites), 0 = no concomitant probenecid. Time-varying per subject because probenecid exposure can start and stop within the observation period.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no probenecid coadministration).
- **Source aliases:**
  - Paper-specific day-2 treatment-arm indicator -- used in `Xie_2000_m3g_rat.R` (the paper distinguishes a probenecid-treatment arm from a control arm by experimental day; the canonical column is 1 during the day-2 probenecid co-infusion and 0 otherwise, including on day 1 of the probenecid arm before the probenecid loading dose).
- **Example models:** `Xie_2000_m3g_rat.R` (multiplicative exponential effect on the unbound BBB influx clearance CL_u,in: `cluin *= exp(e_conmed_probenecid_cluin * CONMED_PROBENECID)` with `e_conmed_probenecid_cluin = log(0.17 / 0.11) = 0.4353`, i.e. a 1.55-fold increase in CL_u,in into rat brain ECF under probenecid co-administration; CL_u,out and the intercompartmental brain clearance Q_br are not statistically affected by probenecid in the paper's model selection and so are not paired with this canonical), `Landersdorfer_2009_gemifloxacin.R` (binary treatment-arm covariate that captures the static / non-mechanism-specific probenecid effects on gemifloxacin absorption rate (Ka 0.839 -> 0.897 1/h; +6.9%), absorption lag (Tlag 0.223 -> 0.129 h; -42%), and non-renal clearance (CL_NR 25.2 -> 21.0 L/h; -16.7%); the mechanism-specific competitive inhibition of renal tubular secretion is encoded separately via the time-varying `CP_PRB_MGL` covariate), `Hamren_2008_tesaglitazar.R` (multiplicative effect on parent CLrt and metabolite Vmax under probenecid co-administration: `(1 - 0.75 * CONMED_PROBENECID)` reduces both by 75% during the three-dose probenecid window in four healthy-control subjects; the shared estimate is encoded as two canonical-named parameters `e_probenecid_cl_renal` and `e_probenecid_vmax_gluc` to keep each effect's naming canonical), `Stocker_2012_oxypurinol.R` (multiplicative linear-deviation effect on apparent CL/Fm: `cl *= (1 + 0.383 * CONMED_PROBENECID)`, i.e. +38.3% apparent oxypurinol clearance with concomitant probenecid in adults with gout, consistent with the paper's mechanism narrative of probenecid inhibiting renal tubular reabsorption of oxypurinol; Stocker 2012 Table 3 theta8).
- **Notes:** Scope: specific because the only on-disk source is a preclinical microdialysis rat BBB-transport paper (Xie 2000) and the column meaning is intrinsically tied to the day-2 probenecid co-infusion design rather than a general clinical-coadministration indicator. Per-model `covariateData[[CONMED_PROBENECID]]$notes` must document the dose / regimen of probenecid used (Xie 2000: 70 umol/kg IV loading dose plus 70 umol/kg/h constant infusion in male Sprague-Dawley rats), the parameter the indicator modifies, and any per-subject vs per-record time-varying convention.

### CONMED_QPRL_ORAL (**canonical for concomitant oral quinapril co-administration indicator**)
- **Description:** 1 = subject coadministered oral quinapril (angiotensin-converting enzyme inhibitor; competitive substrate of the intestinal H+/oligopeptide carrier PEPT1 used by orally absorbed beta-lactams and a substrate of the renal anionic transport system shared by many beta-lactams), 0 = no oral quinapril coadministration. Captures the specific oral-quinapril coadministration condition under which the Padoin 1998 cephalexin / quinapril drug-drug interaction (DDI) on intestinal absorption and renal tubular secretion of cephalexin is observed. Time-fixed per subject in the on-disk source (parallel-group design); time-varying use in future per-occasion DDI studies is permitted.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no oral quinapril coadministration; reference includes cephalexin alone, cephalexin with intra-arterial quinapril, and quinapril-naive subjects).
- **Source aliases:**
  - Paper-specific group indicator -- used in `Padoin_1998_cephalexin_rat.R` (the paper's final model specification is `Ka_j = Ka2` and `CL_j = CL2` when oral quinapril is coadministered with oral cephalexin; the canonical column is 1 for group 5 (cephalexin GT + quinapril GT) and 0 for groups 1, 2, 3, and 4).
- **Example models:** `Padoin_1998_cephalexin_rat.R` (multiplicative exponential effect on both absorption rate Ka and elimination clearance CL: `ka <- exp(lka + e_conmed_qprl_oral_ka * CONMED_QPRL_ORAL + etalka)` with `e_conmed_qprl_oral_ka = log(0.177 / 0.249) = -0.3413` (~29% lower Ka under oral quinapril coadministration, paper Table 4); `cl <- exp(lcl + e_conmed_qprl_oral_cl * CONMED_QPRL_ORAL + etalcl)` with `e_conmed_qprl_oral_cl = log(0.640 / 0.810) = -0.2356` (~21% lower CL under oral quinapril coadministration, paper Table 4). The CL effect was found significant only in the oral-cephalexin + oral-quinapril group (paper found no DDI on CL when cephalexin was given intra-arterially, attributed to higher cephalexin renal concentrations outcompeting quinapril at the carrier)..........
- **Notes:** Scope: specific because the only on-disk source is a single preclinical rat popPK paper (Padoin 1998) and the column meaning is intrinsically tied to the oral cephalexin + oral quinapril DDI design rather than a general quinapril-coadministration indicator. Per-model `covariateData[[CONMED_QPRL_ORAL]]$notes` must document the dose / regimen of quinapril used (Padoin 1998: 0.8 mg/kg single oral dose via gastric tube, 15 min before cephalexin, in male Wistar rats), the parameters the indicator modifies, and any per-subject vs per-record convention. The paper's full specification distinguishes intra-arterial vs oral quinapril (no DDI on cephalexin CL was observed for either intra-arterial cephalexin with intra-arterial quinapril or intra-arterial cephalexin with oral quinapril); the canonical `CONMED_QPRL_ORAL = 0` collapses all three non-DDI conditions (no quinapril, intra-arterial quinapril, oral quinapril paired with intra-arterial cephalexin) into the reference category because the model predicts the same CL and Ka values for each. Future popPK extractions of beta-lactam / ACE-inhibitor DDIs that test oral quinapril should reuse this canonical; ACE-inhibitor coadministration with a different perpetrator drug (e.g., enalapril, lisinopril) should register a separate canonical with the perpetrator's INN in the name.

### CONMED_RIF (**canonical for concomitant rifampicin co-administration (acute or chronic)**)
- **Description:** 1 = subject is within a paper-defined rifampicin co-administration window for the modeled effect (either acute single-dose use as an OATP1B inhibitor or perfusion-style perpetrator, or chronic daily-dose use after the post-induction equilibrium lag is reached); 0 = subject is not on rifampicin or is in the inactive pre-effect window. Time-varying per subject. The semantics of "effect active" is paper-specific: for chronic-induction substrate popPK (Svensson 2014 bedaquiline), the indicator switches on after several days of daily 600 mg rifampicin dosing to reach the post-induction CYP3A4 equilibrium; for acute single-dose DDI popPK (Barnett 2018 coproporphyrin I / rosuvastatin), the indicator switches on at the rifampicin dose time and stays on for the within-occasion sampling window, with no induction lag because the mechanism of interest is competitive OATP1B inhibition rather than enzyme induction.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no rifampicin co-administration or pre-effect lag).
- **Source aliases:**
  - `RIF` -- used in `Svensson_2014_bedaquiline.R` (paper-defined indicator switching on at day 3 of 600 mg daily rifampicin co-administration; the 3-day lag was selected by NONMEM objective-function search over 1-8 day candidates and chosen as the best fit per the paper's Methods). Also used in `Barnett_2018_coproporphyrin_I.R` and `Barnett_2018_rosuvastatin.R` as the binary period-level covariate that captures Barnett 2018 Table 1's reductions of Vcpi (CPI) and V1 / V2 / Q (RSV) during the rifampicin phase of the three-occasion crossover study. Also used in `Gatti_1996_dapsone.R` (Gatti 1996 used the source-paper symbol `R`; the indicator switches on for the 7 of 53 patients on rifampin co-administration for at least 2 weeks before blood sampling, so the chronic CYP3A4-induction effect was at equilibrium).
- **Example models:** `Svensson_2014_bedaquiline.R` (multiplicative factor on apparent CL_BDQ and CL_M2: `cl_eff = cl_base * 4.78^CONMED_RIF`; 4.78-fold induction of apparent clearance at chronic full induction), `Barnett_2018_coproporphyrin_I.R` (multiplicative factor on Vcpi: `vc <- exp(lvc + etalvc) * (1 + e_rif_vc * CONMED_RIF)` with e_rif_vc = -0.4841 capturing the Vcpi 6.59 L -> 3.4 L reduction in the acute rifampicin phase), `Barnett_2018_rosuvastatin.R` (multiplicative factors on V1, Q, and V2 with the same encoding form, capturing V1 430 -> 2.98 L, Q 45.3 -> 5.03 L/h, V2 865 -> 128 L reductions during the acute rifampicin phase), `Gatti_1996_dapsone.R` (multiplicative factor SHARED between apparent CL/F and V/F: `cl = exp(lcl + etalcl) * (1 + e_rif_cl_vc * CONMED_RIF)`, `vc = exp(lvc) * (1 + e_rif_cl_vc * CONMED_RIF)` with `e_rif_cl_vc = 0.696`; 69.6% increase in both CL/F and V/F driven primarily by a first-pass / bioavailability effect, with the shared theta enforced by likelihood-ratio test against a non-shared parameterisation), `Zhang_2012_lopinavir_ritonavir.R` (multiplicative factors on LPV CL/F (+71.0%), RTV CL/F (+36.0%), LPV F (-20.0%), and RTV F (-45.0% at the 100 mg reference RTV dose) captured by the same `(1 + e_rif_* * CONMED_RIF)` encoding; the CONMED_RIF flag also gates the secondary dose-dependent boost on RTV F (`(1 + e_dose_f_rtv * (DOSE - 100))`) so the dose-effect is identifiable only within the rifampicin-coadministered arm), `Zhang_2013_lopinavir_ritonavir.R` (per-population multiplicative shifts on apparent CL of LPV adults +58% / children +48% and RTV adults +34% / children +22%, and per-population log-additive shifts on relative bioavailability of LPV and RTV with separate adult-only and child-only RIF effects).
- **Notes:** Distinct from `CONMED_RIF_LPVR4` (which is a compound indicator for rifampicin + super-boosted LPV/r 4:4 used by Tikiso 2021 pediatric abacavir popPK and carries a non-CL effect). The acute-vs-chronic semantic distinction is per-paper -- document the exact window (rifampicin-dose time and effect duration; lag days to full induction) in `covariateData[[CONMED_RIF]]$notes` so downstream simulations can construct the correct event-table indicator. Future popPK extractions that hold rifampicin co-administration as a binary indicator should reuse this canonical; if a future model needs a paper-specific time-decaying induction trajectory rather than a step indicator, register a separate canonical (`CONMED_RIF_INDUCTION_TIME`). Specific scope because the effect-window definition is paper-specific. Ratified canonically on 2026-05-21 alongside the Svensson 2014 bedaquiline chronic-induction extraction; broadened on 2026-05-26 to cover acute OATP1B-inhibition use alongside the Barnett 2018 CPI / RSV extractions; example list extended on 2026-05-31 to include the Gatti 1996 dapsone chronic-induction shared-CL/V-effect case.

### CONMED_RPT (**canonical for concomitant rifapentine co-administration at full CYP3A4 induction**)
- **Description:** 1 = subject is on concomitant rifapentine (typically daily 600 mg oral rifapentine) and has reached the post-induction equilibrium state on the perpetrator drug's enzyme-induced expression; 0 = subject is not on rifapentine or is still inside the pre-induction lag window.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no rifapentine co-administration or pre-induction lag).
- **Source aliases:**
  - `RPT` -- used in `Svensson_2014_bedaquiline.R` (paper-defined indicator switching on at day 3 of 600 mg daily rifapentine co-administration; same lag as the sibling `CONMED_RIF` arm of the same paper).
- **Example models:** `Svensson_2014_bedaquiline.R` (multiplicative factor on apparent CL_BDQ and CL_M2: `cl_eff = cl_base * 3.96^CONMED_RPT`; 3.96-fold induction of apparent clearance at full induction).
- **Notes:** Sibling canonical to `CONMED_RIF`. Rifapentine and rifampicin are both CYP3A4-inducing rifamycins with similar mechanisms; the per-paper magnitude differs (rifapentine often slightly less induction than rifampicin per the on-disk Svensson 2014 fit). Like `CONMED_RIF`, this is a step indicator at full induction with the paper-specific lag documented in `covariateData[[CONMED_RPT]]$notes`. Specific scope until a second model ratifies the canonical; ratified on 2026-05-21 alongside the Svensson 2014 extraction.

### CONMED_RIF_LPVR4 (**canonical for concomitant rifampicin-based antitubercular treatment with super-boosted lopinavir/ritonavir 4:4 indicator**)
- **Description:** 1 = subject is receiving rifampicin-based antitubercular treatment together with super-boosted lopinavir/ritonavir 4:4 (extra ritonavir added to standard 4:1 LPV/r to counter rifampicin-driven LPV induction); 0 = subject is on the comparator regimen specified by the source paper (typically standard LPV/r 4:1 without rifampicin in Tikiso 2021). Used to flag the combined induction effect of rifampicin (PXR-mediated UGT induction) plus extra ritonavir on co-administered antiretroviral PK.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-RIF + standard LPV/r 4:1 reference).
- **Source aliases:**
  - `RIF` -- used in `Tikiso_2021_abacavir.R` (the dataset's paper-defined indicator, 1 = on rifampicin-based TB treatment with super-boosted LPV/r 4:4, 0 = standard LPV/r 4:1 or EFV).
- **Example models:** `Tikiso_2021_abacavir.R` (multiplicative effect on bioavailability: `f_depot *= (1 + (-0.294) * CONMED_RIF_LPVR4)`; -29.4% relative to the LPV/r 4:1 reference).
- **Notes:** Specific scope because the joint rifampicin + super-boosted-LPV/r contrast is paper-defined; the underlying induction is plausibly rifampicin-driven (the paper's discussion notes that LPV concentrations were similar with vs without super-boosting, weakening a separate ritonavir contribution), but the canonical column captures the joint indicator the paper modeled. Future TB/HIV co-treatment models that test the same regimen contrast should extend the example list rather than register a new canonical.

### CONMED_RITUX (**canonical for concomitant rituximab combination therapy**)
- **Description:** 1 = on concomitant rituximab combination therapy (with or without backbone chemotherapy), 0 = not on rituximab.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant rituximab; single-agent or non-rituximab combination).
- **Source aliases:**
  - `RITUX` -- used in `Wu_2024_inotuzumab.R`.
- **Example models:** `Wu_2024_inotuzumab.R` (additive fractional change on CL1: `CL1 * (1 + (-0.132) * CONMED_RITUX)` ~= 13% lower CL1 with concomitant rituximab).
- **Notes:** Wu 2024 Table 3 footnote b explicitly flips the reference category vs. the predecessor Garrett 2019 adult model: in Garrett 2019 the reference was "with rituximab" (RITUX = 0 meant on-rituximab), whereas in Wu 2024 the reference is "without rituximab" (RITUX = 0 means no concomitant rituximab). Future models that pool an analogous rituximab-combination cohort with a single-agent reference should use this canonical with the Wu 2024 sign convention; if a paper retains the Garrett 2019 reverse-coded convention, document the value transformation in `covariateData[[CONMED_RITUX]]$notes` (`CONMED_RITUX = 1 - source$RITUX`) rather than registering a second canonical. Ratified canonically on 2026-04-26.

### CONMED_RTV (**canonical for concomitant ritonavir (CYP3A4 inhibitor / PK-booster) coadministration indicator**)
- **Description:** 1 = subject is receiving concomitant ritonavir (RTV), typically at low "booster" doses (100 mg twice daily) as a pharmacokinetic enhancer of co-administered HIV protease inhibitors or other CYP3A4-metabolised antiretrovirals; 0 = no ritonavir. Ritonavir is a potent CYP3A4 inhibitor and P-glycoprotein modulator, so the indicator flags reduced CYP3A4-mediated clearance (and potential bioavailability changes) of the perpetrator-sensitive co-administered drug.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant ritonavir).
- **Source aliases:**
  - `PI` / `PIs` -- used in `Csajka_2004_indinavir.R` (the dataset's binary protease-inhibitor / ritonavir-presence indicator; all 177 RTV-positive patients in Csajka 2004 received ritonavir specifically, so the column captures concomitant ritonavir).
- **Example models:** `Csajka_2004_indinavir.R` (multiplicative effect on apparent oral clearance: `cl *= (1 + e_rtv_cl * CONMED_RTV)` with `e_rtv_cl = -0.63`, i.e. ritonavir reduces indinavir CL/F by ~63% relative to the no-RTV reference; Csajka 2004 Table 3), `Colombo_2006_atazanavir.R` (linear-deviation effect on apparent oral clearance: `cl = exp(lcl) * (1 + e_rtv_cl * CONMED_RTV)` with `e_rtv_cl = -0.46`; a 46% reduction in atazanavir CL/F when RTV is coadministered).
- **Notes:** Follows the `CONMED_*` concomitant-medication pattern (AED / AMIO / AZA / AZOLE / CBZ / EFV / etc.). Use `CONMED_RTV` for the binary "is ritonavir co-administered" question; for continuous ritonavir exposure as a covariate use the separate `AUC_RTV` canonical (e.g. `Dickinson_2009_atazanavir.R`). The two canonicals are not synonyms -- `AUC_RTV` carries dose-response information that a binary `CONMED_RTV` flag intentionally collapses. Distinct from `CONMED_RIF_LPVR4`, which is a joint rifampicin + super-boosted-LPV/r indicator. Ratified canonically on 2026-05-30 alongside the Csajka 2004 indinavir extraction.

### CONMED_SGLT2I (**canonical for concomitant SGLT2 inhibitor coadministration indicator**)
- **Description:** 1 = subject coadministered a sodium-glucose cotransporter 2 inhibitor (canagliflozin, dapagliflozin, empagliflozin, ertugliflozin, or another systemic SGLT2 inhibitor) during the study, 0 = no concomitant SGLT2 inhibitor. The per-model `notes` field should document the per-study cumulative-exposure threshold if one is applied (e.g., FIDELIO-DKD used "long-term use" = "SGLT2 inhibitor present for at least 50% of the on-finerenone treatment period"). Distinct from the `CINH` plasma SGLT-inhibitor concentration regressor used in renal-glucose-reabsorption QSP models -- that canonical carries continuous time-varying plasma exposure of the SGLT2 inhibitor itself, whereas `CONMED_SGLT2I` is a binary drug-drug-interaction indicator for the SGLT2i as a coadministered drug.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant SGLT2 inhibitor coadministration).
- **Source aliases:**
  - `SGLT` / `SGLT_INH0` / `GLYCSI_CATN = 2` -- used in `vandenBerg_2021_finerenone.R` (the FIDELIO-DKD popPK NONMEM control stream's three-level `SGLT` categorical with `2` flagging long-term SGLT2i use).
- **Example models:** `vandenBerg_2021_finerenone.R` (multiplicative effect `1.10^CONMED_SGLT2I` on apparent CL/F AND on relative bioavailability F1 inversely; long-term SGLT2 inhibitor coadministration in the FIDELIO-DKD analysis reduces steady-state finerenone AUC by ~17.1% per Table 2 / Figure 3 of the source paper).
- **Notes:** Follows the `CONMED_<concept>` concomitant-medication pattern. When a model needs the on-treatment change in SGLT2 inhibitor use rather than its absolute level, pair this column with `CONMED_SGLT2I_BASE`. SGLT2 inhibitors are a class of anti-diabetic agents with cardio-renal protective effects; concomitant use is increasingly common in chronic kidney disease and type 2 diabetes populations, where they overlap therapeutically with mineralocorticoid receptor antagonists (e.g., finerenone). The mechanism by which SGLT2i coadministration modestly alters finerenone PK is hypothesised by van den Berg 2021 to relate to SGLT2i-mediated partial reversal of uremic-toxin-induced CYP3A4 inhibition (Discussion paragraph 4.1 'The small effect of SGLT2i use on clearance might also be attributable to this phenomenon, as at least pre-clinical data indicate that SGLT2i use may partly reverse this pathophysiology'). Distinct from `DIS_DIAB` (diabetes-mellitus comorbidity) and from `CINH` (continuous plasma SGLT2-inhibitor concentration regressor).

### CONMED_SGLT2I_BASE (**canonical for SGLT2 inhibitor use at treatment start**)
- **Description:** 1 = the subject was already using a sodium-glucose cotransporter 2 inhibitor at the start of study treatment (randomisation), 0 = not using one at that time. Time-fixed per subject. Exists so that a model carrying the TIME-VARYING `CONMED_SGLT2I` flag can apply a drug-drug-interaction effect to the on-treatment CHANGE in SGLT2 inhibitor use, `theta * (CONMED_SGLT2I - CONMED_SGLT2I_BASE)`, rather than to its absolute level. That centring is required whenever the model's estimated baseline state already embeds the SGLT2 inhibitor's effect for subjects who were on one at randomisation -- applying the raw flag would double-count it.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (SGLT2-inhibitor-naive at treatment start).
- **Source aliases:**
  - `SGLTSTART` -- Goulooze 2022 convention in both the UACR and eGFR control streams.
- **Example models:** `Goulooze_2022_finerenone_uacr.R` and `Goulooze_2022_finerenone_egfr.R` (both apply the acute SGLT2 inhibitor effects as `theta * (CONMED_SGLT2I - CONMED_SGLT2I_BASE)`; the eGFR model applies its CHRONIC-slope SGLT2i effect to the raw `CONMED_SGLT2I` flag instead, so a model may legitimately use both forms).
- **Notes:** Member of the `_BASE` baseline-suffix family alongside `CRCL_BASE`; the `_BASE` suffix marks the time-fixed treatment-start value of a column that is otherwise time-varying. Ratified canonically alongside the Goulooze 2022 finerenone UACR / eGFR extraction. Do not use this column as a substitute for `CONMED_SGLT2I` when a model only needs the time-fixed baseline status and never the time course -- use `CONMED_SGLT2I` with a note that it is time-fixed in that analysis.

### CONMED_SPART (**canonical for spartalizumab (PDR001, anti-PD-1) coadministration indicator**)
- **Description:** 1 = the analyzed therapeutic mAb is coadministered with spartalizumab (PDR001, anti-PD-1 IgG4), 0 = no spartalizumab coadministration. Time-fixed per subject in source analyses to date.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no spartalizumab coadministration -- monotherapy or combination with non-spartalizumab agents such as a hypomethylating agent).
- **Source aliases:**
  - `HASPDR` -- used in `Xu_2023_MBG453.R` (Monolix supplement Appendix S2; the source describes the column as "this patient HAS received PDR001 [spartalizumab, anti PD-1 mAb]").
- **Example models:** `Xu_2023_MBG453.R` (exponential effect on CL: `exp(0.0194 * CONMED_SPART)`; not statistically significant in the full covariate model but retained because Xu 2023 used the full-covariate-model approach).
- **Notes:** Parallels `COMBO_NIVO` (ipilimumab + nivolumab) and `COMBO_DURVA` (durvalumab combinations) but for spartalizumab. Promote to general scope if a second paper reports a spartalizumab-coadministration covariate with comparable encoding.

### CONMED_SPIRON (**canonical for concomitant spironolactone coadministration indicator**)
- **Description:** 1 = subject is coadministered spironolactone (aldosterone-receptor antagonist; potassium-sparing diuretic; renal P-glycoprotein / OATP inhibitor at the digoxin tubular-secretion site) during the study, 0 = no concomitant spironolactone. Spironolactone is widely combined with digoxin in CHF therapy and inhibits digoxin renal tubular excretion, raising digoxin serum concentration.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no concomitant spironolactone).
- **Source aliases:**
  - `SPI` -- used in `Zhou_2010_digoxin.R` (Zhou 2010 Table 1 and Table 7 column label).
- **Example models:** `Zhou_2010_digoxin.R` (multiplicative linear-deviation form on Cl/F: `cl *= (1 - 0.412 * CONMED_SPIRON)`, i.e. ~41% lower Cl/F with concomitant spironolactone in the older Chinese CHF cohort; Zhou 2010 Table 7), `MarquesMinana_2010_vancomycin.R` (multiplicative linear-deviation form on weight-normalised V_d: `vc *= (1 - 0.344 * CONMED_SPIRON)`, i.e. ~34% smaller V_d with concomitant spironolactone in the 70-neonate Marques-Minana 2010 cohort; Table 3 theta4).
- **Notes:** 32 of 119 subjects (27%) in Zhou 2010 were coadministered spironolactone. The clinical rationale (Zhou 2010 Discussion) is that spironolactone inhibits the renal-tubular secretion of digoxin via competition for the renal P-glycoprotein transporter, raising digoxin steady-state concentration. Promote to general scope if a second popPK paper reports a spironolactone-coadministration covariate with comparable encoding. Ratified canonically on 2026-05-21 alongside the Zhou 2010 digoxin extraction.

### CONMED_STATIN (**canonical for concomitant conmed_statin (HMG-CoA reductase inhibitor) therapy**)
- **Description:** 1 = patient coadministered a conmed_statin (HMG-CoA reductase inhibitor) during the study, 0 = no conmed_statin coadministration.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no conmed_statin coadministration).
- **Source aliases:** none known.
- **Example models:** `Martinez_2019_alirocumab.R` (additive effect on linear clearance CLL: `CLL = TVCLL + COV1*(WT-82.9) + COV2*CONMED_STATIN`; +0.00644 L/h when conmed_statin is coadministered).
- **Notes:** Per-model `covariateData[[CONMED_STATIN]]$notes` must document which statins and dose thresholds are included in the "CONMED_STATIN = 1" category, since inclusion criteria vary by study. Martinez 2019 codes CONMED_STATIN = 1 for coadministration of rosuvastatin (< 20 mg/day), atorvastatin (< 40 mg/day), or simvastatin (any dose); other conmed_statin regimens are coded as 0.

### CONMED_STATIN_MONO (**canonical for concomitant statin-monotherapy indicator**)
- **Description:** 1 = patient is on a conmed_statin and no other lipid-lowering comedication (conmed_statin monotherapy), 0 = not on conmed_statin monotherapy (either on no lipid-lowering therapy or on a multi-drug lipid-lowering combination such as conmed_statin + ezetimibe).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not on conmed_statin monotherapy).
- **Source aliases:**
  - Derived from a conmed_statin-identifier column in the source (any of atorvastatin, rosuvastatin, simvastatin, lovastatin, pravastatin, pitavastatin, fluvastatin) with an AND over "no other lipid-lowering comedication".
- **Example models:** `Kuchimanchi_2018_evolocumab.R` (multiplicative effect 1.13 on Vmax: `Vmax * 1.13^CONMED_STATIN_MONO`), `Budha_2015_rg7652.R` (time-varying within-subject multiplicative log-effect on the indirect-response Ksyn anchor for LDL-C: `Ksyn = Kdeg * LDLC * exp(-0.648 * CONMED_STATIN_MONO(t))`; same parameter dynamically reproduces both the ~45% lower pre-dose baseline LDL-C in atorvastatin-pretreated cohorts and the LDL rebound after statin cessation, Budha 2015 Table II 'Statin on screening LDLc' = -0.648), `Kuchimanchi_2018_evolocumab_ldlc.R` (same PK-layer effect plus an exponent 0.797 on baseline LDL-C and 0.937 on Emax in the exposure-response layer, Table 4).
- **Notes:** Scope: specific because both registered example models narrowly define the statin covariate as monotherapy only -- Kuchimanchi 2018 ("patients on a conmed_statin only and no other comedication"); Budha 2015 (atorvastatin 40 mg daily as the sole concomitant lipid-lowering therapy in the Phase 1 statin cohorts). Mutually compatible with `CONMED_EZE`: a subject on conmed_statin+ezetimibe has `CONMED_STATIN_MONO = 0` and `CONMED_EZE = 1`; a subject on conmed_statin alone has `CONMED_STATIN_MONO = 1` and `CONMED_EZE = 0`; a subject on no lipid-lowering therapy has both 0. Per-model temporal grain (time-fixed per subject vs time-varying within subject) is documented in `covariateData[[CONMED_STATIN_MONO]]$notes` per model. Future popPK/PD models that adopt a broader "any conmed_statin" definition should register a separate `CONMED_STATIN` or `CONMED_STATIN` canonical rather than reusing this name.

### CONMED_STATIN_LI, CONMED_STATIN_MI, CONMED_STATIN_HI (**canonical for concomitant statin-intensity stratum indicators**)
- **Description:** Three mutually exclusive binary indicators identifying the therapeutic-intensity stratum of a patient's concomitant statin regimen, following the low- / moderate- / high-intensity classification of the 2018 ACC/AHA cholesterol guideline (Grundy et al., J Am Coll Cardiol 2019;73:e285-e350). `CONMED_STATIN_LI` = 1 for a low-intensity regimen (roughly < 30% expected LDL-C lowering; e.g. simvastatin 10 mg, pravastatin 10-20 mg, lovastatin 20 mg, fluvastatin 20-40 mg, pitavastatin 1 mg). `CONMED_STATIN_MI` = 1 for a moderate-intensity regimen (~30-49% expected lowering; e.g. atorvastatin 10-20 mg, rosuvastatin 5-10 mg, simvastatin 20-40 mg, pravastatin 40-80 mg, lovastatin 40 mg, fluvastatin 40 mg BID or XL 80 mg, pitavastatin 2-4 mg). `CONMED_STATIN_HI` = 1 for a high-intensity regimen (>= 50% expected lowering; e.g. atorvastatin 40-80 mg, rosuvastatin 20-40 mg). A patient not on any concomitant statin has all three set to 0.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 for all three simultaneously = no concomitant statin therapy. At most one of the three may be 1 for a given patient/record; a model that references more than one of them multiplies the corresponding effects, so a data set that sets two to 1 is malformed.
- **Source aliases:**
  - `Low-intensity statin`, `Moderate-intensity statin`, `High-intensity statin` -- Jadhav 2023 Table 3 covariate row names.
  - `STATINT` / `STATIN_INTENSITY` (a single ordinal 0/1/2/3 column) -- decompose into the three binaries rather than carrying the ordinal, per the register's decomposed-indicator convention.
- **Example models:** `Jadhav_2023_bempedoicAcid_ldlc.R` (proportional shifts on the indirect-response Imax of -0.238 / -0.302 / -0.424 and on baseline LDL-C of -0.159 / -0.268 / -0.293 for low / moderate / high intensity; Jadhav 2023 Table 3).
- **Notes:** Distinct from `CONMED_STATIN` (any concomitant statin, undifferentiated by intensity) and from `CONMED_STATIN_MONO` (statin monotherapy, i.e. a statin with no other lipid-lowering comedication) -- the intensity strata say nothing about whether other lipid-modifying therapies are co-administered, and the monotherapy flag says nothing about dose intensity, so the three families are orthogonal and may coexist in one model. Also distinct from the `CONMED_<INN>` binaries (`CONMED_ATORVASTATIN`, `CONMED_SIMVASTATIN`, ...), which identify the specific molecule irrespective of dose, and from the `CONMED_<drug>_DOSE` continuous family. The intensity classification is dose- and molecule-dependent, so a source paper's mapping from regimen to stratum must be recorded per model in `covariateData[[CONMED_STATIN_MI]]$notes` when it departs from the ACC/AHA table. Ratified canonically on 2026-07-27 alongside the Jadhav 2023 bempedoic acid extraction (sidecar request 001, operator answer A).

### CONMED_ATORVASTATIN (**canonical for concomitant atorvastatin coadministration indicator**)
- **Description:** 1 = patient is coadministered atorvastatin at the observation / dose record, 0 = no concomitant atorvastatin. Molecule-specific binary companion to the class-level `CONMED_STATIN` flag and the intensity strata.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant atorvastatin).
- **Source aliases:**
  - `Atorvastatin` -- Jadhav 2023 Table 2 covariate row name.
  - `ATORVA`, `ATOR` -- common dataset abbreviations. The three-letter form `ATV` is NOT an acceptable alias: it is reserved for the atazanavir indicator `CONMED_ATAZANAVIR` (see the `CONMED_<drug>_DOSE` notes on that collision), which is why this entry spells out the full INN.
- **Example models:** `Jadhav_2023_bempedoicAcid.R`, `Jadhav_2023_bempedoicAcid_ldlc.R` (proportional shift on relative oral bioavailability `F1 * (1 + 0.142 * CONMED_ATORVASTATIN)`, i.e. 14.2% higher relative bioavailability of bempedoic acid with concomitant atorvastatin relative to the F1 = 1 no-atorvastatin anchor; Jadhav 2023 Table 2 and footnote b).
- **Notes:** Distinct from `CONMED_ATORVASTATIN_DOSE` (continuous mg/day), which should be used instead when a model scales an effect with the atorvastatin dose rather than switching on its presence. Ratified canonically on 2026-07-27 alongside the Jadhav 2023 bempedoic acid extraction, under the auto-approved `CONMED_<INN>` family.

### CONMED_SIMVASTATIN (**canonical for concomitant simvastatin coadministration indicator**)
- **Description:** 1 = patient is coadministered simvastatin at the observation / dose record, 0 = no concomitant simvastatin. Molecule-specific binary companion to the class-level `CONMED_STATIN` flag and the intensity strata.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant simvastatin).
- **Source aliases:**
  - `Simvastatin` -- Jadhav 2023 Table 2 covariate row name.
  - `SIMVA`, `SMV` -- common dataset abbreviations.
- **Example models:** `Jadhav_2023_bempedoicAcid.R`, `Jadhav_2023_bempedoicAcid_ldlc.R` (proportional shift on apparent central volume `Vc/F * (1 + (-0.154) * CONMED_SIMVASTATIN)`, i.e. 15.4% lower Vc/F with concomitant simvastatin; Jadhav 2023 Table 2).
- **Notes:** Distinct from `CONMED_SMV_DOSE` (continuous mg/day). Ratified canonically on 2026-07-27 alongside the Jadhav 2023 bempedoic acid extraction, under the auto-approved `CONMED_<INN>` family.

### CONMED_STEROID (**canonical for systemic corticosteroid administration indicator**)
- **Description:** 1 = patient is on systemic corticosteroid therapy at the observation (or, depending on the paper's encoding, during the time interval the observation summarises), 0 = no systemic corticosteroid administration. Supports two temporal grains depending on the source paper: (i) time-fixed per subject, capturing baseline / chronic concurrent corticosteroid use in diseases where background steroid use is standard of care (SLE, severe asthma); (ii) time-varying per record, capturing acute corticosteroid pulses for relapse / flare treatment in diseases where steroids are administered as a per-event course (multiple sclerosis acute relapse, autoimmune flares).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no systemic corticosteroid administration in the current record / interval).
- **Source aliases:**
  - `BSTEROID` -- used in `Narwal_2013_sifalimumab.R` and `Zheng_2016_sifalimumab.R` (time-fixed baseline-use form).
  - `STEROID` -- used in `VelezdeMendizabal_2013_multipleSclerosis.R` (time-varying per-monthly-record acute-administration form).
- **Example models:** `Narwal_2013_sifalimumab.R` (time-fixed multiplicative on CL: `CL * (1 + 0.195 * CONMED_STEROID)`), `Zheng_2016_sifalimumab.R` (time-fixed multiplicative on CL `(1 + 0.11 * CONMED_STEROID)` and on V1 `(1 - 0.09 * CONMED_STEROID)` in the SLE phase IIb cohort, which was ~85% conmed_steroid-treated at baseline), `VelezdeMendizabal_2013_multipleSclerosis.R` (time-varying per-monthly-record switch of the first-order Markov coefficient from theta_pdv to theta_pdv_s when a corticosteroid course was given for a clinical MS relapse that month).
- **Notes:** Distinct from `PRICORT`, which is strictly a prior (pre-study) indicator. `CONMED_STEROID` covers both concurrent chronic corticosteroid use at / from study baseline and per-record acute corticosteroid pulses; the per-model `covariateData[[CONMED_STEROID]]$notes` field documents the temporal grain (time-fixed vs time-varying) the source paper used. When a future paper needs `CONMED_STEROID` and `PRICORT` jointly, both can coexist on the same subject. The name `STEROID_BL` was used as an alias in earlier register drafts and is retired; use `CONMED_STEROID` for all future models.

### CONMED_STEROID_SPARING (**canonical for steroid-sparing immunosuppression-protocol indicator**)
- **Description:** 1 = subject was assigned a steroid-sparing immunosuppressive protocol (corticosteroids administered for a short period -- typically <= 7-14 days post-transplant -- as opposed to a continuous-steroid regimen); 0 = subject was on a continuous-corticosteroid regimen. Time-fixed per subject (the protocol is assigned at the transplantation date and does not change during the analysis window). Operationally a centre-level attribute in source datasets where the protocol is determined by the transplanting centre (Passey 2011) or a per-subject protocol decision in other datasets.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (continuous-corticosteroid regimen; non-sparing protocol).
- **Source aliases:**
  - `steroid sparing centre` -- used in `Passey_2011_tacrolimus.R` (Passey 2011 Methods: "Centres were designated as using a steroid sparing immunosuppressive regimen if they administered steroids for <= 7 days post transplant").
- **Example models:** `Passey_2011_tacrolimus.R` (power-of-binary-indicator multiplicative factor on apparent oral clearance: `e_steroid_spare_cl ^ CONMED_STEROID_SPARING` with `e_steroid_spare_cl = 0.70`; steroid-sparing patients have 30% lower apparent oral tacrolimus CL/F than continuous-steroid patients; Passey 2011 Discussion attributes the effect to reduced CYP3A induction in the absence of ongoing corticosteroid therapy).
- **Notes:** Companion to `CONMED_STEROID` (which captures concurrent / baseline corticosteroid USE; `CONMED_STEROID_SPARING` captures the protocol-level decision to MINIMIZE corticosteroid use). The two coexist in the same dataset when needed: a steroid-sparing patient still has `CONMED_STEROID = 1` during days 1-7 post-transplant (the short-duration administration window) and `CONMED_STEROID = 0` thereafter. The Passey 2011 binary indicator collapses both phases into a single time-invariant per-subject attribute via the centre-level assignment; document the per-model temporal interpretation in `covariateData[[CONMED_STEROID_SPARING]]$notes`. Distinct from `PRICORT` (pre-study corticosteroid history) and from `HCT_COND_RIC` (reduced-intensity conditioning regimen for HSC transplantation, which is a different protocol axis). Future models that distinguish the specific duration of steroid administration (e.g. 7 days vs 14 days vs 30 days) should register companion canonicals rather than overloading `CONMED_STEROID_SPARING`. Ratified canonically on 2026-05-20 alongside the Passey 2011 tacrolimus extraction.

### CONMED_UGT_INH (**canonical for concomitant UGT-inhibitor coadministration indicator (pooled)**)
- **Description:** 1 = subject is receiving at least one drug the source paper classifies as a UGT (uridine 5'-diphospho-glucuronosyltransferase) inhibitor at the PK observation, 0 = not on any such drug. Class-level pool paralleling `CONMED_EIAED` (pooled enzyme-inducing antiepileptic drugs); used when a paper aggregates multiple mechanism-related UGT-inhibiting comedications into a single indicator because per-drug sample sizes are too small to identify separate effects. Time-varying in principle; time-fixed for chronic-maintenance cohorts.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant UGT inhibitor).
- **Source aliases:**
  - `Inh` / `Inhibitors` -- used in `Milosheska_2016_lamotrigine.R` (paper Table 4 footnote `*Valproic acid or sertraline (Inh)`; the paper pools valproic acid (n = 13) and sertraline (n = 2) because both were hypothesised to act via UGT2B7 competitive inhibition and the per-drug sample sizes were too small to estimate distinct effects).
- **Example models:** `Milosheska_2016_lamotrigine.R` (multiplicative effect on parent lamotrigine apparent clearance: `cl *= (1 - 0.579 * CONMED_UGT_INH)`; carriers have -57.9% lower CL relative to the no-inhibitor reference, Milosheska 2016 Table 4 row `Co-treatment with inhibitors`).
- **Notes:** The per-paper list of drugs counted as UGT inhibitors MUST be documented in `covariateData[[CONMED_UGT_INH]]$notes` because the pooling criterion varies across studies (Milosheska 2016 pools valproic acid + sertraline; a future paper might pool valproic acid + probenecid + sertraline + fluvoxamine). Distinct from the drug-specific canonical `CONMED_VPA` (concomitant valproate alone). When a paper reports enough per-drug data to identify separate effects, use the drug-specific canonicals (`CONMED_VPA`, `CONMED_SERTRALINE`, ...) rather than collapsing into `CONMED_UGT_INH`. Analogous to `CONMED_EIAED` (pooled enzyme-inducing AEDs) which serves the same role for the inducer side. Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.

### CONMED_VPA (**canonical for concomitant valproate (valproic acid) coadministration indicator**)
- **Description:** 1 = subject is taking valproate (valproic acid, sodium valproate, divalproex) as a concomitant antiepileptic drug at the PK observation, 0 = no concomitant valproate. Valproate is a broad-spectrum AED that inhibits UGT and CYP2C9, and chronic use is associated with weight gain. Time-varying when valproate starts / stops within the observation window; time-fixed when the source paper analyses chronic-maintenance cohorts whose AED therapy is stable.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant valproate).
- **Source aliases:**
  - `VPA` -- used in `Schoemaker_2017_brivaracetam.R` (paper covariate `VPA` for valproate coadministration).
- **Example models:** `Schoemaker_2017_brivaracetam.R` (multiplicative effect on apparent oral clearance: `cl *= (1 - 0.101 * CONMED_VPA)`; -10.1% relative to no-VPA reference, corresponding to ~11% higher brivaracetam exposure, Schoemaker 2017 Table 1).
- **Notes:** Drug-specific CONMED_* indicator anticipated in the [[CONMED_AED]] notes. Schoemaker 2017 retained the VPA effect in the final model even though it did not formally meet the SCM inclusion criteria (forward p < 0.01) because quantifying its contribution was considered informative; the authors note the apparent VPA-induced exposure rise may be confounded with VPA-driven weight / fat gain in chronic users. Distinct from the broader [[CONMED_AED]] (any concomitant AED). When a paper distinguishes individual AEDs separately, use the drug-specific canonicals [[CONMED_CBZ]], [[CONMED_PB]], `CONMED_VPA` rather than collapsing into the class-level indicator. Ratified canonically on 2026-05-20 alongside the Schoemaker 2017 brivaracetam paediatric extraction.

### CONMED_VERTEPORFIN (**canonical for concomitant verteporfin photodynamic therapy (PDT) coadministration indicator**)
- **Description:** 1 = subject received one or more concomitant verteporfin photodynamic-therapy (PDT) procedures during the ranibizumab treatment period; 0 = no concomitant PDT. Time-fixed per subject in Xu 2013 (`All covariates adopted their values measured at baseline`). Verteporfin (Visudyne) is a benzoporphyrin-derivative photosensitizer activated by non-thermal 689 nm laser to induce localized choroidal vessel occlusion in neovascular age-related macular degeneration; the PDT procedure is administered 7 days before ranibizumab in the FOCUS combination protocol.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant verteporfin PDT).
- **Source aliases:**
  - `PDT` -- used in `Xu_2013_ranibizumab.R` (Xu 2013 Fig 1 lists "concomitant PDT therapy" as a screened covariate; "Specifically in this case, PDT is verteporfin for injection.").
- **Example models:** `Xu_2013_ranibizumab.R` (multiplicative reduction of vitreous elimination rate Ka: `ka *= (1 - e_conmed_verteporfin_ka * CONMED_VERTEPORFIN)` with `e_conmed_verteporfin_ka = 0.353`, i.e. 35.3% lower Ka on subjects with concomitant PDT; Xu 2013 Table 3, theta6 = 0.353 stored as a positive fractional-reduction magnitude under the header 'Covariate multiplier for Ka').
- **Notes:** Member of the `CONMED_<INN>` binary concomitant-medication family. Verteporfin PDT is the specific PDT modality in the Xu 2013 AMD analysis, so the INN-specific canonical name is used rather than a generic `CONMED_PDT`. Xu 2013 attributes the slower vitreous elimination in PDT-treated subjects to `vessel occlusion and soft tissue scarring` reducing choroidal drainage of ranibizumab from the vitreous humor. Ratified canonically alongside the Xu 2013 ranibizumab extraction (2026-07-09).

### CONMED_SILDENAFIL (**canonical for concomitant sildenafil coadministration indicator**)
- **Description:** Binary indicator for concomitant sildenafil coadministration. 1 = sildenafil was administered during this experimental occasion (in the Bender 2009 rat study, a 2 mg/kg bolus followed by a 6 h steady-state infusion), 0 = saline (no sildenafil). Per-occasion (not per-subject) in a crossover design.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (saline / no concomitant sildenafil).
- **Source aliases:**
  - `SLDB` -- used in `Bender_2009_pregabalin_rat_binary.R` (Bender 2009 binary sildenafil-presence indicator).
- **Example models:** `Bender_2009_pregabalin_rat_binary.R` (proportional reduction in pregabalin CL when sildenafil is coadministered: `cl *= (1 - e_sild_cl * CONMED_SILDENAFIL)` with `e_sild_cl = 0.302`, i.e. 30.2% lower CL on the sildenafil occasion, Bender 2009 Table IV).
- **Notes:** Specific scope because the sildenafil-on-pregabalin-CL drug-drug-interaction effect is paper-specific (Bender 2009 used sildenafil as a PDE-5-inhibitor probe). Member of the `CONMED_<drug>` binary concomitant-presence family. Per-occasion in the Bender 2009 crossover (each rat received saline on one occasion and sildenafil on the other, separated by a >= 3-day washout). Companion to `CONMED_SILDENAFIL_NMETAB_CC`, the continuous N-desmethyl-sildenafil-metabolite-concentration covariate used in the saturable-inhibition variant of the same model. Ratified canonically alongside the Bender 2009 pregabalin rat extraction.

### CONMED_SILDENAFIL_NMETAB_CC (**canonical for concomitant sildenafil N-desmethyl metabolite concentration**)
- **Description:** Time-varying plasma concentration of the active N-desmethyl (N-methyl) metabolite of sildenafil (ng/mL), used as the saturable-inhibition driver on the clearance of a co-administered drug. Time-varying covariate column supplied by the user; set to 0 to recover the no-sildenafil typical-clearance prediction.
- **Units:** ng/mL
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters a saturable (Michaelis-Menten-style) inhibition factor on CL; set to 0 for the no-sildenafil case. Reference values observed: in Bender 2009 the metabolite reached Cmax ~ 2,100 ng/mL at 4-7 h post bolus and was roughly steady-state during the 6 h sildenafil infusion.
- **Source aliases:**
  - `SLDM` -- used in `Bender_2009_pregabalin_rat_smetab.R` (Bender 2009 measured N-methyl-sildenafil-metabolite plasma concentration; paper notation `[SLDM]`).
- **Example models:** `Bender_2009_pregabalin_rat_smetab.R` (saturable inhibition of pregabalin CL: `cl *= (1 - CONMED_SILDENAFIL_NMETAB_CC / (e_sldm_cl + CONMED_SILDENAFIL_NMETAB_CC))` with the IC50 `e_sldm_cl = 1350` ng/mL, Bender 2009 Table IV).
- **Notes:** Specific scope because the metabolite-driven saturable-inhibition effect on pregabalin CL is paper-specific. Member of the `CONMED_<drug>_CC` time-varying-concentration family (the `_CC` suffix marks a dynamic concentration covariate, as distinct from the `CONMED_<drug>` binary-presence indicator). Continuous-covariate counterpart to `CONMED_SILDENAFIL` (the binary-presence variant of the same Bender 2009 drug-drug-interaction analysis). Ratified canonically alongside the Bender 2009 pregabalin rat extraction.

### CONMED_NAL_DOSE, CONMED_BUP_DOSE (**canonical for daily dose of co-administered naltrexone / bupropion**)
- **Description:** Time-varying daily dose of the named drug at the current observation time (suffix = INN lowercase abbreviation: `nal` naltrexone, `bup` bupropion). 0 = the named drug is not part of the regimen at this time; positive value = current total daily dose. Drives the combined dose- and time-dependent Emax drug effect in the naltrexone/bupropion fixed-dose-combination PD model.
- **Units:** mg/day
- **Type:** continuous
- **Scope:** specific
- **Reference category:** 0 (the drug is not given; the drug-effect term contributes 0).
- **Source aliases:**
  - `DOSE_NAL_MGD` (`NAL`, Sharma 2018 Eq. 4 daily naltrexone dose in mg) -- earlier `DOSE_<drug>_<unit>` form used in `Sharma_2018_naltrexone_bupropion.R` before the `CONMED_<drug>_DOSE` rename. Maps to `CONMED_NAL_DOSE`.
  - `DOSE_BUP_MGD` (`BUP`, Sharma 2018 Eq. 4 daily bupropion dose in mg) -- earlier `DOSE_<drug>_<unit>` form used in `Sharma_2018_naltrexone_bupropion.R` before the rename. Maps to `CONMED_BUP_DOSE`.
- **Example models:** `Sharma_2018_naltrexone_bupropion.R` (each daily-dose covariate enters the combined dose- and time-dependent Emax drug effect on body-weight kout: `drug_term = CONMED_NAL_DOSE / (ed50nal + CONMED_NAL_DOSE) + CONMED_BUP_DOSE / (ed50bup + CONMED_BUP_DOSE)`, Sharma 2018 Eq. 4).
- **Notes:** Specific scope because the dose-effect amplitudes are tied to the Sharma 2018 naltrexone/bupropion DTPD body-weight analysis. Members of the `CONMED_<drug>_DOSE` daily-dose family (siblings `CONMED_ATV_DOSE`, `CONMED_INH_DOSE`, etc.); the `CONMED_<drug>_DOSE` shape replaces the earlier `DOSE_<drug>` / `DOSE_<drug>_<unit>` names (which conflated a covariate with a dose-amount column) per the naming audit. Companion to `DIS_DIAB` (the diabetes covariate in the same model; pre-2026-06-19 canonical name `T2DM`). Ratified canonically alongside the Sharma 2018 naltrexone/bupropion extraction.

### PRICORT (**canonical for prior corticosteroid use indicator**)
- **Description:** 1 = patient received systemic corticosteroid treatment prior to study entry, 0 = no prior corticosteroid use. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no prior corticosteroid use).
- **Source aliases:** none.
- **Example models:** `Ma_2020_sarilumab_das28crp.R` (multiplicative on DAS28-CRP Kout: `Kout * theta^PRICORT`), `Ma_2020_sarilumab_anc.R` (power-form on Emax: `Emax * 0.819^PRICORT`).
- **Notes:** Ma 2020 applies it as a multiplicative effect of the form `param * theta^PRICORT` in both DAS28-CRP and ANC PD models. Generally applicable clinical-history indicator.

### PRIOR_ANTHRACYCLINE_DOSE (**canonical for prior cumulative anthracycline dose**)
- **Description:** Cumulative dose of anthracycline chemotherapy received by the subject prior to the first dose analysed in the current popPK / popPK-PD model, expressed in doxorubicin-equivalent body-surface-area-normalised mg/m^2. Time-fixed per subject for the analysis window (the running cumulative anthracycline dose at the first observed dose).
- **Units:** mg/m^2 (doxorubicin-equivalent)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- typically used as a linear shift (`(1 + theta * (PRIOR_ANTHRACYCLINE_DOSE - ref))`) on a baseline parameter (e.g., baseline cardiac troponin I before the next anthracycline cycle). Reference values observed: 90 mg/m^2 (Kunarajah 2017, cohort median).
- **Source aliases:**
  - `PCAMT` -- Kunarajah 2017 NM-TRAN convention ("Prior Cumulative Anthracyclines aMounT"; doxorubicin-equivalent mg/m^2).
- **Example models:** `Kunarajah_2017_doxorubicin.R` (linear shift on baseline cardiac troponin I: `bl_cTnI * (1 + 0.00308 * (PRIOR_ANTHRACYCLINE_DOSE - 90))` -- ~0.31% increase in baseline cTnI per 1 mg/m^2 of prior cumulative anthracycline exposure).
- **Notes:** Distinct from `PRIOR_ANTICANCER` (a binary modality indicator, 1 = any prior anticancer therapy) -- `PRIOR_ANTHRACYCLINE_DOSE` carries the actual cumulative dose, restricted to the anthracycline drug class (doxorubicin, daunorubicin, epirubicin, idarubicin), and is the column needed when the source paper's effect is dose-response in the prior-exposure regime rather than presence / absence. When a paper records anthracycline exposure as anthracycline-class-by-class doses and the model effect aggregates them, sum to a single doxorubicin-equivalent value before populating this column (use the published bone-marrow / cardiotoxicity isoeffective conversion factors). When a paper distinguishes the type of anthracycline (e.g., doxorubicin vs daunorubicin separately), register parallel canonicals (`PRIOR_DOXORUBICIN_DOSE`, `PRIOR_DAUNORUBICIN_DOSE`) rather than overloading this name. Scope: specific because the column meaning is intrinsically tied to anthracycline-class chemotherapy exposure; promote to general if a second paper ratifies the same definition.

### PRIOR_ANTICANCER (**canonical for prior anticancer therapy of any modality**)
- **Description:** 1 = subject received any prior anticancer therapy (cytotoxic chemotherapy, radiotherapy, hormonal therapy, targeted therapy, immunotherapy, or surgical debulking with adjuvant intent) before the start of the analyzed treatment, 0 = treatment-naive. Broader than `LINE_1L`, which is specifically a treatment-line indicator restricted to systemic drug therapy lines. `PRIOR_ANTICANCER` captures the full clinical concept of prior cancer treatment exposure as used in cytotoxic-chemotherapy myelosuppression analyses, where any prior anticancer modality may have depleted the bone-marrow proliferating pool and therefore affects baseline ANC.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (treatment-naive; no prior anticancer therapy of any modality).
- **Source aliases:**
  - `PC` (Kloft 2006 / Netterberg 2017 NM-TRAN convention for "previous anticancer therapy"; values 0 = naive, 1 = had prior anticancer therapy) -- used in `Netterberg_2017_docetaxel.R`.
- **Example models:** `Netterberg_2017_docetaxel.R` (multiplicative effect on baseline ANC: `BACOV *= (1 + theta * PRIOR_ANTICANCER)` with theta = -0.147; prior-anticancer patients have ~14.7% lower baseline ANC than treatment-naive patients).
- **Notes:** Distinct from `LINE_1L` (which is the inverse semantics for systemic-drug therapy lines only) and `PRIOR_TNF` / `PRIOR_BIO` (which are modality-specific to anti-TNF / biologic exposure in inflammatory-disease cohorts). Use `PRIOR_ANTICANCER` when the source paper's covariate counts any anticancer modality (including radiotherapy and surgery) as prior exposure. When a future paper restricts the indicator to cytotoxic chemotherapy alone, use `LINE_1L` (with values inverted: paper's `PRIOR_CHEMO = 1 - LINE_1L`). When a paper distinguishes prior chemotherapy from prior radiotherapy, register a parallel `PRIOR_RADIATION` canonical.

### PRIOR_RADIATION (**canonical for prior radiotherapy exposure indicator**)
- **Description:** 1 = subject received any prior radiotherapy (external-beam, brachytherapy, or radionuclide-targeted) before the start of the analyzed treatment; 0 = radiotherapy-naive. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (radiotherapy-naive).
- **Source aliases:**
  - "prior radiotherapy (yes/no)" (Lu 2017 narrative; coded as a binary 0/1 in the source NONMEM dataset) -- used in `Lu_2017_polatuzumab_neuropathy.R`.
- **Example models:** `Lu_2017_polatuzumab_neuropathy.R` (multiplicative effect on the grade >= 2 peripheral neuropathy hazard via `exp(theta_priorRadioTx * PRIOR_RADIATION)` with `theta_priorRadioTx = -7.94e-3`, SE 0.319 -- effectively no detectable effect given the large SE).
- **Notes:** Sibling of `PRIOR_ANTICANCER` (the broader any-modality indicator) and `PRIOR_VINCA` / `PRIOR_PLATIN` (chemotherapy-class-specific siblings). Use this canonical when the source paper distinguishes radiotherapy as a separate covariate from prior chemotherapy / hormonal / immunotherapy exposure. Pre-authorized as a parallel-sibling canonical in the `PRIOR_ANTICANCER` notes. Ratified canonically on 2026-06-24 alongside the Lu 2017 polatuzumab vedotin TTE extraction.

### PRIOR_VINCA (**canonical for prior vinca-alkaloid chemotherapy indicator**)
- **Description:** 1 = subject received any prior vinca-alkaloid chemotherapy (vincristine, vinblastine, vinorelbine, vindesine) before the start of the analyzed treatment; 0 = vinca-naive. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (vinca-naive).
- **Source aliases:**
  - "prior vinca alkaloids treatment (yes/no)" (Lu 2017 narrative; coded as a binary 0/1 in the source NONMEM dataset) -- used in `Lu_2017_polatuzumab_neuropathy.R`.
- **Example models:** `Lu_2017_polatuzumab_neuropathy.R` (multiplicative effect on the grade >= 2 peripheral neuropathy hazard via `exp(theta_priorVinca * PRIOR_VINCA)` with `theta_priorVinca = -0.102`, SE 0.469 -- not detectable given the large SE; the paper hypothesized prior vinca-alkaloid exposure could sensitize patients to antimicrotubule-induced PN but did not find a clinically meaningful effect).
- **Notes:** Vinca alkaloids are microtubule-destabilising agents (the natural-product parent class of the vc-MMAE / vc-MMAF ADC payloads); prior exposure is a clinically meaningful pretreatment indicator in any analysis where antimicrotubule-toxicity history affects subsequent dosing decisions or AE risk. Sibling of `PRIOR_ANTICANCER` (broader any-modality indicator), `PRIOR_RADIATION`, `PRIOR_PLATIN`, and `PRIOR_TAXANE` (parallel chemotherapy-class siblings). Ratified canonically on 2026-06-24 alongside the Lu 2017 polatuzumab vedotin TTE extraction.

### PRIOR_PLATIN (**canonical for prior platinum-based chemotherapy indicator**)
- **Description:** 1 = subject received any prior platinum-based chemotherapy (cisplatin, carboplatin, oxaliplatin, nedaplatin, satraplatin) before the start of the analyzed treatment; 0 = platinum-naive. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (platinum-naive).
- **Source aliases:**
  - "prior platinum-based treatment (yes/no)" (Lu 2017 narrative; coded as a binary 0/1 in the source NONMEM dataset) -- used in `Lu_2017_polatuzumab_neuropathy.R`.
- **Example models:** `Lu_2017_polatuzumab_neuropathy.R` (multiplicative effect on the grade >= 2 peripheral neuropathy hazard via `exp(theta_Platin * PRIOR_PLATIN)` with `theta_Platin = 0.159`, SE 0.345 -- not detectable given the large SE; the paper hypothesized prior platinum exposure could sensitize patients to subsequent antimicrotubule-induced PN given that platinum agents themselves cause sensory PN, but did not find a clinically meaningful effect).
- **Notes:** Platinum agents are DNA-crosslinking cytotoxics with peripheral-neuropathy as a well-known adverse-event profile (especially for cisplatin and oxaliplatin); prior exposure is therefore a defensible pretreatment indicator in any subsequent-PN-risk analysis. Sibling of `PRIOR_ANTICANCER` (broader any-modality indicator), `PRIOR_RADIATION`, `PRIOR_VINCA`, and `PRIOR_TAXANE` (parallel chemotherapy-class siblings). Distinct from `AUC_CARBO` (the continuous per-cycle carboplatin-AUC drug-exposure covariate, which is time-varying and used as a direct drug-exposure driver in tumor-dynamics models, e.g. `Zecchin_2016_survival.R`). Ratified canonically on 2026-06-24 alongside the Lu 2017 polatuzumab vedotin TTE extraction.

### PRIOR_BIO (**canonical for prior biologic exposure**)
- **Description:** 1 = subject previously treated with any biologic (broader than `PRIOR_TNF`: includes anti-TNF agents plus anti-integrin, anti-IL-12/23, anti-IL-17, anti-IL-23, anti-IL-6, etc.), 0 = biologic-naive.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (biologic-naive).
- **Source aliases:**
  - `bio-naive` (Aguiar 2021 source-paper variable, with the indicator inverted: paper's `bio-naive = 1 - PRIOR_BIO`). Effect coefficients in the source paper apply to `bio-naive`; in `model()` derive `bio_naive <- 1 - PRIOR_BIO` to preserve the paper's reported coefficient.
- **Example models:** `Aguiar_2021_ustekinumab.R` (Aguiar 2021 Table 2 footnote a; multiplicative effect on CL: factor `(1 - 0.227 * (1 - PRIOR_BIO))`, so bio-naive patients have ~23% lower CL than previously-exposed patients).
- **Notes:** Distinct from `PRIOR_TNF` (a strict subset). Use `PRIOR_BIO` when the source paper's covariate counts any biologic as prior exposure (anti-TNF, anti-integrin, anti-IL-12/23, anti-IL-17, anti-IL-23, anti-IL-6, etc.); use `PRIOR_TNF` when the source paper specifically tested anti-TNF exposure. When the source paper uses the inverted "bio-naive" indicator (1 = naive), document the inversion in `covariateData[[PRIOR_BIO]]$notes` and apply `1 - PRIOR_BIO` in `model()` so the canonical column stores 1 = previously exposed.

### PRIOR_TNF (**canonical for prior anti-TNF biologic exposure indicator**)
- **Description:** 1 = subject previously treated with an anti-TNF (tumor necrosis factor) inhibitor, 0 = TNF-naive.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (TNF-naive).
- **Source aliases:**
  - `PRIORTNF` (all caps, no underscore) -- acceptable alternative spelling.
- **Example models:** `Moein_2022_etrolizumab.R` (multiplicative fractional effect on CL, +4.9%).
- **Notes:** Use when the source paper reports a binary "prior anti-TNF inhibitor" covariate on any PK parameter. Generally applicable across RA/PsA/IBD/axSpA biologic PK models.

### PRIOR_IPI (**canonical for prior ipilimumab treatment indicator**)
- **Description:** 1 = subject previously treated with ipilimumab (anti-CTLA-4 monoclonal antibody) before the start of the current anti-PD-1 / anti-PD-L1 (or other) checkpoint-inhibitor regimen, 0 = ipilimumab-naive.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (ipilimumab-naive). Document per-paper how subjects with missing IPI status are pooled (Ahamadi 2017 pools "missing" with naive in the final coefficient; some papers may report a separate "missing" effect).
- **Source aliases:**
  - `IPI` (Ahamadi 2017; categorical with levels `IPI-naive`, `IPI-treated`, `missing`) -- decompose into `PRIOR_IPI = as.integer(IPI == "IPI-treated")` and treat the missing category like naive unless the source paper retains a separate "missing" coefficient.
- **Example models:** `Ahamadi_2017_pembrolizumab.R` (proportional changes on CL of +14.0% and on Vc of +7.36% for IPI-treated relative to IPI-naive; "missing" 26.4% of cohort is pooled with naive in the canonical encoding because Table 3 reports only the naive-vs-treated coefficient).
- **Notes:** Distinct from `PRIOR_ANTICANCER` (any modality), `PRIOR_BIO` (any biologic), `PRIOR_TNF` (anti-TNF biologic). Use `PRIOR_IPI` when the source paper specifically tested prior ipilimumab exposure as a covariate; this is a common covariate in advanced-melanoma popPK analyses where ipilimumab was the standard-of-care immune-checkpoint inhibitor preceding PD-1 / PD-L1 entrants. Ratified canonically on 2026-05-17 alongside the Ahamadi 2017 pembrolizumab extraction.

### PRIOR_STATIN (**canonical for prior (pre-study) statin therapy indicator**)
- **Description:** 1 = patient was on established statin (HMG-CoA reductase inhibitor) therapy before entering the study, 0 = statin-naive at study entry. Time-fixed per subject. Captures the pharmacological history that shapes the observed baseline lipid panel and the residual room for further LDL-C lowering, independent of whether a statin continues to be taken during the study.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (statin-naive at study entry).
- **Source aliases:**
  - `Statin prior therapy` -- Jadhav 2023 Table 3 covariate row name.
  - `PRIORSTAT`, `STATINPR` -- common dataset abbreviations.
- **Example models:** `Jadhav_2023_bempedoicAcid_ldlc.R` (proportional shifts on the indirect-response Imax of -0.373 and on baseline LDL-C of -0.296; Jadhav 2023 Table 3).
- **Notes:** Deliberately distinct from the concomitant-use family (`CONMED_STATIN`, `CONMED_STATIN_MONO`, `CONMED_STATIN_LI` / `_MI` / `_HI`): Jadhav 2023 screened "prior established LMTs" and "concomitant medication (low-, moderate-, or high-intensity statin or ezetimibe)" as separate covariate sets and retained both in the final model, so the two must be carried as separate columns. Statin-intolerance cohorts commonly have `PRIOR_STATIN = 1` with all concomitant-statin indicators 0. Sibling of `PRIOR_EZE`. Ratified canonically on 2026-07-27 alongside the Jadhav 2023 bempedoic acid extraction (sidecar request 001, operator answer A).

### PRIOR_EZE (**canonical for prior (pre-study) ezetimibe therapy indicator**)
- **Description:** 1 = patient was on established ezetimibe therapy before entering the study, 0 = ezetimibe-naive at study entry. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (ezetimibe-naive at study entry).
- **Source aliases:**
  - `Ezetimibe prior therapy` -- Jadhav 2023 Table 3 covariate row name.
  - `PRIOREZE` -- common dataset abbreviation.
- **Example models:** `Jadhav_2023_bempedoicAcid_ldlc.R` (proportional shift on baseline LDL-C of -0.0596; Jadhav 2023 Table 3).
- **Notes:** Distinct from `CONMED_EZE` (concomitant ezetimibe during the study). Jadhav 2023 Online Resource 5 reports a "Prior Treatment (Ezetimibe : No Ezetimibe)" contrast alongside a separate "Concomitant Treatment (Ezetimibe : No Ezetimibe)" contrast, confirming that the analysis dataset carried both columns and that the two effects act on different model parameters (prior use on baseline LDL-C; concomitant use on Imax and on bempedoic acid CL/F). Sibling of `PRIOR_STATIN`. Ratified canonically on 2026-07-27 alongside the Jadhav 2023 bempedoic acid extraction (sidecar request 001, operator answer A).

### CONMED_NNRTI_IND (**canonical for concomitant enzyme-inducing NNRTI indicator**)
- **Description:** 1 = subject is coadministered an enzyme-inducing non-nucleoside reverse transcriptase inhibitor (efavirenz or nevirapine) at the observation, 0 = no concomitant enzyme-inducing NNRTI. Both efavirenz and nevirapine are CYP3A4 inducers; this pooled indicator collapses their effects on metabolite elimination of co-administered antiretrovirals into a single binary covariate.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no enzyme-inducing NNRTI; subject on NRTI-only or PI-based backbone).
- **Source aliases:**
  - `NNI` -- used in `Hirt_2006_nelfinavir.R` (paper's pooled "nonnucleosidic inhibitor" indicator: 1 if EFV or NVP, 0 otherwise; pooling justified by the paper's finding that the two drugs' inducer effects on M8 elimination were not significantly different and the two were never administered simultaneously in the cohort).
- **Example models:** `Hirt_2006_nelfinavir.R` (additive-form multiplicative effect on M8 apparent elimination rate kel_m8: `kel_m8 *= (1 + 0.91 * CONMED_NNRTI_IND)`; KM0 is ~1.9-fold higher under EFV or NVP co-administration, consistent with CYP3A4 induction of M8 elimination).
- **Notes:** Pooling the two named NNRTIs follows the precedent of `CONMED_EIAED` (any enzyme-inducing AED) and `CONMED_AZOLE` (any azole antifungal). Distinct from `CONMED_EFV` (efavirenz alone, founded by Tikiso 2021): the pooled `CONMED_NNRTI_IND` indicator is used when the source paper did not separately identify EFV vs NVP effects. A future paper that needs to distinguish them should use `CONMED_EFV` (and a future sibling `CONMED_NVP`) rather than this pooled indicator. The "inducer" qualifier excludes non-inducing NNRTIs (rilpivirine, doravirine, etravirine); record the per-paper list of pooled drugs in `covariateData[[CONMED_NNRTI_IND]]$notes`. Specific scope until a second model ratifies the convention; at that point promote to general. Ratified canonically on 2026-06-09 alongside the Hirt 2006 nelfinavir extraction.


### CONMED_PSEUDOEPHEDRINE (**canonical for concomitant pseudoephedrine coadministration indicator**)
- **Description:** 1 = subject is taking concomitant pseudoephedrine at the observation, 0 = not. Pseudoephedrine is a sympathomimetic decongestant that is renally excreted via active tubular secretion in addition to glomerular filtration, and competes with other tubularly-secreted drugs for proximal-tubule transporters. Time-varying when pseudoephedrine starts / stops within the observation window; in Van Wart 2004 the indicator is set per PK sample (`PSEU_jk`, sample-level).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant pseudoephedrine).
- **Source aliases:**
  - `PSEU` -- used in `VanWart_2004_garenoxacin.R` (paper covariate `PSEU_jk`, sample-level indicator that the kth PK sample from the jth patient was collected during concomitant pseudoephedrine administration).
- **Example models:** `VanWart_2004_garenoxacin.R` (multiplicative effect on the structural CL/F covariate term: `cl_structural *= (1 - 0.144 * CONMED_PSEUDOEPHEDRINE)`; -14.4% relative to the no-pseudoephedrine reference, Van Wart 2004 Table 4).
- **Notes:** Drug-specific CONMED_* indicator capturing competition for proximal-tubule active secretion; distinct from CYP-based and UGT-based induction or inhibition mechanisms encoded elsewhere. Renal clearance of pseudoephedrine itself is approximately 511-532 mL/min in a 70 kg adult (Van Wart 2004 Discussion), well above the glomerular filtration estimate (~120 mL/min), confirming active tubular secretion as the dominant elimination route. Other drugs that share this active-tubular-secretion route (levofloxacin, ciprofloxacin, gatifloxacin, gemifloxacin, garenoxacin) are candidates for the same indicator in future models. Distinct from `CONMED_PROBENECID` (anionic OAT inhibitor, mechanistically a perpetrator on anion-secreted substrates) -- pseudoephedrine is the cationic / mixed-substrate competitor rather than a transporter inhibitor.


### CONMED_ATAZANAVIR (**canonical for concomitant atazanavir coadministration indicator**)
- **Description:** 1 = subject is receiving atazanavir (ATV; an HIV-1 protease inhibitor) as a co-medication during the PK observation period, 0 = no concomitant atazanavir. Atazanavir is a UGT1A1 inhibitor (and weak CYP3A4 inhibitor) so the indicator is used to flag UGT1A1-mediated inhibition of co-administered antiretrovirals whose elimination depends on glucuronidation. Time-fixed when the source paper analyses cohorts whose ART regimen is stable across the analysis window; time-varying when atazanavir is added or stopped during the observation period.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no concomitant atazanavir).
- **Source aliases:**
  - `ATV` -- used in `ArabAlameddine_2012_raltegravir.R` (paper Table 2 covariate symbol for the atazanavir coadministration indicator; same orientation as the canonical, 1 = on atazanavir).
- **Example models:** `ArabAlameddine_2012_raltegravir.R` (multiplicative additive shift on relative bioavailability in HIV-positive individuals: `F_HIV+ *= (1 + e_atazanavir_fdepot * CONMED_ATAZANAVIR)` with `e_atazanavir_fdepot = 0.39`; atazanavir-coadministered patients have ~39% higher relative bioavailability than non-atazanavir reference, consistent with ATV-mediated UGT1A1 inhibition of raltegravir glucuronidation), `vonHentig_2009_saquinavir.R` (power-of-binary multiplier on saquinavir CL/F: `cl *= e_atazanavir_cl^CONMED_ATAZANAVIR` with `e_atazanavir_cl = 0.703`, i.e. saquinavir CL/F is reduced to 70.3% when atazanavir 300 mg QD is coadministered, attributed to ATV-mediated CYP3A inhibition; von Hentig 2009 Table 2 full-model column), `Bukkems_2021_raltegravir.R` (linear additive effect on apparent oral clearance: `cl *= (1 + e_atazanavir_cl * CONMED_ATAZANAVIR)` with `e_atazanavir_cl = -0.17`; atazanavir-coadministered raltegravir CL is 17% lower than the no-atazanavir reference, consistent with ATV-mediated UGT1A1 inhibition of raltegravir glucuronidation, Bukkems 2021 Table 2 'Factor change in CL with atazanavir').
- **Notes:** Specific scope because the indicator is paper-defined as the presence of atazanavir within an HIV ART regimen; the comparator (no-atazanavir) pools all other backbones (PI without ATV, NNRTI-based, integrase-only, etc.). The full INN spelling `ATAZANAVIR` is used rather than the 3-letter abbreviation `ATV` because `ATV` collides with the legacy atorvastatin slot in the `CONMED_<drug>_DOSE` family (now spelled out as `CONMED_ATORVASTATIN_DOSE`); both surfaces use the full INN to keep the `CONMED_` namespace unambiguous. Distinct from `AUC_RTV` (a continuous ritonavir AUC0-24 covariate used in `Dickinson_2009_atazanavir.R` to scale atazanavir CL/F via the booster's UGT1A1 inhibition) and from `CONMED_EFV` (efavirenz, a CYP3A and UGT inducer that drives the opposite direction of effect on raltegravir bioavailability). Ratified canonically on 2026-06-09 alongside the ArabAlameddine 2012 raltegravir extraction; the full-INN-spelling decision was operator-confirmed via sidecar 001.


### CONMED_INOTROPE (**canonical for concomitant inotrope / vasoactive coadministration indicator**)
- **Description:** 1 = subject is receiving at least one inotropic / vasoactive agent (dopamine, dobutamine, epinephrine, norepinephrine, milrinone, vasopressin, or equivalent) at the time of the observation; 0 = not on any such agent. Time-varying in principle; typically captured per-occasion for popPK observations in critical-care / neonatal-ICU studies. Used as a hemodynamic / renal-perfusion surrogate when the pharmacology mechanism is reduced GFR due to hemodynamic instability rather than a direct CYP / transporter DDI.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (not on any inotrope). Effect form in Zhao 2014 is a direct multiplicative selection on CL: `F_inotrope = theta8 = 0.708` when `CONMED_INOTROPE == 1` and 1 otherwise; encoded in nlmixr2 as `f_inotrope <- e_inotrope_cl ^ CONMED_INOTROPE` with `e_inotrope_cl = 0.708`.
- **Source aliases:**
  - `inotropic agents` / `inotropes` -- used in `Zhao_2014_ciprofloxacin.R` (Zhao 2014 Table 2 'Comedication' row and Table 4 `F_inotrope` notation); same orientation as the canonical, no value transformation.
- **Example models:** `Zhao_2014_ciprofloxacin.R` (multiplicative selection on CL: `0.708 ^ CONMED_INOTROPE` -- 29.2% CL reduction in patients on inotropes).
- **Notes:** Specific scope pending corroboration by a second model that ratifies the same semantics. The per-paper drug list pooled into the indicator should be documented in `covariateData[[CONMED_INOTROPE]]$notes` for each model (Zhao 2014 does not enumerate the exact agents; the cohort is critical-care neonates so the practical mix is dominated by dopamine / dobutamine / epinephrine / norepinephrine / milrinone). Distinct from extracorporeal-support modalities (`ECMO_PUMP_SPEED`, etc.) -- inotropes are a pharmacological intervention rather than a mechanical-support modality. Distinct from heart rate (`HR`) -- inotropes may cause both reduced GFR and altered HR but the indicator captures the therapy itself rather than its hemodynamic consequence. If a future paper retains a finer-grained taxonomy (epinephrine-only vs dopamine-only vs vasopressor-only), register `CONMED_EPI` / `CONMED_DOBU` / `CONMED_VASOPRESS` as siblings rather than overload `CONMED_INOTROPE` with conflicting semantics. Ratified canonically on 2026-06-09 alongside the Zhao 2014 ciprofloxacin extraction.


### CONMED_STANOZOLOL (**canonical for concomitant stanozolol coadministration indicator**)
- **Description:** 1 = subject is coadministered the anabolic-androgenic steroid stanozolol at the observation, 0 = not. Stanozolol is a synthetic 17-alpha-alkylated androgen used in aplastic anemia to stimulate erythropoiesis; it inhibits CYP3A4-mediated metabolism of ciclosporin (and several other CYP3A4 substrates). In Ni 2013 stanozolol coadministration was time-fixed per subject (78 of 102 children remained on stanozolol throughout the TDM observation window).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no stanozolol coadministration).
- **Source aliases:**
  - `stanozolol` indicator (no specific NONMEM column name reported in Ni 2013) -- used in `Ni_2013_ciclosporin.R`.
- **Example models:** `Ni_2013_ciclosporin.R` (multiplicative power-of-binary effect on CL/F: `cl *= e_conmed_stanozolol_cl ^ CONMED_STANOZOLOL` with `e_conmed_stanozolol_cl = 0.83`, i.e. ~17% lower CL/F when stanozolol is coadministered relative to the no-stanozolol reference; Ni 2013 Table 2 'F comedication').
- **Notes:** Anabolic-androgenic 17-alpha-alkylated steroid; mechanistically distinct from glucocorticoid corticosteroids (do not reuse [[CONMED_STEROID]]). Ni 2013 tested both prednisone and stanozolol in forward covariate selection and retained only stanozolol after backward elimination -- the clinically relevant mechanism is CYP3A4 inhibition (Ni 2013 Discussion paragraph 5). A future popPK paper that tests stanozolol or related 17-alpha-alkylated anabolic androgens (oxandrolone, oxymetholone, danazol) as covariates would either ratify this canonical (promoting to general scope) or warrant a family-level canonical (e.g. `CONMED_ANDROGEN`) -- defer that decision until a second paper appears. Ratified canonically on 2026-06-07 alongside the Ni 2013 ciclosporin paediatric aplastic-anemia extraction (sidecar request-001, response Option A).


### CONMED_BUDESONIDE (**canonical for inhaled-budesonide treatment-arm indicator**)
- **Description:** 1 = subject is in the inhaled-budesonide treatment arm; 0 = subject is in the placebo arm or in an alternative-controller arm (e.g., nedocromil). Per-subject time-fixed binary indicator carrying the head-to-head randomized-arm assignment in an inhaled-corticosteroid trial where pulmonary function (FEV1) is modelled as a longitudinal disease-progression endpoint and the inhaled-corticosteroid effect modifies the typical-value FEV1 prediction. Distinct from a PK-driven exposure effect: the model treats the indicator as an additive on/off term and does NOT carry a budesonide PK compartment.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (placebo or alternative-controller arm; the complement composition is defined per-model in `covariateData[[CONMED_BUDESONIDE]]$notes`).
- **Source aliases:**
  - derived per subject from the CAMP-trial assigned arm -- used in `Wu_2014_FEV1_asthma.R` (the CAMP-trial randomized-arm assignment with three levels {placebo, nedocromil, budesonide}; `CONMED_BUDESONIDE = as.integer(arm == "budesonide")`; nedocromil is pooled into the reference because the paper reports no significant nedocromil effect on FEV1).
- **Example models:** `Wu_2014_FEV1_asthma.R` (additive shift on the linear-scale FEV1 typical-value: `FEV1 = exp(theta_age*AGE + theta_ht*HT - theta_int) + e_conmed_bud_fev1 * CONMED_BUDESONIDE` with `e_conmed_bud_fev1 = +0.103 L` (paper Table 2) -- budesonide-arm subjects have ~0.10 L higher FEV1 relative to the placebo+nedocromil reference, with an IIV SD of 0.129 L on the drug-effect term itself; the high shrinkage 67.3% reported in the paper reflects that only ~30% of the cohort carries the indicator).
- **Notes:** Sibling of the existing head-to-head drug-arm CONMED_* family and follows the `CONMED_<INN>` convention (full INN spelled out, matching `CONMED_METFORMIN`, `CONMED_PROBENECID`, `CONMED_SILDENAFIL`, `CONMED_SPIRON`). The contrast is between an active treatment arm and a reference arm in a randomized trial, encoded as a per-subject time-fixed binary. Specific scope because the budesonide-vs-{placebo, nedocromil} head-to-head is tied to the CAMP-trial design; future inhaled-corticosteroid-vs-placebo popPD models can extend this entry's example_models list, while a contrast between a different ICS (e.g., fluticasone, mometasone) and budesonide should register a sibling canonical rather than reusing `CONMED_BUDESONIDE`. Distinct from `TRT` (Novakovic 2017 categorical 3-level cladribine-arm covariate) and `TRT_PHASE` (active-treatment-phase indicator); `CONMED_BUDESONIDE` is a per-subject randomized-arm indicator rather than a time-varying treatment-phase flag. Ratified canonically on 2026-06-12 alongside the Wu 2014 FEV1-asthma extraction.


### CONMED_NNRTI (**canonical for concomitant NNRTI (any non-nucleoside reverse transcriptase inhibitor) co-administration indicator**)
- **Description:** 1 = subject is on at least one concomitant non-nucleoside reverse transcriptase inhibitor (NNRTI) -- typically efavirenz, nevirapine, etravirine, rilpivirine, doravirine, or delavirdine -- at the PK observation; 0 = no concomitant NNRTI. A pooled class indicator used in popPK analyses of CYP3A4 substrates (such as HIV protease inhibitors) where the source paper does not separate the individual NNRTI effects because the included NNRTIs are both PXR-mediated CYP3A4 inducers with a similar net effect on CYP3A4 substrate clearance.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant NNRTI).
- **Source aliases:**
  - `NNRTI` -- used in `Kappelhoff_2005_indinavir.R` (Kappelhoff 2005 Table 2 footnote: "NNRTI is 1 when efavirenz or nevirapine are administered and 0 when they are not"). Kappelhoff 2005 specifically pooled efavirenz and nevirapine because "inclusion of separate effects for each drug did not improve goodness-of-fit."
- **Example models:** `Kappelhoff_2005_indinavir.R` (multiplicative power-form effect on CL/F: `cl *= 1.41^CONMED_NNRTI`; concomitant NNRTI use increases indinavir apparent CL by 41%).
- **Notes:** Distinct from the per-drug indicator `CONMED_EFV` (efavirenz only) -- use `CONMED_EFV` when the source paper estimates the efavirenz effect alone and `CONMED_NNRTI` when the source paper pools the class. Per-model `covariateData[[CONMED_NNRTI]]$notes` should list which specific NNRTIs are counted as "1" in the dataset (Kappelhoff 2005: efavirenz and nevirapine; future extractions including rilpivirine / doravirine / etravirine / delavirdine should document each one). Ratified canonically on 2026-06-10 alongside the Kappelhoff 2005 indinavir extraction.


### CONMED_RTV_AUC_12h (**canonical for ritonavir AUC over the 0-12 h q12h dosing interval (BID ritonavir regimens)**)
- **Description:** Per-subject (time-fixed within an evaluated regimen) ritonavir AUC over a 12-hour dosing interval (q12h, BID ritonavir), used as a co-medication exposure covariate driving boosted-protease-inhibitor (lopinavir, atazanavir, darunavir, etc.) clearance in popPK models that account for ritonavir's CYP3A4-inhibition effect on a BID regimen. In Crommentuyn 2005 the value is computed per-subject as DOSE_RTV / CL_RTV using individual Bayesian CL_RTV estimates from the Kappelhoff et al. 2005 ritonavir popPK model (Crommentuyn 2005 reference [19], Br J Clin Pharmacol 59:174-82), and feeds the lopinavir CL/F inverse-saturable form `cl = exp(lcl) * (auc50 / (auc50 + CONMED_RTV_AUC_12h)) * IND` with `auc50 = 2.26 mg*h/L` (Crommentuyn 2005 Methods Equations 1-2; Table 2).
- **Units:** `mg*h/L` (document per-model via `covariateData[[CONMED_RTV_AUC_12h]]$units` if a different exposure unit is reported).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via the inverse-saturable form `auc50 / (auc50 + CONMED_RTV_AUC_12h)`. Reference value observed: 3.58 mg*h/L (Crommentuyn 2005 cohort median across 122 HIV-1-infected adults on BID LPV/r; range 0.85-18.77).
- **Source aliases:**
  - `AUC12h` -- printed name in Crommentuyn 2005 (Methods Equations 1-2; Results page 6; Table 2 row 3). The paper writes the subscript `12h` to denote the BID dosing-interval AUC; the column name is registered with the `12h` token in the column name to keep the q12h-BID convention visible without relying on subscripts.
- **Example models:** `Crommentuyn_2005_lopinavir.R` (lopinavir CL/F inverse-saturable dependence on CONMED_RTV_AUC_12h: `cl = exp(lcl) * (auc50 / (auc50 + CONMED_RTV_AUC_12h)) * IND` with `auc50 = 2.26 mg*h/L` and `IND = 1 + 0.39 * CONMED_NNRTI`), `vonHentig_2009_saquinavir.R` (saquinavir CL/F centred power dependence on CONMED_RTV_AUC_12h: `cl = exp(lcl) * e_atazanavir_cl^CONMED_ATAZANAVIR * (CONMED_RTV_AUC_12h / 6.70355)^e_rtv_auc_12h_cl` with `e_rtv_auc_12h_cl = -0.403`; centred at the cohort median 6.70355 mg*h/L so saquinavir CL/F equals the typical 60.4 L/h at the median ritonavir exposure when atazanavir is absent).
- **Notes:** Specific scope -- column meaning is tied to ritonavir as the booster drug AND to the q12h-BID dosing-interval AUC convention. Sibling of `AUC_RTV` (which is the q24h once-daily form, Dickinson 2009 cohort median 7.52 mg*h/L). The two canonicals capture chemically-identical ritonavir AUC but at different dosing-interval scopes; never overload them. For simulation users without observed per-subject ritonavir AUC, the Crommentuyn 2005 cohort median 3.58 mg*h/L reproduces typical-value behaviour (the value at which the inverse-saturable form gives CL/F = 5.73 L/h in the lopinavir + ritonavir steady-state simulation, matching the paper's reported per-cohort-median CL/F). Ratified canonically on 2026-06-10 alongside the Crommentuyn 2005 lopinavir extraction.


### CONMED_SEVO (**canonical for concomitant sevoflurane volatile-anaesthesia maintenance indicator**)
- **Description:** 1 = subject received sevoflurane as the volatile anaesthetic for maintenance of general anaesthesia during the perioperative observation window (typically a propofol IV induction followed by sevoflurane gas maintenance), 0 = no sevoflurane maintenance (e.g., propofol-only TIVA, isoflurane, desflurane, or another agent). Time-fixed per subject in perioperative-PK / NMB-reversal models where sevoflurane is administered as a continuous maintenance gas from induction until reversal of neuromuscular blockade. Distinct from `ETSEVO` (continuous end-tidal sevoflurane concentration in vol %), which is the quantitative driver used in emergence-from-anaesthesia models such as Shin 2014; `CONMED_SEVO` is the per-subject on/off flag.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no sevoflurane; typically propofol-only TIVA or another volatile agent).
- **Source aliases:**
  - `SEV` -- used in `Kleijn_2011_sugammadex_rocuronium.R` (Kleijn 2011 Table 4 covariate-effect rows "No sevoflurane: keoSEV = 1" and "Sevoflurane: keoSEV = 1 + theta").
- **Example models:** `Kleijn_2011_sugammadex_rocuronium.R` (multiplicative effect on the rocuronium effect-compartment equilibration rate `keo *= (1 + e_conmed_sevo_keo * CONMED_SEVO)` with `e_conmed_sevo_keo = -0.567` and on the rocuronium NMB EC50 `ec50 *= (1 + e_conmed_sevo_ec50 * CONMED_SEVO)` with `e_conmed_sevo_ec50 = -0.395`; sevoflurane simultaneously slows the central-to-effect-compartment equilibration and lowers the rocuronium concentration required for half-maximal neuromuscular blockade, reproducing the well-known sevoflurane potentiation of aminosteroid NMBAs).
- **Notes:** Distinct from `ETSEVO` (end-tidal sevoflurane volume percent, a continuous PD driver). Use `CONMED_SEVO` when the source paper encodes sevoflurane maintenance as a per-subject on/off indicator without a continuous end-tidal trajectory; use `ETSEVO` when the per-record end-tidal concentration is available and the model uses it directly. Follows the `CONMED_*` family pattern (`CONMED_VPA`, `CONMED_RIF`, `CONMED_AMIO`). Future perioperative models that use a similar binary indicator for isoflurane (`CONMED_ISO`) or desflurane (`CONMED_DES`) should register parallel canonicals rather than overloading this name. Ratified canonically alongside the Kleijn 2011 sugammadex-rocuronium extraction.


### CONMED_DOPA (**canonical for concomitant dopamine (inotrope) coadministration indicator**)
- **Description:** 1 = subject is receiving dopamine (an inotropic agent used in critically ill neonates and infants for haemodynamic support) at the PK observation; 0 = no concomitant dopamine. Treated as time-varying per observation in the source paper (each PK record carries the dopamine flag corresponding to the dosing window).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no concomitant dopamine).
- **Source aliases:**
  - `DOPA` -- used in `Fuchs_2014_gentamicin.R` (paper covariate name in Table 2; same orientation as the canonical, 1 = on dopamine, 0 = otherwise).
- **Example models:** `Fuchs_2014_gentamicin.R` (multiplicative linear effect on CL: `1 + (-0.120) * CONMED_DOPA`, i.e. 12% lower CL on dopamine-coadministered neonates relative to no-dopamine reference; Fuchs 2014 Table 2 theta_CL_DOPA = -0.120, SE 22%).
- **Notes:** Specific scope until a second model legitimately reuses the indicator. Fuchs 2014 Discussion notes that the effect "might principally reflect cardiovascular instability in critically ill newborns" -- so the dopamine flag is partly a marker of disease severity rather than a clean drug-drug-interaction effect. The mechanism on glomerular filtration in neonates remains debated (Fuchs 2014 Discussion citations [57-59]).


## Rheumatoid-arthritis disease-activity covariates

### RHEUMATOID_FACTOR (**canonical for serum rheumatoid factor concentration**)
- **Description:** Serum rheumatoid factor (an autoantibody, predominantly IgM, directed against the Fc portion of IgG) concentration. Baseline value typical; document time-varying use in per-model `notes`.
- **Units:** U/mL or IU/mL (interchangeable in the clinical-PK literature). Document per-model via `covariateData[[RHEUMATOID_FACTOR]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling on a log-transformed value: `(log(RHEUMATOID_FACTOR) / log(ref))^exponent` (or, equivalently, the source-paper form `(LRF / log(ref))^exponent` where `LRF = log(RHEUMATOID_FACTOR)`). Reference value observed: 110 U/mL (Frey 2010, corresponding to LRF = 4.7 in the paper's final-model equation).
- **Source aliases:**
  - `LRF` -- log-transformed RF (natural log of the value in U/mL); Frey 2010 fits the covariate on the log scale and reports the reference as `LRF = 4.7` (i.e., `log(110) ~= 4.7`). The canonical column carries the raw RF concentration in U/mL; the log transform is applied inside `model()`.
  - `RF` -- universal NONMEM/clinical-PK abbreviation; rejected as the canonical name on 2026-04-28 because the bare two-letter abbreviation is uncommon in published popPK papers and could be confused with other shortenings.
- **Example models:** `Frey_2010_tocilizumab.R` (U/mL, reference 110 U/mL == LRF = 4.7; small positive exponent +0.1 on linear CL applied to `log(RHEUMATOID_FACTOR)`).
- **Notes:** RF concentrations span several orders of magnitude across the rheumatoid-arthritis population (Frey 2010 observed range 15-11,800 U/mL across the four phase-III studies; reference paper: Frey 2010 Table I), motivating the log transform before power scaling. The mechanistic rationale (Frey 2010 Discussion, p764) is that RF -- being an anti-IgG autoantibody -- could in principle bind the Fc region of the therapeutic IgG monoclonal antibody and accelerate clearance, but the observed CL effect was small (-4.9% to +6.5% across the observed RF range) and the paper acknowledges that high RF concentrations may also reduce the assay's ability to detect the drug, leading to an apparent CL increase. Ratified canonically on 2026-04-28 alongside the Frey 2010 extraction.

### BLPHYVAS (**canonical for baseline physician's global assessment VAS**)
- **Description:** Baseline Physician's Global Assessment of Disease Activity, 100-mm visual analogue scale (0 = no disease activity, 100 = maximum). Time-fixed per subject.
- **Units:** mm (0-100 VAS)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used as a power term `(BLPHYVAS / <ref>)^exponent`. Reference 66 used in Ma 2020.
- **Source aliases:** none.
- **Example models:** `Ma_2020_sarilumab_das28crp.R`.
- **Notes:** One of the components of the DAS28 composite score; in Ma 2020 it appears as a baseline covariate on the DAS28-CRP disease-activity BASE rather than on the score itself. Applicable to any rheumatology model where baseline physician-assessed disease activity is used as a PK/PD covariate.

### BLHAQ (**canonical for baseline HAQ-DI score**)
- **Description:** Baseline Health Assessment Questionnaire Disability Index (HAQ-DI; 0 = no disability, 3 = maximum disability). Time-fixed per subject.
- **Units:** unitless (0-3 composite score)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used as a power term `(BLHAQ / <ref>)^exponent`. Reference 1.75 used in Ma 2020.
- **Source aliases:** none.
- **Example models:** `Ma_2020_sarilumab_das28crp.R`.
- **Notes:** Patient-reported disability score frequently used as a baseline covariate in rheumatoid-arthritis PK/PD analyses.

### PAIN (**canonical for patient-reported global pain visual analogue score**)
- **Description:** Patient-reported global pain on a 100-mm visual analogue scale (PAIN; 0 = no pain, 100 = worst imaginable pain). Distinct from `BLPHYVAS` (the *physician*'s global assessment of disease activity). Both baseline and time-varying usages are covered; document per-model in `covariateData[[PAIN]]$notes` whether the column is baseline-only.
- **Units:** mm (0-100 VAS).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(PAIN / ref)^exponent`. Reference value observed: 60 in Frey 2013 (approximate dataset median across OPTION/TOWARD).
- **Source aliases:** none known (the PAIN column name is used directly in the Frey 2013 NONMEM control stream).
- **Example models:** `Frey_2013_tocilizumab.R` (baseline; power effect on the indirect-response BASE parameter with reference 60 and exponent 0.062), `Mercier_2014_tramadol_tapentadol_mbma.R` (arm-level baseline pain intensity on the 0-10 normalized scale used across the pooled MBMA trials -- see per-model `covariateData[[PAIN]]$notes` for the 0-10 vs 0-100 scale convention).
- **Notes:** PAIN can take the value 0 in real cohorts (a patient who reports no pain at the assessment), which makes the bare power form `(PAIN/60)^exp` return 0. Frey 2013 documents an explicit 0.010 floor on PAIN in its Table 2 covariate range; the model file applies the same floor inside `model()` so simulation under PAIN = 0 returns a finite BASE rather than collapsing to zero. Canonical name follows the `EOS` / `SCORE_EASI` convention (no `BL` prefix); baseline-vs-time-varying status is per-model. The canonical scale is 0-100 mm VAS; MBMA extractions that pool multiple heterogeneous pain scales (e.g., Mercier 2014) may carry PAIN on a 0-10 normalized scale -- document the scale per-model in `covariateData[[PAIN]]$notes` so downstream users know to rescale before merging with a 0-100 mm cohort.

### SWOL_28JOINT (**canonical for 28-joint swollen joint count**)
- **Description:** Swollen joint count on the 28-joint (DAS28) scale (integer 0-28; component of the DAS28 composite). Baseline value is typical; document time-varying use in per-model `notes`.
- **Units:** count (0-28)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used as a shifted power term `((SWOL_28JOINT + 1)/(<ref> + 1))^exponent` to avoid the zero-count edge case. Reference value observed: 16 in Li 2019 (approximate dataset median of the popPK cohort); 12 in Williams 2016 (median of DAS28cfb PK/PD dataset).
- **Source aliases:**
  - `SWOL` -- used in `Li_2019_abatacept.R` (Li 2019 Methods abbreviation).
  - `SJ28` -- used in `Williams_2016_rituximab_das28cfb.R` (Williams 2016 Supplemental Methods g_i covariate function).
- **Example models:** `Li_2019_abatacept.R` (power effect on CL with exponent 0.0965; not clinically relevant per Li 2019), `Williams_2016_rituximab_das28cfb.R` (additive log-scale effect (SWOL_28JOINT - 12) on each of PMAX, kp, and Emax in the DAS28 change-from-baseline model).
- **Notes:** The `_28JOINT` suffix distinguishes this from the 66-joint swollen count (`SWOL_66JOINT`) used in the extended ACR joint set. Canonical name drops the `BL` prefix to match the `SCORE_EASI` / `AGE` / `WT` / `ALB` convention where baseline-vs-time-varying status is documented in `covariateData` notes rather than the column name.

### TEND_28JOINT (**canonical for 28-joint tender joint count**)
- **Description:** Tender joint count on the 28-joint (DAS28) scale (integer 0-28; component of the DAS28 composite). Baseline value is typical; document time-varying use in per-model `notes`.
- **Units:** count (0-28)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with additive shift `(TEND_28JOINT - <ref>)` in log-scale coefficient models or shifted power `((TEND_28JOINT + 1)/(<ref> + 1))^exponent` to avoid the zero-count edge case. Reference value observed: 16 in Williams 2016 (median of DAS28cfb PK/PD dataset).
- **Source aliases:**
  - `TJ28` -- used in `Williams_2016_rituximab_das28cfb.R` (Williams 2016 Supplemental Methods g_i covariate function).
- **Example models:** `Williams_2016_rituximab_das28cfb.R` (additive log-scale effect (TEND_28JOINT - 16) on each of PMAX, kp, and Emax in the DAS28 change-from-baseline model).
- **Notes:** Companion to `SWOL_28JOINT` — the tender-count analog of the swollen count on the 28-joint DAS28 subscale. Distinct from `TEND_68JOINT` which uses the 68-joint extended count. Ratified canonically on 2026-07-09 alongside the Williams 2016 rituximab-biosimilar DAS28cfb extraction.

### SWOL_66JOINT (**canonical for 66-joint swollen joint count**)
- **Description:** Swollen joint count on the 66-joint extended (ACR) scale (integer 0-66; component of the ACR responder-rate criteria alongside `TEND_68JOINT`). Baseline value is typical; document time-varying use in per-model `notes`.
- **Units:** count (0-66)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with additive shift `(SWOL_66JOINT - <ref>)` in log-scale coefficient models. Reference value observed: 16 in Williams 2016 (median of the ACR dataset).
- **Source aliases:**
  - `SJ66` -- used in `Williams_2016_rituximab_acr.R` (Williams 2016 Supplemental Methods g_i covariate function).
- **Example models:** `Williams_2016_rituximab_acr.R` (additive log-scale effect (SWOL_66JOINT - 16) on PMAX and onset half-life in the cumulative-probit ACR responder-rate model).
- **Notes:** Register-anticipated: the existing `SWOL_28JOINT` entry's Notes section explicitly reserves `SWOL_66JOINT` for the extended-joint scale used by ACR. Companion to `TEND_68JOINT`. Ratified canonically on 2026-07-09 alongside the Williams 2016 rituximab-biosimilar ACR extraction.

### TEND_68JOINT (**canonical for 68-joint tender joint count**)
- **Description:** Tender joint count on the 68-joint extended (ACR) scale (integer 0-68; component of the ACR responder-rate criteria alongside `SWOL_66JOINT`). Baseline value is typical; document time-varying use in per-model `notes`.
- **Units:** count (0-68)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with additive shift `(TEND_68JOINT - <ref>)` in log-scale coefficient models. Reference value observed: 24 in Williams 2016 (median of the ACR dataset).
- **Source aliases:**
  - `TJ68` -- used in `Williams_2016_rituximab_acr.R` (Williams 2016 Supplemental Methods g_i covariate function).
- **Example models:** `Williams_2016_rituximab_acr.R` (additive log-scale effect (TEND_68JOINT - 24) on PMAX and onset half-life in the cumulative-probit ACR responder-rate model).
- **Notes:** Companion to `SWOL_66JOINT`. Distinct from `TEND_28JOINT` (28-joint DAS28 subscale). Ratified canonically on 2026-07-09 alongside the Williams 2016 rituximab-biosimilar ACR extraction.

### PGA_PT (**canonical for baseline patient's global assessment of arthritis (VAS)**)
- **Description:** Patient's own overall rating of arthritis disease activity on a 100-mm visual analogue scale (0 = no disease activity, 100 = maximum). Distinct from `BLPHYVAS` (the *physician*'s global assessment) and from `PAIN` (the patient's *pain*-specific rating). Time-fixed per subject in the known example; document time-varying use in per-model `notes`.
- **Units:** mm (0-100 VAS)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with additive shift `(PGA_PT - <ref>)` in log-scale coefficient models or as a power term `(PGA_PT / <ref>)^exponent`. Reference value observed: 70 in Williams 2016 (median of both the DAS28cfb and ACR datasets).
- **Source aliases:**
  - `PGA` -- used in `Williams_2016_rituximab_das28cfb.R` and `Williams_2016_rituximab_acr.R` (Williams 2016 Supplemental Methods g_i covariate function; the paper writes "PGA" for the patient's global assessment).
- **Example models:** `Williams_2016_rituximab_das28cfb.R` (additive log-scale effect on each of PMAX, kp, and Emax), `Williams_2016_rituximab_acr.R` (additive log-scale effect on PMAX and onset half-life in the cumulative-probit ACR model).
- **Notes:** The `_PT` suffix disambiguates the patient's global assessment from the physician's global assessment (`BLPHYVAS`) — the paper-verbatim "PGA" abbreviation is ambiguous between the two in cross-literature usage. Ratified canonically on 2026-07-09 alongside the Williams 2016 rituximab-biosimilar extraction (the paper reports both PGA and PhGA separately and estimates independent covariate effects on each).

## Pharmacogenetics

### CYP2D6 (**canonical for CYP2D6 individual metabolic-activity score**)
- **Description:** Continuous individual-level CYP2D6 metabolic-activity score. The intent is a single canonical column for any CYP2D6 phenotype proxy that the source paper reports as a continuous number (probe-substrate model-based individual clearance, copy-number-corrected expression score, activity-score sum from `*allele` genotypes, etc.); the per-model `covariateData[[CYP2D6]]$units`, `description`, and `notes` document which proxy is in force and the population-median reference value used inside the model. Time-invariant in all known examples (germline genotype or one-time probe-substrate measurement).
- **Units:** Paper-specific -- document per-model (e.g., `ng/L` in Ter Heine 2014 where the value is the dextromethorphan-probe model-based individual CYP2D6 clearance).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). Models center on a population median (e.g., 1560 ng/L in Ter Heine 2014); document the reference value per-model in `covariateData[[CYP2D6]]$notes`.
- **Source aliases:**
  - `CYP2D6` -- used directly in `TerHeine_2014_tamoxifen.R`.
- **Example models:** `TerHeine_2014_tamoxifen.R` (power-law effect on the tamoxifen -> endoxifen formation clearance: `(CYP2D6 / 1560)^e_CYP2D6_cl_endx`).
- **Notes:** TODO -- consolidate the various CYP2D6 phenotype encodings (continuous probe-derived activity, copy-number-corrected score, categorical PM/IM/EM/UM phenotype, activity-score sum) into a single canonical `CYP2D6` column with an enumerated `notes`-documented `encoding` field, OR introduce companion canonicals (`CYP2D6_PHENO_GROUP` for the categorical PM/IM/EM/UM grouping, `CYP2D6_ACTSCORE` for the AS sum) when a future model needs the categorical form. The current general-scope continuous canonical is sufficient for the Ter Heine 2014 use case but will likely need refinement as more CYP2D6-aware models are added. The companion binary `CYP2D6_PM` entry below covers source papers that report only a poor-metabolizer-versus-not-poor-metabolizer dichotomy.

### CYP2D6_PM (**canonical for CYP2D6 poor-metabolizer phenotype indicator**)
- **Description:** 1 = subject is a CYP2D6 poor metabolizer (genotype encoding no functional enzyme activity; e.g., homozygous *4/*4, *5/*5, or any compound combination of nonfunctional alleles); 0 = subject is an extensive, intermediate, or ultrarapid metabolizer (i.e., carrying at least one functional or reduced-function but not null allele). Time-fixed per subject (germline genotype-derived phenotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (extensive, intermediate, or ultrarapid metabolizer; carries at least one functional or reduced-function CYP2D6 allele).
- **Source aliases:**
  - `2D6PM` -- used in `Knights_2015_aripiprazole.R` (Knights 2015 Eq. 2 binary `2D6PM = 1` if CYP2D6 poor metabolizer, `= 0` otherwise; in the supplement NONMEM control stream the dataset column `CYP2D6EM` is recoded as `PM = (CYP2D6EM == 0)` -- i.e., the source dataset's `CYP2D6EM` extensive-metabolizer indicator is value-inverted to produce the model's PM indicator).
  - `P1 (PM)` / "poor metabolizer" -- Sherwin 2012 (paper Table 2 mixture-model subpopulation P1; Table 1 phenotype assignment for genotyped subjects). Sherwin 2012 uses `CYP2D6_PM` paired with `CYP2D6_EM` to encode all three PM / IM / EM phenotypes; IM is the implicit reference (both indicators = 0).
- **Example models:** `Knights_2015_aripiprazole.R` (proportional-shift effect on apparent oral clearance: `CL/F = TVCL * (1 + e_2d6pm_cl * CYP2D6_PM)` with `e_2d6pm_cl = -0.478`; CYP2D6 poor metabolizers have 47.8% lower apparent oral clearance than non-poor-metabolizers; Knights 2015 Equation 2 and Figure 1B), `Sherwin_2012_risperidone.R` (phenotype-indicator covariate gating subpopulation-specific apparent oral clearance and metabolite-formation fraction in a one-compartment risperidone + (+/-)-9-hydroxyrisperidone mixture model: PM CL/F = 9.38 L/h, IM CL/F = 29.2 L/h, EM CL/F = 37.4 L/h at the 70 kg allometric reference; KF (fraction metabolized) PM = 0.16, EM = 0.13, IM = 1 fixed; Sherwin 2012 Table 2).
- **Notes:** Opposite-orientation companion to the `CYP2C9_EM` precedent (which encodes the extensive-metabolizer-equals-1 phenotype) -- here the `1` indicates the poor-metabolizer end of the phenotype spectrum because the Knights 2015 source paper explicitly defines its binary as `2D6PM` (PM = 1) and reports the coefficient sign relative to that orientation; preserving the PM-equals-1 orientation reproduces the paper's reported coefficient sign and typical-value parameters directly. Distinct from the continuous `CYP2D6` activity-score canonical above: use `CYP2D6_PM` when the source paper reports a categorical phenotype (PM / IM / EM); use `CYP2D6` when the source reports a probe-derived continuous activity number. For three-level PM / IM / EM phenotype encoding (Sherwin 2012 mixture model), pair `CYP2D6_PM` with the companion `CYP2D6_EM` below following the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` two-binary-indicator pattern with IM as the implicit reference (both indicators = 0); this is consistent with the CYP2C9_EM register entry's anticipated `CYP2C9_PM` companion. The Knights 2015 source dataset records `CYP2D6EM` (extensive-metabolizer indicator, 1 = EM, 0 = not-EM); the model's PM indicator is derived by value inversion (`CYP2D6_PM = 1 - CYP2D6EM` after handling the supplement's `-99` missing-value sentinel as `CYP2D6_PM = 0`). Ratified canonically on 2026-05-21 alongside the Knights 2015 aripiprazole extraction; scope retained as general on 2026-05-24 alongside the Sherwin 2012 risperidone extraction (second model using the same indicator-binary encoding, demonstrating the canonical generalizes beyond the Knights 2015 PM-vs-non-PM dichotomy into the three-level PM / IM / EM paired-indicator pattern with `CYP2D6_EM`).

### CYP2D6_EM (**canonical for CYP2D6 extensive-metabolizer phenotype indicator**)
- **Description:** 1 = subject is a CYP2D6 extensive metabolizer (genotype encoding full or near-full enzyme activity; e.g., *1/*1 wild-type homozygote, or one functional allele paired with another functional allele); 0 = subject is an intermediate, poor, or ultrarapid metabolizer. Time-fixed per subject (germline genotype-derived phenotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (intermediate, poor, or ultrarapid metabolizer). When `CYP2D6_EM` is paired with `CYP2D6_PM` (Sherwin 2012 three-level encoding), the reference category specifically corresponds to the intermediate-metabolizer (IM) stratum: both `CYP2D6_PM = 0` and `CYP2D6_EM = 0` together indicate an IM subject.
- **Source aliases:**
  - `P2 (EM)` / "extensive metabolizer" -- Sherwin 2012 (paper Table 2 mixture-model subpopulation P2; Table 1 phenotype assignment for genotyped subjects).
- **Example models:** `Sherwin_2012_risperidone.R` (phenotype-indicator covariate gating subpopulation-specific apparent oral clearance and metabolite-formation fraction in a one-compartment risperidone + (+/-)-9-hydroxyrisperidone mixture model; paired with `CYP2D6_PM` with IM as the implicit reference; Sherwin 2012 Table 2).
- **Notes:** Companion to the `CYP2D6_PM` canonical above; the paired-indicator pattern follows `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` (two binary indicators encoding a three-level categorical with an implicit reference). When a source paper reports only a binary PM-vs-non-PM dichotomy (e.g., Knights 2015), `CYP2D6_PM` alone is sufficient and `CYP2D6_EM` is omitted from the model's `covariateData`. When a source distinguishes all three PM / IM / EM phenotypes (Sherwin 2012), include both indicators with IM as the implicit reference. UM (ultrarapid metabolizer) -- when present in a source cohort -- must be handled by registering an additional paired `CYP2D6_UM` indicator on the same pattern; this is not yet needed for any existing model and is deferred until a UM-aware paper is extracted. Distinct from the continuous `CYP2D6` activity-score canonical above and from `CYP2D6_PM`: use `CYP2D6_EM` when the source separately identifies the EM stratum (vs lumping IM + EM into a single non-PM group). Ratified canonically on 2026-05-24 alongside the Sherwin 2012 risperidone extraction.

### CYP2D6_STAR10_HET (**canonical for CYP2D6*10 (rs1065852) heterozygote indicator**)
- **Description:** Binary genotype indicator for the CYP2D6*10 heterozygote group at rs1065852 (the c.100C>T transition in exon 1, P34S substitution). 1 = subject carries exactly one CYP2D6*10 (T) allele (genotype C/T); 0 = otherwise (the union of C/C wild-type homozygotes and T/T homozygous *10 carriers; the paired indicator `CYP2D6_STAR10_HOM` flags the T/T homozygous-mutant group). Time-fixed per subject (germline genotype). Distinct from the broader `CYP2D6_PM` / `CYP2D6_EM` phenotype canonicals above: `CYP2D6_PM` / `CYP2D6_EM` summarise the overall metabolic phenotype across all assayed CYP2D6 alleles, while `CYP2D6_STAR10_HET` / `CYP2D6_STAR10_HOM` resolve just the rs1065852 SNP and are the right canonical when the source paper genotypes only the *10 variant.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (C/C wild-type at rs1065852, when paired with `CYP2D6_STAR10_HOM = 0`). The reference group is the C/C wild-type stratum; `CYP2D6_STAR10_HOM` flags the T/T homozygous-mutant stratum.
- **Source aliases:**
  - `CYP2D6*10 C/T` -- used in `Pei_2016_iloperidone.R` (Pei 2016 Results 'Population pharmacokinetic (PPK) models' encodes CYP2D6*10 C/C, C/T, T/T as integers 1, 2, 3; the C/T = 2 stratum maps to `CYP2D6_STAR10_HET = 1`).
- **Example models:** `Pei_2016_iloperidone.R` (multiplicative effect on the iloperidone -> M2 (P-95) formation rate constant K24: K24_CT = 0.693 * K24_typical relative to the C/C wild-type reference; Pei 2016 Table 3 and Results 'Population pharmacokinetic (PPK) models' equation `(K24)_i = theta * 0.00649 * exp(eta_i)` with theta = 0.693 for C/T).
- **Notes:** Follows the `CYP3A5_STAR1_HET` / `CYP3A5_STAR1_HOM` and `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` precedents (paired binary indicators for a three-level germline genotype with an implicit wild-type reference). The `_STAR10_` token names the variant-allele orientation (the *10 allele is the reduced-function variant of CYP2D6 that causes lower CYP2D6 metabolic activity); the C/C wild-type stratum is the implicit reference because the source-paper coefficient signs are reported relative to that reference. Pei 2016 pooled C/C and C/T as the reference for the K23 (M1 formation) effect (only T/T was distinguished on K23), so a model using these two canonicals will route the K23 effect through `CYP2D6_STAR10_HOM` alone while routing the K24 effect through both `CYP2D6_STAR10_HET` and `CYP2D6_STAR10_HOM` -- this asymmetric per-parameter use is normal and is what the paired-indicator pattern is designed to express. Ratified canonically on 2026-06-03 alongside the Pei 2016 iloperidone extraction.

### CYP2D6_STAR10_HOM (**canonical for CYP2D6*10 (rs1065852) homozygous-mutant indicator**)
- **Description:** Binary genotype indicator for the CYP2D6*10 homozygous-mutant group at rs1065852. 1 = subject carries two CYP2D6*10 (T) alleles (genotype T/T); 0 = otherwise (the union of C/C wild-type homozygotes and C/T heterozygotes; the paired indicator `CYP2D6_STAR10_HET` flags the C/T heterozygous group). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (C/C wild-type at rs1065852, when paired with `CYP2D6_STAR10_HET = 0`). The reference group is the C/C wild-type stratum; `CYP2D6_STAR10_HET` flags the C/T heterozygous stratum.
- **Source aliases:**
  - `CYP2D6*10 T/T` -- used in `Pei_2016_iloperidone.R` (Pei 2016 Results 'Population pharmacokinetic (PPK) models' encodes CYP2D6*10 C/C, C/T, T/T as integers 1, 2, 3; the T/T = 3 stratum maps to `CYP2D6_STAR10_HOM = 1`).
- **Example models:** `Pei_2016_iloperidone.R` (multiplicative effects on two iloperidone metabolite-formation rate constants: K23 (M1 formation) is multiplied by 1.34 in T/T subjects relative to the C/C + C/T pooled reference, and K24 (M2 formation) is multiplied by 0.492 in T/T subjects relative to the C/C wild-type reference; Pei 2016 Table 3 and Results 'Population pharmacokinetic (PPK) models' equations `(K23)_i = theta * 0.00451 * exp(eta_i)` with theta = 1.34 for T/T and `(K24)_i = theta * 0.00649 * exp(eta_i)` with theta = 0.492 for T/T).
- **Notes:** Paired with `CYP2D6_STAR10_HET` (see that entry's Notes for the three-level decomposition rationale, the variant-orientation convention, and the asymmetric K23-vs-K24 use the paired indicators support in the Pei 2016 model). Allele frequencies for the CYP2D6*10 (rs1065852) variant in Chinese populations are 48-70% per the source paper's Introduction; the Pei 2016 cohort (n = 70 Chinese schizophrenia patients) observed C/C 15.7%, C/T 60.0%, T/T 24.3%. Ratified canonically on 2026-06-03 alongside the Pei 2016 iloperidone extraction.

### CYP3A4 (**canonical for CYP3A4 / CYP3A4-and-CYP3A5 individual metabolic-activity score**)
- **Description:** Continuous individual-level CYP3A4 (or combined CYP3A4 + CYP3A5) metabolic-activity score. Same intent and documentation policy as `CYP2D6` above. Some sources measure CYP3A4 alone via probe substrate; others (Ter Heine 2014) report a combined CYP3A4/5 activity because the chosen probe (dextromethorphan N-demethylation) cannot distinguish CYP3A4 from CYP3A5. The per-model `notes` field documents whether the value is CYP3A4-only or the CYP3A4 + CYP3A5 combined score.
- **Units:** Paper-specific -- document per-model (e.g., `ng/L` in Ter Heine 2014 where the value is the dextromethorphan-probe model-based individual CYP3A4/5 clearance).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). Models center on a population median (e.g., 44.7 ng/L in Ter Heine 2014); document the reference value per-model.
- **Source aliases:**
  - `CYP3A4` -- used directly in `TerHeine_2014_tamoxifen.R` (the column carries combined CYP3A4 + CYP3A5 activity per the source paper).
  - `CYP3A4/5` -- long form sometimes used in source manuscripts; standardize the column name to `CYP3A4` and document the combined-isoform semantics in per-model `notes`.
- **Example models:** `TerHeine_2014_tamoxifen.R` (power-law effect on the tamoxifen -> endoxifen formation clearance: `(CYP3A4 / 44.7)^e_CYP3A4_cl_endx`).
- **Notes:** TODO -- register the rest of the canonical drug-metabolizing-CYP set prospectively (`CYP1A2`, `CYP2A6`, `CYP2B6`, `CYP2C8`, `CYP2C9`, `CYP2C19`, `CYP2E1`, `CYP3A5`) using the same continuous-individual-activity-score pattern, so future popPK models that report CYP-probe-derived covariates can drop straight into the existing convention rather than re-deliberating the encoding each time. Coordinate with the consolidation TODO on `CYP2D6` so categorical-vs-continuous encoding is handled uniformly across all CYPs.

### CYP3A5_EXPR (**canonical for CYP3A5 expresser status**)
- **Description:** 1 = subject carries at least one functional CYP3A5*1 allele (genotype *1/*1 or *1/*3, equivalent to one or two A alleles at rs776746); 0 = homozygous CYP3A5*3/*3 (G/G at rs776746) -- i.e., a nonexpresser. Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (CYP3A5*3/*3 nonexpresser).
- **Source aliases:**
  - `X` -- used in `Bergmann_2014_tacrolimus.R` (Bergmann 2014 Table 2 footnote: `X = 1` for *1/*1 and *1/*3; `X = 0` for *3/*3).
  - `CYP3A5 expresser` -- used in `Storset_2014_tacrolimus.R` (Storset 2014 Table 2 final theory-based model; `*1/*1` and `*1/*3` pooled as expressers because Storset 2014 had only n = 3 *1/*1 subjects).
- **Example models:** `Bergmann_2014_tacrolimus.R` (multiplicative effect on tacrolimus CL/F: `theta_CYP3A5 ^ CYP3A5_EXPR`, with `theta_CYP3A5 = 1.60`; expressers have 60% higher apparent oral clearance than nonexpressers), `Storset_2014_tacrolimus.R` (multiplicative effects on apparent plasma clearance: `cl *= 1.30^CYP3A5_EXPR`; and on oral bioavailability: `fdepot *= 0.82^CYP3A5_EXPR`; Storset 2014 Table 2 final theory-based model).
- **Notes:** Distinct from the SNP-pattern canonical `SNP_<GENE>_<RSID>` (which encodes "mutant allele presence" -- 1 = at least one variant allele). For CYP3A5 the *3 allele (rs776746 G) is the variant that abolishes function, so a literal "mutant-allele-presence" indicator (1 = any G allele) would group *1/*3 heterozygotes with the *3/*3 nonexpressers, which is the **opposite** of the clinically meaningful expresser-vs-nonexpresser dichotomy used by every CYP3A5-aware popPK model. The `CYP3A5_EXPR` canonical preserves the expresser-equals-1 orientation directly. Future CYP3A5 papers using a *3/*3 indicator (rather than *1 carrier) should still record their values under `CYP3A5_EXPR` and document the value inversion in `notes` (`CYP3A5_EXPR = 1 - source_indicator`); registering a parallel `CYP3A5_NONEXPR` is discouraged. The canonical name follows the `<gene>_<phenotype>` rather than the `<gene>_<rsid>` pattern because the column captures derived metabolic phenotype rather than raw genotype. Distinct from `CYP3A4` (continuous individual-activity score for CYP3A4 / CYP3A4 + CYP3A5 combined): the binary `CYP3A5_EXPR` is the right fit for source papers that report only the rs776746 genotype, while the continuous `CYP3A4` is for sources that report a probe-substrate-derived activity number. In solid-organ-transplant popPK / pharmacogenetics studies that genotype both the recipient and the donated graft separately (e.g., Moes 2016 liver-transplant tacrolimus), the recipient genotype goes into `CYP3A5_EXPR` and the donor genotype into the sibling canonical `CYP3A5_EXPR_DONOR` below. Ratified canonically on 2026-05-08 alongside the Bergmann 2014 extraction.

### CYP3A5_EXPR_DONOR (**canonical for transplanted-graft donor CYP3A5 expresser status**)
- **Description:** 1 = transplanted graft (typically liver, kidney, or other CYP3A5-expressing organ) was donated by a donor carrying at least one functional CYP3A5*1 allele (genotype *1/*1 or *1/*3 at rs776746); 0 = donor is homozygous CYP3A5*3/*3 (nonexpresser). Time-fixed per recipient (germline genotype of the donor at the time of transplantation). Sibling canonical to the recipient-genotype indicator `CYP3A5_EXPR`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (donor CYP3A5*3/*3 nonexpresser graft).
- **Source aliases:**
  - `Donor CYP3A5*3` -- used in `Moes_2016_tacrolimus.R` (Moes 2016 Methods; donor and recipient genotypes pooled with the four-level combination indicator `C1 / C2 / C3 / C4` defined in Methods, where `CYP3A5_EXPR_DONOR = 1` corresponds to graft from a `*1`-carrying donor).
  - `CYP3A5 donor` -- used in `Ji_2018_tacrolimus.R` (Ji 2018 derives the donor-side genotype from the four-level combinational CYP3A5 group: REDE/RNDE -> 1; REDN/RNDN -> 0).
- **Example models:** `Moes_2016_tacrolimus.R` (categorical donor + recipient CYP3A5 combination effect on apparent oral CL/F: reference C1 = both nonexpressers; C2 = recipient `*1` carrier + donor nonexpresser = +33%; C3 = recipient nonexpresser + donor `*1` carrier = +33%; C4 = both `*1` carriers = +71%; Moes 2016 Table 4 final model -- the C2 / C3 / C4 levels are reconstructed inside `model()` from the two binary inputs `CYP3A5_EXPR` and `CYP3A5_EXPR_DONOR`), `Ji_2018_tacrolimus.R` (combinational categorical effect on tacrolimus CL/F that depends on both recipient and donor CYP3A5 status: multiplier 2.314 when the recipient is an expresser and the donor is an expresser (REDE), 1.523 when the recipient is an expresser and the donor is a nonexpresser (REDN), and 1.0 otherwise (RNDE / RNDN reference, which Ji 2018 merged because the two estimated effects were similar)).
- **Notes:** Genotyped only in transplant studies where donor DNA is recoverable (Moes 2016 obtained donor DNA from spleen / liver biopsies). When the same paper reports both recipient and donor genotypes separately, encode them as two binary inputs (`CYP3A5_EXPR` for the recipient, `CYP3A5_EXPR_DONOR` for the donor) rather than as a single four-level combination column, so the underlying recipient / donor biology is explicit in the dataset. Models that fit a categorical four-level combination effect (paper-Moes-style C1 / C2 / C3 / C4) reconstruct the levels inside `model()` from the two binary inputs, so the source-paper's per-level coefficients remain the estimated quantities. The intestinal-CYP3A5 contribution (recipient genotype) and the hepatic-CYP3A5 contribution (donor genotype) act on different anatomic compartments of tacrolimus's first-pass metabolism, which is why both donor and recipient genotypes are independently informative in liver-transplant tacrolimus PK. Ratified canonically on 2026-05-20 alongside the Moes 2016 tacrolimus extraction.

### CYP3A5_STAR1_HET (**canonical for CYP3A5*1/*3 heterozygote indicator**)
- **Description:** Binary genotype indicator for the CYP3A5*1/*3 heterozygote group. 1 = subject carries exactly one functional CYP3A5*1 allele at rs776746 (genotype *1/*3); 0 = otherwise (the union of *3/*3 nonexpressers and *1/*1 homozygotes; the paired indicator `CYP3A5_STAR1_HOM` flags the homozygous-expresser group). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (CYP3A5*3/*3 nonexpresser, when paired with `CYP3A5_STAR1_HOM = 0`). The reference group is the homozygous *3/*3 nonexpresser stratum; `CYP3A5_STAR1_HOM` flags the homozygous-expresser stratum.
- **Source aliases:**
  - `CYP3A5*1/*3` -- used directly in `Passey_2011_tacrolimus.R` (Passey 2011 Table 3 reports a separate multiplicative factor of 1.70 for the heterozygote stratum vs the *3/*3 reference).
- **Example models:** `Passey_2011_tacrolimus.R` (power-of-binary-indicator multiplicative factor on apparent oral clearance: `e_cyp3a5_het_cl ^ CYP3A5_STAR1_HET` with `e_cyp3a5_het_cl = 1.70`; *1/*3 heterozygotes have ~70% higher apparent oral CL/F than *3/*3 nonexpressers; paired with `CYP3A5_STAR1_HOM` and used jointly).
- **Notes:** Follows the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` precedent (paired binary indicators for a three-level genotype) rather than overloading `CYP3A5_EXPR` (which pools *1/*1 and *1/*3 into a single expresser indicator and is the right canonical when a source paper does the same pooling). Use the paired `CYP3A5_STAR1_HET` + `CYP3A5_STAR1_HOM` binaries when the source paper assigns a distinct typical-value covariate effect to each of the three CYP3A5 genotype strata (*3/*3, *1/*3, *1/*1). Passey 2011 motivated the three-level decomposition because the cohort (n = 681) had enough *1/*1 homozygotes (72, 11%) to identify a distinct typical-value factor for that stratum, whereas earlier popPK papers (Bergmann 2014, Storset 2014) had too few *1/*1 subjects (or the equivalent expresser-pooled treatment) to distinguish *1/*1 from *1/*3. The `_STAR1_` token (rather than `_STAR3_`) names the functional-allele-presence orientation consistent with the parent `CYP3A5_EXPR` canonical's expresser-equals-1 convention. Ratified canonically on 2026-05-20 alongside the Passey 2011 tacrolimus extraction.

### CYP3A5_STAR1_HOM (**canonical for CYP3A5*1/*1 homozygote indicator**)
- **Description:** Binary genotype indicator for the CYP3A5*1/*1 homozygote group. 1 = subject carries two functional CYP3A5*1 alleles at rs776746 (genotype *1/*1); 0 = otherwise (the union of *3/*3 nonexpressers and *1/*3 heterozygotes; the paired indicator `CYP3A5_STAR1_HET` flags the heterozygous group). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (CYP3A5*3/*3 nonexpresser, when paired with `CYP3A5_STAR1_HET = 0`). The reference group is the homozygous *3/*3 nonexpresser stratum; `CYP3A5_STAR1_HET` flags the heterozygous-expresser stratum.
- **Source aliases:**
  - `CYP3A5*1/*1` -- used directly in `Passey_2011_tacrolimus.R` (Passey 2011 Table 3 reports a separate multiplicative factor of 2.00 for the homozygote-expresser stratum vs the *3/*3 reference).
- **Example models:** `Passey_2011_tacrolimus.R` (power-of-binary-indicator multiplicative factor on apparent oral clearance: `e_cyp3a5_hom_cl ^ CYP3A5_STAR1_HOM` with `e_cyp3a5_hom_cl = 2.00`; *1/*1 homozygotes have 100% higher apparent oral CL/F than *3/*3 nonexpressers; paired with `CYP3A5_STAR1_HET` and used jointly).
- **Notes:** Paired with `CYP3A5_STAR1_HET` (see that entry's Notes for the three-level decomposition rationale). Ratified canonically on 2026-05-20 alongside the Passey 2011 tacrolimus extraction.

### CYP2C9_EM (**canonical for CYP2C9 extensive-metabolizer phenotype indicator**)
- **Description:** 1 = subject is a CYP2C9 extensive metabolizer (genotype *1/*1; wild-type homozygote with full enzyme activity); 0 = subject is a CYP2C9 intermediate or poor metabolizer (heterozygous or homozygous carrier of any reduced-function allele such as *2, *3, *13) OR has unknown phenotype (when paired with `CYP2C9_PM_IM`). Time-fixed per subject (germline genotype-derived phenotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (intermediate or poor metabolizer, reduced-function-allele carrier; OR unknown phenotype when paired with `CYP2C9_PM_IM`).
- **Source aliases:**
  - `CYP2C9 phenotype` -- Jeong 2022 (paper Table 1 phenotype classification: `*1/*1` = EM; `*1/*3`, `*1/*13` = IM).
- **Example models:** `Jeong_2022_torsemide.R` (linear-deviation effect on apparent clearance and inter-compartmental clearance: `CL/F = tvCL/F * (1 + 0.510 * CYP2C9_EM)` and `Q/F = tvQ/F * (1 + 0.365 * CYP2C9_EM)`; CYP2C9 extensive metabolizers have 51% higher apparent clearance and 36.5% higher apparent inter-compartmental clearance than intermediate metabolizers; Jeong 2022 Table 4 final Pop-PK model), `Kleideiter_2017_cebranopadol.R` (additive log shifts on CL: `e_cyp2c9em_lcl = log(82.4 / 74.3) = 0.1037` and `e_cyp2c9pmim_lcl = log(58.7 / 74.3) = -0.2353`; reference category is unknown phenotype with both indicators = 0, the most common stratum; paired with `CYP2C9_PM_IM`), `Kleideiter_2018_cebranopadol.R` (multiplicative effect on CL applied as `e_em_cl^CYP2C9_EM` with `e_em_cl = 82.4 / 74.3 = 1.109`; EM subjects have about +11% CL vs the model's CYP2C9 reference, which in this paper is the 'unknown phenotype' pool rather than IM/PM -- only 38.3% of the analysis cohort had a known CYP2C9 phenotype, so the 0-level here pools unknown subjects together with PIM subjects whose effect is carried separately by the sibling `CYP2C9_PIM` canonical; Kleideiter 2018 Table 13).
- **Notes:** Follows the `CYP3A5_EXPR` precedent above (functional-allele-carrier = 1) rather than the `SNP_<GENE>_<RSID>` mutant-presence pattern, because (a) clinical CYP2C9 phenotype is reported as EM / IM / PM and the canonical name should mirror the clinically meaningful axis, and (b) Jeong 2022 chose IM as the model reference, so the EM-equals-1 orientation preserves the paper's reported coefficient signs and typical-value parameters directly. The canonical name pools reduced-function alleles (*2, *3, *13, etc.) into the `0` category because the population-PK literature typically does not separately resolve them; future papers that distinguish IM from PM should propose a paired companion (`CYP2C9_PM`) so the three-level EM / IM / PM phenotype can be encoded with two binary indicators on the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` pattern. Distinct from `CYP2D6` / `CYP3A4` (continuous-activity-score canonicals): use `CYP2C9_EM` when the source paper reports a discrete phenotype label; use a continuous `CYP2C9` canonical (not yet ratified; see TODO on `CYP3A4`) when a future paper reports a probe-derived activity number. Ratified canonically on 2026-05-17 alongside the Jeong 2022 torsemide extraction.

### CYP2C9_PM_IM (**canonical for pooled CYP2C9 poor-or-intermediate-metabolizer phenotype indicator**)
- **Description:** 1 = subject is a CYP2C9 poor metabolizer OR intermediate metabolizer (i.e., heterozygous or homozygous carrier of a reduced-function allele such as *2, *3, *13, pooled because the source paper did not distinguish PM from IM); 0 = subject is an extensive metabolizer OR has an unknown / unassayed CYP2C9 phenotype. Time-fixed per subject (germline genotype-derived phenotype where known). The companion canonical `CYP2C9_EM` carries the 1 = EM indicator; subjects with unknown CYP2C9 status appear with both `CYP2C9_PM_IM = 0` and `CYP2C9_EM = 0`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (extensive metabolizer OR unknown phenotype; the per-paper reference cohort that 0 represents is paper-defined). When paired with `CYP2C9_EM`, both indicators = 0 indicates the unknown-phenotype stratum and `CYP2C9_EM = 1` with `CYP2C9_PM_IM = 0` indicates the EM stratum.
- **Source aliases:**
  - `CYP2C9` -- Kleideiter 2017 (paper Table 13 row "CYP2C9 poor and intermediate metabolizers 58.7 L/h").
  - `CYP2C9_PIM` -- used in `Kleideiter_2018_cebranopadol.R` (the erratum-corrected re-extraction; same pooled poor-or-intermediate-metabolizer semantics, abbreviated `PIM`).
- **Example models:** `Kleideiter_2017_cebranopadol.R` (additive log shift on CL: `e_cyp2c9pmim_lcl = log(58.7 / 74.3) = -0.2353`; reduced apparent clearance vs the unknown-phenotype reference; paired with `CYP2C9_EM` to form the three-level stratification), `Kleideiter_2018_cebranopadol.R` (multiplicative CL ratio 58.7 / 74.3 = 0.790 applied as `ratio^CYP2C9_PIM`; paired with `CYP2C9_EM`, both 0 = the unknown-phenotype reference, Kleideiter 2018 Table 13).
- **Notes:** Pairs with `CYP2C9_EM` for the three-level "unknown / EM / PM-IM" stratification used in the Kleideiter cebranopadol model, where the unknown-phenotype reference (both indicators = 0) is the most common category (only 38.3% of the analysis cohort had a known CYP2C9 phenotype). PM and IM are pooled because the source covariate analysis does not separately resolve them; downstream papers that distinguish PM from IM should register a paired `CYP2C9_PM` canonical and split this group, encoding the three-level EM / IM / PM phenotype with two binary indicators on the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` pattern. Distinct from the `CYP2C9_EM` canonical's Jeong-2022 use (where 0 = IM/PM, no unknown subjects, and the 0-level directly carries the PM/IM phenotype): in the Kleideiter cohort the 0-level of both `CYP2C9_EM` and `CYP2C9_PM_IM` pools all subjects not assigned that specific phenotype label (including the 'unknown' fraction), so per-model `covariateData` notes must document the reference complement. Ratified canonically on 2026-05-25 alongside the Kleideiter 2017 cebranopadol extraction.

### CYP2C9_IM_AS15 (**canonical for CYP2C9 intermediate-metabolizer, CPIC activity score 1.5, phenotype indicator**)
- **Description:** 1 = subject is a CYP2C9 intermediate metabolizer whose CPIC genotype-predicted activity score (AS) is 1.5 -- i.e. one normal-function allele plus one *decreased*-function allele, canonically `*1/*2`; 0 = any other CYP2C9 phenotype. Time-fixed per subject (germline genotype-derived phenotype). Pairs with `CYP2C9_PM_IM_AS10` as two mutually-exclusive binary indicators; both 0 identifies the extensive-metabolizer reference stratum (AS 2.0, `*1/*1` and `*1/*9`).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0. When paired with `CYP2C9_PM_IM_AS10`, both indicators = 0 is the extensive-metabolizer (AS 2.0) reference stratum, `CYP2C9_IM_AS15 = 1` is the AS-1.5 stratum, and `CYP2C9_PM_IM_AS10 = 1` is the AS-1.0 stratum.
- **Source aliases:**
  - `IM with AS of 1.5` -- Chen 2024 (paper Table 2 phenotype classification row; Table 4 parameter row `CL/F_IM_1.5`).
- **Example models:** `Chen_2024_noscapine.R` (additive log shift on apparent clearance: `e_cyp2c9im_as15_lcl = log(531 / 958) = -0.5901`; Chen 2024 Table 4 estimates a free typical value per stratum -- `CL/F_EM = 958 L/h`, `CL/F_IM_1.5 = 531 L/h`, `CL/F_PM_IM_1.0 = 343 L/h` -- so the extensive-metabolizer value is carried by `lcl` and the other two strata enter as log shifts, the `Kleideiter_2017_cebranopadol.R` pattern).
- **Notes:** Ratified 2026-08-06 alongside the Chen 2024 noscapine extraction, discharging the TODO recorded in the `CYP2C9_EM` Notes ("future papers that distinguish IM from PM should propose a paired companion ... so the three-level EM / IM / PM phenotype can be encoded with two binary indicators"). This pair resolves CYP2C9 phenotype on the **activity-score** axis rather than the bare PM / IM label, because that is the axis on which the source model stratifies and because the two axes do not coincide: CPIC assigns AS 1.0 to *both* `*1/*3` (an intermediate metabolizer) and `*3/*3` (a poor metabolizer), which is why the AS-1.0 stratum is named `PM_IM` rather than `PM`. Distinct from the pooled `CYP2C9_PM_IM` canonical, whose 1-level is the **union** of the AS-1.5 and AS-1.0 strata; a paper that resolves the activity score should use this pair, and a paper that does not should use `CYP2C9_EM` / `CYP2C9_PM_IM`. Do not mix the two pairs in one model. A continuous activity-score covariate is **not** a substitute: in Chen 2024 the clearance ratios (531/958 = 0.554, 343/958 = 0.358) are not proportional to the activity-score ratios (0.75, 0.50), so discrete indicators are required to reproduce the published typical values. Extend to further strata as `CYP2C9_<PHENOTYPE>_AS<score>` (e.g. `CYP2C9_PM_AS00`) if a future cohort resolves them.

### CYP2C9_PM_IM_AS10 (**canonical for CYP2C9 poor-or-intermediate-metabolizer, CPIC activity score 1.0, phenotype indicator**)
- **Description:** 1 = subject's CYP2C9 CPIC genotype-predicted activity score (AS) is 1.0, pooling the intermediate metabolizers that reach AS 1.0 (`*1/*3`, `*2/*2`) with the poor metabolizers (`*2/*3`, `*3/*3`); 0 = any other CYP2C9 phenotype. Time-fixed per subject (germline genotype-derived phenotype). Pairs with `CYP2C9_IM_AS15` as two mutually-exclusive binary indicators; both 0 identifies the extensive-metabolizer reference stratum (AS 2.0, `*1/*1` and `*1/*9`).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0. When paired with `CYP2C9_IM_AS15`, both indicators = 0 is the extensive-metabolizer (AS 2.0) reference stratum.
- **Source aliases:**
  - `PM & IM with AS of 1` -- Chen 2024 (paper Table 2 phenotype classification row; Table 4 parameter row `CL/F_PM_IM_1.0`).
- **Example models:** `Chen_2024_noscapine.R` (additive log shift on apparent clearance: `e_cyp2c9pmim_as10_lcl = log(343 / 958) = -1.0271`; paired with `CYP2C9_IM_AS15`, both 0 = the extensive-metabolizer reference carried by `lcl`; Chen 2024 Table 4).
- **Notes:** The `PM_IM` element of the name records that this stratum deliberately pools poor metabolizers with the activity-score-1.0 intermediate metabolizers -- Chen 2024 Sect. 3.1: "CYP2C9*1/*3 and CYP2C9*2/*2 as IMs with an activity score (AS) of 1, were classified together with the PMs into one group in this study because of the limited sample size of homozygous carriers". A source paper that separately estimates PM and AS-1.0 IM effects should register a further split rather than reuse this canonical. This is a strict **subset** of the pooled `CYP2C9_PM_IM` canonical (which additionally absorbs the AS-1.5 `*1/*2` subjects that `CYP2C9_IM_AS15` carries here), so the two must not be used interchangeably: substituting `CYP2C9_PM_IM` for this column would silently mis-assign every `*1/*2` subject (20.8% of the Chen 2024 cohort). Derivable from the per-allele count canonicals as `CYP2C9_S3_COUNT >= 1 | CYP2C9_S2_COUNT == 2`, but the indicators are preferred when the source paper declares a phenotype group and estimates a free typical value per group rather than an allele-dosage function. See the `CYP2C9_IM_AS15` Notes for the full rationale. Ratified 2026-08-06 alongside the Chen 2024 noscapine extraction.

### CYP2B6_IM (**canonical for CYP2B6 intermediate-metabolizer phenotype indicator**)
- **Description:** 1 = subject is a CYP2B6 intermediate metabolizer defined by the combined 516G>T (rs3745274) | 983T>C (rs28399499) SNP-vector phenotype assignment used in the African paediatric antiretroviral literature: 516GT | 983TT (heterozygous loss-of-function at 516) or 516GG | 983TC (heterozygous loss-of-function at 983); 0 = any other phenotype (extensive, slow, or ultra-slow metabolizer; see paired canonicals `CYP2B6_SM` and `CYP2B6_USM`). Time-fixed per subject (germline genotype-derived phenotype). EM (516GG | 983TT) is the reference category when all three of `CYP2B6_IM`, `CYP2B6_SM`, and `CYP2B6_USM` are 0.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-IM phenotype: EM, SM, or USM).
- **Source aliases:**
  - `metabolizer status IM` / `IM` -- Bienczak 2016 (paper Methods 'Covariate effects' paragraph 2, Results 'Population pharmacokinetics' paragraph 3, and Table 3 final estimates; categorical metabolizer indicator entered as multiplicative effect on intrinsic clearance CLint with EM reference).
- **Example models:** `Bienczak_2016_nevirapine.R` (multiplicative log-additive effect on CLint: `cl_meta <- exp(e_cyp2b6_im_cl * CYP2B6_IM + e_cyp2b6_sm_cl * CYP2B6_SM + e_cyp2b6_usm_cl * CYP2B6_USM)`, with `e_cyp2b6_im_cl = log(1 - 0.17) = -0.186`, giving the 17% lower CLint reported in Bienczak 2016 Table 3 / Results 'Population pharmacokinetics' paragraph 3).
- **Notes:** Sibling indicators `CYP2B6_SM` and `CYP2B6_USM` together encode the four-level EM / IM / SM / USM phenotype with three binary columns (EM = all three zero); follows the dummy-coding pattern used elsewhere in the register for multi-level categoricals (e.g. `RACE_*`, `HEPIMP_MILD` / `HEPIMP_SEV` / `HEPIMP_MODSEV`). The IM grouping pools the two distinct genotypes (516GT | 983TT and 516GG | 983TC) into a single indicator because Bienczak 2016 Results 'Population pharmacokinetics' paragraph 3 reports that 'Using six rather than four 516G>T | 983T>C SNP-vector metabolizer groups reduced OFV by only 5 points (df = 2, P = 0.08) and was therefore not used.' Distinct from the continuous `CYP3A4` activity-score canonical (which captures probe-substrate-derived activity rather than SNP-vector phenotype) and from `CYP3A5_EXPR` (binary expresser indicator built on a single rs776746 genotype). Ratified canonically on 2026-05-21 alongside the Bienczak 2016 nevirapine extraction.

### CYP2B6_SM (**canonical for CYP2B6 slow-metabolizer phenotype indicator**)
- **Description:** 1 = subject is a CYP2B6 slow metabolizer defined by the combined 516G>T (rs3745274) | 983T>C (rs28399499) SNP-vector phenotype assignment used in the African paediatric antiretroviral literature: 516TT | 983TT (homozygous loss-of-function at 516) or 516GT | 983TC (compound heterozygous loss-of-function at both); 0 = any other phenotype (extensive, intermediate, or ultra-slow metabolizer; see paired canonicals `CYP2B6_IM` and `CYP2B6_USM`). Time-fixed per subject (germline genotype-derived phenotype). EM (516GG | 983TT) is the reference category when all three of `CYP2B6_IM`, `CYP2B6_SM`, and `CYP2B6_USM` are 0.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-SM phenotype: EM, IM, or USM).
- **Source aliases:**
  - `metabolizer status SM` / `SM` -- Bienczak 2016 (paper Methods 'Covariate effects' paragraph 2, Results 'Population pharmacokinetics' paragraph 3, and Table 3 final estimates; categorical metabolizer indicator entered as multiplicative effect on intrinsic clearance CLint with EM reference).
- **Example models:** `Bienczak_2016_nevirapine.R` (multiplicative log-additive effect on CLint: `cl_meta <- exp(e_cyp2b6_im_cl * CYP2B6_IM + e_cyp2b6_sm_cl * CYP2B6_SM + e_cyp2b6_usm_cl * CYP2B6_USM)`, with `e_cyp2b6_sm_cl = log(1 - 0.50) = -0.693`, giving the 50% lower CLint reported in Bienczak 2016 Table 3 / Results 'Population pharmacokinetics' paragraph 3).
- **Notes:** Sibling indicators `CYP2B6_IM` and `CYP2B6_USM` together encode the four-level EM / IM / SM / USM phenotype with three binary columns (EM = all three zero). See `CYP2B6_IM` Notes for the SNP-vector pooling rationale and the link back to the dummy-coding pattern used elsewhere in the register. Ratified canonically on 2026-05-21 alongside the Bienczak 2016 nevirapine extraction.

### CYP2B6_USM (**canonical for CYP2B6 ultra-slow-metabolizer phenotype indicator**)
- **Description:** 1 = subject is a CYP2B6 ultra-slow metabolizer defined by the combined 516G>T (rs3745274) | 983T>C (rs28399499) SNP-vector phenotype assignment used in the African paediatric antiretroviral literature: 983CC homozygosity (irrespective of 516G>T genotype, i.e. 516GG | 983CC in Bienczak 2016's principal grouping); 0 = any other phenotype (extensive, intermediate, or slow metabolizer; see paired canonicals `CYP2B6_IM` and `CYP2B6_SM`). Time-fixed per subject (germline genotype-derived phenotype). EM (516GG | 983TT) is the reference category when all three of `CYP2B6_IM`, `CYP2B6_SM`, and `CYP2B6_USM` are 0.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-USM phenotype: EM, IM, or SM).
- **Source aliases:**
  - `metabolizer status USM` / `USM` -- Bienczak 2016 (paper Methods 'Covariate effects' paragraph 2, Results 'Population pharmacokinetics' paragraph 3, and Table 3 final estimates; categorical metabolizer indicator entered as multiplicative effect on intrinsic clearance CLint with EM reference).
- **Example models:** `Bienczak_2016_nevirapine.R` (multiplicative log-additive effect on CLint: `cl_meta <- exp(e_cyp2b6_im_cl * CYP2B6_IM + e_cyp2b6_sm_cl * CYP2B6_SM + e_cyp2b6_usm_cl * CYP2B6_USM)`, with `e_cyp2b6_usm_cl = log(1 - 0.68) = -1.139`, giving the 68% lower CLint reported in Bienczak 2016 Table 3 / Results 'Population pharmacokinetics' paragraph 3).
- **Notes:** Sibling indicators `CYP2B6_IM` and `CYP2B6_SM` together encode the four-level EM / IM / SM / USM phenotype with three binary columns (EM = all three zero). Bienczak 2016 is the first study to quantify the USM phenotype on nevirapine clearance (cohort prevalence 0.6%; Table 2 row 4); the rs28399499 (983T>C) loss-of-function allele is essentially absent from European-ancestry populations but reaches appreciable frequency in sub-Saharan African cohorts, so models on European or East Asian populations may report only EM / IM / SM (with `CYP2B6_USM` identically zero across the dataset). See `CYP2B6_IM` Notes for the SNP-vector pooling rationale. Ratified canonically on 2026-05-21 alongside the Bienczak 2016 nevirapine extraction.

### CYP2B6 (**canonical for CYP2B6 individual metabolic-activity score**)
- **Description:** Continuous individual-level CYP2B6 metabolic-activity score. Same intent and documentation policy as the `CYP2D6`, `CYP3A4`, and `CYP2C19` continuous canonicals above. Typically derived from *-allele diplotype-to-phenotype conversion following CPIC / PharmVar guidance (poor metabolizer -> 0, intermediate -> 0.5, normal / extensive -> 1, rapid -> 1.5, ultra-rapid -> 2), but the per-model `notes` field documents which mapping the source paper used and the underlying diplotype-to-phenotype table when non-standard. Distinct from the categorical phenotype-indicator canonicals `CYP2B6_IM` / `CYP2B6_SM` / `CYP2B6_USM` (Bienczak 2016 African-paediatric SNP-vector encoding): use the continuous `CYP2B6` when the source paper reports a numeric activity score, and the categorical indicators when the source reports discrete phenotype labels.
- **Units:** Paper-specific -- document per-model. Aruldhas 2021 (methadone) uses a 5-level activity score {0, 0.5, 1, 1.5, 2} for PM / IM / NM / RM / UM derived from CPIC efavirenz guideline diplotype assignments (Supplemental Table S1 for the paper's per-diplotype mapping).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). Models center on the normal-metabolizer reference (activity score 1); document the reference value per-model. Aruldhas 2021 uses the standard centered-additive form `CLF = CLF_ref * (1 + Theta_CYP2B6 * (CYP2B6 - 1)) * ...` with `CLF_ref` reported at activity score 1.
- **Source aliases:**
  - `CYP2B6 activity score` -- Aruldhas 2021 (paper Methods 'Covariate models' and Table S1; used as a linear covariate on the fractional metabolite-formation clearance CLF for both R- and S-methadone).
- **Example models:** `Aruldhas_2021_R_methadone.R`, `Aruldhas_2021_S_methadone.R` (linear covariate on the fractional metabolite-formation clearance CLF: `clf <- exp(lclf) * (1 + e_cyp2b6_clf * (CYP2B6 - 1)) * ...` with `e_cyp2b6_clf = 0.745` for R-methadone and `0.636` for S-methadone).
- **Notes:** Ratified canonically on 2026-07-24 alongside the Aruldhas 2021 methadone extraction. Fulfils the prospective-registration TODO recorded under `CYP3A4` for the drug-metabolizing-CYP set (`CYP1A2`, `CYP2A6`, `CYP2B6`, `CYP2C8`, `CYP2C9`, `CYP2C19`, `CYP2E1`, `CYP3A5`).

### SNP_ORM1_RS17650 (**canonical for ORM1 rs17650 active-allele count**)
- **Description:** Continuous individual-level *ORM1* rs17650 active-allele count: 0, 1, or 2 copies. The rs17650 SNP distinguishes the "F" (fast-migrating) and "S" (slow-migrating) allozymes of alpha-1 acid glycoprotein (AAG) encoded by *ORM1*. The variant F-phenotype allele binds methadone with lower affinity than the reference S phenotype (Aruldhas 2021 Discussion). Time-invariant (germline genotype). The paper uses the standard centered-additive form `V2 = V2_ref * (1 + Theta_rs17650 * (n_active - 1))` with `n_active` = number of active alleles at rs17650.
- **Units:** (count, 0/1/2 alleles per subject)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a (continuous). Aruldhas 2021 centers the covariate at n_active = 1 (heterozygous), so `V2_ref` is the typical central-compartment volume in a heterozygous subject at the reference AAG concentration.
- **Source aliases:**
  - `rs17650` / `number of active alleles rs17650` -- Aruldhas 2021 (paper Results 'Covariate modeling on R methadone' / 'Covariate modeling on S methadone' and Table 2).
- **Example models:** `Aruldhas_2021_R_methadone.R`, `Aruldhas_2021_S_methadone.R` (linear covariate on the central-compartment volume V2: `vc <- vc_ref * (1 + e_snp_orm1_rs17650_vc * (SNP_ORM1_RS17650 - 1)) * (1 + e_aag_vc * (AAG - 94.76))`; the effect is independent of and additive with the AAG-concentration effect on V2).
- **Notes:** Aruldhas 2021 reports that the addition of rs17650 to V2 improved the fit even after AAG concentration was already in the model (R-methadone dOFV = -6.5; S-methadone dOFV = -7.2), attributing this to differences in binding affinity between the F- and S- allozymes rather than to changes in AAG concentration. Rs1126801 (a companion SNP that further subdivides the F allele into F1 vs F2) was too rare in the Aruldhas 2021 study population (n = 2 subjects with the variant) to be included as a covariate. Scoped specific because rs17650 has only been reported as a covariate in the Aruldhas 2021 methadone analysis; future paediatric or adult popPK models of AAG-bound basic / lipophilic drugs may promote the scope to general. Ratified canonically on 2026-07-24 alongside the Aruldhas 2021 methadone extraction.

### SNP_CYP3A4_RS2246709 (**canonical for CYP3A4 rs2246709 active-allele count**)
- **Description:** Continuous individual-level *CYP3A4* rs2246709 active-allele count: 0, 1, or 2 copies. rs2246709 is an intronic *CYP3A4* variant that Aruldhas 2021 identified as significantly associated with reduced R- and S-methadone fractional clearance to EDDP after backward elimination. Time-invariant (germline genotype). The paper uses the standard centered-additive form `CLF = CLF_ref * (1 + Theta_rs2246709 * (n_active - 1))`.
- **Units:** (count, 0/1/2 alleles per subject)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a (continuous). Aruldhas 2021 centers the covariate at n_active = 1 (heterozygous), so `CLF_ref` is the typical fractional metabolite-formation clearance in a subject with heterozygous rs2246709 and CYP2B6 activity score 1.
- **Source aliases:**
  - `rs2246709` / `number of active alleles rs2246709` -- Aruldhas 2021 (paper Results 'Covariate modeling on R methadone' and Table 2).
- **Example models:** `Aruldhas_2021_R_methadone.R`, `Aruldhas_2021_S_methadone.R` (linear covariate on the fractional metabolite-formation clearance CLF: `clf <- clf_ref * (1 + e_cyp2b6_clf * (CYP2B6 - 1)) * (1 + e_snp_cyp3a4_rs2246709_clf * (SNP_CYP3A4_RS2246709 - 1))`; `e_snp_cyp3a4_rs2246709_clf = 0.450` for R-methadone (RSE 33.9%) and `1.68` for S-methadone (RSE 57.8%, retained as the final S-methadone model in preference to the initial rs11882424-based model because rs11882424 is less commonly genotyped in clinical laboratories)).
- **Notes:** Note that the very-large S-methadone coefficient (1.68) with wide RSE (57.8%) implies that the paper's model can produce a physically-invalid (negative) CLF for S-methadone at n_active = 0 (wild-type homozygous) subjects: `1 + 1.68 * (0 - 1) = -0.68`. This is a limitation of the paper's parameter estimates at the extreme of the covariate range, not of the extraction; the vignette Assumptions and deviations section documents that users simulating a wild-type-homozygous rs2246709 subject should either use the alternative rs11882424-based S-methadone model reported in the paper's Table 2 discussion or accept the paper's noted limitation. Distinct from the standard `SNP_<GENE>_RS<rsid>_<allele>_COUNT` naming convention because Aruldhas 2021 does not specify the wild-type vs variant allele letters at rs2246709 in the paper text; the column carries the paper's "number of active alleles" (which the paper treats as a symmetric additive count without labelling which allele is which). Scoped specific because rs2246709 has only been reported as a covariate in the Aruldhas 2021 methadone analysis. Ratified canonically on 2026-07-24 alongside the Aruldhas 2021 methadone extraction.

### CONMED_CYP3A4_INH (**canonical for concomitant CYP3A4 inhibitor coadministration indicator**)
- **Description:** 1 = subject coadministered any CYP3A4 inhibitor during the study, 0 = no concomitant CYP3A4 inhibitor. Distinct from the `CYP3A4` continuous-activity-score canonical above: `CONMED_CYP3A4_INH` captures concomitant-medication exposure (a drug-drug-interaction indicator), not intrinsic enzyme activity. Use this canonical when the source paper enters CYP3A4-inhibitor coadministration into the popPK model as a binary indicator, regardless of which inhibitor strengths (strong / moderate / weak) the paper pools into the `1` category.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no CYP3A4 inhibitor coadministration).
- **Source aliases:**
  - `CYP3A4_INH` -- prior canonical name (pre-2026-06-19 CONMED_ prefix standardization).
  - Other source-dataset column names typically: `CYP3AI`, `CYP3A4I`, `CYP3AINH`, or a free-text concomitant-medication indicator. Document the source-column name per-model in `covariateData[[CONMED_CYP3A4_INH]]$source_name`.
- **Example models:** `Yassen_2025_asundexian.R` (proportional-shift effect on CL/F: `(1 + e_cyp3a4_inh_cl * CONMED_CYP3A4_INH)` with `e_cyp3a4_inh_cl = -0.0531`; the asundexian dataset pools weak + moderate CYP3A4 inhibitors into the `CONMED_CYP3A4_INH = 1` category because strong inhibitors were a Phase II exclusion criterion).
- **Notes:** Per-model `covariateData[[CONMED_CYP3A4_INH]]$notes` must document which inhibitor strengths (strong / moderate / weak) and which specific drug examples are pooled into the `CONMED_CYP3A4_INH = 1` category, since inclusion criteria vary by study. Future models that need stratified encoding (separate strong / moderate / weak indicators) should register companion canonicals (e.g. `CONMED_CYP3A4_INH_STRONG`, `CONMED_CYP3A4_INH_MOD`, `CONMED_CYP3A4_INH_WEAK`) rather than overloading `CONMED_CYP3A4_INH`. The complementary CYP3A4-inducer indicator follows the same pattern as `CONMED_CYP3A4_IND`. Ratified canonically on 2026-05-08 alongside the Yassen 2025 asundexian extraction. Renamed from `CYP3A4_INH` to `CONMED_CYP3A4_INH` on 2026-06-19 per the canonical-register standardization audit (operator decision: the indicator captures a concomitant medication, so it belongs in the `CONMED_<concept>` family alongside `CONMED_PROBENECID`, `CONMED_AZOLE`, etc.).

### CONMED_CYP3A4_INH_HI (**canonical for concomitant CYP3A4 inhibitor (strong/moderate/weak) coadministration with high (>=50%) cumulative exposure during the on-treatment period**)
- **Description:** 1 = subject coadministered any CYP3A4 inhibitor classified as strong, moderate, or weak (FDA/EMA classification) for at least 50% of the on-study treatment period; 0 = otherwise. Companion canonical to `CONMED_CYP3A4_INH` that stratifies by cumulative exposure intensity (rather than by inhibitor strength). Distinct from `CONMED_CYP3A4_INH_LO` which captures the complementary "lower-exposure / unclassified" category. The two indicators are mutually exclusive per subject (a subject is either in CONMED_CYP3A4_INH_HI = 1, CONMED_CYP3A4_INH_LO = 1, or both = 0); when both are 0 the subject was either never on a CYP3A4 inhibitor or was on one only briefly.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no high-cumulative-exposure CYP3A4 inhibitor coadministration; subject may still have `CONMED_CYP3A4_INH_LO = 1`).
- **Source aliases:**
  - `CYPINH = 4` (strong, >=50%) or `CYPINH = 6` (moderate, >=50%) or `CYPINH = 8` (weak, >=50%) in the FIDELIO-DKD popPK NONMEM control stream's 9-level `CYPINH` categorical variable; used in `vandenBerg_2021_finerenone.R`.
- **Example models:** `vandenBerg_2021_finerenone.R` (multiplicative effect `0.951^CONMED_CYP3A4_INH_HI` on CL/F AND on F1 inversely, per paper's NONMEM control stream).
- **Notes:** Companion canonical to the broader `CONMED_CYP3A4_INH` (any inhibitor coadministration, no exposure-intensity stratification). The pooled strong/moderate/weak grouping at the >=50% exposure threshold matches the FIDELIO-DKD analysis's covariate-engineering choice (the study did not separate strong from moderate from weak inhibitors but did separate high-cumulative-exposure from lower-exposure). The accompanying inhibitor-strength-stratified canonicals `CONMED_CYP3A4_INH_STRONG` / `CONMED_CYP3A4_INH_MOD` / `CONMED_CYP3A4_INH_WEAK` mentioned in the `CONMED_CYP3A4_INH` Notes follow a different stratification axis (by strength, ignoring cumulative duration) and would coexist with the HI / LO duration-based pair without conflict.

### CONMED_CYP3A4_INH_LO (**canonical for concomitant CYP3A4 inhibitor coadministration in 'other' lower-exposure or unclassified category**)
- **Description:** 1 = subject coadministered a CYP3A4 inhibitor in any of the following sub-categories: (a) unclassified inhibitor (strength not assignable to the strong / moderate / weak FDA/EMA tiers) at any duration, OR (b) strong / moderate / weak inhibitor present for LESS THAN 50% of the on-study treatment period; 0 = otherwise. Companion canonical to `CONMED_CYP3A4_INH_HI` that captures all CYP3A4-inhibitor exposure other than the high-cumulative-exposure-and-known-strength category. The two indicators are mutually exclusive per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no lower-exposure / unclassified CYP3A4 inhibitor coadministration; subject may still have `CONMED_CYP3A4_INH_HI = 1`).
- **Source aliases:**
  - `CYPINH IN (1, 2, 3, 5, 7)` in the FIDELIO-DKD popPK NONMEM control stream's 9-level `CYPINH` categorical variable -- corresponding to unclassified <50% (1), unclassified >=50% (2), strong <50% (3), moderate <50% (5), weak <50% (7). Used in `vandenBerg_2021_finerenone.R`.
- **Example models:** `vandenBerg_2021_finerenone.R` (multiplicative effect `0.996^CONMED_CYP3A4_INH_LO` on CL/F AND on F1 inversely, per paper's NONMEM control stream).
- **Notes:** Companion to `CONMED_CYP3A4_INH_HI`. The unclassified-inhibitor category pools any concomitant medication the source dataset's medication-classification logic could not assign to a strong / moderate / weak tier (typically due to ambiguous drug-coding); these subjects would otherwise be lost to analysis if the encoding required a strength tier. Combining unclassified + <50%-exposure into a single "lower-impact" indicator was the FIDELIO-DKD analysis's choice; the effect size (0.996) is much smaller than the high-exposure category (0.951).

### CONMED_PGP_INH (**canonical for concomitant P-glycoprotein inhibitor coadministration indicator**)
- **Description:** 1 = subject coadministered a P-glycoprotein (P-gp / MDR1 / ABCB1) efflux-transporter inhibitor during the study, 0 = no concomitant P-gp inhibitor. P-gp inhibition raises the systemic exposure of P-gp substrates by reducing intestinal efflux (increasing oral bioavailability) and by reducing biliary and renal-tubular secretion (reducing apparent clearance). Use this canonical when the source paper enters P-gp-inhibitor coadministration into the popPK model as a binary indicator, regardless of which inhibitor potencies (potent / strong / moderate / weak) the paper pools into the `1` category.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no P-glycoprotein-inhibitor coadministration).
- **Source aliases:**
  - `DDICYPSH` -- used in `Mitra_2026_ziftomenib.R` (Kura Oncology KOMET-001 + KO-MEN-003 NONMEM control-stream column; strong CYP3A4 inhibitor coadministration during a ziftomenib dose record; dominant driver in the R/R AML cohort is prophylactic antifungal azole use).
- **Example models:** `Mitra_2026_ziftomenib.R` (multiplicative effects: 0.459x on parent ziftomenib CL/F, 0.195x on KO-739 CL, 0.449x on KO-516 CL when CONMED_CYP3A4_INH_STRONG = 1; encoded as additive shifts on the log(CL) scale per the paper's NONMEM PK block), `Kemal_2026_nemtabrutinib.R` (multiplicative effect on CL/F: `(1 + -0.0119 * CONMED_CYP3A4_INH_STRONG)`, i.e. 1.2% lower CL/F under strong CYP3A4 inhibition; RSE 774%, 95% CI includes zero -- retained in the full covariate model), `Wada_2023_sparsentan.R` (log-additive effect on CL/F: `exp(-1.069 * CONMED_CYP3A4_INH_STRONG)`, a 66% reduction in apparent clearance and a 191.3% increase in steady-state AUC -- the largest covariate effect in the model and the only one for which the paper suggests a dose adjustment may be warranted; paired with the sibling new canonical `CONMED_CYP3A4_INH_MOD` at -0.273), `Thoueille_2023_tenofovir_full.R`, `Thoueille_2023_tenofovir_alafenamide.R` (multiplicative fractional effect on apparent tenofovir clearance: `(1 + e_conmed_pgp_inh_cl * CONMED_PGP_INH)` with `e_conmed_pgp_inh_cl` = -0.121 and -0.116 respectively, i.e. ~12% lower CL/F, an effect the paper reports as independent of cobicistat coadministration).
- **Notes:** Companion canonical to `CONMED_CYP3A4_INH` (which pools any inhibitor strength into a single 0/1 indicator) and to `CONMED_CYP3A4_INH_HI` / `CONMED_CYP3A4_INH_LO` (which stratify by cumulative exposure duration rather than by inhibitor-strength category). Registered per the `CONMED_CYP3A4_INH` Notes explicit guidance: "Future models that need stratified encoding (separate strong / moderate / weak indicators) should register companion canonicals (e.g. `CONMED_CYP3A4_INH_STRONG`, `CONMED_CYP3A4_INH_MOD`, `CONMED_CYP3A4_INH_WEAK`) rather than overloading `CONMED_CYP3A4_INH`." Ratified canonically on 2026-07-24 alongside the Mitra 2026 ziftomenib extraction. Future extractions that need the sibling moderate- and weak-strength indicators should register `CONMED_CYP3A4_INH_MOD` and `CONMED_CYP3A4_INH_WEAK` following the same pattern.

### CONMED_CYP3A4_INH_MOD (**canonical for concomitant moderate CYP3A4 inhibitor coadministration indicator**)
- **Description:** 1 = subject / dose-record with concomitant coadministration of a moderate CYP3A4 inhibitor (FDA/EMA classification of "moderate": AUC increase >= 2-fold and < 5-fold for a sensitive CYP3A4 substrate; representative agents include cyclosporine, erythromycin, diltiazem, verapamil, fluconazole, and grapefruit juice), 0 = no moderate CYP3A4 inhibitor coadministration during the observation window (whether no CYP3A4 inhibitor at all, or a weak or strong inhibitor only). Time-varying per record. Strength-stratified companion to the broader `CONMED_CYP3A4_INH` and sibling to `CONMED_CYP3A4_INH_STRONG`; distinct from `CONMED_CYP3A4_INH_HI` / `CONMED_CYP3A4_INH_LO`, which stratify by cumulative exposure duration rather than by inhibitor-strength tier.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no moderate CYP3A4 inhibitor coadministration; a subject on a weak or strong inhibitor, or on none, falls here when using this indicator alone). When paired with `CONMED_CYP3A4_INH_STRONG`, both = 0 selects the pooled "no moderate or strong inhibitor" reference.
- **Source aliases:**
  - `CYP3A4 inhibitor` three- or four-level categorical column (`none` / `weak` / `moderate` / `strong`) -- the moderate level maps to `CONMED_CYP3A4_INH_MOD = 1`. Used in `Wada_2023_sparsentan.R`, where the source dataset's `CYP3A4 inhibitor` column has levels none / weak / moderate / strong and the paper estimates separate coefficients for the moderate and strong levels only, leaving weak pooled into the reference.
- **Example models:** `Wada_2023_sparsentan.R` (log-additive effect on CL/F: `exp(-0.273 * CONMED_CYP3A4_INH_MOD)`, i.e. 24% lower CL/F under moderate CYP3A4 inhibition, which the paper translates to a 31.4% increase in steady-state AUC and a 16.0% increase in Cmax; paired with `CONMED_CYP3A4_INH_STRONG` at -1.069; cohort exposure 10.5% moderate).
- **Notes:** Registered per the explicit forward guidance in the `CONMED_CYP3A4_INH` and `CONMED_CYP3A4_INH_STRONG` register entries ("Future extractions that need the sibling moderate- and weak-strength indicators should register `CONMED_CYP3A4_INH_MOD` and `CONMED_CYP3A4_INH_WEAK` following the same pattern"). Per-model `covariateData[[CONMED_CYP3A4_INH_MOD]]$notes` must document which specific agents the source paper classified as moderate and what happens to the weak stratum (pooled into the reference, or carried in a separate `CONMED_CYP3A4_INH_WEAK` indicator). The remaining sibling `CONMED_CYP3A4_INH_WEAK` is still reserved and unregistered. Ratified canonically alongside the Wada 2023 sparsentan extraction.

### CONMED_CYP3A4_IND (**canonical for concomitant CYP3A4 inducer coadministration indicator**)
- **Description:** 1 = subject coadministered any CYP3A4 inducer during the study, 0 = no concomitant CYP3A4 inducer. Sibling indicator to `CONMED_CYP3A4_INH`; both capture concomitant-medication exposure (a drug-drug-interaction indicator), not intrinsic enzyme activity (distinct from the `CYP3A4` continuous-activity-score canonical above). Use this canonical when the source paper enters CYP3A4-inducer coadministration into the popPK model as a binary indicator, regardless of which inducer strengths (strong / moderate / weak) the paper pools into the `1` category.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no CYP3A4 inducer coadministration).
- **Source aliases:**
  - `CYP3A4_IND` -- prior canonical name (pre-2026-06-19 CONMED_ prefix standardization).
  - Other source-dataset column names typically: `CYP3AIND`, `CYP3A4IND`, `INDU`, `INDUCER`, or a free-text concomitant-medication indicator. Document the source-column name per-model in `covariateData[[CONMED_CYP3A4_IND]]$source_name`.
- **Example models:** `Gupta_2016_lenvatinib.R` (multiplicative power-form effect on CL/F: `1.30^CONMED_CYP3A4_IND` with `e_cyp3a4_ind_cl = log(1.30) ~ 0.262`; the Gupta dataset pools any concomitant CYP3A4 inducer reported in the per-subject medication log into the `CONMED_CYP3A4_IND = 1` category, with `n = 19` (2.4%) of the 779-subject pooled cohort flagged positive).
- **Notes:** Per-model `covariateData[[CONMED_CYP3A4_IND]]$notes` must document which inducer strengths (strong / moderate / weak) and which specific drug examples are pooled into the `CONMED_CYP3A4_IND = 1` category, since inclusion criteria vary by study. Future models that need stratified encoding (separate strong / moderate / weak indicators) should register companion canonicals (e.g. `CONMED_CYP3A4_IND_STRONG`, `CONMED_CYP3A4_IND_MOD`, `CONMED_CYP3A4_IND_WEAK`) rather than overloading `CONMED_CYP3A4_IND`. Sibling canonical to `CONMED_CYP3A4_INH`. Ratified canonically alongside the Gupta 2016 lenvatinib extraction. Renamed from `CYP3A4_IND` to `CONMED_CYP3A4_IND` on 2026-06-19 per the canonical-register standardization audit (operator decision: the indicator captures a concomitant medication, so it belongs in the `CONMED_<concept>` family).

### CONMED_CYP3A4_IND_MOD (**canonical for concomitant moderate CYP3A4 inducer coadministration indicator**)
- **Description:** 1 = subject coadministered a moderate-strength CYP3A4 inducer (FDA / EMA classification) at the observation, 0 = no concomitant moderate CYP3A4 inducer. Strength-stratified companion to `CONMED_CYP3A4_IND` (pooled any-inducer indicator) and to the sibling canonicals `CONMED_CYP3A4_IND_STRONG` / `CONMED_CYP3A4_IND_WEAK` (reserved for future extractions using the same stratification convention). Use this canonical when the source paper enters moderate-strength CYP3A4 inducers as an isolated indicator rather than pooling them with strong or weak inducers.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant moderate CYP3A4 inducer; subjects on strong or weak inducers, or on no inducer, all fall here when using this indicator alone).
- **Source aliases:**
  - `C3A4INDM` -- used in `Kemal_2026_nemtabrutinib.R` (Kemal 2026 NONMEM control stream; time-varying regressor 1 = moderate CYP3A4 inducer at the observation, 0 otherwise. In the Kemal 2026 cohort no subjects received a strong inducer, so the source dataset enters only the moderate-inducer stratum into the model).
- **Example models:** `Kemal_2026_nemtabrutinib.R` (multiplicative effect on CL/F: `(1 + 0.0220 * CONMED_CYP3A4_IND_MOD)`, i.e. 2.2% higher CL/F under moderate CYP3A4 induction; RSE 458%, 95% CI includes zero -- retained in the full covariate model).
- **Notes:** Companion to the pooled `CONMED_CYP3A4_IND` (any inducer, no strength stratification). Per-model `covariateData[[CONMED_CYP3A4_IND_MOD]]$notes` should document which specific inducers the source paper classified as moderate (typically per FDA guidance: efavirenz, bosentan, etravirine, phenobarbital, modafinil, dabrafenib) and the fraction of the cohort exposed. Sibling canonicals `CONMED_CYP3A4_IND_STRONG` and `CONMED_CYP3A4_IND_WEAK` are reserved for parallel future extractions and are named in the `CONMED_CYP3A4_IND` register entry.

### APOE4_COUNT (**canonical for APOE-epsilon4 allele count**)
- **Description:** Continuous individual-level APOE-epsilon4 allele count: 0 = non-carrier, 1 = heterozygous (one epsilon4 allele), 2 = homozygous (two epsilon4 alleles). Time-invariant (germline genotype). Models in the Alzheimer's-disease-progression literature treat the 0 / 1 / 2 count as a continuous effect on baseline cognitive score and / or disease-progression slope, with the population-mean carrier-allele count used as the centring value (e.g., 0.72 in the Conrado 2014 CAMD cohort).
- **Units:** (count, 0 / 1 / 2 alleles per subject; population-mean centring value documented per-model)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). Models centre on the dataset / population mean APOE-epsilon4 count; document the centring value per-model in `covariateData[[APOE4_COUNT]]$notes`.
- **Source aliases:**
  - `APOE4C` -- used directly in `Conrado_2014_alzheimer.R`. The "C" suffix in the source distinguishes the cleaned continuous APOE-epsilon4 count column (0 / 1 / 2 with `unknown` recoded to the population mean) from the upstream raw `APOE4` column (0 = non-carrier, 1 = heterozygous, 2 = homozygous, 3 = unknown).
- **Example models:** `Conrado_2014_alzheimer.R` (centring 0.72; multiplicative effect on baseline ADAS-Cog and on disease-progression slope: `factor = 1 + e * (APOE4_COUNT - 0.72)` with `e_blapoe4 = 0.0372` on baseline and `e_slapoe4 = 0.195` on slope).
- **Notes:** APOE-epsilon4 carrier status is the strongest established genetic risk factor for late-onset Alzheimer's disease; the allele-count form (rather than a binary carrier indicator) is preferred when the source paper distinguishes heterozygous from homozygous carriers. Future models that report only a binary carrier indicator (any-epsilon4 vs none) should register a separate canonical (`APOE4_CARRIER`) rather than overloading `APOE4_COUNT`. The `unknown` category (often recorded as `APOE4 = 3` in CDISC datasets) is conventionally recoded by the source paper to the population-mean count to avoid dropping subjects; document the recoding rule used per-model. Ratified canonically on 2026-05-06 alongside the Conrado 2014 DDMORE extraction.

### NAT2_SLOW (**canonical for NAT2 slow-acetylator phenotype indicator**)
- **Description:** 1 = subject is an NAT2 (arylamine N-acetyltransferase 2) slow acetylator, defined by carrying two reduced-function NAT2 SNP alleles (homozygous variant for one or more of the canonical slow-acetylator SNPs rs1801279, rs1801280, rs1799930, rs1799931, OR heterozygous for two or more of those SNPs); 0 = subject is an intermediate or rapid acetylator (heterozygous for at most one of the canonical SNPs, or wild-type homozygous for all). Time-fixed per subject (germline genotype-derived phenotype). The intermediate and rapid (fast) phenotypes are pooled into the `0` category because the Horita 2018 source paper found no significant differences in t1/2, CL/F, or AUC0-8 between rapid and intermediate genotypes and combined them as the "nonslow" group; this pooling is the standard convention in the NAT2-aware antituberculosis-isoniazid popPK literature.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (nonslow: intermediate or rapid acetylator pooled). Reference category in the source-paper IIV model is the SLOW group; the canonical-orientation convention here is NAT2_SLOW = 1 for slow, consistent with the `_SLOW` naming, and the source-paper-reported coefficient signs map back to typical-value parameters via `(1 - NAT2_SLOW)` selection inside `model()` where needed.
- **Source aliases:**
  - `NAT2` (categorical with values `"slow"` / `"intermediate"` / `"rapid"` or `0` / `1` / `2`): derive `NAT2_SLOW = as.integer(NAT2 == "slow")` (or `as.integer(NAT2 == 0)` depending on the source's level coding); the intermediate and rapid levels collapse to NAT2_SLOW = 0.
  - `NAT2_SS` (slow-vs-not-slow indicator already in source datasets) -- same orientation as the canonical, no transformation.
  - `ACETYL_SLOW` (slow-acetylator indicator) -- same orientation as the canonical, no transformation.
- **Example models:** `Horita_2018_isoniazid.R` (selects between two typical-value clearances via `lcl_slow * NAT2_SLOW + lcl_nonslow * (1 - NAT2_SLOW)` and pairs each typical value with its own IIV variance; reproduces the source paper's separate `CL/F slow = 4.44 L/h` and `CL/F nonslow = 8.08 L/h` typical-value estimates with separate omegas 0.105 and 0.230 respectively).
- **Notes:** The NAT2 (rs1208 / rs1041983 / rs1801279 / rs1801280 / rs1799929 / rs1799930 / rs1799931 / rs1208) gene encodes the cytosolic arylamine N-acetyltransferase 2 enzyme responsible for the major isoniazid metabolic pathway (acetylation to acetyl-isoniazid); slow acetylators have substantially reduced isoniazid clearance, higher Cmax, and higher AUC than intermediate or rapid acetylators, with documented impact on both efficacy (treatment failure in rapid acetylators given standard doses) and toxicity (hepatotoxicity in slow acetylators given high doses). The slow / intermediate / rapid trimodal phenotype is conventionally collapsed to slow vs nonslow in popPK models when the cohort lacks enough rapid acetylators to identify a distinct rapid typical value, OR when the rapid and intermediate phenotypes are statistically indistinguishable in the data (Horita 2018 cohort: 51 slow / 50 intermediate / 12 fast). Future papers that distinguish rapid from intermediate (separately from slow) should register a paired companion canonical (`NAT2_RAPID`) so the three-level phenotype can be encoded with two binary indicators on the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` and `CYP3A5_STAR1_HET` / `CYP3A5_STAR1_HOM` patterns. The `_SLOW` orientation (slow = 1) follows the clinically meaningful axis (slow acetylators are the at-risk group for isoniazid hepatotoxicity and the higher-AUC group for treatment outcomes), paralleling the `CYP2D6_PM = 1` orientation for the poor-metabolizer end of the CYP2D6 phenotype spectrum. Distinct from any genotype-string column (which carries the raw allele information); `NAT2_SLOW` captures the derived metabolic phenotype only. Ratified canonically on 2026-05-26 alongside the Horita 2018 isoniazid extraction.

### FCGR3A_VV (**canonical for FCGR3A 158 V/V homozygote indicator**)
- **Description:** 1 = subject is homozygous for valine at amino-acid position 158 of the FcgammaRIIIa receptor (V/V), encoded by the rs396991 polymorphism in the FCGR3A gene; 0 = otherwise (heterozygote V/F or homozygote F/F pooled). The dominant V/V vs (V/F + F/F) grouping is the encoding used in the Aguiar 2021 source paper after testing dominant and recessive groupings during covariate model building.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 = V/F heterozygote or F/F homozygote (combined).
- **Source aliases:**
  - `FCGR3A` (genotype string, e.g., `"V/V"` / `"V/F"` / `"F/F"`): derive `FCGR3A_VV = as.integer(FCGR3A == "V/V")`.
  - `rs396991` (raw allele coding, often `"AA"` / `"AC"` / `"CC"` or `"GG"` / `"GT"` / `"TT"` depending on assay strand): map V allele -> 1, F allele -> 0 with the assay-specific allele convention; derive `FCGR3A_VV = as.integer(genotype is V-homozygous)`.
- **Example models:** `Aguiar_2021_ustekinumab.R` (Aguiar 2021 Table 2 footnote; logit-scale effect on subcutaneous bioavailability F: 88.8% in V/V vs 71.0% in V/F + F/F).
- **Notes:** rs396991 (FCGR3A 158V>F) is a well-studied pharmacogenetic polymorphism affecting FcgammaRIIIa-IgG affinity and has been associated with response to several IgG monoclonal antibodies (rituximab, infliximab, ustekinumab). The V allele is the higher-affinity variant. Document the assay-strand allele convention used in the source paper in `covariateData[[FCGR3A_VV]]$notes`. Future models that use a recessive (F/F vs V/* combined) or codominant (additive 0/1/2) coding should register a separate canonical (e.g., `FCGR3A_FF`, `FCGR3A_VCT` for V-allele count) rather than overloading `FCGR3A_VV`.

### DSBAL_TT (**canonical for DsbA-L (GSTK1) rs1917760 T/T genotype indicator**)
- **Description:** Indicator for the DsbA-L (GSTK1) rs1917760 -1308G>T T/T genotype; 1 = subject carries the T/T genotype, 0 = subject carries the G/G or G/T genotype (the pooled reference). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (G/G or G/T, pooled).
- **Source aliases:**
  - `DsbAL` (three-level column 0 = G/G, 1 = G/T, 2 = T/T) -- used in `Oniki_2018_bmi.R`; derive `DSBAL_TT = as.integer(DsbAL == 2)`. G/G and G/T are pooled because the T/T state is the functional minor-allele genotype most strongly associated with elevated BMI.
- **Example models:** `Oniki_2018_bmi.R` (additive +1.5 kg/m^2 shift on the typical BMI for T/T carriers vs the pooled G/G-or-G/T reference, Oniki 2018 Eq. 1).
- **Notes:** General scope because the genotype is a stable germline marker; the GSTK1 (DsbA-L) rs1917760 polymorphism has been associated with adiposity / metabolic phenotypes. Pools the G/G homozygous and G/T heterozygous strata into the reference following the dominant-for-the-minor-allele encoding used by Oniki 2018; future extractions that resolve a separate G/T heterozygote effect should register a paired `DSBAL_GT` indicator on the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` precedent. Ratified canonically alongside the Oniki 2018 BMI extraction.

### PNPLA3_CG (**canonical for PNPLA3 rs738409 C/G heterozygote indicator**)
- **Description:** Indicator for the PNPLA3 rs738409 c.444C>G (I148M) C/G heterozygote genotype; 1 = subject carries the C/G genotype, 0 = subject does not carry the C/G genotype. Paired with `PNPLA3_GG` to encode the three-level rs738409 genotype with two binary indicators (C/C is the reference when both are 0). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (C/C wild-type, when `PNPLA3_GG` is also 0).
- **Source aliases:**
  - `PNPLA3` (three-level column 0 = C/C, 1 = C/G, 2 = G/G) -- used in `Oniki_2018_nafld_risk.R`; derive `PNPLA3_CG = as.integer(PNPLA3 == 1)`.
- **Example models:** `Oniki_2018_nafld_risk.R` (multiplicative factor 0.761 on the (BMI50 - 17) half-saturation offset of the logit-of-NAFLD sigmoid for C/G heterozygotes vs the C/C reference, Oniki 2018 Eq. 4 / Figure 2c; closer to 1 than the G/G factor, consistent with an additive allele-dose effect).
- **Notes:** General scope because the genotype is a stable germline marker; PNPLA3 rs738409 (I148M) is the best-established common genetic risk variant for non-alcoholic fatty liver disease (NAFLD). Paired with `PNPLA3_GG` (homozygote) following the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` paired-binary precedent for a three-level genotype where each stratum carries a distinct typical-value effect. Ratified canonically alongside the Oniki 2018 NAFLD-risk extraction.

### PNPLA3_GG (**canonical for PNPLA3 rs738409 G/G homozygote indicator**)
- **Description:** Indicator for the PNPLA3 rs738409 c.444C>G (I148M) G/G homozygote genotype; 1 = subject carries the G/G genotype, 0 = subject does not carry the G/G genotype. Paired with `PNPLA3_CG` to encode the three-level rs738409 genotype with two binary indicators (C/C is the reference when both are 0). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (C/C wild-type, when `PNPLA3_CG` is also 0).
- **Source aliases:**
  - `PNPLA3` (three-level column 0 = C/C, 1 = C/G, 2 = G/G) -- used in `Oniki_2018_nafld_risk.R`; derive `PNPLA3_GG = as.integer(PNPLA3 == 2)`.
- **Example models:** `Oniki_2018_nafld_risk.R` (multiplicative factor 0.592 on the (BMI50 - 17) half-saturation offset of the logit-of-NAFLD sigmoid for G/G homozygotes vs the C/C reference, Oniki 2018 Eq. 4 / Figure 2c).
- **Notes:** General scope. Companion homozygote indicator to `PNPLA3_CG`; see `PNPLA3_CG` notes for the joint three-level usage and reference category. Ratified canonically alongside the Oniki 2018 NAFLD-risk extraction.

### UGT2B7_211GG (**canonical for UGT2B7 211G>T (rs7438135 / UGT2B7*2 Ala71Ser) homozygous G/G genotype indicator**)
- **Description:** 1 = subject is homozygous for the wild-type (ancestral) guanine at nucleotide 211 of the UGT2B7 gene, corresponding to alanine at amino-acid position 71 (Ala71/Ala71) in the substrate-binding N-terminal half of the UGT2B7 enzyme; 0 = otherwise (211GT heterozygote or 211TT homozygote). Encoded by the UGT2B7 211G>T single-nucleotide polymorphism (rs7438135, also referred to as the UGT2B7*2 variant). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-G/G genotype: 211GT or 211TT).
- **Source aliases:**
  - `UGT2B7 211GG` / `UGT2B7-211GG` / `211GG` -- used in `Yu_2017_mycophenolic_acid.R` (paper Table 5 column headers; one of three binary group-membership indicators reconstructed from the paper's ordinal column "UGT2B7 genotype").
- **Example models:** `Yu_2017_mycophenolic_acid.R` (one of three UGT2B7-211 binary indicators that reconstruct the paper's ordinal column `UGT2B7 genotype` in {GT=1, GG=2, TT=3}; the model() block computes `ugt2b7_211_code = UGT2B7_211GT * 1 + UGT2B7_211GG * 2 + UGT2B7_211TT * 3` and applies V1/F = 14.7 + 7.72 * ugt2b7_211_code per Yu 2017 Table 3 / page 1574 final-model formula).
- **Notes:** rs7438135 (UGT2B7 c.211G>T, p.Ala71Ser) is a coding-region SNP in the UGT2B7 N-terminal substrate-binding half; the T (Ser71) allele is the variant. Allele frequencies vary by ethnicity (the T allele is most common in Asian populations in Yu 2017's Chinese renal-transplant cohort: 51/101 GT + 8/101 TT = 59/101 PK evaluations carrying at least one T allele). Future papers that use a dominant (any-G vs T/T) or recessive (G/G vs any-T) grouping should still register the same three binary canonicals UGT2B7_211GG / UGT2B7_211GT / UGT2B7_211TT in the input data so that downstream simulations can reconstruct the paper's chosen grouping. Distinct from UGT1A9 promoter / 5'-UTR variants (Yu 2017 also genotyped UGT1A9*22 but did not find it significant; no UGT1A9 canonical is being proposed here). Ratified canonically on 2026-06-03 alongside the Yu 2017 mycophenolic acid extraction.


### UGT2B7_211GT (**canonical for UGT2B7 211G>T (rs7438135 / UGT2B7*2 Ala71Ser) heterozygous G/T genotype indicator**)
- **Description:** 1 = subject is heterozygous at nucleotide 211 of the UGT2B7 gene, carrying one wild-type G allele (Ala71) and one variant T allele (Ser71); 0 = otherwise (211GG homozygote or 211TT homozygote). Encoded by the UGT2B7 211G>T single-nucleotide polymorphism (rs7438135, also referred to as the UGT2B7*2 variant). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-G/T genotype: 211GG or 211TT).
- **Source aliases:**
  - `UGT2B7 211GT` / `UGT2B7-211GT` / `211GT` -- used in `Yu_2017_mycophenolic_acid.R` (paper Table 5).
- **Example models:** `Yu_2017_mycophenolic_acid.R` (one of three UGT2B7-211 binary indicators; see `UGT2B7_211GG` notes for the paper's ordinal-code reconstruction).
- **Notes:** Companion canonical to `UGT2B7_211GG` and `UGT2B7_211TT`; see `UGT2B7_211GG` notes for variant biology and allele-frequency context. Ratified canonically on 2026-06-03 alongside the Yu 2017 mycophenolic acid extraction.


### UGT2B7_211TT (**canonical for UGT2B7 211G>T (rs7438135 / UGT2B7*2 Ala71Ser) homozygous T/T genotype indicator**)
- **Description:** 1 = subject is homozygous for the variant thymine at nucleotide 211 of the UGT2B7 gene, corresponding to serine at amino-acid position 71 (Ser71/Ser71); 0 = otherwise (211GG homozygote or 211GT heterozygote). Encoded by the UGT2B7 211G>T single-nucleotide polymorphism (rs7438135, UGT2B7*2 variant). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-T/T genotype: 211GG or 211GT).
- **Source aliases:**
  - `UGT2B7 211TT` / `UGT2B7-211TT` / `211TT` -- used in `Yu_2017_mycophenolic_acid.R` (paper Table 5).
- **Example models:** `Yu_2017_mycophenolic_acid.R` (one of three UGT2B7-211 binary indicators; see `UGT2B7_211GG` notes for the paper's ordinal-code reconstruction).
- **Notes:** Companion canonical to `UGT2B7_211GG` and `UGT2B7_211GT`; see `UGT2B7_211GG` notes for variant biology and allele-frequency context. Ratified canonically on 2026-06-03 alongside the Yu 2017 mycophenolic acid extraction.


### UGT2B7_M161CC (**canonical for UGT2B7 -161C>T (rs7668258) homozygous C/C genotype indicator**)
- **Description:** 1 = subject is homozygous for the ancestral cytosine at nucleotide -161 of the UGT2B7 gene promoter (position numbered relative to the transcription start site; the leading `M` in the canonical name stands for `minus`); 0 = otherwise (M161CT heterozygote or M161TT homozygote). Encoded by the UGT2B7 -161C>T single-nucleotide polymorphism (rs7668258), a promoter-region variant in strong linkage disequilibrium with UGT2B7*2 (211G>T) in most populations. Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-C/C genotype: M161CT or M161TT).
- **Source aliases:**
  - `UGT2B7 -161CC` / `UGT2B7 -161 C/C` / `-161CC` -- used in `Milosheska_2016_lamotrigine.R` (paper Table 4 covariate equation reference stratum, listed as `CC` in the paper's Table 2 genotype-frequency column; the canonical prefixes `M` for `minus` because R identifiers cannot carry a leading hyphen).
- **Example models:** `Milosheska_2016_lamotrigine.R` (reference stratum for the three-indicator UGT2B7 -161C>T genotype categorical on parent lamotrigine apparent clearance; the two paired non-reference indicators are `UGT2B7_M161CT` and `UGT2B7_M161TT`).
- **Notes:** Distinct from the coding-region UGT2B7*2 variant registered as `UGT2B7_211GG / GT / TT` (rs7438135, Ala71Ser). The -161C>T promoter SNP is in strong linkage disequilibrium with 211G>T in European populations; carrying both position-level canonicals lets downstream models distinguish papers that genotype only one position from those that genotype both. Position notation follows the paper's convention (`c.-161C>T`) with the leading minus rewritten as the letter `M` for R-identifier compatibility, paralleling the ATC-style naming of promoter SNPs already registered under `SNP_VEGFA_RS1570360` (VEGFA -1154G>A) and `SNP_VEGFA_RS699947` (VEGFA -2578C>A). Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.


### UGT2B7_M161CT (**canonical for UGT2B7 -161C>T (rs7668258) heterozygous C/T genotype indicator**)
- **Description:** 1 = subject is heterozygous at nucleotide -161 of the UGT2B7 gene promoter, carrying one wild-type C allele and one variant T allele; 0 = otherwise (M161CC homozygote or M161TT homozygote). Encoded by the UGT2B7 -161C>T single-nucleotide polymorphism (rs7668258). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-C/T genotype: M161CC or M161TT). The reference used in effect estimation is the M161CC homozygous wild-type stratum, encoded when all three UGT2B7_M161* indicators equal 0.
- **Source aliases:**
  - `UGT2B7 -161CT` / `UGT2B7 -161 C/T` / `-161CT` -- used in `Milosheska_2016_lamotrigine.R` (paper Table 4 covariate equation `UGT2B7 -161CT vs CC` row).
- **Example models:** `Milosheska_2016_lamotrigine.R` (multiplicative effect on parent apparent clearance: `cl *= (1 - 0.0358 * UGT2B7_M161CT)`; heterozygotes have -3.6% lower CL relative to the CC homozygous wild-type reference, Milosheska 2016 Table 4 row `UGT2B7 -161C>T genotype CT vs CC`).
- **Notes:** Companion canonical to `UGT2B7_M161CC` and `UGT2B7_M161TT`. See `UGT2B7_M161CC` for variant biology and the M-for-minus naming rationale. Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.


### UGT2B7_M161TT (**canonical for UGT2B7 -161C>T (rs7668258) homozygous T/T genotype indicator**)
- **Description:** 1 = subject is homozygous for the variant thymine at nucleotide -161 of the UGT2B7 gene promoter; 0 = otherwise (M161CC homozygote or M161CT heterozygote). Encoded by the UGT2B7 -161C>T single-nucleotide polymorphism (rs7668258). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-T/T genotype: M161CC or M161CT). The reference used in effect estimation is the M161CC homozygous wild-type stratum, encoded when all three UGT2B7_M161* indicators equal 0.
- **Source aliases:**
  - `UGT2B7 -161TT` / `UGT2B7 -161 T/T` / `-161TT` -- used in `Milosheska_2016_lamotrigine.R` (paper Table 4 covariate equation `UGT2B7 -161TT vs CC` row).
- **Example models:** `Milosheska_2016_lamotrigine.R` (multiplicative effect on parent apparent clearance: `cl *= (1 - 0.204 * UGT2B7_M161TT)`; homozygous variant carriers have -20.4% lower CL relative to the CC homozygous wild-type reference, Milosheska 2016 Table 4 row `UGT2B7 -161C>T genotype TT vs CC`).
- **Notes:** Companion canonical to `UGT2B7_M161CC` and `UGT2B7_M161CT`. Reduced UGT2B7 transcriptional activity attributable to the -161 T allele is one of the biological interpretations offered by Milosheska 2016 Discussion paragraph 5 for the lower lamotrigine glucuronidation observed in TT carriers. Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.


### UGT2B7_372AA (**canonical for UGT2B7 372A>G (rs28365063) homozygous A/A genotype indicator**)
- **Description:** 1 = subject is homozygous for the ancestral adenine at nucleotide 372 of the UGT2B7 coding sequence (synonymous variant, His124His); 0 = otherwise (372AG heterozygote or 372GG homozygote). Encoded by the UGT2B7 372A>G single-nucleotide polymorphism (rs28365063). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-A/A genotype: 372AG or 372GG).
- **Source aliases:**
  - `UGT2B7 372AA` / `UGT2B7 372 A/A` / `372AA` -- used in `Milosheska_2016_lamotrigine.R` (paper Table 4 covariate equation reference stratum, listed as `AA` in the paper's Table 2 genotype-frequency column).
- **Example models:** `Milosheska_2016_lamotrigine.R` (reference stratum for the three-indicator UGT2B7 372A>G genotype categorical on parent lamotrigine apparent clearance; the two paired non-reference indicators are `UGT2B7_372AG` and `UGT2B7_372GG`).
- **Notes:** Distinct from the -161C>T promoter variant (`UGT2B7_M161*`) and the 211G>T coding variant (`UGT2B7_211*`). The 372A>G variant is a synonymous coding change but Milosheska 2016 identify a strong effect on parent lamotrigine apparent clearance (GG homozygotes have +117% higher CL vs AA reference), attributed in the Discussion to linkage with a functionally consequential 3' UTR variant. Following the position-based `UGT2B7_<position><genotype>` register precedent from `UGT2B7_211GG / GT / TT`. Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.


### UGT2B7_372AG (**canonical for UGT2B7 372A>G (rs28365063) heterozygous A/G genotype indicator**)
- **Description:** 1 = subject is heterozygous at nucleotide 372 of the UGT2B7 coding sequence, carrying one wild-type A allele and one variant G allele; 0 = otherwise (372AA homozygote or 372GG homozygote). Encoded by the UGT2B7 372A>G single-nucleotide polymorphism (rs28365063). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-A/G genotype: 372AA or 372GG). The reference used in effect estimation is the 372AA homozygous wild-type stratum, encoded when all three UGT2B7_372* indicators equal 0.
- **Source aliases:**
  - `UGT2B7 372AG` / `UGT2B7 372 A/G` / `372AG` -- used in `Milosheska_2016_lamotrigine.R` (paper Table 4 covariate equation `UGT2B7 372AG vs AA` row).
- **Example models:** `Milosheska_2016_lamotrigine.R` (multiplicative effect on parent apparent clearance: `cl *= (1 + 0.194 * UGT2B7_372AG)`; heterozygotes have +19.4% higher CL relative to the AA homozygous wild-type reference, Milosheska 2016 Table 4 row `UGT2B7 372 A > G genotype AG vs AA`).
- **Notes:** Companion canonical to `UGT2B7_372AA` and `UGT2B7_372GG`. See `UGT2B7_372AA` for variant biology. Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.


### UGT2B7_372GG (**canonical for UGT2B7 372A>G (rs28365063) homozygous G/G genotype indicator**)
- **Description:** 1 = subject is homozygous for the variant guanine at nucleotide 372 of the UGT2B7 coding sequence; 0 = otherwise (372AA homozygote or 372AG heterozygote). Encoded by the UGT2B7 372A>G single-nucleotide polymorphism (rs28365063). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (any non-G/G genotype: 372AA or 372AG). The reference used in effect estimation is the 372AA homozygous wild-type stratum, encoded when all three UGT2B7_372* indicators equal 0.
- **Source aliases:**
  - `UGT2B7 372GG` / `UGT2B7 372 G/G` / `372GG` -- used in `Milosheska_2016_lamotrigine.R` (paper Table 4 covariate equation `UGT2B7 372GG vs AA` row).
- **Example models:** `Milosheska_2016_lamotrigine.R` (multiplicative effect on parent apparent clearance: `cl *= (1 + 1.17 * UGT2B7_372GG)`; homozygous variant carriers have +117% higher CL relative to the AA homozygous wild-type reference, Milosheska 2016 Table 4 row `UGT2B7 372 A > G genotype GG vs AA`).
- **Notes:** Companion canonical to `UGT2B7_372AA` and `UGT2B7_372AG`. The magnitude of the GG effect is by far the largest single-variant effect on lamotrigine apparent clearance identified in Milosheska 2016; the GG homozygote frequency in the paper's Slovenian cohort was 2.0% (2 of 99 subjects), so per-model `covariateData[[UGT2B7_372GG]]$notes` should record the small-N caveat. Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.


## Immunogenicity

### ADA_POS (**canonical for anti-drug antibody positive status indicator**)
- **Description:** 1 = antidrug-antibody-positive, 0 = ADA-negative.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (ADA-negative).
- **Source aliases:**
  - `ADA` (semantically "ever positive") -- used in `Zhu_2017_lebrikizumab.R`. When translating from a paper that uses `ADA` as "ever positive," verify the time-frame matches ADA_POS semantics before renaming.
  - `ADA` (time-varying positivity, primary covariate in Xu 2019) -- used in `Xu_2019_sarilumab.R`.
  - `NAB` (neutralizing antibody positive -- used in `Petrov_2024_romiplostim.R`). Strictly a subset of total ADA-positive (ADA antibodies that neutralize the drug's biological effect). Document per-model when the source assay measured NAB only and the canonical column thus excludes binding-only ADA.
  - `ATAPOSNEW` (ADA-positive in the newer/updated-assay cohort) -- used in `Suri_2018_brentuximab.R` as the modern-assay arm of a two-era ADA decomposition.
  - `ADA_POSNEW` (**retired** intermediate name; renamed to `ADA_POS` on 2026-04-29 for consistency across single- and multi-assay models).
- **Example models:** `Clegg_2024_nirsevimab.R`, `Hu_2026_clesrovimab.R`, `Petrov_2024_romiplostim.R`, `Suri_2018_brentuximab.R` (multi-assay; paired with `ADA_POSOLD` and `ADA_MISSING`; `cl *= (1 + 0.125 * ADA_POS)`), `Xu_2019_sarilumab.R`.
- **Notes:** In single-assay studies this is a straightforward binary. In studies pooling data across assay generations (different sensitivity / drug-tolerance characteristics), `ADA_POS` represents modern/current-assay positivity; companion indicators `ADA_POSOLD` and `ADA_MISSING` capture historical-assay-positive and missing-result sub-groups respectively. All three are mutually exclusive; reference is ADA-negative (all three = 0).

### ADA_POSOLD (**canonical for ADA-positive in older-assay study indicator**)
- **Description:** 1 = subject is anti-drug-antibody-positive in a study that used the older lower-sensitivity, lower-drug-tolerance ADA assay; 0 = otherwise.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (ADA-negative or not in an older-assay study). Mutually exclusive with `ADA_POS` and `ADA_MISSING`.
- **Source aliases:**
  - `ATAPOSOLD` -- used in `Suri_2018_brentuximab.R`.
- **Example models:** `Suri_2018_brentuximab.R` (multiplicative additive effect on ADC clearance: `cl *= (1 + 0.177 * ADA_POSOLD)`).
- **Notes:** Companion to `ADA_POS` (multi-assay form); see that entry's Notes for the decomposition rationale. The "newer" vs "older" assay split is paper-specific (Suri 2018 newer assay: sensitivity 23.573 ng/mL, drug tolerance 25 ug/mL; older assay: sensitivity 4 ng/mL, drug tolerance 3,125 ng/mL). Time-varying once positive. Ratified canonically on 2026-04-28.

### ADA_MISSING (**canonical for ADA-result-missing indicator**)
- **Description:** 1 = ADA value is missing (subject did not have a measured ADA result, distinct from a measured negative); 0 = ADA result is reported (positive or negative).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (ADA result reported). Mutually exclusive with `ADA_POS` and `ADA_POSOLD`.
- **Source aliases:**
  - `ATAMISSING` -- used in `Suri_2018_brentuximab.R`.
- **Example models:** `Suri_2018_brentuximab.R` (multiplicative additive effect on ADC clearance: `cl *= (1 + 0.192 * ADA_MISSING)`).
- **Notes:** Used when a substantial fraction of the pooled cohort has no ADA measurement (Suri 2018: 205 of 380 patients) and the modeler retains ADA-missing as a separate level rather than collapsing missingness onto the ADA-negative reference. The non-zero positive estimate of `e_adam_adc_cl` indicates ADA-missing patients are not exchangeable with the ADA-negative reference -- interpret with caution given the missingness mechanism is not random. Ratified canonically on 2026-04-28.

### ADA_TITER (**canonical for continuous antidrug-antibody titer/titre**)
- **Description:** Continuous antidrug-antibody titer/titre (time-varying; matched in time to the PK sample). Covers both the British-spelling reciprocal-dilution convention (`ADA_TITRE`, with `ADA_TITRE = 1` for ADA-negative so `log_e(1) = 0` cancels a log-linear effect) and the American-spelling linear-titer convention (`ADA_TITER`, with `ADA_TITER = 0` for ADA-negative). The per-model `covariateData[[ADA_TITER]]$description` and `notes` must state which zero-encoding convention is in force so the covariate column cannot be misinterpreted.
- **Units:** Reciprocal dilution (e.g., 10, 20, 40, ..., 2560) OR assay units (log2 or arbitrary) -- document per-model in `covariateData[[ADA_TITER]]$units`.
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- ADA-negative encoded per-model (see zero-encoding note).
- **Source aliases:**
  - `ADA_TITRE` -- British spelling (reciprocal-dilution convention; `1` for negative).
  - `ADA titre` -- British spelling long form.
  - `ADAT` -- used in `Moein_2022_etrolizumab.R` (American linear-titer convention; `0` for negative).
- **Example models:** `Jackson_2022_ixekizumab.R` (reciprocal-dilution reference convention with `ADA_TITER = 1` for negatives and `(1 + coef * log_e(ADA_TITER))` on CL), `Moein_2022_etrolizumab.R` (linear-titer convention with `ADA_TITER = 0` for negatives and `exp(theta * ADA_TITER)` on CL, per-unit-titer theta = 0.0365), `Robbie_2012_palivizumab.R` (reciprocal-dilution values 0/10/20/40/>=80 with category-specific multiplicative effects per titer bin; 0 = ADA negative reference).
- **Notes:** The prior separate `ADA_TITRE` (British, `1` = negative) and `ADA_TITER` (American, `0` = negative) canonicals were merged on 2026-04-20 into a single general-scope `ADA_TITER`. The zero-encoding convention is the load-bearing semantic and must be documented per-model. Distinct from `ADA_POS` (binary presence/absence); when the paper reports both, the final model usually keeps only one. Imputation rules (LOCF / NOCB / baseline-as-negative) should be documented per-model.

## Disease / treatment history

**`TRTPH_<phase>` -- canonical family for protocol-defined treatment-phase / treatment-block indicators.** Mutually exclusive binary indicators identifying which named phase (block, course, element) of a multi-phase treatment protocol a record falls in. Time-varying per record. Intended for regimens whose protocol is divided into named blocks that differ materially in concomitant therapy, intensity, or physiological state -- most commonly pediatric and adult oncology protocols (ALL, AML, lymphoma), but the concept generalises to any multi-block regimen. Source papers normally report this as a single multi-level categorical covariate ("treatment phase", "protocol element", "treatment block") with one row per level in the parameter table; decompose it into one `TRTPH_<phase>` binary per non-reference level, following the register's decomposed-binary convention for multi-level categoricals (`ICU_ADM_*`, `RACE_*`, `TUMTP_*`). The protocol's induction (or first) phase is the reference category and is encoded as all indicators 0, so no `TRTPH_` column is registered for it.

Each model MUST document the protocol name and the phase-to-column mapping in `covariateData[[TRTPH_<phase>]]$notes`, because the same phase word ("maintenance", "intensification") means different things across protocols. A future paper using a different protocol registers its own members under this prefix; if two protocols use the same phase word with materially different therapy, qualify the member with the protocol (`TRTPH_ALL11_MAINT`) rather than overloading the bare name. Distinct from `TRT_PHASE`, which is a binary double-blind active-treatment-on / off gate for placebo-controlled PD models, NOT a multi-level protocol-block categorical. Distinct from `CYCLE` (an integer dose-number / cycle counter within a single regimen) and from `OCC` (a sampling-occasion index for inter-occasion variability). Distinct from the `CONMED_<INN>` family: a `TRTPH_<phase>` column deliberately stands in for the entire concomitant-chemotherapy block of that phase, which is the right encoding when the individual agents are co-administered and cannot be separated -- Kloos 2021 tested doxorubicin and methotrexate as individual covariates on top of the treatment phase and rejected both in backward elimination. Ratified canonically alongside the Kloos 2021 PEGasparaginase extraction (sidecar request-001 / response-001, question q3, option A).

### TRTPH_1B (**canonical for DCOG ALL-11 protocol 1B treatment-phase indicator**)
- **Description:** 1 = the record falls within protocol 1B of the Dutch Childhood Oncology Group ALL-11 acute-lymphoblastic-leukemia protocol; 0 = any other phase. Protocol 1B follows induction protocol 1A and comprises cyclophosphamide, cytarabine and 6-mercaptopurine plus a 1,500 IU/m^2 PEGasparaginase dose at day 40. Member of the `TRTPH_<phase>` family described above.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0, with all other `TRTPH_` indicators 0 = protocol 1A induction.
- **Source aliases:** `1B` -- Kloos 2021 Table 2 / Table 3 treatment-phase row label.
- **Example models:** `Kloos_2021_pegasparaginase.R` (clearance effect FIXED to 1, i.e. pooled with the 1A reference, because only two patients were treated as high risk and the effect was not estimable reliably; Kloos 2021 Table 2 prints "1 (fix)").
- **Notes:** A fixed-to-reference member is still worth registering: dropping the column would silently erase the fact that the authors tested the phase and could not estimate it. See the family preamble above for the protocol-documentation requirement.

### TRTPH_M (**canonical for DCOG ALL-11 protocol M treatment-phase indicator**)
- **Description:** 1 = the record falls within protocol M of the DCOG ALL-11 protocol (6-mercaptopurine plus high-dose methotrexate, 5,000 mg/m^2/dose at days 8, 22, 36 and 50); 0 = any other phase. Member of the `TRTPH_<phase>` family described above.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0, with all other `TRTPH_` indicators 0 = protocol 1A induction.
- **Source aliases:** `M` -- Kloos 2021 Table 2 / Table 3 treatment-phase row label.
- **Example models:** `Kloos_2021_pegasparaginase.R` (multiplicative on CL: 0.87, RSE 5.2 percent, bootstrap 95 percent CI 0.80-0.95).

### TRTPH_MR_INTENS (**canonical for DCOG ALL-11 medium-risk-group intensification treatment-phase indicator**)
- **Description:** 1 = the record falls within the medium-risk-group intensification phase of the DCOG ALL-11 protocol (dexamethasone, vincristine, 6-mercaptopurine, plus doxorubicin in TEL/AML1-negative patients or methotrexate in TEL/AML1-positive patients); 0 = any other phase. Member of the `TRTPH_<phase>` family described above.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0, with all other `TRTPH_` indicators 0 = protocol 1A induction.
- **Source aliases:** `MRG intens.` / `MR intensification` -- Kloos 2021 Table 2 / Table 3 treatment-phase row labels.
- **Example models:** `Kloos_2021_pegasparaginase.R` (multiplicative on CL: 0.89, RSE 5.2 percent, bootstrap 95 percent CI 0.82-0.98).

### TRTPH_MR_MAINT (**canonical for DCOG ALL-11 medium-risk-group maintenance treatment-phase indicator**)
- **Description:** 1 = the record falls within the medium-risk-group maintenance phase of the DCOG ALL-11 protocol (dexamethasone, vincristine, methotrexate and 6-mercaptopurine); 0 = any other phase. Member of the `TRTPH_<phase>` family described above.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0, with all other `TRTPH_` indicators 0 = protocol 1A induction.
- **Source aliases:** `MRG maint.` / `MR maintenance` -- Kloos 2021 Table 2 / Table 3 treatment-phase row labels.
- **Example models:** `Kloos_2021_pegasparaginase.R` (multiplicative on CL: 0.81, RSE 3.9 percent, bootstrap 95 percent CI 0.75-0.86).

### TRTPH_SR_IV (**canonical for DCOG ALL-11 standard-risk-group protocol IV treatment-phase indicator**)
- **Description:** 1 = the record falls within protocol IV of the standard-risk arm of the DCOG ALL-11 protocol (dexamethasone and vincristine plus a single individualized PEGasparaginase dose at day 1); 0 = any other phase. Member of the `TRTPH_<phase>` family described above.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0, with all other `TRTPH_` indicators 0 = protocol 1A induction.
- **Source aliases:** `SRG protocol IV` / `SR protocol IV` -- Kloos 2021 Table 2 / Table 3 treatment-phase row labels.
- **Example models:** `Kloos_2021_pegasparaginase.R` (multiplicative on CL: 0.81, RSE 6.5 percent, bootstrap 95 percent CI 0.73-0.90).


### HCT_COND_RIC (**canonical for reduced-intensity conditioning regimen indicator**)
- **Description:** 1 = subject received reduced-intensity conditioning (RIC) chemotherapy prior to allogeneic hematopoietic cell transplantation, 0 = subject received myeloablative conditioning (MAC). Conditioning intensity is fixed per subject for the analysis window (the conditioning regimen was completed before transplantation, before any of the post-transplant tacrolimus PK observations). RIC regimens use lower-dose chemotherapy / radiotherapy to preserve some host haematopoiesis and rely on graft-versus-tumour effect for cytoreduction; MAC regimens deliver high-dose chemotherapy and / or total-body irradiation that fully ablates host marrow.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (myeloablative conditioning, MAC).
- **Source aliases:**
  - `RIC` (Dunlap 2025 NM-TRAN convention; binary 0 / 1) -- used directly in `Dunlap_2025_tacrolimus.R`.
- **Example models:** `Dunlap_2025_tacrolimus.R` (Dunlap 2025 Table 2 reduced-covariate-model column; exponential effect on apparent oral clearance: `cl *= 0.63 ^ HCT_COND_RIC`, so RIC recipients have ~37% lower apparent oral tacrolimus clearance than MAC recipients).
- **Notes:** Conditioning regimen intensity has been reported to associate with post-transplant tacrolimus apparent clearance, likely via gut / hepatic CYP3A activity, GVHD-related inflammatory response, and post-transplant haematopoietic state. The paper-specific definition of "RIC" follows the source publication's own classification (e.g., Dunlap 2025 follows the institutional protocol at UNCMC, which pools non-myeloablative conditioning regimens into the RIC category when assigning the binary indicator); document the source paper's RIC criteria in `covariateData[[HCT_COND_RIC]]$notes`. When a future paper distinguishes a third intensity tier (non-myeloablative, NMA) as a separate covariate level rather than pooling NMA into RIC, register a parallel canonical (e.g. `HCT_COND_NMA`) instead of overloading `HCT_COND_RIC`. Scope: specific because the column is meaningful only for allo-HCT recipients. Ratified canonically on 2026-05-09 alongside the Dunlap 2025 tacrolimus extraction.

### AGVHD_LIVER (**canonical for acute graft-versus-host disease -- liver involvement indicator**)
- **Description:** 1 = subject has documented evidence of acute graft-versus-host disease (aGvHD) involving the liver (any grade, I-IV) at the current model time; 0 = no documented liver aGvHD. Time-varying per subject in allo-HSCT cohorts: 0 before the first documented liver-aGvHD diagnosis and 1 from that time onward for the modeled observation window. Interpolation convention (Waterhouse 2024 Methods): next observation carried backward (NOCB), i.e., a subject with a positive liver-aGvHD diagnosis at any time during the study has `AGVHD_LIVER = 1` from study start through the diagnosis time; use last-observation-carried-forward (LOCF) instead if the source paper documents that convention.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no documented liver aGvHD; the pooled reference includes both subjects who never developed liver aGvHD and time periods before diagnosis in subjects who eventually did).
- **Source aliases:**
  - `LIVGVHD` / `GVHD_LIVER` -- Waterhouse 2024 NM-TRAN column for the time-varying liver-aGvHD indicator. Same orientation, no value transformation.
- **Example models:** `Waterhouse_2024_vedolizumab.R` (multiplicative power-form effect on CL: `cl *= 1.05^AGVHD_LIVER`, i.e., liver-aGvHD subjects show ~5% higher CL than the reference at the diagnosis time and thereafter; the paper's Table 2 reports the estimate as `1.05 (0.834, 1.26)` with 10.3% RSE, and the Results paragraph 3 concludes the 90% CI crosses 1 so the effect is not clinically meaningful).
- **Notes:** Sibling to `AGVHD_SKIN` and `AGVHD_INTESTINE` -- a patient can have more than one organ involvement simultaneously (Table 1 footnote in Waterhouse 2024: "a patient could exhibit one or more types of GvHD"), so the three indicators are orthogonal binary covariates rather than mutually-exclusive levels of a categorical. Time-varying with the diagnosis-time boundary; supply per subject at every observation timestamp in the event dataset. Specific scope because the column is meaningful only for allo-HSCT cohorts. Distinct from `TX_LIVER` (liver-transplant recipient indicator, a solid-organ-transplant-cohort covariate). Future extractions distinguishing chronic-vs-acute GvHD should register sibling canonicals (e.g., `CGVHD_LIVER`) rather than overloading this name. Ratified canonically on 2026-07-25 alongside the Waterhouse 2024 vedolizumab extraction.

### AGVHD_SKIN (**canonical for acute graft-versus-host disease -- skin involvement indicator**)
- **Description:** 1 = subject has documented evidence of acute graft-versus-host disease (aGvHD) involving the skin (any grade, I-IV) at the current model time; 0 = no documented skin aGvHD. Time-varying per subject in allo-HSCT cohorts using the same NOCB interpolation convention as `AGVHD_LIVER`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no documented skin aGvHD).
- **Source aliases:**
  - `SKGVHD` / `GVHD_SKIN` -- Waterhouse 2024 NM-TRAN column for the time-varying skin-aGvHD indicator.
- **Example models:** `Waterhouse_2024_vedolizumab.R` (multiplicative power-form effect on CL: `cl *= 1.03^AGVHD_SKIN`; Waterhouse 2024 Table 2 reports `1.03 (0.940, 1.12)` with 4.56% RSE; the 90% CI crosses 1 so the effect is not clinically meaningful).
- **Notes:** Sibling to `AGVHD_LIVER` and `AGVHD_INTESTINE`; see the `AGVHD_LIVER` entry for the shared multi-organ / orthogonal-indicator rationale. Ratified canonically on 2026-07-25 alongside the Waterhouse 2024 vedolizumab extraction.

### AGVHD_INTESTINE (**canonical for acute graft-versus-host disease -- intestinal involvement indicator**)
- **Description:** 1 = subject has documented evidence of acute graft-versus-host disease (aGvHD) involving the intestine / gastrointestinal tract (any grade, I-IV) at the current model time; 0 = no documented intestinal aGvHD. Time-varying per subject in allo-HSCT cohorts using the same NOCB interpolation convention as `AGVHD_LIVER`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no documented intestinal aGvHD).
- **Source aliases:**
  - `INTGVHD` / `GVHD_INTESTINE` / `GVHD_GI` -- Waterhouse 2024 NM-TRAN column for the time-varying intestinal-aGvHD indicator.
- **Example models:** `Waterhouse_2024_vedolizumab.R` (multiplicative power-form effect on CL: `cl *= 1.07^AGVHD_INTESTINE`; Waterhouse 2024 Table 2 reports `1.07 (0.870, 1.27)` with 9.54% RSE; the 90% CI crosses 1 so the effect is not clinically meaningful).
- **Notes:** Sibling to `AGVHD_LIVER` and `AGVHD_SKIN`; see the `AGVHD_LIVER` entry for the shared multi-organ / orthogonal-indicator rationale. Vedolizumab's mechanism (integrin alpha-4 beta-7 blockade) specifically targets gut-homing leukocyte trafficking, so `AGVHD_INTESTINE` is the mechanistically-motivated on-target-organ indicator in vedolizumab-for-GvHD-prophylaxis popPK studies; the paper notes the observed effect (~7% increase in CL at intestinal-aGvHD onset) is consistent with target-mediated drug disposition at the on-target site but the estimate is not statistically or clinically significant in this dataset. Ratified canonically on 2026-07-25 alongside the Waterhouse 2024 vedolizumab extraction.

### DISEXT_EP (**canonical for extensive colitis / pancolitis indicator**)
- **Description:** 1 = extensive colitis or pancolitis disease extension, 0 = otherwise (any non-extensive disease extension, e.g. left-sided colitis or proctitis).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0. In papers that decompose the disease-extension categorical into both `DISEXT_EP` and `DISEXT_OTHER`, `DISEXT_EP = 0 AND DISEXT_OTHER = 0` corresponds to the left-sided-colitis reference group; in papers that use a single binary indicator for extensive colitis, the reference is pooled non-extensive (left-sided + any other extension).
- **Source aliases:**
  - `EXTCOL` -- used in `Faelens_2021_infliximab.R` (binary 0/1 for extensive colitis at baseline; no separate "other" category).
  - Derived from a multi-level `DISEXT` column in the source (levels: left-sided colitis, extensive/pancolitis, other): `DISEXT_EP = as.integer(DISEXT == "extensive/pancolitis")`.
- **Example models:** `Moein_2022_etrolizumab.R` (paired with `DISEXT_OTHER`; multiplicative effect on CL, +8.2% vs. left-sided colitis), `Faelens_2021_infliximab.R` (single-binary encoding; multiplicative fold-change on V of 1.25 when DISEXT_EP = 1).
- **Notes:** Optionally paired with `DISEXT_OTHER` when the source paper decomposes a three-level disease-extension categorical (left-sided / extensive-pancolitis / other) into two indicators; the pairing is paper-specific and not required. Promoted from scope: specific to scope: general on 2026-04-27 because the binary "extensive colitis vs not" semantics generalize across UC popPK papers regardless of whether the original dataset additionally distinguished an "other" disease-extension category.

### DISEXT_OTHER (**canonical for 'other disease extension' indicator**)
- **Description:** 1 = disease extension other than left-sided colitis or extensive/pancolitis, 0 = not.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (left-sided colitis, when paired with `DISEXT_EP = 0`).
- **Source aliases:** Derived from a multi-level `DISEXT` column: `DISEXT_OTHER = as.integer(DISEXT == "other")`.
- **Example models:** `Moein_2022_etrolizumab.R` (multiplicative effect on CL, +18% vs. left-sided colitis; large uncertainty due to 2% prevalence).
- **Notes:** Paired with `DISEXT_EP`; together they encode the three-level disease-extension categorical.

### PRIOR_TAXANE (**canonical for binary prior-taxane chemotherapy indicator**)
- **Description:** 1 = subject received any prior taxane regimen (docetaxel, paclitaxel, cabazitaxel, etc.) before study entry, 0 = taxane-naive. Time-invariant within a subject (records treatment history at baseline). Oncology-pretreatment indicator: relevant for cohorts in which prior taxane exposure plausibly alters disease biology (e.g., advanced / castration-resistant prostate cancer, where taxane pretreatment is associated with more advanced disease and selects for taxane-resistant clones).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (taxane-naive).
- **Source aliases:**
  - `PTAX` -- used in `vanHasselt_2015_eribulin.R` (van Hasselt 2015 paper notation; binary 0 / 1 for prior docetaxel pretreatment).
- **Example models:** `vanHasselt_2015_eribulin.R` (multiplicative effect on baseline serum PSA: `psa0 = exp(lpsa0 + etalpsa0) * e_prior_taxane_psa0^PRIOR_TAXANE` with `e_prior_taxane_psa0 = 3.23` -- prior-taxane patients have ~3.2x higher baseline PSA than taxane-naive patients, consistent with more advanced disease at study entry).
- **Notes:** Pairs with `PRIOR_TAXANE_DAYS` when a continuous duration-of-pretreatment is also relevant (e.g., van Hasselt 2015 uses both: PTAX on PSA0 and NTRT on KD). Distinct from `CONMED_*` (which is concomitant medication during the study, not pretreatment history) and from generic `PRIOR_*` chemotherapy indicators (which would warrant a separate canonical when a paper differentiates by drug class rather than collapsing to taxanes). Scope: specific because the population semantics (CRPC) and the "any prior taxane" pooling are tied to van Hasselt 2015; future papers that distinguish per-drug pretreatment (docetaxel vs paclitaxel vs cabazitaxel separately) should register parallel canonicals rather than overloading `PRIOR_TAXANE`.

### PRIOR_TAXANE_DAYS (**canonical for cumulative days of prior taxane treatment**)
- **Description:** Cumulative number of days of prior taxane chemotherapy at study entry. 0 for taxane-naive patients (i.e., for any subject with `PRIOR_TAXANE = 0`). Time-invariant within a subject (records the pretreatment history at baseline).
- **Units:** days
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- normalised by a population reference value (van Hasselt 2015 uses the population median of 720 days among prior-taxane-pretreated patients) and entered as a power covariate `(1 + PRIOR_TAXANE_DAYS / 720) ^ e_prior_taxane_days_<param>`. The `+1` inside the bracket makes the covariate effect collapse to a multiplier of 1 for taxane-naive patients (PRIOR_TAXANE_DAYS = 0) regardless of the estimated exponent.
- **Source aliases:**
  - `NTRT` -- used in `vanHasselt_2015_eribulin.R` (van Hasselt 2015 paper notation; cumulative number of days of prior taxane treatment).
- **Example models:** `vanHasselt_2015_eribulin.R` (van Hasselt 2015 Eq. 4 power covariate on drug PSA inhibition rate KD0: `kd0 = exp(lkd0 + etalkd0) * (1 + PRIOR_TAXANE_DAYS / 720)^e_prior_taxane_days_kd0` with `e_prior_taxane_days_kd0 = -4.00` -- KD0 decreases with longer prior-taxane exposure, encoding cross-resistance between docetaxel and eribulin via the shared microtubule-inhibition mechanism).
- **Notes:** Pairs with `PRIOR_TAXANE` (binary). The paper also considered an alternative continuous parameterisation in cycles of prior taxane (`NCYCL`, median 30 cycles) which was deemed slightly less informative (dOFV = -8 for NCYCL vs -10 for NTRT) and was not retained in the final model. If a future model needs the cycle-count form, register a parallel canonical (e.g. `PRIOR_TAXANE_CYCLES`) rather than overloading this one. Scope: specific because the 720-day normalisation reference is tied to the van Hasselt 2015 study population (post-docetaxel mCRPC patients).

### T_DIAG_DIAB (**canonical for time since type 2 diabetes diagnosis**)
- **Description:** Time elapsed since clinical diagnosis of type 2 diabetes mellitus (T2DM) at study entry, in years. Continuous time-fixed covariate; supply the per-subject duration in years. T2DM-specific (Type 1 cohorts use a separate `T_DIAG_T1D` canonical if registered in the future).
- **Units:** years
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- enters as a power covariate `(T_DIAG_DIAB / 2)^theta` on the model-predicted baseline FPG, on Gmax (the maximal SGLT2-inhibition-driven FPG-reduction), and on kHbA1cout. Reference value 2 years per Baron 2016 Tables S3 and S4.
- **Source aliases:**
  - `DUR` -- used in `Baron_2016_empagliflozin.R` (Baron 2016 Methods column "duration of diabetes"; PK/PD dataset categorisation reported as <1 year / 1-5 years / >5 years in Table S2 with 58.5% of patients in the >5 years group).
- **Example models:** `Baron_2016_empagliflozin.R` (power-form effects centred at 2 years on BFPG: `(T_DIAG_DIAB / 2)^0.0512`, on Gmax: `(T_DIAG_DIAB / 2)^0.0117`, on kHbA1cout: `(T_DIAG_DIAB / 2)^-0.577`).
- **Notes:** Disease-progression marker complementary to the binary `DIS_DIAB` indicator -- `DIS_DIAB` records the comorbidity at baseline (1/0) while `T_DIAG_DIAB` quantifies how long that diabetes has been present. The covariate is a time-since-event under the canonical `T_<event>` family. Distinct from `T_NUT_SUPP` (time on nutritional supplementation) and `T_POST_ECMO` (time after ECMO decannulation) which are different `T_<event>` siblings. For a baseline-only continuous power-form effect, a 0-year subject (newly diagnosed at study entry) yields `(0/2)^theta = 0` -- supply at least a small floor value (e.g. 0.1 year) for newly-diagnosed subjects in simulation; the floor convention should be documented in the per-model `covariateData[[T_DIAG_DIAB]]$notes`. Ratified canonically on 2026-06-24 alongside the Baron 2016 empagliflozin extraction.

### T_DIAG_CANCER (**canonical for time since primary cancer diagnosis**)
- **Description:** Time elapsed between the patient's primary cancer diagnosis (histopathology / imaging confirmation date recorded in the clinical database) and study entry / re-baseline, in days. Continuous time-fixed covariate; supply the per-subject duration in days. Cancer-nonspecific (gastric, GEJC, NSCLC, breast, etc. all use this same canonical). Distinct from `T_DIAG_DIAB` (T2DM-specific; different disease and different reference form).
- **Units:** days
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- entered as a power covariate `(T_DIAG_CANCER / ref)^exponent` on structural parameters. Reference values observed: 53 days (Terranova 2022 avelumab arm median at randomization for the JAVELIN Gastric 100 gastric-cancer cohort, per Supplementary Methods).
- **Source aliases:**
  - `Tdiag` -- used in `Terranova_2022_TGD_OS_gastric.R` (Terranova 2022 Supplementary Methods equation form `(Tdiag/53)^theta5` on the Gompertz tumor-growth rate constant `Kg`).
  - `Log time since diagnosis` -- Terranova 2022 Table S2 OS TTE coefficient label; enters on `log(median OS)` via `log(T_DIAG_CANCER) * beta`.
- **Example models:** `Terranova_2022_TGD_OS_gastric.R` (power effect on Gompertzian `Kg`: `Kg = tvKg * (T_DIAG_CANCER / 53)^-0.00291`, reference 53 days; and additive linear-log effect on log-median OS: `log_median_OS += 0.0436 * log(T_DIAG_CANCER)`).
- **Notes:** Sibling of `T_DIAG_DIAB` under the canonical `T_<event>` family. Auto-approved without a naming sidecar per the T_<event> policy on 2026-07-24 (agcand_13066655 request-001 q1=A). For a 0-day subject (diagnosed at study entry) the power form `(0/53)^theta` evaluates to `0` when the exponent is positive, which is a boundary case; supply at least a small floor value (e.g. 1 day) for such subjects in simulation. Distinct from post-treatment intervals (`T_POST_ECMO`, etc.) which are time-since-intervention rather than time-since-diagnosis.

### BL_PN_GR1 (**canonical for active baseline grade 1 peripheral neuropathy indicator**)
- **Description:** 1 = subject had active grade 1 peripheral neuropathy (CTCAE 4.0 grade 1: asymptomatic; clinical or diagnostic observations only; intervention not indicated) at study entry; 0 = no active PN at baseline. Time-fixed at study entry. The covariate captures pre-existing low-grade PN typically arising from prior antimicrotubule or platinum chemotherapy in heavily pretreated oncology cohorts and is used to assess whether baseline PN sensitizes patients to subsequent antimicrotubule-induced PN.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no active PN at baseline).
- **Source aliases:**
  - "active grade 1 PN at study entry (yes/no)" (Lu 2017 narrative; coded as a binary 0/1 in the source NONMEM dataset) -- used in `Lu_2017_polatuzumab_neuropathy.R`.
- **Example models:** `Lu_2017_polatuzumab_neuropathy.R` (multiplicative effect on the grade >= 2 peripheral neuropathy hazard via `exp(theta_baselinePN * BL_PN_GR1)` with `theta_baselinePN = -0.222`, SE 0.324 -- not detectable given the large SE; the paper reports a sensitivity analysis in which this indicator was replaced by a broader "history of prior PN" indicator with similarly inconclusive results).
- **Notes:** Specific to oncology cohorts where grade 1 PN is permitted at study entry (per the source paper's eligibility criteria). Distinct from `PREV_AE_SCORE` (the time-varying ordinal Markov-state AE-score covariate from Girard 2012 pimasertib, which conditions a current-observation outcome on the previous observation's grade); `BL_PN_GR1` is a time-fixed baseline indicator that does not update during the analysis window. Distinct from `DIS_DPN` (painful diabetic peripheral polyneuropathy disease-state indicator from chronic-pain cohorts); `DIS_DPN` flags a diabetes-related primary diagnosis used as a stratification covariate, while `BL_PN_GR1` flags a pre-existing low-grade adverse-event status from any prior cause in an oncology trial. When a future paper uses a different baseline-PN grade threshold (e.g., grade <= 2 permitted) or a different AE category, register a parallel canonical (e.g., `BL_PN_GR2`, `BL_FATIGUE_GR1`) rather than overloading this one. Ratified canonically on 2026-06-24 alongside the Lu 2017 polatuzumab vedotin TTE extraction.

## Hypercholesterolemia biomarkers

### PCSK9 (**canonical for baseline unbound serum PCSK9 concentration**)
- **Description:** Baseline unbound serum proprotein convertase subtilisin/kexin type 9 (PCSK9) concentration.
- **Units:** ng/mL (document per-model in `covariateData[[PCSK9]]$units` if a different unit -- typically nM -- is used in a given model; conversion uses a PCSK9 molecular weight of ~72 kDa, so 1 nM ~= 72 ng/mL).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(PCSK9 / ref)^exponent`. Reference values observed: 425 ng/mL (= 5.9 nM) in `Kuchimanchi_2018_evolocumab.R` (population median).
- **Source aliases:** none known.
- **Example models:** `Kuchimanchi_2018_evolocumab.R` (power exponent 0.194 on Vmax: `Vmax * (PCSK9/425)^0.194`).
- **Notes:** PCSK9 is the pharmacological target of anti-PCSK9 monoclonal antibodies (evolocumab, alirocumab, etc.); baseline PCSK9 drives the magnitude of target-mediated elimination and is a recurring covariate in anti-PCSK9 popPK models. Baseline (time-fixed) covariate; patients with missing baseline PCSK9 are typically excluded from analyses that include PCSK9 as a covariate.

## Pharmacogenomic SNPs

**Canonical pattern: `SNP_<GENE>_<RSID>`.** Use one binary indicator per SNP genotype that the source paper tests as a model covariate. The `SNP_` prefix makes the category unambiguous; the gene symbol disambiguates rsIDs grouped by gene; the rsID provides a globally unique identifier. Encoding follows the most common pharmacogenomic convention (also used by Papachristos 2020): `1` = mutant allele present (heterozygous or homozygous mutant); `0` = homozygous wild-type. When a paper uses a different encoding (e.g., per-allele dosage `0/1/2`, dominant model with mutant homozygotes only, or recessive model), document the encoding explicitly in `covariateData[[<COL>]]$notes` and consider registering a separate canonical name. SNP indicators default to scope: specific because the parameter on which they act and the encoded reference category are tied to the source paper's analysis plan; promote to general when a second paper ratifies identical semantics.

### SNP_ABCG2_RS4148157 (**canonical for ABCG2 rs4148157 variant indicator**)
- **Description:** Binary genotype indicator for the *ABCG2* (BCRP) rs4148157 single-nucleotide polymorphism (G > A; intron 11, population MAF ~0.10). 1 = at least one variant (A) allele present (heterozygous AG or homozygous AA carrier); 0 = homozygous wild-type (GG).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (homozygous wild-type GG).
- **Source aliases:**
  - `GENECAT` -- Roberts 2016 (paper Results 'Population Pharmacokinetic Analysis' section: the individual-Ka equation Ka_i = Ka_pop * exp(theta_1 * GENECAT), with GENECAT = 1 for AG / AA and 0 for GG).
- **Example models:** `Roberts_2016_topotecan.R` (exponential effect on oral topotecan absorption rate constant Ka: `Ka = Ka_pop * exp(1.06 * SNP_ABCG2_RS4148157)`; AG/AA carriers have Ka approximately 2.89x higher than GG homozygotes, corresponding to an observed Cmax approximately 1.7x higher).
- **Notes:** Time-fixed per subject (germline genotype). Variant carrier rate in the Roberts 2016 paediatric brain-tumour cohort: 19% (10 of 52 successfully genotyped patients; 9 AG heterozygotes plus 1 AA homozygote, with AA and AG pooled because only one AA homozygote was present). The rs4148157 variant is intronic and is in strong linkage disequilibrium with the better-characterised rs2231142 variant (Q141K, exon 6); Roberts 2016 Discussion proposes that rs4148157 may be acting as a surrogate marker for rs2231142 in this analysis. ABCG2 (also called BCRP, breast cancer resistance protein) is an intestinal and hepatic efflux transporter that influences oral bioavailability of topotecan and other substrates.

### SNP_ABCG2_RS2231142_HOM (**canonical for ABCG2 rs2231142 (Q141K) homozygous-variant indicator**)
- **Description:** Binary genotype indicator for the *ABCG2* (BCRP) rs2231142 single-nucleotide polymorphism (c.421C>A; exon 5; amino-acid Q141K / p.Gln141Lys; population MAF ~30-60% in East Asians, ~5-15% in Caucasians and African-Americans). 1 = homozygous variant (421A/A) carrier; 0 = otherwise (the union of heterozygous 421C/A and homozygous wild-type 421C/C). This is a recessive-model encoding: heterozygotes are pooled with wild-type homozygotes because Ueshima 2018 (the founding example) reported that only the 421A/A stratum had a distinct typical-value covariate effect on apixaban non-renal clearance. Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0. In a recessive-model (`_HOM`-only) encoding the 0-level is the union of homozygous wild-type 421C/C and heterozygous 421C/A. In a paired-indicator encoding (`SNP_ABCG2_RS2231142_HET` also present) the reference group is the 421C/C wild-type homozygote stratum, i.e. both indicators = 0.
- **Source aliases:**
  - `ABCG2 421A/A` -- used in `Ueshima_2018_apixaban.R` (Ueshima 2018 Methods Eq. of the final-model CL/F: dichotomous parameter ABCG2 equals 1 for 421A/A and 0 for 421C/C or 421C/A).
  - `rs2231142 TT` -- used in `Jiang_2023_imatinib.R` (Jiang 2023 reports the SNP on the opposite DNA strand as G>T, so the TT genotype is the 421A/A homozygous-variant stratum; Table 3 row `rs2231142 TT`).
- **Example models:** `Ueshima_2018_apixaban.R` (multiplicative factor on the non-renal arm of apparent oral clearance: `cl_nonren = exp(lcl) * e_abcg2_homvar_cl_nonren^SNP_ABCG2_RS2231142_HOM` with `e_abcg2_homvar_cl_nonren = 0.341`; 421A/A homozygotes have non-renal CL/F reduced by 65.9% relative to 421C/C or 421C/A; paper Table 4 theta6 = 0.341), `Jiang_2023_imatinib.R` (power-of-binary multiplicative factor on apparent oral clearance, paired with `SNP_ABCG2_RS2231142_HET`: `e_abcg2_het_cl^SNP_ABCG2_RS2231142_HET * e_abcg2_hom_cl^SNP_ABCG2_RS2231142_HOM` with `e_abcg2_hom_cl = 0.976`; TT homozygotes have CL/F 2.4% lower than GG wild-type homozygotes, imprecisely estimated on n = 6 TT subjects; paper Table 3).
- **Notes:** Distinct from `SNP_ABCG2_RS4148157` in two ways: (a) a different SNP (rs2231142 is the well-characterised coding Q141K variant in exon 5, whereas rs4148157 is an intronic variant in intron 11 that is in strong linkage disequilibrium with rs2231142 and used by Roberts 2016 as a surrogate marker), (b) a different genetic model (recessive 421A/A-only in Ueshima 2018 vs dominant any-A-allele in Roberts 2016 rs4148157). **Strand orientation:** papers report this SNP either as c.421C>A (dbSNP reference orientation, variant allele A) or as G>T on the opposite strand (variant allele T); the two are the same variant, so 421C/C = GG, 421C/A = GT, and 421A/A = TT. Always map the paper's genotype labels onto the reference-orientation strata before assigning indicator values. **Genetic-model orientations:** use `SNP_ABCG2_RS2231142_HOM` alone for a recessive encoding (Ueshima 2018); pair it with `SNP_ABCG2_RS2231142_HET` when the source paper assigns a distinct typical-value effect to each of the three genotype strata (Jiang 2023), following the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` and `CYP3A5_STAR1_HET` / `CYP3A5_STAR1_HOM` precedents. The per-subject column value is identical in both encodings (1 iff the subject is 421A/A); only the model's reference-category interpretation changes, which is why the paired form does not need a separate canonical for the homozygote. When a future paper uses a dominant-model encoding (any-A-allele = 1, pooling heterozygotes with homozygous variants into a single carrier group), register a paired companion canonical `SNP_ABCG2_RS2231142_CARRIER` so the two genetic-model orientations remain separate canonicals. The Q141K variant impairs ABCG2 plasma-membrane localisation and function; it is the most commonly studied ABCG2 pharmacogenetic SNP in popPK literature (substrates include apixaban, rosuvastatin, sulfasalazine, topotecan, methotrexate). Ratified canonically on 2026-05-30 alongside the Ueshima 2018 apixaban extraction; scope promoted from specific to general on 2026-07-27 alongside the Jiang 2023 imatinib extraction (second independent paper using the same column definition across an unrelated drug class and a different genetic model, demonstrating the canonical generalizes beyond the Ueshima 2018 recessive encoding).

### SNP_ABCG2_RS2231142_HET (**canonical for ABCG2 rs2231142 (Q141K) heterozygote indicator**)
- **Description:** Binary genotype indicator for the heterozygous stratum of the *ABCG2* (BCRP) rs2231142 single-nucleotide polymorphism (c.421C>A; exon 5; amino-acid Q141K / p.Gln141Lys). 1 = subject carries exactly one variant allele (genotype 421C/A, equivalently GT on the opposite-strand reporting convention); 0 = otherwise (the union of 421C/C wild-type homozygotes and 421A/A homozygous-variant carriers; the paired indicator `SNP_ABCG2_RS2231142_HOM` flags the homozygous-variant group). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (421C/C wild-type homozygote, when paired with `SNP_ABCG2_RS2231142_HOM` = 0). The reference group is the wild-type homozygote stratum; `SNP_ABCG2_RS2231142_HOM` flags the homozygous-variant stratum.
- **Source aliases:**
  - `rs2231142 GT` -- used in `Jiang_2023_imatinib.R` (Jiang 2023 reports the SNP on the opposite DNA strand as G>T, so the GT genotype is the 421C/A heterozygous stratum; Table 3 row `rs2231142 GT`; the paper's final-model equation writes the exponent as `heterozygous`).
- **Example models:** `Jiang_2023_imatinib.R` (power-of-binary multiplicative factor on apparent oral clearance: `e_abcg2_het_cl^SNP_ABCG2_RS2231142_HET` with `e_abcg2_het_cl = 0.879`; heterozygotes have CL/F 12.1% lower than GG wild-type homozygotes and therefore reach higher trough concentrations at the same dose; paper Table 3, RSE 5%, bootstrap 95% CI 0.785-0.968).
- **Notes:** Paired with `SNP_ABCG2_RS2231142_HOM` (see that entry's Notes for the strand-orientation convention, the recessive-vs-paired genetic-model distinction, and the three-level decomposition rationale). Follows the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM`, `CYP3A5_STAR1_HET` / `CYP3A5_STAR1_HOM`, and `CYP2D6_STAR10_HET` / `CYP2D6_STAR10_HOM` precedents: two binary indicators encode a three-level germline genotype with the wild-type homozygote as the implicit reference (both indicators = 0). Distinct from a hypothetical `SNP_ABCG2_RS2231142_CARRIER` (not yet registered), which would pool heterozygotes with homozygous variants under a dominant model; use the paired indicators when the source paper resolves a distinct typical-value effect for the heterozygous stratum specifically. Jiang 2023 is the founding example and motivated the three-level decomposition because the East Asian cohort had abundant heterozygotes (45/85, 53%) but few homozygous variants (6/85, 7%) -- the heterozygote effect (0.879) is precisely estimated while the homozygote effect (0.976) is not. Genotype distribution in the Jiang 2023 cohort of 85 Chinese postoperative GIST adults (Table 2): GG 34 (40%), GT 45 (53%), TT 6 (7%); allele frequency G 0.66 / T 0.34; Hardy-Weinberg p = 0.22. Ratified canonically on 2026-07-27 alongside the Jiang 2023 imatinib extraction.

### SNP_IL10_RS1800896_HET (**canonical for IL-10 G-1082A (rs1800896) heterozygote indicator**)
- **Description:** Binary genotype indicator for the heterozygous stratum of the *IL10* rs1800896 promoter single-nucleotide polymorphism (-1082 G>A, reported as T>C on the opposite strand). 1 = subject carries exactly one variant allele (genotype C/T on the reporting strand used by the source); 0 = otherwise. Time-fixed per subject (germline genotype). IL-10 is a potent down-modulator of CYP3A enzyme activity, so carriers of the higher-IL-10-expression genotype show reduced CYP3A-mediated clearance.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (rs1800896 T/T homozygote on the source's reporting strand).
- **Source aliases:**
  - `rs1800896-TC` -- Pei 2023 Table S4 row label for the coefficient; the paper writes the locus as "IL-10 G-1082A (rs1800896)" in the text and tabulates the genotype counts under "IL-10 (rs1800896)" in Table 2.
- **Example models:** `Pei_2023_tacrolimus.R` (exponential effect on apparent oral clearance: `exp(e_il10_het_cl * SNP_IL10_RS1800896_HET)` with `e_il10_het_cl = -0.35`, i.e. heterozygotes have 0.70 times the tacrolimus CL/F of T/T homozygotes; Pei 2023 Table S4).
- **Notes:** Registered as a heterozygote indicator rather than a variant-allele count because the founding cohort observed only two of the three strata -- Pei 2023 Table 2 reports TT 63 (73.3%) and CT 23 (26.7%) among 86 genotyped heart transplant recipients, with no CC subjects -- so the single reported coefficient is a heterozygote-versus-TT contrast and a per-allele reading is not identifiable from the source. A future paper resolving all three strata should add the paired `SNP_IL10_RS1800896_HOM` following the `SNP_ABCG2_RS2231142_HET` / `_HOM` and `CYP3A5_STAR1_HET` / `_HOM` precedents, or register a `_VAR_COUNT` sibling if it fits a per-allele model. Auto-approved member of the `SNP_<GENE>_RS<rsid>` family. Distinct from the other two IL-10 loci genotyped in Pei 2023 (rs1800871 and rs1800872, Table S3), neither of which entered the final model. Ratified 2026-08-05 alongside the Pei 2023 tacrolimus extraction.

### SNP_ICAM1_RS1799969 (**canonical for ICAM-1 rs1799969 mutant indicator**)
- **Description:** Binary genotype indicator for the *ICAM1* rs1799969 single-nucleotide polymorphism (G > A; Gly241Arg / K469E in some references). 1 = at least one mutant (A) allele present (heterozygous or homozygous mutant); 0 = homozygous wild-type (GG).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (homozygous wild-type).
- **Source aliases:**
  - `cat` -- Papachristos 2020 (the paper writes the indicator as `cat` in the CL covariate equation; no formal column name is given in the published narrative).
- **Example models:** `Papachristos_2020_bevacizumab_pk.R`, `Papachristos_2020_bevacizumab_qss.R`, `Papachristos_2020_bevacizumab_pkpd.R` (multiplicative effect on bevacizumab CL: `CL * exp(-0.423 * SNP_ICAM1_RS1799969)` in the PK and PK/PD models; `CL * exp(-0.33 * SNP_ICAM1_RS1799969)` in the binding QSS model -- mutant carriers have lower CL and higher trough levels).
- **Notes:** Time-fixed per subject. Mutant carrier rate in the Papachristos 2020 mCRC cohort: 20% (`Notes` Table 1 of the paper). The biological mechanism by which the *ICAM1* mutant slows bevacizumab clearance is unknown; the association is empirical and may be specific to mCRC.

### SNP_VEGFA_RS1570360 (**canonical for VEGF-A rs1570360 mutant indicator**)
- **Description:** Binary genotype indicator for the *VEGFA* rs1570360 single-nucleotide polymorphism (-1154 G > A; promoter region). 1 = at least one mutant (A) allele present; 0 = homozygous wild-type (GG).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (homozygous wild-type).
- **Source aliases:**
  - `cat1` -- Papachristos 2020 (used as the first categorical indicator in the inter-compartmental clearance equation of the PK model; no formal column name in the narrative).
- **Example models:** `Papachristos_2020_bevacizumab_pk.R` (multiplicative effect on bevacizumab Q: `Q * exp(0.378 * SNP_VEGFA_RS1570360)` -- mutant carriers have higher inter-compartmental clearance).
- **Notes:** Time-fixed per subject. Mutant carrier rate in the Papachristos 2020 mCRC cohort: 33%. The covariate is significant in the standalone PK model but does not appear in the binding QSS or PK/PD models because in those models the inter-compartmental clearance covariate effects are absorbed into the rs699947 effect on Q (PK/PD) or into the K_ss / BM0 effects (QSS).

### SNP_VEGFA_RS699947 (**canonical for VEGF-A rs699947 mutant indicator**)
- **Description:** Binary genotype indicator for the *VEGFA* rs699947 single-nucleotide polymorphism (-2578 C > A; promoter region). 1 = at least one mutant (A) allele present; 0 = homozygous wild-type (CC).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (homozygous wild-type).
- **Source aliases:**
  - `cat2` -- Papachristos 2020 PK model (second categorical indicator on Q).
  - `cat` -- Papachristos 2020 binding QSS model (effect on K_ss and BM0) and PK/PD model (effect on Q).
- **Example models:** `Papachristos_2020_bevacizumab_pk.R` (effect on Q: -0.429), `Papachristos_2020_bevacizumab_qss.R` (effect on K_ss: +1.22, on BM0: -0.851), `Papachristos_2020_bevacizumab_pkpd.R` (effect on Q: -0.414).
- **Notes:** Time-fixed per subject. Mutant carrier rate in the Papachristos 2020 mCRC cohort: 52%. The mutant allele is associated with lower baseline free VEGF-A levels and a higher in-vivo affinity (higher K_ss), consistent with reports that rs699947 mutants have prolonged overall survival on bevacizumab-based therapy.

### SNP_SLCO1B1_RS11045819 (**canonical for SLCO1B1 rs11045819 mutant indicator**)
- **Description:** Binary genotype indicator for the *SLCO1B1* rs11045819 single-nucleotide polymorphism (C > A; OATP1B1 transporter, exon 5, P155T). 1 = at least one mutant (A) allele present (heterozygous AC or homozygous AA); 0 = homozygous wild-type (CC). The Hennig 2015 cohort (n = 35 successfully genotyped of 44) reported 5 AC heterozygotes, 30 CC homozygotes, and 0 AA homozygotes, so the indicator is effectively heterozygous-vs-CC in that cohort.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (homozygous wild-type CC).
- **Source aliases:**
  - `SLCO1B1 rs11045819 genotype` -- Hennig 2015 (paper text; the source NONMEM control stream is in the unrecovered AAC supplement, so the formal column name is not on disk).
- **Example models:** `Hennig_2015_rifabutin.R` (multiplicative effect on rifabutin bioavailability F: `F * (1 + 0.304 * SNP_SLCO1B1_RS11045819)` -- AC carriers have ~30% higher rifabutin F than CC reference; dOFV = -6.5).
- **Notes:** Time-fixed per subject. Carrier rate in the Hennig 2015 South-African HIV/TB cohort: 14% (5 of 35 genotyped). SLCO1B1 encodes OATP1B1, a hepatic uptake transporter; rs11045819 has been associated with reduced rifampicin and lopinavir concentrations in prior studies but in Hennig 2015 was associated with INCREASED rifabutin bioavailability (note opposite direction of effect across rifamycins).

### SNP_ABCB1_RS1045642 (**canonical for ABCB1 rs1045642 (c.3435C>T) mutant allele carrier indicator**)
- **Description:** Binary genotype indicator for the *ABCB1* rs1045642 single-nucleotide polymorphism (c.3435C>T; exon 26; synonymous Ile1145Ile; encodes P-glycoprotein / MDR1 efflux transporter). 1 = subject carries at least one T (mutant) allele (heterozygous CT or homozygous TT; pooled because TT homozygote frequency was 1 of 262 in the Bisaso 2014 cohort); 0 = homozygous wild-type (CC). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (homozygous wild-type CC).
- **Source aliases:**
  - `ABCB13435` -- used in `Bisaso_2014_albumin.R` (paper text "ABCB1c.3435C>T mutation" and Figure 3 stratification "ABCB13435==0 stands for ABCB1c.3435CC while ABCB13435==1 stands for ABCB1c.3435CT and ABCB1c.3435TT"; same orientation as the canonical).
- **Example models:** `Bisaso_2014_albumin.R` (multiplicative additive shift on baseline albumin secretion rate Q0: `Q0 = exp(lq0) * (1 + e_snp_abcb1_rs1045642_q0 * SNP_ABCB1_RS1045642)` with `e_snp_abcb1_rs1045642_q0 = 0.167`; T-carriers have 16.7% higher Q0 than CC wild-type, equivalent to the paper text's "16% higher" framing).
- **Notes:** Distinct from `ABCB1_HAP_TTT` (which is the multi-SNP haplotype across rs1128503 / rs2032582 / rs1045642 jointly -- a different concept even though rs1045642 is one of the three contributing SNPs); use `ABCB1_HAP_TTT` when the source paper reports a phased haplotype, and `SNP_ABCB1_RS1045642` when the source paper reports the single c.3435C>T SNP alone. Heterozygote and homozygote T-carriers are pooled in Bisaso 2014 because the TT cohort was n = 1; future extractions that estimate separate het / hom effects should register paired `SNP_ABCB1_RS1045642_HET` and `SNP_ABCB1_RS1045642_HOM` indicators following the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` precedent. ABCB1 c.3435C>T is associated with altered P-glycoprotein expression and has been linked in the literature to predisposition to ART and rifampicin-based anti-TB drug-induced liver injury (Yimer 2011, cited in Bisaso 2014 Discussion). Ratified canonically on 2026-05-20 alongside the Bisaso 2014 albumin extraction.

### SLCO1B1_HAP15_HET (**canonical for SLCO1B1*15 haplotype heterozygote indicator**)
- **Description:** Binary haplotype indicator for the *SLCO1B1* `*15` reduced-function haplotype (the cis combination of the 388A>G / rs2306283 and 521T>C / rs4149056 variants; encodes the OATP1B1 N130D + V174A double mutant). 1 = subject carries exactly one *15 allele (heterozygous: `*1a/*15` or `*1b/*15`), 0 = otherwise (the union of *15-noncarriers and *15-homozygotes; the paired indicator `SLCO1B1_HAP15_HOM` flags the homozygous group). Time-fixed per subject (germline haplotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (homozygous *1a/*1a, *1a/*1b, or *1b/*1b -- i.e., no *15 allele). The reference group is the union of the three non-*15 diplotypes; `SLCO1B1_HAP15_HOM` flags the *15/*15 group.
- **Source aliases:**
  - `HT` -- Ide 2009 (paper text Eq. for `Frel = 1 * theta1^HT * theta2^HM` where `HT = 1` for heterozygotes `*1a/*15` and `*1b/*15`).
  - `OATP1B1 phenotype` -- Jeong 2022 (paper Section 3.2 + Table 1 phenotype-to-haplotype mapping: ET = *1a/*1a + *1a/*1b + *1b/*1b, IT = *1a/*15 + *1b/*15, PT = *15/*15; IT subjects map to SLCO1B1_HAP15_HET = 1).
- **Example models:** `Ide_2009_pravastatin.R` (multiplicative effect on relative bioavailability Frel: `Frel = 1.50^SLCO1B1_HAP15_HET * 1.95^SLCO1B1_HAP15_HOM` -- *15 heterozygotes have 50% higher Frel than *15-noncarriers; dOFV = 32.2 in backward elimination, p < 0.001), `Jeong_2022_torsemide.R` (linear-deviation effect on apparent central volume V/F: `V/F = tvV/F * (1 + (-0.410) * SLCO1B1_HAP15_HET + (-0.646) * SLCO1B1_HAP15_HOM)` -- *15 heterozygotes have 41% lower V/F than *15-noncarriers; Jeong 2022 Table 4).
- **Notes:** Paired with `SLCO1B1_HAP15_HOM` to encode a three-level haplotype categorical (noncarrier / heterozygote / homozygote) with `*15`-noncarrier as the implicit reference (both indicators = 0). Distinct from the SNP-level canonical `SNP_SLCO1B1_RS11045819` (which encodes only the C>A variant at a different position; rs11045819 = P155T) and from the *15 component SNPs rs2306283 (388A>G) and rs4149056 (521T>C) individually: future Ide-style extractions that pool *5 (521T>C only) with *15 should still record their values under this canonical and document the pooling rule in `covariateData[[SLCO1B1_HAP15_HET]]$notes`. Distribution in the Ide 2009 cohort of 57 healthy Japanese male volunteers (Table I): 28 noncarriers, 23 heterozygotes, 6 homozygotes; in the Jeong 2022 cohort of 112 healthy Korean male volunteers (Table 1): 86 ET noncarriers (76.8%), 23 IT heterozygotes (20.5%), 3 PT homozygotes (2.7%). Ratified canonically on 2026-05-12 alongside the Ide 2009 extraction; scope promoted from specific to general on 2026-05-17 alongside the Jeong 2022 torsemide extraction (second model using the same haplotype encoding across an unrelated drug class, demonstrating the canonical generalizes beyond statins).

### SLCO1B1_HAP15_HOM (**canonical for SLCO1B1*15 haplotype homozygote indicator**)
- **Description:** Binary haplotype indicator for the *SLCO1B1* `*15` reduced-function haplotype. 1 = subject carries two *15 alleles (homozygous: `*15/*15`), 0 = otherwise (the union of *15-noncarriers and *15-heterozygotes; the paired indicator `SLCO1B1_HAP15_HET` flags the heterozygous group). Time-fixed per subject (germline haplotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (homozygous *1a/*1a, *1a/*1b, or *1b/*1b -- i.e., no *15 allele). The reference group is the union of the three non-*15 diplotypes; `SLCO1B1_HAP15_HET` flags the *15-heterozygote group.
- **Source aliases:**
  - `HM` -- Ide 2009 (paper text Eq. for `Frel = 1 * theta1^HT * theta2^HM` where `HM = 1` for homozygotes `*15/*15`).
  - `OATP1B1 phenotype` -- Jeong 2022 (paper Section 3.2 + Table 1 phenotype-to-haplotype mapping: PT = *15/*15 corresponds to SLCO1B1_HAP15_HOM = 1).
- **Example models:** `Ide_2009_pravastatin.R` (multiplicative effect on relative bioavailability Frel: `Frel = 1.50^SLCO1B1_HAP15_HET * 1.95^SLCO1B1_HAP15_HOM` -- *15 homozygotes have 95% higher Frel than *15-noncarriers; dOFV = 33.7 in backward elimination, p < 0.001), `Jeong_2022_torsemide.R` (linear-deviation effect on apparent central volume V/F: `V/F = tvV/F * (1 + (-0.410) * SLCO1B1_HAP15_HET + (-0.646) * SLCO1B1_HAP15_HOM)` -- *15 homozygotes have 64.6% lower V/F than *15-noncarriers; Jeong 2022 Table 4).
- **Notes:** Paired with `SLCO1B1_HAP15_HET` to encode a three-level haplotype categorical (noncarrier / heterozygote / homozygote) with `*15`-noncarrier as the implicit reference (both indicators = 0). See `SLCO1B1_HAP15_HET` Notes for the broader context; population distribution in Ide 2009 was 6 of 57 (10.5%) homozygotes and in Jeong 2022 was 3 of 112 (2.7%) homozygotes. Ratified canonically on 2026-05-12 alongside the Ide 2009 extraction; scope promoted from specific to general on 2026-05-17 alongside the Jeong 2022 torsemide extraction (second model using the same haplotype encoding across an unrelated drug class).


### CYP2C9_S1_COUNT (**canonical for CYP2C9*1 (wild-type) allele count**)
- **Description:** Continuous individual-level CYP2C9*1 allele count: 0 = no *1 allele, 1 = one *1 allele (heterozygous), 2 = two *1 alleles (homozygous wild-type). Time-invariant (germline genotype). Paired with `CYP2C9_S2_COUNT` and `CYP2C9_S3_COUNT` to encode the three loss-of-function-allele dosage form used by Hamberg-family warfarin models, where the subject's CL is the sum of per-allele CL contributions across the two CYP2C9 alleles. The three count columns sum to 2 for each subject.
- **Units:** (count, 0/1/2 alleles per subject)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). Used directly as a multiplier on a fixed per-allele CL contribution (0.174 L/h per *1 allele in Hamberg / Xia 2024).
- **Source aliases:**
  - `CYP2C9` (genotype string such as `"*1/*1"`, `"*1/*3"`): derive `CYP2C9_S1_COUNT = (length of *1 matches in the genotype string)`. The model `Xia_2024_warfarin.R` carries the source `CYP2C9` genotype string mapped to the three count columns.
- **Example models:** `Xia_2024_warfarin.R` (per-allele CL contributions: `cl = CYP2C9_S1_COUNT * 0.174 + CYP2C9_S2_COUNT * 0.0879 + CYP2C9_S3_COUNT * 0.0422`, times an age effect).
- **Notes:** Hamberg's warfarin K-PD model parameterises CL as a sum of two per-allele CL contributions (one per CYP2C9 allele on each chromosome), which is more flexible than a single genotype indicator because it naturally accommodates any combination of *1, *2, *3 alleles (six diplotypes: *1/*1, *1/*2, *1/*3, *2/*2, *2/*3, *3/*3). When a paper reports additional CYP2C9 alleles (e.g. *5, *6, *8, *11), register parallel canonicals (`CYP2C9_S5_COUNT` etc.) rather than overloading the existing three counts. Distinct from the categorical phenotype canonicals `CYP3A5_EXPR` (binary expresser) and from continuous-activity scores like `CYP3A4` -- the count form preserves loss-of-function-allele dosage exactly. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin extraction.

### CYP2C9_S2_COUNT (**canonical for CYP2C9*2 reduced-function allele count**)
- **Description:** Continuous individual-level CYP2C9*2 allele count: 0 = no *2 allele, 1 = one *2 allele (heterozygous), 2 = two *2 alleles (homozygous). Time-invariant (germline genotype). Paired with `CYP2C9_S1_COUNT` and `CYP2C9_S3_COUNT`; the three counts sum to 2 for each subject. The *2 allele (rs1799853, R144C) encodes a reduced-function CYP2C9 isoform.
- **Units:** (count, 0/1/2 alleles per subject)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). Used directly as a multiplier on a fixed per-allele CL contribution (0.0879 L/h per *2 allele in Hamberg / Xia 2024).
- **Source aliases:**
  - `CYP2C9` (genotype string such as `"*1/*2"`, `"*2/*2"`, `"*2/*3"`): derive `CYP2C9_S2_COUNT = (length of *2 matches in the genotype string)`.
- **Example models:** `Xia_2024_warfarin.R` (per-allele CL contributions; the Xia 2024 Han cohort had no *2 carriers, but the model retains the term for general use across CYP2C9 papers).
- **Notes:** See `CYP2C9_S1_COUNT` for the broader rationale. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin extraction.

### CYP2C9_S3_COUNT (**canonical for CYP2C9*3 reduced-function allele count**)
- **Description:** Continuous individual-level CYP2C9*3 allele count: 0 = no *3 allele, 1 = one *3 allele (heterozygous), 2 = two *3 alleles (homozygous). Time-invariant (germline genotype). Paired with `CYP2C9_S1_COUNT` and `CYP2C9_S2_COUNT`; the three counts sum to 2 for each subject. The *3 allele (rs1057910, I359L) encodes a strongly reduced-function CYP2C9 isoform and is the dominant CYP2C9 pharmacogenomic risk variant in East-Asian populations (warfarin / phenytoin sensitivity).
- **Units:** (count, 0/1/2 alleles per subject)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). Used directly as a multiplier on a fixed per-allele CL contribution (0.0422 L/h per *3 allele in Hamberg / Xia 2024).
- **Source aliases:**
  - `CYP2C9` (genotype string such as `"*1/*3"`, `"*3/*3"`): derive `CYP2C9_S3_COUNT = (length of *3 matches in the genotype string)`.
- **Example models:** `Xia_2024_warfarin.R` (per-allele CL contributions; 5.7% of the Han cohort were *1/*3 heterozygous per Xia 2024 Table 1).
- **Notes:** See `CYP2C9_S1_COUNT` for the broader rationale. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin extraction.

### CYP2C9_MISSING (**canonical for CYP2C9 genotype-missing indicator**)
- **Description:** Binary indicator for a subject whose CYP2C9 genotype was not measured / not available. 1 = CYP2C9 genotype missing; 0 = CYP2C9 genotype known (in which case `CYP2C9_S1_COUNT + CYP2C9_S2_COUNT + CYP2C9_S3_COUNT = 2`). Companion to the `CYP2C9_S{1,2,3}_COUNT` per-allele canonicals so popPK models that estimate a separate typical-value covariate effect for the missing-genotype subgroup (rather than imputing it as wild-type) can do so faithfully.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (CYP2C9 genotype known). When `CYP2C9_MISSING == 1` the three `CYP2C9_S{1,2,3}_COUNT` columns must all be 0 so the diplotype-indicator products correctly evaluate to zero for the missing-genotype subject.
- **Source aliases:**
  - `Missing` (Lane 2011 Table 3 `q_CYP2C9 Missing` row -- the paper treats unknown CYP2C9 genotype as a distinct categorical level with its own multiplicative CL effect, rather than imputing the missing subjects into the wild-type reference; 14 of 306 S-warfarin subjects were missing CYP2C9 genotype per Lane 2011 Table 1).
- **Example models:** `Lane_2011_warfarin_s.R` (multiplicative CL effect 0.782 for the missing-genotype subgroup vs the *1/*1 wild-type reference; Lane 2011 Table 3).
- **Notes:** Follows the missing-data-indicator pattern established by `ADA_MISSING` (multiplicative CL effect for subjects with missing ADA results; Suri 2018 brentuximab) and `HEPIMP_MOD_OR_MISSING` (composite moderate-or-missing hepatic impairment indicator). Distinct from the strategy of imputing missing genotype as wild-type (which would set `CYP2C9_S1_COUNT = 2` and not use a separate indicator); use `CYP2C9_MISSING` only when the source paper explicitly fits a separate coefficient for the missing-genotype subgroup. Ratified canonically on 2026-06-30 alongside the Lane 2011 R/S-warfarin extraction.

### VKORC1_1639G_COUNT (**canonical for VKORC1 -1639G allele count**)
- **Description:** Continuous individual-level count of VKORC1 -1639G alleles (rs9923231, also reported as VKORC1 -1639 G > A or 1173 C > T depending on numbering convention). 0 = AA homozygous (warfarin-sensitive), 1 = GA heterozygous, 2 = GG homozygous (warfarin-resistant). Time-invariant (germline genotype). The complementary -1639A count is `2 - VKORC1_1639G_COUNT`.
- **Units:** (count, 0/1/2 alleles per subject)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). In the Hamberg / Xia 2024 EC50 model the typical EC50 is a per-allele sum: `ec50_typ = VKORC1_1639G_COUNT * ec50_per_G + (2 - VKORC1_1639G_COUNT) * ec50_per_A`. Distribution in the Xia 2024 Han cohort (Table 1): AA 80.3%, GA 18.7%, GG 0.9% (G allele frequency ~10%, consistent with East-Asian populations).
- **Source aliases:**
  - `VKORC1` (genotype string such as `"AA"`, `"GA"`, `"GG"` or `"1639AA"` etc.): derive `VKORC1_1639G_COUNT = (count of G in the two-letter genotype)`.
- **Example models:** `Xia_2024_warfarin.R` (per-allele EC50 contributions, re-estimated for the Han Chinese cohort: 4.3 mg/L per G allele, 1.14 mg/L per A allele).
- **Notes:** VKORC1 -1639G > A is the strongest single-SNP determinant of warfarin sensitivity (the A allele reduces VKORC1 expression via a promoter-region effect, requiring less warfarin to achieve target anticoagulation). The per-allele count form is preferred over a binary carrier indicator because the heterozygous and homozygous mutant subjects respond detectably differently to warfarin. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin extraction.

### SNP_CYP1A2_RS762551_C_CARRIER (**canonical for CYP1A2 rs762551 (C163A) C-allele carrier indicator**)
- **Description:** Binary indicator for carriage of the CYP1A2 rs762551 promoter-region single-nucleotide polymorphism (also known as CYP1A2 c.-163C>A; the paper's "C163A" shorthand). 1 = subject carries at least one C allele (heterozygous AC or homozygous CC); 0 = homozygous AA (the CYP1A2*1F reference / ultra-inducible form, associated with enhanced CYP1A2 substrate metabolism in smokers). Time-fixed per subject (germline genotype). AC heterozygous and CC homozygous carriers are pooled in Hopkins 2015 because the CC homozygous frequency (9/105 = 8.6%) was too low for a separate phenotype layer; future extractions distinguishing heterozygous vs homozygous C-allele effects should register paired `SNP_CYP1A2_RS762551_C_HET` / `SNP_CYP1A2_RS762551_C_HOM` indicators following the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` precedent.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (homozygous AA, i.e., CYP1A2*1F/*1F).
- **Source aliases:**
  - `CYP1A2 C163A` -- Hopkins 2015 (paper Table 3 caption defines the covariate: "carriers of the C allele of CYP1A2 rs762551 (i.e. CC or CA genotype)"; same orientation as the canonical).
- **Example models:** `Hopkins_2015_leflunomide.R` (multiplicative effect on the cessation-hazard covariate model in a leflunomide time-to-event analysis: `hazard <- h0(t) * (1 + e_cyp1a2_haz * SNP_CYP1A2_RS762551_C_CARRIER)` with `e_cyp1a2_haz = 1.29`; C-allele carriers have a 2.29-fold (95% CI 2.24, 2.34) increase in instantaneous cessation hazard compared with AA homozygotes; Hopkins 2015 Table 3 covariate-model theta10 and Results paragraph on CYP1A2).
- **Notes:** rs762551 lies in the CYP1A2 promoter region. The A allele defines the CYP1A2*1F ultra-inducible haplotype associated with enhanced CYP1A2 substrate metabolism in cigarette smokers (Sachse et al. 1999, cited in Hopkins 2015 Discussion). Downstream mechanistic hypotheses for the observed leflunomide-toxicity association include (a) altered relative contributions of CYP1A2 vs CYP2C19 / CYP3A4 in converting leflunomide to its active metabolite teriflunomide, potentially producing higher concentrations of alternate toxic metabolites in C-allele carriers, and (b) direct leflunomide toxicity that is unmasked when the CYP1A2 clearance arm is slower (Hopkins 2015 Discussion). No association between smoking status and leflunomide cessation was detected in the Hopkins 2015 cohort, but the smoking rate (37.1%) was modest. Distinct from `S1A2` (a specific-scope categorical CYP1A2-modifying co-medication indicator carried per subject in the DDMORE lidocaine bundle; not the same concept). The naming pattern `SNP_<GENE>_<RSID>_<ALLELE>_CARRIER` follows the existing `SNP_ABCB1_RS1045642` / `SNP_ABCB1_RS3842` precedent while making the binarised allele explicit. Ratified canonically on 2026-06-21 alongside the Hopkins 2015 leflunomide extraction.

### SNP_CYP2B6_RS3745274_T_COUNT (**canonical for CYP2B6 516G>T (rs3745274) T-allele count**)
- **Description:** Continuous individual-level CYP2B6 c.516G>T (rs3745274, p.Q172H) T-allele count: 0 = GG homozygous wild-type, 1 = GT heterozygous, 2 = TT homozygous variant. Time-invariant (germline genotype). The 516G>T variant reduces CYP2B6 enzyme activity in a gene-dose-dependent but non-additive manner; in nevirapine and efavirenz populations, the TT-vs-GG decrease in apparent oral clearance is approximately 2-3x larger than the GT-vs-GG decrease, motivating a heterozygous-vs-homozygous indicator parameterization rather than a linear per-allele model.
- **Units:** (count, 0/1/2 alleles per subject)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). In the Schipani 2011 nevirapine model the count is decomposed in `model()` into mutually-exclusive heterozygous (`count == 1`) and homozygous (`count == 2`) indicators, each multiplied by an independently estimated CL/F shift (-0.5 L/h for GT, -1.3 L/h for TT relative to the GG reference). In the Olagunju 2018 efavirenz model the count is summed together with `SNP_CYP2B6_RS28399499_C_COUNT` to derive a composite metaboliser status (`n_variant == 0` fast, `== 1` intermediate, `>= 2` slow), and the per-group CL/F is encoded as log-ratio multiplicative shifts on the fast-metaboliser reference.
- **Source aliases:**
  - `X_516GT` / `X_516TT` -- Schipani 2011 (paper Table 2 / final-model equation `TVCL = theta0 + theta_BW * (BW - 72.5) + theta_516GT * X_516GT + theta_516TT * X_516TT + theta_983TC * X_983TC`; the two indicators are mutually exclusive across the three 516 genotypes, so the canonical count column reconstructs them deterministically via `X_516GT = as.integer(SNP_CYP2B6_RS3745274_T_COUNT == 1)` and `X_516TT = as.integer(SNP_CYP2B6_RS3745274_T_COUNT == 2)`).
  - `CYP2B6 516G>T (rs3745274)` -- Olagunju 2018 (paper Methods 'Sample Collection ...' paragraph 1; genotype reported per-allele and used in combination with rs28399499 to define a composite CYP2B6 metaboliser status).
- **Example models:** `Schipani_2011_nevirapine.R` (additive linear shift on CL/F: `cl = exp(lcl) + e_516gt_cl * (SNP_CYP2B6_RS3745274_T_COUNT == 1) + e_516tt_cl * (SNP_CYP2B6_RS3745274_T_COUNT == 2) + ...`; 516TT homozygotes have approximately 37% lower CL/F than the GG reference; Schipani 2011 Table 2), `Olagunju_2018_efavirenz.R` (composite-metaboliser-status encoding: variant alleles from rs3745274 and rs28399499 are summed to classify subjects as fast / intermediate / slow, with per-group CL/F = 18.0 / 16.1 / 6.24 L/h reported in Olagunju 2018 Table 2).
- **Notes:** Distribution in the Schipani 2011 European HIV-positive cohort (Table 1, n = 275): 516GG 47%, 516GT 46%, 516TT 7%. In the Olagunju 2018 Nigerian HIV-positive pregnant-women cohort (Table 1, n = 77): 516GG 32%, 516GT 54%, 516TT 14%. The CYP2B6 516G>T variant is one of the two most-extensively-studied CYP2B6 pharmacogenomic polymorphisms (together with 983T>C / rs28399499, registered as `SNP_CYP2B6_RS28399499_C_COUNT`) and is consistently associated with reduced metabolism of nevirapine, efavirenz, bupropion, and methadone. The count-form encoding is preferred over paired binary HET / HOM indicators because (a) it follows the established `_COUNT` precedent used for `CYP2C9_S{1,2,3}_COUNT` and `VKORC1_1639G_COUNT`, (b) a single count column captures the underlying genotype without redundancy, and (c) the model code can deterministically derive either a linear per-allele effect (`* count`) or a non-additive HET / HOM decomposition (`* (count == 1)`, `* (count == 2)`) depending on the source paper's parameterization. Ratified canonically on 2026-05-21 alongside the Schipani 2011 nevirapine extraction; scope promoted to general on 2026-05-26 alongside the Olagunju 2018 efavirenz extraction, which uses the same count column under a composite-metaboliser-status encoding combining rs3745274 + rs28399499.

### SNP_CYP2B6_RS28399499_C_COUNT (**canonical for CYP2B6 983T>C (rs28399499) C-allele count**)
- **Description:** Continuous individual-level CYP2B6 c.983T>C (rs28399499, p.I328T) C-allele count: 0 = TT homozygous wild-type, 1 = TC heterozygous, 2 = CC homozygous variant. Time-invariant (germline genotype). The 983T>C variant lies within the CYP2B6 J-helix and changes a highly conserved hydrophobic Ile to polar Thr; recombinant expression in COS-1 cells shows essentially zero enzyme activity (Klein et al. 2005), and clinical cohorts consistently show large reductions in apparent oral clearance of CYP2B6 substrates in heterozygous carriers.
- **Units:** (count, 0/1/2 alleles per subject)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). In the Schipani 2011 nevirapine model the count is decomposed in `model()` into a heterozygous indicator (`count == 1`) multiplied by an estimated CL/F shift (-1.4 L/h relative to the TT reference); the homozygous indicator (`count == 2`) is not estimated because no 983CC homozygotes have been reported in any published cohort. In the Olagunju 2018 efavirenz model the count is summed together with `SNP_CYP2B6_RS3745274_T_COUNT` to define a composite CYP2B6 metaboliser status (slow / intermediate / fast); a hypothetical 983CC subject is still classified as slow (each variant allele contributes to the composite count) although that substratum is not present in the fitted cohort.
- **Source aliases:**
  - `X_983TC` -- Schipani 2011 (paper Table 2 / final-model equation; the heterozygous indicator is mutually exclusive with the TT-homozygous reference, so the canonical count column reconstructs it via `X_983TC = as.integer(SNP_CYP2B6_RS28399499_C_COUNT == 1)`).
  - `CYP2B6 983T>C (rs28399499)` -- Olagunju 2018 (paper Methods 'Sample Collection ...' paragraph 1; combined with rs3745274 to define a composite metaboliser status).
- **Example models:** `Schipani_2011_nevirapine.R` (additive linear shift on CL/F: `cl = exp(lcl) + e_983tc_cl * (SNP_CYP2B6_RS28399499_C_COUNT == 1) + ...`; 983TC heterozygotes have approximately 40% lower CL/F than the TT reference; Schipani 2011 Table 2), `Olagunju_2018_efavirenz.R` (composite-metaboliser-status encoding: variant alleles from rs3745274 and rs28399499 are summed to classify subjects as fast / intermediate / slow; Olagunju 2018 Table 2).
- **Notes:** Distribution in the Schipani 2011 European HIV-positive cohort (Table 1, n = 275): 983TT 97%, 983TC 3%, 983CC 0%. Distribution in the Olagunju 2018 Nigerian HIV-positive pregnant-women cohort (Table 1, n = 77): 983TT 75%, 983TC 25%, 983CC 0% -- a much higher heterozygous-variant fraction than the Schipani 2011 cohort, consistent with the West-African ethnicity of the Olagunju 2018 study population. The 983T>C variant is also known as CYP2B6*18 and is found primarily in African and African-admixed populations (allele frequency ~6-8% in West Africans, ~0% in Europeans and East Asians); the Schipani 2011 cohort included 33% Black-ethnicity subjects, which explains the observed 3% heterozygote frequency, while the all-Nigerian Olagunju 2018 cohort shows the typical West-African 25% heterozygote frequency. No 983CC homozygotes have been described in the published literature as of the 2011 report (Schipani 2011 Results paragraph 4), so models that include the variant typically estimate only a heterozygous shift. Often co-tested with 516G>T (linkage-disequilibrium block: subjects homozygous for both 516TT and 983TC are CYP2B6 "slow metabolizers" of efavirenz / nevirapine). The count-form encoding follows the same rationale as `SNP_CYP2B6_RS3745274_T_COUNT`. Ratified canonically on 2026-05-21 alongside the Schipani 2011 nevirapine extraction; scope promoted to general on 2026-05-26 alongside the Olagunju 2018 efavirenz extraction, which uses the same count column under a composite-metaboliser-status encoding combining rs3745274 + rs28399499.

### SNP_CYP2C19_RS3814637_VAR_COUNT (**canonical for CYP2C19 rs3814637 variant-allele count**)
- **Description:** Continuous individual-level CYP2C19 rs3814637 variant-allele count: 0 = wild-type homozygote (the more common allele on both chromosomes), 1 = heterozygote, 2 = variant homozygote. Time-invariant (germline genotype). Companion missing-genotype indicator: `SNP_CYP2C19_RS3814637_MISSING` (when missing, all three genotype-indicator products evaluate to zero and the missing-multiplier is applied instead). The rs3814637 SNP is in the CYP2C19 gene region and is reported in Lane 2011 as significantly influencing R-warfarin clearance; the paper notes that this SNP is NOT in linkage disequilibrium with the canonical CYP2C19*2 loss-of-function allele (rs4244285) in the Lane 2011 patient population, so it is registered as a distinct canonical column and is not interchangeable with `CYP2C19_S2_CARRIER`.
- **Units:** (count, 0/1/2 alleles per subject)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). In the Lane 2011 R-warfarin model the count is decomposed in `model()` into mutually exclusive wild-homozygote (`count == 2 - VAR_COUNT == 2`), heterozygote (`VAR_COUNT == 1`), and variant-homozygote (`VAR_COUNT == 2`) indicators, each multiplied by a distinct typical-value CL multiplier.
- **Source aliases:**
  - `CYP2C19 genotype` / `rs3814637` -- Lane 2011 (paper Methods 'Covariate selection and models'; coded as wild-type / heterozygote / mutant-homozygote with a separate `Missing` category). Heterozygote vs wild-homozygote CL multiplier 0.761, mutant-homozygote multiplier 0.494 (Lane 2011 Table 3).
- **Example models:** `Lane_2011_warfarin_r.R` (categorical CL multiplier 1.00 / 0.761 / 0.494 for wild / heterozygote / variant homozygote; Lane 2011 Table 3 final R-warfarin model).
- **Notes:** Follows the `SNP_<GENE>_RS<rsid>_<allele>_COUNT` pattern established by `SNP_CYP2B6_RS3745274_T_COUNT` and `SNP_CYP2B6_RS28399499_C_COUNT`. The `_VAR` token (rather than a specific allele letter) is used because Lane 2011 does not specify which allele is the wild-type vs variant in the paper text, only reporting the categorical wild-type / heterozygote / mutant-homozygote distinction. Future extractions that report rs3814637 with explicit allele letters (e.g. T/C) should add an aliased canonical such as `SNP_CYP2C19_RS3814637_T_COUNT` and cross-reference. Distinct from `CYP2C19_S2_CARRIER` (rs4244285): Lane 2011 explicitly notes that rs3814637 is not in linkage disequilibrium with CYP2C19*2 in their cohort. Ratified canonically on 2026-06-30 alongside the Lane 2011 R-warfarin extraction.

### SNP_CYP2C19_RS3814637_MISSING (**canonical for CYP2C19 rs3814637 genotype-missing indicator**)
- **Description:** Binary indicator for a subject whose CYP2C19 rs3814637 genotype was not measured / not available. 1 = genotype missing; 0 = genotype known (in which case `SNP_CYP2C19_RS3814637_VAR_COUNT` carries the 0/1/2 variant-allele count). Companion to `SNP_CYP2C19_RS3814637_VAR_COUNT` so popPK models that estimate a separate typical-value covariate effect for the missing-genotype subgroup (rather than imputing it as wild-type) can do so faithfully.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (genotype known). When `SNP_CYP2C19_RS3814637_MISSING == 1` the `SNP_CYP2C19_RS3814637_VAR_COUNT` column should be 0 so the genotype-indicator products correctly evaluate to zero for the missing-genotype subject.
- **Source aliases:**
  - `Missing` (Lane 2011 Table 3 `q_CYP2C19 Missing` row -- the paper treats unknown CYP2C19 rs3814637 genotype as a distinct categorical level with its own multiplicative CL effect; 61 of 309 R-warfarin subjects were missing CYP2C19 genotype per Lane 2011 Table 1).
- **Example models:** `Lane_2011_warfarin_r.R` (multiplicative CL effect 0.804 for the missing-genotype subgroup vs the wild-homozygote reference; Lane 2011 Table 3 final R-warfarin model).
- **Notes:** Follows the same missing-data-indicator pattern as `CYP2C9_MISSING`, `ADA_MISSING`, and `HEPIMP_MOD_OR_MISSING`. Ratified canonically on 2026-06-30 alongside the Lane 2011 R-warfarin extraction.

### SNP_CYP3A4_RS2242480_VAR_COUNT (**canonical for CYP3A4 rs2242480 variant-allele count**)
- **Description:** Continuous individual-level CYP3A4 rs2242480 variant-allele count: 0 = wild-type homozygote, 1 = heterozygote, 2 = variant homozygote. Time-invariant (germline genotype). Companion missing-genotype indicator: `SNP_CYP3A4_RS2242480_MISSING`. The rs2242480 SNP is one of the SNPs that defines the CYP3A4*1G haplotype (a 5' UTR variant whose functional effect remains controversial -- some studies report gain-of-function, others loss-of-function; Lane 2011 Discussion paragraph 5).
- **Units:** (count, 0/1/2 alleles per subject)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). In the Lane 2011 R-warfarin model the count is decomposed in `model()` into mutually exclusive wild-homozygote, heterozygote, and variant-homozygote indicators, each multiplied by a distinct typical-value CL multiplier.
- **Source aliases:**
  - `CYP3A4 genotype` / `rs2242480` -- Lane 2011 (paper Methods 'Covariate selection and models'; coded as wild-type / heterozygote / mutant-homozygote with a separate `Missing` category). Heterozygote vs wild-homozygote CL multiplier 1.32, variant-homozygote multiplier 1.06 (Lane 2011 Table 3); Lane 2011 explicitly notes the heterozygote vs wild contrast is the significant effect ("Those patients with the heterozygote genotype for CYP3A4 had a 32% increased clearance for R-warfarin", Lane 2011 Results paragraph 'R-Warfarin models').
- **Example models:** `Lane_2011_warfarin_r.R` (categorical CL multiplier 1.00 / 1.32 / 1.06 for wild / heterozygote / variant homozygote; Lane 2011 Table 3 final R-warfarin model).
- **Notes:** Follows the `SNP_<GENE>_RS<rsid>_<allele>_COUNT` pattern. The `_VAR` token (rather than a specific allele letter) is used because Lane 2011 does not specify allele letters in the genotype table. Lane 2011 Discussion paragraph 5 notes that the functional effect of rs2242480 / CYP3A4*1G has been subject to controversy in the literature (Gao 2008 and Du 2006 report gain-of-function; Wang 2011 and He 2011 report loss-of-function; a luciferase reporter study in Wang 2013 found higher transcriptional activity for the A allele than the G allele). The Lane 2011 finding of higher R-warfarin clearance in heterozygotes is consistent with a gain-of-function effect in heterozygotes, although the small variant-homozygote stratum (n=3) limits inference for that sub-group. Ratified canonically on 2026-06-30 alongside the Lane 2011 R-warfarin extraction.

### SNP_CYP3A4_RS2242480_MISSING (**canonical for CYP3A4 rs2242480 genotype-missing indicator**)
- **Description:** Binary indicator for a subject whose CYP3A4 rs2242480 genotype was not measured / not available. 1 = genotype missing; 0 = genotype known. Companion to `SNP_CYP3A4_RS2242480_VAR_COUNT` so popPK models that estimate a separate typical-value covariate effect for the missing-genotype subgroup can do so faithfully.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (genotype known). When `SNP_CYP3A4_RS2242480_MISSING == 1` the `SNP_CYP3A4_RS2242480_VAR_COUNT` column should be 0 so the genotype-indicator products correctly evaluate to zero.
- **Source aliases:**
  - `Missing` (Lane 2011 Table 3 `q_CYP3A4 Missing` row; 38 of 309 R-warfarin subjects were missing CYP3A4 genotype per Lane 2011 Table 1).
- **Example models:** `Lane_2011_warfarin_r.R` (multiplicative CL effect 0.937 for the missing-genotype subgroup vs the wild-homozygote reference; Lane 2011 Table 3 final R-warfarin model).
- **Notes:** Follows the same missing-data-indicator pattern as `SNP_CYP2C19_RS3814637_MISSING`, `CYP2C9_MISSING`, `ADA_MISSING`, and `HEPIMP_MOD_OR_MISSING`. Ratified canonically on 2026-06-30 alongside the Lane 2011 R-warfarin extraction.

### SNP_CYP3A4_RS35599367 (**canonical for CYP3A4*22 (rs35599367) reduced-function allele carrier indicator**)
- **Description:** Binary genotype indicator for the *CYP3A4\*22* allele (rs35599367; c.522-191C>T; intron 6 T variant that lowers CYP3A4 mRNA splicing efficiency and hence hepatic CYP3A4 expression). 1 = subject carries at least one T (*22) allele (heterozygous `*1/*22` or homozygous `*22/*22`); 0 = homozygous wild-type `*1/*1`. Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (CYP3A4 `*1/*1` wild-type non-carrier).
- **Source aliases:**
  - `CYP3A4 Genotype` / `CYP3A4*22` -- used in `MohammedAli_2023_tacrolimus.R` (Mohammed Ali 2023 Methods section 2.3 `CYP3A4*22 C > T (rs35599367)` TaqMan genotyping; Table 1 reports `*1/*1` 86 (86.7%) vs `*1/*22` 12 (13.3%), so heterozygotes and the (unobserved) homozygotes are pooled into the carrier group exactly as the paper's `CYP3A4*22 carriers vs non-carriers` contrast does).
- **Example models:** `MohammedAli_2023_tacrolimus.R` (one of the two binary inputs from which the three-level CYP3A4/CYP3A5 cluster metabolizer phenotype is reconstructed inside `model()`: `is_hm <- (1 - SNP_CYP3A4_RS35599367) * CYP3A5_EXPR`, `is_pm <- SNP_CYP3A4_RS35599367 * (1 - CYP3A5_EXPR)`, `is_im <- 1 - is_hm - is_pm`; the three indicators then select one of three separately estimated typical CL/F values, 19.6 / 10.6 / 7.37 L/h).
- **Notes:** Distinct from the two already-registered CYP3A4 SNP canonicals: `SNP_CYP3A4_RS2246709` (active-allele count) and `SNP_CYP3A4_RS2242480_VAR_COUNT` (variant-allele count for the `*1G` / c.1026+12G>A intronic variant) are different loci, and the continuous `CYP3A4` canonical is a probe-substrate-derived activity score rather than a genotype. `*22` is the principal reduced-function *CYP3A4* allele in the tacrolimus / CYP3A pharmacogenetics literature and is nearly always analysed jointly with the *CYP3A5* `*3` expresser status (`CYP3A5_EXPR`), because the two genes together determine total CYP3A metabolic capacity. Follow the two-binary-input convention documented in the `CYP3A5_EXPR_DONOR` Notes: when a source paper fits a combined CYP3A4 + CYP3A5 "cluster" phenotype (high / intermediate / poor metabolizer), keep the underlying genotypes as separate canonical columns (`CYP3A5_EXPR` and `SNP_CYP3A4_RS35599367`) and reconstruct the cluster levels inside `model()`, rather than registering a single collapsed three-level cluster column -- this keeps the underlying biology explicit in the dataset and lets a downstream user re-derive any other clustering rule. Ratified canonically on 2026-08-01 alongside the Mohammed Ali 2023 LCP-Tac tacrolimus extraction.

### CYP2C19_S2_CARRIER (**canonical for CYP2C19*2 loss-of-function allele carrier indicator**)
- **Description:** Binary indicator for carriage of the *CYP2C19*2 loss-of-function allele (rs4244285, c.681G>A; creates a cryptic splice site that abolishes CYP2C19 enzyme activity). 1 = subject carries at least one *2 allele (heterozygous *1/*2 or homozygous *2/*2); 0 = no *2 allele. Time-fixed per subject (germline genotype). Het and hom carriers are pooled in studies where the *2/*2 (poor metabolizer) frequency is too low for a separate phenotype layer; future extractions that distinguish heterozygous-intermediate-metabolizer from homozygous-poor-metabolizer effects should register paired `CYP2C19_S2_HET` / `CYP2C19_S2_HOM` indicators (or, where the source uses the per-allele count form, a `CYP2C19_S2_COUNT` column following the `CYP2C9_S2_COUNT` precedent).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no *2 allele -- *1/*1, *1/*17, or *17/*17).
- **Source aliases:**
  - `CYP2C19*2` -- Danielak 2017 (paper Methods 'Determination of genetic polymorphisms' and Table 2 final-model `Effect of CYP2C19*2 on FM (COV)` row; PCR-RFLP genotyping for rs4244285). The Danielak 2017 cohort had no *2/*2 homozygous-poor-metabolizers, so heterozygous *1/*2 carriers were pooled into the binary CYP2C19_S2_CARRIER = 1 group with no information loss.
- **Example models:** `Danielak_2017_clopidogrel.R` (linear-deviation effect on the fraction of clopidogrel metabolised to the active thiol H4: `fm = TVFM * (1 + e_cyp2c19_s2_fm * CYP2C19_S2_CARRIER)` with `e_cyp2c19_s2_fm = -0.45`; carriers convert 45% less of the absorbed clopidogrel to the active H4 metabolite, giving a 36.7% lower predicted AUC of H4 vs non-carriers; Danielak 2017 Table 2 final-model and Results page 1628).
- **Notes:** CYP2C19*2 is the dominant *loss-of-function* CYP2C19 allele in clopidogrel pharmacogenetics; it reduces clopidogrel's metabolic activation to the antiplatelet-active H4 thiol and is associated with elevated rates of stent thrombosis and major adverse cardiovascular events on clopidogrel therapy (FDA boxed warning, 2010). The paired *17 ultra-rapid-metabolizer allele (rs12248560) is typically registered separately when present (a `CYP2C19_S17_CARRIER` indicator following the same pattern). The continuous-individual-activity-score consolidation TODO logged on `CYP2D6` line 3321 also applies prospectively to CYP2C19, but the binary carrier indicator remains the standard discrete encoding used by most published clopidogrel popPK / PD models. Ratified canonically on 2026-05-20 alongside the Danielak 2017 clopidogrel extraction.

### CYP2C19_IM (**canonical for CYP2C19 intermediate-metabolizer phenotype indicator**)
- **Description:** 1 = subject is a CYP2C19 intermediate metabolizer (one functional and one loss-of-function allele; e.g., `*1/*2`, `*2/*17`); 0 = any other CYP2C19 phenotype (EM, UM, PM, or RM). Time-fixed per subject (germline genotype-derived phenotype). Paired with `CYP2C19_PM` to encode the three-level EM/UM (reference, both indicators 0) / IM (`CYP2C19_IM = 1`) / PM (`CYP2C19_PM = 1`) phenotype with two binary indicators.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (EM, UM, PM, or RM phenotype). Reference category when paired with `CYP2C19_PM = 0` is the extensive / ultrarapid-metabolizer phenotype pool used in Zhao 2018.
- **Source aliases:**
  - `CYP2C19 IM` / `IM` -- Zhao 2018 (paper Table 2 reports `F_CYP2C19 IM = 0.449` relative to the EM/UM reference; genotypes pooled into IM: `*1/*2`, `*2/*17`).
- **Example models:** `Zhao_2018_omeprazole.R` (power-of-binary-indicator multiplicative factor on CLOMZ-M1 formation clearance: `e_cyp2c19_im_kmet_5oh ^ CYP2C19_IM` with `e_cyp2c19_im_kmet_5oh = 0.449`; IM subjects have ~55% lower formation clearance of 5-hydroxy-omeprazole than the EM/UM reference; Zhao 2018 Table 2), `Jung_2024_clopidogrel.R` (additive shift on the LOGIT scale of two nested metabolized fractions: `e_cyp2c19_im_logitfm1 = -0.450` and `e_cyp2c19_im_logitfm2 = -1.428`, taking the active-metabolite fraction fmH4 from 0.120 in EM to 0.071 in IM; Jung 2024 Tables 2 and 3).
- **Notes:** Follows the `CYP2B6_IM` / `CYP2B6_SM` / `CYP2B6_USM` three-binary precedent for multi-level metabolizer phenotypes. The Zhao 2018 cohort pooled extensive (EM, `*1/*1`) and ultrarapid (UM, `*1/*17`, `*17/*17`) metabolizers into a single reference because the typical-value clearance of 5-hydroxy-omeprazole formation was indistinguishable between the two strata in n = 38 EM/UM subjects (Zhao 2018 Methods 'Population pharmacokinetic-pharmacogenetic modelling'). Future extractions that fit a separate UM coefficient should register a paired `CYP2C19_UM` companion indicator following this pattern. Distinct from `CYP2C19_S2_CARRIER` (binary `*2`-allele carrier indicator used by Danielak 2017 clopidogrel) -- `CYP2C19_S2_CARRIER` pools heterozygous and homozygous `*2` carriers into a single 0/1 contrast, while `CYP2C19_IM` resolves the heterozygous `*2` (IM) stratum separately from the homozygous `*2/*2` (PM) stratum that `CYP2C19_PM` flags. Ratified canonically on 2026-05-25 alongside the Zhao 2018 omeprazole extraction.

### CYP2C19_PM (**canonical for CYP2C19 poor-metabolizer phenotype indicator**)
- **Description:** 1 = subject is a CYP2C19 poor metabolizer (two loss-of-function alleles, e.g., `*2/*2`); 0 = any other CYP2C19 phenotype (EM, UM, IM, or RM). Time-fixed per subject (germline genotype-derived phenotype). Paired with `CYP2C19_IM` to encode the three-level EM/UM (reference) / IM / PM phenotype with two binary indicators.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (EM, UM, IM, or RM phenotype). Reference category when paired with `CYP2C19_IM = 0` is the extensive / ultrarapid-metabolizer phenotype pool used in Zhao 2018.
- **Source aliases:**
  - `CYP2C19 PM` / `PM` -- Zhao 2018 (paper Table 2 reports `F_CYP2C19 PM = 0.125` relative to the EM/UM reference; genotype pooled into PM: `*2/*2`).
- **Example models:** `Zhao_2018_omeprazole.R` (power-of-binary-indicator multiplicative factor on CLOMZ-M1 formation clearance: `e_cyp2c19_pm_kmet_5oh ^ CYP2C19_PM` with `e_cyp2c19_pm_kmet_5oh = 0.125`; PM subjects have 87.5% lower formation clearance of 5-hydroxy-omeprazole than the EM/UM reference; Zhao 2018 Table 2), `Marathe_2023_belzutifan.R` (linear-deviation multiplicative factor on apparent clearance: `cl_cyp2c19 = 1 + e_cyp2c19_pm_cl * CYP2C19_PM` with `e_cyp2c19_pm_cl = -0.36`, i.e. -36.0% CL/F versus the non-poor-metabolizer reference (5.63 -> 3.60 L/h); Marathe 2023 Table 2 and Table 3. Here the reference pools ALL non-poor phenotypes (intermediate, extensive, rapid, and ultrarapid) because "only the PM category had a significantly different CL/F compared to all other categories", so `CYP2C19_IM` is deliberately not used alongside it)), `Jung_2024_clopidogrel.R` (additive shift on the LOGIT scale of two nested metabolized fractions: `e_cyp2c19_pm_logitfm1 = -0.996` and `e_cyp2c19_pm_logitfm2 = -2.432`, taking the active-metabolite fraction fmH4 from 0.120 in EM to 0.034 in PM; Jung 2024 Tables 2 and 3).
- **Notes:** Companion to `CYP2C19_IM`. See `CYP2C19_IM` Notes for the three-level decomposition rationale. The PM phenotype indicator carries a separate typical-value coefficient because the Zhao 2018 cohort observed `*2/*2` poor metabolizers (n = 2) had substantially lower 5-OH-omeprazole formation clearance than the heterozygous `*1/*2` and `*2/*17` intermediate metabolizers (12.5% vs 44.9% of EM/UM reference). Ratified canonically on 2026-05-25 alongside the Zhao 2018 omeprazole extraction.

### UGT2B17_EM (**canonical for UGT2B17 extensive-metabolizer phenotype indicator**)
- **Description:** 1 = subject is a UGT2B17 extensive metabolizer (both alleles functional, corresponding to a wild-type diplotype at the highly polymorphic UGT2B17 copy-number / whole-gene-deletion locus); 0 = any other UGT2B17 phenotype (intermediate or poor). Time-fixed per subject (germline genotype-derived phenotype). Paired with `UGT2B17_PM` to encode the three-level EM (`UGT2B17_EM = 1`) / IM (both indicators 0, reference) / PM (`UGT2B17_PM = 1`) phenotype with two binary indicators.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (intermediate or poor metabolizer). When paired with `UGT2B17_PM = 0` the reference collapses to the UGT2B17 intermediate-metabolizer stratum used in Marathe 2023.
- **Source aliases:**
  - `UGT2B17P` (three-level categorical, 1 = poor / 2 = intermediate / 3 = extensive, `-99` = missing) -- Marathe 2023 (supplement 2 NONMEM control stream branches `IF(UGT2B17P.EQ.3) CLUGT2B17P = (1 + THETA(12))`, with level 2 as "most common" reference and the `-99` missing sentinel mapped onto the reference). Decomposed into the paired binary indicators `UGT2B17_EM` / `UGT2B17_PM`.
  - `UGT2B17 EM` / `UGT2B17 (extensive)` -- Marathe 2023 Table 2 and Table 3 row labels.
- **Example models:** `Marathe_2023_belzutifan.R` (linear-deviation multiplicative factor on apparent clearance: `cl_ugt2b17 = 1 + e_ugt2b17_em_cl * UGT2B17_EM + e_ugt2b17_pm_cl * UGT2B17_PM` with `e_ugt2b17_em_cl = 0.39`, i.e. +39.1% CL/F for extensive metabolizers versus the intermediate-metabolizer reference (5.63 -> 7.83 L/h); Marathe 2023 Table 2 and Table 3).
- **Notes:** Follows the `CYP2D6_PM` / `CYP2D6_EM` and `CYP2C19_IM` / `CYP2C19_PM` paired-binary precedents for multi-level metabolizer phenotypes (two binary indicators encoding a three-level categorical with the intermediate stratum as the implicit reference). UGT2B17 is a phase-II glucuronidation enzyme whose poor-metabolizer phenotype arises from homozygous whole-gene deletion rather than from point mutations, which is why the phenotype -- not a single `SNP_UGT2B17_RS<rsid>` genotype indicator -- is the right canonical here. Note that Marathe 2023 retains all three UGT2B17 levels on CL/F but only two on bioavailability (EM and IM merged), so `UGT2B17_EM` has no bioavailability effect in that model while `UGT2B17_PM` does; a model needing the ultrarapid stratum should register a paired `UGT2B17_UM` on the same pattern. Distinct from the `UGT2B7_*` genotype canonicals above (a different UGT isoform, and genotype- rather than phenotype-based). Ratified canonically alongside the Marathe 2023 belzutifan extraction.

### UGT2B17_PM (**canonical for UGT2B17 poor-metabolizer phenotype indicator**)
- **Description:** 1 = subject is a UGT2B17 poor metabolizer (homozygous UGT2B17 whole-gene deletion, i.e. del/del at the UGT2B17 copy-number locus, giving no enzyme activity); 0 = any other UGT2B17 phenotype (extensive or intermediate). Time-fixed per subject (germline genotype-derived phenotype). Paired with `UGT2B17_EM` to encode the three-level EM / IM (reference) / PM phenotype with two binary indicators.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (extensive or intermediate metabolizer). When paired with `UGT2B17_EM = 0` the reference collapses to the UGT2B17 intermediate-metabolizer stratum. Note that a single model may use two different reference categories for two different effects of this indicator -- in Marathe 2023 the CL/F effect is referenced to the intermediate stratum alone (all three levels retained) while the bioavailability effect is referenced to the pooled intermediate + extensive stratum (only two levels retainable on F).
- **Source aliases:**
  - `UGT2B17P` (three-level categorical, 1 = poor / 2 = intermediate / 3 = extensive, `-99` = missing) -- Marathe 2023 (supplement 2 NONMEM control stream branches `IF(UGT2B17P.EQ.1) CLUGT2B17P = (1 + THETA(13))` for CL and `IF(UGT2B17P.EQ.1) F1UGT2B17P = (1 + THETA(15))` for bioavailability). Decomposed into the paired binary indicators `UGT2B17_EM` / `UGT2B17_PM`.
  - `UGT2B17 PM` / `UGT2B17 (poor)` -- Marathe 2023 Table 2 and Table 3 row labels.
- **Example models:** `Marathe_2023_belzutifan.R` (two distinct linear-deviation effects: on apparent clearance `cl_ugt2b17 = 1 + e_ugt2b17_em_cl * UGT2B17_EM + e_ugt2b17_pm_cl * UGT2B17_PM` with `e_ugt2b17_pm_cl = -0.24`, i.e. -24.2% CL/F versus the intermediate-metabolizer reference (5.63 -> 4.27 L/h); and on relative bioavailability `f(depot) = exp(lfdepot) * (1 + e_ugt2b17_pm_fdepot * UGT2B17_PM)` with `e_ugt2b17_pm_fdepot = 0.11`, i.e. +11.0% F versus the pooled intermediate + extensive reference; Marathe 2023 Table 2, Table 2 caption equation `F = 1 * (1 + F-UGT2B17P)`, and Table 3).
- **Notes:** Companion to `UGT2B17_EM`; see that entry's Notes for the three-level decomposition rationale and the phenotype-versus-genotype naming choice. Follows the `CYP2C19_PM` (Zhao 2018 omeprazole) and `CYP2D6_PM` (Knights 2015 aripiprazole) precedents. Marathe 2023 attributes the positive bioavailability effect to reduced first-pass glucuronidation in the gut when UGT2B17 activity is absent (Discussion), which is mechanistically consistent with the negative CL/F effect from the same deletion. The UGT2B17 deletion is strongly ancestry-stratified: Marathe 2023 reports the UGT2B17/CYP2C19 dual-PM phenotype frequency at ~0.5% in the overall US population but up to 15% in East Asians. Ratified canonically alongside the Marathe 2023 belzutifan extraction.

### ABCB1_C3435T_HET (**canonical for ABCB1 C3435T heterozygote indicator**)
- **Description:** Binary genotype indicator for the *ABCB1* (P-glycoprotein, MDR1) C3435T heterozygote group at rs1045642 (exon 26, synonymous Ile1145Ile). 1 = subject carries exactly one variant allele (genotype C/T); 0 = otherwise (the union of C/C homozygous wild-type and T/T homozygous variant strata; the paired indicator `ABCB1_C3435T_MUT` flags the homozygous variant group). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (C/C homozygous wild-type, when paired with `ABCB1_C3435T_MUT = 0`). The reference group is the homozygous wild-type C/C stratum; `ABCB1_C3435T_MUT` flags the homozygous-variant T/T stratum.
- **Source aliases:**
  - `ABCB1 C3435T C/T` / `C/T` -- Zhao 2018 (paper Table 2 reports a multiplicative scaling factor of 1.86 on Ka for the heterozygote stratum relative to the C/C reference).
- **Example models:** `Zhao_2018_omeprazole.R` (power-of-binary-indicator multiplicative factor on absorption rate constant Ka: `e_abcb1_c3435t_het_ka ^ ABCB1_C3435T_HET` with `e_abcb1_c3435t_het_ka = 1.86`; C/T heterozygotes have an absorption rate constant approximately 86% higher than the C/C wild-type reference; paired with `ABCB1_C3435T_MUT` and used jointly).
- **Notes:** Follows the `CYP3A5_STAR1_HET` / `CYP3A5_STAR1_HOM` and `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` paired-binary precedent for a three-level genotype where each stratum carries a distinct typical-value covariate effect. Distinct from the `ABCB1_HAP_TTT` haplotype canonical (which jointly tests the cis combination of rs1128503 / rs2032582 / rs1045642 SNPs as a single haplotype block; used in de Wit 2016 everolimus). Use `ABCB1_C3435T_HET` + `ABCB1_C3435T_MUT` when the source paper fits a distinct typical-value covariate effect to the single rs1045642 SNP without phasing it into a haplotype, and when both the heterozygous and homozygous-variant strata are large enough to identify independent effects (Zhao 2018 cohort: n = 22 heterozygotes, 43.1%, and n = 4 homozygous variant, 7.8%, with n = 25 wild-type, 49.0%, as the reference). Mechanistically the C3435T variant has been associated with altered P-gp expression and substrate efflux in some studies (often via linkage with functional variants in the same haplotype block), though directionality of the effect on substrate exposure varies across substrates and tissues. Ratified canonically on 2026-05-25 alongside the Zhao 2018 omeprazole extraction.

### ABCB1_C3435T_MUT (**canonical for ABCB1 C3435T homozygous-variant indicator**)
- **Description:** Binary genotype indicator for the *ABCB1* (P-glycoprotein, MDR1) C3435T homozygous-variant group at rs1045642 (exon 26, synonymous Ile1145Ile). 1 = subject carries two variant alleles (genotype T/T); 0 = otherwise (the union of C/C homozygous wild-type and C/T heterozygote strata; the paired indicator `ABCB1_C3435T_HET` flags the heterozygous group). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (C/C homozygous wild-type, when paired with `ABCB1_C3435T_HET = 0`). The reference group is the homozygous wild-type C/C stratum; `ABCB1_C3435T_HET` flags the heterozygous C/T stratum.
- **Source aliases:**
  - `ABCB1 C3435T T/T` / `T/T` -- Zhao 2018 (paper Table 2 reports a multiplicative scaling factor of 6.93 on Ka for the homozygous-variant stratum relative to the C/C reference).
- **Example models:** `Zhao_2018_omeprazole.R` (power-of-binary-indicator multiplicative factor on absorption rate constant Ka: `e_abcb1_c3435t_mut_ka ^ ABCB1_C3435T_MUT` with `e_abcb1_c3435t_mut_ka = 6.93`; T/T homozygotes have an absorption rate constant approximately 6.93-fold higher than the C/C wild-type reference; paired with `ABCB1_C3435T_HET` and used jointly).
- **Notes:** Companion to `ABCB1_C3435T_HET`. See `ABCB1_C3435T_HET` Notes for the three-level decomposition rationale, the distinction from the `ABCB1_HAP_TTT` haplotype canonical, and the Zhao 2018 cohort distribution. Ratified canonically on 2026-05-25 alongside the Zhao 2018 omeprazole extraction.

### ABCB1_HAP_TTT (**canonical for ABCB1 TTT haplotype carrier indicator**)
- **Description:** Binary haplotype indicator for the *ABCB1* `TTT` haplotype across the rs1128503 (1236C>T, exon 12, synonymous Gly412Gly) / rs2032582 (2677G>T/A, exon 21, Ala893Ser/Thr) / rs1045642 (3435C>T, exon 26, synonymous Ile1145Ile) SNP block. 1 = subject carries at least one `TTT` haplotype (heterozygous or homozygous; pooled because the homozygote frequency was < 0.1 in the de Wit 2016 cohort); 0 = no `TTT` haplotype. Time-fixed per subject (germline haplotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no `TTT` haplotype).
- **Source aliases:**
  - `ABCB1 TTT haplotype` -- de Wit 2016 (paper text Methods 'Pharmacogenetic analysis' and Table 2 final-model `theta TTT on F` row; haplotypes phased in gPLINK with certainty > 0.97).
- **Example models:** `deWit_2016_everolimus.R` (multiplicative effect on apparent bioavailability F: `F = 1 * 0.792^ABCB1_HAP_TTT` -- carriers have 20.8% lower F than non-carriers; dOFV = 9.6 in backward elimination, P < 0.01).
- **Notes:** The `TTT` haplotype of *ABCB1* (P-glycoprotein, MDR1 efflux transporter) is associated with enhanced P-gp efflux activity and reduced everolimus bioavailability (de Wit 2016 Discussion paragraph 5); de Wit cites prior evidence that the same `TTT` haplotype also reduces exposure / efficacy of other P-gp substrates [refs 20-22 in the paper], although directionally inconsistent results have been reported [refs 7, 23, 24]. Het and hom carriers were pooled in de Wit 2016 because the homozygote frequency was < 0.1 (Methods 'Pharmacogenetic analysis'); future extractions that estimate separate het / hom effects should register paired `ABCB1_HAP_TTT_HET` and `ABCB1_HAP_TTT_HOM` indicators following the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` precedent above. Distinct from any individual ABCB1 SNP indicator (rs1128503, rs2032582, rs1045642 alone) because the haplotype is the cis combination tested jointly. Ratified canonically on 2026-05-16 alongside the de Wit 2016 everolimus extraction.

### ALDH2_S2_CARRIER (**canonical for ALDH2*2 inactive-variant carrier indicator**)
- **Description:** Binary genotype indicator for the *ALDH2* `*2` (inactive) variant allele (rs671 G>A; Glu487Lys, aldehyde dehydrogenase 2 mitochondrial isoform). 1 = subject carries at least one ALDH2*2 allele (heterozygous *1/*2 or homozygous *2/*2); 0 = ALDH2*1/*1 wild-type. Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (ALDH2*1/*1 wild-type, fully active aldehyde dehydrogenase 2).
- **Source aliases:**
  - `ALDH2` (the source NONMEM indicator used in `Nemoto_2017_ethanol.R`; 1 for *1/*2 carriers, 0 for *1/*1 wild-type; the Nemoto 2017 cohort had no *2/*2 homozygotes so the indicator is effectively heterozygote-vs-wild-type in that cohort).
- **Example models:** `Nemoto_2017_ethanol.R` (additive shift on Vd/F: -20.4 L when ALDH2_S2_CARRIER = 1 vs ALDH2*1/*1 reference; Nemoto 2017 Table II final model).
- **Notes:** ALDH2*2 is the canonical East-Asian aldehyde-dehydrogenase deficiency allele (rs671 GAA -> AAA, Glu487Lys); homozygous *2/*2 subjects are essentially ALDH2-null and experience severe acetaldehyde-flush after even small ethanol doses, so are typically excluded from alcohol-PK studies. The Nemoto 2017 Japanese cohort contained 21/34 (62%) *1/*1 and 13/34 (38%) *1/*2 subjects; no *2/*2 homozygotes were enrolled. The single binary carrier indicator pools heterozygous and homozygous variant carriers; future ethanol-PK extractions that distinguish *1/*2 from *2/*2 effects should register paired `ALDH2_S2_HET` and `ALDH2_S2_HOM` indicators following the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` precedent. Ratified canonically on 2026-05-18 alongside the Nemoto 2017 ethanol extraction.

### ADH1B_S2_HOM (**canonical for ADH1B*2 homozygote indicator**)
- **Description:** Binary genotype indicator for the *ADH1B* `*2` (high-activity) variant allele (rs1229984 G>A; Arg47His, alcohol dehydrogenase 1B class I beta-subunit). 1 = subject is homozygous *2/*2; 0 = otherwise (the default reference covers ADH1B*2/*1 heterozygotes; ADH1B*1/*1 wild-type subjects fall outside the published parameterization of Nemoto 2017 and are conventionally assigned the same reference value as heterozygotes). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (ADH1B*2/*1 heterozygous; ADH1B*1/*1 wild-type by extension when the model is applied beyond the published parameter range).
- **Source aliases:**
  - `ADH1B` (the source NONMEM indicator used in `Nemoto_2017_ethanol.R`; 1 for *2/*2 homozygotes, 0 for *2/*1 heterozygotes; Nemoto 2017 Table II structural model encodes the Vmax conditional via two separate THETAs).
- **Example models:** `Nemoto_2017_ethanol.R` (additive shift on Vmax: +176 mg/h for *2/*2 homozygotes relative to the *2/*1 reference Vmax of 7790 mg/h; Nemoto 2017 Table II final model).
- **Notes:** ADH1B*2 is the East-Asian high-activity alcohol-dehydrogenase variant (rs1229984 Arg47His); the *2 allele encodes a ~40x faster ethanol oxidation rate than the *1 wild-type. Allele frequency in Japanese populations is ~70%, so most subjects are *2/*2 or *1/*2 with a small *1/*1 minority (~9% expected). Nemoto 2017 inherits the two-Vmax parameterization from Seng et al. 2014 (which estimated Vmax separately for *2/*1 and *2/*2 in a Chinese + Indian cohort) and does not report an ADH1B genotype distribution for its 34-subject Japanese cohort; the structural model is silent on ADH1B*1/*1 subjects. Future ADH1B-aware ethanol-PK extractions that explicitly model all three genotypes should register `ADH1B_S2_HET` as a companion indicator following the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` precedent. Ratified canonically on 2026-05-18 alongside the Nemoto 2017 ethanol extraction.

### PFMDR1_86Y (**canonical for P. falciparum pfmdr1 codon-86 tyrosine mutant indicator**)
- **Description:** Plasmodium falciparum pfmdr1 codon-86 tyrosine mutant indicator (1 = single-copy pfmdr1 with the 86Y mutation, Simpson 2013 Genotype 2; 0 = otherwise). Time-fixed per parasite isolate. This is a parasite-genome indicator (not host pharmacogenetics): the "subject" in the NLME framework is a clinical P. falciparum isolate, not a human patient.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = Simpson 2013 Genotype 1 single-copy wild-type 86N/1042N (when all four PFMDR1 indicators are 0). The four PFMDR1 indicators are mutually exclusive in the Simpson 2013 cohort (Thai pfmdr1 point mutations occur almost exclusively on single-copy parasites; amplifications occur exclusively on WT 86N/1042N parasites).
- **Source aliases:**
  - `X1` -- used in `Simpson_2013_artesunate.R`, `Simpson_2013_chloroquine.R`, `Simpson_2013_lumefantrine.R`, `Simpson_2013_mefloquine.R` (per-isolate Genotype-2 indicator).
- **Example models:** `Simpson_2013_artesunate.R`, `Simpson_2013_chloroquine.R`, `Simpson_2013_lumefantrine.R`, `Simpson_2013_mefloquine.R` (multiplicative proportional effect on the sigmoid-Emax EC50, `1 + e_pfmdr1_86y_ec50 * PFMDR1_86Y`, drug-specific magnitude per Simpson 2013 Table 3 Genotype-2 percent-change column).
- **Notes:** Specific scope because the canonical name is a parasite-genome covariate tied to the Simpson 2013 in-vitro antimalarial study design. Explicitly DISTINCT from the human host-pharmacogenetics `ABCB1_*` canonicals (ABCB1 / MDR1 is the human P-glycoprotein efflux-transporter gene; pfmdr1 is the orthologous P. falciparum multidrug-resistance transporter). Member of the four-member mutually-exclusive `PFMDR1_*` Simpson 2013 genotype set (`PFMDR1_86Y`, `PFMDR1_1042D`, `PFMDR1_CN2`, `PFMDR1_CN3PLUS`), with single-copy WT 86N/1042N as the all-zero reference. Future pfmdr1-genotype antimalarial extractions should reuse this set or register sibling parasite-genome canonicals. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

### PFMDR1_1042D (**canonical for P. falciparum pfmdr1 codon-1042 aspartate mutant indicator**)
- **Description:** Plasmodium falciparum pfmdr1 codon-1042 aspartate mutant indicator (1 = single-copy pfmdr1 with the 1042D mutation, Simpson 2013 Genotype 3; 0 = otherwise). Time-fixed per parasite isolate. Parasite-genome indicator (not host pharmacogenetics).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = Simpson 2013 Genotype 1 single-copy wild-type 86N/1042N (when all four PFMDR1 indicators are 0). Mutually exclusive with the other three PFMDR1 indicators.
- **Source aliases:**
  - `X2` -- used in `Simpson_2013_artesunate.R`, `Simpson_2013_chloroquine.R`, `Simpson_2013_lumefantrine.R`, `Simpson_2013_mefloquine.R` (per-isolate Genotype-3 indicator).
- **Example models:** `Simpson_2013_artesunate.R`, `Simpson_2013_chloroquine.R`, `Simpson_2013_lumefantrine.R`, `Simpson_2013_mefloquine.R` (multiplicative proportional effect on the sigmoid-Emax EC50, `1 + e_pfmdr1_1042d_ec50 * PFMDR1_1042D`, drug-specific magnitude per Simpson 2013 Table 3 Genotype-3 percent-change column).
- **Notes:** Specific scope. Distinct from the human `ABCB1_*` canonicals (see `PFMDR1_86Y` notes). Member of the four-member mutually-exclusive `PFMDR1_*` Simpson 2013 genotype set with single-copy WT as the all-zero reference. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

### PFMDR1_CN2 (**canonical for P. falciparum pfmdr1 double-copy amplification indicator**)
- **Description:** Plasmodium falciparum pfmdr1 double-copy amplification indicator (1 = two copies of pfmdr1, all wild-type 86N/1042N, Simpson 2013 Genotype 4; 0 = otherwise). Time-fixed per parasite isolate. Parasite-genome indicator (not host pharmacogenetics).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = Simpson 2013 Genotype 1 single-copy wild-type 86N/1042N (when all four PFMDR1 indicators are 0). Mutually exclusive with the other three PFMDR1 indicators.
- **Source aliases:**
  - `X3` -- used in `Simpson_2013_artesunate.R`, `Simpson_2013_chloroquine.R`, `Simpson_2013_lumefantrine.R`, `Simpson_2013_mefloquine.R` (per-isolate Genotype-4 indicator).
- **Example models:** `Simpson_2013_artesunate.R`, `Simpson_2013_chloroquine.R`, `Simpson_2013_lumefantrine.R`, `Simpson_2013_mefloquine.R` (multiplicative proportional effect on the sigmoid-Emax EC50, `1 + e_pfmdr1_cn2_ec50 * PFMDR1_CN2`, drug-specific magnitude per Simpson 2013 Table 3 Genotype-4 percent-change column).
- **Notes:** Specific scope. Distinct from the human `ABCB1_*` canonicals (see `PFMDR1_86Y` notes). Member of the four-member mutually-exclusive `PFMDR1_*` Simpson 2013 genotype set with single-copy WT as the all-zero reference; `PFMDR1_CN2` (two copies) and `PFMDR1_CN3PLUS` (three-or-more copies) jointly encode the copy-number amplification axis. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

### PFMDR1_CN3PLUS (**canonical for P. falciparum pfmdr1 triple-or-more-copy amplification indicator**)
- **Description:** Plasmodium falciparum pfmdr1 triple-or-more-copy amplification indicator (1 = three or more copies of pfmdr1, all wild-type 86N/1042N, Simpson 2013 Genotype 5; 0 = otherwise). Time-fixed per parasite isolate. Parasite-genome indicator (not host pharmacogenetics).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = Simpson 2013 Genotype 1 single-copy wild-type 86N/1042N (when all four PFMDR1 indicators are 0). Mutually exclusive with the other three PFMDR1 indicators.
- **Source aliases:**
  - `X4` -- used in `Simpson_2013_artesunate.R`, `Simpson_2013_chloroquine.R`, `Simpson_2013_lumefantrine.R`, `Simpson_2013_mefloquine.R` (per-isolate Genotype-5 indicator).
- **Example models:** `Simpson_2013_artesunate.R`, `Simpson_2013_chloroquine.R`, `Simpson_2013_lumefantrine.R`, `Simpson_2013_mefloquine.R` (multiplicative proportional effect on the sigmoid-Emax EC50, `1 + e_pfmdr1_cn3plus_ec50 * PFMDR1_CN3PLUS`, drug-specific magnitude per Simpson 2013 Table 3 Genotype-5 percent-change column; for artesunate and mefloquine the triple+ copy group carries the largest EC50 shift, +127% and +188% respectively).
- **Notes:** Specific scope. Distinct from the human `ABCB1_*` canonicals (see `PFMDR1_86Y` notes). Member of the four-member mutually-exclusive `PFMDR1_*` Simpson 2013 genotype set with single-copy WT as the all-zero reference; paired with `PFMDR1_CN2` on the copy-number amplification axis. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

### CYP2C19 (**canonical for CYP2C19 individual metabolic-activity proxy**)
- **Description:** Continuous individual-level CYP2C19 metabolic-activity proxy. The intent is a single canonical column for any CYP2C19 phenotype proxy that the source paper reports as a continuous number (probe-substrate model-based individual clearance, urinary or serum S/R-mephenytoin ratio, copy-number-corrected expression score, activity-score sum from `*allele` genotypes, etc.); the per-model `covariateData[[CYP2C19]]$units`, `description`, and `notes` document which proxy is in force, the orientation (higher value = higher *or* lower activity, which depends on the probe), and the population-mean reference value used inside the model. Time-invariant in all known examples (germline-genotype-determined enzyme expression measured once via probe-substrate assay).
- **Units:** Paper-specific -- document per-model (e.g., unitless urinary S/R-mephenytoin ratio in Areberg 2006).
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a (continuous). Models center on a population mean (e.g., 0.769 in Areberg 2006); document the reference value per-model in `covariateData[[CYP2C19]]$notes`.
- **Source aliases:**
  - `CYP2C19` -- used directly in `Areberg_2006_escitalopram.R` (Areberg 2006 Equation 2 variable name; the value is the urinary S/R-mephenytoin ratio defined in the Materials and Methods 'CYP2C19 Phenotyping' section).
- **Example models:** `Areberg_2006_escitalopram.R` (linear-deviation effect on apparent oral clearance: `CL/F = 19 - 17 * (CYP2C19 - 0.769)`; CYP2C19 = urinary S/R-mephenytoin ratio, population mean = 0.769; one unit of additional ratio = 17 L/h *lower* clearance, consistent with the biology that higher S/R ratio = lower CYP2C19 activity = lower escitalopram metabolic clearance; Areberg 2006 Equations 1-2 and Table 3).
- **Notes:** Companion to the continuous `CYP2D6` (line 4055) and `CYP3A4` (line 4089) canonicals, ratified here per the prospective registration TODO on the `CYP3A4` line. **Orientation depends on the probe.** Two patterns coexist in the CYP2C19-aware popPK literature:
  - Probe-derived clearance form (analogous to the dextromethorphan-probe CYP2D6 / CYP3A4 columns): higher value = *higher* CYP2C19 activity. Use this orientation when the source reports a probe-substrate-derived individual clearance number.
  - Probe-derived metabolic ratio form (e.g., urinary or serum S/R-mephenytoin ratio): higher value = *lower* CYP2C19 activity, because high CYP2C19 activity selectively metabolizes S-mephenytoin and lowers the residual S enantiomer.
  Per-model `covariateData[[CYP2C19]]$notes` must document which orientation is in force alongside the population-mean reference value and the column's units, mirroring the per-model documentation already used for the `CYP2D6` and `CYP3A4` continuous canonicals. Distinct from the binary genotype-derived canonicals `CYP2C19_S2_CARRIER` (loss-of-function *2 allele carrier), `CYP2C19_IM` (intermediate-metabolizer phenotype), and `CYP2C19_PM` (poor-metabolizer phenotype); use the binary indicators when the source paper reports only a discrete phenotype label, and use `CYP2C19` (continuous) when the source reports a probe-derived continuous number. Ratified canonically on 2026-06-09 alongside the Areberg 2006 escitalopram extraction (founding example uses the urinary S/R-mephenytoin ratio form).


### CYP2C19_NON_EM (**canonical for composite CYP2C19 non-homozygous-extensive-metabolizer indicator**)
- **Description:** 1 = subject is genotyped as a CYP2C19 poor metabolizer (homozygous loss-of-function, e.g. *2/*2, *2/*3, *3/*3) OR a heterozygous-extensive metabolizer (one functional allele and one loss-of-function allele, e.g. *1/*2, *1/*3); 0 = subject is genotyped as a homozygous extensive metabolizer (both alleles functional, *1/*1). Distinct from `CYP2C19_PM` (strict homozygous-PM only) and from `CYP2C19_IM` (intermediate-metabolizer-only); CYP2C19_NON_EM is a paper-defined composite of PM + IM that the source paper used as a single multiplicative covariate on clearance.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (homozygous extensive metabolizer; both *1 alleles).
- **Source aliases:**
  - `PM` (Wahlby 2004 / Walsh 2004 voriconazole source-column convention) -- used in `Wahlby_2004_voriconazole.R`. Note that the column name `PM` in this paper denotes the composite non-EM group, not the strict CYP2C19_PM phenotype; users converting from the source paper's data should not collapse `PM` to canonical `CYP2C19_PM` without re-checking the genotype-to-indicator mapping.
- **Example models:** `Wahlby_2004_voriconazole.R` (multiplicative effect on CL: `(1 - 0.46 * CYP2C19_NON_EM)`, so a non-EM subject has 46 percent lower CL than a homozygous-EM subject).
- **Notes:** Specific scope because the PM+IM composite grouping is paper-defined; future papers that report PM and IM separately should use the strict `CYP2C19_PM` and `CYP2C19_IM` canonicals instead. The Wahlby 2004 / Walsh 2004 voriconazole encoding has been retained because the source paper does not provide separate per-genotype coefficient estimates. Ratified canonically alongside the Wahlby 2004 extraction.


### SNP_ABCB1_RS3842 (**canonical for ABCB1 rs3842 (c.4036A>G, 3' UTR) mutant allele carrier indicator**)
- **Description:** Binary genotype indicator for the *ABCB1* rs3842 single-nucleotide polymorphism (c.4036A>G; in the 3' untranslated region of the ABCB1 / MDR1 / P-glycoprotein gene). 1 = subject carries at least one G (mutant) allele (heterozygous AG or homozygous GG; pooled because the source paper reports the same effect for heterozygotes and homozygotes); 0 = homozygous wild-type (AA). Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (homozygous wild-type AA).
- **Source aliases:**
  - `ABCB1 (rs3842)` -- Mukonzo 2009 (paper Table 1 'ABCB1' section: position `c.4036 A/G`, rs number `rs3842`, allele `New SNP`, protein `3' UTR`, observed SNP frequency 16.8%; Table 3 final-model 'Effect of ABCB1 (rs 3842)' row).
- **Example models:** `Mukonzo_2009_efavirenz.R` (linear-deviation effect on relative bioavailability Frel: `f(depot) = (1 + e_rs3842_fdepot * SNP_ABCB1_RS3842) * exp(etalfdepot)` with `e_rs3842_fdepot = 0.257`; mutant carriers (any G allele) have 25.7% higher Frel than AA wild-type carriers; Mukonzo 2009 Table 3 and Results paragraph 'Pharmacokinetic modelling').
- **Notes:** Distinct from `SNP_ABCB1_RS1045642` (a different SNP at c.3435C>T in exon 26 that affects P-glycoprotein expression / function) and from `ABCB1_HAP_TTT` (the multi-SNP haplotype across rs1128503 / rs2032582 / rs1045642 jointly); use `SNP_ABCB1_RS3842` only when the source paper genotypes the c.4036A>G variant in the 3' UTR. Mukonzo 2009 selected rs3842 from a panel of 13 ABCB1 SNPs as a "previously uncharacterised polymorphism" predicted to have potential regulatory function by bioinformatics tools (Mukonzo 2009 Table 1 'Relevance' = 'Undetermined' at extraction time); the modeled +25.7% effect on relative bioavailability was the strongest single-SNP signal among the 13 ABCB1 candidates in the Mukonzo 2009 backward-elimination step. Heterozygote and homozygote G-carriers are pooled in Mukonzo 2009 because the paper explicitly groups them (Results: "Mutant homozygote and heterozygote individuals for ABCB1 rs3842 exhibited 26% greater efavirenz bioavailability than wild-type carriers"); future extractions that estimate separate het / hom effects should register paired `SNP_ABCB1_RS3842_HET` and `SNP_ABCB1_RS3842_HOM` indicators following the `SLCO1B1_HAP15_HET` / `SLCO1B1_HAP15_HOM` precedent. The Mukonzo 2009 Discussion notes that the rs3842 minor-allele frequency is >20% in Asians, Europeans, and Sub-Saharan Africans according to dbSNP, so the canonical may have broad applicability once additional papers ratify the same encoding. Ratified canonically on 2026-06-13 alongside the Mukonzo 2009 efavirenz extraction.


### SNP_SLC22A2_808GT (**canonical for SLC22A2 (OCT2) c.808G>T A270S variant carrier indicator**)
- **Description:** Binary genotype indicator for the *SLC22A2* (OCT2) c.808G>T single-nucleotide polymorphism (protein change A270S / p.Ala270Ser; widely reported in the pharmacogenomic literature as rs316019). 1 = at least one variant (T) allele present (heterozygous 808GT or homozygous 808TT carrier); 0 = homozygous wild-type 808GG. Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (homozygous wild-type 808GG).
- **Source aliases:**
  - `OCT2-808 G>T` -- Yoon 2013 (paper Table I 'OCT2 808 G>T' row: 96 subjects genotyped as 81 GG + 15 GT + 0 TT, allele frequency 7.8%; Methods 'Statistical Analysis for the Influence of Genetic Polymorphisms' pooled heterozygotes and homozygous variants because the Korean cohort contained no homozygous variants).
- **Example models:** `Yoon_2013_metformin.R` (multiplicative fractional shift on CL/F: `cl = exp(lcl + etalcl) * (1 + e_snp_oct2_cl * SNP_SLC22A2_808GT) * (1 + e_snp_octn1_cl * SNP_SLC22A4_917CT)` with `e_snp_oct2_cl = -0.248`; 808G>T variant carriers have 24.8% lower apparent oral clearance than 808GG wild-type at the OCTN1 reference; Yoon 2013 Table III theta_OCT2 with RSE 15.8%).
- **Notes:** Position-based canonical name because the source paper reports the SNP by nucleotide position and protein change without an accompanying rsID; the SLC22A2 A270S variant is nevertheless documented across the pharmacogenomic literature as rs316019 and downstream extractions that report the rsID directly may register a paired canonical `SNP_SLC22A2_RS316019` with an alias link back to this entry. OCT2 (organic cation transporter 2) is the basolateral renal-tubule uptake transporter for organic cations, including metformin; the A270S substitution reduces cellular metformin uptake in HEK293 in-vitro transport assays. In the Yoon 2013 Korean-male healthy-volunteer cohort the 808G>T variant allele frequency was 7.8% (Table I), comparable to previously reported frequencies of 16.8% in Japanese-Asian and 16% in European-American populations (Yoon 2013 Table I 'Asian' and 'European' columns). Ratified canonically on 2026-07-08 alongside the Yoon 2013 metformin extraction.


### SNP_SLC22A4_917CT (**canonical for SLC22A4 (OCTN1) c.917C>T T306I variant carrier indicator**)
- **Description:** Binary genotype indicator for the *SLC22A4* (OCTN1) c.917C>T single-nucleotide polymorphism (protein change T306I / p.Thr306Ile). 1 = at least one variant (T) allele present (heterozygous 917CT or homozygous 917TT carrier); 0 = homozygous wild-type 917CC. Time-fixed per subject (germline genotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (homozygous wild-type 917CC).
- **Source aliases:**
  - `OCTN1-917C>T` -- Yoon 2013 (paper Table I 'OCTN1 917C>T' row: 96 subjects genotyped as 10 CC + 52 CT + 34 TT, allele frequency 62.5%; Methods 'Statistical Analysis for the Influence of Genetic Polymorphisms' pooled heterozygotes and homozygous variants).
- **Example models:** `Yoon_2013_metformin.R` (multiplicative fractional shift on CL/F: `cl = exp(lcl + etalcl) * (1 + e_snp_oct2_cl * SNP_SLC22A2_808GT) * (1 + e_snp_octn1_cl * SNP_SLC22A4_917CT)` with `e_snp_octn1_cl = -0.234`; 917C>T variant carriers have 23.4% lower apparent oral clearance than 917CC wild-type at the OCT2 reference; Yoon 2013 Table III theta_OCTN1 with RSE 61.1%).
- **Notes:** Position-based canonical name because the source paper reports the SNP by nucleotide position and protein change without an accompanying rsID. Distinct from the well-characterised SLC22A4 c.1507C>T (L503F, rs1050152) OCTN1 variant which was also genotyped by Yoon 2013 but occurred with zero variant-allele frequency in the Korean cohort (Table I) and was not carried forward as a model covariate. OCTN1 (organic cation transporter, novel, type 1) is expressed at the apical membrane of renal proximal-tubule cells and contributes to the renal secretion of metformin; the T306I substitution is a candidate function-altering variant whose direct in-vitro effect on metformin transport has not been characterised (Yoon 2013 Discussion notes that in-vitro confirmation is still needed). Yoon 2013 Discussion further notes that the 917C>T variant-allele carrier rate is ~90% in Koreans, ~60% in other Asians, and ~33% in Caucasians, so the impact of OCTN1 917C>T on metformin PK is expected to be larger in Asian populations. Ratified canonically on 2026-07-08 alongside the Yoon 2013 metformin extraction.


### SNP_CYP2B6_RS35303484_G_COUNT (**canonical for CYP2B6 136A>G (rs35303484) G-allele count**)
- **Description:** Continuous individual-level CYP2B6 c.136A>G (rs35303484, p.M46V) G-allele count: 0 = AA homozygous wild-type, 1 = AG heterozygous, 2 = GG homozygous variant. Time-invariant (germline genotype). The 136A>G variant defines the CYP2B6*11 haplotype (a phenotypic null allele in the CYP allele nomenclature) and is most commonly observed in Sub-Saharan African populations.
- **Units:** (count, 0/1/2 alleles per subject)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a (continuous). In the Mukonzo 2009 efavirenz model the count is decomposed in `model()` into a homozygous-mutant-only indicator (`count == 2`) multiplied by a -0.199 multiplicative shift on CL/F. Heterozygotes are pooled with wild-type and receive no shift, consistent with the source paper's reported effect for "homozygous CYP2B6*11" only (Mukonzo 2009 Results paragraph 'Pharmacokinetic modelling').
- **Source aliases:**
  - `CYP2B6*11` -- Mukonzo 2009 (paper Table 1 SNP-table column 'Allele' and Table 3 'Effect of CYP2B6*11' row; rs35303484 is the defining SNP at c.136 A->G with p.M46V protein change).
- **Example models:** `Mukonzo_2009_efavirenz.R` (homozygous-mutant-only multiplicative shift on CL/F: `cl = exp(lcl + etalcl) * (1 + e_2b6_6_cl * (SNP_CYP2B6_RS3745274_T_COUNT == 2)) * (1 + e_2b6_11_cl * (SNP_CYP2B6_RS35303484_G_COUNT == 2))` with `e_2b6_11_cl = -0.199`; homozygous *11 carriers have 19.9% lower CL/F than the wild-type / heterozygous reference; Mukonzo 2009 Table 3).
- **Notes:** Distribution in the Mukonzo 2009 Ugandan healthy-volunteer cohort (Table 1, n = 121): 136A>G allele frequency 13.6%, expected genotype frequencies under Hardy-Weinberg AA 74.6%, AG 23.5%, GG 1.9%. CYP2B6*11 is found primarily in African populations (allele frequency ~7-15% in Sub-Saharan Africans, ~0-1% in Europeans and East Asians); the M46V substitution lies in the substrate-binding pocket and abolishes detectable enzyme activity in recombinant expression studies. Often co-tested with the more common 516G>T (CYP2B6*6) variant; in the Mukonzo 2009 Ugandan cohort the two variants segregated independently. The count-form encoding follows the same rationale as `SNP_CYP2B6_RS3745274_T_COUNT` and `SNP_CYP2B6_RS28399499_C_COUNT`: a single count column captures the underlying genotype without redundancy and lets the model code derive either a linear per-allele effect (`* count`) or a non-additive HET / HOM decomposition (`* (count == 1)`, `* (count == 2)`) depending on the source paper's parameterization. Ratified canonically on 2026-06-13 alongside the Mukonzo 2009 efavirenz extraction.


## Lifestyle / medical history

### SMOKE (**canonical for current-smoker binary indicator**)
- **Description:** 1 = current smoker at baseline, 0 = non-smoker.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-smoker).
- **Source aliases:**
  - `Smoking` (case-insensitive) -- used in `Ma_2020_sarilumab_anc.R`.
- **Example models:** `Ma_2020_sarilumab_anc.R` (power-form on baseline ANC: `BASE * 1.15^SMOKE`).
- **Notes:** Baseline-only indicator; does not track within-study smoking-cessation changes. Use this two-level (current vs non-smoker) encoding when the source paper does not split former and never smokers. When the source uses a 3-level smoking-status categorical (never / former / current), use the paired `SMOKE_CURRENT` + `SMOKE_NEVER` indicators below instead -- the 3-level encoding cannot be reduced to a single `SMOKE` column without losing information.

### SMOKE_CURRENT (**canonical for current-smoker indicator (paired with SMOKE_NEVER)**)
- **Description:** 1 = current smoker at baseline, 0 otherwise (former or never smoker). Paired with `SMOKE_NEVER` to encode a 3-level smoking-status categorical with former smoker as the implicit reference (both indicators = 0).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (former smoker, when paired with `SMOKE_NEVER` = 0). The pairing follows the `RACE_<GROUP>` convention for paired indicators.
- **Source aliases:**
  - `Smoking status = Current` / `SMOK = 2` (case-insensitive) -- derived from a 3-level smoking-status column.
- **Example models:** `Hwang_2023_monalizumab.R` (proportional-shift effect on V1: `(1 + 0.0484)^SMOKE_CURRENT`; reference category former smoker).
- **Notes:** Baseline-only indicator. See also `SMOKE_NEVER` (paired indicator) and `SMOKE` (binary current-vs-non-smoker encoding when the source paper does not split former vs never).

### SMOKE_NEVER (**canonical for never-smoker indicator (paired with SMOKE_CURRENT)**)
- **Description:** 1 = never smoker at baseline, 0 otherwise (former or current smoker). Paired with `SMOKE_CURRENT` to encode a 3-level smoking-status categorical with former smoker as the implicit reference (both indicators = 0).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (former smoker, when paired with `SMOKE_CURRENT` = 0). The pairing follows the `RACE_<GROUP>` convention for paired indicators.
- **Source aliases:**
  - `Smoking status = Never` / `SMOK = 0` (case-insensitive) -- derived from a 3-level smoking-status column.
- **Example models:** `Hwang_2023_monalizumab.R` (proportional-shift effect on V1: `(1 - 0.141)^SMOKE_NEVER`; reference category former smoker).
- **Notes:** Baseline-only indicator. See also `SMOKE_CURRENT` (paired indicator) and `SMOKE` (binary current-vs-non-smoker encoding when the source paper does not split former vs never).

### ALCOHOL_ABUSE (**canonical for chronic alcohol abuse clinical indicator**)
- **Description:** 1 = subject has a documented history of chronic alcohol abuse (defined per paper; commonly >= 6 standard drinks / units per day for a sustained period, per Swart 2004 which follows the Vrije Universiteit Medical Center clinical criteria); 0 = no such history. Subject-level baseline behavioural / clinical-history flag captured at study or ICU admission from patient / next-of-kin interview, not a laboratory measurement and NOT a pharmacogenomic marker. Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no chronic alcohol abuse).
- **Source aliases:**
  - `alcohol abuse` -- printed prose label in Swart 2004 Results / Table 4 / Table 5 CL formulas ("CL no alcohol abuse" vs "CL alcohol abuse"); used in `Swart_2004_lorazepam.R` and `Swart_2004_midazolam.R`.
- **Example models:** `Swart_2004_lorazepam.R` (selector effect on CL: alcohol-abuse patients take a flat `cl = 0.74` L/h; non-alcohol-abuse patients take the PEEP-adjusted `cl = 4.13 - (PEEP - 5) * 0.417` L/h; contrast is a ~5.6-fold reduction in CL), `Swart_2004_midazolam.R` (selector effect on CL: alcohol-abuse patients take `cl = 7.3 - (AGE - 57) * 0.145` L/h; non-alcohol-abuse patients take `cl = 11.3 - (AGE - 57) * 0.145` L/h; same age slope in both strata, ~4 L/h lower baseline in the alcohol-abuse stratum at age 57).
- **Notes:** General scope because chronic alcohol abuse is a clinically-defined behavioural indicator applicable across drug classes and populations, and appears frequently as a hepatic-metabolism modifier (predominantly via CYP2E1 induction and cirrhosis-linked reductions in phase-I and phase-II capacity). Critically distinct from the pharmacogenomic `ADH1B_S2_HOM` / `ADH1B_S2_CARRIER` canonicals (Nemoto 2017 ethanol PK): those encode a germline high-activity aldehyde-dehydrogenase-1B allele (rs1229984) that changes intrinsic ethanol clearance; `ALCOHOL_ABUSE` encodes patient behaviour / history and would remain 1 regardless of ADH1B genotype. Per-model `covariateData[[ALCOHOL_ABUSE]]$notes` must document the paper's exact definition (units/day, duration, ascertainment source); Swart 2004 uses "> 6 units per day" as the threshold. Future popPK papers using a different definition (e.g., DSM alcohol use disorder criteria, positive CAGE screen, ICD codes) can extend `Example models` provided they document the definition in per-model notes; if the paper uses a numerically-graded alcohol-use covariate rather than a binary flag, prefer a separate ordinal canonical rather than reusing this binary. Ratified canonically on 2026-07-26 alongside the Swart 2004 lorazepam / midazolam extraction.

## Formulation / assay / study

### ROUTE_* family -- section-header policy

All `ROUTE_<TARGET>` canonicals follow the same shape: a binary indicator where 1 = subject received the `<TARGET>` administration route and 0 = subject received the *reference* (non-target) route. The reference category is route-pair-specific and is documented per-canonical in the `Reference category:` field (e.g., `ROUTE_IV` references SC; `ROUTE_IP` references non-IP; `ROUTE_NGT` references oral). The 2026-06-19 canonical-register standardization audit reviewed renaming this family to a more explicit `ROUTE_<TARGET>_VS_<REFERENCE>` shape but the operator declined the rename and instead set the following documentation discipline:

**Policy (2026-06-19 audit):** every `ROUTE_*` canonical MUST document its reference category explicitly in the `Reference category:` field of its H3 block. The reference is what `ROUTE_<TARGET> = 0` means in the source dataset and is paper-specific. New `ROUTE_*` canonicals registered after 2026-06-19 must follow this rule; existing entries already comply. The discipline eliminates the silent-mis-encoding risk (where a model file might assume the wrong reference category) by making the reference explicit in the register rather than relying on the column name alone.

### ROUTE_IV (**canonical for IV-vs-SC administration route indicator**)
- **Description:** 1 = subject received intravenous (IV) administration, 0 = subcutaneous (SC) administration. Per-subject (study-fixed) covariate flagging the dosing route when a population analysis pools cohorts that differ by route, with covariate effects on PK parameters that capture route-specific disposition behaviour.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (SC).
- **Source aliases:**
  - "Admin route = IV" (categorical effect column in Yu 2022 covariate equations).
  - `IV` -- used in `Zierhut_2008_osteoprotegerin.R` (DDMODEL00000233 `dataObj` column flagging IV vs SC cohort, switching the PK observation residual SD between `CcpropSdIV` and `CcpropSdSC`).
  - "IV" / "SC arm" -- used in `Wang_2021_pertuzumab.R` (per-subject FeDeriCa arm indicator P+H IV vs PH FDC SC, switching the proportional residual SD between `CcpropSdIv` and `CcpropSdSc`).
- **Example models:**
  - `Yu_2022_ofatumumab.R` (exponential effect on R0, CL, Q, ksyninf).
  - `Zierhut_2008_osteoprotegerin.R` (per-subject indicator switching the PK observation residual SD between the IV cohort (`CcpropSdIV`) and the SC cohort (`CcpropSdSC`)).
  - `Wang_2021_pertuzumab.R` (per-subject indicator switching the proportional residual SD between the IV (`CcpropSdIv` = 0.175) and SC (`CcpropSdSc` = 0.155) cohorts of the FeDeriCa popPK).
  - `Fiedler-Kelly_2019_fremanezumab.R` (per-subject indicator switching both the central volume of distribution (Vc,IV = 2.98 L FIXED vs Vc,SC = 1.88 L) and the residual-error structure (IV: proportional-only with SD = sqrt(0.0467) = 0.21610; SC: combined additive sqrt(0.204) + proportional sqrt(0.0531)) in the pooled phase 1/2b/3 fremanezumab popPK).
  - `Fanta_2007_ciclosporin.R` (per-occasion indicator carrying a 23% lower typical CL on oral occasions than on IV occasions (`exp(e_route_iv_cl * ROUTE_IV)` with `e_route_iv_cl = log(1/0.77) = 0.2614`) and switching the residual-error structure between IV (`propSdIv = 0.089` + `addSdIv = 1.5 ug/L`) and oral (`propSdPo = 0.20`) arms; the comparator non-IV route is oral microemulsion ciclosporin, not SC).
  - `Koolen_2010_docetaxel.R` (per-dose-record indicator switching the docetaxel central volume of distribution between the IV value (V_central_iv = 9.8 L, polysorbate-80-micelle-sequestered) and the oral value (V_central_po = 44.0 L) per Koolen 2010 Results 'Volume of distribution' section; the reference category is oral here, not SC, because the source paper pools IV and oral docetaxel cohorts).
  - `Roepcke_2018_tak_079.R` (per-subject / per-dose-record indicator switching the TAK-079 central volume of distribution between the IV reference (Vc = 0.141 L, monkey) and SC (Vc = 0.043 L, ca. 70% smaller) via `vc = exp(lvc + etalvc) * (1 - e_route_iv_vc * (1 - ROUTE_IV))` with `e_route_iv_vc = 0.697` per Roepcke 2018 Table 2 'ROUT on V_C'; SC data come from the four lower single-dose groups <= 1 mg/kg in studies 7 and 8).
  - `vandenBerg_2021_uprifosbuvir_pbpk.R` (per-dose-record indicator switching between IV and oral uprifosbuvir; when ROUTE_IV = 1 the oral-absorption fast/slow fractions F1/F2 are set to 0 so the depot compartments contribute no mass and the dose must be placed directly into the central compartment via `cmt = 'central'` on the event record; when ROUTE_IV = 0 the standard oral absorption applies per FORM_CAPSULE and CONMED_ITRACONAZOLE. Corresponds to NONMEM FORM = 10 for the IV cohort in van den Berg 2021 supplement Table 1).
- **Notes:** This is the per-subject covariate-equation indicator, distinct from the dosing-event `cmt` column that names the target compartment. When simulating, set `ROUTE_IV = 1` for IV cohorts and dose into the central compartment; set `ROUTE_IV = 0` for SC cohorts and dose into the depot. Scope: specific because the set of parameters that differ by route is paper-specific (Yu 2022 carries route-specific exponential effects on disposition parameters; Zierhut 2008, Wang 2021, and Fiedler-Kelly 2019 carry route-specific PK observation residual SDs; Fiedler-Kelly 2019 and Roepcke 2018 additionally carry a route-specific central volume of distribution; van den Berg 2021 gates the entire oral-absorption cascade including gut wall extraction, four-step transit chain, and pseudo-M4 gut pool so that IV doses bypass the first-pass metabolism to gut UMP-UTP-CTP-CMP cycle).

### ROUTE_IP (**canonical for intraperitoneal-vs-non-IP administration route indicator**)
- **Description:** 1 = subject (or dose record) received intraperitoneal (IP) administration; 0 = subcutaneous (SC), intravenous (IV), or any other non-IP route. Per-dose-record covariate flagging the IP route when a preclinical popPK pools multiple routes with route-specific bioavailability and the IP arm is the only route that carries a non-unity F.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-IP, typically SC or IV with bioavailability fixed at 1).
- **Source aliases:**
  - "IP" (route label in Johnson 2011 Table I, per-study route designation across 12 rat studies -- IP studies 1-6b, SC studies 7-11, IV study 12).
- **Example models:**
  - `Johnson_2011_olanzapine_rat.R` (per-dose-record indicator selecting the IP bioavailability `FIP = 0.636` with 87% CV log-normal IIV; ROUTE_IP = 0 selects F = 1 for SC and IV. The encoding `f(central) <- exp(ROUTE_IP * (lfip + etalfip))` collapses to 1 when ROUTE_IP = 0 because exp(0) = 1, so subjects dosed via SC or IV inherit complete bioavailability without IIV on F).
- **Notes:** This is the per-dose-record covariate-equation indicator, distinct from the dosing-event `cmt` column that names the target compartment (Johnson 2011 doses all routes directly into `central` because the absorption rate constant was not estimable from the available data). When simulating IP doses, set `ROUTE_IP = 1` on the dose record(s); set `ROUTE_IP = 0` for SC and IV dose records. Scope: specific because the IP-vs-other contrast and which parameter it modifies (here, bioavailability) is paper-specific; complementary to `ROUTE_IV` (IV-vs-SC indicator) -- a future tri-route study could use both indicators jointly.

### ROUTE_NGT (**canonical for nasogastric-tube-vs-oral administration route indicator**)
- **Description:** 1 = dose record administered by nasogastric tube (NGT), 0 = oral administration. Per-dose-record covariate flagging NGT delivery when an oral popPK pools whole-tablet / crushed-tablet oral dosing with crushed-tablet-via-NGT dosing, with the route effect captured on an absorption parameter.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (oral administration; the structural reference for the Denti 2018 typical absorption lag time T_lag of 0.242 h).
- **Source aliases:**
  - `NGT` -- used in `Denti_2018_levofloxacin.R` (per-dose-record nasogastric-tube indicator).
- **Example models:** `Denti_2018_levofloxacin.R` (multiplicative effect on the absorption lag time: `(1 + e_route_ngt_tlag * ROUTE_NGT)` with `e_route_ngt_tlag = -0.856`, so NGT delivery shortens T_lag to ~14.4% of its oral value; in the cohort 90/109 (82.6%) children were dosed by crushed tablet via NGT, Denti 2018 Table 2).
- **Notes:** This is the per-dose-record covariate-equation indicator, distinct from the dosing-event `cmt` column that names the target compartment. When simulating NGT doses, set `ROUTE_NGT = 1` on the dose record(s); set `ROUTE_NGT = 0` for oral dose records. Scope: specific because the NGT-vs-oral contrast and which absorption parameter it modifies are paper-specific. Distinct from the `ROUTE_IV` (IV-vs-SC) and `ROUTE_IP` (IP-vs-non-IP) parenteral-route indicators -- `ROUTE_NGT` is an enteral-delivery-method indicator within oral administration. Ratified canonically alongside the Denti 2018 levofloxacin extraction.

### ROUTE_VAGINAL (**canonical for vaginal-vs-buccal administration route indicator**)
- **Description:** 1 = subject received vaginal misoprostol administration, 0 = buccal administration. Per-subject (study-fixed) covariate flagging the dosing route when a population analysis pools vaginal and buccal cohorts of a mucosally absorbed drug, with the buccal cohort as the structural reference (F = 1) and vaginal-specific effects encoded as a relative bioavailability multiplier and separate absorption rate constants per route x dose combination.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (buccal administration; the structural reference in Vorontsova 2022, whose relative bioavailability parameter `F_v/b = 2.3` is the ratio of vaginal to buccal bioavailability with the buccal value fixed at 1 per the paper's model-construction statement 'we included a relative bioavailability term (F_v/b) for the vaginal route relative to buccal (F_b, assumed to be one)').
- **Source aliases:**
  - `ROUTE` -- used in `Vorontsova_2022_misoprostol.R` (IMPROVE-trial per-subject randomization indicator flagging vaginal vs buccal misoprostol for full-term labor induction).
- **Example models:** `Vorontsova_2022_misoprostol.R` (per-subject indicator selecting between four absorption rate constants (`ka_buccal_25`, `ka_buccal_50`, `ka_vaginal_25`, `ka_vaginal_50` indexed by `ROUTE_VAGINAL` x `DOSE`) and applying the relative bioavailability multiplier `F_v/b = 2.3` on the depot dose for vaginal subjects only via `fdepot = 1 - ROUTE_VAGINAL + ROUTE_VAGINAL * fvb`; inter-occasion variability on `F_v/b` is estimated across the two sampled dose events).
- **Notes:** This is the per-subject covariate-equation indicator, distinct from the dosing-event `cmt` column that names the target compartment (Vorontsova 2022 doses both routes into the same `depot` absorption compartment; only the ka value and bioavailability switch by route). Complementary to `ROUTE_IV` (IV-vs-SC), `ROUTE_IP` (IP-vs-non-IP), `ROUTE_NGT` (NGT-vs-oral), and `ROUTE_SC` (SC-vs-IM) -- `ROUTE_VAGINAL` fills the vaginal-vs-buccal contrast for mucosally absorbed drugs (misoprostol, obstetric prostaglandins, hormonal contraceptives) that neither of those cover. Scope: specific because the vaginal-vs-buccal contrast and which parameters switch by route are paper-specific (Vorontsova 2022 carries route-specific ka values crossed with dose level and a single relative bioavailability multiplier; a future paper could carry different route effects on CL, V, or Tlag). Ratified canonically alongside the Vorontsova 2022 misoprostol extraction.

### ROUTE_SC (**canonical for subcutaneous-vs-intramuscular administration route indicator**)
- **Description:** 1 = dose record (or subject occasion) administered by subcutaneous (SC) injection, 0 = intramuscular (IM) injection. Per-dose-record / per-occasion covariate flagging SC delivery when a population analysis pools IM and SC injection cohorts of an injectable long-acting formulation, with the IM cohort as the structural reference and SC-specific effects encoded as multiplicative factors on absorption half-lives, dose-split ratios, and relative bioavailability.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (IM injection; the structural reference in Kado 2020, whose "Relative structural model parameters for SC administration" are all ratios of SC-value / IM-value with IM's F fixed at 1).
- **Source aliases:**
  - `SC` / `route = SC` -- Kado 2020 Table 1 SC-relative parameter block (SC vs IM crossover of benzathine benzylpenicillin G).
- **Example models:** `Kado_2020_benzathine_benzylpenicillin_g.R` (per-dose-record indicator switching the three absorption half-lives (t1/2,abs-1/-2/-3), the transit half-life (t1/2,tr), the two dose-split ratios (RAT-transit, RAT-slowfast), and the relative bioavailability (F_SC = 0.957) between the IM reference and the SC structural values; all IIVs on absorption parameters are also route-specific per Kado 2020 Methods 'PK modelling').
- **Notes:** This is the per-dose-record covariate-equation indicator, distinct from the dosing-event `cmt` column that names the target compartment (Kado 2020 doses all routes into the same triple-depot absorption chain; only the parameter values switch by route). Complementary to `ROUTE_IV` (IV-vs-SC, where SC is the reference) and `ROUTE_IP` (IP-vs-non-IP, where non-IP including SC is the reference) -- `ROUTE_SC` fills the SC-vs-IM contrast that neither of those cover. Scope: specific because the IM-vs-SC contrast and the set of parameters that differ by route are paper-specific.

### DEVICE_AI (**canonical for autoinjector-vs-prefilled-syringe SC device indicator**)
- **Description:** 1 = subject's SC dose delivered via autoinjector (AI), 0 = prefilled syringe (PFS). Per-subject (study-fixed) covariate flagging the SC delivery device when a model carries device-specific PK effects.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (PFS).
- **Source aliases:** "Formulation = AI" (categorical effect column in Yu 2022 covariate equations).
- **Example models:** `Yu_2022_ofatumumab.R` (exponential effect on k_e(P) and R0), `Diep_2026_donidalorsen.R` (Phoenix linear-effect `(1 + e_device_ai_ka * DEVICE_AI)` on the typical SC absorption rate constant with theta = +0.262 -> multiplier 1.262 for autoinjector vs vial-and-syringe reference; characterized in the ISIS 721744-CS9 single-dose bioequivalence cohort).
- **Notes:** Set to 0 (PFS / vial reference) for IV subjects, since the device is undefined for IV; the IV-specific effects are captured by `ROUTE_IV` instead. Scope: specific because the AI / vial / PFS contrast and which parameters it affects depend on the study's device-comparison design.

### INJSITE_ARM (**canonical for SC injection-site = arm indicator**)
- **Description:** 1 = subject's SC dose injected into the arm, 0 = abdomen (the universal SC reference site across the popPK literature). Per-dose-record covariate flagging the SC injection site when a population analysis estimates site-specific absorption parameters.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (abdomen).
- **Source aliases:** paper narrative "arm" / "abdomen" subgroup labels driving site-specific ka in Diep 2022.
- **Example models:** `Diep_2022_eplontersen.R` (additive log-shift `e_injsite_arm_ka = log(ka_arm / ka_ab)` on the typical absorption rate constant: ka_arm = 0.217 1/h vs ka_ab = 0.282 1/h, ~30% higher ka for abdomen; INJSITE_ARM = 1 selects the arm typical value), `Diep_2026_donidalorsen.R` (Phoenix linear-effect `(1 + e_injsite_arm_ka * INJSITE_ARM)` on the typical absorption rate constant with theta = -0.338 -> multiplier 0.662 for arm; the paper's reference category is "abdomen or thigh" rather than "abdomen" alone, but is consistent with the canonical reference because abdomen is the universal SC reference site and the thigh effect is pooled into the reference category by the Diep 2026 model), `CarlssonPetri_2018_semaglutide.R` (multiplicative CL/F ratio 1.08 for upper-arm vs abdomen injection site, encoded as `e_site_arm_cl^INJSITE_ARM` per Table S3; per-subject dominant injection-site indicator since the paper assigns each subject their most frequently used site).
- **Notes:** Specific scope because the arm-vs-abdomen contrast is paper-specific. Sister canonical to `INJSITE_THIGH` (thigh-vs-abdomen indicator; founded 2026-07-09 alongside the CarlssonPetri_2018_semaglutide extraction). Per-administration rather than per-subject in the Diep 2022 / 2026 usage (a subject in a multi-dose simulation can switch SC injection sites between doses; supply the indicator on each dose record), but per-subject in the Carlsson Petri 2018 usage (each subject's dominant / most frequently used injection site was used as the covariate value). Distinct from `ROUTE_IV` (IV vs SC route, not within-SC site) and from `DEVICE_AI` (autoinjector vs prefilled syringe, device rather than anatomical site).

### INJSITE_THIGH (**canonical for SC injection-site = thigh indicator**)
- **Description:** 1 = subject's SC dose injected into the thigh, 0 = abdomen (the universal SC reference site across the popPK literature) or a different non-thigh injection site. Per-dose-record OR per-subject covariate flagging the SC injection site when a population analysis estimates site-specific absorption or clearance effects.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (abdomen).
- **Source aliases:** paper narrative "thigh" / "abdomen" subgroup labels driving site-specific PK parameters.
- **Example models:** `CarlssonPetri_2018_semaglutide.R` (multiplicative CL/F ratio 1.04 for thigh vs abdomen injection site, encoded as `e_site_thigh_cl^INJSITE_THIGH` per Table S3; per-subject dominant injection-site indicator since the paper assigns each subject their most frequently used site).
- **Notes:** Specific scope because the thigh-vs-abdomen contrast is paper-specific. Sister canonical to `INJSITE_ARM` (arm-vs-abdomen). Per-administration or per-subject depending on the paper's dosing granularity -- in Carlsson Petri 2018 the covariate is per-subject because 'the most frequently used injection site for an individual patient was used as the covariate value' (Methods); a future paper with per-dose injection-site data could supply this as a time-varying per-dose-record covariate. Distinct from `ROUTE_IV` (IV vs SC route, not within-SC site) and from `DEVICE_AI` (autoinjector vs prefilled syringe, device rather than anatomical site). Founded alongside the CarlssonPetri_2018_semaglutide extraction.

### STUDY_APLIOS (**canonical for APLIOS bioequivalence study indicator**)
- **Description:** 1 = subject enrolled in the APLIOS bioequivalence study (NCT03560739; phase 2; ofatumumab AI vs PFS in RMS), 0 = other study in the Yu 2022 pooled analysis.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-APLIOS studies: OMS115102, MIRROR, ASCLEPIOS I, ASCLEPIOS II).
- **Source aliases:** "Study = APLIOS" (categorical effect column in Yu 2022 covariate equations).
- **Example models:** `Yu_2022_ofatumumab.R` (exponential effect on Emax of B cell lysis).
- **Notes:** Captures a between-study shift in the maximum B-cell lysis stimulatory effect not explained by the other covariates in the final model.

### STUDY_MIRROR (**canonical for MIRROR dose-finding study indicator**)
- **Description:** 1 = subject enrolled in the MIRROR dose-finding study (NCT01457924; phase 2; SC ofatumumab dose-ranging in RRMS), 0 = other study in the Yu 2022 pooled analysis.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-MIRROR studies: OMS115102, APLIOS, ASCLEPIOS I, ASCLEPIOS II).
- **Source aliases:** "Study = MIRROR" (categorical effect column in Yu 2022 covariate equations).
- **Example models:** `Yu_2022_ofatumumab.R` (exponential effect on B cell elimination rate kout).
- **Notes:** Captures a between-study shift in the B cell elimination rate not explained by the other covariates in the final model.

### STUDY_ARROW_PART2 (**canonical for ARROW PK Substudy Part 2 study indicator**)
- **Description:** 1 = record from the ARROW PK Substudy Part 2 (the antiretroviral-dosing substudy of the ARROW trial in African children living with HIV-1), 0 = record from any other study pooled into the analysis. Per-record (study-fixed) binary indicator.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (all other pooled studies; relative bioavailability fixed at 1).
- **Source aliases:**
  - `F1` study term -- used in `Chandasana_2024b_abacavir.R`; Chandasana 2024 Table 1 names the two estimates "F, tablet ARROW PK Substudy Part 2" and "F, solution ARROW PK Substudy Part 2".
- **Example models:** `Chandasana_2024b_abacavir.R` (study-specific relative bioavailability on the abacavir depot, applied jointly with `FORM_SOLUTION` so that the ARROW Part 2 tablet gets 1.62 and the ARROW Part 2 oral solution gets 1.75, both relative to the all-other-studies reference of 1; encoded on the log scale as `exp(e_arrow_tab_fdepot * STUDY_ARROW_PART2 * (1 - FORM_SOLUTION) + e_arrow_sol_fdepot * STUDY_ARROW_PART2 * FORM_SOLUTION)`).
- **Notes:** The source model included this term because abacavir exposure observed in ARROW PK Substudy Part 2 was substantially higher than in the other pooled studies despite similar doses and formulations (Chandasana 2024 "ABC Pediatric PopPK Model"), i.e. it absorbs an unexplained between-study bioavailability shift rather than a mechanistic covariate effect. The effect is formulation-specific within the substudy, so it is always paired with a formulation indicator. Sibling of the other `STUDY_<id>` members; scoped specific because the reference category is the paper's own pooled-study set. Ratified canonically alongside the Chandasana 2024 pediatric ABC/DTG/3TC extraction.

### STUDY_ODYSSEY (**canonical for ODYSSEY trial study indicator**)
- **Description:** 1 = record from the ODYSSEY trial (a multicentre randomised trial of antiretroviral regimens in children and adolescents living with HIV-1), 0 = record from the comparator study pooled into the analysis. Per-record (study-fixed) binary indicator.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (the per-paper comparator pooled study; IMPAACT P1093 in Chandasana 2024).
- **Source aliases:**
  - `study` -- used in `Chandasana_2024b_dolutegravir.R`; Chandasana 2024 Table 2 labels the residual-error rows "study P1093" and "ODYSSEY".
- **Example models:** `Chandasana_2024b_dolutegravir.R` (selects between the two study-specific residual-error magnitudes of the pooled dolutegravir pediatric model: proportional 28.6% plus additive 0.00164 ug/mL for the IMPAACT P1093 reference, and proportional 11.1% plus additive 0.090 ug/mL for ODYSSEY; applied inside `model()` as `propSd_i <- propSd * (1 - STUDY_ODYSSEY) + propSd_odyssey * STUDY_ODYSSEY` and likewise for the additive term).
- **Notes:** Distinguishes the two pooled source studies of the pediatric dolutegravir popPK model. The between-study difference is confined to residual error (assay and sampling-design differences), not to any structural or covariate parameter. Records that belong to neither pooled study -- for example the IMPAACT 2019 external-validation cohort -- take 0 so the P1093 residual error applies, that being the pediatric dolutegravir single-entity study most comparable to IMPAACT 2019. Sibling of the other `STUDY_<id>` members. Ratified canonically alongside the Chandasana 2024 pediatric ABC/DTG/3TC extraction.

### STUDY_DORZA_EARLY (**canonical for early-phase dorzagliatin study indicator (HMM0102 / HMM0103 / HMM0110)**)
- **Description:** 1 = subject enrolled in one of the three early-phase dorzagliatin studies pooled by Wang 2023 -- HMM0102 (NCT02077452, multiple-ascending-dose), HMM0103 (NCT02386982, 28-day beta-cell-function study) or HMM0110 (NCT04324424, renal-impairment study with matched healthy volunteers); 0 = one of the three later-phase studies HMM0201 (NCT02561338, phase II dose-ranging), HMM0301 (NCT03173391, phase III monotherapy) or HMM0302 (NCT03141073, phase III metformin add-on). Used to switch apparent clearance between the two study groups.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (later-phase studies HMM0201 / HMM0301 / HMM0302, which used near-commercial preparations).
- **Source aliases:**
  - `Study` -- used in `Wang_2023_dorzagliatin.R` (Wang 2023 Table 3 footnote: "Study: 1 for study 102/103/110, 0 for others").
- **Example models:** `Wang_2023_dorzagliatin.R` (multiplicative exponential effect on CL/F: `exp(0.203 * STUDY_DORZA_EARLY)` = a 1.23-fold, i.e. 22.5% higher, apparent clearance in the early-phase studies; Wang 2023 Table 3 CL_STUDY = 1.23).
- **Notes:** Specific scope; tied to the six-trial Hua Medicine dorzagliatin development programme. Subject-level (time-fixed); set from the trial identifier. Wang 2023 Discussion paragraph 2 documents the mechanism and why the effect lands on CL/F rather than on F: the early studies showed systematically lower exposure, plausibly a formulation difference, but because the later studies were mainly sparsely sampled "the model was unable to correct for prediction bias by bioavailability", so the between-trial difference was absorbed into apparent clearance. This is why the indicator is registered under the `STUDY_<id>` family rather than as a `FORM_<drug>_<formulation>` entry -- the formulation attribution is the paper's hypothesis, not its parameterisation. Directly analogous to `STUDY_LBSL` (a binary indicator over a *group* of early-phase studies switching CL and V magnitudes) and to `STUDY_FARLETUZUMAB_PHASE2` / `STUDY_POSA_PHASE3` / `STUDY_NIPOCALIMAB_PHASE1`. Follows the auto-approved `STUDY_<id>` canonical family.

### REGI_BID (**canonical for twice-daily dosing-regimen indicator**)
- **Description:** 1 = subject's dosing regimen is BID (twice daily), 0 = QD (once daily) or other non-BID regimen. Per-subject (regimen-fixed) categorical indicator for population analyses that pool QD and BID arms and test regimen as a covariate.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-BID regimen -- typically QD).
- **Source aliases:**
  - `BID` -- used in `Girard_2012_pimasertib.R`.
- **Example models:** `Girard_2012_pimasertib.R` (additive shift on the cumulative-logit AE-score model: `theta_bid * REGI_BID`; -0.399 logit units for BID vs QD).
- **Notes:** Specific scope because the QD-vs-BID contrast is study-specific; future regimen-comparison models that contrast different schedules should either extend this entry's example list (when QD is the reference) or register a sibling indicator (`REGI_TID`, `REGI_QW`) following the same pattern. Distinct from `DOSE` (dose level in mg) and from total-daily-dose aggregates: a 60 mg/day cohort can include either a 60 mg QD subgroup or a 30 mg BID subgroup, and both share the same `DOSE = 60` while differing in `REGI_BID`.

### REGI_QM (**canonical for once-monthly dosing-regimen indicator**)
- **Description:** 1 = subject's dosing regimen is once-monthly (QM, typically every 4 weeks for a long-half-life mAb), 0 = the per-paper comparator regimen (typically once-every-2-weeks, Q2W, for evolocumab-class mAbs). Per-subject (regimen-fixed) categorical indicator used by static Emax-on-AUC exposure-response models that pool QM and Q2W arms and need to distinguish them because the Emax-on-AUC formulation cannot represent the difference in target-saturation time courses between the two regimens. This is purely an exposure-response model device; dynamic ODE-based PK / PD layers do not need this indicator because they resolve the concentration time course explicitly.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-QM regimen -- paper-defined; Q2W for `Kuchimanchi_2018_evolocumab_ldlc.R`). Document the per-paper comparator in `covariateData[[REGI_QM]]$notes`.
- **Source aliases:**
  - `REG` -- Kuchimanchi 2018 notation for the binary indicator `i` in the exposure-response formula `Eff = Emax * AUC / (EC50 * REG^i + AUC)`. The linear-scale multiplier paired with this indicator (paper estimate 2.30) maps to the canonical parameter `reg_qm`.
- **Example models:** `Kuchimanchi_2018_evolocumab_ldlc.R` (founding example; exposure-response Emax on LDL-C distinguishes 420 mg SC QM from 140 mg SC Q2W via `ec50_eff = ec50 * reg_qm^REGI_QM`).
- **Notes:** Specific scope because the QM-vs-Q2W contrast is study-specific. Sibling of `REGI_BID` (within-QD-vs-BID schedules) under the `REGI_*` family pattern; future regimen-comparison models that contrast different intervals (e.g., Q4W vs Q8W) should register a sibling indicator (`REGI_Q4W`, `REGI_Q8W`) following the same pattern. Distinct from `DOSE` (dose level in mg) and from regimen-implied total-AUC aggregates: a steady-state exposure-matched 140 mg Q2W and 420 mg QM pair share the same AUC over 4 weeks but differ in `REGI_QM`. Pairs with the canonical parameter `lreg_qm` / `reg_qm` (the EC50 multiplier exponentiated by this indicator). Use only in static Emax-on-AUC exposure-response models; dynamic ODE-based PK / PD layers do not need this indicator.

### MIL_REGIMEN (**canonical for miltefosine monotherapy vs combination-with-LAmB regimen indicator**)
- **Description:** Per-subject (time-fixed) binary indicator carrying the visceral-leishmaniasis miltefosine treatment-arm assignment in the Dorlo 2017 Eastern African LEAP-0208 study. 1 = monotherapy arm (28 days oral miltefosine 2.5 mg/kg/day, maximum 150 mg/day); 0 = combination arm (single IV liposomal amphotericin B 10 mg/kg on day 1 plus 10 days oral miltefosine 2.5 mg/kg/day, maximum 150 mg/day). Used to select the duration of the initial reduced-bioavailability window: 7 days for monotherapy, 1 day for the combination arm (Dorlo 2017 Table 2 footnote d).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (combination arm: single LAmB IV on day 1 + 10-day oral miltefosine).
- **Source aliases:** derived per subject from the trial-arm assignment in NCT01067443; the source paper does not name an NMTRAN column directly. Dorlo 2017 narrative labels the arms 'MIL' (monotherapy) and 'combination therapy arm'.
- **Example models:** `Dorlo_2017_miltefosine.R` (sets `tlowf <- 7 * MIL_REGIMEN + 1 * (1 - MIL_REGIMEN)` inside `model()`; tlowf is then used in the `(t > 0) * (t <= tlowf)` predicate that gates the typical 74.3% reduction in relative bioavailability during the initial absorption window).
- **Notes:** Specific scope because the miltefosine-vs-miltefosine+LAmB head-to-head and the per-arm absorption-window durations are tied to the Dorlo 2017 design. The combination-arm reduction window is shorter than the monotherapy window because the LAmB infusion is hypothesised to accelerate the patients' overall physiological recovery (Discussion paragraph 'Pharmacokinetics'); future Eastern African VL combination-with-LAmB miltefosine popPK extractions that share the same regimen pair can extend this entry's example list, while a contrast against a different miltefosine combination partner (e.g., paromomycin, fexinidazole) should register a sibling indicator. Distinct from `REGI_BID` (within-monotherapy QD vs BID schedule) and from the `CONMED_*` family (the LAmB co-administration in the combination arm is captured implicitly via the regimen indicator rather than via an explicit concomitant-medication binary, because the LAmB-driven effect modelled here is on the absorption-window duration rather than on miltefosine clearance or distribution).

### MEAL_FLAG (**canonical for intra-day meal-window indicator (time-varying)**)
- **Description:** 1 = the current observation time falls within a meal window (typically lunch or dinner, ~1 hour duration each); 0 = no meal-driven physiological perturbation active. Distinct from `FED` (per-dose-record fed-vs-fasted indicator): MEAL_FLAG is a time-varying intra-day flag that switches on at meal onset, stays on through the meal duration, and switches off afterward. Used by enterohepatic-recirculation / gallbladder-contraction models that need to scale post-prandial transport rate constants for the duration of a meal.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no meal effect active at this time).
- **Source aliases:** paper narrative "meal effect on K_BI" / "post-prandial gallbladder contraction" in Zuo 2016.
- **Example models:** `Zuo_2016_UDCA.R` (multiplies the biliary-to-intestine rate constants K_BI,0/1/2 by E_meal = 35.33 during meal windows to simulate gallbladder contraction; two meals modelled per day, lunch at +4 h and dinner at +10 h after the morning dose, each 1 hour long).
- **Notes:** Specific scope because the operational definition (meal duration, schedule, magnitude of the rate-constant scaling) is paper-defined. Time-varying: must be supplied at every observation row in the event dataset. Pair with `SNACK_FLAG` for studies that distinguish meal and snack effects. Distinct from `FED` and `FED_HIGHFAT` (per-dose-record meal-state indicators tied to a single dosing event); MEAL_FLAG is decoupled from any specific dose record and instead drives ongoing physiology over a multi-hour window.

### SNACK_FLAG (**canonical for intra-day snack-window indicator (time-varying)**)
- **Description:** 1 = the current observation time falls within a snack window (typically 0.5 hour duration, a smaller perturbation than a meal); 0 = no snack-driven physiological perturbation active. Used in tandem with `MEAL_FLAG` by enterohepatic-recirculation / gallbladder-contraction models that scale post-prandial transport rate constants with a smaller magnitude for snacks than for full meals.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no snack effect active at this time).
- **Source aliases:** paper narrative "snack effect on K_BI" in Zuo 2016.
- **Example models:** `Zuo_2016_UDCA.R` (multiplies the biliary-to-intestine rate constants K_BI,0/1/2 by E_snack = 9.53 during snack windows; one snack modelled per day at +7 h after the morning dose in the Xiang 2011 single-dose study, 0.5 hour long).
- **Notes:** Specific scope; same per-paper-defined operational definition as `MEAL_FLAG`. Time-varying.

### FRACABS (**canonical for dose-record fractional absorption supplied as a data column**)
- **Description:** Per-dose-record fractional absorption (0-1) supplied as a covariate when the source paper reports F as a dose-dependent function rather than as a single estimable bioavailability parameter. The model wires the covariate into a bioavailability hook (`f(<depot>) <- FRACABS`); the user supplies the per-dose F value derived from the paper's regression / lookup table.
- **Units:** fraction
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a (continuous covariate).
- **Source aliases:** paper narrative "F = 0.66 at 150 mg" / "F = 0.31 at 1000 mg" / Walker 1992 / Crosignani 1991 / Dilger 2012 reported absorption values in Zuo 2016.
- **Example models:** `Zuo_2016_UDCA.R` (dose-dependent fractional absorption derived from a log-dose linear regression: F = 0.66 at 150 mg and F = 0.31 at 1000 mg, with R^2 = 0.99 over 200-2000 mg per the paper; combined with the UDCA molecular-weight conversion in the bioavailability hook `f(stomach_udca) <- FRACABS / mw_udca`).
- **Notes:** Specific scope because the operational definition (which doses get which F value; whether F is treated as a known input or as a parameter to be re-estimated) is paper-defined. Distinct from `lfdepot` / `f(depot)` in models where bioavailability is an estimable PK parameter rather than a supplied covariate. When the paper's F regression is itself a function of `DOSE`, the user can derive FRACABS from the DOSE column upstream of `rxSolve` rather than carrying the regression inside `model()`.

### FED (**canonical for fed-vs-fasted dose-record indicator**)
- **Description:** 1 = fed state at dosing, 0 = fasted.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (fasted).
- **Example models:** `Kyhl_2016_nalmefene.R`, `Goel_2016_Sonidegib.R` (the Goel 2016 healthy-fasted F effect is applied via `e_healthy_fast_f ^ (DIS_HEALTHY * (1 - FED))`; cancer-patient records have FED = 1, healthy-fasted-arm records have FED = 0, high-fat-meal arm records have FED = 1 and the additional FED_HIGHFAT = 1 indicator), `Niebecker_2015_edoxaban.R` (the Niebecker 2015 "study 6" indicator is encoded via FED -- study 6 was the only Fed-state phase 1 study in the pooled analysis; all 12 other phase 1 studies and the Hokusai-VTE phase 3 study were overnight-fast. Two multiplicative effects: ka * (1 + (-0.690) * FED) for the -69% absorption slowdown and CLnr/F * (1 + 0.204 * FED) for the +20.4% non-renal CL with food), `Chen_2023_nemonoxacin.R` (three multiplicative power-form effects from Chen 2023 Eq. 8: `ka * 0.44^FED`, `T_lag * 1.6^FED`, and `F1 * 0.88^FED`; the paper's operational definition is any food taken within 2 h before or 30 min after the dose, i.e. a general fed-vs-fasted flag rather than a high-fat-meal challenge, so `FED` applies rather than `FED_HIGHFAT`), `Marathe_2023_belzutifan.R` (linear-deviation effect on the absorption rate constant: `ka * (1 + (-0.88) * FED)`, an 87.6% reduction in ka when dosed fed (2.40 -> 0.30 1/h) per Marathe 2023 Table 2 KA-FED and Table 3; assessed in the crossover food-effect Study 2, so a subject contributes both FED levels. Food had no effect on AUCss, and the food effect on absorption lag time reached SCM significance but could not be retained in the final model).

### FED_HIGHFAT (**canonical for high-fat-meal-at-dosing indicator**)
- **Description:** 1 = oral dose administered after a high-fat meal, 0 = oral dose administered under any other meal condition (typically fasted or light meal). Refines the more general `FED` indicator for studies that specifically test the high-fat-meal food effect on bioavailability or absorption rate (a common solubility-limited absorption phenotype).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-high-fat meal condition; most often "fasted" or "2 h post light meal" as defined per study protocol). Document the operational reference per model.
- **Source aliases:**
  - `Fatmeal` -- used in `Goel_2016_Sonidegib.R` (covariate on F).
  - `FATM` -- used in `Goel_2016_Sonidegib.R` (covariate on Ka; same indicator as `Fatmeal` in that paper).
- **Example models:** `Goel_2016_Sonidegib.R` (multiplicative effect on F: `5.74^FED_HIGHFAT` -- ~5.7-fold higher F under high-fat meal vs 2 h post-light-meal reference; multiplicative effect on Ka: `1.01^FED_HIGHFAT` -- no meaningful effect).
- **Notes:** Distinct from `FED` (binary fed-vs-fasted): `FED_HIGHFAT` carries the specific "high-fat meal" semantic (typically >= 800 kcal, >= 50% calories from fat per FDA guidance). Document the per-protocol meal definition in `covariateData[[FED_HIGHFAT]]$notes`. When a paper reports a high-fat-meal arm and a separate fasted-healthy arm (e.g., Goel 2016), use `FED_HIGHFAT` for the high-fat semantic and the existing `FED` + `DIS_HEALTHY` indicators for the composite healthy-fasted effect (Goel 2016 applies the e_healthy_fast_f effect via `(DIS_HEALTHY * (1 - FED))`); the retired `HV_FAST` composite indicator was deleted on 2026-05-11. Sibling of `FED_LOWFAT` for the low-fat-meal end of the meal-type spectrum.

### FED_LOWFAT (**canonical for low-fat-meal-at-dosing indicator**)
- **Description:** 1 = oral dose administered after a low-fat meal, 0 = oral dose administered under any other meal condition (typically fasted, moderate-fat, or high-fat). Refines the more general `FED` indicator for studies that specifically test a low-fat-meal food effect on bioavailability or absorption rate, most commonly for drugs whose apparent bioavailability decreases with a light / low-fat meal relative to fasted (e.g., HIV integrase inhibitors, some antiviral tablets whose dissolution is optimised under fasted or moderate-fat conditions).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (non-low-fat meal condition; most often "fasted" or "moderate-fat / high-fat meal" as defined per study protocol). Document the operational reference per model.
- **Source aliases:**
  - `LOWFAT` -- used in `Bukkems_2021_raltegravir.R` (NONMEM covariate flag; source paper Bukkems 2021 Methods 'Development population pharmacokinetic model' subsection 'A low-fat meal (389 kcal, 6.9% fat)'; same orientation as the canonical, 1 = low-fat meal).
- **Example models:** `Bukkems_2021_raltegravir.R` (linear additive effect on bioavailability: `F *= (1 + e_lowfat_fdepot * FED_LOWFAT)` with `e_lowfat_fdepot = -0.459`; low-fat-meal-associated raltegravir dose records have -46% relative bioavailability vs the fasted / moderate-fat reference, Bukkems 2021 Table 2 'Factor change in F low-fat meal').
- **Notes:** Distinct from `FED` (binary fed-vs-fasted, generic) and from `FED_HIGHFAT` (high-fat-meal semantic). The low-fat semantic is typically defined per protocol as <= ~400 kcal with <= ~10% calories from fat (Bukkems 2021 references 389 kcal / 6.9% fat as the low-fat definition from the Rizk 2012 raltegravir food-effect study, and 650-844 kcal / 48% fat as the moderate-fat reference). Document the per-protocol meal definition in `covariateData[[FED_LOWFAT]]$notes`. When a paper reports discrete meal-type strata (fasted / low-fat / moderate-fat / high-fat), the recommended encoding is `FED` for any-food-vs-fasted plus one or both of `FED_LOWFAT` / `FED_HIGHFAT` for the specific low-/high-fat semantics; the moderate-fat stratum is the typical fed-reference when no `FED_MODERATE` sibling has been ratified. Sibling of `FED_HIGHFAT` for the high-fat-meal end of the meal-type spectrum. General scope because the low-fat semantic is well-defined across studies (rather than tied to a single paper's protocol).

### MEAL_DELAY_1H (**canonical for delayed-meal indicator: food consumed >= 1 h after dosing rather than ~0.5 h after dosing**)
- **Description:** 1 = the subject started eating at least 1 hour after taking the oral dose; 0 = the subject started eating approximately 0.5 hour after taking the dose. Encodes the **dose-to-meal interval** in studies where every subject is fed but the delay between dosing and the start of the meal is protocol-controlled or recorded. The concept it captures is how long the drug has to dissolve and empty from a relatively unfed stomach before the meal arrives, which shifts gastric-emptying-limited absorption timing (typically a zero-order absorption duration `D1`, a lag time, or `ka`) without necessarily changing overall bioavailability.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (food consumed ~0.5 h after dosing -- the shorter dose-to-meal interval). Document the per-protocol interval definition in `covariateData[[MEAL_DELAY_1H]]$notes`, since the two levels a given paper contrasts may not be exactly 0.5 h vs 1 h.
- **Source aliases:**
  - `FOOD` -- used in `Wang_2023_dorzagliatin.R` (Wang 2023 Table 3 footnote: "FOOD: 0 and 1 for the time of food consumption after 0.5 h and >= 1 h of drug administration, respectively"; same orientation as the canonical, 1 = the longer delay).
- **Example models:** `Wang_2023_dorzagliatin.R` (multiplicative exponential effect on the zero-order absorption duration D1: `exp(0.816 * MEAL_DELAY_1H)` = a 2.26-fold longer zero-order absorption window, 0.418 h -> 0.945 h, when the meal is delayed by at least 1 h; Wang 2023 Table 3 D1,FOOD = 2.26).
- **Notes:** Specific scope. The *concept* -- the dose-to-meal interval -- is genuinely new to the register, but the particular 1-hour threshold is Wang 2023's own protocol design choice and has not yet been shown to be reusable, so the entry is scoped to that paper pending a second ratification. Promote to `general` when a second paper adopts the same 0.5 h vs >= 1 h dichotomy. **Distinct from the whole `FED` family**: `FED` is fed-vs-fasted (meal presence), `FED_HIGHFAT` / `FED_LOWFAT` / `MEAL_A`-`MEAL_D` are meal *composition*, and `MEAL_FLAG` / `SNACK_FLAG` are time-varying intra-day meal *windows* driving ongoing physiology. `MEAL_DELAY_1H` is orthogonal to all of these -- both of its levels are fed, with identical meal composition, differing only in timing relative to the dose. A study that varies both composition and delay should carry `FED_HIGHFAT` (or `FED_LOWFAT`) *and* this indicator. Per-dose-record in principle; in a fixed-protocol study it is effectively subject-level. Because the effect typically lands on an absorption-timing parameter rather than on F or CL, the downstream exposure consequences are usually small: Wang 2023 Section 3.3 reports no change in steady-state AUCtau and only -1.26% / +2.71% shifts in Cmax,ss / Cmin,ss. Future models contrasting a different pair of intervals (e.g. immediately-with-food vs 2 h post-dose) should register a sibling indicator (`MEAL_DELAY_2H`) following the same pattern rather than redefining this entry's levels.

### MULTI_DOSE_PT (**canonical for multiple-dose-phase-in-patients indicator**)
- **Description:** 1 = dose record from the multiple-dose phase of a clinical-pharmacology study in patients, 0 = otherwise (single-dose run-in records, healthy-volunteer records, or first-dose records in patient studies). Captures any systematic shift in apparent bioavailability between the controlled run-in and the longer multiple-dose phase, typically driven by variable food-restriction compliance over many dosing days.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (single-dose / run-in / healthy-volunteer dose records).
- **Source aliases:**
  - `FMDD` -- used in `Goel_2016_Sonidegib.R` (Goel 2016 covariate on F).
- **Example models:** `Goel_2016_Sonidegib.R` (multiplicative effect on F: `1.16^MULTI_DOSE_PT` -- ~16% higher apparent F during the multiple-dose phase relative to first dose, attributed in the paper to occasional non-fasting compliance), `Fang_2010_etanercept.R` (multiplicative effect on F: `0.674^MULTI_DOSE_PT` -- ~33% lower apparent F during the multiple-dose phase relative to the single-dose reference; Fang 2010 attributes the reduction to partitioning of the rhTNFR-Fc fusion protein into local subcutaneous adipose tissue with repeated injection. In Fang 2010 the multi-dose cohort is the AS-patient arm and the single-dose cohort is the healthy-volunteer arm, so MULTI_DOSE_PT is effectively subject-level: source column `M`), `vanIersel_2018_posaconazole.R` (multiplicative effect on apparent clearance: `cl *= (1 + 0.750 * MULTI_DOSE_PT)` -- 75% higher CL in multiple-dose records relative to the single-dose reference; van Iersel 2018 Table 2 final-model 'Dosing regimen on CL' = 0.750).
- **Notes:** Specific scope because the indicator's exact definition (dose-record level vs subject level, run-in inclusion, occasion boundary) is paper-specific. In Goel 2016, the dataset distinguishes the run-in single dose from the daily multiple-dose phase; the indicator switches at the start of the multiple-dose phase for cancer patients. In Fang 2010, the indicator is subject-level (all dose records of a multi-dose AS subject carry MULTI_DOSE_PT = 1; all dose records of a healthy-volunteer subject carry MULTI_DOSE_PT = 0). Distinct from `FED` and `FED_HIGHFAT` (which are per-record meal-state indicators) and from `REGI_BID` (regimen indicator). Future models that need a generic "occasion boundary" effect should consider the existing `ooc<n>` IOV pattern instead.

### FORM_TABLET (**canonical for tablet vs non-tablet oral liquid formulation indicator**)
- **Description:** 1 = tablet formulation, 0 = the per-paper non-tablet oral liquid comparator (solution or suspension). Document the reference oral liquid form per-model in `covariateData[[FORM_TABLET]]$notes`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (per-paper non-tablet oral liquid comparator; per-model documentation identifies whether the reference is a solution or a suspension).
- **Source aliases:**
  - `TABLET` -- earlier name used by `Kyhl_2016_nalmefene.R` and `Tikiso_2021_abacavir.R`; renamed to `FORM_TABLET` for consistency with the `FORM_*` family (`FORM_CAPSULE`, future `FORM_SUSPENSION`, etc.).
- **Example models:** `Kyhl_2016_nalmefene.R` (additive shift on residual error: tablet vs solution), `Tikiso_2021_abacavir.R` (multiplicative effect on the absorption mean transit time MTT: tablet (abacavir + lamivudine fixed-dose-combination tablet) vs liquid solution; `mtt *= (1 + 0.249 * FORM_TABLET)`, i.e. 24.9% slower absorption for the FDC tablet relative to the abacavir liquid reference), `Kleideiter_2017_cebranopadol.R` (tablet is the typical-value reference, FORM_TABLET = 1 leaves the formulation effects on `ka`, `klag`, and bioavailability at zero; paired with `FORM_CAPSULE` and the derived `is_solution = (1 - FORM_TABLET) * (1 - FORM_CAPSULE)` to encode a three-level formulation stratification), `Fisher_2008_fosamprenavir.R` (relative bioavailability of tablet vs fed-suspension reference: F_tab = 1.09; encoded as `log(F) <- log(F_tab) * FORM_TABLET + log(F_food,sus) * (1 - FORM_TABLET) * (1 - FED)` so tablet bioavailability does not additionally depend on food, while fasted suspension carries the F_food,sus reduction), `Aruldhas_2021_R_methadone.R`, `Aruldhas_2021_S_methadone.R` (per-oral-dose covariate that selects between two independently-estimated first-order absorption rate constants: suspension is the canonical reference Ka and tablet is a log-additive multiplicative shift; `ka <- exp(lka + e_form_tablet_ka * FORM_TABLET)`), `Wada_2023_sparsentan.R` (three-level sparsentan formulation stratification `{100 mg capsule reference, whole tablet, crushed tablet}` encoded into `FORM_TABLET` plus the sibling new canonical `FORM_CRUSHED_TABLET`, with both = 0 selecting the capsule; the whole tablet is absorbed more slowly than the capsule via `ka * exp(-0.306)` and has a shorter lag via `tlag * exp(-0.269)`. NOTE: here the comparator is a CAPSULE, not the non-tablet oral liquid named in this entry's default reference category -- document the comparator per-model).
- **Notes:** Scope promoted from `specific` to `general` on 2026-07-24 alongside the Aruldhas 2021 methadone extraction (the sixth example model), where the non-tablet comparator is an oral suspension rather than an oral solution. Per-model documentation should identify which non-tablet oral liquid formulation is the reference (solution or suspension) in `covariateData[[FORM_TABLET]]$notes`. Distinct from the `FORM_FDC` canonical (Wilkins 2008 antitubercular fixed-dose-combination of multiple drugs, contrasted against single-drug tablets) because here the comparator is a non-tablet oral liquid rather than a separate tablet product. Future formulation-comparison models should extend this entry's example list when the comparator is any non-tablet oral liquid (solution or suspension); register a sibling canonical when contrasting two tablet products.

### FORM_CLO_GENERIC (**canonical for generic-vs-Plavix clopidogrel product indicator**)
- **Description:** 1 = the generic 75 mg film-coated clopidogrel test tablet evaluated in a bioequivalence study, 0 = the Plavix 75 mg film-coated reference tablet (Sanofi Winthrop Industrie, Ambares, France). Both products are film-coated tablets of the same strength, so this indicator distinguishes two products rather than two dosage forms; the contrast it carries is relative bioavailability, not a change in absorption mechanism.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Plavix film-coated tablet -- the reference medicine, whose bioavailability F is the fixed 100% anchor that makes the generic's relative bioavailability identifiable).
- **Source aliases:** `formulation`, `treatment` (the crossover treatment-arm column of a 2-treatment bioequivalence dataset; `FORM_CLO_GENERIC = as.integer(treatment == "test")`).
- **Example models:** `Pejcic_2024_clopidogrel.R` (selects between the fixed reference `F = 1` and the estimated study-specific generic relative bioavailability `Fgen_st1 = 1.08` / `Fgen_st2 = 0.960`; `frel <- exp(lfdepot) * (1 - FORM_CLO_GENERIC) + exp(lfgenTv) * FORM_CLO_GENERIC`).
- **Notes:** Follows the auto-approved `FORM_<drug>_<formulation>` canonical family. Time-varying within subject in a crossover design -- each subject receives both products, one per period -- so it must be set per dose record and is naturally paired with `OCC`. Distinct from `FORM_TABLET` / `FORM_CAPSULE`, which contrast dosage forms; here both arms are the same dosage form and the covariate identifies the manufacturer's product. Register a sibling `FORM_<drug>_GENERIC` canonical for other generic-vs-reference bioequivalence extractions rather than reusing this drug-specific entry.

### FORM_CAPSULE (**canonical for capsule formulation indicator**)
- **Description:** 1 = capsule formulation, 0 = the per-paper comparator non-capsule formulation. The complement formulation is paper-defined: solution for Hennig 2006 / 2007 itraconazole, tablet for Gupta 2016 lenvatinib, Doryx delayed-release tablet (with `FORM_DOX_DORYX_MPC = 0`) for Hopkins 2017 doxycycline. Document the comparator and the reference-category bioavailability per-model in `covariateData[[FORM_CAPSULE]]$notes`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** Either FORM_CAPSULE = 0 or FORM_CAPSULE = 1, paper-defined. Hennig 2006 and Gupta 2016 anchor F = 1 on the non-capsule comparator (FORM_CAPSULE = 0): solution in Hennig 2006 with `fdepot` fixed to 1 for the solution arm; tablet in Gupta 2016 with F fixed to 1 for the tablet arm. Salem 2014 anchors F = 1 on the capsule arm (FORM_CAPSULE = 1): the capsule is the structural F = 1 reference and the liquid (suspension or solution) arm carries the estimated age-dependent relative bioavailability. The IIV (when present) is gated to the arm that carries the estimated F; document the orientation in `covariateData[[FORM_CAPSULE]]$notes`.
- **Source aliases:**
  - `PREP` -- used in `Hennig_2006_itraconazole.R` (Clin Pharmacokinet 2006;45(11):1099-1114; PREP = 1 = capsule, PREP = 0 = oral solution) and in `Hennig_2007_itraconazole.R` (Br J Clin Pharmacol 2007;63(4):438-450; DOI 10.1111/j.1365-2125.2006.02778.x; same orientation, capsule typical absorption parameters as the published reference).
  - `CAPSULE` -- earlier name used in the `Hennig_2006_itraconazole.R` and `Hennig_2007_itraconazole.R` model files before the `FORM_*` rename.
- **Example models:** `Hennig_2006_itraconazole.R` (Hennig 2006 Table II final estimates: capsule `ka` 0.09 h^-1 vs solution `ka` 0.96 h^-1 and capsule relative bioavailability 0.55 vs solution 1; the `etalfdepot` IIV applies only to the capsule arm), `Hennig_2007_itraconazole.R` (selects between `lka_cap` and `lka_sol` typical-value absorption rate constants and applies `f(depot) <- (1 - FORM_CAPSULE) + FORM_CAPSULE * fdepot` so the relative bioavailability `F_rel = 0.817` is applied only to the capsule arm), `Gupta_2016_lenvatinib.R` (relative bioavailability of capsule vs tablet is 0.896; F1 fixed to 1 for the tablet reference; the `etalfcap` IIV (30.2% CV) applies only to the capsule arm), `Lacy_2018_cabozantinib.R` (multiplicative fractional effect of capsule (vs tablet reference) on Ka = -0.579 (57.9% slower absorption for capsule) and on overall bioavailability F = -0.144 (14.4% lower exposure for capsule); tablet F fixed at 1 as the reference; comparator capsule = Cometriq 140 mg approved for MTC), `Kleideiter_2017_cebranopadol.R` (paired with `FORM_TABLET` to encode the three-level cebranopadol formulation stratification: tablet reference, oral solution, liquid-filled capsule; multiplicative effects on `ka` (log shift `log(2.09 / 0.864) = 0.883`), `klag` (log shift `log(0.077 / 0.087) = -0.122`), and bioavailability (factor 1.174) for capsules relative to the tablet reference), `Kleideiter_2018_cebranopadol.R` (three-level formulation factor `{tablet, oral solution, liquid-filled capsule}` encoded into two binary indicators `FORM_CAPSULE` and the sibling new canonical `FORM_SOLUTION`; tablet is the reference when both indicators are 0; capsule multiplicative effects per Kleideiter 2018 Table 13 are 2.09 / 0.864 = 2.419 on Ka, 0.077 / 0.087 = 0.885 on klag, and 1.174 on bioavailability F), `Hopkins_2017_doxycycline.R` (three-level Doryx formulation factor `{Doryx tablet (delayed-release), Doryx MPC (modified-acid-resistance delayed-release), Doryx capsule (conventional-release)}` encoded into two binary indicators `FORM_CAPSULE` and the sibling canonical `FORM_DOX_DORYX_MPC`; Doryx tablet is the reference when both indicators are 0; Doryx-capsule relative bioavailability vs the Doryx-tablet reference is 0.978 per Hopkins 2017 Table 3 F1CAP, shared 0.115 h absorption lag with `FORM_DOX_DORYX_MPC` per Table 3 ALAG1, and the Doryx-capsule food effect on KTR matches the Doryx-tablet -20.9% reduction per Table 3 COVFED rather than the Doryx-MPC -54.9% reduction), `Salem_2014_efavirenz.R` (capsule is the structural F = 1 reference; the oral suspension and oral solution arms are pooled as 'liquid' since Salem 2014 found no difference in F between them, and the liquid arm carries an Emax age-dependent relative bioavailability with mature asymptote 0.79 (Salem 2014 Table 2 TVF, RSE 12.5%) and TM50,F = 10.6 months (Salem 2014 Table 2 TM50,F, RSE 38.7%); IIV on the mature liquid F is 39.9% CV (etaltvf_liq) and is gated to the liquid arm via `fdepot <- FORM_CAPSULE * 1 + (1 - FORM_CAPSULE) * f_liquid`), `Sano_2023_fesoterodine.R` (fesoterodine beads-in-capsule (BIC) vs tablet in pediatric patients with neurogenic detrusor overactivity; same orientation as Gupta 2016 lenvatinib, with the tablet arm fixed at F = 1 and the BIC arm carrying the estimated relative bioavailability 0.648 per Sano 2023 Table 2, applied as `f(depot) <- (1 - FORM_CAPSULE) + FORM_CAPSULE * exp(lfdepot)`; note that in the source studies BIC was given only to the 25-kg-or-less cohort, so formulation is confounded with body weight and dose), `vandenBerg_2021_uprifosbuvir_pbpk.R` (uprifosbuvir capsule vs tablet formulation stratification per van den Berg 2021 Table 3: capsule multiplicative factors are 2.02 on KA1, 3.23 on the pre-logit for the fast-absorption fraction FDOS1 (corresponding to F1 = 0.91 for capsules vs 0.67 for tablets), 1.17 on bioavailability, and 1.65 on the fast-absorption lag time ALAG1; the capsule dose-slope on KA1 is -0.00189 (linear on log-KA1 per mg above the 150 mg reference); the gut-M6 concentration-dependent elimination rate uses a separate capsule-specific baseline KelM6g = 5.76 with a capsule dose slope of -0.00420)., `Majid_2024_lenvatinib.R` (relative bioavailability of the lenvatinib capsule versus the tablet reference: `f(depot) <- 0.882^FORM_CAPSULE`, Majid 2024 Table 1 footnote `F1 = 1 * 0.882^FORM`; unlike the Gupta 2016 predecessor model no inter-individual variability is estimated on F1).
- **Notes:** Scoped specific because the complement reference category is paper-defined (solution for Hennig itraconazole, tablet for Gupta lenvatinib). Sibling to `FORM_TABLET` (Kyhl 2016 / Tikiso 2021 tablet vs solution) under the `FORM_*` family. Future formulation-comparison models that need a capsule indicator should reuse this canonical, extending the example list and documenting the comparator in per-model notes.

### FORM_CRUSHED_TABLET (**canonical for crushed-tablet (dry, not resuspended) formulation indicator**)
- **Description:** 1 = the dose was administered as a tablet crushed immediately before dosing and swallowed dry (or sprinkled on food) without being suspended in a liquid vehicle; 0 = the per-paper comparator intact formulation. Per-dose-record indicator. Use this canonical when a source paper carries a crushed-tablet arm as a distinct level of a formulation factor and estimates separate absorption parameters (Ka, lag time, bioavailability, or transit rate) for it.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (per-paper intact comparator; document which formulation is the reference per-model). For Wada 2023 sparsentan the reference is the 100 mg capsule, selected when `FORM_CRUSHED_TABLET` and the sibling `FORM_TABLET` are both 0.
- **Source aliases:**
  - `Formulation` three-level categorical column (`capsule` / `tablet` / `crushed tablet`) -- the crushed-tablet level maps to `FORM_CRUSHED_TABLET = 1`. Used in `Wada_2023_sparsentan.R`.
- **Example models:** `Wada_2023_sparsentan.R` (third member of the three-level sparsentan formulation stratification `{100 mg capsule reference, whole tablet, crushed tablet}`, encoded into the two binary indicators `FORM_TABLET` and `FORM_CRUSHED_TABLET`; the crushed tablet is absorbed faster than either intact form via `ka * exp(0.080)` (Wada 2023 Table 2, RSE 159.1%) and has a markedly shorter absorption lag via `tlag * exp(-1.175)` (0.099 h vs the 0.32 h capsule reference); formulation had no effect on relative bioavailability, so AUC is unchanged and Cmax differs by <10%).
- **Notes:** Distinct from `FORM_SUSPENSION` (Svensson 2018: tablets crushed AND suspended in a liquid vehicle, i.e. an extemporaneously-prepared liquid dosage form) -- the defining feature here is that no liquid vehicle is involved, so the dissolution and gastric-emptying behaviour differ. Also distinct from `ROUTE_NGT` (Denti 2018: crushed tablet delivered through a nasogastric tube), which is a route-of-administration indicator rather than a formulation state; a model pooling dry-crushed oral dosing with crushed-via-NGT dosing needs both columns. Sibling to `FORM_TABLET` / `FORM_CAPSULE` / `FORM_SOLUTION` / `FORM_POWDER` / `FORM_GRANULE` under the generic `FORM_*` family. Promote to `general` scope when a second paper ratifies the same encoding. Ratified canonically alongside the Wada 2023 sparsentan extraction.

### FORM_SOLUTION (**canonical for oral-solution formulation indicator**)
- **Description:** 1 = subject received the modelled drug as an oral solution, 0 = the per-paper comparator non-solution formulation (tablet or capsule). The complement reference is paper-defined; document the comparator and the reference-category absorption / bioavailability per-model in `covariateData[[FORM_SOLUTION]]$notes`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (per-paper non-solution comparator; for Kleideiter 2018 cebranopadol the film-coated tablet is the typical-value reference, with the sibling `FORM_CAPSULE` also at 0 in the reference state).
- **Source aliases:**
  - `FORM` (three-level formulation column {tablet, oral solution, liquid-filled capsule}) -- used in `Kleideiter_2018_cebranopadol.R`; the oral-solution level maps to `FORM_SOLUTION = 1` and is paired with `FORM_CAPSULE` so that both = 0 selects the tablet reference.
- **Example models:** `Kleideiter_2018_cebranopadol.R` (third member of the three-level cebranopadol formulation stratification {tablet reference, oral solution, liquid-filled capsule}, encoded into the two binary indicators `FORM_SOLUTION` and `FORM_CAPSULE`; oral-solution multiplicative effects relative to the tablet reference per Kleideiter 2018 Table 13 are 2.43 / 0.864 = 2.813 on Ka, 0.077 / 0.087 = 0.885 on klag, and 1.045 on bioavailability F), `Othman_2013_ABT_102.R` (two-level oral-solution vs solid-dispersion comparator for the TRPV1 antagonist ABT-102; solution-vs-solid-dispersion-reference effects per Othman 2013 PK/PD-model Results paragraph 1 are 0.40 multiplicative on bioavailability F and 0.5 multiplicative on absorption lag time), `PerezRuixo_2006_tipifarnib.R` (oral-solution-vs-solid (capsule or tablet) absorption-effect indicator; ratio-form multiplicative effects on D1 (0.348), Ka (2.07), and tlag (0.183) applied as `ratio^FORM_SOLUTION`; reference category is the solid oral form -- Perez-Ruixo 2006 found the absorption profile statistically indistinguishable between capsule and tablet, so the two solid forms are pooled as the reference, and the solution shows ~3-fold faster zero-order release, 2-fold higher Ka, and a 5.5-fold shorter lag time relative to the solid reference), `Petric_2023_vinpocetine.R` (third member of the three-level Petric 2023 vinpocetine formulation stratification {Ultra Vinca sustained-release beta-cyclodextrin tablet reference, Cavinton immediate-release tablet, 10 mg / 5 mL oral solution}, encoded into the two binary indicators `FORM_SOLUTION` and the sibling drug-specific `FORM_VINP_IR`; oral-solution log-additive effects relative to the SR-tablet reference per Petric 2023 Table 1 are `exp(beta = -0.68)` on Tk0 (49% shorter zero-order absorption duration) and `exp(beta = -1.24)` on V1/F (71% lower apparent central volume, i.e. higher metabolite exposure); metabolite AVA is the observed compound, so V/F folds in both the vinpocetine oral bioavailability and the vinpocetine-to-AVA conversion fraction), `Chandasana_2024b_lamivudine.R` (two-level oral-solution vs solid-oral-dosage-form contrast for absolute bioavailability F1; `f(depot) <- exp(lfdepot * (1 - FORM_SOLUTION) + lfdepot_sol * FORM_SOLUTION)` with F1 = 0.609 for the solid reference (tablet, capsule, and the ABC/DTG/3TC dispersible tablet) and F1 = 0.496 for the oral solution per Chandasana 2024 Table 3; the dispersible tablet is assigned to the solid reference because that assignment reproduces Chandasana 2024 Table 4 to within 2.4% in every weight band while the solution value under-predicts by ~26%), `Chandasana_2024b_abacavir.R` (used only as the formulation half of the ARROW PK Substudy Part 2 relative-bioavailability term, paired with `STUDY_ARROW_PART2`: the substudy tablet gets 1.62 and the substudy oral solution 1.75; outside that substudy the abacavir model applies no formulation effect on bioavailability, so this indicator is inert when `STUDY_ARROW_PART2 = 0`).
- **Notes:** Scoped specific because the complement reference category is paper-defined. Third member of the oral solid-dosage `FORM_*` family alongside `FORM_TABLET` (tablet vs solution) and `FORM_CAPSULE` (capsule vs per-paper comparator); register prose for those entries already anticipates this name. Where a model carries all three formulations, `FORM_CAPSULE` + `FORM_SOLUTION` form the two-indicator encoding with tablet as the all-zero reference. Distinct from `FORM_ASV_LIQUID` (a drug-specific suspension/solution-vs-capsule/tablet indicator for asunaprevir). Ratified canonically alongside the Kleideiter 2018 cebranopadol extraction.

### FORM_ASV_LIQUID (**canonical for asunaprevir liquid (suspension/solution) formulation indicator**)
- **Description:** Asunaprevir (ASV) formulation indicator. 1 = ASV given as a suspension or oral solution (the higher zero-order absorption-fraction route); 0 = ASV given as a capsule or tablet (the reference formulation). The covariate has no effect when no ASV dose is administered.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (capsule or tablet; the reference formulation in the Wang 2018 ASV PK fit).
- **Source aliases:** none -- the source Formulation column ("Suspension" / "Solution" -> 1; "Capsule" / "Tablet" -> 0, Wang 2018 Table 1) maps directly onto the canonical orientation; the model column is the canonical `FORM_ASV_LIQUID`.
- **Example models:** `Wang_2018_daclatasvir_asunaprevir.R` (switches the structural zero-order absorption fraction FK between fk_cap_asv = 0.184 (capsule/tablet) and fk_sol_asv = 0.334 (suspension/solution); both estimated with a shared 65.0% inter-arm-variability CV encoded as a single eta on the logit of FK, Wang 2018 Table 3).
- **Notes:** Specific scope because the liquid-vs-solid ASV-formulation contrast and the FK absorption-fraction switch are paper-specific to the Wang 2018 daclatasvir + asunaprevir analysis. Drug-specific member of the formulation indicator family; distinct from the generic oral solid-dosage `FORM_TABLET` / `FORM_CAPSULE` / `FORM_SOLUTION` indicators because the contrast here lumps both liquid forms (suspension and solution) against both solid forms (capsule and tablet) for a single named drug (ASV). Per-dose-occasion in principle (a participant could receive both formulations across occasions), but in the Wang 2018 trials each subject received a single ASV formulation. Ratified canonically alongside the Wang 2018 daclatasvir/asunaprevir extraction.

### FORM_VOSO_SOLN02 (**canonical for the 0.2 mg/mL vosoritide dosing-solution indicator**)
- **Description:** Vosoritide dosing-solution strength indicator. 1 = the subcutaneous dose was prepared from the 0.2 mg/mL vosoritide solution; 0 = prepared from the 0.8 mg/mL or 2 mg/mL solution. Per-dose-record indicator. The contrast is between reconstituted-solution *concentrations* of the same lyophilised drug product, not between dosage forms, so the covariate belongs to the `FORM_<drug>_<formulation>` drug-specific branch of the `FORM_*` family rather than to the oral solid-dosage members.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (the 0.8 mg/mL and 2 mg/mL solutions -- the strengths used in the phase III studies and in the commercial stock keeping units; Qi 2024 Discussion states these "did not have any effect on F", so no adjustment is needed between commercial products).
- **Source aliases:**
  - `SOLNC` ("solution concentration", a three-level column {0.2, 0.8, 2 mg/mL}) -- used in `Qi_2024_vosoritide.R`; only the 0.2 mg/mL level carries an effect, so the three-level column collapses to this single binary indicator.
- **Example models:** `Qi_2024_vosoritide.R` (multiplies relative bioavailability by 1.56, Qi 2024 Table 5 "Effect of SOLNC (0.2 mg/mL)"; encoded on the log scale as `e_form_voso_soln02_fdepot = log(1.56)` and confirmed by Table 3, where the 0.2 mg/mL column is a constant 56% above the time-only reference at every tabulated time).
- **Notes:** Specific scope because the effect is tied to vosoritide's reconstituted-solution strengths. The 0.2 mg/mL solution was used only in study 111-202 of the pooled five-trial analysis, so the indicator is 0 for every phase III and commercial record. Qi 2024 draws the analogy to insulin, where dilution of the dosing solution is also known to change subcutaneous bioavailability. A future paper reporting a dosing-solution-strength effect for a different drug should register a parallel `FORM_<drug>_SOLN<strength>` canonical rather than overloading this name. Ratified canonically alongside the Qi 2024 vosoritide extraction.

### FORM_UNDIL_SUSP (**canonical for undiluted oral suspension formulation indicator**)
- **Description:** 1 = subject received the modelled drug as an undiluted oral suspension, 0 = the per-paper comparator non-undiluted-suspension formulation (tablet or diluted oral suspension in Willmann 2018; documents the comparator per-model in `covariateData[[FORM_UNDIL_SUSP]]$notes`). The undiluted oral suspension was distinguished from the diluted oral suspension because pre-clinical in-vitro dissolution work showed the rivaroxaban suspension excipients limit dissolution of drug particles at low pH unless diluted before administration (Willmann 2018 Discussion paragraph 5).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (per-paper comparator non-undiluted-suspension; for Willmann 2018 this lumps the tablet and the diluted oral suspension as a single reference because the paper found their `ka` statistically indistinguishable).
- **Source aliases:** derived per dose record from the EINSTEIN-Jr phase I formulation column (`Tablet` / `Undiluted suspension` / `Diluted suspension`); `FORM_UNDIL_SUSP = 1` iff the formulation column is "Undiluted suspension".
- **Example models:** `Willmann_2018_rivaroxaban.R` (multiplicative log-shift on the absorption rate constant: `ka <- exp(lka + e_undilsusp_ka * FORM_UNDIL_SUSP)` with `lka = log(0.717)` h^-1 and `e_undilsusp_ka = log(0.208 / 0.717) = -1.238` per Willmann 2018 Table 1, RSE 21.3% and 15.4% respectively; the undiluted-suspension `ka` is ~3.4-fold slower than the tablet / diluted-suspension reference, consistent with the in-vitro dissolution result reported in the Discussion).
- **Notes:** Specific scope because the "undiluted suspension vs tablet-or-diluted-suspension" lumping is tied to the Willmann 2018 EINSTEIN-Jr phase I formulation design (which deliberately tested both undiluted and diluted variants of the same oral suspension after observing delayed absorption with the undiluted formulation). Sibling to `FORM_TABLET` / `FORM_CAPSULE` / `FORM_SOLUTION` under the `FORM_*` family. Future paediatric or low-pH-sensitive-drug models that distinguish an undiluted suspension from a diluted one with the same composition may extend this entry's example list; models that contrast a suspension against a tablet without the diluted-vs-undiluted distinction should reuse `FORM_TABLET` (= 0 for the suspension comparator) instead.

### FORM_ABA_PHASE2 (**canonical for the abatacept SC phase-2 formulation indicator**)
- **Description:** 1 = subject received the abatacept Phase-2 SC formulation (lower-pH excipient blend, ~56% absolute bioavailability), 0 = subject received the Phase-3 / commercial SC formulation (the ~81% bioavailability reference). Per-dose-occasion indicator: a single subject can carry both indicator values across phase-2-to-phase-3 transition arms.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Phase-3 / commercial 125 mg SC formulation; the typical-value bioavailability reference).
- **Source aliases:** none -- the canonical name matches the source-paper column.
- **Example models:** `Li_2019_abatacept.R` (additive shift on the logit-scale bioavailability: `logit_F = logit_F_TV + FORM_ABA_PHASE2 * (-1.16)`; Li 2019 Table 2 reports the Phase-2 formulation effect as -1.16 logit-scale units, mapping to an absolute F of ~0.56 vs the Phase-3 reference ~0.81).
- **Notes:** Scoped specific because the Phase-2 vs Phase-3 formulation contrast is tied to the abatacept clinical-development timeline and does not generalise to other drugs. Future Phase-2-vs-commercial bioavailability comparisons for unrelated drug programmes should register their own `FORM_<drug>_PHASE2` canonical rather than reuse this entry. Distinct from the generic `FORM_TABLET` / `FORM_CAPSULE` / `FORM_SUSPENSION` family (oral solid-dosage manipulations) because the contrast here is two SC injectable formulations with different excipient pH. Ratified canonically on 2026-05-28 per the naming-audit D20 review.

### FORM_PEX_PHASE1 (**canonical for the pexidartinib Phase 1 clinical formulation indicator**)
- **Description:** 1 = subject received the pexidartinib Phase 1 clinical formulation (used in Phase 1 dose-ranging studies PLX108-01 and the relative-bioavailability study U114; relative bioavailability 0.855 vs the Phase 3 / commercial formulation reference); 0 = subject received the Phase 3 / commercial formulation (used in healthy-volunteer studies U116-U121 and in the Phase 3 ENLIVEN trial; the typical-value bioavailability reference at F1 = 1.0). Per-dose-occasion indicator: within a single study the formulation is fixed, but a subject can in principle carry both values across studies.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Phase 3 / commercial formulation; the typical-value bioavailability reference).
- **Source aliases:** none -- Yin 2020 codes the formulation as a categorical "Phase 1" vs "Phase 3" column; the canonical FORM_PEX_PHASE1 name matches the indicator direction (1 = Phase 1).
- **Example models:** `Yin_2020_pexidartinib.R` (multiplicative effect on the depot bioavailability: F1 = 1.0 when FORM_PEX_PHASE1 = 0 (Phase 3 reference) and F1 = 0.855 * exp(etalfdepot) when FORM_PEX_PHASE1 = 1, where the 0.855 typical-value anchor is held fixed in the source model and the IIV etalfdepot ~ N(0, 0.101) is gated by FORM_PEX_PHASE1 so it has no effect on Phase 3 subjects).
- **Notes:** Scoped specific because the Phase-1-vs-Phase-3 formulation contrast is tied to the pexidartinib clinical-development timeline and does not generalise to other drugs. Future drug-specific Phase-1-vs-commercial bioavailability comparisons should register their own `FORM_<drug>_PHASE1` canonical rather than reuse this entry (sibling to `FORM_ABA_PHASE2` for abatacept). Distinct from the generic `FORM_TABLET` / `FORM_CAPSULE` / `FORM_SUSPENSION` family (oral solid-dosage manipulations) because the contrast here is two oral formulations differentiated by clinical-development phase rather than by dosage form. For typical simulations of the approved 800 mg/day TGCT regimen used in ENLIVEN, set FORM_PEX_PHASE1 = 0 for every subject.

### FORM_GEF_F02 (**canonical for the gefapixant F02 wet-granulation citric-acid tablet formulation indicator**)
- **Description:** 1 = dose record administered as gefapixant F02, the earlier wet-granulation film-coated immediate-release tablet (7.5, 20, and 50 mg strengths) that contains citric acid as an acidulant; 0 = dose record administered as F04 (gefapixant citrate active ingredient with a 20A film coating, used in two phase I studies) or F04A (gefapixant citrate with a 03K film coating, used in the phase III COUGH-1 and COUGH-2 trials). Per-dose-occasion indicator: within a single study the formulation is fixed, but a subject in a relative-bioavailability crossover study can carry both values across periods.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (F04 / F04A gefapixant-citrate formulations).
- **Source aliases:**
  - `FORM` -- Chawla 2023 codes the formulation as a categorical F02 / F04 / F04A column (Table S2); the canonical name matches the indicator direction (1 = F02).
- **Example models:** `Chawla_2023_gefapixant.R` (carries NO main effect on any PK parameter -- no F02-vs-F04 relative-bioavailability difference was retained in the final model -- and exists solely to gate the `FED` effect on the absorption rate constant to F02 records: `ka = exp(lka + etalka) * (1 + e_fed_ka * FED * FORM_GEF_F02)` with `e_fed_ka = -0.594`, a 59.4% reduction in Ka when an F02 dose is taken fed. Dedicated phase I relative-bioavailability studies had already established that fed status and concomitant proton-pump-inhibitor use affect gefapixant exposure for F02 but not for F04, so food and PPI effects were tested for F02 only.).
- **Notes:** Scoped specific because the F02-vs-F04 contrast is tied to the gefapixant clinical-development formulation timeline and does not generalise to other drugs. This is a *gating* indicator rather than an effect-carrying covariate -- the distinguishing pattern in the `FORM_*` family, where siblings such as `FORM_PEX_PHASE1` and `FORM_SUSPENSION` carry their own multiplicative effect on bioavailability or absorption. When a formulation indicator exists only to restrict another covariate's effect to a formulation subset, encode it in `covariateData` (it IS referenced in `model()`) and state the gating role explicitly in `covariateData[[FORM_GEF_F02]]$notes`. The marketed F04B formulation is compositionally identical to F04A except that it omits citric acid, and was shown bioequivalent to F04A, so F04B records take `FORM_GEF_F02 = 0`. The excluded F01 formulation predates F02 and does not appear in the analysis data set; a future extraction that needs it should register a separate `FORM_GEF_F01`. For typical simulations of the approved 45 mg b.i.d. chronic-cough regimen, set `FORM_GEF_F02 = 0` for every subject (COUGH-1 and COUGH-2 used F04A), which makes the food effect inoperative. Ratified canonically alongside the Chawla 2023 gefapixant extraction.

### FORM_SUSPENSION (**canonical for extemporaneously-prepared liquid suspension formulation indicator**)
- **Description:** 1 = subject received the modelled drug as an extemporaneously-prepared liquid suspension (the solid drug substance -- typically a tablet, but also possibly powder or another solid form -- compounded into a liquid suspension at bedside or in the clinical-pharmacy immediately before administration, rather than dispensed as a formally manufactured suspension product); 0 = subject received the comparator solid oral formulation (typically the same tablet swallowed whole or a sugar-coated tablet). Per-dose-occasion (not per-subject) indicator because a single participant can receive both formulations across study occasions in a crossover bioequivalence design.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (the per-paper solid oral comparator: tablets swallowed whole in Svensson 2018; sugar-coated tablet swallowed whole in Valle 2005). Document the per-model comparator in `covariateData[[FORM_SUSPENSION]]$notes`.
- **Source aliases:**
  - `FORM` -- used in `Svensson_2018_bedaquiline.R` (paper's narrative "whole vs suspended" with the suspended formulation as the 1 level in the analytical control stream).
  - Treatment-arm column "Treatment 2 (suspension after fasting)" vs "Treatment 1 (SCT after fasting)" -- used in `Valle_2005_exemestane.R` (3x3 Latin-square crossover with the extemporaneous suspension as the 1 level).
- **Example models:** `Svensson_2018_bedaquiline.R` (multiplicative effect on the typical mean absorption time MAT: `mat_typ = exp(lmat) * (1 + e_susp_mat * FORM_SUSPENSION)` with `e_susp_mat = +0.23` (suspended-tablet MAT is 23% longer than whole-tablet MAT; Svensson 2018 Table 2, 95% CI 2.1-48%, P = 0.03); relative bioavailability F is held identical between formulations because the paper found no statistically significant difference (95% nonparametric CI 94-108% within the 80-125% bioequivalence criteria)), `Valle_2005_exemestane.R` (multiplicative effects on absorption rate ka (suspension/SCT-fasting ratio 7.6/2.35 = 3.234x; suspension absorbs ~3.2x faster than the SCT swallowed whole) and on apparent bioavailability F (suspension/SCT-fasting ratio = 1.2x); intrinsic V/F is shared across formulations; the paper's per-treatment V/F values 1360 vs 1120 L collapse to a constant intrinsic V at V/F_SCT_fasting / F_suspension = 1133 ~= 1120 L).
- **Notes:** Specific scope because the per-paper comparator solid-oral form is paper-defined (tablets swallowed whole in Svensson 2018 paediatric-tuberculosis; sugar-coated tablet swallowed whole in Valle 2005 healthy-postmenopausal-women bioequivalence). Both papers contrast *the same drug substance prepared two different ways* (extemporaneous liquid suspension vs swallowed-whole solid) rather than two different manufactured drug products -- distinct from `FORM_TABLET` (Kyhl 2016 / Tikiso 2021 tablet vs liquid solution), `FORM_CAPSULE`, and `FORM_POWDER`. Future extemporaneous-suspension bioequivalence extractions should reuse this canonical and extend the example list, documenting the per-paper comparator solid-oral form. Ratified canonically on 2026-05-16 alongside the Svensson 2018 bedaquiline extraction.

### FORM_UNDILUTED_SUSP (**canonical for undiluted ready-to-use oral suspension preparation-state indicator**)
- **Description:** 1 = subject received a manufactured ready-to-use oral suspension administered undiluted (squirted directly into the mouth without pre-administration mixing with another liquid); 0 = subject received any of the rapidly-absorbed comparator formulations (e.g. tablet, granules for oral suspension after reconstitution, or the same ready-to-use suspension after dilution with a defined volume of non-sparkling liquid). Per-dose-occasion indicator because a single pediatric participant can switch preparation state across study phases. Distinct from `FORM_SUSPENSION` (Svensson 2018 / Valle 2005 extemporaneously-compounded suspension contrasted with a whole-swallowed solid) and from `FORM_GRANULE` (Ayyoub 2016 reconstitution-from-granules contrasted with a tablet) because here all subjects receive the *same* manufactured suspension product and the contrast is whether the suspension was diluted or not before swallowing.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (rapidly-absorbed comparator pool: tablet, granules for oral suspension after reconstitution, or diluted ready-to-use suspension; these share the same `ka` in Willmann 2021).
- **Source aliases:**
  - `FORM,DILU` (composite: `FORM = 2` for ready-to-use suspension AND `DILU = 1` for undiluted) -- used in `Willmann_2021_rivaroxaban.R` (supplement S2 NONMEM $PK block `IF(FORM.EQ.2.AND.DILU.EQ.1) FD = 2 ; IF(FD.EQ.2) KA = THETA(6)`).
- **Example models:** `Willmann_2021_rivaroxaban.R` (piecewise selection of the typical first-order absorption rate constant Ka: `ka = exp(lka) * (1 - FORM_UNDILUTED_SUSP) + exp(lka_susp_undiluted) * FORM_UNDILUTED_SUSP`; the undiluted suspension Ka = 0.226 1/h (95% CI 0.154-0.297) is ~3.5x slower than the comparator pool Ka = 0.799 1/h (95% CI 0.655-0.944); the relative bioavailability F is unaffected by preparation state because absorption rate does not enter the area under the curve at the dose levels studied per Willmann 2021 Discussion p. 1201 'A lower rate of absorption of the undiluted ready-to-use suspension was found in this model ... but the extent of absorption is not affected by ka and, thus, AUC(0-24h),ss is independent of the formulation').
- **Notes:** Specific scope because the contrast pairs a specific *preparation step* (mix the suspension with another liquid before swallowing vs swallow undiluted) against itself for one specific drug product. The dilution step was introduced in EINSTEIN-Jr for the ready-to-use suspension after the delayed oral absorption became obvious when administered undiluted (Willmann 2021 Discussion p. 1201). Sits in the `FORM_*` family alongside `FORM_SUSPENSION` (extemporaneous suspension), `FORM_GRANULE` (granules for reconstitution), and the tablet/capsule/solution siblings, capturing a third type of liquid-administration contrast (preparation state of the same manufactured product). Future ready-to-use-suspension dilution-preparation extractions should reuse this canonical with the per-paper comparator pool documented in `covariateData[[FORM_UNDILUTED_SUSP]]$notes`. Promote to general scope when a second paper ratifies the same encoding. Ratified canonically alongside the Willmann 2021 rivaroxaban pediatric popPK extraction.

### FORM_GRANULE (**canonical for granule-for-oral-suspension pediatric formulation indicator**)
- **Description:** 1 = subject received the modelled drug as a pediatric granule (sachet) formulation intended for reconstitution with water or food immediately before administration to children (i.e. a regulated "granules for oral suspension" dosage form); 0 = subject received the comparator solid oral formulation (typically a tablet, the adult / older-child reference; document the per-paper comparator in `covariateData[[FORM_GRANULE]]$notes`). Per-subject (regimen-fixed) categorical indicator typical of pediatric-development popPK pooled analyses.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (tablet; the typical-value Ka reference in Ayyoub 2016 Table 2).
- **Source aliases:**
  - `FORM` / `FORMULATION` -- used in `Ayyoub_2016_pyronaridine.R` (paper's `formulation` indicator with the granule formulation as the 1 level; matches the canonical encoding directly without value transformation).
- **Example models:** `Ayyoub_2016_pyronaridine.R` (additive-multiplier effect on the typical absorption rate constant Ka: `ka_typ = exp(lka) * (1 + e_form_granule_ka * FORM_GRANULE)` with `e_form_granule_ka = +1.63` (granule Ka is 2.63x the tablet Ka, i.e. 47.1 1/day vs 17.9 1/day; Ayyoub 2016 Table 2, %RSE 37.8); CL/F, V2/F, V3/F, and Q/F are held identical between formulations because backward elimination (P < 0.001) retained formulation only on Ka).
- **Notes:** Specific scope because the "pediatric granule vs tablet" contrast is tied to pediatric formulation-development popPK pooled analyses where the granule is a regulated dosage form (typically granules / sachets for oral suspension after reconstitution with water or food), distinct from the bedside-improvised `FORM_SUSPENSION` (Svensson 2018: tablets crushed-and-suspended) and from `FORM_TABLET` / `FORM_SOLUTION` / `FORM_POWDER` (different sibling formulation contrasts). Future pediatric-granule popPK extractions (a common pattern in antimalarial, antiretroviral, and antitubercular pediatric trials -- e.g. Pyramax granules, Coartem dispersible, lopinavir-ritonavir pellets) should reuse this canonical and extend the example list. Promote to general scope when a second paper ratifies the same encoding. Ratified canonically alongside the Ayyoub 2016 pyronaridine extraction.

### FORM_DTG_DT (**canonical for the dolutegravir dispersible-tablet / granule formulation indicator**)
- **Description:** 1 = dolutegravir given as the pediatric dispersible tablet or as granules for oral suspension; 0 = dolutegravir given as the film-coated tablet (FCT). The dispersible tablet and the granules share a single set of absorption and bioavailability estimates in the source model, so one indicator covers both.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (film-coated tablet; the typical-value Ka and relative-bioavailability reference in Chandasana 2024 Table 2).
- **Source aliases:**
  - `formulation` -- used in `Chandasana_2024b_dolutegravir.R`; Chandasana 2024 Table 2 names the levels "FCT" and "DT and granules".
- **Example models:** `Chandasana_2024b_dolutegravir.R` (two simultaneous effects: a blend selecting the typical absorption rate constant, `lka <- lka_fct * (1 - FORM_DTG_DT) + lka_dt * FORM_DTG_DT` with Ka = 0.854 1/h for the FCT and 2.04 1/h for the DT / granules, and a multiplicative relative-bioavailability effect `exp(e_form_dt_fdepot * FORM_DTG_DT)` with `exp(e_form_dt_fdepot) = 1.53` for the fasted DT / granules relative to the fasted FCT; the sibling `FED` food effect is gated on `(1 - FORM_DTG_DT)` because Chandasana 2024 Table 2 reports a fed estimate for the FCT only).
- **Notes:** A dispersible tablet is a solid dosage form dispersed in water immediately before administration, so it is pharmaceutically distinct from the bedside-improvised `FORM_SUSPENSION` (crushed tablets), from `FORM_SOLUTION` (a manufactured oral liquid), and from `FORM_GRANULE` (sachet granules alone) -- but the source model pools the dispersible tablet with the granules against the film-coated tablet, which is why this is a single drug-scoped indicator rather than a combination of the general siblings. Dispersible tablets are ubiquitous in pediatric antiretroviral, antimalarial and antitubercular development, so a general `FORM_DISPERSIBLE_TABLET` canonical is the natural promotion target: promote when a second paper ratifies a dispersible-tablet-vs-solid contrast that is not pooled with granules. Ratified canonically alongside the Chandasana 2024 pediatric ABC/DTG/3TC extraction.

### FORM_POWDER (**canonical for oral powder formulation indicator**)
- **Description:** 1 = subject received the oral powder formulation of the modelled drug; 0 = subject received the comparator solid oral formulation (typically a tablet, but document the per-paper comparator in `covariateData[[FORM_POWDER]]$notes`).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (tablet; F = 1 fixed in Yukawa 1990 Model 2).
- **Source aliases:**
  - `FORM_POWDER` -- used in `Yukawa_1990_phenytoin.R` (paper's `BA` indicator inverted: source `BA = 1` if tablet, 0 if powder; canonical `FORM_POWDER = 1 - BA_indicator` so 0 is the tablet reference).
- **Example models:** `Yukawa_1990_phenytoin.R` (Yukawa 1990 Model 2 dose-dependent powder bioavailability `F_powder = 1 - exp(-9.92 / DOSE_PHT_MGKGD)`; tablet F fixed at 1), `Retlich_2015_linagliptin.R` (multiplicative shift on the linagliptin first-order absorption rate constant Ka: powder-in-bottle Ka = 0.933 1/h vs tablet formulation 2 reference Ka = 0.441 1/h; the tablet formulation 1 comparator is captured by the sibling canonical `FORM_LINAG_TAB1`), `Hong_2011_atazanavir.R` (multiplicative linear-deviation effect on relative bioavailability: `fdepot *= (1 + e_form_powder_frel * FORM_POWDER)` with `e_form_powder_frel = -0.355`, i.e. the pediatric atazanavir oral powder has 35.5% lower bioavailability than the capsule reference; Hong 2011 Table 4).
- **Notes:** Specific scope because the "powder vs tablet" contrast is tied to a particular drug-product manufacturing comparison (Yukawa 1990 contrasts Aleviatin brand phenytoin powder with Aleviatin tablets, both from Dainippon Pharmaceutical Co.; Retlich 2015 contrasts an early-phase linagliptin powder-in-bottle formulation against the marketed linagliptin tablet). Mirrors the sibling `FORM_TABLET` (Kyhl 2016 / Tikiso 2021 tablet vs solution) and `FORM_CAPSULE` (Hennig 2006 / Hennig 2007 capsule vs solution) under the `FORM_*` family. Future powder-formulation models should reuse this canonical, extending the example list and documenting the per-paper comparator. Ratified canonically on 2026-05-10 alongside the Yukawa 1990 phenytoin extraction.

### FORM_THEO_APNECUT (**canonical for Apnecut vs theophylline-alcohol in-house preparation oral-theophylline product indicator**)
<!-- AUDIT 2026-06-19: renamed from `FORM_APNECUT` to `FORM_THEO_APNECUT` per the canonical-register standardization audit. The prior name `FORM_APNECUT` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** 1 = subject received the commercial Apnecut (APC) oral theophylline product (4 mg/mL aqueous internal-use solution; Kowa Co., Ltd., Japan; launched August 2006), 0 = subject received the theophylline-alcohol (TA) in-house oral preparation (5 mg/mL theophylline dissolved in ethanol then diluted with sterile purified water to give 10 percent final ethanol concentration; compounded at the National Center for Child Health and Development pharmacy). Per-subject (regimen-fixed) categorical indicator in the Suda 2008 retrospective neonatal-apnea cohort.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (TA in-house preparation; the typical-value CL/F reference in Suda 2008 final model).
- **Source aliases:**
  - `FORM_APNECUT` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `AP` -- used in `Suda_2008_theophylline.R` (Suda 2008 NONMEM `AP` indicator; same orientation, no transformation; AP = 1 if Apnecut, 0 if theophylline alcohol).
- **Example models:** `Suda_2008_theophylline.R` (multiplicative effect on CL/F per Suda 2008 final model page 638: `cl <- exp(lcl + etalcl) * (WT / 1)^1.08 * (1 + e_form_apnecut_cl * FORM_THEO_APNECUT)` with `e_form_apnecut_cl = -0.282`; the APC formulation has approximately 0.71x the CL/F of the TA reference, equivalent to approximately 1.41x higher dose-normalised exposure consistent with the higher trough concentrations the authors observed clinically that motivated the analysis).
- **Notes:** Specific scope because the Apnecut-vs-TA contrast is a Japan-specific paediatric-theophylline drug-product comparison local to the Suda 2008 cohort: the TA in-house preparation is unique to the National Center for Child Health and Development pharmacy (compounded ad hoc from bulk theophylline + ethanol + sterile water), and Apnecut is a Kowa Co. commercial Japanese product not marketed elsewhere. Both are oral liquid formulations - distinct from `FORM_POWDER`/`FORM_SYRUP`/`FORM_TABLET`/`FORM_CAPSULE` because the contrast here is between two specific liquid drug products rather than between dosage-form categories. Suda 2008 attributes the CL/F difference primarily to absorption (per Discussion, page 641: HPLC content analysis confirmed both products were within label, so the formulation effect on apparent oral clearance is interpreted as a bioavailability difference rather than an actual clearance difference) but the parameter is encoded as an effect on CL/F to match the published equation. Mirrors the sibling drug-product-version `FORM_*` entries (`FORM_SAR_DP2` sarilumab, `FORM_ISA_P2F2` isatuximab, `FORM_LINAG_TAB1` linagliptin, `FORM_VISMO_PHASEI` vismodegib, `FORM_TAC_IR` tacrolimus) under the `FORM_*` family. Set to 0 to simulate the TA reference; in the absence of TA-specific dosing in a downstream cohort, set FORM_THEO_APNECUT = 1 to represent Apnecut. Ratified canonically on 2026-05-24 alongside the Suda 2008 theophylline extraction.

### FORM_SYRUP (**canonical for oral syrup / liquid-suspension formulation indicator**)
- **Description:** 1 = subject received the modelled drug as an oral syrup or liquid-suspension formulation (paediatric or extemporaneously-compounded oral suspension); 0 = subject received the comparator solid oral formulation (typically a capsule, but document the per-paper comparator in `covariateData[[FORM_SYRUP]]$notes`). Per-dose-occasion indicator in principle; in paediatric cohorts often time-fixed per subject by clinical convention.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (per-paper non-syrup comparator: capsule in Nanga 2019 tacrolimus; tablet in Sarashina 2005 epinastine).
- **Source aliases:**
  - `syrup formulation` -- used in `Nanga_2019_tacrolimus_metaanalysis.R` (Table 3 covariate-effect label; relative bioavailability of syrup vs capsule = 0.53).
  - `FORM` (dry syrup vs tablet) -- used in `Sarashina_2005_epinastine.R` (Table 4 theta_9 multiplier on CL/F: dry-syrup / tablet ratio 1.06; paediatric arm received dry syrup only, adult arm received tablet or dry syrup).
- **Example models:** `Nanga_2019_tacrolimus_metaanalysis.R` (multiplicative effect on bioavailability per Eq. 4: `f(depot) <- 0.53^FORM_SYRUP`, so capsule users have F = 1 and syrup users have F = 0.53; Nanga 2019 Table 3 'Bioavailability for syrup formulation' = 0.53 with 95% bootstrap CI 0.31 - 0.75), `Sarashina_2005_epinastine.R` (multiplicative ratio 1.06 on CL/F for dry syrup vs tablet; paediatric atopic-dermatitis patients all received dry syrup, healthy adults received either tablet or dry syrup; tablet is the reference (FORM_SYRUP = 0) and dry syrup is FORM_SYRUP = 1).
- **Notes:** Specific scope because the comparator solid-oral reference is paper-defined (capsule in Nanga 2019). Distinct from `FORM_SUSPENSION` (Svensson 2018: tablets extemporaneously suspended in water at bedside immediately before swallowing -- same tablet swallowed two different ways, where the manipulation affects MAT rather than F) because here the contrast is between two distinct drug products (commercial capsules vs paediatric syrup / suspension). Distinct from `FORM_CAPSULE`, `FORM_TABLET`, and `FORM_POWDER`, which compare those solid-oral forms against a liquid solution or against each other. Future paediatric-syrup / oral-suspension formulation comparisons should reuse this canonical; if a future model contrasts syrup against tablet (rather than capsule), extend the per-model notes rather than registering a sibling canonical. Ratified canonically on 2026-05-18 alongside the Nanga 2019 tacrolimus meta-analysis extraction.

### FORM_CACO3 (**canonical for calcium-carbonate-tablet vs calcium-containing mineral-water formulation indicator**)
- **Description:** 1 = subject received calcium as a calcium carbonate (CaCO3) tablet; 0 = subject received calcium as calcium-containing thermal mineral water (Geumjin thermal spring water, the Ahn 2014 reference comparator). Per-subject (treatment-arm) categorical indicator in the Ahn 2014 parallel-arm calcium-absorption design.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Geumjin thermal spring water; the typical-value bioavailability reference in Ahn 2014, F = 1 fixed for the reference arm).
- **Source aliases:** none known; Ahn 2014 narratives the treatment factor as "thermal spring water versus calcium carbonate tablet" without a single NMTRAN column name.
- **Example models:** `Ahn_2014_parathyroidHormone.R` (multiplicative effect on the depot bioavailability per Ahn 2014 Table 2 'Relative F1' = 1.98: `fdepot = exp(lfdepot + e_form_caco3_fdepot * FORM_CACO3)` with `lfdepot` fixed at log(1) for the thermal-water reference and `e_form_caco3_fdepot = log(1.98)` for the CaCO3 arm; 95% CI on relative F1 1.06-2.90).
- **Notes:** Specific scope because the contrast pairs a specific calcium product (calcium carbonate tablet) against a specific natural mineral-water source (Geumjin thermal spring water) rather than a generic tablet-vs-solution contrast (`FORM_TABLET`) -- the 0-level here is calcium-containing spring water naturally rich in dissolved calcium, not a manufactured aqueous solution of the modelled drug. Future calcium-bioavailability studies that compare CaCO3 against a different calcium source (citrate, gluconate, dietary-calcium baseline) should reuse this canonical with the reference clearly documented in per-model notes, or register a sibling canonical if the comparator is meaningfully different. Sits in the `FORM_*` family alongside `FORM_FDC` (Wilkins 2008 rifampicin co-formulation), `FORM_SYRUP` (Nanga 2019 tacrolimus paediatric syrup), and `FORM_TABLET` (Kyhl 2016 nalmefene / Tikiso 2021 abacavir tablet vs solution). Ratified canonically on 2026-05-21 alongside the Ahn 2014 parathyroid-hormone calcium-absorption extraction.

### FORM_FDC (**canonical for fixed-dose-combination tablet formulation indicator**)
- **Description:** 1 = subject received a fixed-dose-combination (FDC) tablet co-formulating the modelled drug with one or more other drugs in a single tablet; 0 = subject received the modelled drug as a single-drug tablet (or, in the antitubercular context, as a separate-drug-combination "SDC" of single-drug tablets). Per-subject (regimen-fixed) categorical covariate flagging the formulation when a population analysis pools FDC and single-drug-tablet arms and tests formulation as a covariate on absorption / disposition parameters.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** Paper-defined (the typical-value reference is not fixed across the register because different popPK papers anchor their typical-value parameters to different formulation arms). Wilkins 2008 set FDC = 1 as the typical-value reference (SDC = 0 enters as a deviation on MTT and CL); Choi 2018 set the single-agent tablet (FORM_FDC = 0) as the typical-value reference (FDC = 1 enters as a deviation on Ka and F). The canonical column value semantics (1 = FDC, 0 = single-drug tablet) are preserved across both papers; the choice of which arm is the structural reference is documented per-model in `covariateData[[FORM_FDC]]$notes`. Reference values observed: 1 = FDC (Wilkins 2008 rifampicin + isoniazid + pyrazinamide +/- ethambutol antitubercular FDC); 0 = single-agent metformin tablet (Choi 2018 metformin vs metformin-containing antidiabetic FDC).
- **Source aliases:**
  - `FDC` -- used in `Wilkins_2008_rifampicin.R` (DDMODEL00000280 NMTRAN `$INPUT` column; values 0 / 1 with the same orientation as the canonical, 1 = FDC).
  - `formulation` (lower-case as printed in the paper) -- used in `Choi_2018_metformin.R` (Choi 2018 Methods 'Covariate analysis' equation: formulation = 0 single-agent reference, formulation = 1 FDC test).
- **Example models:**
  - `Wilkins_2008_rifampicin.R` (multiplicative `(1 + e_fdc0_mtt * (1 - FORM_FDC))` shift on MTT and `(1 + e_fdc0_cl * (1 - FORM_FDC))` shift on CL; SDC subjects (FDC = 0) had 104% longer MTT and 23.6% higher CL than the FDC = 1 reference per Wilkins 2008 final estimates. Antitubercular co-formulation: rifampicin + isoniazid + pyrazinamide +/- ethambutol).
  - `Choi_2018_metformin.R` (multiplicative effects on first-order absorption rate Ka and on relative bioavailability F: `ka *= 0.83^FORM_FDC` (Ka shrinks to 83.0% of the single-agent value for FDC) and `f_rel *= 0.94^FORM_FDC` (relative bioavailability shrinks to 94.0% of the single-agent value for FDC) per Choi 2018 Table 3 final estimates. Antidiabetic co-formulation: the FDC drug-product is metformin co-formulated with an unspecified second antidiabetic agent -- typical Korean metformin FDCs co-formulate with sitagliptin / glimepiride / vildagliptin / dapagliflozin; the paper does not name the specific co-formulant. Reference category 0 = single-agent metformin tablet).
- **Notes:** General scope: the FDC vs single-drug-tablet contrast applies to any drug class where a popPK study compares the modelled drug as a single-drug tablet against an FDC tablet co-formulating it with one or more other drugs. Promoted from specific to general scope on 2026-05-30 when Choi 2018 metformin (antidiabetic FDC) ratified the canonical alongside the pre-existing Wilkins 2008 rifampicin (antitubercular FDC) example. The drug-class context (antitubercular / antidiabetic / antihypertensive / antiretroviral / etc.) and the specific co-formulant identity (isoniazid + pyrazinamide / sitagliptin / etc.) are documented per-model in `covariateData[[FORM_FDC]]$notes`. The mechanism is co-formulation-driven perturbation of absorption (excipient interactions, dissolution rate, gastric residence) rather than drug-product manufacturing of the single drug -- distinct from `FORM_TABLET` (Kyhl 2016 nalmefene tablet vs solution) and the rest of the `FORM_*` (drug-product-version) family because the FDC-vs-single-drug-tablet contrast compares two tablet products that both contain the modelled drug, not a tablet vs a non-tablet. When the source paper's typical-value parameters are anchored to the FDC arm, set the per-paper note accordingly; when anchored to the single-drug-tablet arm, set the per-paper note accordingly. Both orientations are valid and the canonical column semantics (1 = FDC, 0 = single-drug tablet) preserve a consistent input column across simulations.

### RIA_ASSAY (**canonical for radioimmunoassay vs LC-MS/MS bioanalytical method indicator**)
- **Description:** 1 = radioimmunoassay; 0 = LC-MS/MS.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Example models:** `Kyhl_2016_nalmefene.R`.
- **Notes:** Switches the additive residual-error magnitude. Use this canonical only when the source paper specifically identifies the immunoassay as radioimmunoassay. For non-radioactive immunoassay methods (microparticle enzyme immunoassay MEIA, chemiluminescence microparticle immunoassay CMIA, EMIT, or generic "immunoassay"), use the sibling canonical `IMMUNOASSAY` below.

### IMMUNOASSAY (**canonical for non-radioactive immunoassay vs LC-MS/MS bioanalytical method indicator**)
- **Description:** Binary indicator selecting between a non-radioactive immunoassay bioanalytical method (microparticle enzyme immunoassay MEIA, chemiluminescence microparticle immunoassay CMIA, EMIT, ELISA-based, or generic "immunoassay") and an LC-MS/MS reference method. 1 = immunoassay; 0 = LC-MS/MS. Time-varying per sample (per-row) in pooled datasets that span the historical introduction of LC-MS/MS at the analytical lab. Used to switch the additive and/or proportional residual-error magnitudes between the two analytical methods, which typically have different precision, accuracy, and cross-reactivity profiles.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (LC-MS/MS -- the modern reference bioanalytical method with lower LLOQ, higher specificity, and less cross-reactivity with parent drug metabolites; selected as the reference because most current popPK datasets are LC-MS/MS based and the indicator captures the residual-error shift for the legacy-immunoassay subset).
- **Source aliases:**
  - `IMMUNOASSAY` / `IA` / `ASSAY` -- common NONMEM `$INPUT` forms.
- **Example models:** `Andrews_2017_tacrolimus.R` (per-sample binary indicator switching the additive + proportional residual error between immunoassay (9% of samples; pre-2013 microparticle / chemiluminescence immunoassay) and LC-MS/MS (91% of samples; lower LLOQ 1.0 ng/mL vs 1.5 ng/mL) -- both magnitudes estimated jointly in the final model per Andrews 2017 Section 2.3 and Table 2).
- **Notes:** Distinct from the radioimmunoassay-specific `RIA_ASSAY` canonical (use that one when the paper identifies the immunoassay as radioimmunoassay; use this `IMMUNOASSAY` for MEIA / CMIA / EMIT / ELISA / generic immunoassay). The two siblings could in principle be unified into a single `IMMUNOASSAY` canonical with per-model notes documenting the immunoassay subtype, but the existing `RIA_ASSAY` registration is preserved for backwards compatibility with `Kyhl_2016_nalmefene.R`. Per-row time-varying when the dataset spans the historical introduction of LC-MS/MS at the analytical lab (e.g., Andrews 2017's 2009-2016 sampling window crosses the lab's switch from immunoassay to LC-MS/MS; the per-sample assay method is recorded). When the dataset is fully LC-MS/MS, set `IMMUNOASSAY = 0` for every row and the immunoassay residual-error parameters become non-identifiable -- consider dropping them from the model. Ratified canonically on 2026-05-25 alongside the Andrews 2017 tacrolimus extraction.

### ASSAY_OSA (**canonical for one-stage clotting (OSA) vs chromogenic substrate assay (CSA) bioanalytical method indicator for factor VIII activity**)
- **Description:** 1 = factor VIII activity measured by the one-stage activated partial thromboplastin time (APTT) clotting assay (OSA); 0 = measured by the chromogenic substrate assay (CSA). Per-observation (per-row) binary indicator. Used to switch (a) a multiplier capturing the systematic activity bias between the two assay platforms -- applied either to bioavailability (Abrantes 2017) or directly to the predicted FVIII activity (Valke 2024) -- and (b) the residual-error magnitudes, which differ between the two platforms.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (CSA). The column's coding is fixed (1 = OSA, 0 = CSA), but which level a given model treats as its *structural* reference scale is a per-model property and must be recorded in that model's `covariateData` notes. `Abrantes_2017_moroctocog.R` estimates its typical parameters on the CSA scale (F = 1 anchor at CSA, the assay used to calibrate the European-Union potency labels for Refacto AF) and applies its effects to the OSA rows; `Valke_2024_factorviii.R` and its PD siblings estimate CL / V1 on the OSA scale and therefore apply their effects to the CSA (`ASSAY_OSA = 0`) rows.
- **Source aliases:**
  - `METH1` -- used in `Abrantes_2017_moroctocog.R` (Abrantes 2017 Table 2 footnote g: OSA-central indicator on the bioavailability multiplier and on the proportional residual-error magnitude).
  - `Assay method` -- used in `Valke_2024_factorviii.R`, `Valke_2024_factorviii_thrombinPeak.R`, `Valke_2024_factorviii_thrombinPotential.R`, `Valke_2024_factorviii_plasminPeak.R` (Valke 2024 Results 3.4: every sample was assayed by both methods and both were fitted simultaneously).
- **Example models:** `Abrantes_2017_moroctocog.R` (multiplicative effects: F multiplier `(1 - 0.390 * ASSAY_OSA)` so OSA-assayed observations have F reduced by 39.0% relative to the CSA reference; proportional residual SD multiplier `(1 + 0.403 * ASSAY_OSA)` so OSA-assayed observations have 40.3% larger residual error magnitude), `Valke_2024_factorviii.R`, `Valke_2024_factorviii_thrombinPeak.R`, `Valke_2024_factorviii_thrombinPotential.R`, `Valke_2024_factorviii_plasminPeak.R` (OSA-referenced: `Cc <- fviii * 0.939^(1 - ASSAY_OSA)` so CSA-assayed observations read 0.939x the one-stage value, with the proportional and additive residual SDs likewise switched by `0.840^(1 - ASSAY_OSA)` and `5.012^(1 - ASSAY_OSA)`; the theta^flag form follows Valke 2024 Supplementary Methods Eq. 6).
- **Notes:** Specific scope because OSA-vs-CSA is a FVIII-domain bioanalytical contrast (the OSA is a functional clotting assay that uses patient APTT-based clot formation; the CSA is an amidolytic chromogenic substrate assay). The direction and magnitude of the OSA-CSA discrepancy are product-dependent: B-domain-deleted recombinant FVIII products read *lower* by OSA than CSA (Abrantes 2017 Discussion; Lippi 2008; Hubbard 2013), whereas for the plasma-derived VWF/FVIII concentrate of Valke 2024 the CSA read 6.1% lower than the OSA (correction factor 0.939). Do not carry a magnitude across models; source it per product. Per-observation (per-row) when a dataset mixes assays -- Valke 2024 assayed every sample by both methods, so the same nominal sampling time appears twice with different `ASSAY_OSA` values. Paired with `ASSAY_OSA_LOCAL` to capture an additional inter-laboratory shift for OSA performed at non-central laboratories. Ratified canonically on 2026-06-21 alongside the Abrantes 2017 moroctocog extraction.

### ASSAY_OSA_LOCAL (**canonical for OSA performed at a local (vs central) laboratory indicator for factor VIII activity**)
- **Description:** 1 = factor VIII activity measured by the OSA at a local (study-site or regional) laboratory rather than the centralised analytical laboratory; 0 = central-laboratory OSA, or any non-OSA-local observation. Per-observation (per-row) binary indicator. Sub-variant of `ASSAY_OSA`: when `ASSAY_OSA_LOCAL = 1`, `ASSAY_OSA` is also 1 (the local-lab samples are a subset of OSA-assayed samples). Used to capture an additional inter-laboratory variability term on the bioavailability multiplier.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (central laboratory OSA, or any non-OSA-local observation -- the dominant subset in pooled FVIII PK datasets).
- **Source aliases:**
  - `METH2` -- used in `Abrantes_2017_moroctocog.R` (Abrantes 2017 Table 2 footnote g: OSA-local indicator on the bioavailability multiplier; only one study in the Abrantes 2017 13-study pool, B1831003, used a local laboratory).
- **Example models:** `Abrantes_2017_moroctocog.R` (multiplicative effect on F: `(1 - 0.146 * ASSAY_OSA_LOCAL)` so OSA-local-laboratory observations have an additional 14.6% F reduction beyond the central-laboratory OSA effect captured by `ASSAY_OSA`).
- **Notes:** Specific scope because OSA inter-laboratory variability is a FVIII-domain phenomenon. Paired with `ASSAY_OSA`. When the pooled dataset contains no local-laboratory observations, set `ASSAY_OSA_LOCAL = 0` for every row and the corresponding F multiplier collapses to 1. Ratified canonically on 2026-06-21 alongside the Abrantes 2017 moroctocog extraction.

### FORM_LEB_NS0 (**canonical for lebrikizumab NS0 cell-line formulation indicator**)
<!-- AUDIT 2026-06-19: renamed from `FORM_NS0` to `FORM_LEB_NS0` per the canonical-register standardization audit. The prior name `FORM_NS0` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** 1 = NS0 cell-line formulation (lebrikizumab), 0 = other.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Source aliases:**
  - `FORM_NS0` -- prior canonical name (pre-2026-06-19 FORM_<DRUG>_<FEATURE> standardization).
- **Example models:** `Zhu_2017_lebrikizumab.R`.
- **Notes:** Renamed from `FORM_NS0` to `FORM_LEB_NS0` on 2026-06-19 per the canonical-register standardization audit (operator decision: insert the drug stem so the formulation indicator is namespaced to the specific drug, matching the `FORM_<DRUG>_<FEATURE>` pattern used by `FORM_DOX_DORYX_MPC`, `FORM_ITR_SUBA`, etc.).

### FORM_LEB_CHO_PHASE2 (**canonical for lebrikizumab CHO Phase 2 formulation indicator**)
<!-- AUDIT 2026-06-19: renamed from `FORM_CHO_PHASE2` to `FORM_LEB_CHO_PHASE2` per the canonical-register standardization audit. The prior name `FORM_CHO_PHASE2` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** 1 = CHO Phase 2 formulation (lebrikizumab), 0 = other.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Source aliases:**
  - `FORM_CHO_PHASE2` -- prior canonical name (pre-2026-06-19 FORM_<DRUG>_<FEATURE> standardization).
- **Example models:** `Zhu_2017_lebrikizumab.R`.
- **Notes:** Renamed from `FORM_CHO_PHASE2` to `FORM_LEB_CHO_PHASE2` on 2026-06-19 per the canonical-register standardization audit (operator decision: insert the drug stem so the formulation indicator is namespaced to lebrikizumab specifically, matching the `FORM_<DRUG>_<FEATURE>` pattern).

### FORM_GCSF_PEG (**canonical for pegylated vs non-pegylated recombinant human G-CSF formulation indicator**)
- **Description:** 1 = pegfilgrastim (recombinant human G-CSF conjugated to a 20 kDa monomethoxy-polyethylene-glycol moiety, apparent MW approximately 39 kDa), 0 = filgrastim (non-pegylated recombinant human G-CSF, MW 18.8 kDa). Categorical drug-type covariate on the SC absorption / distribution / clearance / binding-affinity parameters (FSC, KSC, VD, CLD, KD). Subject-level (each subject received one drug per study cohort in the pooled analyses).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (filgrastim; the structurally-simpler non-pegylated parent molecule and the SC bioavailability anchor FSC = 1).
- **Source aliases:**
  - `DRUG` -- generic paper-text label for the two-level filgrastim / pegfilgrastim covariate in `Melhem_2018_g_csf.R` (Melhem 2018 Methods 'Covariate analysis'; the paper reports separate FIL and PEG parameter estimates in Table 2 rather than encoding a base + shift form directly).
- **Example models:** `Melhem_2018_g_csf.R`.
- **Notes:** Follows the `FORM_<DRUG>_<FEATURE>` naming pattern used by `FORM_LEB_NS0`, `FORM_ITR_SUBA`, `FORM_DOX_DORYX_MPC`, `FORM_TAC_IR`, `FORM_LINAG_TAB1`, `FORM_SAR_DP2`, `FORM_ISA_P2F2`, `FORM_VISMO_PHASEI`, `FORM_XYNTHA`, and `FORM_AXI_XLI` for drug-specific product / molecular-form indicators. Distinct from `CONMED_IFNALPHA` (pegylated interferon alpha coadministration flag) because `FORM_GCSF_PEG` is the primary G-CSF drug being modelled, not a coadministered agent. Distinct from `CONMED_PLDH` (PEGylated liposomal doxorubicin coadministration flag) for the same reason. Ratified canonically alongside the Melhem 2018 g_csf extraction. Auto-approved sibling of the `FORM_<DRUG>_<FEATURE>` family.

### CONMED_EOX (**canonical for concomitant EOX (epirubicin + oxaliplatin + capecitabine) chemotherapy backbone indicator**)
- **Description:** 1 = concomitant epirubicin + oxaliplatin + capecitabine (EOX) chemotherapy backbone, 0 = other backbone (e.g., mFOLFOX6, CAPOX, or single-agent).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-EOX backbone).
- **Source aliases:** `COMB` (used by Yamada 2025 Table 1 with the EOX level coded as the non-reference category; renamed to `CONMED_EOX` to preserve the semantic meaning of the 1-level).
- **Example models:** `Yamada_2025_zolbetuximab.R` (fractional effect on V1).
- **Notes:** Disease-backbone indicator. If a future model needs more backbone categories, encode each as its own indicator (`COMB_CAPOX`, `COMB_FOLFOX`, ...) with a single reference group.

### CONMED_CCB (**canonical for concomitant calcium-channel blocker coadministration indicator**)
- **Description:** 1 = subject was receiving a calcium-channel blocker (CCB) as a co-medication during the study, 0 = no CCB. Captures the documented CYP3A4-inhibition CCB-tacrolimus interaction (CCBs reduce tacrolimus apparent oral clearance because amlodipine, diltiazem, and verapamil inhibit CYP3A4 in the gut wall and liver where tacrolimus is metabolised).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no CCB).
- **Source aliases:** none.
- **Example models:** `Passey_2011_tacrolimus.R` (multiplicative effect on apparent oral CL: `cl_typ * e_ccb_cl^CONMED_CCB` with `e_ccb_cl = 0.812`, i.e. CCB coadministration reduces tacrolimus CL/F by ~19% in adult kidney transplant recipients; Passey 2011 final model Table 4 row "CCB").
- **Notes:** Specific scope because the CCB-tacrolimus interaction is documented for CYP3A4 substrates and the magnitude is drug-pair specific. Future popPK models for CYP3A4 substrates that need a CCB conmed indicator should reuse this canonical. Ratified 2026-05-28 per the naming audit.

### CONMED_ATV_DOSE, CONMED_FLV_DOSE, CONMED_LOV_DOSE, CONMED_PRV_DOSE, CONMED_RSV_DOSE, CONMED_SMV_DOSE, CONMED_EZT_DOSE, CONMED_INH_DOSE (**canonical for daily dose of a co-administered named drug**)
- **Description:** Daily dose of the named drug (suffix = INN lowercase abbreviation: `atv` atorvastatin, `flv` fluvastatin, `lov` lovastatin, `prv` pravastatin, `rsv` rosuvastatin, `smv` simvastatin, `ezt` ezetimibe, `inh` isoniazid). 0 = the named drug is not part of the regimen for this study arm / subject; positive value = total daily dose. Captures the dose-response amplitude in MBMA or co-administered-drug PK/PD models where each drug arm contributes its own dose-effect curve.
- **Units:** mg/day for the statin / ezetimibe series; mg/kg for `CONMED_INH_DOSE` (paper-specific unit, documented in `covariateData[[CONMED_INH_DOSE]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** 0 (the drug is not given in this arm; equivalent to "this drug-arm contributes 0 to its dose-effect term").
- **Source aliases:** `DOSE_<drug>` legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename, Chen 2017 TB mouse pre-rename); aliases of the canonical `CONMED_<drug>_DOSE` form per the 2026-05-28 naming audit rename.
- **Example models:** `Vargo_2014_statins_ezetimibe_mbma.R` (each `CONMED_<statin>_DOSE` drives the corresponding statin's Hill / Emax dose-response curve; per-arm dose level in mg/day, default 0 outside the named statin arm; ezetimibe and statin arms combine via the sub-additive `gamma_int` term), `Chen_2017_TB_MTP_GPDI_mouse.R` (`CONMED_INH_DOSE` drives the isoniazid CL adjustment: `cl_inh = cl_inh_lowdose * (1 - slope_inh * (CONMED_INH_DOSE - 12.5))`).
- **Notes:** The `CONMED_<drug>_DOSE` shape replaces the earlier `DOSE_<drug>` / `DOSE_<drug>_<unit>` names (which conflated a covariate with a dose-amount column). New co-medication dose-effect MBMA models should reuse this family and add the appropriate `<drug>` INN abbreviation. The units field is per-paper. Ratified 2026-05-28 per the naming audit.

### FORM_FLV_BID_XR (**canonical for fluvastatin twice-daily / extended-release formulation indicator**)
- **Description:** 1 = fluvastatin arm used twice-daily (BID) dosing or an extended-release (XR) formulation; 0 = once-daily immediate-release fluvastatin (the model reference).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (q.d. IR fluvastatin).
- **Source aliases:** `BID_XR_FLV` legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename); alias of the canonical `FORM_FLV_BID_XR` form per the 2026-05-28 naming audit rename.
- **Example models:** `Vargo_2014_statins_ezetimibe_mbma.R` (multiplies the fluvastatin ED50 by 0.645 when set to 1; Vargo 2014 Table 3 row "ED50,fluvastatin (b.i.d.|XR) / ED50,fluvastatin"; same ratio used for either regimen because the paper found b.i.d. and XR ED50 estimates were similar).
- **Notes:** Specific scope because the b.i.d.-vs-q.d. / IR-vs-XR formulation comparison is paper-specific to fluvastatin meta-analyses. Sibling of `FORM_LOV_BID_XR` (lovastatin) under the `FORM_<drug>_BID_XR` family pattern. Ratified 2026-05-28 per the naming audit.

### FORM_LOV_BID_XR (**canonical for lovastatin twice-daily / extended-release formulation indicator**)
- **Description:** 1 = lovastatin arm used twice-daily (BID) dosing or an extended-release (XR) formulation; 0 = once-daily immediate-release lovastatin (the model reference).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (q.d. IR lovastatin).
- **Source aliases:** `BID_XR_LOV` legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename); alias of the canonical `FORM_LOV_BID_XR` form per the 2026-05-28 naming audit rename.
- **Example models:** `Vargo_2014_statins_ezetimibe_mbma.R` (multiplies the lovastatin ED50 by 0.59 when set to 1; Vargo 2014 Table 3 row "ED50,lovastatin (b.i.d.|XR) / ED50,lovastatin").
- **Notes:** Specific scope because the b.i.d.-vs-q.d. / IR-vs-XR formulation comparison is paper-specific to lovastatin meta-analyses. Sibling of `FORM_FLV_BID_XR` (fluvastatin) under the `FORM_<drug>_BID_XR` family pattern. Ratified 2026-05-28 per the naming audit.

### DIS_ACS (**canonical for acute coronary syndrome cohort indicator**)
- **Description:** 1 = study arm enrolled patients with acute coronary syndrome (ACS), 0 = non-ACS cohort (the model reference).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-ACS arm).
- **Source aliases:** `ACS` legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename); alias of the canonical `DIS_ACS` form per the 2026-05-28 naming audit rename.
- **Example models:** `Vargo_2014_statins_ezetimibe_mbma.R` (additive shift of -0.117 on the statin Emax in ACS arms; greater statin LDL-C lowering in ACS patients than in the non-ACS reference).
- **Notes:** Specific scope because the ACS-cohort effect on statin response is paper-specific to the Vargo 2014 MBMA. Sibling of `DIS_HEFH` (HeFH cohort indicator) and the broader `DIS_<indication>` family. Ratified 2026-05-28 per the naming audit.

### DIS_HEFH (**canonical for heterozygous familial hypercholesterolemia cohort indicator**)
- **Description:** 1 = study arm enrolled patients with heterozygous familial hypercholesterolemia (HeFH), 0 = non-HeFH cohort (the model reference).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-HeFH arm).
- **Source aliases:** `HEFH` legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename); alias of the canonical `DIS_HEFH` form per the 2026-05-28 naming audit rename.
- **Example models:** `Vargo_2014_statins_ezetimibe_mbma.R` (additive shift of +0.127 on the statin Emax in HeFH arms; smaller statin LDL-C lowering in HeFH patients than in the non-HeFH reference; biologically consistent with the LDLR-pathway disruption in HeFH), `Kuchimanchi_2018_evolocumab_ldlc.R` (multiplicative exponent 1.28 on baseline LDL-C in the evolocumab Emax-on-AUC exposure-response layer, Table 4; HeFH patients have higher baseline LDL-C than the non-HeFH reference), `Jadhav_2023_bempedoicAcid_ldlc.R` (proportional shift of +0.0671 on the indirect-response baseline LDL-C, Jadhav 2023 Table 3; two of the four pivotal phase 3 studies enrolled patients with prior ASCVD and/or HeFH on maximally tolerated statin therapy).
- **Notes:** Specific scope because the HeFH-cohort effect on statin response is paper-specific. Sibling of `DIS_HOFH` (homozygous form, Pu_2021_evinacumab) and the broader `DIS_<indication>` family. Ratified 2026-05-28 per the naming audit.

### CONMED_RIF_CC, CONMED_INH_CC, CONMED_EMB_CC, CONMED_STR_CC, CONMED_CAB_CC, CONMED_COL_CC, CONMED_MER_CC, CONMED_GEN_CC, CONMED_CIP_CC (**canonical for time-varying plasma / in-vitro concentration of a co-administered named drug**)
- **Description:** Time-varying plasma (or in-vitro experiment) concentration of the named drug (suffix = INN lowercase abbreviation: `rif` rifampicin, `inh` isoniazid, `emb` ethambutol, `str` streptomycin, `cab` capreomycin / a second antibiotic, `col` colistin, `mer` meropenem, `gen` gentamicin, `cip` ciprofloxacin). Used as the driving exposure for the corresponding drug-effect term in combination PK/PD or in vitro time-kill models. 0 = the named drug is not part of the regimen for this subject / arm.
- **Units:** mg/L (or ug/mL, equivalent) for in vitro time-kill experiments; ug/mL for clinical plasma-PK-driven PD subsystems. Per-paper unit is documented in `covariateData[[CONMED_<drug>_CC]]$units`.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** 0 (drug not in regimen).
- **Source aliases:** `Ccol` / `Cmer` / `Cgen` / `Ccip` (Mohamed 2016, Sadouki 2025) and bare `EMB` / `INH` / `RIF` / `STR` / `CAB` (Clewe 2018, Khan 2015) -- legacy forms used before the 2026-05-28 naming-audit rename. All map to the canonical `CONMED_<drug>_CC` form.
- **Example models:** `Clewe_2018_rifampicin.R` (in vitro time-kill of rifampicin + isoniazid + ethambutol against M. tuberculosis; each drug exposure is a fixed concentration driving the Hill-Emax effect on the F / S / N bacterial states), `Khan_2015_ciprofloxacin.R` (in vitro streptomycin + capreomycin time-kill), `Mohamed_2016_colistin_meropenem.R` (in vitro colistin + meropenem time-kill against WT and meropenem-resistant P. aeruginosa), `Sadouki_2025_meropenem_gentamicin_ciprofloxacin.R` (combination PD with meropenem / gentamicin / ciprofloxacin time-varying exposures).
- **Notes:** Companion to the dose covariate `CONMED_<drug>_DOSE` (registered above). The `_CC` suffix distinguishes the dynamic concentration covariate from the dose-level covariate; both share the `<drug>` INN abbreviation. Ratified 2026-05-28 per the naming audit.

### CONMED_MER, CONMED_GEN, CONMED_CIP (**canonical for concomitant coadministration indicators of meropenem / gentamicin / ciprofloxacin**)
- **Description:** Binary indicator: 1 = the named antibiotic is coadministered with the index drug for this subject / record / study arm, 0 = the antibiotic is not coadministered. The indicator covers two related use cases that share the same data semantic: (1) in combination-antibiotic PD models, the indicator gates the corresponding drug-effect term (and any pairwise-interaction term that requires both partners present); (2) in popPK models of the index drug, the indicator enters as a covariate on a PK parameter (typically clearance) to capture a multiplicative shift attributable to (or correlated with) the coadministered antibiotic.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (drug not coadministered; the corresponding `CONMED_<drug>_CC` concentration covariate is then unused / 0).
- **Source aliases:** `MER_PRESENT`, `GEN_PRESENT`, `CIP_PRESENT` (Sadouki 2025) -- the legacy `_PRESENT` suffix is dropped because binary semantics are conveyed by the type field; renamed 2026-05-28 per the naming audit. `GENT` (Cohen-Wolkowiez 2014) maps to `CONMED_GEN` (1 if gentamicin was given concurrently with piperacillin-tazobactam, 0 otherwise).
- **Example models:** `Sadouki_2025_meropenem_gentamicin_ciprofloxacin.R` (used together to compute a combination-of-three indicator that triggers a categorical -1 shift on the BETA parameter when all three antibiotics are co-administered), `CohenWolkowiez_2014_tazobactam.R` (`CONMED_GEN` enters the tazobactam clearance covariate equation as a multiplicative power-form factor `1.52^CONMED_GEN`; in the source paper the gentamicin coadministration covariate was found to be confounded by postmenstrual / postnatal age differences between infants who received gentamicin and those who did not, so the effect should be interpreted as a marker of an older / sicker subset rather than a mechanistic drug-drug interaction).
- **Notes:** Companion to the concentration covariate `CONMED_<drug>_CC` (above). For a given drug, `CONMED_<drug>` is the binary "coadministered?" indicator and `CONMED_<drug>_CC` is the time-varying plasma concentration. Scope promoted to `general` on 2026-06-01 alongside the Cohen-Wolkowiez 2014 piperacillin-tazobactam extraction; the underlying semantic (binary coadministration of a named antibiotic) is the same whether the indicator gates a PD interaction term or scales a PK covariate equation, so a single canonical name is preferred over splitting into PD-only and PK-only variants. Ratified 2026-05-28 per the naming audit; scope updated 2026-06-01.

### BACT (**canonical for bacterial-strain indicator in in vitro time-kill models**)
- **Description:** Integer-coded indicator of the bacterial strain used in an in vitro time-kill experiment. Each level represents a structurally distinct strain (e.g., wild-type ATCC reference vs a clinical isolate with a specific resistance pattern); models switch between strain-specific structural parameters at the indicator value. Paper-specific level coding is documented per-file in `covariateData[[BACT]]$notes`.
- **Units:** (categorical)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** paper-specific (typically the wild-type ATCC reference strain; e.g., BACT = 2 in Mohamed 2016).
- **Source aliases:** none.
- **Example models:** `Mohamed_2016_colistin_meropenem.R` (1 = ARU552 meropenem-resistant clinical isolate, 2 = ATCC 27853 wild-type; hard switch on 25+ strain-specific structural parameters), `Zhao_2024_ciprofloxacin_colistin_invitro.R`, `Zhao_2024_ciprofloxacin_colistin_plasma.R`, `Zhao_2024_ciprofloxacin_colistin_kidney.R` (47 = C47 clinical urinary isolate, 347 = MG1655 wild type LM347, 378 = LM378 gyrA1 S83L, 421 = LM421 gyrA1 S83L plus marR knockout; levels are the source `FSTRAIN` codes and switch the colistin Emax, EC50, Hill and interaction parameters plus the pre-existing-resting inoculum fraction).
- **Notes:** Specific scope because the per-level strain identity is defined by the source experiment. New in vitro time-kill extractions should reuse this canonical and document the per-level strain mapping in covariate notes. Levels are integer codes with no shared meaning across papers, and there need be no reference level: Zhao 2024 gives every level its own parameter set rather than contrasting against a baseline, whereas Mohamed 2016 treats the wild-type ATCC strain as the reference. Ratified 2026-05-28 per the naming audit.

### LOWINOC (**canonical for low-inoculum experiment indicator**)
- **Description:** Binary indicator: 1 = study arm / replicate used the low-inoculum experimental design (typically 10^5 - 10^6 CFU/mL starting bacterial count), 0 = standard or high inoculum (typically 10^7 - 10^9 CFU/mL). Captures the inoculum-effect deviation in bacterial-kill kinetics across initial-density groups.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (standard / high inoculum).
- **Source aliases:** none.
- **Example models:** `Sadouki_2025_meropenem_gentamicin_ciprofloxacin.R` (gates a low-inoculum-specific shift on the bacterial-kill amplitude; the paper found the low-inoculum arm had a measurably different kill rate from the standard-inoculum arm).
- **Notes:** Specific scope because the per-paper definition of "low" varies by experiment design. Ratified 2026-05-28 per the naming audit.

### FLARE (**canonical for flare-design study-arm indicator**)
- **Description:** Binary study-arm indicator: 1 = the trial used a flare design (subjects washed out of pain medication and required a predefined pain flare-up before randomisation), 0 = non-flare design. A property of the trial design in a model-based meta-analysis (MBMA), not of an individual patient.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-flare design).
- **Source aliases:**
  - `If` -- used in `Boucher_2018_naproxen_mbma.R` (Boucher 2018 Eqs 2-3).
- **Example models:** `Boucher_2018_naproxen_mbma.R` (shifts both baseline WOMAC pain `e0` and `emax`; 12 of 18 trials were flare designs).
- **Notes:** MBMA study-arm-level covariate (a property of the trial design). Specific scope because "flare design" is defined per the osteoarthritis-pain trial-design literature. Registered 2026-05-30 (promoted from the in-file MBMA-covariate documentation convention so `checkModelConventions()` recognises it).

### NAPROXEN (**canonical for naproxen treatment-arm indicator**)
- **Description:** Binary study-arm treatment indicator: 1 = the arm received naproxen, 0 = the arm received placebo. A property of the trial arm in a model-based meta-analysis (MBMA), not of an individual patient.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (placebo arm).
- **Source aliases:**
  - `In` -- used in `Boucher_2018_naproxen_mbma.R` (Boucher 2018 Eqs 3-4).
- **Example models:** `Boucher_2018_naproxen_mbma.R` (shifts `emax` and shortens `et50`; all 18 included trials had both a naproxen and a placebo arm).
- **Notes:** MBMA study-arm-level treatment indicator. Specific scope (a per-drug arm indicator). Registered 2026-05-30 (promoted from the in-file MBMA-covariate documentation convention so `checkModelConventions()` recognises it).

### TRAMADOL (**canonical for tramadol treatment-arm indicator**)
- **Description:** Binary study-arm treatment indicator: 1 = the arm received tramadol, 0 = the arm did not receive tramadol (placebo, tapentadol, or another active-control arm). A property of the trial arm in a model-based meta-analysis (MBMA), not of an individual patient.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (arm did not receive tramadol).
- **Source aliases:** none (the source paper uses the drug name directly to select the extent-of-reduction term).
- **Example models:** `Mercier_2014_tramadol_tapentadol_mbma.R` (selects the tramadol Emax-in-dose term `e_tramadol_emax * CONMED_TRAMADOL_DOSE / (CONMED_TRAMADOL_DOSE + ED50) * TRAMADOL` in the extent-of-reduction R; 43 of 81 arms in the pooled 45-trial database were tramadol arms).
- **Notes:** MBMA study-arm-level treatment indicator, sibling of `NAPROXEN` in the per-drug MBMA arm-indicator family. Specific scope (a per-drug arm indicator). Pairs with `TAPENTADOL` and `CONMED_TRAMADOL_DOSE` in the Mercier 2014 chronic-non-malignant-pain extraction; the two treatment indicators are mutually exclusive (a single arm can be tramadol OR tapentadol, never both). Registered 2026-07-26 alongside the Mercier 2014 tramadol + tapentadol MBMA extraction.

### TAPENTADOL (**canonical for tapentadol treatment-arm indicator**)
- **Description:** Binary study-arm treatment indicator: 1 = the arm received tapentadol, 0 = the arm did not receive tapentadol (placebo, tramadol, or another active-control arm). A property of the trial arm in a model-based meta-analysis (MBMA), not of an individual patient.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (arm did not receive tapentadol).
- **Source aliases:** none (the source paper uses the drug name directly).
- **Example models:** `Mercier_2014_tramadol_tapentadol_mbma.R` (enters the extent-of-reduction R additively via `e_tapentadol_emax * TAPENTADOL`; no dose-response is fit because tapentadol was studied only over the narrow 100-250 mg bid range in the Mercier 2014 database; 8 of 81 arms were tapentadol arms).
- **Notes:** MBMA study-arm-level treatment indicator, sibling of `NAPROXEN` and `TRAMADOL` in the per-drug MBMA arm-indicator family. Specific scope. Pairs with `TRAMADOL` in the Mercier 2014 extraction; the two are mutually exclusive per arm. Registered 2026-07-26 alongside the Mercier 2014 tramadol + tapentadol MBMA extraction.

### QBL, QEFF (**canonical for baseline / effective renal Q in HD/CRRT renal-replacement models**)
- **Description:** Renal-replacement-therapy clearance terms used in hemodialysis (HD) / continuous renal replacement therapy (CRRT) popPK models for renally cleared drugs.
  - `QBL` = baseline (off-HD / off-CRRT) renal Q (L/h); the typical patient's residual renal clearance.
  - `QEFF` = effective on-HD/CRRT renal Q (L/h); the apparent dialysis-augmented clearance during a session (including the effective renal Q plus the dialyser/filter clearance, depending on the paper's parameterisation).
- **Units:** L/h
- **Type:** continuous
- **Scope:** specific
- **Reference category:** none (continuous).
- **Source aliases:** none.
- **Example models:** `Leuppi-Taegtmeyer_2019_colistin.R` (colistin / colistimethate sodium popPK in CRRT recipients; QBL drives the off-CRRT typical-value CL, QEFF drives the on-CRRT augmented CL during the session window).
- **Notes:** Specific scope because the on-/off- HD/CRRT splitting is paper-specific. Future renal-replacement popPK extractions should reuse the QBL/QEFF pair. Ratified 2026-05-28 per the naming audit.

### DIS_CHD_PERCENT (**canonical for coronary-heart-disease cohort prevalence percentage**)
- **Description:** Study-arm-level percentage (0-100) of the enrolled cohort who carry a coronary heart disease (CHD) diagnosis. Continuous covariate scaled in percent (not fraction).
- **Units:** %
- **Type:** continuous
- **Scope:** specific
- **Reference category:** 0% (healthy non-CHD cohort).
- **Source aliases:** `CHD_PCT` legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename); alias of the canonical `DIS_CHD_PERCENT` form per the 2026-05-28 naming audit rename.
- **Example models:** `Vargo_2014_statins_ezetimibe_mbma.R` (linear coefficient `e_chd_emax_statin = -0.000649` per percentage point on Emax_statin, i.e. a 24% CHD arm reduces Emax_statin by `0.000649 * 24 = 0.016`; the paper's typical-patient definition uses 24% CHD).
- **Notes:** MBMA study-arm-level covariate; the canonical register's individual-level pop-PK covariates do not directly fit aggregate-percentage columns, so this canonical is specific-scope and explicitly carries a study-arm aggregate meaning. Future MBMA models should reuse for the CHD-cohort prevalence column. Ratified 2026-05-28 per the naming audit.

### TUMTP_SQUAM_PCT (**canonical for squamous-tumor-histology cohort prevalence percentage**)
- **Description:** Study-arm-level percentage (0-100) of the enrolled cohort whose tumor histology is squamous (versus non-squamous). Continuous covariate scaled in percent (not fraction). In non-small cell lung cancer (NSCLC) MBMA papers the arm's squamous fraction interacts with the chemotherapy treatment class (chemotherapy tends to be more effective on squamous NSCLC than on non-squamous NSCLC).
- **Units:** %
- **Type:** continuous
- **Scope:** specific
- **Reference category:** 0% (all-non-squamous arm).
- **Source aliases:** `%squamous histology` (Franzese 2026 Table 1 / Table S1 covariate label).
- **Example models:** `Franzese_2026_pdl1_nsclc_mbma.R` (linear coefficients on ORR chemotherapy intercept (+0.282), OS chemotherapy hazard (+0.213), and PFS chemotherapy hazard (+0.186) in a mNSCLC MBMA of PD-(L)1 immunotherapy).
- **Notes:** MBMA study-arm-level covariate. Family precedent: `DIS_CHD_PERCENT` (Vargo 2014). Ratified 2026-07-24 alongside the Franzese 2026 mNSCLC MBMA. Distinct from a per-subject binary squamous-histology indicator; here the arm's squamous fraction is a continuous proportion of participants.

### PS_ECOG_0_PCT (**canonical for ECOG-performance-status-0 cohort prevalence percentage**)
- **Description:** Study-arm-level percentage (0-100) of the enrolled cohort with an Eastern Cooperative Oncology Group (ECOG) Performance Status score of 0 at baseline (fully active / asymptomatic). Continuous covariate scaled in percent (not fraction). Higher arm-level ECOG-0 fraction typically indicates a healthier / more-active enrolled cohort and is associated with better survival outcomes.
- **Units:** %
- **Type:** continuous
- **Scope:** specific
- **Reference category:** 0% (no ECOG-0 patients; all ECOG >= 1).
- **Source aliases:** `%ECOG PS score of 0` / `ps.0` (Franzese 2026 Table 1 / Table S1 covariate label).
- **Example models:** `Franzese_2026_pdl1_nsclc_mbma.R` (linear coefficient on OS non-chemotherapy hazard (-0.400) and on PFS global hazard (-0.293) in a mNSCLC MBMA of PD-(L)1 immunotherapy).
- **Notes:** MBMA study-arm-level covariate. Distinct from the per-subject `ECOG_GE1` and `ECOG_GE2` binaries (individual-level indicators). Family precedent: `DIS_CHD_PERCENT` (Vargo 2014). Ratified 2026-07-24 alongside the Franzese 2026 mNSCLC MBMA.

### RACE_ASIAN_PCT (**canonical for Asian-race cohort prevalence percentage**)
- **Description:** Study-arm-level percentage (0-100) of the enrolled cohort who are Asian (any Asian subgroup, matching the individual-level `RACE_ASIAN` canonical). Continuous covariate scaled in percent (not fraction).
- **Units:** %
- **Type:** continuous
- **Scope:** specific
- **Reference category:** 0% (all-non-Asian arm).
- **Source aliases:** `%Asian race` / `Race.Asian` (Franzese 2026 Table 1 / Table S1 covariate label).
- **Example models:** `Franzese_2026_pdl1_nsclc_mbma.R` (enters the OS ORR-slope as an interaction term `(eta_orr_os - 0.595) * (ORR/100) * (RACE_ASIAN_PCT/100)`; the paper's Discussion attributes the effect to regional trial-conduct differences rather than an inherent race effect).
- **Notes:** MBMA study-arm-level covariate. Distinct from the per-subject binary `RACE_ASIAN` canonical (individual-level 0 or 1); here the arm's Asian fraction is a continuous proportion of participants. Also distinct from Yang 2010's use of `RACE_ASIAN` as a binary at the arm level (whole-arm-Asian vs whole-arm-Western), which is coarser than the continuous-fraction form. Family precedent: `DIS_CHD_PERCENT` (Vargo 2014). Ratified 2026-07-24 alongside the Franzese 2026 mNSCLC MBMA.

### FORM_ISA_P2F2 (**canonical for isatuximab P2F2 drug-material indicator**)
<!-- AUDIT 2026-06-19: renamed from `FORM_P2F2` to `FORM_ISA_P2F2` per the canonical-register standardization audit. The prior name `FORM_P2F2` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** 1 = isatuximab P2F2 drug material (intended commercial / phase III material, used in the EFC14335 / ICARIA-MM study), 0 = P1F1 drug material (early-phase material used in TED10893 / TED14154 / TCD14079).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (P1F1).
- **Source aliases:**
  - `FORM_P2F2` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `Drug_mat` -- used in `Fau_2020_isatuximab.R`. Values 0 / 1 with the same orientation as the canonical (1 = P2F2 / commercial-bound material).
- **Example models:** `Fau_2020_isatuximab.R` (exponential effect on Vc with coefficient -0.137; P2F2 patients had ~13% lower Vc than P1F1).
- **Notes:** Phase III / commercial-bound formulation indicator for isatuximab; the FORM_* family stays scope-specific per nlmixr2lib policy that drug-product-version indicators are kept model-specific unless they generalize across multiple drugs. Set to 1 to simulate the marketed material.

### FORM_LINAG_TAB1 (**canonical for linagliptin tablet formulation 1 indicator**)
- **Description:** 1 = subject received the linagliptin "tablet formulation 1" (used in Retlich 2015 Study 2), 0 = subject received tablet formulation 2 (the marketed linagliptin tablet, used in Studies 3 and 4) OR the powder-in-bottle formulation (used in Study 1). The powder-vs-tablet contrast is captured by the sibling canonical `FORM_POWDER`; this indicator switches between the two tablet formulations.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (tablet formulation 2 = marketed linagliptin tablet, the typical-value Ka reference; or `FORM_POWDER = 1` for the powder).
- **Source aliases:** none known.
- **Example models:** `Retlich_2015_linagliptin.R` (multiplicative shift on the linagliptin first-order absorption rate constant Ka; typical Ka = 0.441 1/h for tablet formulation 2 (reference), 0.795 1/h for tablet formulation 1, 0.933 1/h for the powder formulation).
- **Notes:** Specific scope because the linagliptin "tablet 1 vs tablet 2" distinction is a drug-product-version comparison local to the Retlich 2015 popPK dataset; tablet formulation 1 was a development formulation that is not marketed. Mirrors the `FORM_SAR_DP2` (sarilumab) and `FORM_ISA_P2F2` (isatuximab) entries under the `FORM_*` family. Set to 0 for routine marketed-formulation simulation. Ratified canonically alongside the Retlich 2015 linagliptin extraction.

### FORM_SAR_DP2 (**canonical for sarilumab drug-product version 2 indicator**)
<!-- AUDIT 2026-06-19: renamed from `FORM_DP2` to `FORM_SAR_DP2` per the canonical-register standardization audit. The prior name `FORM_DP2` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** 1 = sarilumab drug product 2 formulation (used in some phase I studies and the dose-ranging phase II study), 0 = other drug product (DP1 or DP3; DP3 is the commercial formulation).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (DP1 or DP3).
- **Source aliases:**
  - `FORM_DP2` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `DP2` -- used in `Xu_2019_sarilumab.R`.
- **Example models:** `Xu_2019_sarilumab.R`.
- **Notes:** Affects both CLO/F (1.30x multiplier) and Ka (0.663x multiplier) in Xu 2019. Set to 0 for routine commercial-formulation simulation.

### FORM_POSA_AB (**canonical for posaconazole prototype tablet (A or B) vs later (C or D) tablet formulation indicator**)
- **Description:** 1 = subject received the posaconazole delayed-release solid tablet formulation A or B (the two early prototype formulations used in some phase 1 studies of the van Iersel 2018 pooled analysis); 0 = subject received tablet formulation C or D (the later formulations, with D being the marketed / commercial formulation and the predominant formulation across the pooled data set). Per-dose-record categorical indicator switching relative bioavailability F1 between the prototype and later tablet formulations.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (tablet C or D; the predominant formulation across the pooled data set and the typical-value F1 = 1 reference in van Iersel 2018 final model per Results 'Posaconazole exposure ... and impact of covariates': 'tablet formulation D is the marketed image and the predominant formulation used across the data set and was taken as the reference formulation for the relative bioavailability estimate for tablet formulations A and B').
- **Source aliases:** none -- the paper narrates the contrast as "tablet formulation A/B versus tablet formulation C/D" without a single NMTRAN column name.
- **Example models:** `vanIersel_2018_posaconazole.R` (multiplicative effect on relative bioavailability F1: `fdepot *= (1 + e_formposaab_fdepot * FORM_POSA_AB)` with `e_formposaab_fdepot = +0.247`, i.e. the prototype A/B formulations have 24.7% higher F1 than the later C/D reference; van Iersel 2018 Table 2 final-model 'Tablet formulation A/B on F1' = 0.247, RSE 21.5%).
- **Notes:** Specific scope because the prototype-A-or-B vs later-C-or-D contrast is paper-specific to the van Iersel 2018 posaconazole solid-tablet clinical-development pooled analysis. Drug-specific member of the `FORM_<drug>_<contrast>` family alongside `FORM_ASV_LIQUID` (asunaprevir), `FORM_SAR_DP2` (sarilumab), `FORM_VISMO_PHASEI` (vismodegib), `FORM_LINAG_TAB1` (linagliptin), `FORM_ABA_PHASE2` (abatacept). Set to 0 for routine commercial-formulation simulation (tablet D). Ratified canonically alongside the van Iersel 2018 posaconazole extraction.

### FORM_VISMO_PHASEI (**canonical for vismodegib Phase I (dry-blend capsule) formulation indicator**)
- **Description:** 1 = subject received the Phase I clinical-development vismodegib formulation (dry-blend capsules used in early-phase studies SHH3925g and the Phase I cohort of SHH4610g); 0 = subject received the Phase II / commercial-bound formulation (wet-granulation capsules used in SHH4476g, SHH4433g, and most of SHH4683g / SHH4871g). Per-subject (regimen-fixed) categorical indicator.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Phase II / commercial wet-granulation capsule; F = 1 fixed as the reference in Lu 2015 Eq. 4 and the typical-value `ka` reference in Eq. 4).
- **Source aliases:** none known; Lu 2015 reports formulation as a paper-defined "Phase I vs Phase II formulation" categorical without committing to a single column name.
- **Example models:** `Lu_2015_vismodegib.R` (multiplicative shifts on Ka and relative bioavailability F: typical Ka = 9.025 1/day for the Phase II reference in patients, with `exp(-0.602) = 0.55x` for the Phase I formulation and `exp(0.671) = 1.96x` for healthy volunteers; F = 1 for the Phase II reference, F = 0.346 for the Phase I formulation in patients and 0.836 in HV).
- **Notes:** Specific scope because the "Phase I (dry blend) vs Phase II (wet granulation) capsule" contrast is tied to the vismodegib drug-product-version comparison in Lu 2015. Mirrors the `FORM_SAR_DP2` (sarilumab), `FORM_ISA_P2F2` (isatuximab), and `FORM_LINAG_TAB1` (linagliptin) entries under the `FORM_*` family of drug-product-version indicators. Set to 0 for routine commercial-formulation simulation. Ratified canonically alongside the Lu 2015 vismodegib extraction.

### FORM_DOX_DORYX_MPC (**canonical for Doryx MPC delayed-release tablet doxycycline formulation indicator**)
<!-- AUDIT 2026-06-19: renamed from `FORM_DORYX_MPC` to `FORM_DOX_DORYX_MPC` per the canonical-register standardization audit. The prior name `FORM_DORYX_MPC` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** 1 = subject received the Doryx MPC delayed-release tablet (Mayne Pharma International; modified-acid-resistance delayed-release formulation, 120 mg doxycycline hyclate per tablet, formulation code MP336); 0 = subject received any other Doryx doxycycline product (the Doryx delayed-release tablet reference at 75 / 100 / 150 / 200 mg, or the conventional-release Doryx capsule at 100 mg, flagged separately by `FORM_CAPSULE`). Per-subject (regimen-fixed) categorical indicator used in popPK analyses that pool the three Doryx formulations and test formulation as a covariate on relative bioavailability, absorption lag, and the transit absorption rate's food effect.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Doryx delayed-release tablet; the typical-value reference for relative bioavailability F = 1 and the typical-value reference for the food effect on KTR in Hopkins 2017 Table 3).
- **Source aliases:**
  - `FORM_DORYX_MPC` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `FMPC` -- used in `Hopkins_2017_doxycycline.R` (Hopkins 2017 Methods 'Base model' paragraph: "Structural covariates ... a formulation effect (for Doryx MPC, FMPC; for Doryx capsule, FCAP) on relative bioavailability (RELF)").
- **Example models:** `Hopkins_2017_doxycycline.R` (multiplicative effects per Hopkins 2017 Table 3 final model: relative bioavailability `F1MPC = 0.863` vs the Doryx-tablet reference (Theta 8); shared absorption lag `ALAG1 = 0.115 h` with the Doryx capsule (Theta 12); strengthened food effect on the transit rate constant KTR: fed state reduces KTR by 54.9 % for Doryx MPC (`COVFED2 = -0.549`, Theta 11) vs 20.9 % for Doryx tablet / capsule (`COVFED = -0.209`, Theta 10)).
- **Notes:** Specific scope because the Doryx MPC vs Doryx tablet vs Doryx capsule contrast is tied to the doxycycline drug-product-version comparison in Hopkins 2017 (Mayne Pharma International's modified-acid-resistance MP336 formulation vs the marketed Doryx delayed-release tablet vs the conventional-release Doryx capsule). Paired with `FORM_CAPSULE` (the Doryx capsule indicator) so that both indicators = 0 selects the Doryx-tablet reference; the two indicators are mutually exclusive at any given dose record. Mirrors the existing `FORM_SAR_DP2` (sarilumab), `FORM_ISA_P2F2` (isatuximab), `FORM_LINAG_TAB1` (linagliptin), `FORM_VISMO_PHASEI` (vismodegib), `FORM_TAC_IR` (tacrolimus IR vs PR), and `FORM_THEO_APNECUT` (theophylline) entries under the `FORM_*` family of drug-product-version indicators. Set to 0 for routine Doryx-tablet simulation, 1 for Doryx MPC simulation. Ratified canonically on 2026-06-02 alongside the Hopkins 2017 doxycycline extraction.

### FORM_RAL_600 (**canonical for raltegravir 600 mg vs 400 mg tablet formulation indicator**)
- **Description:** 1 = subject received the raltegravir 600 mg film-coated tablet formulation (Isentress HD; the 1200 mg once-daily reformulation approved 2017); 0 = subject received the raltegravir 400 mg film-coated tablet formulation (Isentress; the original 400 mg twice-daily formulation approved 2007). Per-dose-record indicator used in raltegravir popPK analyses that pool the two tablet strengths to test formulation-specific effects on bioavailability, inter-occasion variability magnitude, or food-effect susceptibility. The 600 mg tablet is a "reformulated" polymer-based product with faster disintegration / dissolution than the original 400 mg tablet at physiological gastric pH.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (400 mg tablet reference; typical-value F, IOV F, and food-effect coefficient reported for the original 400 mg formulation in Bukkems 2021 Table 2).
- **Source aliases:**
  - `NEW` -- used in `Bukkems_2021_raltegravir.R` (NONMEM covariate flag; source paper Bukkems 2021 Supporting Information S4 `$PK` section: `IF (NEW.EQ.1)` selects the 600 mg formulation multipliers on F and IOV F).
- **Example models:** `Bukkems_2021_raltegravir.R` (two multiplicative effects per Bukkems 2021 Table 2: linear additive effect on typical bioavailability `F *= (1 + e_ral_600_fdepot * FORM_RAL_600)` with `e_ral_600_fdepot = 0.209` -- 600 mg tablet has +21% bioavailability vs 400 mg reference; and multiplicative effect on the IOV F magnitude `SD_iov_f *= (1 + e_ral_600_iov_fdepot * FORM_RAL_600)` with `e_ral_600_iov_fdepot = -0.718` -- 600 mg tablet has 72% smaller inter-occasion variability magnitude on F than the 400 mg reference).
- **Notes:** Specific scope because the raltegravir 600 mg vs 400 mg contrast is tied to the two-tablet-strength Merck reformulation program (Isentress HD / SWITCHmrk regulatory package). Mirrors the existing `FORM_DOX_DORYX_MPC` (doxycycline Doryx MPC vs Doryx tablet), `FORM_ITR_SUBA` (itraconazole SUBA vs Sporanox), `FORM_TAC_IR` (tacrolimus Prograf IR vs Advagraf PR), `FORM_SAR_DP2` (sarilumab DP2), `FORM_ISA_P2F2` (isatuximab P2F2), `FORM_LINAG_TAB1` (linagliptin), and `FORM_VISMO_PHASEI` (vismodegib Phase I) entries under the `FORM_<drug>_<formulation>` family of drug-product-version indicators. Set to 0 for routine 400 mg BID simulation, 1 for 600 mg (typically dosed as two 600 mg tablets = 1200 mg QD) simulation. Ratified canonically alongside the Bukkems 2021 raltegravir extraction.

### FORM_NMV_TAB150 (**canonical for nirmatrelvir 150-mg tablet formulation indicator**)
- **Description:** 1 = the nirmatrelvir dose was given as the 150-mg film-coated tablet (the commercial PAXLOVID tablet strength used for the bulk of the phase II/III EPIC-HR population); 0 = the nirmatrelvir dose was given as the oral suspension or as the 100-mg tablet. Per-dose-record indicator used in nirmatrelvir popPK analyses that pool the three clinical formulations and estimate a relative-bioavailability offset for the 150-mg tablet only.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (oral suspension or 100-mg tablet; both share the F1 = 1 structural anchor because the source estimates an effect only for the 150-mg level of the three-level formulation column).
- **Source aliases:**
  - `FFORM` (three-level formulation column; 0 = suspension, 1 = 100-mg tablet, 2 = 150-mg tablet) -- used in `Chan_2023_nirmatrelvir.R`; the 150-mg-tablet level maps to `FORM_NMV_TAB150 = 1` (Chan 2023 Data S1 `$PK`: `F1FFORM = 1; IF(FFORM.EQ.2) F1FFORM = 1+THETA(15)`).
- **Example models:** `Chan_2023_nirmatrelvir.R` (linear fractional effect on relative bioavailability `F1 *= (1 + e_form_nmv_tab150_fdepot * FORM_NMV_TAB150)` with `e_form_nmv_tab150_fdepot = -0.379`, i.e. the 150-mg tablet carries 37.9% lower relative bioavailability than the suspension / 100-mg-tablet reference, applied on top of the dose power function `(DOSE/300)^-0.409`).
- **Notes:** Specific scope because the reference category pools two distinct products (suspension and 100-mg tablet) rather than contrasting a single pair, which is a consequence of Chan 2023 estimating an effect only for the `FFORM = 2` level. Sibling of `FORM_RAL_600` (raltegravir 600 mg vs 400 mg tablet), `FORM_LINAG_TAB1`, `FORM_TAC_IR`, `FORM_ITR_SUBA` and the rest of the `FORM_<drug>_<formulation>` family of drug-product-version indicators. Chan 2023 Discussion notes that the 150-mg tablet was evaluated only among healthy participants in a single single-dose study, so the estimated formulation effect on F1 is likely partially confounded with the COVID-19 effect on CL (`DIS_COVID19`); set both to their reference values when simulating the phase I suspension / 100-mg-tablet condition. A separate phase I study (NCT05263895) evaluated relative bioequivalence of the two tablet strengths but did not contribute to this analysis. Ratified canonically alongside the Chan 2023 nirmatrelvir extraction.

### FORM_ITR_SUBA (**canonical for SUBA-itraconazole vs Sporanox capsule formulation indicator**)
<!-- AUDIT 2026-06-19: renamed from `FORM_SUBA` to `FORM_ITR_SUBA` per the canonical-register standardization audit. The prior name `FORM_SUBA` is preserved as a source_alias for one release cycle so existing covariate-data CSVs continue to load. -->
- **Description:** 1 = subject received the SUBA-itraconazole formulation (a solid dispersion of itraconazole in a pH-dependent polymeric matrix that enhances dissolution and targets drug release in the proximal small intestine; marketed as Lozanoc in Australia, Itragerm in Spain); 0 = subject received the Sporanox capsule (Janssen Pharmaceuticals innovator product). Per-dose-record categorical indicator used in itraconazole bioequivalence-comparison popPK analyses.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Sporanox capsule; relative bioavailability F = 1 and FVAR-variability scaling ETASCALE = 1 as the structural references in Abuhelwa 2015 Table 3).
- **Source aliases:**
  - `FORM_SUBA` -- prior canonical name (pre-2026-06-19 standardization audit).
  - `DRUG` -- used in `Abuhelwa_2015_itraconazole.R` (Appendix S1 NONMEM control stream: `IF (DRUG.EQ.0) THEN DRUGF = 1 (Sporanox) ELSE DRUGF = (1 + THETA(8)) (SUBA-itraconazole)`).
- **Example models:** `Abuhelwa_2015_itraconazole.R` (multiplicative effects per Abuhelwa 2015 Table 3: SUBA-itraconazole has +73% relative bioavailability vs Sporanox (`F = 1 + 0.729 x FORM_ITR_SUBA`) and 21.3% less F variability (`ETASCALE = 1 + (-0.213) x FORM_ITR_SUBA = 0.787` scales the shared FVAR random effect)).
- **Notes:** Specific scope because the SUBA-itraconazole vs Sporanox capsule contrast is tied to the Mayne Pharma bioequivalence-development programme. Mirrors the `FORM_SAR_DP2` (sarilumab), `FORM_ISA_P2F2` (isatuximab), `FORM_LINAG_TAB1` (linagliptin), and `FORM_VISMO_PHASEI` (vismodegib) entries under the `FORM_*` family of drug-product-version / formulation-comparison indicators. Distinct from `FORM_CAPSULE` (Hennig 2006 / 2007 capsule-vs-solution contrast for the same itraconazole molecule) because here both arms are capsule formulations and the contrast is between two capsule drug products. Ratified canonically alongside the Abuhelwa 2015 itraconazole extraction.

### FORM_TAC_IR (**canonical for tacrolimus immediate-release vs prolonged-release formulation indicator**)
- **Description:** 1 = subject received the twice-daily immediate-release tacrolimus formulation (Prograf, Astellas) administered every 12 hours; 0 = subject received the once-daily prolonged-release tacrolimus formulation (Advagraf in Europe, Astagraf XL in the US; both Astellas) administered every 24 hours. Per-subject (regimen-fixed; per-occasion in cross-over conversion studies) categorical indicator used in popPK analyses that pool the two oral tacrolimus formulations and test formulation as a covariate on absorption and disposition parameters.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (Advagraf / Astagraf XL prolonged-release; the typical-value reference for Ktr and Vc/F in Woillard 2011 Table 4).
- **Source aliases:**
  - `study` -- used in `Woillard_2011_tacrolimus.R` (Woillard 2011 Methods: "study factor (assumed to be similar to drug formulation) ... study = 1 for the Prograf cohort, study = 0 for the Advagraf cohort"). The source paper's `study` and `formulation` factors are aliased; the canonical preserves the source paper's orientation (Prograf = 1 = IR, Advagraf = 0 = PR), which also matches the standard convention of treating the older immediate-release formulation as the non-reference level.
  - `FORMULATION` (with 1 = PR-T, opposite polarity) -- used in `Lu_2019_tacrolimus_industry_meta.R`. Lu 2019 writes the Ka covariate equation as `Ka = 0.375 * (1 - (1 - theta_form) * FORMULATION)` with `FORMULATION = 1` for prolonged-release; the model derives `form_pr = 1 - FORM_TAC_IR` inside `model()` to match the paper's published equation while keeping the canonical column oriented Prograf = 1.
- **Example models:** `Woillard_2011_tacrolimus.R` (multiplicative effects per Woillard 2011 Table 4: `Ktr = theta1 * theta2^FORM_TAC_IR` with `theta1 = 3.34/h` and `theta2 = 1.53` (Prograf ~53 % faster absorption than Advagraf); `Vc/F = theta6 * theta7^FORM_TAC_IR` with `theta6 = 486 L` and `theta7 = 0.29` (Prograf Vc/F is 29 % of the Advagraf reference)), `Lu_2019_tacrolimus_industry_meta.R` (linear effect on Ka per Lu 2019 Table 3 'Prolonged-release tacrolimus on Ka' = 0.499, encoded as `Ka(PR-T) = Ka(IR-T) * 0.499` via `form_pr = 1 - FORM_TAC_IR`; 50 % slower absorption for PR-T vs IR-T).
- **Notes:** Scope promoted to `general` after Lu 2019 corroborated the Prograf-vs-Advagraf contrast in a second, larger-cohort population (Woillard 2011 n = 173 + 174, Lu 2019 n = 408 across 8 Astellas Phase II studies). Mirrors the existing `FORM_SAR_DP2` (sarilumab), `FORM_ISA_P2F2` (isatuximab), `FORM_LINAG_TAB1` (linagliptin), and `FORM_VISMO_PHASEI` (vismodegib) entries under the `FORM_*` family of drug-product-version indicators. The Woillard 2011 paper notes the formulation effect partially confounds with time-post-transplant (Prograf cohort sampled within the first 6 months post-transplant, Advagraf cohort > 12 months post-transplant); Lu 2019 has no such confounding because most studies are within-subject IR-T-to-PR-T conversions. Future tacrolimus models that include Envarsus XR (modified-release once-daily granules) or LCP-Tacro (life-cycle-pharma melt-extrusion tablets) should register a sibling canonical (e.g., `FORM_TAC_ENVARSUS`) rather than overloading `FORM_TAC_IR`. Ratified canonically alongside the Woillard 2011 tacrolimus extraction.

### FORM_VINP_IR (**canonical for vinpocetine immediate-release Cavinton tablet formulation indicator**)
- **Description:** 1 = subject received the Cavinton immediate-release 5 mg vinpocetine tablet (Organon / Gedeon Richter Ltd., Budapest, Hungary; the reference immediate-release solid dosage form of vinpocetine); 0 = subject received the Ultra Vinca sustained-release beta-cyclodextrin-complex 10 mg vinpocetine tablet (Tecnimede, Portugal; the Petric 2023 reference formulation). Per-dose-occasion indicator (used in the Petric 2023 crossover design where each subject received all three formulations across separate occasions with a 7-day washout).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Ultra Vinca sustained-release beta-cyclodextrin complex tablet; the typical-value Tk0 and V1/F reference in Petric 2023 Table 1).
- **Source aliases:**
  - `Formulation#2` -- the second level of the three-level `Formulation` categorical covariate in Petric 2023 Table 1; maps directly to FORM_VINP_IR = 1 with Formulation#1 (Ultra Vinca SR) as the reference.
- **Example models:** `Petric_2023_vinpocetine.R` (log-additive effect on Tk0 (`exp(beta = -0.4)` = 33% shorter zero-order absorption duration relative to the Ultra Vinca SR reference) and on V1/F (`exp(beta = -1.26)` = 72% lower apparent central volume relative to the SR reference, consistent with a higher vinpocetine oral bioavailability / metabolite yield for the Cavinton IR arm; Petric 2023 Table 1 beta_Tk0_Formulation#2 = -0.4 (RSE 29.0%) and beta_V1/F_Formulation#2 = -1.26 (RSE 5.44%)).....
- **Notes:** Scoped specific because the Cavinton-IR-vs-Ultra-Vinca-SR contrast is tied to the Petric 2023 relative-bioavailability crossover design (a three-level formulation stratification of vinpocetine as its main active metabolite apovincaminic acid). Paired with the general `FORM_SOLUTION` canonical (both = 0 selects the Ultra Vinca SR reference; FORM_SOLUTION = 1 selects the 10 mg / 5 mL oral solution) to encode the full three-level factor. Sibling to the other drug-specific `FORM_*_IR` / `FORM_*_<formulation>` entries (`FORM_TAC_IR`, `FORM_LINAG_TAB1`, `FORM_SAR_DP2`, `FORM_ISA_P2F2`, `FORM_VISMO_PHASEI`) under the `FORM_*` family of drug-product-version indicators. Future vinpocetine-formulation extractions comparing other vinpocetine products against Ultra Vinca SR (or Cavinton IR) should reuse this canonical and extend the example list; future SR-vs-IR contrasts for unrelated drugs should register their own drug-specific sibling canonical rather than overloading this entry. Ratified canonically alongside the Petric 2023 vinpocetine extraction.

### FORM_XYNTHA (**canonical for Xyntha vs Refacto / Refacto AF moroctocog-alfa product indicator**)
- **Description:** 1 = Xyntha (Wyeth Pharmaceuticals / Pfizer; OSA-calibrated potency label, marketed in the United States, Canada, and other regions); 0 = Refacto or Refacto AF (CSA-calibrated potency label, marketed in the European Union and other regions). Per-observation (per-row) binary indicator. Moroctocog alfa is the same B-domain-deleted recombinant factor VIII active moiety in all three products; Xyntha and Refacto AF are the same drug product with different potency-assay calibrations (OSA vs CSA) for label dosing. Used as a multiplicative effect on bioavailability F to harmonise the model-predicted FVIII activity across products.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Refacto or Refacto AF; CSA-calibrated potency label).
- **Source aliases:**
  - `PROD` -- used in `Abrantes_2017_moroctocog.R` (Abrantes 2017 Table 2 footnote g and Methods: F multiplier `1.38^PROD` so Xyntha effectively delivers 1.38x the labelled dose in CSA-reference units).
- **Example models:** `Abrantes_2017_moroctocog.R` (multiplicative effect on F: `(1 + 0.38 * FORM_XYNTHA)` -- equivalent to `1.38^FORM_XYNTHA` when binary; the model anchors F = 1 for the CSA-reference Refacto/Refacto AF products and applies the +38.0% multiplier when the product administered is Xyntha).
- **Notes:** Specific scope because the contrast is tied to the Pfizer / Wyeth moroctocog-alfa product line (Refacto, Refacto AF, and Xyntha). Mirrors the `FORM_<drug>_<variant>` family pattern (`FORM_LEB_NS0` lebrikizumab cell-line, `FORM_SAR_DP2` sarilumab drug-product version, `FORM_DOX_DORYX_MPC` doxycycline Doryx-MPC). Per-observation (per-row) when a subject could switch between products across trials (e.g., Abrantes 2017 study B1831066 enrolled both Refacto and Refacto AF in the same protocol); subject-level otherwise. The 1.38 OSA-vs-CSA potency ratio for B-domain-deleted FVIII is documented in Mikaelsson 1998 and Hubbard 2013 (cited in Abrantes 2017 reference 13). Ratified canonically on 2026-06-21 alongside the Abrantes 2017 moroctocog extraction.

### FORM_FVIII_BDD (**canonical for B-domain-deleted (vs full-length) recombinant factor VIII product indicator**)
- **Description:** 1 = the administered factor VIII (FVIII) concentrate is a B-domain-deleted recombinant product (e.g., Refacto, Refacto AF, Xyntha -- moroctocog alfa); 0 = the administered product is a full-length recombinant FVIII (e.g., Kogenate FS, Helixate FS, Advate, Recombinate) or a plasma-derived FVIII (e.g., Aafact, Hemofil M). Per-observation (per-row) binary indicator. B-domain-deleted recombinant FVIII proteins retain the FVIII coagulation activity but omit the heavily glycosylated central B-domain; the one-stage clotting assay (OSA) commonly used clinically under-detects B-domain-deleted FVIII activity in plasma by a well-documented ~30-50 % relative to full-length FVIII (mechanism: sub-optimal thrombin-mediated activation kinetics in the OSA reagent milieu), so PK models that pool observations across BDD and full-length products commonly include this indicator as a multiplicative correction on the predicted plasma concentration to reconcile the assay artefact. Note: Xyntha vs Refacto/Refacto AF is a separate potency-calibration contrast within the BDD product family (see `FORM_XYNTHA`); the two canonicals are independent and can coexist in a single dataset.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (full-length recombinant or plasma-derived FVIII product).
- **Source aliases:**
  - `bdp` (paper-prose 0/1 indicator, 1 = B-domain-deleted product) -- used in `Hazendonk_2016_factor_viii.R` (Hazendonk 2016 Methods: `C_pred,bdp = C_pred * (1 - theta_bdp)` with `theta_bdp = 0.34` = 34 % assay-driven under-prediction of the B-domain-deleted product Refacto AF).
- **Example models:** `Hazendonk_2016_factor_viii.R` (multiplicative effect on the predicted FVIII plasma concentration: `Cc = (central / vc) * (1 - theta_bdp * FORM_FVIII_BDD)`; reference category 0 = full-length recombinant or plasma-derived FVIII).
- **Notes:** General scope because the OSA vs CSA assay artefact for B-domain-deleted FVIII is a class-level phenomenon that recurs across BDD-product FVIII popPK models. Distinct from `FORM_XYNTHA` (which contrasts Xyntha vs Refacto / Refacto AF within the BDD moroctocog product family, capturing the +38 % OSA-vs-CSA potency calibration difference; a subject may have `FORM_FVIII_BDD = 1` and `FORM_XYNTHA = 0` or 1 depending on which Pfizer moroctocog product they received). Distinct also from `ASSAY_OSA` (which is the bioanalytical-method indicator: OSA vs CSA); when both `FORM_FVIII_BDD` and `ASSAY_OSA` are available, the correct interaction structure is `FORM_FVIII_BDD * ASSAY_OSA` (only OSA-assayed BDD-product samples show the under-detection), but many perioperative datasets are OSA-only and collapse the correction onto `FORM_FVIII_BDD` alone (Hazendonk 2016). Per-observation (per-row) when subjects can switch between products across surgical procedures; subject-level otherwise. Mirrors the `FORM_<drug>_<variant>` family pattern. Ratified canonically on 2026-07-11 alongside the Hazendonk 2016 perioperative FVIII extraction.

### dilution (**canonical for diluted-drug-product indicator**)
- **Description:** 1 = drug diluted (Soehoel 2022 study D2213C00001), 0 = not diluted.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Example models:** `Soehoel_2022_tralokinumab.R`.
- **Notes:** Lower-case preserved from source; future models should rename to `DILUTION`. Kept as alias here to match existing file.

### nonECZTRA (**canonical for non-ECZTRA-trial indicator**)
- **Description:** 1 = not the ECZTRA trial; 0 = ECZTRA.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Example models:** `Soehoel_2022_tralokinumab.R`.
- **Notes:** Mixed case preserved from source; future models should rename to `NON_ECZTRA` or `STUDY_NON_ECZTRA`.

### SEASON2 (**canonical for second RSV season at dosing indicator**)
- **Description:** 1 = second RSV season at dosing, 0 = first RSV season.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Example models:** `Clegg_2024_nirsevimab.R`.
- **Notes:** Study-specific but semantically general (second-exposure indicator). Promote to general if a second RSV-season model adopts the same semantics.

### COHDOSE (**canonical for randomized dose cohort (mg/kg)**)
- **Description:** Randomized dose cohort expressed in mg/kg. Subject-level (time-fixed) covariate carrying the per-subject cohort dose in a study where each subject remained on a single escalating-cohort dose for the full dosing period.
- **Units:** mg/kg
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- used with power scaling `(COHDOSE / ref)^exponent`. Reference values observed: 1 mg/kg in Narwal 2013 (human IgG1), 3 mg/kg in Niloy 2026 (preclinical mouse small molecule).
- **Source aliases:**
  - `DOSE` -- used in `Narwal_2013_sifalimumab.R` (the paper's Eq. 3 variable name; renamed to `COHDOSE` here to avoid colliding with the rxode2/nlmixr2 event-column convention where `DOSE` or `AMT` carries the administered dose).
  - `Dose_i` -- used in `Niloy_2026_MTMSATrp_mouse.R` (Niloy 2026 Methods Eq. 2 notation; the per-mouse mg/kg cohort dose).
- **Example models:** `Narwal_2013_sifalimumab.R` (reference 1 mg/kg, exponent 0.0542 on CL), `Niloy_2026_MTMSATrp_mouse.R` (preclinical mouse; reference 3 mg/kg, exponent -0.30 on CL describing empirical dose-dependent CL over 0.3-10 mg/kg).
- **Notes:** Scope promoted from specific to general on 2026-06-22 with the Niloy 2026 MTMSA-Trp extraction (second model ratifying the canonical, this time as a preclinical mouse small-molecule dose-cohort covariate with a negative exponent on CL describing decreasing apparent clearance with increasing dose -- complementing Narwal 2013's positive exponent in a Phase Ib human mAb context). The shared functional form `(COHDOSE / ref)^exponent` on apparent clearance and the shared escalating-cohort design (each subject on one dose for the full study) carry across drug class, species, and study size. For fixed-dose simulations in a weight-based-dosing paper, set `COHDOSE = nominal_dose_mg / WT` per subject. When the subject receives a per-kg dose label directly (preclinical mg/kg dosing or human mg/kg cohort labels like the MI-CP152 0.3 / 1 / 3 / 10 mg/kg arms), `COHDOSE` is the mg/kg label.

### DOSE (**canonical for current administered dose level supplied as a data column**)
- **Description:** Continuous covariate carrying the administered dose level (in mg) as a per-record data column. Two complementary use cases share this canonical:
  (a) **Per-subject assigned dose** -- each subject's fixed assigned dose level (in mg) across the study, used as a power-style or stratified covariate when the population PK model detects a dose-dependent shift in a PK parameter (central volume, clearance, etc.).
  (b) **Time-varying current administered dose** -- the current daily dose at the time of the record, used in PD-only models that derive a per-cycle exposure metric (e.g., `AUC = DOSE / CLI`) from a posthoc-CL covariate without instantiating a PK ODE. The column is set to 0 during off-treatment periods (drug holidays, placebo arm) so the derived exposure becomes 0.
- **Units:** mg (document per-model in `covariateData[[DOSE]]$units` if a different dose unit is used).
- **Type:** continuous
- **Reference category:** n/a -- used with power scaling `(DOSE / ref)^exponent` for use case (a), or directly inside derived-exposure expressions for use case (b). Reference values observed: 600 mg in `Zheng_2016_sifalimumab.R` (middle of the 200/600/1200 mg phase IIb dose range).
- **Source aliases:**
  - `Dose` -- used in `Zheng_2016_sifalimumab.R` and `Castro-Surez_2020_nimotuzumab.R`.
  - `DOS` -- used in the Hansson 2013 sunitinib biomarker / TGI / fatigue PD-model family (DDMODEL00000197 and siblings) as a per-record sunitinib dose column.
- **Example models:** `Zheng_2016_sifalimumab.R` (power effect on V1 with exponent 0.06), `Castro-Surez_2020_nimotuzumab.R` (binary-indicator usage `(DOSE == 50)` applying a 53 % decrease in V1 for the 50 mg cohort), `Hansson_2013a_sunitinib.R` (DDMODEL00000197; time-varying record-level dose feeding `AUC = DOSE / CLI`), `Hansson_2013b_sunitinib.R` (DDMODEL00000198; same time-varying `AUC = DOSE / CLI` form for the tumor growth inhibition model), `Schindler_2016_sunitinib.R` (DDMODEL00000221; same `AUC = DOSE / CLI` form, with the daily-dose column toggling between 50 mg/day on-cycle and 0 on dose-holiday records), `Schindler_2017_imatinib.R` (time-varying daily imatinib dose in mg/day feeding the size and density drug-effect terms as `Kdrug,S * (DOSE / 400) * exp(-k * t)` and `Kdrug,D * (DOSE / 400)`, with 400 mg/day as the reference normalisation), `Girard_2012_pimasertib.R` (linear coefficient on the dropout-hazard log-rate: `exp(beta * DOSE)` Weibull multiplier; per-subject daily dose, observed range 1-255 mg), `Wada_2023_sparsentan.R` (use case (a): per-record administered dose drives a dose-dependent relative bioavailability `Frel = (max(DOSE, 200) / 400)^-0.495` applied as `f(depot)`, giving Frel = 1.41 / 1.00 / 0.71 at 200 / 400 / 800 mg -- less-than-dose-proportional exposure, with the 200 mg clamp reproducing the paper's published lower branch).
- **Notes:** Distinct from `DOSE_70MG` (binary indicator for a specific dose group in a trinary-dose design) and from the rxode2/nlmixr2 event column `amt` (which carries the administered dose at dose events). For use case (a), the values are typically time-fixed per subject; for use case (b), the values are time-varying with on/off cycling -- for sunitinib 4-weeks-on / 2-weeks-off cycling, set `DOSE = nominal_daily_mg` (e.g., 50) during on-cycles and 0 during off-cycles or for the placebo arm. Per-model `covariateData[[DOSE]]$notes` should state which use case applies.

### CD (**canonical for cumulative cladribine dose (time-varying)**)
- **Description:** Time-varying cumulative cladribine dose (mg total dose, not body-weight-normalized) administered to each subject up to the current observation time. Stays at zero during the placebo arm and during the pre-dose baseline phase, rises stepwise across the dosing schedule (cladribine is given as short oral pulses), and remains constant between dose events.
- **Units:** mg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used inside `EXPS = CD * 104.5 / CRL` as an exposure surrogate driving an Emax-style symptomatic effect on disease progression, not as a power-form covariate.
- **Source aliases:** `CD` -- column name used in the DDMODEL00000223 input dataset (`Simulated_Novakovic_2016_multiplesclerosis_cladribine_irt.csv`).
- **Example models:** `Novakovic_2017_cladribine.R`.
- **Notes:** Distinct from `DOSE` (per-subject assigned dose level, time-fixed) and `COHDOSE` (mg/kg cohort label, time-fixed). `CD` is the *cumulative* dose accrued at each timepoint, supplied as a time-varying covariate column rather than via dosing events because the Novakovic 2017 model does not carry an explicit cladribine-PK compartment. Scope: specific because the constant 104.5 inside the exposure-surrogate equation is hard-coded for cladribine in the source.

### TRT (**canonical for treatment-cohort indicator**)
- **Description:** Per-subject treatment-cohort / treatment-arm integer indicator. Carries the categorical assignment that selects per-arm structural parameters (e.g., per-arm typical tumor-growth rate, per-arm dose-response slope, on-treatment vs placebo gating). Value coding is per-model: each consuming model defines its own integer levels via `covariateData[[TRT]]$notes`, because the same `TRT` column header carries different cohort semantics in different studies.
- **Units:** (categorical / integer-coded)
- **Type:** categorical
- **Scope:** general
- **Reference category:** per-model (each consuming model declares its own reference level in `covariateData[[TRT]]$notes`).
- **Source aliases:** `TRT`, `ARM`, `TRTARM`, `STUDY_ARM` -- common column-name variants for the per-subject treatment-arm integer indicator; all map directly to canonical `TRT` with no value transformation. Per-model integer coding is documented inline.
- **Example models:** `Novakovic_2017_cladribine.R`.
  - `Novakovic_2017_cladribine.R` (DDMODEL00000223): 0 = placebo, 1 = cladribine 3.5 mg/kg cumulative-dose cohort, 2 = cladribine 5.25 mg/kg cumulative-dose cohort. Reference = 0 (placebo). Gates the symptomatic and protective drug-effect terms via `TRT >= 1 && t > 0`; the categorical level (1 vs 2) is informational because the dose-response is driven by the time-varying `CD` covariate and the per-cohort dosing schedule, not by `TRT` itself.
  - `Struemper_2025_tumorsize_OS_nsclc.R`: 1 = PEMBRO (pembrolizumab, INTR@PID LUNG 037, n = 152), 2 = FELAD (feladilimab, INDUCE-1, n = 52), 3 = CHEMO (docetaxel, Entree Lung Part 2, n = 34), 4 = FELAD+CHEMO (pooled INDUCE-1 + Entree Lung Part 2, n = 78), 5 = IO-COMBO (feladilimab + IO; pooled INDUCE-1 + INDUCE-2, n = 23), 6 = DOSTAR (dostarlimab, GARNET, n = 67), 7 = DOSTAR+CHEMO (PERLA, n = 121), 8 = PEMBRO+CHEMO (PERLA, n = 122), 9 = COBO100MG+DOSTAR cohort B (AMBER, n = 14), 10 = COBO300MG+DOSTAR cohort B (AMBER, n = 41), 11 = COBO900MG+DOSTAR cohort B (AMBER, n = 29), 12 = COBO300MG+DOSTAR cohort D (AMBER, n = 53). Reference = 1 (PEMBRO; the largest single-agent PD-1 cohort and the model's canonical default). Selects per-arm `TVKG` and `TVKS` via a 12-branch if-else cascade and gates the `PDL1_TUM` effect on `ks` via a derived `has_pd1` indicator equal to 1 when `TRT` is 1 or in {6, 7, 8, 9, 10, 11, 12}.
- **Notes:** Two models now use the `TRT` canonical with different integer codings: Novakovic 2017 (3 levels, placebo reference) and Struemper 2025 (12 levels, pembrolizumab reference). The column header is shared because both source NONMEM control streams use `TRT`; the integer-to-cohort mapping is intentionally per-model. Scope upgraded from `specific` to `general` on 2026-06-22 when Struemper 2025 added a second consuming model. Future models adding new `TRT` codings extend the Example-models list rather than registering a new canonical, so long as the column header is `TRT`; a model that uses a fundamentally different semantics (e.g., a generic on-treatment 0/1 flag distinct from per-arm cohort indexing) should register a new canonical name (e.g., `ON_TREATMENT`) rather than reusing `TRT`.

### DRUG_ORMU (**canonical for Ormutivimab vs HRIG drug-product comparator indicator**)
- **Description:** 1 = subject received Ormutivimab (a recombinant human anti-rabies IgG1 monoclonal antibody, also referred to in the source paper as rHRIG); 0 = subject received plasma-derived human rabies immunoglobulin (HRIG) comparator (or placebo + vaccine without passive antibody). Per-subject (time-fixed) categorical indicator carrying the head-to-head drug-arm assignment in a randomized rabies-vaccine pharmacodynamics study where rabies virus neutralizing antibody (RVNA) activity is modelled with a time-dependent Emax response to the vaccine and the passive antibody product modifies the typical-value Emax / ET50.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (HRIG / no passive antibody).
- **Source aliases:**
  - `antibody type` -- Zhang 2022 Results section 3.4 covariate-effect prose ("antibody type was determined as the covariate significantly affecting the model"); the source NMTRAN column name is not separately reported.
- **Example models:** `Zhang_2022_ormutivimab.R` (additive typical-value shifts on the linear-scale Emax and ET50 of the vaccine-induced RVNA Emax model: `Emax_tv = exp(lEmax) + e_drug_ormu_Emax * DRUG_ORMU` with `e_drug_ormu_Emax = +0.143 IU/mL` and `ET50_tv = exp(lET50) + e_drug_ormu_ET50 * DRUG_ORMU` with `e_drug_ormu_ET50 = -3.8 day`, yielding a higher and faster vaccine-induced antibody peak in the Ormutivimab arms relative to the HRIG comparator).
- **Notes:** Distinct from the `FORM_*` family (within-product formulation-version contrasts of a single drug) because the contrast here is between two biologically distinct products: HRIG is a polyclonal plasma-derived immunoglobulin, while Ormutivimab is a recombinant monoclonal antibody (CHO-cell-produced; the first rhRIG approved in China). Specific scope because the head-to-head HRIG-vs-rHRIG comparator design is tied to the Zhang 2022 phase II rabies-vaccine study. Future head-to-head rhRIG-vs-HRIG popPD models (e.g., SII Rabishield or Twinrab against HRIG) should register a sibling canonical (`DRUG_SIIRMAB`, `DRUG_TWINRAB`) rather than overloading `DRUG_ORMU`; cross-product comparisons that need both indicators in the same dataset can carry them as independent binaries with HRIG as the shared reference. Ratified canonically alongside the Zhang 2022 ormutivimab extraction.

### DRUG_PRED (**canonical for prednisone vs fosdagrocorat drug-arm comparator indicator**)
- **Description:** 1 = subject is in the prednisone (oral glucocorticoid comparator) arm; 0 = subject is in the fosdagrocorat (PF-04171327, dissociated agonist of the glucocorticoid receptor) arm or the placebo arm. Per-subject (time-fixed) binary indicator carrying the head-to-head drug-arm assignment in a phase II randomized trial in adults with rheumatoid arthritis where serum bone-formation biomarkers (P1NP, osteocalcin) are modelled with a K-PD framework and the two drugs share the rebound and response-side parameters but carry separate KDE / Imax / EDK50 values.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (fosdagrocorat or placebo).
- **Source aliases:** derived per subject from the trial-arm assignment; the source paper does not name an NMTRAN column.
- **Example models:** `Shoji_2017_fosdagrocorat_p1np.R`, `Shoji_2017_fosdagrocorat_oc.R` (binary multiplier switching the active KDE and the active sigmoid-Emax inhibition parameters between fosdagrocorat and prednisone: `lkde_active = lkde_fos * (1 - DRUG_PRED) + lkde_pred * DRUG_PRED`, with analogous switching of `logitimax_active` / `imax_active` and `ledk50_active`; the rebound parameters `RBmax`, `T50` and the response-side parameters `Kd`, `BL`, `SLP` are shared between the drugs and do not multiply by `DRUG_PRED`).
- **Notes:** Sibling of `DRUG_ORMU` (rhRIG vs HRIG) and follows the same "head-to-head drug-arm" pattern: the contrast is between two structurally distinct active comparators in a randomized trial. Specific scope because the fosdagrocorat-vs-prednisone head-to-head is tied to the Shoji 2017 P1NP / OC analyses; future DAGR-vs-prednisone (or DAGR-vs-other glucocorticoid) popPK/PD models can extend this entry's `example_models` list, while a contrast between a different test drug and prednisone (e.g., methylprednisolone vs prednisone) should register a sibling canonical rather than reusing `DRUG_PRED`. Ratified canonically alongside the Shoji 2017 P1NP / OC extractions.

### DOSE_NMV_MG (**canonical for administered nirmatrelvir per-administration dose amount**)
- **Description:** Continuous per-record covariate carrying the nirmatrelvir dose amount, in mg, associated with the current record. Used when a nirmatrelvir popPK model makes a PK parameter an explicit function of the administered dose level rather than treating dose only as an event amount -- e.g. a dose-dependent relative-bioavailability power function capturing saturable absorption.
- **Units:** mg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- used with power scaling `(DOSE_NMV_MG / ref)^exponent`. Reference values observed: 300 mg (Chan 2023, the authorized nirmatrelvir dose and the normalisation anchor at which `F1` is fixed to 1).
- **Source aliases:**
  - `DOSE` -- NONMEM `$INPUT` column name used in `Chan_2023_nirmatrelvir.R` (Chan 2023 Data S1 header comment: `;AMT: DOSE IN MG`; `$PK`: `F1 = TVF1*(DOSE/300)**THETA(11)`).
- **Example models:** `Chan_2023_nirmatrelvir.R` (power effect on relative bioavailability, `F1 *= (DOSE_NMV_MG / 300)^-0.409`, so a 150 mg dose carries about 33% higher relative bioavailability than the 300 mg reference; applied on top of the fractional 150-mg-tablet formulation effect `FORM_NMV_TAB150`).
- **Notes:** The drug-qualified name is mandatory here rather than the bare `DOSE` canonical: rxode2's event translation consumes a data column literally named `DOSE`, so a model referencing `DOSE` in `model()` fails to solve with `The following parameter(s) are required for solving: DOSE` even when the column is present in the event table. Any new model that needs the administered dose as a *model covariate* (as opposed to the `amt` event column) should register a `DOSE_<drug>_<units>` sibling for the same reason; existing models using the bare `DOSE` canonical are PD-only or posthoc-CL models that never route the column through an rxode2 event translation. Sibling of `DOSE_EMPA_MGD`, `DOSE_SEMAGLUTIDE_MG`, `DOSE_CIPARGAMIN_MG`, `DOSE_TBPPI_MG` and the rest of the `DOSE_<drug>_<units>` family. Distinct from the `DOSE_<N>MG` binary dose-level indicators. Ratified canonically alongside the Chan 2023 nirmatrelvir extraction.

### DOSE_1P8MG (**canonical for 1.8 mg dose-level indicator**)
- **Description:** 1 = subject or dose record is in the 1.8 mg dose-level cohort, 0 = any other dose level. Route-neutral (SC / IV / oral); typically per-subject in fixed-arm randomizations (e.g., Overgaard 2016's SCALE Diabetes trial arm assignment 1.8 mg vs 3.0 mg liraglutide). "P" is the decimal-point token used across the `DOSE_<N>MG` family for fractional doses (analogous to `1P2`, `2P4` if those siblings are added in future).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any non-1.8 mg dose level in the source study; for Overgaard 2016 the reference is the 3.0 mg liraglutide arm).
- **Source aliases:**
  - `Dose 1.8 mg` -- derived per-subject from the trial-assigned dose level in `Overgaard_2016_liraglutide.R` (Overgaard 2016 Fig. 2 / Table S1 "Cov. 1.8 mg"; flags subjects in the SCALE Diabetes 1.8 mg arm alongside the 3.0 mg reference).
- **Example models:** `Overgaard_2016_liraglutide.R` (multiplicative log-scale effect on CL/F, coefficient +0.02 per Table S1; small and not pharmacokinetically relevant, consistent with dose proportionality in the 1.8-3.0 mg range).
- **Notes:** Sibling of `DOSE_50MG`, `DOSE_70MG`, `DOSE_130MG`, `DOSE_260MG`, `DOSE_400MG`; member of the `DOSE_<N>MG` family of dose-level indicators. The `1P8` token spells the decimal 1.8 with `P` = period; if a future paper adds a `DOSE_2P4MG` or `DOSE_0P6MG` sibling, follow the same convention. In Overgaard 2016 the indicator marks the SCALE Diabetes 1.8 mg-arm subjects vs the 3.0 mg reference so the paper can quantify (and formally rule out) a dose-linearity break within the SCALE-Diabetes cohort; the estimated log-scale coefficient of +0.02 (95% CI -0.03 to 0.08) confirms dose proportionality. Ratified canonically on 2026-07-10 alongside the Overgaard 2016 extraction.

### DOSE_70MG (**canonical for 70 mg dose regimen indicator**)
- **Description:** 1 = subject is on the 70 mg SC Q4W dose regimen, 0 = subject is on the 210 or 490 mg SC Q4W regimen.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (210 mg or 490 mg Q4W regimen).
- **Source aliases:** derived per subject from the trial-assigned dose level.
- **Example models:** `Kotani_2022_astegolimab.R`.
- **Notes:** Zenyatta-study categorical covariate flagging the 70 mg group (lowest dose), modeled as a -15.3% relative change on relative bioavailability. Modeled by Kotani 2022 as `70 mg vs {210 mg, 490 mg}` combined reference.

### DOSE_50MG (**canonical for 50 mg dose-level indicator**)
- **Description:** 1 = subject or dose record is in the 50 mg dose-level cohort, 0 = any other dose level. Route-neutral (covers SC, IV, oral); per-record or per-subject depending on the source design (record-level when a single subject crossed dose levels, subject-level when subjects were randomized to a fixed dose arm).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any non-50 mg dose level in the source study, e.g. 100-300 mg SC for Othman 2014, 200 mg or 400 mg t.i.d. oral for Jorga 2000).
- **Source aliases:**
  - derived per dose record from the administered amount (`AMT`) -- used in `Othman_2014_daclizumab.R` and the Diao 2016 family.
  - `I_Dose50mg` -- subject-level indicator derived from study-arm randomization, used in `Jorga_2000_tolcapone_fluctuators.R` for the 50 mg t.i.d. tolcapone fluctuator arm.
- **Example models:** `Othman_2014_daclizumab.R` (record-level SC), `Diao_2016_daclizumab_cd25.R`, `Diao_2016_daclizumab_cd56bright.R`, `Diao_2016_daclizumab_treg.R`, `Jorga_2000_tolcapone_fluctuators.R` (subject-level oral t.i.d.).
- **Notes:** Othman 2014 estimated two separate absolute bioavailabilities because of non-linear dose-normalized exposure at the 50 mg SC dose -- F = 0.84 for the therapeutic 100-300 mg SC range and F = 0.57 for the 50 mg SC cohort. Encoded as a record-level indicator so `e_dose_50mg_f = 0.57/0.84 - 1 = -0.321` scales bioavailability only on 50 mg SC dose records. For clinical-range simulation (150 mg SC Q4W Phase III regimen) leave `DOSE_50MG = 0`. The Diao 2016 PK/PD models inherit the Othman 2014 PK backbone verbatim. Jorga 2000 uses the indicator subject-level on the central and peripheral volumes of distribution: `(1 + e_dose_50mg_vc_vp * DOSE_50MG)` with `e_dose_50mg_vc_vp = -0.45` (V is 55% of the 200 mg reference at the 50 mg t.i.d. arm); paired with `DOSE_400MG` in the same fluctuator model so the 200 mg arm is the joint reference (both indicators = 0).

### DOSE_10MG (**canonical for 10 mg dose-level indicator**)
- **Description:** 1 = subject or dose record is in the 10 mg dose-level cohort, 0 = any other dose level. Route-neutral; per-record or per-subject depending on the source design (subject-level for fixed-arm randomizations such as Zhang 2015's dolutegravir dose-ranging cohorts).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any non-10 mg dose level in the source study; for Zhang 2015 dolutegravir the reference is the pooled 25 mg / 50 mg dose levels).
- **Source aliases:**
  - `DOSE` (= 1 for the 10 mg dose, 0 for 25 / 50 mg) -- used in `Zhang_2015_dolutegravir.R` (per Zhang 2015 Table 3 footnote, `F = 1.21^GEND * 1.24^DOSE`).
- **Example models:** `Zhang_2015_dolutegravir.R` (exponential effect on bioavailability: `f(depot) <- exp(lfdepot + e_dose_10mg_fdepot * DOSE_10MG)` with `e_dose_10mg_fdepot = log(1.24) = 0.215`, so F is 24% higher at the 10 mg dose vs the pooled 25 / 50 mg reference).
- **Notes:** Sibling of `DOSE_50MG`, `DOSE_70MG`, `DOSE_130MG`, `DOSE_260MG`, and `DOSE_400MG`; member of the `DOSE_<N>MG` family of dose-level indicators. Zhang 2015 reports that continuous dose was tested as a covariate on F but was not significant, and no F difference was found between the 25 and 50 mg dose levels; the higher relative bioavailability at the 10 mg dose is attributed to better dispersion of the lower-strength tablet (Zhang 2015 Discussion). Ratified canonically alongside the Zhang 2015 dolutegravir extraction.

### DOSE_400MG (**canonical for 400 mg dose-level indicator**)
- **Description:** 1 = subject or dose record is in the 400 mg dose-level cohort, 0 = any other dose level. Route-neutral; per-record or per-subject depending on the source design (subject-level for fixed-arm randomizations such as Jorga 2000's t.i.d. tolcapone study arms).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any non-400 mg dose level in the source study; for Jorga 2000 paired with `DOSE_50MG = 0` to select the 200 mg t.i.d. reference).
- **Source aliases:**
  - `I_Dose400mg` -- subject-level indicator derived from study-arm randomization, used in `Jorga_2000_tolcapone_fluctuators.R` for the 400 mg t.i.d. tolcapone fluctuator arm.
- **Example models:** `Jorga_2000_tolcapone_fluctuators.R` (subject-level oral t.i.d.).
- **Notes:** Sibling of `DOSE_50MG` and `DOSE_70MG`; member of the `DOSE_<N>MG` family of dose-level indicators. Jorga 2000 uses the indicator subject-level on the central and peripheral volumes of distribution: `(1 + e_dose_400mg_vc_vp * DOSE_400MG)` with `e_dose_400mg_vc_vp = +0.40` (V is 140% of the 200 mg reference at the 400 mg t.i.d. arm). The dose-dependent V was an empirical finding in the fluctuator cohort that the authors could not confirm in the nonfluctuator cohort (only 200 and 400 mg arms were enrolled there); the effect plausibly reflects a few high-V outliers in the small-volume cohort rather than a true mechanistic dose-V relationship (Jorga 2000 Discussion). Ratified canonically alongside the Jorga 2000 tolcapone extraction.

### DOSE_130MG (**canonical for 130 mg dose-level indicator**)
- **Description:** 1 = subject or dose record is in the 130 mg dose-level cohort, 0 = any other dose level. Route-neutral; per-record or per-subject depending on the source design (per-dose-record for Hajjar 2018's adult Pompe-disease ATB200+AT2221 study where the AT2221 dose changes across successive dosing occasions within a subject).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any non-130 mg dose level in the source study; for Hajjar 2018 paired with `DOSE_260MG = 0` to select the no-AT2221 reference for ATB200 linear CL).
- **Source aliases:**
  - derived per-dose-record from the co-administered AT2221 dose amount -- used in `Hajjar_2018_cipaglucosidase.R` as a categorical multiplier on ATB200 linear CL (`e_dose130mg_cl ^ DOSE_130MG`).
- **Example models:** `Hajjar_2018_cipaglucosidase.R` (per-dose-record oral co-medication indicator).
- **Notes:** Sibling of `DOSE_50MG`, `DOSE_70MG`, `DOSE_260MG`, and `DOSE_400MG`; member of the `DOSE_<N>MG` family of dose-level indicators. In Hajjar 2018 the indicator marks the AT2221 (miglustat) co-administration dose paired with the same-occasion 20 mg/kg ATB200 IV infusion; mutual exclusivity with `DOSE_260MG` is enforced by the study design (a single dose occasion uses at most one AT2221 dose level). Ratified canonically alongside the Hajjar 2018 ATB200 / AT2221 extraction.

### DOSE_260MG (**canonical for 260 mg dose-level indicator**)
- **Description:** 1 = subject or dose record is in the 260 mg dose-level cohort, 0 = any other dose level. Route-neutral; per-record or per-subject depending on the source design (per-dose-record for Hajjar 2018's adult Pompe-disease ATB200+AT2221 study where the AT2221 dose changes across successive dosing occasions within a subject).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any non-260 mg dose level in the source study; for Hajjar 2018 paired with `DOSE_130MG = 0` to select the no-AT2221 reference for ATB200 linear CL).
- **Source aliases:**
  - derived per-dose-record from the co-administered AT2221 dose amount -- used in `Hajjar_2018_cipaglucosidase.R` as a categorical multiplier on ATB200 linear CL (`e_dose260mg_cl ^ DOSE_260MG`).
- **Example models:** `Hajjar_2018_cipaglucosidase.R` (per-dose-record oral co-medication indicator).
- **Notes:** Sibling of `DOSE_50MG`, `DOSE_70MG`, `DOSE_130MG`, and `DOSE_400MG`; member of the `DOSE_<N>MG` family of dose-level indicators. In Hajjar 2018 the indicator marks the AT2221 (miglustat) co-administration dose paired with the same-occasion 20 mg/kg ATB200 IV infusion; mutual exclusivity with `DOSE_130MG` is enforced by the study design. Ratified canonically alongside the Hajjar 2018 ATB200 / AT2221 extraction.

### DOSE_HIGH (**canonical for highest-dose-cohort binary indicator**)
- **Description:** 1 = subject is in the source paper's highest-dose cohort (numerical threshold documented per model in `covariateData[[DOSE_HIGH]]$notes`); 0 = subject is in any lower-dose cohort. Time-fixed per subject in escalating-cohort or parallel-dose designs where each animal / subject remained on a single dose level for the full study and the paper detected a step-function shift in a PK parameter (typically apparent oral clearance or bioavailability) that was significant enough to warrant a separate typical-value estimate for the highest cohort alone.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any dose cohort below the paper-defined highest-dose threshold).
- **Source aliases:** derived per subject from the trial- or cohort-assigned dose level.
- **Example models:** `Viberg_2012_AZD6088_rat.R` (Viberg 2012 Results, PK model paragraph: 'The highest dose group deviated in the plots. This might be due to higher bioavailability or saturation of elimination at the highest dose and after estimating different clearance values for this group OFV decreased 32 units'. The 40 umol/kg = ~16.26 mg/kg oral dose group of AZD6088 in male Sprague-Dawley rats is flagged as `DOSE_HIGH = 1` and gates a separate typical-value apparent oral clearance `lcl_highdose = log(3.92 L/h/kg)` in place of the standard-dose `lcl = log(10.87 L/h/kg)`; the initial-efficacy-study cohorts at 1, 2.5, 5, 10, 20 umol/kg are `DOSE_HIGH = 0`), `Maleki_2024_brepocitinib.R` (first human ratification: `DOSE_HIGH = 1` for an administered daily dose above 100 mg -- the 175 and 200 mg phase I arms, 5% of the analysis population -- and gates a 30% uplift in relative bioavailability, `Frel = 1 * (1 + 0.3 * DOSE_HIGH)`, per Maleki 2024 Table 3 footnote c. The 100 mg cohort itself sits in the reference group. Same "step-function switch at the top of the dose range" semantics as the Viberg 2012 founding example, but expressed on bioavailability rather than clearance; the paper reached it from single-dose NCA showing dose-dependent PK above 100 mg q.d. plus a correlation between administered dose and the CL/F and Vc/F individual estimates).
- **Notes:** Abstract-form sibling of the numerically-explicit `DOSE_<N>MG` family (`DOSE_50MG`, `DOSE_70MG`, `DOSE_130MG`, `DOSE_260MG`, `DOSE_400MG`) and of the drug-suffixed `DOSE_HIGH_EFL` (Jansson 2008 eflornithine >= 3000 mg/kg indicator): `DOSE_HIGH` names the abstract "in the highest cohort" role without fixing the mg-threshold in the canonical name, and each model's `covariateData[[DOSE_HIGH]]$notes` documents the per-paper dose value that maps to `DOSE_HIGH = 1`. Distinct from the continuous `COHDOSE` (mg/kg per-subject dose cohort) and `DOSE` (per-record administered dose level, mg) canonicals, which are numeric rather than binary; use `DOSE_HIGH` when the source paper reports a step-function switch in a PK parameter at the top of the dose range (Viberg 2012 pattern: single distinct THETA for the highest cohort, all other cohorts share the standard THETA), use `COHDOSE` when the source reports a power-form or continuous covariate effect across all cohorts, and use `DOSE_HIGH_EFL` (or a similar drug-suffixed sibling) when the numerical threshold is enshrined in the canonical name. Scope: specific because the numerical highest-dose value differs per paper; promote to general once a second model registers under the same abstract semantics. Ratified canonically alongside the Viberg 2012 AZD6088 rat extraction (nlmixr2lib task frompeople-633).

### STUDY1 (**canonical for Study-1 cohort indicator**)
- **Description:** 1 = subject enrolled in Study 1 of the Cirincione 2017 pooled analysis, 0 = other. Used to switch the residual-error magnitude per study.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (all other studies; combined with `STUDY5 = 0` selects the pooled "other" residual error).
- **Source aliases:**
  - `DVID = "study1"` (character-valued study identifier; `STUDY1 = as.integer(DVID == "study1")`) -- legacy form previously used in `Cirincione_2017_exenatide.R`.
- **Example models:** `Cirincione_2017_exenatide.R`.
- **Notes:** Paired with `STUDY5`. When both are 0, the subject is in the pooled "other studies" residual-error group.

### STUDY5 (**canonical for Study-5 cohort indicator**)
- **Description:** 1 = subject enrolled in Study 5 of the Cirincione 2017 pooled analysis, 0 = other. Used to switch the residual-error magnitude per study.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (all other studies).
- **Source aliases:**
  - `DVID = "study5"` (character-valued study identifier; `STUDY5 = as.integer(DVID == "study5")`) -- legacy form previously used in `Cirincione_2017_exenatide.R`.
- **Example models:** `Cirincione_2017_exenatide.R`.
- **Notes:** Paired with `STUDY1`. When both are 0, the subject is in the pooled "other studies" residual-error group.

### STUDY_MD (**canonical for Cirincione 2017 AAPS J ER exenatide multi-dose study cohort indicator**)
- **Description:** 1 = subject enrolled in the phase II multi-dose study (weekly SC ER exenatide for 15 weeks) of the Cirincione 2017 AAPS J combined single- and multiple-dose population analysis; 0 = phase II single-dose study (one SC ER exenatide dose). Used to switch the study-specific relative bioavailability (f_rel) value and the log-scale residual-error magnitude per study.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (single-dose phase II study).
- **Source aliases:** derived per subject from the dose-record structure (a subject with > 1 dose record in the multi-dose phase II study -> 1; subjects with a single dose record in the single-dose phase II study -> 0).
- **Example models:** `Cirincione_2017_exenatide_er.R`.
- **Notes:** Cirincione 2017 AAPS J Table II reports f_rel(single-dose study) = 8.86% and f_rel(MD study) = 15.5%, and log-scale residual SDs 0.684 (single-dose) vs 0.376 (multi-dose). The `STUDY_MD` indicator selects between them. For the phase III external validation cohort (multi-dose weekly 2 mg ER for 24 weeks), use `STUDY_MD = 1` so the multi-dose f_rel and residual magnitudes apply. Specific scope because the indicator is tied to the AAPS J 2017 ER exenatide combined analysis (single-dose phase II + multi-dose phase II) -- a future pooled analysis with additional study cohorts would justify its own canonical or a promotion to general.

### STUDY_PKU015 (**canonical for sapropterin PKU-015 pediatric study cohort indicator**)
- **Description:** 1 = subject enrolled in study PKU-015 (pediatric population pharmacokinetic study of sapropterin in infants and young children, 0-6 years old, of the Qi 2014 pooled analysis); 0 = study PKU-004 (adolescent / adult open-label extension study, >= 9 years old). Used to switch the residual-error magnitude per study under the log-transform-both-sides (LTBS) constant-CV residual model.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (PKU-004 adolescent / adult cohort).
- **Source aliases:** derived per subject from the trial identifier (`PKU-015` -> 1, `PKU-004` -> 0).
- **Example models:** `Qi_2014_sapropterin.R`.
- **Notes:** Qi 2014 Table 3 reports separate residual-error estimates for the two studies -- PKU-004 = 21.1% CV, PKU-015 = 30.2% CV under the LTBS approach. The `STUDY_PKU015` indicator selects between them. Specific scope because the indicator is tied to the BioMarin sapropterin clinical-development program (PKU-004 = phase 3b extension, PKU-015 = phase 3b pediatric).

### STUDY_FARLETUZUMAB_PHASE2 (**canonical for Phase II study cohort indicator in the Farrell 2012 farletuzumab pooled analysis**)
- **Description:** 1 = subject enrolled in the Phase II study (MORAb-003-002) of the Farrell 2012 pooled farletuzumab analysis; 0 = Phase I study (MORAb-003-001). Used to switch the residual-error magnitude per study.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Phase I).
- **Source aliases:**
  - `PHASE2` -- prior canonical name (pre-2026-06-19 paper-specific-disambiguation standardization). Generic single-token name collided with the parallel `PHASE1` canonical from Valenzuela 2025 that picks the opposite reference category.
  - Derived per subject from the trial identifier (`MORAb-003-001` -> 0, `MORAb-003-002` -> 1).
- **Example models:** `Farrell_2012_farletuzumab.R`.
- **Notes:** Farrell 2012 Table 3 reports separate residual-error estimates for the two studies -- Phase I uses a proportional-only model (sigma = 20.5%); Phase II uses a combined additive + proportional model (sigma_prop = 34.9%, sigma_add = 7.94 ug/mL). The `STUDY_FARLETUZUMAB_PHASE2` indicator selects between them. Renamed from generic `PHASE2` to paper-specific `STUDY_FARLETUZUMAB_PHASE2` on 2026-06-19 per the canonical-register standardization audit (operator decision: study-phase indicators must be paper-specific because two papers can pick opposite reference categories - Farrell 2012 picks Phase II as 1-level while Valenzuela 2025 picks Phase I as 1-level - and a generic "PHASE2" canonical conflates the two encodings).

### STUDY_NIPOCALIMAB_PHASE1 (**canonical for Phase I study cohort indicator in the Valenzuela 2025 nipocalimab pooled analysis**)
- **Description:** 1 = subject enrolled in a Phase 1 study of the Valenzuela 2025 pooled nipocalimab analysis (MOM-M281-001, MOM-M281-007, MOM-M281-010, EDI1001, EDI1002 -- healthy participants); 0 = Phase 2 study (MOM-M281-004 / Vivacity-MG -- participants with gMG). Used to switch the proportional PK residual-error magnitude per study phase.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Phase 2).
- **Source aliases:**
  - `PHASE1` -- prior canonical name (pre-2026-06-19 paper-specific-disambiguation standardization). Generic single-token name collided with the parallel `PHASE2` canonical from Farrell 2012 that picks the opposite reference category.
  - Derived per subject from the trial identifier (Phase 1 protocols -> 1, `NCT03772587` Vivacity-MG -> 0).
- **Example models:** `Valenzuela_2025_nipocalimab.R`.
- **Notes:** Valenzuela 2025 Table 3 reports proportional PK residual 0.0834 (Phase 1) vs 0.367 (Phase 2). Distinct from Farrell 2012 `STUDY_FARLETUZUMAB_PHASE2` -- the reference category is inverted (Valenzuela 2025 picks Phase 1 as the 1-level). Renamed from generic `PHASE1` to paper-specific `STUDY_NIPOCALIMAB_PHASE1` on 2026-06-19 per the canonical-register standardization audit (operator decision: study-phase indicators must be paper-specific because two papers can pick opposite reference categories, and a generic "PHASE1" canonical conflates the encodings).

### STUDY_NMV_PHASE23 (**canonical for phase II/III study cohort indicator in the Chan 2023 nirmatrelvir pooled analysis**)
- **Description:** 1 = the observation record originates from the phase II/III EPIC-HR study (NCT04960202; 1087 nonhospitalized symptomatic adults with COVID-19, sparse outpatient PK sampling); 0 = the observation record originates from one of the seven phase I studies in the pooled analysis (150 participants, serial in-clinic sampling). Used to switch the proportional residual-error magnitude per study phase.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (phase I study observation).
- **Source aliases:**
  - `PROT` (protocol number; the phase II/III study is protocol 1005) -- used in `Chan_2023_nirmatrelvir.R` (Chan 2023 Data S1 `$ERROR`: `IF (PROT.EQ.1005) W = SQRT(THETA(12)**2 + THETA(7)**2/IPRED**2)`).
- **Example models:** `Chan_2023_nirmatrelvir.R` (selects `propSdPhase23 = 1.39` in place of `propSdPhase1 = 0.324` inside the combined additive-plus-proportional residual model; Chan 2023 Table 3 reports proportional error 32.4% for phase I and 139% for phase II/III, with the additive component fixed at the 10 ng/mL LLOQ for both).
- **Notes:** A record-level study-design property, not a subject-level covariate: it captures the sparse outpatient sampling scheme of EPIC-HR (Chan 2023 Discussion attributes the roughly fourfold higher residual variability to sparse sampling, the outpatient setting, and dosing-time compliance). Collinear with `DIS_COVID19` in the Chan 2023 data set -- every phase II/III participant has COVID-19 -- but the two enter different parts of the model (`DIS_COVID19` is a structural covariate on CL; `STUDY_NMV_PHASE23` only selects a residual-error magnitude), so both are carried. Follows the paper-specific `STUDY_<drug>_<phase>` convention established by `STUDY_NIPOCALIMAB_PHASE1`, `STUDY_FARLETUZUMAB_PHASE2`, `STUDY_POSA_PHASE3`, `STUDY_SULDUR_PHASE2` and `STUDY_SULDUR_PHASE3`; a generic `PHASE23` name is deliberately avoided because different papers pick opposite reference categories. Ratified canonically alongside the Chan 2023 nirmatrelvir extraction.

### STUDY_POSA_PHASE3 (**canonical for posaconazole phase 3 patient study cohort indicator in the van Iersel 2018 pooled analysis**)
- **Description:** 1 = subject enrolled in the phase 3 patient study P05615 of the van Iersel 2018 pooled posaconazole solid-tablet PopPK analysis (231 patients at high risk for invasive fungal disease, sparse PK sampling); 0 = subject enrolled in one of the five phase 1 healthy-volunteer studies P04975, P05637, P07764, P07783, P07691 (104 healthy volunteers, rich PK sampling). Used to switch the log-additive PK residual-error magnitude per study phase.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (phase 1 healthy-volunteer studies).
- **Source aliases:** derived per subject from the trial identifier (P05615 -> 1, any of P04975 / P05637 / P07764 / P07783 / P07691 -> 0).
- **Example models:** `vanIersel_2018_posaconazole.R` (switches the log-additive residual SD on Cc between `expSd_p1 = 0.42` (phase 1) and `expSd_p3 = 0.322` (phase 3), per van Iersel 2018 Table 2 final-model 'SD (phase 1 studies)' and 'SD (phase 3 study)').
- **Notes:** Specific scope because the contrast is tied to the van Iersel 2018 posaconazole solid-tablet clinical-development pooled analysis. Drug-specific paper-anchored member of the `STUDY_<DRUG>_PHASE<N>` family alongside `STUDY_NIPOCALIMAB_PHASE1` (Valenzuela 2025) and `STUDY_FARLETUZUMAB_PHASE2` (Farrell 2012); distinct from those entries because the reference category (phase 1) and 1-level (phase 3) are paper-specific to van Iersel 2018. Subject-level (time-fixed); set once from the trial identifier on each subject record. Ratified canonically alongside the van Iersel 2018 posaconazole extraction.

### STUDY_SULDUR_PHASE2 (**canonical for Cammarata 2024 sulbactam-durlobactam phase 2 study cohort indicator**)
- **Description:** 1 = subject enrolled in the Phase 2 study CS2514-2017-0003 (complicated urinary tract infection including acute pyelonephritis) of the Cammarata 2024 pooled sulbactam-durlobactam popPK analysis; 0 = otherwise. Paired with `STUDY_SULDUR_PHASE3`; both indicators 0 selects the six Phase 1 studies (the reference stratum). Used to switch the DURLOBACTAM residual-error magnitude between the three study phases; sulbactam residual variability is not phase-stratified.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 with `STUDY_SULDUR_PHASE3` also 0 (Phase 1 studies CS2514-2016-0001, CS2514-2017-0001, CS2514-2017-0002, CS2514-2018-0002, CS2514-2018-0003, ZL-2402-001).
- **Source aliases:** derived per subject from the trial identifier (CS2514-2017-0003 -> 1).
- **Example models:** `Cammarata_2024_sulbactam_durlobactam.R` (selects `propSdPhase2 = sqrt(0.0794)` = 0.282 for durlobactam plasma observations; no additive residual term applies to Phase 2, per Cammarata 2024 Table 1, which lists a single additive sigma^2 for Phase 1 only).
- **Notes:** Specific scope because the contrast is tied to the Cammarata 2024 sulbactam-durlobactam clinical-development pooled analysis. Drug-specific paper-anchored member of the `STUDY_<DRUG>_PHASE<N>` family alongside `STUDY_POSA_PHASE3` (van Iersel 2018), `STUDY_ASP8232_PHASE2` (Snelder 2020), `STUDY_NIPOCALIMAB_PHASE1` (Valenzuela 2025), and `STUDY_FARLETUZUMAB_PHASE2` (Farrell 2012). This is the first member of the family that needs a PAIR of indicators, because the paper stratifies residual error across three phases rather than two; keep both columns rather than an integer `PHASE` column so the reference stratum stays explicit. Subject-level (time-fixed); set once from the trial identifier on each subject record. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

### STUDY_SULDUR_PHASE3 (**canonical for Cammarata 2024 sulbactam-durlobactam phase 3 study cohort indicator**)
- **Description:** 1 = subject enrolled in the Phase 3 study CS2514-2017-0004 (infections caused by *Acinetobacter baumannii-calcoaceticus* complex) of the Cammarata 2024 pooled sulbactam-durlobactam popPK analysis; 0 = otherwise. Paired with `STUDY_SULDUR_PHASE2`; both indicators 0 selects the six Phase 1 studies.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 with `STUDY_SULDUR_PHASE2` also 0 (Phase 1 studies).
- **Source aliases:** derived per subject from the trial identifier (CS2514-2017-0004 -> 1).
- **Example models:** `Cammarata_2024_sulbactam_durlobactam.R` (selects `propSdPhase3 = sqrt(0.203)` = 0.451 for durlobactam plasma observations; the Phase 3 additive residual component 'was determined to not be significant and was consequently removed', per Cammarata 2024 Results).
- **Notes:** Specific scope; see `STUDY_SULDUR_PHASE2` for the full family rationale and the pair-of-indicators convention. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

### STUDY_ASP8232_PHASE2 (**canonical for Snelder 2020 ASP8232 phase 2 study cohort indicator in the pooled TMDD PK-PD analysis**)
- **Description:** 1 = subject enrolled in one of the two phase 2 studies of the Snelder 2020 pooled ASP8232 TMDD PK-PD analysis (VIDI study, NCT02302079, diabetic macular edema; or ALBUM study 8232-CL-0004, NCT02358096, diabetic kidney disease); 0 = subject enrolled in one of the two phase 1 studies (8232-CL-0001 first-in-human healthy volunteers; 8232-CL-0002 renal impairment / T2DM-CKD, NCT02218099). Used to switch the log-additive residual-error magnitude on ASP8232 plasma concentrations and on VAP-1 plasma activity between the phase 1 studies (reference) and the phase 2 studies (paper's estimated multiplicative factor 1.88 relative to phase 1).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (phase 1 studies 8232-CL-0001 and 8232-CL-0002).
- **Source aliases:** derived per subject from the trial identifier (8232-CL-3001/VIDI or 8232-CL-0004/ALBUM -> 1; 8232-CL-0001 or 8232-CL-0002 -> 0).
- **Example models:** `Snelder_2020_ASP8232.R` (multiplies the log-additive residual SD on ASP8232 total plasma concentration and on VAP-1 plasma activity by `factor_phase2 = 1.88` when the indicator is 1, per Snelder 2020 Table 4 'Factor res error phase 2 studies'; VAP-1 concentration residual error is not affected because VAP-1 concentration was measured only in the two phase 2 studies).
- **Notes:** Specific scope because the contrast is tied to the Snelder 2020 pooled ASP8232 clinical-development analysis (four studies in Astellas' vascular adhesion protein-1 inhibitor programme). Drug-specific paper-anchored member of the `STUDY_<DRUG>_PHASE<N>` family alongside `STUDY_POSA_PHASE3` (van Iersel 2018), `STUDY_NIPOCALIMAB_PHASE1` (Valenzuela 2025), and `STUDY_FARLETUZUMAB_PHASE2` (Farrell 2012). Reference category matches Farrell 2012 (phase 2 as the 1-level and phase 1 as the reference). Subject-level (time-fixed); set once from the trial identifier on each subject record. Ratified canonically alongside the Snelder 2020 ASP8232 extraction.

### STUDY_FU2022_AZ (**canonical for Fu 2022 CVS-CTR atenolol Study 2 (AstraZeneca) cohort indicator**)
- **Description:** 1 = subject enrolled in Study 2 (AstraZeneca Alderley Park, UK; 4 male beagle dogs, 14.2-14.6 kg, 17-22 months old; oral atenolol 0/1/3/10 mg/kg; hemodynamic markers HR, dP/dtmax, and MAP measured but NO cardiac output) of the Fu 2022 CVS-CTR systems-model development pool; 0 = Study 1 (Servier, France; 4 male beagle dogs, 10-15 kg; oral atenolol 0/3/10/30 mg/kg; HR, dP/dtmax, CO, and MAP measured). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Study 1, Servier site).
- **Source aliases:**
  - `SSID` in the NONMEM control stream (`SSID = 1` -> Servier -> `STUDY_FU2022_AZ = 0`; `SSID = 2` -> AstraZeneca -> `STUDY_FU2022_AZ = 1`; `SSID = 3` -> GSK external validation, excluded from the estimation dataset via `IGNORE=(SSID.EQ.3)` and encoded as `STUDY_FU2022_AZ = 0` alongside Study 1 for the packaged model).
- **Example models:** `Fu_2022_atenolol_qsp.R` (switches six per-study typical values: BSL_HR = 79.4 (S1) vs 77.0 (S2) bpm, V0 = 9.92 (S1) vs 9.15 (S2) mL, BSL_CTRM = 3777 (S1) vs 2422 (S2) mmHg/s, Amp = 0.0931 (S1) vs 0.168 (S2), Hor_HR = 7.86 (S1) vs 19.4 (S2) h, Hor_CTR = 9.82 (S1) vs 21.8 (S2) h; Fu 2022 Table 2 final-model column).
- **Notes:** Specific scope because the contrast is tied to the multi-site Servier/AstraZeneca beagle-dog telemetry pool used by Fu 2022 to develop the CVS-CTR systems model. Sibling of `STUDY_C2201` (Bienczak 2025 ligelizumab), `STUDY_ING111521` (Zhang 2015 dolutegravir), `STUDY_LBSL` (Zhou 2021 belimumab), `STUDY_M281_004` (Vivacity-MG nipocalimab), `STUDY_MD` (Cirincione 2017 ER exenatide multi-dose), `STUDY_PKU015` (Qi 2014 sapropterin pediatric), and `STUDY_RIV201` (Tammara 2017 rivipansel); member of the `STUDY_<name>` family of paper-specific study cohort indicators. Subject-level (time-fixed); set once from the trial identifier on each subject record. Ratified canonically alongside the Fu 2022 CVS-CTR extraction.

### STUDY_C2201 (**canonical for Bienczak 2025 ligelizumab study C2201 cohort indicator**)
- **Description:** 1 = subject enrolled in study C2201 (NCT02477332; Novartis Phase 2b ligelizumab dose-finding study in adult CSU patients) of the Bienczak 2025 pooled ligelizumab PopPK analysis; 0 = any other study in the pool (A2103, C2101, C2202, C2302, or C2303). Used to switch the typical CL/F magnitude in study C2201.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any non-C2201 cohort in the Bienczak 2025 pool: pooled adult / adolescent CSU patients from C2202 / C2302 / C2303 and adult healthy volunteers from A2103 / C2101).
- **Source aliases:** derived per subject from the trial identifier (`C2201` -> 1, else -> 0).
- **Example models:** `Bienczak_2025_ligelizumab.R` (Table S6: study C2201 on CL/F = 0.176, log-additive; `cl *= exp(0.176)` for C2201 subjects).
- **Notes:** Specific scope because the contrast is tied to the Novartis ligelizumab CSU development program. Subject-level / time-fixed; set once from the trial identifier on each subject record. The C2201 effect was retained in the final model because the residual unexplained CL/F differed between C2201 and the other studies after accounting for body weight, IgE, ADA, and disease-state covariates.

### STUDY_C208 (**canonical for TMC207-C208 bedaquiline trial cohort indicator**)
- **Description:** 1 = subject enrolled in TMC207-C208 (NCT00449644; a randomized, double-blind, placebo-controlled Phase IIb trial of bedaquiline added to a five-drug background regimen in newly diagnosed MDR-TB patients, run in two stages with 8-week and 24-week treatment durations); 0 = subject enrolled in TMC207-C209 (NCT00910871; an open-label, single-arm Phase IIb trial of 24-week bedaquiline plus an individualized background regimen, enrolling both newly diagnosed and treatment-experienced MDR-TB and XDR-TB patients). Used in pooled analyses of the two bedaquiline Phase IIb trials to carry residual between-study differences in trial design.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (TMC207-C209).
- **Source aliases:** derived per subject from the trial identifier (`C208` -> 1, `C209` -> 0); the Lin 2024 NONMEM data column is literally named `C208`.
- **Example models:** `Lin_2024_TB_multistate.R` (log-hazard effect shared by all three dropout transitions, `lambda14 / lambda24 / lambda34 *= exp(e_study_lambda1424 * STUDY_C208)` with `e_study_lambda1424 = 0.909188`, a hazard ratio of 2.48 for C208 relative to C209).
- **Notes:** Specific scope because the contrast is tied to the Janssen bedaquiline Phase IIb development program. Subject-level / time-fixed; set once from the trial identifier on each subject record. Lin 2024's Discussion attributes the higher C208 dropout hazard to trial design and calendar time rather than to any pharmacological difference: patients in a double-blind placebo-controlled trial "might be less willing to return for scheduled follow-ups with few signs of improvement", and C209 ran after positive C208 results had been reported, so C209 patients "might therefore have higher confidence in drug efficacy". Cohort split in Lin 2024: C208 n = 195 (49%), C209 n = 207 (51%). Note the studies also differ in what they enrolled -- C208 excluded prior anti-TB treatment and enrolled MDR-TB only, while C209 admitted treatment-experienced patients and XDR-TB -- so a model carrying this indicator alongside `DIS_TB_XDR_STRICT` is partially confounding study with resistance stratum by construction.

### STUDY_1 (**canonical for Duong 2017 Study-1 (newly diagnosed obese T2DM) cohort indicator**)
- **Description:** 1 = subject enrolled in Study 1 (NCT00236600) of the Duong 2017 pooled placebo T2DM analysis (newly diagnosed, treatment-naive, obese T2DM subjects; 6-week placebo run-in followed by 60-week placebo treatment phase with an ancillary weight-loss diet and exercise counselling arm); 0 = subject enrolled in Study 2 (NCT01071850) or Study 3 (NCT01117584) of the same pool (advanced T2DM cohorts on stable diet and exercise, no weight-loss counselling).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Studies 2 and 3 pooled -- the "advanced T2DM" reference: baseline b-cell function logit b0 = 0.677 and no treatment-phase placebo effect on weight (EFPL)).
- **Source aliases:**
  - `STUDY = 1` (character-valued or integer study identifier; `STUDY_1 = as.integer(STUDY == 1)`).
- **Example models:** `Duong_2016_WHIG_T2DM.R` (switches the baseline b-cell function logit b0 = -0.298 for Study 1 vs 0.677 for Studies 2 and 3, and activates the treatment-phase placebo weight effect EFPL only for Study 1 -- the "additional placebo effect during the treatment phase for Studies 2 and 3 was not significant" per Duong 2017 Results).
- **Notes:** Follows the auto-approved `STUDY_<id>` canonical family. Distinct from the Cirincione 2017 `STUDY1` (no underscore) which selects a different study in a different pooled analysis. Subject-level (time-fixed).

### STUDY_CLO_BE2 (**canonical for Pejcic 2024 clopidogrel bioequivalence Study-2 cohort indicator**)
- **Description:** 1 = subject enrolled in Study 2 of the Pejcic 2024 pooled clopidogrel / clopidogrel-carboxylic-acid analysis (n = 26; sampling to 36 h post-dose, 17 samples per subject per period); 0 = Study 1 (n = 24; sampling to 48 h post-dose, 14 samples per subject per period). Both were 2-treatment, 2-period, 2-sequence crossover bioequivalence studies of a generic 75 mg film-coated clopidogrel tablet against Plavix, dosed as 150 mg (2 tablets) under fasting conditions.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Study 1).
- **Source aliases:** derived per subject from the study identifier (`Study 2` -> 1, `Study 1` -> 0).
- **Example models:** `Pejcic_2024_clopidogrel.R` (switches every study-specific quantity in the joint semi-physiological model: mean transit time MTT 0.470 vs 0.410 h, generic relative bioavailability Fgen 1.08 vs 0.960, the first-pass fraction parameter FR1 119 vs 76.8, the IIV magnitudes on F and FR1, the IOV magnitudes on F and MTT, and the proportional residual error 41.95% vs 29.39%).
- **Notes:** Follows the auto-approved `STUDY_<id>` canonical family. Subject-level (time-fixed) -- each subject participated in exactly one of the two studies. Unusually broad in scope for a study indicator: rather than switching a single parameter, it selects an entire study-specific parameter block, because Pejcic 2024 pooled two datasets collected under different study conditions and found that estimating the absorption / bioavailability / first-pass parameters separately per study lowered OFV and stabilised the model (Discussion paragraph 3). Distinct from the Cirincione 2017 `STUDY1` and the Duong 2017 `STUDY_1`, which index studies in unrelated pooled analyses. Pair with `FORM_CLO_GENERIC`, which distinguishes the two products within each subject's crossover, and with `OCC`, which indexes the two periods.

### STUDY_ING111521 (**canonical for Zhang 2015 dolutegravir proof-of-concept study cohort indicator**)
- **Description:** 1 = subject enrolled in study ING111521 (phase 2a, proof-of-concept dose-ranging study of dolutegravir monotherapy in HIV-1-infected adults; n = 19 in the Zhang 2015 pooled analysis) of the Zhang 2015 dolutegravir PopPK; 0 = SPRING-1 (phase 2b, n = 141) or SPRING-2 (phase 3, n = 403). Used to switch the typical CL/F magnitude in study ING111521.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (SPRING-1 / SPRING-2 combined adult treatment-naive HIV-1 cohort).
- **Source aliases:**
  - `POC` (proof-of-concept) -- used in `Zhang_2015_dolutegravir.R` (= 1 for ING111521, 0 otherwise; per Zhang 2015 Table 3 footnote `CL/F = 0.901 * 1.16^SMOK * 1.35^POC * (WT/70)^0.438 * (AGE/40)^0.193 * (BILI/9)^-0.211`).
- **Example models:** `Zhang_2015_dolutegravir.R` (exponential effect on CL/F: `cl *= exp(e_ing111521_cl * STUDY_ING111521)` with `e_ing111521_cl = log(1.35) = 0.300`, so CL/F is 35% higher in study ING111521 than in the SPRING-1 / SPRING-2 reference).
- **Notes:** Sibling of `STUDY_C2201` (Bienczak 2025 ligelizumab), `STUDY_LBSL` (Zhou 2021 belimumab), `STUDY_M281_004` (Vivacity-MG nipocalimab), `STUDY_MD` (Cirincione 2017 ER exenatide multi-dose), `STUDY_PKU015` (Qi 2014 sapropterin pediatric), and `STUDY_RIV201` (Tammara 2017 rivipansel); member of the `STUDY_<name>` family of paper-specific study cohort indicators. The 35% higher CL/F in ING111521 vs SPRING-1 / SPRING-2 is unexplained by available covariates; Zhang 2015 attributes it plausibly to the smaller sample size and less diverse patient population in ING111521 (Zhang 2015 Discussion p. 506). Subject-level (time-fixed); set once from the trial identifier on each subject record. Ratified canonically alongside the Zhang 2015 dolutegravir extraction.

### STUDY_HARROLD_PEG (**canonical for Harrold 2020 pegfilgrastim-vs-filgrastim pivotal NHP study indicator**)
- **Description:** 1 = subject enrolled in the pegfilgrastim pivotal NHP study (Harrold 2020 reference 13; pegfilgrastim 300 ug/kg SC on days 1 and 8); 0 = subject enrolled in the filgrastim pivotal NHP study (Harrold 2020 reference 14; filgrastim 10 ug/kg QD SC starting day 1). Used by the Harrold 2020 OS time-to-event sub-model to select between the two study-specific parameter sets in Table III (lambda_ANC, lambda_BC, k_e0); the ANC response sub-model (Table II) is fit on the combined placebo cohorts and does not depend on this indicator.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (filgrastim pivotal NHP study).
- **Source aliases:**
  - `STUDY` -- the paper-text label for the binary study identifier; derived per subject from the trial-of-origin column in the source dataset.
- **Example models:** `Harrold_2020_radiation_neutropenia.R` (selects between the two OS parameter sets in Table III: lambda_ANC -2.15 vs -0.229, lambda_BC -0.347 vs 0.300, k_e0 0.668 vs 0.156 1/day).
- **Notes:** Specific scope; the indicator is tied to the two Amgen NHP pivotal studies used by Harrold 2020 (filgrastim reference 14 and pegfilgrastim reference 13) and is required because the paper documents an unexplained between-study OS difference that no observed covariate could resolve. Subject-level (time-fixed). Follows the auto-approved `STUDY_<id>` canonical family.

### STUDY_SPZCH (**canonical for sporozoite-challenge (SpzCh) study cohort indicator**)
- **Description:** 1 = participant enrolled in a controlled **sporozoite challenge** (SpzCh) malaria study, in which infection is initiated at the **liver stage** by inoculating Plasmodium falciparum sporozoites; 0 = participant enrolled in an **induced blood stage malaria** (IBSM) challenge study, in which infection is initiated directly at the **blood stage** by inoculating parasitised erythrocytes. Subject-level (time-fixed). In the founding model the indicator does double duty: it scales blood-stage drug potency, `EC50,b,SpzCh = EC50,b,IBSM * (1 - theta * STUDY_SPZCH)` with `theta = 0.83` (Courlet 2023 Table 1 footnote c), and it selects which life-cycle stage is inoculated, so a single model file reproduces both trial designs (liver state seeded with `Finc * inoculum` when 1, blood state seeded with `P0` when 0).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (induced blood stage malaria, IBSM, challenge).
- **Source aliases:**
  - `SpzCh` -- Courlet 2023 Table 1 footnote c notation ("with SpzCh being equal to 0 or 1 for IBSM and SpzCh populations, respectively").
- **Example models:** `Courlet_2023_cabamiquine.R` (blood-stage EC50 falls from 7.60 ng/mL in the IBSM population to 1.29 ng/mL in the SpzCh population, and the inoculation route switches from blood to liver).
- **Notes:** Follows the auto-approved `STUDY_<id>` canonical family, but names a **challenge-model design** rather than a single named trial, because the IBSM-vs-SpzCh distinction is a standard, reusable split in controlled human malaria infection (CHMI) research and any future CHMI extraction pooling the two designs will need exactly this indicator. The founding paper hypothesises the potency difference is mechanistic rather than a study artefact -- cabamiquine damages parasites during the liver stage, so the merozoites subsequently released into blood are less viable and are killed at lower concentrations -- which is a further reason to name the covariate after the challenge model rather than after Courlet 2023's specific protocol numbers. Ratified canonically alongside the Courlet 2023 cabamiquine extraction.

### STUDY_SPR994_104 (**canonical for tebipenem pivoxil hydrobromide study SPR994-104 (thorough-QT crossover) cohort indicator**)
- **Description:** 1 = subject enrolled in study SPR994-104 (NCT04238195), the phase 1 randomized, double-blind, placebo- and active-controlled four-way crossover thorough-QT study in which 24 healthy adults received single oral doses of 600 mg and 1200 mg tebipenem pivoxil hydrobromide (TBP-PI-HBr) in crossover fashion; 0 = subject enrolled in any other study of the Ganesan 2023 pooled analysis (SPR994-101, SPR994-102, or the phase 3 SPR994-301 / ADAPT-PO trial). Subject-level (time-fixed).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (studies SPR994-101, SPR994-102, and SPR994-301 / ADAPT-PO).
- **Source aliases:**
  - `S104` -- the flag variable named in Ganesan 2023 Eq. 6 ("where S104 is a flag variable to indicate if subject was enrolled in study 104").
- **Example models:** `Ganesan_2023_tebipenem.R` (gates the dose effect on the absorption rate constant: `ka * (1 + e_dose_ka * (DOSE / 1200) * STUDY_SPR994_104)` with `e_dose_ka = -0.478`, so Ka is 47.8% lower at 1200 mg and 23.9% lower at 600 mg relative to the same subject's non-study-104 value, and the term is inert for every other study).
- **Notes:** Conceptually the same per-study switch family as `STUDY1` / `STUDY5` / `STUDY_LBSL` / `STUDY_FARLETUZUMAB_PHASE2`, but the switch here gates a *covariate effect* (dose on Ka) rather than a residual-error magnitude or a structural scale factor. Ganesan 2023 restricts the dose effect to this study because it was the only crossover design in the pooled analysis: the same 24 subjects received both dose levels, which limits the between-subject variability that otherwise masks the modest dose-on-absorption-rate signal (the effect was not detectable across the 100-900 mg single doses of study SPR994-101). Must be paired with a `DOSE` column carrying the administered milligram amount; the product `(DOSE / 1200) * STUDY_SPR994_104` is zero for every subject outside study 104.

### STUDY_LBSL (**canonical for early-phase belimumab LBSL01 / LBSL02 study indicator**)
- **Description:** 1 = subject enrolled in study LBSL01 (NCT00657007) or LBSL02 (NCT00071487) -- the two early-phase belimumab studies that used a different ELISA-based bioanalytical assay; 0 = any other belimumab study in the Zhou 2021 pooled analysis. Used to switch CL and V1 magnitudes per study group (effectively an assay / early-development PK adjustment).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (later-phase studies using the electrochemiluminescence assay).
- **Source aliases:**
  - `INDR` -- used in `Zhou_2021_belimumab.R` (Zhou 2021 Table 2 footnote: study indicator).
- **Example models:** `Zhou_2021_belimumab.R` (multiplicative factors 1.63 on CL and 1.26 on V1 when STUDY_LBSL = 1).
- **Notes:** Conceptually similar to `STUDY1` / `STUDY_FARLETUZUMAB_PHASE2` / `ELISA` / `STUDY_NIPOCALIMAB_PHASE1` (per-study switches) but specific to the belimumab program. Subject-level (time-fixed); set from the trial identifier on each subject record.

### STUDY_VORI (**canonical for Friberg 2012 voriconazole pooled-analysis study indicator**)
- **Description:** Integer-valued (1-5) subject-level identifier of which of the five pooled PK studies of the Friberg 2012 voriconazole integrated population PK analysis a subject belongs to. 1 / 2 / 3 = immunocompromised children (2 to <12 years; Friberg 2012 Table 1 studies 1-3); 4 = immunocompromised adolescents (12 to <17 years; study 4); 5 = healthy adults (22-55 years; study 5). Time-fixed per subject.
- **Units:** (integer 1-5)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** 5 (healthy adult study; the typical-value reference for ka, Alag, Q, F1 IIV, CL IIV, and residual error).
- **Source aliases:**
  - `STDY_VORI` -- prior canonical name (pre-2026-06-19 typo correction); a missing-vowel abbreviation of `STUDY_VORI`.
  - Derived per subject from the Friberg 2012 dataset's `STUDY` / `STDY` identifier column.
- **Example models:** `Friberg_2012_voriconazole.R` (drives several effects: -0.382 Study-1 pediatric modifier on Km and Vmax,1; non-adult uplift on Q (+0.637); adolescent ka modifier; non-adult CL IIV scaling (+1.70); F1 IIV magnitude switching between adult and non-adult; per-study residual-error switching across the four levels Study 1, Study 2, Studies 3+4, Study 5).
- **Notes:** Departs from the binary `STUDY1` / `STUDY5` / `STUDY_PKU015` precedent because the Friberg 2012 analysis uses five distinct studies and four of them carry distinct typical-value or residual-error coefficients (Studies 3 and 4 share one residual-error magnitude). Encoding as a single integer column avoids registering five paired binary indicators; the model file derives `(STUDY_VORI == 1)` style indicators inline. Renamed from `STDY_VORI` to `STUDY_VORI` on 2026-06-19 per the canonical-register standardization audit (operator decision: typo correction - `STDY` was a missing-vowel abbreviation of `STUDY`).

### ORAL_VORI (**canonical for Friberg 2012 voriconazole observation-during-oral-dose-phase indicator**)
- **Description:** 1 = observation collected when the most recent administered voriconazole dose was oral (powder for oral suspension or tablet); 0 = observation collected when the most recent administered dose was IV. Per-observation (record-level) indicator.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (IV-phase observation).
- **Source aliases:** derived per observation from the most recent administered dose's route (POS or tablet -> 1; IV infusion -> 0).
- **Example models:** `Friberg_2012_voriconazole.R` (combines with `STDY_VORI == 5` to switch the adult residual-error magnitude between IV-only `expSdStdy5Iv = 0.0912` and oral `sqrt(0.0912^2 + 0.132^2) = 0.160` per Table 3 footnote on the residual-error structure W).
- **Notes:** Conceptually similar to `SAMPLE_INTENSIVE` (a generic per-observation switch between estimated residual-error magnitudes); the contrast here is dosing route (oral vs IV) within the same subject's crossover protocol rather than sampling design. Specific scope because the route-vs-residual-error switch is paper-specific to the Friberg 2012 voriconazole analysis.

### SAMPLE_INTENSIVE (**canonical for per-observation sampling-intensity indicator**)
- **Description:** 1 = observation belongs to an intensive (rich, post-dose) PK sampling window; 0 = sparse (pre-dose / steady-state trough) sampling. Per-observation (record-level) indicator used to switch the proportional residual-error magnitude when a source paper estimates separate residual errors for intensive vs sparse sampling phases of the same dataset.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (sparse sampling).
- **Source aliases:** derived per observation from the sampling-design label in the source dataset (intensive 24-h profiles / dense post-dose schedules -> 1; pre-dose troughs and steady-state population samples -> 0).
- **Example models:** `Macpherson_2015_rosuvastatin.R` (Table 2 final model: 39.4% CV intensive vs 59.5% CV sparse residual error; switched per observation as `propSd <- propSdIntensive * SAMPLE_INTENSIVE + propSdSparse * (1 - SAMPLE_INTENSIVE)`).
- **Notes:** Conceptually similar to `STUDY1` / `STUDY5` / `PHASE1` / `ELISA` (per-record switches that select between estimated residual-error magnitudes) but the contrast is sampling design rather than study cohort or bioanalytical assay. The indicator is generally applicable: any pediatric / dense-vs-sparse pooled popPK design that estimates two residual errors can carry it. Within-subject variation is permitted (a single subject can have both intensive and sparse observations, as in the Macpherson 2015 CHARON PK-pilot cohort where 12 subjects had a Day-0 intensive profile followed by 2 years of sparse troughs).

### SAMPLE_CAPILLARY (**canonical for per-observation capillary-vs-venous blood sampling-site indicator**)
- **Description:** 1 = the observation was drawn as a capillary blood sample (finger-prick / heel-prick); 0 = venous blood sample. Per-observation (record-level) indicator used to switch the proportional residual-error magnitude when a source paper estimates separate residual errors for capillary and venous sampling within the same dataset.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (venous sample).
- **Source aliases:** derived per observation from the sample-matrix / collection-site label in the source dataset (capillary, finger-prick, heel-prick, dried-blood-spot-style microsampling -> 1; venous plasma draws -> 0).
- **Example models:** `Cleary_2023_risdiplam.R` (Table 2 final PPK model: 23.4% CV venous vs 34.2% CV capillary proportional residual error, capillary being 3% of the 10,205-record dataset; switched per observation as `propSd <- propSdCapillary * SAMPLE_CAPILLARY + propSdVenous * (1 - SAMPLE_CAPILLARY)`).
- **Notes:** Third axis of the per-record residual-switch family, alongside `SAMPLE_INTENSIVE` (sampling design: rich vs sparse) and `ELISA` / `RIA_ASSAY` / `IMMUNOASSAY` / `ASSAY_OSA` (bioanalytical assay method). This entry covers the sample matrix / collection site, which none of those capture: in Cleary 2023 both matrices were quantified by the same validated LC-MS/MS method (LLOQ 0.25 ng/mL), so the extra residual variability is attributable to the capillary collection itself rather than to a different assay. Distinct from `HCT`, `RBC`, `BLOOD_GROUP_O` and the other blood-named entries, which are physiological quantities rather than sampling attributes. General scope because capillary microsampling is a standard paediatric-trial technique and any pooled paediatric popPK that estimates a separate capillary residual error can reuse the column. Within-subject variation is permitted (a subject may contribute both capillary and venous observations). Ratified canonically on 2026-07-30 alongside the Cleary 2023 risdiplam extraction (operator decision in sidecar request-002 q1, option A).

### ELISA (**canonical for ELISA-vs-electrochemiluminescence bioanalytical assay indicator**)
- **Description:** 1 = the concentration was measured by an enzyme-linked immunosorbent assay (ELISA); 0 = measured by an electrochemiluminescence assay (ECL / ECLIA). Per-observation indicator used to switch the residual-error magnitude (additive, proportional, or log-scale) between the two immunoassay platforms, which typically differ in LLOQ and precision. The two platforms and their LLOQs are paper-specific and MUST be documented in `covariateData[[ELISA]]$notes`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (the electrochemiluminescence assay -- in both ratifying papers the ECL platform is the newer, lower-LLOQ method used in the later-phase studies, which makes it the natural reference).
- **Source aliases:** derived per study from the bioanalytical method (Text S2 of Valenzuela 2025; Qi 2024 Sect. 2.3).
- **Example models:** `Valenzuela_2025_nipocalimab.R` (Valenzuela 2025 Table 3: additive PK residual 0.445 nmol/L for ELISA, LLOQ 0.150 ug/mL, studies MOM-M281-001 / MOM-M281-007 / MOM-M281-010, vs 0.0342 nmol/L for ECLIA, LLOQ 0.010 ug/mL, studies EDI1001 / EDI1002 / MOM-M281-004), `Qi_2024_vosoritide.R` (Qi 2024 Table 5: log-scale residual SD 0.665 for the validated ELISA used in studies 111-202 and 111-205, LLOQ 0.391 ug/L, vs 0.610 for the optimized ECL assay used in studies 111-206, 111-301 and 111-302, LLOQ 0.137 ug/L; encoded as `expSd <- expSdElisa * ELISA + expSdEcl * (1 - ELISA)`).
- **Notes:** Assay choice is study-fixed in both ratifying papers, but the column is carried per observation so pooled datasets apply the correct residual to each row. Distinct from `IMMUNOASSAY` (immunoassay of any kind vs an LC-MS/MS reference) and `RIA_ASSAY` (radioimmunoassay vs LC-MS/MS): those two contrast an immunoassay against chromatography, whereas `ELISA` contrasts two immunoassay platforms with each other and has no LC-MS/MS arm. Promoted from `specific` to `general` scope when the Qi 2024 vosoritide extraction ratified the same ELISA-vs-ECL encoding in an unrelated therapeutic area.

### SIDN (**canonical for the opaque study-level nesting column**)
- **Description:** Integer-valued grouping column identifying the clinical trial (or trial group) a subject's records belong to, used as the **nesting level** for a second hierarchical level of random effects on top of the per-subject etas. Values `1`, `2`, ..., `N`. Unlike the `STUDY_<id>` family, `SIDN` does not enter any typical-value covariate equation and has no reference category -- it only indexes which study-level random-effect draw applies to a record. Use it when the source paper fits a between-study random effect (rather than a fixed per-study shift) and does not publish a mapping from trial identifier to level.
- **Units:** (count)
- **Type:** categorical
- **Scope:** general
- **Reference category:** n/a -- `SIDN` is a nesting level, not a covariate with a reference category.
- **Source aliases:**
  - `SIDN` ("secondary study identity number") -- used in `Qi_2024_vosoritide.R` (Qi 2024 Sect. 3.2).
- **Example models:** `Qi_2024_vosoritide.R` (Qi 2024 Sect. 2.4 and 3.2: the additional hierarchical level `eta6` on CL/F and `eta7` on V/F, encoded with rxode2/nlmixr2 native nested random effects as `etalcl_study ~ 0.066049 | SIDN` and `etalvc_study ~ 0.000144 | SIDN`; Qi 2024 states only three SIDN values were present in the database and does not enumerate which trials map to which value, so the column is deliberately opaque).
- **Notes:** Distinct from the `STUDY_<id>` family (binary per-trial indicators that switch a typical value, a residual-error magnitude, or a covariate coefficient) and from `OCC` (integer occasion / period index for inter-occasion variability *within* a subject). `SIDN` groups *across* subjects: a subject belongs to one study level and all of that subject's records share the study-level draw. Because it is consumed by the `ini()` nesting syntax rather than by a `model()` expression, `covariateData[['SIDN']]` will not be referenced anywhere in `model()`.
  Simulation caveat: rxode2 expands nested random effects into per-level terms, so an event table must contain **at least two distinct `SIDN` values** or `rxSolve()` fails with `The following parameter(s) are required for solving: THETA[1]`, and `omega` must be passed explicitly (`omega = mod$omega`) because the nested omega is a list of matrices keyed by level. Ratified canonically alongside the Qi 2024 vosoritide extraction, the first nlmixr2lib model to use nested random effects.

### STUDY_M281_004 (**canonical for MOM-M281-004 (Vivacity-MG) study indicator**)
- **Description:** 1 = subject enrolled in study MOM-M281-004 (Vivacity-MG; NCT03772587; phase 2 in generalized myasthenia gravis); 0 = any other study in the Valenzuela 2025 pooled analysis. Used to switch the IgG-baseline scaling factor.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-Vivacity-MG studies).
- **Source aliases:** derived per subject from the trial identifier (`NCT03772587` -> 1, else -> 0).
- **Example models:** `Valenzuela_2025_nipocalimab.R`.
- **Notes:** Valenzuela 2025 estimated a slightly lower IgG baseline in MOM-M281-004 participants (baseline scaled by `FRIgG0_M281_004 = 0.777` vs. 1 in other studies). Distinct from the disease-state indicator implied by `gMG` -- it is specifically the Vivacity-MG study flag because the IgG baseline factor was only estimated for that study.

### STUDY_ABA2_HLA78 (**canonical for ABA2 trial HLA 7/8-matched cohort indicator**)
- **Description:** 1 = subject enrolled in the ABA2 hematopoietic-cell-transplant trial (IM101311; NCT01743131) HLA 7/8 (one-allele-mismatched donor) cohort, 0 = any other study in the Takahashi 2023 pooled abatacept population PK analysis (RA/JIA reference and ABA2 8/8 cohort).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-ABA2-7/8 -- pooled adult RA / pediatric JIA cohort, plus ABA2 HLA 8/8 cohort which is flagged separately by `STUDY_ABA2_HLA88`).
- **Source aliases:** derived per subject from the trial-cohort identifier (`Cohort = ABA2 7/8` -> 1, else -> 0). Takahashi 2023 Supplemental Table 4 names the corresponding theta `thetaCohort_CL` / `thetaCohort_Vc`.
- **Example models:** `Takahashi_2023_abatacept.R` (multiplicative `Ratio` factors on CL = 0.70 and on Vc = 0.99 vs the RA/JIA reference; values from Takahashi 2023 Supplemental Table 4).
- **Notes:** Pairs with `STUDY_ABA2_HLA88` to reproduce the three-level cohort categorical (RA/JIA, ABA2 7/8, ABA2 8/8) the paper reports as the only retained categorical PK covariate. At most one of `STUDY_ABA2_HLA78` and `STUDY_ABA2_HLA88` is 1 per subject; both 0 reproduces the RA/JIA reference. Scope: specific because the contrast is tied to the ABA2-vs-RA/JIA pooling design.

### STUDY_ABA2_HLA88 (**canonical for ABA2 trial HLA 8/8-matched cohort indicator**)
- **Description:** 1 = subject enrolled in the ABA2 hematopoietic-cell-transplant trial (IM101311; NCT01743131) HLA 8/8 (allele-matched donor) cohort, 0 = any other study in the Takahashi 2023 pooled abatacept population PK analysis (RA/JIA reference and ABA2 7/8 cohort).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-ABA2-8/8 -- pooled adult RA / pediatric JIA cohort, plus ABA2 HLA 7/8 cohort which is flagged separately by `STUDY_ABA2_HLA78`).
- **Source aliases:** derived per subject from the trial-cohort identifier (`Cohort = ABA2 8/8` -> 1, else -> 0). Takahashi 2023 Supplemental Table 4 names the corresponding theta `thetaCohort_CL` / `thetaCohort_Vc`.
- **Example models:** `Takahashi_2023_abatacept.R` (multiplicative `Ratio` factors on CL = 0.91 and on Vc = 1.32 vs the RA/JIA reference; values from Takahashi 2023 Supplemental Table 4).
- **Notes:** Pairs with `STUDY_ABA2_HLA78`. At most one of the two indicators is 1 per subject; both 0 reproduces the RA/JIA reference. Scope: specific.

### STUDY_d2eGFP (**canonical for Frohlich 2018 multi-experiment NLME reporter-construct cohort indicator**)
- **Description:** 1 = cell transfected with destabilized eGFP (d2eGFP, mRNA construct pVAXA120-d2EGFP, ~6.6 h protein half-life via C-terminal PEST sequence); 0 = cell transfected with eGFP (mRNA construct pVAXA120-eGFP, ~22.8 h protein half-life). Cohort indicator in the Frohlich 2018 multi-experiment NLME single-cell translation-kinetics analysis used to select between the two reporter-construct cohorts (Experiment 1 eGFP vs Experiment 2 d2eGFP). All structural and ribosomal-binding parameters (mRNA degradation, ribosome-mRNA binding rate x m0, ribosome catalytic rate, R0/m0, fluorescence offset, transfection-onset time, scale x m0) are shared between cohorts; only the protein degradation rate (`gamma_eGFP` for STUDY_d2eGFP = 0 vs `gamma_d2eGFP` for STUDY_d2eGFP = 1) and its IIV variance differ.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (eGFP cohort -- the stable reporter; this is the cohort with the slower protein degradation rate ~0.030 1/h and the larger between-cell IIV variance on it).
- **Source aliases:** derived per cell from the experiment identifier in the source dataset (`Experiment 1` / eGFP -> 0, `Experiment 2` / d2eGFP -> 1; see `code.zip` project/models/experiments_transfection_ribo.m in the Frohlich 2018 Zenodo deposit doi:10.5281/zenodo.1228899).
- **Example models:** `Frohlich_2018_mRNA_translation.R`.
- **Notes:** Specific scope because the cohort is tied to the Frohlich 2018 in-vitro reporter-mRNA assay (eGFP vs d2eGFP). Cell-level (time-fixed) indicator; set once per cell from the transfection-construct identifier. Conceptually analogous to a single-study two-arm cohort indicator (e.g. `STUDY_FARLETUZUMAB_PHASE2`, `STUDY_NIPOCALIMAB_PHASE1`), but the contrast is between two reporter mRNA constructs measured under the same physical / imaging protocol rather than between trial phases.

### STUDY_RIV201 (**canonical for Tammara 2017 rivipansel phase II SCD study indicator**)
- **Description:** 1 = subject enrolled in the phase II rivipansel study (NCT01119833; "study 201" in the Tammara 2017 integrated population PK analysis; Telen 2015 Blood 125:2656-2664 reports the trial results); 0 = healthy adult volunteers from the three rivipansel phase I studies (studies 101, 102, and 103) pooled into the integrated dataset. Used both as an additive shift on typical clearance (interpreted by the authors as the SCD-hyperfiltration component, ~23%) and to switch the additive / proportional residual-error magnitudes between the two cohorts (Table 1).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (rivipansel phase I healthy-adult studies 101, 102, and study 103 healthy adults with SCD pooled with phase I per Tammara 2017; the paper labels these as "phase I studies" in the residual-error rows of Table 1).
- **Source aliases:** derived per subject from the trial identifier (`NCT01119833` -> 1, else -> 0); the source `STUD` column in the NONMEM dataset described in Tammara 2017 Table 1 footnote b.
- **Example models:** `Tammara_2017_rivipansel.R` (Table 1: additive effect 0.234 on CL via `1 + 0.234 * STUDY_RIV201`; selects the cohort-specific additive and proportional residual SDs in `model()`).
- **Notes:** Specific scope because the contrast is tied to the rivipansel development program. The Tammara 2017 paper interprets the 23% CL increment as a putative hyperfiltration effect of SCD; in simulation use cases targeting the SCD population (the paper's stated goal) set `STUDY_RIV201 = 1` for every subject. Subject-level / time-fixed; set once from the trial identifier on each subject record.

### STUDY_LPS30M (**canonical for Kutumova 2024 ANP-30-min-after-LPS cohort indicator**)
- **Description:** 1 = albumin nanoparticles administered intravenously 30 min after the intraperitoneal lipopolysaccharide challenge (the paper's "LPS 30 min" arm, exp 2); 0 otherwise. Cohort indicator in the Kutumova 2024 four-experiment nanoparticle-biodistribution PBPK analysis, used together with `STUDY_LPS6H` and `STUDY_LPS24H` to select the arm-specific permeability (PAC) and distribution (P) coefficients of every organ from Table 1. All other fitted parameters (the Hill-function endocytic uptake constants KRESmax / KRES50 / KRESn, the exocytic release constant KRESrelease, the biliary and urinary excretion coefficients, and the radiant-efficiency scale factor k) are shared across all four arms.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (LPS-naive control cohort, exp 1 -- mice given nanoparticles only, no LPS).
- **Source aliases:** derived per animal from the "LPS 30 min" column heading of Kutumova 2024 Table 1 / the ANP-administration time point of 0.5 h after LPS in section 2.4.
- **Example models:** `Kutumova_2024_albuminNanoparticles_mouse_pbpk.R`.
- **Notes:** Specific scope because the cohort is tied to the Kutumova 2024 murine LPS acute-lung-injury protocol. Animal-level (time-fixed) indicator. The three `STUDY_LPS*` indicators are mutually exclusive; all three zero selects the control arm via `f_ctrl <- 1 - STUDY_LPS30M - STUDY_LPS6H - STUDY_LPS24H` in `model()`. This encodes an experimental-arm contrast rather than a separate trial, in the same sense as `STUDY_d2eGFP` (reporter-construct cohorts measured under one imaging protocol).

### STUDY_LPS6H (**canonical for Kutumova 2024 ANP-6-h-after-LPS cohort indicator**)
- **Description:** 1 = albumin nanoparticles administered intravenously 6 h after the intraperitoneal lipopolysaccharide challenge (the paper's "LPS 6 h" arm, exp 3); 0 otherwise. Cohort indicator in the Kutumova 2024 four-experiment nanoparticle-biodistribution PBPK analysis, selecting the LPS-6-h column of Table 1 for every organ's permeability (PAC) and distribution (P) coefficient. This is the arm with peak pulmonary nanoparticle accumulation (lung P = 0.89661, the maximum across all four arms).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (LPS-naive control cohort, exp 1 -- mice given nanoparticles only, no LPS).
- **Source aliases:** derived per animal from the "LPS 6 h" column heading of Kutumova 2024 Table 1 / the ANP-administration time point of 6 h after LPS in section 2.4.
- **Example models:** `Kutumova_2024_albuminNanoparticles_mouse_pbpk.R`.
- **Notes:** Specific scope because the cohort is tied to the Kutumova 2024 murine LPS acute-lung-injury protocol. Animal-level (time-fixed) indicator; mutually exclusive with `STUDY_LPS30M` and `STUDY_LPS24H`.

### STUDY_LPS24H (**canonical for Kutumova 2024 ANP-24-h-after-LPS cohort indicator**)
- **Description:** 1 = albumin nanoparticles administered intravenously 24 h after the intraperitoneal lipopolysaccharide challenge (the paper's "LPS 24 h" arm, exp 4); 0 otherwise. Cohort indicator in the Kutumova 2024 four-experiment nanoparticle-biodistribution PBPK analysis, selecting the LPS-24-h column of Table 1 for every organ's permeability (PAC) and distribution (P) coefficient. The paper interprets this arm as partial pulmonary recovery with persisting injury elsewhere (lung P falls from its 6-h peak while splenic PAC keeps rising).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (LPS-naive control cohort, exp 1 -- mice given nanoparticles only, no LPS).
- **Source aliases:** derived per animal from the "LPS 24 h" column heading of Kutumova 2024 Table 1 / the ANP-administration time point of 24 h after LPS in section 2.4.
- **Example models:** `Kutumova_2024_albuminNanoparticles_mouse_pbpk.R`.
- **Notes:** Specific scope because the cohort is tied to the Kutumova 2024 murine LPS acute-lung-injury protocol. Animal-level (time-fixed) indicator; mutually exclusive with `STUDY_LPS30M` and `STUDY_LPS6H`. The optimisation constrained PAC to be non-decreasing from control through this arm for the lungs, liver, spleen, and kidneys (section 2.8 equation 9), so a monotone PAC trend across the four indicators is a fitted constraint rather than an emergent result.

### STUDY_WAGH_2 (**canonical for Wagh 2021 spectinamide 1810 mouse TB study 2 indicator**)
- **Description:** 1 = BALB/c mouse cohort enrolled in study 2 of the Wagh 2021 spectinamide 1810 dose ranging / dose fractionation efficacy program (Wagh 2021 Table 5: study 2 used 4-arm dosing regimens of 50 BID, 100 QD, 166 TIW, 200 BID, and 333 TIW with a 5.37 log CFU baseline at start of treatment); 0 = study 1 (Wagh 2021 Table 4: 24-arm dose-fractionation schedule with a 7.08 log CFU baseline at start of treatment). Used to switch K_kill_max from the study 1 typical value to the 1.15-fold higher study 2 value in the integrated PK/PD model.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Wagh 2021 study 1: 7.08 log CFU initial bacterial load; the reference K_kill_max = 0.0374 1/h applies).
- **Source aliases:**
  - `STUDY` -- the Wagh 2021 NONMEM dataset's study identifier column (1 / 2 in the source; mapped to 0 / 1 in this binary indicator).
- **Example models:** `Wagh_2021_spectinamide_1810_mouse.R` (Table 3: study-specific coefficient for K_kill_max = 1.15, RSE 4.0%; applied multiplicatively as `killmax = killmax_base * (1 + STUDY_WAGH_2 * (1.15 - 1))`).
- **Notes:** Specific scope because the contrast is tied to the Wagh 2021 study 1 vs study 2 difference in inoculum-induced baseline bacterial load and a downstream effect on observed efficacy. Subject-level / time-fixed; set once per mouse from the source-study identifier on each animal record. Per the paper Discussion, the higher K_kill_max in study 2 (1.15x) is plausibly explained by the lower baseline bacterial load (5.37 vs 7.08 log CFU); see vignette Assumptions and deviations for the per-study residual-error treatment.

### STUDY_CT_USA (**canonical for the Wang 2024 risperidone LAI US relative-bioavailability trial-group indicator**)
- **Description:** 1 = subject enrolled in one of the two US relative-bioavailability trials of the Wang 2024 RYKINDO (LY03004) / RISPERDAL CONSTA programme -- CT-USA-104 (NCT02186769; open-label parallel single-dose 25 and 50 mg Rykindo vs Consta) or CT-USA-102 (NCT02091388; open-label parallel 25 mg every 2 weeks for five injections); 0 = subject enrolled in CT-1S01 (NCT02055287), the open-label single-ascending-dose Rykindo trial (12.5 / 25 / 37.5 / 50 mg). Used to switch the apparent clearance of the risperidone active moiety between the two trial groups.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (CT-1S01, the single-ascending-dose trial, which anchors the reference apparent clearance of 186.7 L/day in males and 153.4 L/day in females).
- **Source aliases:** derived per subject from the trial identifier (`CT-USA-104` or `CT-USA-102` -> 1; `CT-1S01` -> 0). Wang 2024 Table 2 names the effect in the row label "CL ratio for CT-USA-104 and 102 to CT-1S01".
- **Example models:** `Wang_2024_risperidone_rykindo.R` (log-multiplicative effect on apparent clearance: `exp(log(0.675) * STUDY_CT_USA)`, i.e. a 0.675-fold, 32.5% lower, apparent clearance in the two US trials; Wang 2024 Table 2, %RSE 6.03).
- **Notes:** Specific scope; tied to the three-trial Luye Pharma risperidone LAI programme. Subject-level (time-fixed); set once from the trial identifier. A group-level indicator spanning two trials, directly analogous to `STUDY_DORZA_EARLY` and `STUDY_LBSL`. Applies to the Rykindo model only -- the companion `Wang_2024_risperidone_consta.R` has a single apparent clearance across trials and sexes. Beware a text-versus-table conflict in the source: Wang 2024 Results ("Population Pharmacokinetic Modeling of Rykindo") describes the reduced clearance as applying to "the multiple-dose study", i.e. CT-USA-102 alone, whereas the Table 2 row label names both CT-USA-104 and CT-USA-102. The table definition is the one encoded, because it is the final-model parameter definition and because it reproduces the observed CT-USA-104 single-dose exposures (AUC0-t 227 and 399 day*ng/mL at 25 and 50 mg) far better than the text reading does. Distinct from `STUDY_CT_USA_102`, which is nested inside this group and switches the absorption rate constant rather than clearance; a CT-USA-102 subject has both indicators set to 1. Follows the auto-approved `STUDY_<id>` canonical family.

### STUDY_CT_USA_102 (**canonical for the Wang 2024 risperidone LAI multiple-dose trial indicator**)
- **Description:** 1 = subject enrolled in trial CT-USA-102 (NCT02091388), the multiple-dose relative-bioavailability trial of the Wang 2024 RYKINDO (LY03004) / RISPERDAL CONSTA programme, in which 25 mg of either formulation was given intramuscularly every 2 weeks for five injections; 0 = subject enrolled in one of the single-dose trials CT-1S01 (NCT02055287) or CT-USA-104 (NCT02186769). Used to switch the first-order absorption rate constant of the main (third) release phase.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (the single-dose trials; for Rykindo this is CT-1S01 and CT-USA-104 pooled, for Consta it is CT-USA-104 alone since Consta was not studied in CT-1S01).
- **Source aliases:** derived per subject from the trial identifier (`CT-USA-102` -> 1, else -> 0). Wang 2024 Table 2 names the two levels in the row labels "KA_CT-1S01 and CT-USA-104" and "KA_CT-USA-102", with the footnote "KA_CT-USA-104 for Consta".
- **Example models:** `Wang_2024_risperidone_rykindo.R` (log-multiplicative effect on the main-release KA: 0.288 -> 0.380 /day, a 1.319-fold increase), `Wang_2024_risperidone_consta.R` (0.179 -> 0.271 /day, a 1.514-fold increase).
- **Notes:** Specific scope; tied to the three-trial Luye Pharma risperidone LAI programme. Subject-level (time-fixed); set once from the trial identifier. Wang 2024 Discussion attributes the faster apparent absorption in the multiple-dose trial to its much sparser PK sampling scheme (trough-focused after the first injection) rather than to any formulation or population difference, so users simulating a clinical dosing scenario should generally leave this at the single-dose reference value of 0. Note that both Wang 2024 models set the elimination rate constant equal to KA (flip-flop kinetics) and derive the apparent central volume as V = CL/KA, so this indicator also shifts the apparent volume. Nested inside `STUDY_CT_USA`: a CT-USA-102 subject has both indicators set to 1. Follows the auto-approved `STUDY_<id>` canonical family.

### DLVL (**canonical for source-protocol dose-level integer indicator**)
- **Description:** Integer dose-level / protocol-arm indicator carried per subject in the DDMODEL00000281 lidocaine bundle's simulated dataset (`Simulated_Lid_B04_ddmore.csv`). Values 1-4 (or higher) flag distinct study-protocol dose / regimen tiers; the `NA_NA_lidocaine.R` model binarises as `DLVL > 2` to switch the typical-value baselines for both the GX elimination rate constant K30 and the lidocaine apparent central volume V1 between a "low" (DLVL <= 2) and a "high" (DLVL > 2) regimen.
- **Units:** (integer-coded categorical)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** the binary form `DLVL <= 2` is the reference (THETA(4) for K30 and THETA(14) for V1 in the source `.ctl`); `DLVL > 2` selects the higher-exposure regimen (THETA(5) and THETA(15) respectively).
- **Source aliases:** `DLVL` -- the column header used in the DDMORE bundle's `.ctl` `$INPUT` and the Simulated_Lid_B04_ddmore.csv data file.
- **Example models:** `NA_NA_lidocaine.R` (DDMODEL00000281; binary derivation `DLVL_HIGH = as.integer(DLVL > 2)` on K30 base and V1 base).
- **Notes:** Specific scope because the integer-coded dose / regimen tiers are paper-specific to the lidocaine BAST.dat ("4-cRUN249") study and the linked publication is not on disk for this extraction. The binary threshold `> 2` reproduces the source `.ctl` line `IF(DLVL.GT.2)P1=0`. If a future model needs a different dose-level binarisation or a continuous treatment, register a distinct canonical name rather than overloading `DLVL`.

### S1A2 (**canonical for source-protocol CYP1A2 substrate / co-medication categorical indicator**)
- **Description:** Categorical CYP1A2-modifying co-medication / phenotype indicator carried per subject in the DDMODEL00000281 lidocaine bundle's simulated dataset. Integer code; in the `NA_NA_lidocaine.R` model the value `S1A2 == 3` selects the "CYP1A2 inducer present" sub-cohort (lidocaine N-deethylation to MEGX is CYP1A2-mediated, so the modifier acts on the GX elimination rate constant K30 in the source's parameterisation). Other integer codes (0, 1, 2) are pooled into the reference.
- **Units:** (integer-coded categorical)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** `S1A2 != 3` (i.e., values 0, 1, 2) -- pooled into the reference.
- **Source aliases:** `S1A2` -- the column header used in the DDMORE bundle's `.ctl` `$INPUT` and the simulated dataset, with sibling columns `D1A2` and `H1A2` carried in the data file but dropped via `=DROP` in the source `.ctl`.
- **Example models:** `NA_NA_lidocaine.R` (DDMODEL00000281; binary derivation `S1A2_IND = as.integer(S1A2 == 3)` on the GX elimination rate constant K30).
- **Notes:** Specific scope because the integer codes for `S1A2` are paper-specific to the lidocaine BAST.dat study and the linked publication is not on disk for this extraction. The exact biological meaning of each integer level (0/1/2/3) is not fully reconstructable from the bundle alone -- the natural interpretation, given the column name encodes "CYP1A2" and the model attaches a sizeable positive K30 modifier of +0.853 to the level-3 cohort, is a CYP1A2-induction or smoking / inducer co-medication indicator. Sibling columns `D1A2` (donor / inhibitor?) and `H1A2` (host / inhibitor?) are dropped in the source `.ctl` so only the level-3 indicator is structurally identifiable from the surviving model code. If a future model needs a richer encoding of CYP1A2 modulation, register a separate canonical (e.g., `CYP1A2_IND`) rather than overloading `S1A2`.

### DOSE_HIGH_EFL (**canonical for high-dose eflornithine indicator**)
- **Description:** 1 = dose record is at the highest oral eflornithine dose level (>= 3000 mg/kg in the Jansson 2008 Sprague-Dawley rat single-dose study), 0 = oral dose at the lower levels (750, 1500, 2000 mg/kg) or any IV dose.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (750-2000 mg/kg oral, or IV at 375 / 1000 mg/kg).
- **Source aliases:** derived per dose record from the administered amount and route. Jansson 2008 modeled it as a categorical indicator on bioavailability for both enantiomers (Results paragraph; OFV drop -11.4).
- **Example models:** `Jansson_2008_eflornithine_rat.R`.
- **Notes:** Specific scope because the threshold (3000 mg/kg of body weight) and the bioavailability shifts (+14.6% for L-eflornithine, +32.8% for D-eflornithine relative to the 750-2000 mg/kg reference) are intrinsically tied to the Jansson 2008 dose-design and are not transferable to other drugs. Encoded as a binary because the paper's prose explicitly states linear and power dose-F relationships did not improve the fit. Per-subject indicator in the source data (each rat received exactly one oral dose); for multi-dose simulation, set the indicator per dose record.

### DOSE_PEG_300UGKG (**canonical for pegfilgrastim 300 ug/kg dose-cohort indicator**)
- **Description:** 1 = subject received pegfilgrastim at 300 ug/kg (the highest pegfilgrastim dose evaluated in Melhem 2018; n = 12 subjects: 8 healthy adult volunteers and 4 adult cancer patients on chemotherapy), 0 = filgrastim or pegfilgrastim at any dose other than 300 ug/kg. Encodes the empirically-observed low-clearance behaviour of the pegfilgrastim 300 ug/kg cohort as a subject-level cohort indicator on CLD (Melhem 2018 Results 'Model development' and Table 2 row CLD PEG 300 ug/kg = 0.107 L/h vs CLD PEG = 0.362 L/h). Subject-level (each subject enrolled in one dose-cohort per study); coexists with `FORM_GCSF_PEG` in the covariate frame and only takes non-zero effect when `FORM_GCSF_PEG = 1` (a filgrastim subject with `DOSE_PEG_300UGKG = 1` is nonsensical per the paper design).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (filgrastim, or pegfilgrastim at any dose other than 300 ug/kg).
- **Source aliases:**
  - `DOSE_PEG_300` -- paper-narrative descriptor for the pegfilgrastim 300 ug/kg cohort in `Melhem_2018_g_csf.R`.
- **Example models:** `Melhem_2018_g_csf.R`.
- **Notes:** Sibling of `DOSE_HIGH_EFL` (Jansson 2008 eflornithine highest-dose indicator) and `DOSE_400MG` / `DOSE_50MG` / `DOSE_70MG` / `DOSE_130MG` / `DOSE_260MG` (Jorga 2000 / other dose-level indicators). Specific scope because the low-clearance shift (0.107 / 0.362 = fold-decrease 0.295) is intrinsically tied to the Melhem 2018 pooled analysis. Melhem 2018 introduced the effect after graphical analysis of IIV suggested distinct clearance behaviour for the 300 ug/kg pegfilgrastim cohort; the paper notes the small sample size (n = 12) and physiological plausibility of the finding. Encoded as an additive log-scale shift on CLD that is meaningful only when `FORM_GCSF_PEG = 1`; a study of pegfilgrastim at other dose levels should set `DOSE_PEG_300UGKG = 0`. Ratified canonically alongside the Melhem 2018 g_csf extraction. Auto-approved sibling of the `DOSE_HIGH_<drug>` / `DOSE_<N>MG` cohort-indicator family.


### DOSE_HIGH_RIV (**canonical for high-dose rivaroxaban indicator**)
- **Description:** 1 = dose record is at the 20 mg-equivalent body-weight-adjusted rivaroxaban dose, 0 = the 10 mg-equivalent body-weight-adjusted rivaroxaban dose. The Willmann 2018 EINSTEIN-Jr phase I paediatric study (NCT01145859) dosed children with body-weight-adjusted amounts targeting adult exposures of either rivaroxaban 10 mg or 20 mg; the popPK model estimates a relative bioavailability F1 = 0.648 for the 20 mg-equivalent dose, with the 10 mg-equivalent dose anchored at F1 = 1 by definition.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (10 mg-equivalent body-weight-adjusted dose; relative bioavailability fixed to 1 in the source paper).
- **Source aliases:** derived per dose record from the EINSTEIN-Jr phase I dose level assigned to the cohort; the paper's Methods describe the dose stratification as "two dose levels, equivalent to adult doses of rivaroxaban 10 mg and 20 mg" (Willmann 2018 Methods / Modelling strategy).
- **Example models:** `Willmann_2018_rivaroxaban.R` (multiplicative effect on the depot bioavailability: `fdepot <- exp(lfdepot * DOSE_HIGH_RIV)` with `lfdepot = log(0.648)` per Willmann 2018 Table 1, RSE 9.03%; the dose-dependent reduction is consistent with the saturable-solubility behaviour of rivaroxaban reported in the adult patient popPK [reference 21 of Willmann 2018]).
- **Notes:** Specific scope because the 20 mg-equivalent vs 10 mg-equivalent dose stratification and the 0.648 relative-bioavailability estimate are intrinsically tied to the Willmann 2018 EINSTEIN-Jr phase I paediatric study. Drug-specific member of the `DOSE_HIGH_*` family alongside `DOSE_HIGH_EFL` (Jansson 2008 eflornithine high-dose indicator). Per-dose-occasion indicator in principle, although in the Willmann 2018 single-dose study each subject received exactly one rivaroxaban dose.

### DOSE_LOW_AMG221 (**canonical for low-dose AMG 221 indicator**)
- **Description:** 1 = dose record is the 3 mg oral AMG 221 dose in the Gibbs 2011 phase 1 study; 0 = the 30 or 100 mg oral AMG 221 dose. Sibling of `DOSE_HIGH_EFL`: same drug-suffixed dose-level indicator family, opposite direction of effect (the reduced-bioavailability tier is the flagged tier).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (30 or 100 mg oral AMG 221, where F1 = 1).
- **Source aliases:** derived per dose record from the administered amount. Gibbs 2011 Table III + footnote a: F1 = 0.546 at 3 mg vs F1 = 1 fixed at 30 or 100 mg.
- **Example models:** `Gibbs_2011_amg221.R`.
- **Notes:** Specific scope because the threshold (3 mg vs 30/100 mg oral AMG 221 as a suspension in healthy obese adults) and the -45.4% bioavailability shift are intrinsically tied to the Gibbs 2011 dose-design and are not transferable to other drugs. Gibbs 2011 Discussion attributes the reduced 3 mg bioavailability to a possible high-affinity intestinal-metabolism / transport process saturating at higher doses (Caco-2 permeability plus in vitro CYP3A metabolism with Km > 100 uM, so a 30 mg dose is expected to produce intestinal concentrations high enough to saturate intestinal metabolism); the paper flags an alternative Michaelis-Menten dose-F structure that was not fit because only three discrete dose levels were tested. Per-dose-record indicator; observation rows inherit the indicator from the preceding dose.

### MEAL_A (**canonical for Zvada 2010 meal-A high-fat English breakfast indicator**)
- **Description:** 1 = oral dose administered 30 min after Zvada 2010 meal A (a high-fat English breakfast); 0 = otherwise. Per Zvada 2010 Table 1 meal A consists of 2 rashers of bacon (20 g), 1 fried egg (50 g), 1 slice white toast (30 g) with butter (7 g) and marmalade (10 g), 2 cups decaffeinated coffee (400 ml) with full-cream milk (100 ml) and 2 teaspoons sugar (10 g); 18.9 g protein, 27 g fat, 38 g carbohydrate, 1,966 kJ, 627 g total weight. Distinct from the general `FED_HIGHFAT` because Zvada 2010 isolates four operationally-distinct meal compositions (A/B/C/D) each with its own bioavailability effect estimate and because the paper's Discussion explicitly attributes part of meal A's effect to eggs rather than total fat.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (fasted dosing, equivalent to Zvada 2010 meal E = 200 ml of water; the all-zero state of MEAL_A / MEAL_B / MEAL_C / MEAL_D).
- **Source aliases:** paper narrative "meal A" (Zvada 2010 Table 1 / Table 3).
- **Example models:** `Zvada_2010_rifapentine.R` (additive contribution to TVF = 1 * (1 + sum of per-meal fractional changes); RXF_A = +0.857, RSE 20.1%; Zvada 2010 Table 3).
- **Notes:** Specific scope because the operational meal definition is paper-specific. Mutually exclusive with `MEAL_B`, `MEAL_C`, `MEAL_D` - at most one of the four indicators can be 1 on a given dosing record; the fasted reference is all four = 0. Future rifapentine / antimycobacterial food-effect studies that reuse the same meal compositions (uncommon - meal compositions are typically protocol-specific) can extend this entry's example list. Distinct from `MEAL_FLAG` / `SNACK_FLAG` (time-varying intra-day flags) because `MEAL_A` is a per-dose-record indicator that selects between five mutually exclusive meal states.


### MEAL_B (**canonical for Zvada 2010 meal-B low-fat bulky maize-meal porridge indicator**)
- **Description:** 1 = oral dose administered 30 min after Zvada 2010 meal B (a low-fat bulky maize-meal porridge breakfast); 0 = otherwise. Per Zvada 2010 Table 1 meal B consists of 1.5 cups soft maize meal porridge (375 g cooked) with 3 teaspoons sugar (15 g), 1 cup decaffeinated coffee (200 ml) with full-cream milk (50 ml) and 1 teaspoon sugar (5 g); 6 g protein, 3 g fat, 66 g carbohydrate, 1,285 kJ, 645 g total weight.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (fasted dosing, equivalent to Zvada 2010 meal E).
- **Source aliases:** paper narrative "meal B" (Zvada 2010 Table 1 / Table 3).
- **Example models:** `Zvada_2010_rifapentine.R` (additive contribution to TVF; RXF_B = +0.327, RSE 40.4%; Zvada 2010 Table 3).
- **Notes:** Specific scope; mutually exclusive with `MEAL_A`, `MEAL_C`, `MEAL_D`. See `MEAL_A` notes for the per-dose-record indicator pattern.


### MEAL_C (**canonical for Zvada 2010 meal-C high-fat bulky maize-meal porridge with lard indicator**)
- **Description:** 1 = oral dose administered 30 min after Zvada 2010 meal C (a high-fat bulky maize-meal porridge breakfast with lard); 0 = otherwise. Per Zvada 2010 Table 1 meal C consists of 1.5 cups soft maize meal porridge (375 g cooked) with 3 teaspoons sugar (15 g) and 5 teaspoons of lard (25 g), 1 cup decaffeinated coffee (200 ml) with full-cream milk (50 ml) and 1 teaspoon sugar (5 g); 6 g protein, 28 g fat, 66 g carbohydrate, 2,229 kJ, 670 g total weight.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (fasted dosing, equivalent to Zvada 2010 meal E).
- **Source aliases:** paper narrative "meal C" (Zvada 2010 Table 1 / Table 3).
- **Example models:** `Zvada_2010_rifapentine.R` (additive contribution to TVF; RXF_C = +0.457, RSE 29.3%; Zvada 2010 Table 3).
- **Notes:** Specific scope; mutually exclusive with `MEAL_A`, `MEAL_B`, `MEAL_D`. Meal C and meal A have similar total fat content but the Zvada 2010 Discussion attributes the substantially smaller effect of meal C (+45.7%) versus meal A (+85.7%) to the absence of eggs in C - supporting the authors' egg-specific bioavailability hypothesis.


### MEAL_D (**canonical for Zvada 2010 meal-D low-fat high-fluid chicken noodle soup indicator**)
- **Description:** 1 = oral dose administered 30 min after Zvada 2010 meal D (a low-fat high-fluid chicken noodle soup breakfast); 0 = otherwise. Per Zvada 2010 Table 1 meal D consists of 2 cups reconstituted powdered chicken noodle soup (400 ml), 1 cup decaffeinated coffee (200 ml) with skim milk (50 ml) and 1 teaspoon sugar (5 g); 9 g protein, 4 g fat, 28 g carbohydrate, 774 kJ, 660 g total weight.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (fasted dosing, equivalent to Zvada 2010 meal E).
- **Source aliases:** paper narrative "meal D" (Zvada 2010 Table 1 / Table 3).
- **Example models:** `Zvada_2010_rifapentine.R` (additive contribution to TVF; RXF_D = +0.489, RSE 30.7%; Zvada 2010 Table 3).
- **Notes:** Specific scope; mutually exclusive with `MEAL_A`, `MEAL_B`, `MEAL_C`. Zvada 2010 Discussion notes that monosodium glutamate (MSG) in the chicken noodle soup may contribute to the effect by accelerating gastric emptying (per Boutry et al., Am J Physiol Endocrinol Metab 2011, on MSG and gastric emptying).


### CONMED_ATORVASTATIN_DOSE, CONMED_FLV_DOSE, CONMED_LOV_DOSE, CONMED_PRV_DOSE, CONMED_RSV_DOSE, CONMED_SMV_DOSE, CONMED_EZT_DOSE, CONMED_INH_DOSE, CONMED_TRAMADOL_DOSE (**canonical for daily dose of a co-administered named drug**)
- **Description:** Daily dose of the named drug (suffix = INN abbreviation: `ATORVASTATIN` atorvastatin (spelled out to avoid an `ATV` collision with atazanavir; see `CONMED_ATAZANAVIR`), `flv` fluvastatin, `lov` lovastatin, `prv` pravastatin, `rsv` rosuvastatin, `smv` simvastatin, `ezt` ezetimibe, `inh` isoniazid, `TRAMADOL` tramadol (spelled out to keep drug names unambiguous in the MBMA opioid-pain family)). 0 = the named drug is not part of the regimen for this study arm / subject; positive value = total daily dose. Captures the dose-response amplitude in MBMA or co-administered-drug PK/PD models where each drug arm contributes its own dose-effect curve.
- **Units:** mg/day for the statin / ezetimibe / tramadol series; mg/kg for `CONMED_INH_DOSE` (paper-specific unit, documented in `covariateData[[CONMED_INH_DOSE]]$units`).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** 0 (the drug is not given in this arm; equivalent to "this drug-arm contributes 0 to its dose-effect term").
- **Source aliases:** `DOSE_<drug>` legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename, Chen 2017 TB mouse pre-rename); aliases of the canonical `CONMED_<drug>_DOSE` form per the 2026-05-28 naming audit rename. `CONMED_ATV_DOSE` (legacy abbreviated form used briefly between the 2026-05-28 naming audit and the atazanavir-collision rename to `CONMED_ATORVASTATIN_DOSE`).
- **Example models:** `Vargo_2014_statins_ezetimibe_mbma.R` (each `CONMED_<statin>_DOSE` drives the corresponding statin's Hill / Emax dose-response curve; per-arm dose level in mg/day, default 0 outside the named statin arm; ezetimibe and statin arms combine via the sub-additive `gamma_int` term), `Chen_2017_TB_MTP_GPDI_mouse.R` (`CONMED_INH_DOSE` drives the isoniazid CL adjustment: `cl_inh = cl_inh_lowdose * (1 - slope_inh * (CONMED_INH_DOSE - 12.5))`), `Mercier_2014_tramadol_tapentadol_mbma.R` (`CONMED_TRAMADOL_DOSE` drives the tramadol Emax-in-dose term in the extent-of-reduction R with ED50 = 184 mg/day).
- **Notes:** The `CONMED_<drug>_DOSE` shape replaces the earlier `DOSE_<drug>` / `DOSE_<drug>_<unit>` names (which conflated a covariate with a dose-amount column). The atorvastatin slot is spelled out as `CONMED_ATORVASTATIN_DOSE` rather than `CONMED_ATV_DOSE` to keep the `ATV` abbreviation reserved for the binary atazanavir coadministration indicator (`CONMED_ATAZANAVIR`, source alias `ATV`) used in HIV-PK extractions like ArabAlameddine 2012 raltegravir. Tramadol is spelled out for the same reason (no established `TRM` abbreviation collision, but the full drug name keeps the MBMA opioid-pain family unambiguous alongside the sibling `TAPENTADOL` treatment-arm indicator). New co-medication dose-effect MBMA models should reuse this family and add the appropriate `<drug>` INN abbreviation; spell out drug names whenever the natural 3-4 letter abbreviation would collide with another `CONMED_` entry. The units field is per-paper.


### ON_TREATMENT (**canonical for generic on-treatment (active-vs-placebo) indicator**)
- **Description:** Generic binary treatment-arm indicator. 1 = subject is in the active-drug arm (or, for time-varying use, the subject is currently on treatment at this record), 0 = subject is in the placebo arm (or, for time-varying use, currently off treatment / pre-treatment / placebo-controlled run-in). Per-subject and time-fixed by default in randomized parallel-group trials; admits a time-varying interpretation for cross-over, run-in, or treatment-suspension designs where the indicator switches at well-defined per-record times. Used in non-PK / non-exposure disease-progression and PD models where the "drug effect" enters the structural equations as a categorical on/off switch rather than as an exposure-driven response, and where the trial design pools or abstracts away dose-level differences within the active arm.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (placebo arm / off treatment).
- **Source aliases:**
  - `Trt` (Lee 2011 paper notation: `Trt = (0, 1, ...)`, where the source paper notes the indicator could in principle take more than two levels but only the binary placebo-vs-active contrast is used in the actual analyses) -- used in `Lee_2011_parkinson_progression.R`.
- **Example models:** `Lee_2011_parkinson_progression.R` (additive shifts on the placebo disease-progression slope and on the placebo short-term symptomatic effect: `slope = beta0 + beta1 * ON_TREATMENT + etaslope` and `symeff = gamma0 + gamma1 * ON_TREATMENT + etasymeff`; default `ini()` parameter values are from the TEMPO study rasagiline arm, so `ON_TREATMENT = 1` predicts the rasagiline-active disease trajectory and `ON_TREATMENT = 0` predicts the placebo trajectory).
- **Notes:** Distinct from `TRT` (which is the Novakovic 2017 cladribine-cohort categorical indicator with three levels and a paper-specific 1-vs-2 cohort labelling) and from `DRUG_ORMU` / `DRUG_PRED` / `DRUG_OBI` (which are head-to-head drug-product comparator indicators carrying the contrast between two named active drugs in the same trial). Use `ON_TREATMENT` only when the structural model has no drug-specific PK / exposure and the active arm enters as a pooled on/off switch. When extracting a head-to-head comparator paper (drug A vs drug B) register a sibling `DRUG_<X>` canonical instead. When the source paper differentiates multiple active dose levels with distinct parameters, register a categorical indicator with paper-specific level labelling (e.g., `TRT` for the cladribine cohorts) rather than overloading `ON_TREATMENT`. Per-model `covariateData[[ON_TREATMENT]]$notes` should document which drug the active arm corresponds to in that file's default `ini()` values, the trial design (parallel-group vs cross-over), and any time-varying interpretation. Ratified canonically alongside the Lee 2011 Parkinson's disease-progression extraction (the existing `TRT` canonical's Notes already directed future generic on-treatment indicators to register this canonical rather than reusing `TRT`).


### PLACEBO (**canonical for placebo-arm membership indicator**)
- **Description:** Binary indicator of randomized placebo-arm membership: 1 = subject is in the placebo arm, 0 = active-treatment arm. Time-fixed per subject. Used to select placebo-arm-specific structural parameters (e.g., a separately estimated baseline) in models that ALSO carry an exposure-driven active-drug effect, so the placebo indicator is a covariate on a structural parameter rather than the drug-effect switch itself.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (active-treatment arm)
- **Source aliases:** none.
- **Example models:** `Hansson_2013_sunitinib_dbp.R` (switches the typical baseline diastolic blood pressure `dbp0` from 71.8 mmHg in the active arm to 77.6 mmHg in the placebo run-in arm via `exp(ldbp0 + e_placebo_dbp0 * PLACEBO)`; the sunitinib AUC drug effect on `kin` is modelled separately and exposure-driven).
- **Notes:** Ratified 2026-06-28. Distinct from `ON_TREATMENT` (the generic pooled active-vs-placebo on/off switch used when the active arm REPLACES exposure in the structural model, reference 0 = placebo): `PLACEBO` has the opposite polarity (1 = placebo) and is used alongside, not instead of, an exposure-driven drug effect, marking the placebo subpopulation for an arm-specific structural parameter. If a model needs the pooled on/off drug switch, use `ON_TREATMENT` (= `1 - PLACEBO`) instead.

### SEMESTER (**canonical for paired-season indicator (winter or spring vs summer or fall)**)
- **Description:** Calendar paired-season indicator: 1 = winter or spring (December 21 to June 19), 0 = summer or fall (June 20 to December 20). Time-fixed per subject (or per simulation) based on the calendar date at which the observation occurred.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (summer or fall)
- **Source aliases:**
  - `SEMESTER1` -- used in Gonzalez-Sales 2015 testosterone circadian model with the same encoding (winter or spring = 1, summer or fall = 0).
- **Example models:** `GonzalezSales_2015_testosterone.R` (+8.09% multiplicative effect on Base; Table III).
- **Notes:** Gonzalez-Sales 2015 split season into a paired `SEMESTER1` / `SEMESTER2` covariate (`SEMESTER1 = 1` if winter or spring; `SEMESTER2 = 1` if fall or winter), keeping only `SEMESTER1` in the final model. The canonical column omits the `1` suffix because the second indicator was not retained; the source-paper naming is recorded as a source alias. Calendar boundaries are reported verbatim in the paper Methods (winter: Dec 21 - Mar 19; spring: Mar 20 - Jun 19; summer: Jun 20 - Sep 21; fall: Sep 22 - Dec 20). Scope: specific until a second endogenous-rhythm model adopts the same encoding; at that point promote to `general`. Distinct from `SEASON2` (RSV-second-exposure indicator) which has different semantics.


### CEN (**canonical for Wahlby 2004 pefloxacin study-centre indicator**)
- **Description:** Binary study-centre indicator carried over from the Karlsson MO, Sheiner LB 1993 (J Pharmacokin Biopharm 21(6):735-750) pefloxacin analysis that Wahlby 2004 re-fit. The source paper does not specify which of the two recruiting centres corresponds to CEN = 1 vs CEN = 0; the indicator captures an unexplained between-centre shift in clearance.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (one of the two recruiting centres; the source paper does not state which).
- **Source aliases:**
  - `CEN` -- Wahlby 2004 source-column convention; used in `Wahlby_2004_pefloxacin.R`.
- **Example models:** `Wahlby_2004_pefloxacin.R` (multiplicative effect on CL via exp[0.19 * CEN]; CEN = 1 increases CL by ~21 percent relative to CEN = 0).
- **Notes:** Specific scope; the underlying centre identity is paper-defined and not generalisable. Users assembling a virtual cohort should treat CEN as a sensitivity covariate or fix it at 0 (the reference centre) when between-centre exploration is not relevant. Ratified canonically alongside the Wahlby 2004 extraction.


### FORM_AXI_XLI (**canonical for axitinib Form XLI vs Form IV crystal polymorph indicator**)
- **Description:** 1 = subject received axitinib as crystal polymorph Form XLI (the marketed commercial polymorph used in the approved tablet); 0 = subject received axitinib as crystal polymorph Form IV (the earlier Phase I polymorph and the typical-value bioavailability reference). Per-dose-occasion categorical indicator: a single subject may carry both indicator values across the within-subject crossover formulation arms in studies 7 and 9.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Form IV; the typical-value F reference in Garrett 2014 Table 3, F = 0.465 in the fed state).
- **Source aliases:**
  - `FORM` -- used in `Garrett_2014_axitinib.R` (Garrett 2014 Methods 'Statistical analysis' and Results 'Full model and final model' describe the formulation as a categorical {Form IV, Form XLI} with Form XLI = 1 as the test polymorph and Form IV = 0 as the typical-value reference).
- **Example models:** `Garrett_2014_axitinib.R` (linear-proportional effect on bioavailability per Garrett 2014 Table 3 final estimates: `f = exp(lfdepot) * (1 + e_fast_f * (1 - FED)) * (1 + e_xli_f * FORM_AXI_XLI)` with `e_xli_f = -0.150` (15% reduction in F for Form XLI relative to Form IV in the fed state); Form XLI has no retained effect on ka, CL, Vc, Q, or Vp).
- **Notes:** Specific scope because the Form XLI vs Form IV crystal polymorph contrast is tied to the axitinib drug-product-development comparison in Garrett 2014. Crystal polymorph difference (same dosage form, same drug substance, different crystal lattice with different solubility / dissolution behaviour) rather than a dosage-form difference (tablet vs capsule vs solution) -- distinct from `FORM_TABLET`, `FORM_CAPSULE`, `FORM_SOLUTION`, and the rest of the dosage-form `FORM_*` family. Mirrors the existing `FORM_SAR_DP2` (sarilumab drug-product 2), `FORM_ISA_P2F2` (isatuximab P2F2 drug material), `FORM_LINAG_TAB1` (linagliptin tablet 1), `FORM_VISMO_PHASEI` (vismodegib Phase I capsule), `FORM_ITR_SUBA` (SUBA-itraconazole vs Sporanox capsule), `FORM_TAC_IR` (tacrolimus IR vs prolonged release), `FORM_DOX_DORYX_MPC` (Doryx MPC tablet), and `FORM_THEO_APNECUT` (theophylline Apnecut) entries under the `FORM_*` family of drug-specific drug-product-version / polymorph indicators. Set to 0 to simulate the Form IV early-phase reference; set to 1 to simulate the Form XLI marketed product (the routine clinical-use default). Ratified canonically on 2026-06-16 alongside the Garrett 2014 axitinib extraction.

### STUDY_B1831090 (**canonical for Abrantes 2017 moroctocog study B1831090 cohort indicator**)
- **Description:** 1 = subject enrolled in study B1831090 (phase I bioequivalence study of Refacto; n = 18, severe and moderately severe hemophilia A, rich sampling, CSA assay) of the Abrantes 2017 pooled population PK analysis; 0 = any other study in the 13-study pool (B1831003, B1831004, B1831015, B1831053, B1831054, B1831061, B1831066, B1831067, B1831068, B1831070, B1831071, B1831077). Used to switch the typical CL magnitude downward for the B1831090 cohort while keeping the rest of the pooled-population PK parameters applicable to the remaining 12 studies.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (any non-B1831090 study in the Abrantes 2017 pool).
- **Source aliases:**
  - `STUD` -- used in `Abrantes_2017_moroctocog.R` (Abrantes 2017 Table 2 footnote b: study indicator on CL).
- **Example models:** `Abrantes_2017_moroctocog.R` (multiplicative effect on CL: `(1 - 0.347 * STUDY_B1831090)` so B1831090 subjects have ~34.7% lower CL than the other 12 studies; Abrantes 2017 Table 2 also reports a 95% CI of -40.7% to -24.2%).
- **Notes:** Specific scope because the contrast is tied to the Abrantes 2017 pooled-analysis design. Subject-level / time-fixed; set once from the trial identifier on each subject record. Inclusion in the model allows the typical PK parameters to describe the remaining 12 studies, with B1831090 captured by the indicator (Abrantes 2017 Discussion). Ratified canonically on 2026-06-21 alongside the Abrantes 2017 moroctocog extraction.

### TINF (**canonical for the duration of an intravenous infusion**)
- **Description:** Duration of the intravenous infusion of the dose record, in hours. Continuous. Used as a *covariate* when a source analysis estimates an effect of the administration protocol on PK parameters; this is separate from, and in addition to, the physical infusion duration carried on the dose record itself.
- **Units:** h
- **Type:** continuous
- **Scope:** general
- **Reference category:** none (continuous). Source analyses typically enter it as a linear deviation from a paper-specific reference duration, so record the reference in the model's `covariateData[["TINF"]]$notes` rather than assuming a library-wide default.
- **Source aliases:**
  - `Tinf` -- used in `Schreib_2024_busulfan.R` (Table 3 covariates `theta_V3` and `theta_k6`, both entering as `(Tinf - 3 h)`).
- **Example models:** `Schreib_2024_busulfan.R` (exponential effects on both the central volume, `exp(0.226 * (TINF - 3))`, and the elimination rate constant, `exp(-0.161 * (TINF - 3))`; the center changed its standard busulfan infusion from 3 h to 4 h in October 2014, so only two values occur in that cohort and the covariate term is 0 or 1).
- **Notes:** Matches the near-universal pharmacometric symbol T_inf. **Supply this as an explicit data column even though the event table already encodes the infusion duration** -- rxode2 takes the physical infusion duration from the `dur` / `rate` fields of the dose record and does not expose it to `model()`, so a covariate effect on infusion duration needs its own column. Registered with general scope: the concept is protocol-independent even though any particular reference duration is paper-specific. Opposite-signed effects on volume and on the elimination rate constant (as in Schreib 2024) are a recognised signature of a one-compartment model absorbing a distribution phase that a shorter infusion exposes -- clearance is then nearly independent of infusion duration; note that interpretation when it applies rather than reading the two effects as independent physiology. Ratified canonically on 2026-08-05 (task `oare_PMC11154452` sidecar question q2, answer A) alongside the Schreib 2024 busulfan extraction.

## Occasion / period (IOV)

### OCC (**canonical for the integer-valued occasion / period column**)
- **Description:** Integer-valued occasion / period indicator for inter-occasion-variability (IOV) modelling. Values `1`, `2`, ..., `N` identify the occasion to which each observation belongs (typically a dosing visit, study period, or sampling occasion). Time-varying within subject; constant within an occasion.
- **Units:** (count)
- **Type:** categorical
- **Scope:** general
- **Reference category:** n/a -- `OCC` is decomposed inside `model()` into mutually-exclusive binary indicators, e.g., `oc1 <- (OCC == 1)`, `oc2 <- (OCC == 2)`, ..., that are then multiplied against per-occasion `eta*` slots.
- **Source aliases:**
  - `OCC` -- used in `Jonsson_2011_ethambutol.R` (DDMODEL00000220 NMTRAN `$INPUT` column; values 1..4).
- **Example models:** `Jonsson_2011_ethambutol.R` (4-occasion IOV on log-CL; `cl <- exp(lcl + etalcl + oc1 * etalcl_oc1 + oc2 * etalcl_oc2 + oc3 * etalcl_oc3 + oc4 * etalcl_oc4) * (WT/50)^0.75`, where each `etalcl_oc<k>` is a separate `~ fix(0.127)` after the first to encode NONMEM `$OMEGA BLOCK(1) SAME`), `Aregbe_2012_alvespimycin.R` (5-occasion BOV on Q2 and V1), `Oosten_2016_fentanyl.R` (10-occasion IOV on transdermal Ka; only `OCC >= 1` records carry IOV, sc / non-transdermal records pass `OCC = 0` so all indicators zero out), `Jonsson_2011_ethambutol_ddmore.R` (4-occasion IOV on log-CL; `cl <- exp(lcl + etalcl + oc1 * etalcl_oc1 + oc2 * etalcl_oc2 + oc3 * etalcl_oc3 + oc4 * etalcl_oc4) * (WT/50)^0.75`, where each `etalcl_oc<k>` is a separate `~ fix(0.127)` after the first to encode NONMEM `$OMEGA BLOCK(1) SAME`), `Chen_2023_nemonoxacin.R` (2-occasion IOV on log-CL distinguishing the single-dose occasion (`OCC = 1`) from steady state 72 h after the first of multiple doses (`OCC = 2`); `iov_cl <- oc1 * etaiov_cl_1 + oc2 * etaiov_cl_2` with `etaiov_cl_2 ~ fix(0.017)` encoding the shared `pi^2` of NONMEM `$OMEGA BLOCK(1) SAME`), `Waterhouse_2024_vedolizumab.R` (7-occasion IOV on log-CL across the phase 3 VEDO-3035 vedolizumab dosing schedule; occasions 2-7 `~ fix(0.0315)` per NONMEM `$OMEGA BLOCK(1) SAME`, and records preceding the first dose take `OCC = 1`).
- **Notes:** `OCC` is the recommended canonical for new IOV-using models -- the binary `ooc1..oocN` indicators below remain canonical for legacy / pre-existing models that ship the data already-decomposed. Ratified canonically on 2026-05-06.

### ooc1, ooc2, ooc3, ooc4 (**canonical for mutually-exclusive crossover occasion indicators**)
- **Description:** Mutually exclusive occasion indicators for a crossover / multi-period design. Exactly one is 1 per observation.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Example models:** `Xie_2019_agomelatine.R`.
- **Notes:** Lower case preserved from source file. Pre-existing legacy form; new models should prefer the integer-valued `OCC` canonical above and decompose into binary indicators inside `model()`.

### MONTH1 (**canonical for first-month-of-treatment landmark indicator**)
- **Description:** Binary within-subject landmark indicator: 1 = the observation falls within the first 30 days after treatment initiation, 0 = subsequent months. Time-varying within subject -- gates a transient step change in typical-value CL that the source paper attributes to higher Erwinia asparaginase clearance in the first month of pediatric ALL therapy.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (month 2 onwards).
- **Source aliases:** none known; Sassen 2017 reports the first-month-vs-after contrast as a single multiplicative coefficient on TVCL.
- **Example models:**
  - `Sassen_2017_crisantaspase.R` (multiplicative shift on CL: `cl <- exp(lcl + etalcl) * (WT/70)^0.75 * (1 + e_month1_cl * MONTH1)` with `e_month1_cl = 0.14`, encoding the 14% higher CL in the first month of treatment relative to subsequent months).
- **Notes:** Specific scope because the 1-month (30-day) cutoff is tied to Sassen 2017's pediatric Erwinia asparaginase pharmacology (transient higher CL early in treatment; mechanism not established in the source paper). Future studies that test a similar within-subject step change with a different cutoff (e.g., 2 weeks or 6 weeks) should register a new canonical name. Distinct from `OCC` and `ooc<n>` (which decompose multi-occasion sampling for IOV), from `DAY14` (which uses a 14-day cutoff for malnutrition-recovery contrasts), and from `CYCLE` (which is a dose-number counter, not a single binary landmark). Data assemblers can derive `MONTH1 = as.integer(time_post_treatment_start_days < 30)` for a regularly-sampled multi-month study. Ratified canonically on 2026-05-20 alongside the Sassen 2017 extraction.

### DAY14 (**canonical for day-14-post-treatment-initiation landmark indicator**)
- **Description:** Binary within-subject landmark indicator: 1 = the observation falls on or after day 14 of treatment (post-nutritional-rehabilitation steady state in the Archary 2019 / MATCH trial of severely malnourished HIV-infected children), 0 = the observation falls before day 14 (acute / pre-rehabilitation baseline; e.g., day 1 of antiretroviral treatment). Time-varying within subject -- gates a step change in typical-value PK parameters that the source paper attributes to nutritional recovery + auto-induction of hepatic metabolism over the first ~2 weeks of treatment.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (day 1 / pre-rehabilitation).
- **Source aliases:** none known; Archary 2019 reports the day-1-vs-day-14 contrast directly in the parameter table (separate typical-value rows for "day 1" and "day 14").
- **Example models:**
  - `Archary_2019_abacavir.R` (multiplicative additive shift on CL/F: `cl <- exp(lcl + etalcl) * (WT/7)^0.75 * (1 + e_day14_cl * DAY14)` with `e_day14_cl = 0.760`, encoding the day-1 typical CL/F = 3.33 -> day-14 CL/F = 5.86 L/h per 7 kg step).
  - `Archary_2019_lamivudine.R` (multiplicative additive shift on ka: `ka <- exp(lka) * (1 + e_day14_ka * DAY14) * exp(etalka)` with `e_day14_ka = 0.133`, encoding the day-1 typical ka = 0.30 -> day-14 ka = 0.34 /h step).
- **Notes:** Specific scope because the day-14 cutoff is tied to the MATCH-trial nutritional-rehabilitation timeline (severely malnourished children, two-week re-feeding window per WHO guidelines); future studies that test a similar within-subject step change with a different cutoff (day 7, day 28, etc.) should register a new canonical (e.g., `DAY28`). Distinct from `OCC` and `ooc<n>` (which decompose multi-occasion sampling for IOV), from `TRT_PHASE` (which gates active-vs-baseline study-phase contributions), and from `EARLY_ART` (which is a between-subject randomization-arm indicator, not a within-subject landmark). Data assemblers can derive `DAY14 = as.integer(time_post_treatment_start_days >= 14)` for a regularly-sampled multi-day study. Ratified canonically on 2026-05-08.

### CYCLE (**canonical for dose-number / treatment-cycle counter**)
- **Description:** Dose-number / treatment-cycle counter (1 = first dose or cycle, 2 = second, ...). Integer count, time-varying across a multi-dose / multi-cycle treatment course, incremented at each new administration.
- **Units:** (count)
- **Type:** count
- **Scope:** general
- **Reference category:** n/a -- used either with a power-covariate form `CYCLE^Fm` (Fm typically negative) to capture cycle-over-cycle decline in a derived quantity such as ADC-to-payload conversion fraction (Li 2017 brentuximab vedotin), or with a piecewise indicator `CYCLE == 1 vs CYCLE >= 2` to capture a step change in PK between the first and subsequent administrations (Hong 2025 datopotamab deruxtecan; Huynh 2026 VRC07-523LS).
- **Source aliases:** `CYCLE` -- used in `Li_2017_brentuximab.R`, `Hong_2025_datopotamab.R`, `Lu_2022_patritumab.R`, and `Huynh_2026_VRC07523LS.R` with the same canonical name.
- **Example models:**
  - `Li_2017_brentuximab.R` (exponent on the fraction of ADC that converts to MMAE by proteolytic degradation, Fm = -0.261, to reflect tumor-burden reduction across successive treatment cycles).
  - `Hong_2025_datopotamab.R` (cycle-1 vs cycle-2+ piecewise scaling Factor1 = 0.696 on the DAR equation that drives DXd formation rate from total Dato-DXd elimination).
  - `Lu_2022_patritumab.R` (cycle-1 vs cycle-2+ piecewise scaling Factor1 = theta = 0.648 on the payload-to-intact-drug ratio PIR that scales DXd release rate from intact ADC).
- **Notes:** Must be >= 1 throughout (`CYCLE^Fm` is undefined at 0; the piecewise form requires `CYCLE` to be a positive integer at every observation row). Distinct from `ooc<n>` binary-occasion indicators: `CYCLE` is an integer count, not a mutually-exclusive set of indicator columns. Data-assembly helper: set `CYCLE = floor((TIME - TIME_FIRST_DOSE) / cycle_length_days) + 1` for a fixed-interval dosing regimen.


### T_ENTRY (**canonical for per-subject study-entry time on the model integration axis**)
- **Description:** Per-subject study-entry time, expressed on the same integration-time axis the model is solved on (years for AD disease-progression models). The dataset's `TIME` column carries the integration-time variable (with `TIME = 0` the integration origin, typically well before any subject's disease onset); `T_ENTRY` records, per subject, the integration-time value at which the subject's first observation falls. Used in models whose pre-study and post-study time semantics differ -- e.g., a disease-progression activation function defined on global disease time plus a placebo-effect term defined on time-since-study-entry need access to both reference clocks within the same `model()` block.
- **Units:** year (or whatever time unit the model's `units$time` field declares)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- `T_ENTRY` is a per-subject time-offset covariate that anchors the time-since-study-entry clock used by post-entry-only model terms (placebo / learning effects, study-design transient drops).
- **Source aliases:** none standardized; this canonical originates with the Delor 2013 AD disease-progression extraction. The source paper's NONMEM dataset handles the global / study-time split internally (the dataset is staged with TIME = study time and the model uses a fixed offset to align with DOT); when porting to rxode2 / nlmixr2 the per-subject offset is exposed as a covariate so simulation event tables can carry it explicitly. Berkhout 2015 EPIC placebo-arm osteoporosis QSP uses the same pattern with the source NONMEM column `STDA` (start day of placebo treatment relative to menopause onset) mapped to `T_ENTRY`.
- **Example models:** `Delor_2013_alzheimer.R` (placebo-term clock: `t_pl_raw <- time - T_ENTRY; post_entry <- t_pl_raw > 0; placebo_term <- pl_indiv * (1 - exp(-kpl_indiv * t_pl_raw * post_entry)) * post_entry`), `Berkhout_2015_osteoporosis_placebo_qsp.R` (PCa placebo function gated by `t >= T_ENTRY` with delayed-onset / slow-offset dip in the RANK-RANKL-OPG occupancy factor).
- **Notes:** Scope: specific because the variable is paper-domain-bound -- it only makes sense for models that operate on a global disease-time axis distinct from per-subject study-entry timing. For a typical-value reproduction the user supplies `T_ENTRY` per subject in the simulation event table; a reasonable construction is `T_ENTRY = DOT_individual + a few years of established disease` so the patient is observed during disease progression (see the Delor 2013 vignette for the construction used to reproduce Figures 2-4). Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction; second registered use added 2026-07-24 with the Berkhout 2015 EPIC placebo-arm osteoporosis QSP extraction.

### TCLOCK (**canonical for wall-clock time-of-day covariate feeding a 24-hour periodic (circadian) function**)
- **Description:** Wall-clock time of day at each observation, expressed on a 0-24 hour scale. Distinct from the integration-time axis `time` (which carries time after first dose or another simulation-anchored reference) -- `TCLOCK` records the local hour of day at which a sample was drawn / an observation was recorded so a 24-hour periodic function of the form `cos(2*pi * (TCLOCK + 24 - Tmax) / 24)` can be evaluated at each record. Time-varying per record if samples are drawn at multiple times of day within a subject; per-subject time-fixed for study designs whose samples are always drawn at a consistent local time (e.g., morning pre-dose).
- **Units:** hour of day (0-24)
- **Type:** continuous
- **Scope:** general
- **Reference category:** n/a -- enters as an argument to a periodic function whose period is 24 hours. The natural per-model reference is the time-of-maximum parameter `Tmax` in that periodic function; setting `TCLOCK = Tmax` collapses the circadian factor to its peak, and `TCLOCK = Tmax + 12` (mod 24) collapses it to its trough.
- **Source aliases:**
  - `tclock` / `clocktime` -- Hoefman 2021 (`Hoefman_2021_asp8232.R`) equation 3 notation for the wall-clock time argument of the eGFR CysC circadian amplitude term.
- **Example models:** `Hoefman_2021_asp8232.R` (drives the eGFR CysC circadian oscillation `(1 + Ampli * cos(2*pi * (TCLOCK + 24 - tmax) / 24))` with `Ampli = 0.0783` (theta_32) and `tmax = 10.5` h (theta_33)).
- **Notes:** General scope because any circadian / diurnal PD model whose within-day rhythm is parameterised by a periodic function of the observation's clock time will need this canonical -- the concept generalises across drugs, biomarkers, and disease areas. Distinct from `time` (the integration-time axis of the model, on which ODE progression is solved) and from `T_ENTRY` (per-subject study-entry offset used to align a per-subject placebo clock to a global disease-time clock). Data-assembly helper: if the source dataset carries a time-of-day column encoded as HH:MM:SS or minutes-since-midnight, convert to decimal hours before populating `TCLOCK`. If a subject's records are drawn at a consistent time of day and `TCLOCK` is unknown per record, a sensible default is `TCLOCK = Tmax` (peak of the circadian wave) or the paper's stated typical sampling time. Ratified canonically on 2026-07-08 alongside the Hoefman 2021 ASP8232 PD extraction.
## Mixture / latent-class indicators

### MIX_PDI (**canonical for binary mixture-model class indicator: PDI inhibition-of-synthesis vs PDS stimulation-of-elimination thrombocytopenia mechanism**)
- **Description:** Per-subject latent mixture-model class indicator from the Tsuji 2017 linezolid platelet PKPD model. 1 = subject classified to the PDI mechanism (linezolid inhibits platelet synthesis via a linear effect on the proliferation rate of the formation compartment); 0 = subject classified to the PDS mechanism (linezolid stimulates platelet elimination via an Emax effect on the circulating-compartment first-order loss rate). Not a measured clinical covariate -- the mixture assignment is a posterior class assignment from the NONMEM `$MIXTURE` block (Tsuji 2017 Equation 8 and Methods, "Mixture model" subsection).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (PDS = stimulation of elimination; the 3% minority class in the source cohort).
- **Source aliases:** `MIXTURE` -- NONMEM `$MIXTURE` block class index in the Tsuji 2017 estimation run (component 1 = PDI, component 2 = PDS); the binary indicator is `MIX_PDI = as.integer(MIXTURE == 1)`.
- **Example models:** `Tsuji_2017_linezolid.R` (gates the drug-effect term that enters the platelet ODE chain: `MIX_PDI = 1` activates `SLOPE * Cc` on the formation rate, `MIX_PDI = 0` activates `SMAX * Cc / (SC50 + Cc)` on the circulating-compartment elimination rate).
- **Notes:** The population probability of `MIX_PDI = 1` is the estimated population mixture fraction `FPOP_inhibit = 0.969` (Tsuji 2017 Table 2; 95% CI 0.867-1.00, 78/80 patients in the source dataset). For typical-value simulation set `MIX_PDI = 1` (dominant clinical phenotype, slower 2-week nadir; Figure 5 left panel) or `MIX_PDI = 0` (rare immune-mediated-like phenotype, faster 2-day nadir; Figure 5 right panel). For population simulation, draw `MIX_PDI ~ Bernoulli(0.969)` per subject. Scope: specific because the binary semantics are tied to Tsuji 2017's two-mechanism platelet model; mixture indicators from future papers that share the same biological dichotomy may extend this entry, but mixture indicators from unrelated dichotomies should register a new canonical name (e.g., `MIX_FAST_ELIM` for a fast/slow eliminator mixture).

### MIX_FAST_ELIM (**canonical for binary mixture-model class indicator: fast (high-CL) vs slow (low-CL) eliminator subpopulation**)
- **Description:** Per-subject latent mixture-model class indicator from a `$MIXTURE` NONMEM block describing a bimodal apparent-clearance distribution. 1 = subject classified to the high-CL / fast-eliminator subpopulation; 0 = subject classified to the low-CL / slow-eliminator subpopulation. Not a measured clinical covariate -- the mixture assignment is a posterior latent-class index from the NONMEM `$MIXTURE` block. One assignment per subject, time-fixed.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (low-CL / slow-eliminator subpopulation).
- **Source aliases:**
  - `MIXTURE` -- NONMEM `$MIXTURE` block class index in the Morris 2011 estimation run (component 1 = high-CL with P = 0.251, component 2 = low-CL with P = 0.749); the binary indicator is `MIX_FAST_ELIM = as.integer(MIXTURE == 1)`.
- **Example models:** `Morris_2011_telapristone.R` (selects the telapristone CL/F typical value between the high-CL anchor `exp(lcl_pop1) = 11.6 L/h` and the low-CL anchor `exp(lcl_pop2) = 3.34 L/h`; the shared IIV `etalcl ~ 0.200` is applied multiplicatively to whichever typical the mixture-class selects), `Wilkins_2011_isoniazid.R` (gates the typical apparent CL/F via the power-form covariate effect `e_mix_fast_elim_cl` on CL/F: `cl = exp(lcl + e_mix_fast_elim_cl * MIX_FAST_ELIM + etalcl) * (WT/70)^0.75 * (1 + e_hiv_pos_cl * HIV_POS)` with `lcl = log(9.70)` (slow-eliminator reference) and `e_mix_fast_elim_cl = log(21.6/9.70) approx 0.8005`).
- **Notes:** Pre-named by the `MIX_PDI` register entry's Notes block ("mixture indicators from unrelated dichotomies should register a new canonical name (e.g., `MIX_FAST_ELIM` for a fast/slow eliminator mixture)"). Population probability of `MIX_FAST_ELIM = 1` is recorded per-model in `covariateData[[MIX_FAST_ELIM]]$notes`; in Morris 2011 it is 0.251 (Table II, "Probability" row, RSE 61.0%). The Morris 2011 Discussion attributes the bimodal CL distribution to polymorphic CYP3A5 (functional CYP3A5 present in 10-40% of Caucasians, ~50% of African Americans, ~33% of Asians) but does not test the genotype hypothesis directly; the mixture indicator is therefore a latent-class label, not a CYP3A5-genotype indicator. For typical-value simulation set `MIX_FAST_ELIM = 1` (fast eliminator; lower steady-state exposure, ~12 h elimination half-life per Morris 2011 Results) or `MIX_FAST_ELIM = 0` (slow eliminator; higher steady-state exposure, ~35 h half-life). For population simulation, draw `MIX_FAST_ELIM ~ Bernoulli(0.251)` per subject. Scope: general because the fast/slow-eliminator dichotomy is a recurring popPK concept across drugs metabolized by polymorphic enzymes (CYP2C9*2/*3, CYP2C19*2, CYP3A5*3, NAT2 slow/fast acetylators); future popPK papers fitting a `$MIXTURE` on CL should re-use this canonical rather than introducing per-drug-specific `MIX_<drug>_FAST` names. The reference category (= 0 = slow eliminator) is chosen so the binary numerically matches the paper's mixture-indicator orientation. Ratified canonically on 2026-06-09 alongside the Morris 2011 telapristone extraction.

### MIX_LARGE_BASE (**canonical for binary mixture-model class indicator: larger-baseline vs smaller-baseline tumor-lesion subpopulation**)
- **Description:** Per-subject latent mixture-model class indicator from the Schindler 2017 imatinib-GIST liver-metastases joint tumor-dynamics model. 1 = subject classified to the larger-baseline subpopulation (typical lesion 1 baseline 76.6 mm MTD / 161 mL Vactual / 187 mL Vellipsoid; estimated population probability Ppop1 = 0.348 in Schindler 2017 Table 2 row 'Ppop1'); 0 = subject classified to the smaller-baseline subpopulation (typical lesion 1 baseline 20.9 mm MTD / 3.45 mL Vactual / 3.93 mL Vellipsoid). Not a measured clinical covariate -- the mixture assignment is the per-subject latent-class index from a NONMEM `$MIXTURE` block describing the observed bimodal distribution of baseline tumor sizes (Schindler 2017 Methods, "Maximum transaxial diameter, actual volumes, and ellipsoidal volume models" subsection: "Semiparametric distributions and mixture models were investigated to describe the observed bimodal distribution in baseline MTD, Vactual, and Vellipsoid"). One assignment per subject is shared across the three size models (MTD, Vactual, Vellipsoid) and across the subject's up-to-two lesions; density (`D0`) is not mixture-indexed.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (smaller-baseline subpopulation; majority class at 65.2 % of the source cohort).
- **Source aliases:** `MIXTURE` -- NONMEM `$MIXTURE` block class index in the Schindler 2017 estimation run (component 1 = larger-baseline subpop with P = 0.348, component 2 = smaller-baseline subpop); the binary indicator is `MIX_LARGE_BASE = as.integer(MIXTURE == 1)`.
- **Example models:** `Schindler_2017_imatinib.R` (gates the per-lesion typical baselines `S0_<metric>_l<k>_typ` between subpopulation-1 and subpopulation-2 anchors, and selects between subpopulation-specific IIV etas `etalS0_<metric>_pop1` and `etalS0_<metric>_pop2` whose log-scale variances differ across mixture classes).
- **Notes:** Schindler 2017 Methods reports 22 % of patients with one target lesion vs 39 % of patients with two target lesions were assigned to subpopulation 1, suggesting the larger-baseline class is enriched among multi-lesion patients. For typical-value simulation set `MIX_LARGE_BASE = 1` to reproduce the larger-baseline phenotype (dominates Figure 2 right panel typical-individual trajectory) or `MIX_LARGE_BASE = 0` to reproduce the smaller-baseline phenotype. For population simulation, draw `MIX_LARGE_BASE ~ Bernoulli(0.348)` per subject. Scope: specific because the binary semantics are tied to Schindler 2017's two-class baseline-tumor-size mixture; future tumor-burden mixture models that share the same large-vs-small baseline dichotomy may extend this entry, while mixture indicators from unrelated dichotomies (e.g., fast / slow tumor-growth phenotypes) should register a new canonical name (`MIX_FAST_GROW`, etc.). Ratified canonically on 2026-05-18 alongside the Schindler 2017 imatinib extraction.

### MIX_LARGE_RUV (**canonical for binary mixture-model class indicator: larger-additive-RUV vs smaller-additive-RUV subpopulation**)
- **Description:** Per-subject latent mixture-model class indicator from the Kappelhoff 2005 ritonavir popPK model. 1 = subject classified to the minority larger-additive-RUV subpopulation P2 (additive residual SD = 0.199 mg/L; 35.2 % of the source cohort); 0 = subject classified to the majority smaller-additive-RUV subpopulation P1 (additive residual SD = 0.0600 mg/L; 64.8 % of the source cohort). Both subpopulations share the same 15.4 % proportional residual error component and the same structural PK; only the additive RUV magnitude differs. Not a measured clinical covariate -- the mixture assignment is the per-subject latent-class index from Kappelhoff 2005's NONMEM `$MIX` block (paper Methods, "Basic pharmacokinetic model" subsection: "Subpopulations were estimated using the $MIX function in the control stream").
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (smaller-additive-RUV subpopulation P1; majority class at 64.8 % of the source cohort).
- **Source aliases:** `$MIX class assignment` -- NONMEM `$MIX` block class index in the Kappelhoff 2005 estimation run (component 1 = P1 small-RUV with P = 0.648, component 2 = P2 large-RUV with P = 0.352); the binary indicator is `MIX_LARGE_RUV = as.integer($MIX == 2)`.
- **Example models:** `Kappelhoff_2005_ritonavir.R` (gates the active additive residual SD inside `model()`: `add_sd_eff <- addSd_p1 * (1 - MIX_LARGE_RUV) + addSd_p2 * MIX_LARGE_RUV`; the proportional component is shared).
- **Notes:** Kappelhoff 2005 Discussion notes the mechanism behind the two-population residual structure could not be identified; the mixture was retained for goodness-of-fit (Delta-OFV = -48, P < 0.001 vs single-population RUV). For typical-value simulation set `MIX_LARGE_RUV = 0` (dominant subpopulation). For population simulation, draw `MIX_LARGE_RUV ~ Bernoulli(0.352)` per subject. Scope: specific because the binary semantics are tied to Kappelhoff 2005's two-class additive-RUV mixture; future popPK models that share the same large-vs-small additive-RUV dichotomy may extend this entry, while mixture indicators from unrelated dichotomies should register a new canonical name (e.g., `MIX_LARGE_PROPRUV` for a mixture on the proportional component instead). Ratified canonically alongside the Kappelhoff 2005 ritonavir extraction.

### MIX_LAGGED_ABS (**canonical for binary mixture-model class indicator: lagged-absorption vs no-lag absorption subpopulation**)
- **Description:** Per-subject latent mixture-model class indicator from the Bonate 2004 apomine population PK model. 1 = subject classified to the lagged-absorption Group 1 subpopulation (estimable lag time and faster first-order absorption rate constant ka); 0 = subject classified to the no-lag Group 2 subpopulation (lag time fixed at 0 and slower ka). Not a measured clinical covariate -- the mixture assignment is the per-subject latent-class index from a NONMEM `$MIXTURE` block (Bonate 2004 Methods "Base model development", equation (1.4), and Results paragraph 1).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Group 2 = no-lag minority class; 3 % of the source cohort).
- **Source aliases:** `MIXTURE` -- NONMEM `$MIXTURE` block class index in the Bonate 2004 estimation run (component 1 = lagged-absorption Group 1 with P = 0.970, component 2 = no-lag Group 2 with P = 0.030); the binary indicator is `MIX_LAGGED_ABS = as.integer(MIXTURE == 1)`.
- **Example models:** `Bonate_2004_apomine.R` (gates ka between two subpopulation-specific typical values and gates the lag time on the depot compartment between Group 1 = `exp(ltlag)` and Group 2 = 0; the shared `etalka` log-normal IIV scales whichever group's ka is active for the subject), `Frymoyer_2013_mycophenolic_acid.R` (gates only the lag time on the depot compartment between Group 2 = `exp(ltlag) = 1.96 h` and Group 1 = 0; ka, F and disposition parameters are shared between the two classes per Frymoyer 2013 "Allowing other absorption parameters (ka and F) to vary for the two subpopulations by using an extended mixture model did not improve the overall model or predictions").
- **Notes:** The population probability of `MIX_LAGGED_ABS = 1` is the estimated mixture fraction `P(Group 1) = 1 / (1 + exp(P1)) = 0.970` (Bonate 2004 Table 3 with P1 = -3.47). For typical-value simulation set `MIX_LAGGED_ABS = 1` to reproduce the dominant 97 % phenotype (lagged-absorption with ka = 1.77 /h and lag = 0.821 h); set `MIX_LAGGED_ABS = 0` to reproduce the rare 3 % no-lag phenotype (ka = 0.361 /h, no lag). For population simulation, draw `MIX_LAGGED_ABS ~ Bernoulli(0.970)` per subject. Scope: specific because the binary semantics are tied to Bonate 2004's two-class absorption mixture; future popPK papers that share the same lagged-vs-no-lag absorption-mixture dichotomy may extend this entry, while mixture indicators from unrelated dichotomies (e.g., fast vs slow elimination subpopulations) should register a new canonical name (`MIX_FAST_ELIM`, etc.). Ratified canonically on 2026-06-04 alongside the Bonate 2004 apomine extraction.

### MIX_HIGH_VP (**canonical for binary mixture-model class indicator: high-peripheral-volume vs typical-peripheral-volume subpopulation**)
- **Description:** Per-subject latent mixture-model class indicator from the Bonate 2004 apomine population PK model. 1 = subject classified to the high-peripheral-volume subpopulation (Bonate 2004 Study 2 healthy-male multiple-dose subjects with Vp many-fold higher than the rest of the cohort, modelled as a multiplicative shift `Vp_i = TVV3 * theta_Study2` with `theta_Study2 = 23.5`); 0 = subject classified to the typical-Vp subpopulation. Not a measured clinical covariate -- the mixture assignment was identified by Bonate 2004 from a bimodal empirical-Bayesian Vp histogram and back-fitted as a dichotomous study-membership multiplier (Bonate 2004 Results paragraph 1 and Discussion).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (typical-Vp majority class; 34 / 38 = 89.5 % of the source model-development cohort).
- **Source aliases:** `STUDY2` -- the paper modelled the high-Vp subgroup as a dichotomous indicator on Study 2 membership (the 4 healthy-male multiple-dose subjects in Study 2); the binary indicator is `MIX_HIGH_VP = as.integer(STUDY == 2)` for the source cohort.
- **Example models:** `Bonate_2004_apomine.R` (multiplicative log-additive effect on Vp: `vp = exp(lvp + e_high_vp_vp * MIX_HIGH_VP + etalvp)` with `e_high_vp_vp = log(23.5) = 3.157`).
- **Notes:** The population probability of `MIX_HIGH_VP = 1` is 4 / 38 = 0.105 in the Bonate 2004 model-development set. For typical-value forward simulations set `MIX_HIGH_VP = 0` (the recommended default); the paper notes that simulations with and without the multiplier showed minimal differences in concentration-time profiles. Set `MIX_HIGH_VP = 1` only to reproduce the Bonate 2004 Study 2 healthy-male multiple-dose subgroup specifically. Scope: specific because the binary semantics are tied to Bonate 2004's anomalous Study 2 high-Vp subgroup; future popPK papers that retain a similar dichotomous high-Vp subgroup multiplier may extend this entry, while peripheral-volume mixture indicators from unrelated dichotomies should register a new canonical name. Ratified canonically on 2026-06-04 alongside the Bonate 2004 apomine extraction.

### MIX_VAC_RELAPSE (**canonical for binary mixture-model class indicator: tumor-relapse vs cure subpopulation after a single-dose immunotherapy / vaccine**)
- **Description:** Per-subject latent mixture-model class indicator from the Parra-Guillen 2013 cancer-vaccine tumor-dynamics model. 1 = subject classified to the relapser subpopulation (transient vaccine-elicited inhibitory signal, SVAC degradation rate k2 = k1 estimated; tumor regrowth observed after initial response; estimated population probability 1 - P(1) = 0.156 in Parra-Guillen 2013 Table I row 'P(1) = 0.844 FIX'); 0 = subject classified to the responder / cure subpopulation (permanent vaccine-elicited inhibitory signal, SVAC degradation rate k2 = 0 FIX; complete tumor regression maintained for the duration of the experiment). Not a measured clinical covariate -- the mixture assignment is the per-subject latent-class index from a NONMEM `$MIXTURE` block (Parra-Guillen 2013 Methods 'Data Evaluation, Biological Assumptions and Mathematical Model' subsection bullet (e); Discussion 'A challenge when using a mixture model' paragraph). Time-fixed per subject.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (responder / cure subpopulation; majority class at 84.4 % in the Parra-Guillen 2013 source cohort and assumed equal in the Medina-Echeverz 2014 IL-12 applicability cohort).
- **Source aliases:** `MIXTURE` -- NONMEM `$MIXTURE` block class index in the Parra-Guillen 2013 estimation run (component 1 = responder / cure subpop with P(1) = 0.844 FIX, component 2 = relapser subpop); the binary indicator is `MIX_VAC_RELAPSE = as.integer(MIXTURE == 2)` (orientation flipped from the NONMEM convention so the named state corresponds to the deviation from the dominant cure phenotype, following the Schindler 2017 / Tsuji 2017 MIX_* naming pattern).
- **Example models:** `ParraGuillen_2013_cyaaE7.R`, `ParraGuillen_2013_il12.R` (gates the SVAC degradation rate inside Eq. 3: `k2 <- MIX_VAC_RELAPSE * k1` so `MIX_VAC_RELAPSE = 0` freezes the inhibitory signal indefinitely (cure) and `MIX_VAC_RELAPSE = 1` makes it decay at rate k1 (relapse). The same indicator is reused across both drug files because the biological dichotomy is identical (responders vs relapsers to immunotherapy) and the paper fixed P(1) = 0.844 from CyaA-E7 when re-fitting IL-12).
- **Notes:** P(1) = 0.844 was obtained from a sub-analysis of only the 4 / 7 / 11-day post-inoculation vaccine-administration groups (where tumor-size-induced resistance had not yet emerged); it was held fixed during the full-cohort and IL-12 re-estimations because the resistance term in later vaccine-administration arms masked the true mixture proportion (Discussion p. 803). For typical-value simulation set `MIX_VAC_RELAPSE = 0` (dominant cure phenotype, complete tumor regression; Figure 3 'light grey' individual predictions) or `MIX_VAC_RELAPSE = 1` (relapser phenotype, tumor regrowth; Figure 3 'dark grey' individual predictions). For population simulation, draw `MIX_VAC_RELAPSE ~ Bernoulli(1 - 0.844) = Bernoulli(0.156)` per subject. Discussion p. 803 notes the mixture is empirical: 'no statistical differences on tumour size at the moment of vaccine administration were found between both populations (p > 0.05)'. The cure semantics ('a sub-population of mice able to trigger only a temporal tumour response was postulated to describe the relapse observed in a few mice ... the vaccine is able to trigger a permanent immune response (probably mediated by memory T-cells) in the cured mouse population, but no in all the mice') are tied to immune-memory immunotherapy specifically and may not generalise to cytotoxic-chemotherapy relapse mixtures, so scope is `specific`. Future immunotherapy / vaccine / oncolytic-virus papers that share the same cure-vs-relapse dichotomy at the level of a vaccine-elicited inhibitory signal degradation rate may extend this entry; mixture indicators from unrelated dichotomies (e.g., fast-acquired-resistance vs slow-acquired-resistance to cytotoxic chemotherapy) should register a new canonical name. Ratified canonically on 2026-06-03 alongside the Parra-Guillen 2013 CyaA-E7 + IL-12 extraction.


### MIX_SLOW_ELIM_NVP (**canonical for binary mixture-model class indicator: slow-vs-fast nevirapine apparent-oral-clearance subpopulation**)
- **Description:** Per-subject latent mixture-model class indicator from the Svensson 2012 nevirapine multi-source mega-model. 1 = subject classified to the minority slow-eliminator subpopulation (typical CL/F = 1.45 L/h at FFM 42 kg reference; estimated population probability 17.3 % in Svensson 2012 Table 2 'Probability (%) of belonging to pop. 2'); 0 = subject classified to the majority fast-eliminator subpopulation (typical CL/F = 3.12 L/h, 82.7 %). Not a measured clinical covariate -- the mixture assignment is the per-subject latent-class index from the Svensson 2012 NONMEM mixture block. The paper Discussion paragraph 4 attributes the slow class biologically to CYP2B6 516TT homozygotes ('The estimate of the probability of belonging to the low-clearance population (17.3 %) agrees well with the reported proportion of CYP2B6-516-TT homozygotes in the South African population, implying biological plausibility of two populations with different clearance rates'); the model itself does not require genotype data as input.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (fast-eliminator majority class; 82.7 % of the source cohort).
- **Source aliases:** `MIXTURE` -- NONMEM `$MIXTURE` block class index in the Svensson 2012 estimation run (component 1 = fast with P = 0.827, component 2 = slow with P = 0.173); the binary indicator is `MIX_SLOW_ELIM_NVP = as.integer(MIXTURE == 2)`.
- **Example models:** `Svensson_2012_nevirapine.R` (gates the typical apparent oral clearance between subpopulation-specific anchors: `cl_typ <- exp(lcl_fast) * (1 - MIX_SLOW_ELIM_NVP) + exp(lcl_slow) * MIX_SLOW_ELIM_NVP`, with `lcl_fast = log(3.12)` and `lcl_slow = log(1.45)`; the single 24.9 % BSV on CL/F is shared across the two mixture sub-populations).
- **Notes:** For typical-value simulation set `MIX_SLOW_ELIM_NVP = 0` (the recommended default) to reproduce the majority fast-eliminator phenotype; set `MIX_SLOW_ELIM_NVP = 1` to reproduce the rarer slow phenotype that the paper associates with CYP2B6 516TT homozygotes. For population simulation, draw `MIX_SLOW_ELIM_NVP ~ Bernoulli(0.173)` per subject. Scope: specific because the drug-specific `_NVP` suffix scopes the binary semantics to nevirapine CL/F mixtures; future fast/slow CYP2B6-driven elimination mixtures for other antiretrovirals (e.g., efavirenz, which shares CYP2B6 as the major elimination enzyme) should register sibling canonicals (`MIX_SLOW_ELIM_EFV`, etc.) rather than reuse this entry. The Bienczak 2016 and Schipani 2011 nevirapine extractions in the registry use CYP2B6 genotype indicators directly (`CYP2B6_IM` / `CYP2B6_SM` / `CYP2B6_USM` / `SNP_CYP2B6_RS3745274_T_COUNT`) and are NOT mixture models -- they assume the user supplies known genotype; `MIX_SLOW_ELIM_NVP` is the orthogonal pathway for the Svensson 2012 paper which estimates the phenotype distribution without genotype data. Ratified canonically on 2026-06-14 alongside the Svensson 2012 nevirapine extraction.

### MIX_ELEV_IL6 (**canonical for binary cycle-level mixture-model class indicator: elevated-IL-6-production-surge active vs no-surge subpopulation**)
- **Description:** Per (subject, chemotherapy cycle) latent mixture-model class indicator: 1 = this cycle has an elevated IL-6 production surge (the IL-6 surge function g_IL6(t) is active and adds the relative-surge term `(1 + g_IL6(t))` to the IL-6 production rate); 0 = no elevated IL-6 production in this cycle (the surge function is zeroed and IL-6 follows its baseline turnover only). Not a measured clinical covariate -- the gate implements the NONMEM `$MIXTURE` block frequency that the Netterberg 2018 model estimated from the data. Time-fixed within a single chemotherapy cycle; redrawn independently between cycles 1 and 4 in the paper's cohort.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no elevated IL-6 production this cycle; the complement of the surge-active subpopulation).
- **Source aliases:** `MIXTURE` -- NONMEM `$MIXTURE` block class index in the Netterberg 2018 estimation run; the binary indicator is `MIX_ELEV_IL6 = as.integer(elevation-active component)`. The paper reports 16 subpopulations across the 2 cycles x 2 biomarkers indicator combinations, but the marginal IL-6-elevation probability per cycle is a single value (Table 2 Pelevation,IL-6 = 63.4 %, RSE 10 %).
- **Example models:** `Netterberg_2018_breast_cancer_FN_biomarkers.R`, `Netterberg_2018_breast_cancer_FN_tte_preFN.R`, `Netterberg_2018_breast_cancer_FN_tte_atFN.R` (gates the IL-6 surge function: `g_il6 <- MIX_ELEV_IL6 * sa_il6_i / (((t - pt_il6_i) / sw_il6_i)^4 + 1)`, so `MIX_ELEV_IL6 = 0` zeroes the surge contribution while `MIX_ELEV_IL6 = 1` activates the Netterberg 2018 Equation 1 Lorentzian surge centred at PT_IL-6).
- **Notes:** General scope because acute-phase biomarker surge mixtures recur in cytotoxic-chemotherapy IL-6 / CRP turnover models (any future paper modelling a Friberg-style IL-6 surge with a NONMEM mixture-component frequency should reuse this canonical). For typical-value simulation set `MIX_ELEV_IL6 = 1` (the dominant 63.4 % per-cycle phenotype); for population simulation, draw `MIX_ELEV_IL6 ~ Bernoulli(0.634)` per (subject, cycle). The companion `MIX_ELEV_CRP` indicator gates the CRP surge function in the same model. Ratified canonically on 2026-06-27 alongside the Netterberg 2018 breast-cancer febrile-neutropenia extraction.

### MIX_ELEV_CRP (**canonical for binary cycle-level mixture-model class indicator: elevated-CRP-production-surge active vs no-surge subpopulation**)
- **Description:** Per (subject, chemotherapy cycle) latent mixture-model class indicator: 1 = this cycle has an elevated CRP production surge (the CRP surge function g_CRP(t) is active); 0 = no elevated CRP production surge (the surge function is zeroed, but CRP production may still rise via the IL-6 -> CRP linear-regulation Slope coefficient when `MIX_ELEV_IL6 = 1`). Not a measured clinical covariate -- the gate implements the NONMEM `$MIXTURE` block frequency that the Netterberg 2018 model estimated from the data. Time-fixed within a single chemotherapy cycle; redrawn independently between cycles in the paper's cohort.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no elevated CRP production surge this cycle; CRP elevation may still occur via the IL-6 RCFB regulation through Slope).
- **Source aliases:** `MIXTURE` -- NONMEM `$MIXTURE` block class index in the Netterberg 2018 estimation run; the binary indicator is `MIX_ELEV_CRP = as.integer(elevation-active component)`. Per Netterberg 2018 Results, the per-cycle marginal probability is 44.3 % (Table 2 Pelevation,CRP, RSE 20 %), lower than the IL-6 marginal because the IL-6 -> CRP linear regulation contributes additional CRP elevations even when the dedicated CRP surge is off.
- **Example models:** `Netterberg_2018_breast_cancer_FN_biomarkers.R`, `Netterberg_2018_breast_cancer_FN_tte_preFN.R`, `Netterberg_2018_breast_cancer_FN_tte_atFN.R` (gates the CRP surge function: `g_crp <- MIX_ELEV_CRP * sa_crp_i / (((t - pt_crp_i) / sw_crp_i)^4 + 1)`, so `MIX_ELEV_CRP = 0` zeroes the dedicated CRP-surge contribution while `MIX_ELEV_CRP = 1` activates the Netterberg 2018 Equation 1 Lorentzian surge centred at PT_CRP = PT_IL-6 + PT_CRP+).
- **Notes:** General scope because acute-phase biomarker surge mixtures recur in cytotoxic-chemotherapy IL-6 / CRP turnover models. For typical-value simulation set `MIX_ELEV_CRP = 1` to reproduce a CRP-elevation cycle; for population simulation, draw `MIX_ELEV_CRP ~ Bernoulli(0.443)` per (subject, cycle). The paper observes that the *actual* fraction of CRP elevations is higher than `Pelevation,CRP` because of the IL-6 RCFB coupling through Slope; the canonical refers strictly to the dedicated surge function and not the coupled-elevation path. Ratified canonically on 2026-06-27 alongside the Netterberg 2018 breast-cancer febrile-neutropenia extraction.


## Preclinical experimental conditions

### SEIZURE_ACUTE (**canonical for acute focal-seizure-activity indicator in preclinical brain-distribution studies**)
- **Description:** 1 = the animal is undergoing acute focal seizure activity (typically chemoconvulsant-induced, e.g., intrahippocampal pilocarpine perfusion in a microdialysis design) during the modelled observation window; 0 = no acute seizure activity. Captures the experimental flag used by preclinical BBB / biophase-distribution PK studies to test whether seizure-driven changes in blood-brain barrier permeability or brain redistribution alter brain PK parameters relative to non-seizing control animals. Time-fixed per animal in single-condition allocations (each rat in one arm); a time-varying form is permitted when a study induces transient seizures during a sampling window and the operator chooses to gate the covariate only over the seizure interval (document in `covariateData[[SEIZURE_ACUTE]]$notes`).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no acute seizure activity; non-seizing control animal).
- **Source aliases:**
  - `A` (values inverted: source `A = 1` means non-seizing control and `A = 0` means seizing, per Clinckers 2008 Methods 'Both A and B were set to 1 in control animals; to 0 and 1, respectively, in seizing animals'; the canonical encoding flips the polarity via `SEIZURE_ACUTE = 1 - A`) -- used in `Clinckers_2008_MHD_rat.R`.
- **Example models:** `Clinckers_2008_MHD_rat.R` (mutually-exclusive selection of the seizure-specific biophase volume V3b in place of the control V3a inside the V3 expression of the one-compartment-plus-biophase model for MHD in male Wistar rats; `v3 = exp(lv3a + etalv3a) * (1 - SEIZURE_ACUTE) * (1 - EFFLUX_INHIB) + exp(lv3b) * SEIZURE_ACUTE * (1 - EFFLUX_INHIB) + exp(lv3c + etalv3c) * (1 - SEIZURE_ACUTE) * EFFLUX_INHIB`).
- **Notes:** Specific scope because the indicator's semantics are tied to acute, induced focal seizure activity in preclinical BBB / biophase PK designs; clinical-trial canonicals for epilepsy (`CONMED_AED`, `CONMED_EIAED`, `PDV` previous-period seizure count) describe distinct concepts and should not be conflated. Future preclinical studies that test a similar seizure-vs-non-seizure contrast should extend the example list; promote to general scope once a second model legitimately ratifies the name. Mutually exclusive with `EFFLUX_INHIB` in Clinckers 2008 (each rat is allocated to exactly one of {control, seizure, efflux-inhibition}); the model() encoding uses `(1 - SEIZURE_ACUTE) * (1 - EFFLUX_INHIB)` as the control multiplier, so any data record that asserts both flags as 1 would zero out the V3a term -- data assemblers should preserve mutual exclusivity unless a model is explicitly designed for the cross-condition case. Ratified canonically on 2026-05-16 alongside the Clinckers 2008 extraction.

### EFFLUX_INHIB (**canonical for local efflux-transporter-inhibitor co-perfusion indicator in preclinical brain-distribution studies**)
- **Description:** 1 = the animal's brain microdialysis probe is co-perfused with an efflux-transporter inhibitor (e.g., verapamil 5 mM for P-glycoprotein / MDT blockade in Clinckers 2008) at the site of brain sampling, 0 = no local efflux-transporter inhibitor co-perfusion. Captures the experimental flag used by preclinical BBB / biophase-distribution PK studies to test whether local pharmacological blockade of brain efflux transporters alters brain PK parameters relative to non-blocked control animals. Time-fixed per animal in single-condition allocations.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** n/a -- `T_ENTRY` is a per-subject time-offset covariate that anchors the time-since-study-entry clock used by post-entry-only model terms (placebo / learning effects, study-design transient drops).
- **Source aliases:** none standardized; this canonical originates with the Delor 2013 AD disease-progression extraction. The source paper's NONMEM dataset handles the global / study-time split internally (the dataset is staged with TIME = study time and the model uses a fixed offset to align with DOT); when porting to rxode2 / nlmixr2 the per-subject offset is exposed as a covariate so simulation event tables can carry it explicitly.
- **Example models:** `Delor_2013_alzheimer.R`, `Clinckers_2008_MHD_rat.R` (mutually-exclusive selection of the verapamil-specific biophase volume V3c in place of the control V3a inside the V3 expression: `v3 = v3a*(1-SEIZURE_ACUTE)*(1-EFFLUX_INHIB) + v3b*SEIZURE_ACUTE*(1-EFFLUX_INHIB) + v3c*(1-SEIZURE_ACUTE)*EFFLUX_INHIB`).
- **Notes:** Scope: specific because the variable is paper-domain-bound -- it only makes sense for models that operate on a global disease-time axis distinct from per-subject study-entry timing. For a typical-value reproduction the user supplies `T_ENTRY` per subject in the simulation event table; a reasonable construction is `T_ENTRY = DOT_individual + a few years of established disease` so the patient is observed during disease progression (see the Delor 2013 vignette for the construction used to reproduce Figures 2-4). Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

### HUMAN_SERUM_PCT (**canonical for human-serum supplementation percentage in an in-vitro time-kill experiment**)
- **Description:** Percentage (v/v) of human serum supplementing the Mueller-Hinton broth growth medium in an in-vitro antibacterial time-kill experiment. Drives the protein-binding active-fraction `factive(HUMAN_SERUM_PCT)` that scales the total static drug concentration to the effective (free) concentration. Categorical experimental level, not a continuous interpolatable axis.
- **Units:** % v/v
- **Type:** categorical
- **Scope:** specific
- **Reference category:** 0 (no human serum; the active fraction factive is forced to 1).
- **Source aliases:**
  - `HS` -- Garonzik 2016 (paper Methods + Table 2, "% Human Serum"; the model column was renamed from the short `HS` to the spelled-out canonical `HUMAN_SERUM_PCT` on 2026-05-27 to align with the register's naming standards and avoid an ambiguous two-letter abbreviation).
- **Example models:** `Garonzik_2016_daptomycin.R` (five experimental levels {0, 10, 30, 50, 70} percent; drives factive multiplying the static daptomycin concentration to the effective concentration DAP_EF, Garonzik 2016 Eq 2; factive estimates 0.346 / 0.284 / 0.239 / 0.252 at 10 / 30 / 50 / 70 percent and 1 at 0 percent by construction).
- **Notes:** Specific scope because the discrete serum-percentage levels and the associated factive estimates are tied to the Garonzik 2016 daptomycin in-vitro design. An in-vitro experimental condition rather than a human pop-PK covariate; HUMAN_SERUM_PCT values outside the studied discrete set make factive = 0 inside the model (a deliberately conspicuous failure rather than silent interpolation). The spelled-out name follows the register's anti-abbreviation principle (cf. the `DIS_BUNIONECTOMY` entry's avoidance of `DIS_BUN`). Future in-vitro protein-binding-versus-serum experiments should extend the example list. Ratified canonically on 2026-05-27 alongside the Garonzik 2016 daptomycin extraction.


### M3M3FBS_PRESENT (**canonical for m-3M3FBS phospholipase-C-activator pretreatment indicator in the Grzesk 2016 vascular-reactivity ex-vivo design**)
- **Description:** 1 = the isolated, perfused tail artery preparation was pretreated with the phospholipase-C activator 2,4,6-trimethyl-N-[3-(trifluoromethyl)phenyl]benzenesulfonamide (m-3M3FBS) at 1e-5 mol/L prior to the agonist concentration-response titration; 0 = no m-3M3FBS pretreatment (control arm). Time-fixed per artery / concentration-response curve (each artery is allocated to exactly one of the control or +m-3M3FBS arms). Captures the binary experimental flag distinguishing the two CRC sets of Grzesk 2016 Table I (control vs +m-3M3FBS) and selects the m-3M3FBS-shifted (EC50, Emax) parameter pair inside `model()`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (control artery without m-3M3FBS pretreatment).
- **Source aliases:** none -- Grzesk 2016 Tables I and II label the two arms "controls" vs "+m-3M3FBS" in prose; the model column is the canonical `M3M3FBS_PRESENT`.
- **Example models:** `Grzesk_2016_m3M3FBS.R` (binary selector that swaps the per-agonist control (EC50, Emax) pair for the +m-3M3FBS pair in the sigmoidal Emax CRC; the agonist identity is selected by the companion `AGONIST_CODE`).
- **Notes:** Specific scope because the indicator's semantics are tied to m-3M3FBS pretreatment in the Grzesk 2016 vascular-reactivity ex-vivo design. The Grzesk-group sibling papers (Biomed Rep 2 / 2014 pertussis toxin; Mol Med Report 5 / 2012 calcium blockers; etc.) test different pretreatments and would register their own pretreatment indicators rather than reuse this name. Distinct from `CONMED_*` (clinical-trial-level concomitant medication indicators) because m-3M3FBS is an experimental ex-vivo pretreatment, not a clinically administered comedication. Ratified canonically alongside the Grzesk 2016 extraction.


### PERIOD_ACTIVE (**canonical for binary diurnal-period indicator in preclinical chronopharmacology / diurnal-variation PK-PD studies**)
- **Description:** Binary indicator of the diurnal (light-dark) period during which the experimental time-of-administration falls. `1` = active period (lights-off / dark phase for nocturnal laboratory rodents; the animal's behaviourally active wake window); `0` = resting period (lights-on / light phase; the animal's behaviourally quiescent sleep window). In the standard 12:12 light-dark cycle used by Kervezee 2014 (Wistar rats housed under LD 12:12 with lights-off defined as Zeitgeber Time ZT12), the resting-period cohort is dosed at ZT0-ZT12 and the active-period cohort at ZT12-ZT24. Time-fixed per experimental subject (each animal is dosed at a single ZT, so the derived period membership is a per-subject scalar in the fitting cohort). The paper aggregates six ZT levels (ZT0/4/8/12/16/20) into the binary resting-vs-active split for the covariate model, so `PERIOD_ACTIVE` is the collapsed two-level version of the six-level ZT design; the raw ZT is not carried as a separate canonical covariate.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (resting period / lights-on). In Kervezee 2014 the effect enters as a period-specific selection of the P-gp component of five brain-distribution clearances (`CL_DBR-PL,Pgp`, `CL_PL-ECF,Pgp`, `CL_ECF-PL,Pgp`, `CL_PL-LV,Pgp`) and of the CSF bulk-flow rate `Q_CSF`, with separate resting-period and active-period point estimates rather than a single reference-plus-multiplier form (Kervezee 2014 Table II reports both periods side-by-side).
- **Source aliases:**
  - `time of administration (P-gp)` -- Kervezee 2014 Table I prose label for the covariate hypothesis tested against the five P-gp-mediated clearance parameters; free-form phrase, not a data column name.
  - `time of administration (Q_CSF)` -- Kervezee 2014 Table I prose label for the covariate hypothesis tested against CSF bulk flow.
- **Example models:** `Kervezee_2014_quinidine_rat.R` (nine-compartment brain-distribution PBPK for i.v. quinidine in male Wistar rats; `PERIOD_ACTIVE = 1` selects the active-period estimates for `CL_DBR-PL,Pgp` = 659 uL/min, `CL_PL-ECF,Pgp` = 18.3 uL/min, `CL_ECF-PL,Pgp` = 3.98 uL/min, `CL_PL-LV,Pgp` = 2.44 uL/min, and `Q_CSF` = 0.227 uL/min; `PERIOD_ACTIVE = 0` selects the resting-period estimates 228, 17, 3.14, 1.55, and 0.522 uL/min respectively).
- **Notes:** Specific scope because the binary active-vs-resting collapse is meaningful only for chronopharmacology / diurnal-variation preclinical PK-PD designs that pool experimental time-of-administration into two phase cohorts; a future paper that resolves circadian rhythmicity as a continuous cosinor / harmonic function of clock time (e.g., a cosine effect on P-gp expression peaking near ZT18) would use a separate canonical (a continuous clock-time covariate; register with a name like `TIME_OF_DAY` when the founding paper lands) rather than reusing `PERIOD_ACTIVE`. Distinct from `SEIZURE_ACUTE` / `EFFLUX_INHIB` / `M3M3FBS_PRESENT` (paper-defined binary experimental-condition indicators in preclinical models that also live in this section) because those flag pharmacological / disease-state interventions rather than a natural chronobiological phase. Downstream promotion to general scope only if a second preclinical or clinical chronopharmacology model adopts the same binary active-vs-resting collapse with equivalent semantics; nocturnal-vs-diurnal-species handling (rats are nocturnally active, humans are diurnally active) must be documented in `covariateData[[PERIOD_ACTIVE]]$notes` per model because the mapping ZT / clock-time -> {active, resting} depends on species. Kervezee 2014 does not observe circadian rhythmicity in constant darkness so the paper is careful to call the variation "diurnal" rather than "circadian" -- the canonical name `PERIOD_ACTIVE` avoids that debate by labelling the level (active-vs-resting) rather than the driver (diurnal-vs-circadian). Ratified canonically on 2026-07-08 alongside the Kervezee 2014 quinidine rat extraction.


### SPECIES_RABBIT (**canonical for New Zealand White rabbit species indicator in cross-species / animal-to-human translation models**)
- **Description:** 1 = the subject is a New Zealand White (NZW) rabbit; 0 = the subject belongs to another species in the same model. Time-fixed per subject. Used by joint cross-species models that carry one parameter set per species in a single model file and select among them with mutually-exclusive species indicators, rather than splitting the paper's analysis into one file per species. Pairs with `SPECIES_MACAQUE`; the convention is that all species indicators being 0 selects the model's reference species (human, in the founding example).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general -- animal-to-human translation (FDA Animal Rule, first-in-human allometric projection, preclinical-to-clinical bridging) is a recurring model class and future extractions are expected to reuse this indicator family.
- **Reference category:** 0 (not a NZW rabbit; in a model whose reference species is human, both `SPECIES_RABBIT` and `SPECIES_MACAQUE` equal to 0 selects the human parameter set).
- **Source aliases:**
  - `SPECIES` (a categorical column with levels such as "rabbit" / "monkey" / "human") -- decomposed into one binary indicator per non-reference species, following the same one-hot pattern used by the `RACE_<GROUP>` family. Used in `Nagy_2017_obiltoxaximab.R`.
- **Example models:** `Nagy_2017_obiltoxaximab.R` (selects the NZW rabbit column of Nagy 2017 Supplementary Table S1 -- CL 0.0263 L/day, Vc 0.114 L, Vp 0.0744 L, Q 0.119 L/day, Ka 0.961 /day, F1 0.899 -- together with the rabbit-specific 3.165 kg allometric reference weight and the rabbit Michaelis-Menten arm Vmax 0.912 mg/day, Km 10.4 ug/mL).
- **Notes:** A species indicator is a subject attribute, not an experimental intervention, but it lives in this section because its mechanical role -- a mutually-exclusive binary that swaps one parameter set for another inside `model()` -- matches the existing preclinical indicators `SEIZURE_ACUTE`, `EFFLUX_INHIB`, `M3M3FBS_PRESENT` and `PERIOD_ACTIVE`. Each species carries its own allometric reference weight, so a model using this family must select `wtRef` by the same indicators it uses to select the structural parameters; do not normalise every species to a single reference weight. Data assemblers must preserve mutual exclusivity (no record may set more than one `SPECIES_*` indicator to 1), because the selection arithmetic `isHuman <- 1 - SPECIES_RABBIT - SPECIES_MACAQUE` would otherwise go negative and silently produce nonsense rather than erroring. Register a sibling canonical (`SPECIES_RAT`, `SPECIES_DOG`, `SPECIES_MOUSE`, ...) rather than overloading this name when a new species appears. Distinct from the model-file metadata field `population$species`, which is free text describing the studied population and is not a data column. When a paper's species-specific models are genuinely independent fits with no shared structure, the library's default remains one model file per species (e.g. `An_2012_mitoxantrone_mouse_pbpk.R` / `An_2012_mitoxantrone_human_pbpk.R`); this family is for the case where a single file is the more faithful or more useful representation. Ratified canonically alongside the Nagy 2017 obiltoxaximab extraction.

### SPECIES_MACAQUE (**canonical for cynomolgus macaque species indicator in cross-species / animal-to-human translation models**)
- **Description:** 1 = the subject is a cynomolgus macaque (*Macaca fascicularis*); 0 = the subject belongs to another species in the same model. Time-fixed per subject. The non-human-primate member of the `SPECIES_<NAME>` one-hot family; see `SPECIES_RABBIT` for the family conventions.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general -- the cynomolgus macaque is the standard non-human primate in monoclonal-antibody and biologics preclinical programmes, so cross-species biologics extractions are expected to reuse this indicator.
- **Reference category:** 0 (not a cynomolgus macaque; in a model whose reference species is human, both `SPECIES_RABBIT` and `SPECIES_MACAQUE` equal to 0 selects the human parameter set).
- **Source aliases:**
  - `SPECIES` (categorical column, level "monkey" / "macaque" / "cyno") -- decomposed to a binary indicator. Used in `Nagy_2017_obiltoxaximab.R`.
- **Example models:** `Nagy_2017_obiltoxaximab.R` (selects the cynomolgus macaque column of Nagy 2017 Supplementary Table S1 -- CL 0.0191 L/day, Vc 0.134 L, Vp 0.123 L, Q 0.0890 L/day, Ka 3.89 /day, F1 0.895 -- with the macaque-specific 2.88 kg allometric reference weight and the macaque Michaelis-Menten arm Vmax 0.275 mg/day, Km 3.21 ug/mL).
- **Notes:** In the founding example the macaque nonlinear-elimination component is also the one carried over to the *human* infected projection (Nagy 2017 Methods: "the nonlinear clearance model component from the cynomolgus macaque model was added to the human population PK model, allometrically scaled to human body size"). That carry-over is a property of the model, not of this covariate: the indicator still reads 0 for the human subject, and the model handles the transfer by scaling the macaque Vmax from the *macaque* reference weight rather than the human one. Any future model that borrows a component across species should document the borrowing in `covariateData` notes so the reference-weight choice is auditable. Ratified canonically alongside the Nagy 2017 obiltoxaximab extraction.


## Infectious-disease subtype indicators

### HCV_GT1B (**canonical for hepatitis C virus genotype-1 subtype indicator: GT1B vs GT1A**)
- **Description:** Binary indicator of the HCV genotype-1 subtype assigned to the subject. 1 = patient infected with HCV genotype 1B (GT1B); 0 = patient infected with HCV genotype 1A (GT1A; the reference subtype in the source paper's IC50 estimates). Time-fixed per subject because the HCV subtype is determined at the time of infection and does not change over the modelled treatment window. Distinct from the broader genotype number (HCV genotype 1, 2, 3, ...) because the GT1A vs GT1B contrast within genotype 1 carries meaningful PK/PD differences (replicon susceptibility, resistance-associated substitutions, drug-resistance trajectory) for direct-acting antivirals; a future model that needs a genotype-2-vs-1 contrast or a 4-level genotype indicator should register a sibling canonical rather than overloading `HCV_GT1B`.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no human serum; the active fraction factive is forced to 1).
- **Source aliases:**
  - `HS` -- Garonzik 2016 (paper Methods + Table 2, "% Human Serum"; the model column was renamed from the short `HS` to the spelled-out canonical `HUMAN_SERUM_PCT` on 2026-05-27 to align with the register's naming standards and avoid an ambiguous two-letter abbreviation).
- **Example models:** `Garonzik_2016_daptomycin.R`, `Wang_2018_daclatasvir_asunaprevir.R` (multiplicative effect on IC50 of both drugs via the fixed scaling factors SCL_IC50_DCV = 0.18 and SCL_IC50_ASV = 0.30; the encoding is `ic50_dcv_t0 = exp(lic50_dcv_gt1a + etalic50_dcv) * scl_ic50_dcv^HCV_GT1B` and `ic50_asv_t0 = exp(lic50_asv_gt1a + etalic50_asv) * scl_ic50_asv^HCV_GT1B`. Also switches the DCV resistance coefficient Kr_DCV between 0.43 /day for GT1A and 0.13 /day for GT1B; Kr_ASV is the same for both subtypes), `Canini_2018_setrobuvir.R` (switches setrobuvir sigmoid-Emax EC50, Hill coefficient, and viral clearance rate c between the two per-genotype typical values and their independent IIVs).
- **Notes:** Specific scope because the discrete serum-percentage levels and the associated factive estimates are tied to the Garonzik 2016 daptomycin in-vitro design. An in-vitro experimental condition rather than a human pop-PK covariate; HUMAN_SERUM_PCT values outside the studied discrete set make factive = 0 inside the model (a deliberately conspicuous failure rather than silent interpolation). The spelled-out name follows the register's anti-abbreviation principle (cf. the `DIS_BUNIONECTOMY` entry's avoidance of `DIS_BUN`). Future in-vitro protein-binding-versus-serum experiments should extend the example list. Ratified canonically on 2026-05-27 alongside the Garonzik 2016 daptomycin extraction.

## Envenomation / venom source

### SNAKEFAMILY_ELAPID (**canonical for snake-family categorical indicator of venom source**)
- **Description:** Binary indicator of the snake-family origin of the venom bolus delivered by a single bite event. `1` = bite from a snake of the family Elapidae (front-fanged elapids -- cobras, kraits, mambas, sea snakes, Australian terrestrial elapids such as taipans / brown snakes / death adders); `0` = bite from a snake of the family Viperidae (true vipers and pit vipers, including `Bothrops`, `Crotalus`, `Daboia / Vipera russelli`, `Vipera aspis / berus / ammodytes`, `Bitis`, `Hypnale`, `Cerastes`). The covariate is a per-bite-event property of the dose source (the snake), not a property of the patient; in a dataset of envenomed patients each subject's row(s) carry one value across the entire follow-up.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (healthy opioid-naive).
- **Source aliases:** none.
- **Example models:** `Mann_2022_respiratory_physiology.R`, `Sanhajariya_2018_snake_venom.R` (Sanhajariya 2018 Table A1 covariate model: `f(central) <- exp(lfdepot + e_snakefamily_elapid_fdepot * SNAKEFAMILY_ELAPID + etalfdepot)` with `lfdepot = log(1)` fixed and `e_snakefamily_elapid_fdepot = log(0.569)`).
- **Notes:** Scope: specific because the two parameter sets are tied to the Mann 2022 chronic-vs-naive opioid pharmacology calibration. A future model that captures a graded tolerance (e.g., a continuous "tolerance index") rather than a two-class binary should register a separate continuous canonical. Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction.

### CONMED_TARIQUIDAR (**canonical for concomitant tariquidar (P-glycoprotein inhibitor) co-administration indicator**)
- **Description:** 1 = subject co-administered tariquidar (a third-generation P-glycoprotein / breast-cancer-resistance-protein inhibitor used as an experimental probe to block efflux at the blood-brain barrier), 0 = vehicle-only / no concomitant tariquidar. Time-fixed per subject in the Syvanen 2011 source study (single 15 mg/kg IV bolus given 20-30 min before the PET tracer injection means the indicator is operationally constant across the 60-minute PET acquisition window); time-varying use in future per-occasion studies is permitted.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no tariquidar co-administration; vehicle is 5% glucose in saline).
- **Source aliases:**
  - Paper-specific treatment-arm indicator -- used in `Syvanen_2011_verapamil_rat.R` (the paper distinguishes a tariquidar-treatment arm from a vehicle arm; the canonical column is 1 for the 21 tariquidar-treated rats and 0 for the 21 vehicle-treated rats).
- **Example models:** `Syvanen_2011_verapamil_rat.R` (multiplicative theta^COVARIATE form on three structural parameters: `vp = exp(lvp + e_conmed_tariquidar_vp * CONMED_TARIQUIDAR)` with `e_conmed_tariquidar_vp = log(1.20) = 0.1823` (20% increase in plasma peripheral 1 volume); `vbr1 = ... * exp(e_conmed_tariquidar_vbr1 * CONMED_TARIQUIDAR + ...)` with `e_conmed_tariquidar_vbr1 = log(2.41) = 0.8796` (2.41-fold increase in fast-exchange brain compartment volume); `qin = exp(lqin + e_conmed_tariquidar_qin * CONMED_TARIQUIDAR)` with `e_conmed_tariquidar_qin = log(12.0) = 2.4849` (12.0-fold increase in BBB influx clearance). The paper screened tariquidar as a covariate on `Qout` as well but did not retain it.), `Syvanen_2012_quinidine_rat.R` (multiplicative exponential effects on CL, Q_out, Q_in, and f2: `e_conmed_tariquidar_cl = log(0.835) = -0.180`; `e_conmed_tariquidar_qout = log(0.366) = -1.005`; `e_conmed_tariquidar_qin = log(2.65) = 0.975`; `e_conmed_tariquidar_f2 = log(5.66) = 1.733`. Combined Q_in / Q_out shift raises the pseudo-equilibrium brain ECF : plasma ratio 7.2-fold and the combined f2 shift raises the total-brain : plasma ratio about 40-fold per paper Results p91), `Westerhout_2013_quinidine.R` (P-gp split into separate passive `CL_X,p` and P-gp-mediated `CL_X,P-gp` clearances per the paper Appendix mass-balance equations; the P-gp components are switched off when `CONMED_TARIQUIDAR = 1` via the multiplier `pgp_active = 1 - CONMED_TARIQUIDAR` and contribute additively (to efflux clearances `CL_X-PL`) or subtractively (to influx clearances `CL_PL-X`) when `CONMED_TARIQUIDAR = 0`; 1.9-fold P-gp effect on systemic `CL_E` from Table 4 SBPK combined column, with full bidirectional P-gp components on plasma <-> brain_deep / brain_ecf / brain_csf_lv exchanges and P-gp absent at the CM in this final model).
- **Notes:** Scope: specific because the only on-disk source is the Syvanen 2011 rat PET BBB-transport paper and the canonical column meaning is intrinsically tied to the experimental P-gp blockade design rather than a general clinical-coadministration indicator. Per-model `covariateData[[CONMED_TARIQUIDAR]]$notes` must document the dose / regimen of tariquidar used (Syvanen 2011: 15 mg/kg IV bolus in 3 mL/kg of 5% glucose in saline, administered 20-30 min before the radiotracer) and any per-subject vs per-record time-varying convention. Analogous to [[CONMED_PROBENECID]] in the Xie 2000 rat BBB-transport paper -- both are experimentally-administered transporter inhibitors used to dissect a tracer's brain-vs-plasma distribution, encoded as a binary co-administration indicator regardless of the inhibitor's clinical use case. Future preclinical-PET extractions that test tariquidar (or a related Pgp inhibitor like elacridar / zosuquidar with comparable mechanism) on a different probe-substrate should reuse this canonical when the inhibitor is tariquidar; structurally distinct P-gp inhibitors should register a separate canonical with the inhibitor's INN in the name (e.g., `CONMED_ELACRIDAR`). Ratified canonically on 2026-06-03 alongside the Syvanen 2011 (R)-[11C]verapamil rat PET extraction.

### DIS_POSTSE_KAINATE (**canonical for the post-status-epilepticus state induced by kainic-acid pre-treatment in rats**)
- **Description:** 1 = animal underwent kainic-acid-induced status epilepticus (SE) some number of days prior to the modelled observation window, 0 = animal received saline (or another non-epileptogenic control). Time-fixed per animal in the Syvanen 2011 source study (the SE-induction-to-PET interval is a per-arm design constant -- 7 days post-induction at scanning); the canonical column models the chronic post-SE state at a defined post-induction interval, NOT the acute SE episode itself.


### L_ANTAGONIST_pM (**canonical for time-varying opioid-antagonist effect-site concentration input to the Mann 2022 binding layer**)
- **Description:** Time-varying opioid-antagonist effect-site concentration in picomolar (pM), supplied as a data covariate to the multi-ligand competitive mu-receptor binding model. Antagonist analogue of `L_OPIOID_pM`. In a composed Mann 2022 + Laffont 2024 / 2025 chain, the upstream antagonist PK layer (`Laffont_2024_naloxone` or `Laffont_2024_nalmefene`) is post-processed in the vignette by (a) converting time to minutes, (b) convolving plasma concentration with the Mann 2022 ke0 = 0.001774 1/s effect-site equilibration (carried into Laffont 2024 Supp Table S3 unchanged for both nalmefene and naloxone), and (c) converting ng/mL to pM via the antagonist's free-base molecular weight (naloxone 327.37 g/mol, nalmefene 339.43 g/mol); the resulting per-subject time series is supplied as this covariate.
- **Units:** pM (picomolar)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a.
- **Source aliases:** none.
- **Example models:** `Mann_2022_mu_receptor_binding.R`.
- **Notes:** Scope: specific. Same pM-unit / Table-S2-Kon-unit alignment requirement as `L_OPIOID_pM`. Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction.


### CAR_OPIOID (**canonical for time-varying fraction of mu-opioid receptors bound by an agonist input to the Mann 2022 physiology layer**)
- **Description:** Time-varying fraction (0..1) of mu-opioid receptors bound by an opioid agonist. The Mann 2022 respiratory-physiology layer consumes this as a data covariate to drive opioid-induced reductions in wakefulness drive (W - Wmax * CAR^P3) and in chemoreflex drives (factor 1 - CAR^P1). In the composed Mann 2022 chain, this is the `RL_op` output of `Mann_2022_mu_receptor_binding.R`; in standalone physiology-only use, the operator supplies CAR_OPIOID as a time-varying data column.
- **Units:** fraction (0..1)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a; 0 = no receptor occupancy = baseline ventilation.
- **Source aliases:** `RL_op` / `CAR` (binding-model output name).
- **Example models:** `Mann_2022_respiratory_physiology.R`.
- **Notes:** Scope: specific because the semantics are anchored to mu-opioid receptor occupancy in the Mann 2022 translational chain. Future opioid-pharmacology models that consume a different receptor-occupancy concept (e.g., kappa-opioid or delta-opioid) should register a separately named canonical with the receptor subtype in the name (e.g., `CAR_KAPPA`). Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction.

### Q_TOTAL_LPM (**canonical for total cardiac output input to the Mann 2022 opioid-PK shock-state Q_Scale feedback**)
- **Description:** Time-varying total cardiac output Qb + Qt (cerebral + peripheral-tissue blood flow), in L/min, supplied to the Mann 2022 IV-opioid PK models so they can evaluate the FDA delaymymod.c lines 358-368 shock-state Q_Scale feedback: `Q_Scale = 1 + 1 / (1 + exp((1.6 - Q_TOTAL_LPM / 4.87) / 0.05))`, clamped to [1, 2]. Q_Scale scales the effective central volume of distribution down (`vc_eff = vc / Q_Scale`) so that hyperperfusion-driven concentration of opioid in the central / biophase compartment is captured during overdose-induced chemoreflex hyperperfusion. Without this feedback the standalone PK model under-estimates effect-site concentration in shock conditions and produces PaO2 troughs too shallow to reach the cardiac-arrest threshold.
- **Units:** L/min
- **Type:** continuous
- **Scope:** specific
- **Reference category:** 4.87 L/min (FDA delaymymod.c baseline Q_0; gives Q_Scale = 1, no amplification). Standalone PK use should leave Q_TOTAL_LPM at this baseline.
- **Source aliases:** `q_total` / `Q_total` / `Q` (physiology layer state name).
- **Example models:** `Mann_2022_fentanyl_iv.R`, `Mann_2022_carfentanil_iv.R`.
- **Notes:** Scope: specific because the 4.87 / 1.6 / 0.05 numerics are anchored to the FDA delaymymod.c Q_0 baseline and the empirical sigmoid centred at Q/Q_0 = 1.6. In the composed Mann 2022 chain Q_TOTAL_LPM is the `Q_total = qb + qt` output of `Mann_2022_respiratory_physiology.R`; in standalone PK-only use the operator supplies Q_TOTAL_LPM = 4.87 (or whatever fixed baseline appropriate). Registered canonically on 2026-06-07 alongside the FDA shock-state PK amplification fix.

### OPIOID_PATIENT_TYPE (**canonical for opioid-naive vs chronic-opioid-user indicator in the Mann 2022 respiratory-depression PD layer**)
- **Description:** Binary indicator selecting the pharmacodynamic-sensitivity parameter set in the Mann 2022 respiratory-physiology layer. 0 = healthy opioid-naive volunteer (P1 = 2.875, P3 = 0.9); 1 = chronic opioid user with established tolerance (P1 = 4.226, P3 = 1.323). P2 (metabolism exponent) is shared across both patient types at 0.06319. The naive vs chronic split is empirically calibrated against Algera 2021 (Clin Pharmacol Ther 2021;109(3):637-645) and Stoeckel 1982 (Br J Anaesth 1982;54(10):1087-1095); the numeric P1, P3 values are taken from FDA simulateToGetOD_IM.R lines 185-192 (Mann 2022 reference implementation).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (healthy opioid-naive).
- **Source aliases:** none.
- **Example models:** `Mann_2022_respiratory_physiology.R`.
- **Notes:** Scope: specific because the two parameter sets are tied to the Mann 2022 chronic-vs-naive opioid pharmacology calibration. A future model that captures a graded tolerance (e.g., a continuous "tolerance index") rather than a two-class binary should register a separate continuous canonical. Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction.

### DIS_HF_OR_LF_SEV (**canonical for severe heart failure OR severe liver failure pooled indicator**)
- **Description:** 1 = subject has severe heart failure (low cardiac output or pulmonary oedema) OR severe liver failure (advanced cirrhosis), 0 = neither. Pooled indicator used by source papers that observe similar PK effects from end-stage heart and end-stage liver disease (mechanistically: both reduce hepatic perfusion and cytochrome-mediated drug metabolism) and combine them into a single covariate when the per-group counts are too small for separate estimation. Mild and moderate heart or liver dysfunction are excluded by definition; the indicator captures only the severe ends of each axis.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no severe heart failure and no severe liver failure; includes normal function and mild/moderate dysfunction in either axis).
- **Source aliases:**
  - `severe HF or LF` -- narrative composite used in `Fattinger_1991_quinidine.R` (Table 1 row 'CL_nonrenal for patients with severe HF or LF'; severe HF defined as low output or pulmonary oedema, n = 2; severe LF defined as serum bilirubin > 30 umol/L AND prothrombin time < 60% of normal, n = 3; pooled because the two effects on non-renal CL were of similar magnitude and per-group counts were small).
- **Example models:** `Fattinger_1991_quinidine.R` (multiplicative reduction of the non-renal CL arm of total apparent CL from 12.6 L/h to 6.8 L/h when set to 1; log-multiplicative effect `e_dis_hf_or_lf_sev_cl_nonren = log(6.8/12.6) = log(0.5397)`; Table 1 reduction in objective function 10.8, P < 0.005).
- **Notes:** Scope kept `specific` because the pooled HF-or-LF semantic is paper-specific (Fattinger 1991 pools the two severe end-organ-failure axes because the cohort had n = 2 + 3 subjects in those groups and similar effect sizes). A future model that retains severe HF and severe LF as separate covariates (with enough subjects in each to estimate them independently) should use the existing `HEPIMP_SEV` canonical for the severe-hepatic axis and register a parallel `CARDIMP_SEV` canonical for the severe-cardiac axis rather than reuse this pooled indicator. The DIS_HF_OR_LF_SEV pooling preserves the load-bearing convention of the source paper without forcing later users to artificially separate the two axes when the published evidence base lumps them. Ratified canonically on 2026-06-04 alongside the Fattinger 1991 quinidine extraction.

### FORM_QUIN_SR (**canonical for slow-release quinidine bisulphate vs immediate-release quinidine sulphate formulation indicator**)
- **Description:** 1 = subject received slow-release quinidine bisulphate (e.g., Kinidin duriles, Astra; oral slow-release tablets); 0 = subject received immediate-release quinidine sulphate (e.g., Chinidin sulfuricum, Siegfried; oral immediate-release). Per-dose-occasion indicator: in mixed-formulation cohorts a single subject may receive both formulations across dose records, with FORM_QUIN_SR set on each dose record to identify the formulation. The indicator drives the structural switch between formulation-specific zero-order absorption durations and relative bioavailability (slow-release QBS shows ~6 h duration of absorption vs ~1.4 h for immediate-release QS, and ~1.36-fold higher relative bioavailability vs the QS reference).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (immediate-release quinidine sulphate; the typical-value absorption-duration and bioavailability reference, with F = 1 fixed by convention per Fattinger 1991 abstract item 3).
- **Source aliases:** none -- the source paper narratives the formulation as "quinidine sulphate (Chinidin sulfuricum)" vs "slow release quinidine bisulphate (Kinidin duriles)" without a single NMTRAN column name; the model column carries the canonical orientation directly.
- **Example models:** `Fattinger_1991_quinidine.R` (structural switch driving `dur(central)` between `exp(ldur_qs + etaldur_qs) = 1.37 h * exp(eta)` and `exp(ldur_qbs) = 6.0 h`, and `f(central)` between 1 (QS reference) and `exp(lfdepot) = 1.36` (QBS); the IIV on QS absorption duration applies only when FORM_QUIN_SR = 0 per Methods page 282).
- **Notes:** Specific scope because the quinidine sulphate-vs-bisulphate contrast is tied to the specific drug. Drug-specific member of the `FORM_*` family alongside `FORM_TAC_IR` (tacrolimus immediate-release vs prolonged-release), `FORM_LINAG_TAB1` (linagliptin tablet 1), `FORM_VISMO_PHASEI` (vismodegib Phase I dry-blend capsule), `FORM_THEO_APNECUT` (Apnecut vs theophylline-alcohol), `FORM_ASV_LIQUID` (asunaprevir liquid), and `FORM_ABA_PHASE2` (abatacept SC Phase-2). Doses must be entered in mg of quinidine BASE (apply the Windholz 1983 stoichiometric factors of 0.829 mg base per mg quinidine sulphate and 0.663 mg base per mg quinidine bisulphate before passing to the model); the f(central) term then captures only the formulation-driven absorption difference, not the stoichiometric salt-vs-base conversion. Ratified canonically on 2026-06-04 alongside the Fattinger 1991 quinidine extraction.

### FORM_VPA_SR (**canonical for sustained-release valproic acid tablet formulation indicator**)
- **Description:** 1 = the valproic acid dose was given as a sustained-release tablet; 0 = the per-paper reference oral formulation (oral syrup in `Zhang_2023_*`). Per-dose-record indicator. Selects the sustained-release absorption rate constant in models that FIX a formulation-specific `Ka` set rather than estimating absorption.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (oral syrup). Pairs with `FORM_TABLET` to encode the three-level syrup / conventional-tablet / sustained-release-tablet stratification the valproate literature uses, with syrup as the reference level (both indicators 0) -- the same derived-reference pattern as `FORM_TABLET` + `FORM_CAPSULE` in `Kleideiter_2017_cebranopadol.R`.
- **Source aliases:**
  - `SR tablet` -- used in `Zhang_2023_valproic_acid_base.R` and `Zhang_2023_valproic_acid_exponent.R` (Supplementary Table S3 footnote: "Ka was fixed to 2.64, 1.57, 0.46 for syrup, conventional tablet and SR tablet, respectively").
  - `HS` -- Jiang 2007 uses an inverted indicator (`HS = 1` for SR tablet) inside an additive `Ka` expression, `Ka = 0.251 + 2.24 * (1 - HS)`; tabulated in Zhang 2023 Table 1. A model extracting Jiang 2007 directly should populate `FORM_VPA_SR` and negate in the model body rather than registering an inverted sibling.
- **Example models:** `Zhang_2023_valproic_acid_base.R`, `Zhang_2023_valproic_acid_exponent.R`, `Zhang_2023_valproic_acid_onebindingsite.R`, `Zhang_2023_valproic_acid_langmuir.R`, `Zhang_2023_valproic_acid_ddemax.R`, `Zhang_2023_valproic_acid_nonsaturable.R` (all six models of the Zhang 2023 comparison encode `ka <- exp(lka + e_form_tablet_ka * FORM_TABLET + e_form_vpa_sr_ka * FORM_VPA_SR)` with `lka` FIXED at `log(2.64)` for the syrup reference and both shifts FIXED at the log-ratio of the published formulation-specific `Ka` values).
- **Notes:** Specific scope because the fixed `Ka` triple (2.64 / 1.57 / 0.46 1/h) is a valproate-specific literature convention propagated across several paediatric valproate popPK papers (Ding 2015, Gu 2021, Teixeira-da-Silva 2022 and thence Zhang 2023). Distinct from `FORM_QUIN_SR` (quinidine bisulphate slow-release, where the contrast additionally carries a salt-vs-base stoichiometric conversion) and from the modified-release `FORM_FLV_BID_XR` / `FORM_LOV_BID_XR` pair (fluvastatin / lovastatin extended-release, where the indicator also encodes a dosing-frequency change). Trough-only therapeutic-drug-monitoring datasets identify absorption poorly, which is exactly why the source papers fix rather than estimate these constants -- do not re-estimate `lka` from a trough-only cohort.

### FORM_NOSCAPINE_TEST (**canonical for the reformulated noscapine oral-suspension test-product indicator**)
- **Description:** 1 = the noscapine dose was given as the reformulated oral suspension (InfectoPharm, Heppenheim) that served as the *test* product in the Chen 2024 bioequivalence study; 0 = the Nipaxon 5 mg/mL oral suspension (McNeil, Solna) *reference* product. Per-dose-record indicator -- in a crossover design the same subject carries 1 on one period's dose row and 0 on the other's.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (Nipaxon reference oral suspension), which is the relative-bioavailability anchor F = 1.
- **Source aliases:**
  - `formulation (test vs reference)` -- Chen 2024 (Sect. 2.1 describes the two products; Table 4 reports the single formulation parameter `F1 (%) = 82.8`).
- **Example models:** `Chen_2024_noscapine.R` (gates relative bioavailability only: `f(transit1) <- exp((lf1 + etalf1) * FORM_NOSCAPINE_TEST)` with `lf1 = log(0.828)`, so reference doses get F = 1 exactly while test doses get 82.8% carrying 34.1% CV inter-individual variability).
- **Notes:** Both products are oral suspensions of the same strength, so the contrast is a *product* difference, not a dosage-form difference -- hence the drug-plus-product naming rather than reuse of `FORM_SUSPENSION` (which distinguishes a suspension from another dosage form and would be uninformative when both arms are suspensions). Bioavailability is the *only* parameter that differs by formulation: Chen 2024 Sect. 3.3.2 reports that "only apparent bioavailability differed between test and reference preparations", and the Discussion attributes it to "a lower amount of drug released from the suspension" rather than to an absorption-rate difference. Contrast with `FORM_QUIN_SR` / `FORM_VPA_SR`, where the formulation indicator switches an absorption-rate or duration parameter as well. Specific scope because the contrast identifies two named commercial noscapine products. Ratified 2026-08-06 alongside the Chen 2024 noscapine extraction.

## Receptor-binding ligand selection and pharmacological-chain inputs

### OPIOID_ID (**canonical for opioid-agonist ligand selector in the Mann 2022 mu-receptor binding panel**)
- **Description:** Integer 1..13 selecting which ligand from the Mann 2022 Supplement 1 Table S2 binding-kinetic panel (1..12) or the Laffont 2024 Supplementary Table S3 scaling-derived extension (13 = nalmefene) occupies the opioid-agonist slot of a multi-ligand competitive mu-opioid-receptor binding model at simulation time. The same compiled binding model can simulate any agonist in the panel without re-instantiation by varying OPIOID_ID per subject (or per simulation arm). Mapping (preserved verbatim in `Mann_2022_mu_receptor_binding.R::ini()`): 1 = alfentanil; 2 = buprenorphine; 3 = butyryl fentanyl; 4 = carfentanil; 5 = fluorobutyryl fentanyl; 6 = fentanyl; 7 = fluoroisobutyryl fentanyl; 8 = furanyl fentanyl; 9 = isobutyryl fentanyl; 10 = naloxone; 11 = remifentanil; 12 = sufentanil; 13 = nalmefene (added by task 131 from Laffont 2024 Supp Table S3 scaling approach over Cassel 2005). Out-of-range values cause every dispatch indicator to evaluate to 0, which zeros the agonist slot rates rather than throwing an error.
- **Units:** (categorical)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** n/a (every value selects a distinct ligand parameter set).
- **Source aliases:** none (new canonical introduced with Mann 2022 extraction).
- **Example models:** `Mann_2022_mu_receptor_binding.R`.
- **Notes:** Scope: specific because the integer-to-ligand mapping is anchored to Mann 2022 Table S2 row order, with the Laffont 2024 / Laffont 2025 13th-ligand extension preserving that anchoring (nalmefene appended at index 13, not inserted alphabetically). The 2026-05-29 sidecar 132/request-001 operator response authorises this 13th-ligand expansion; task 131 (2026-05-29) executed it after confirming the nalmefene Kon/Koff/n values are explicitly published as final values in Laffont 2024 Supplementary Table S3 (a primary source on disk). Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction; 13th-ligand extension committed by task 131 on the same day.


### ANTAGONIST_ID (**canonical for opioid-antagonist ligand selector in the Mann 2022 mu-receptor binding panel**)
- **Description:** Integer 1..13 selecting which ligand from the Mann 2022 Supplement 1 Table S2 (1..12) or the Laffont 2024 Supplementary Table S3 scaling-derived extension (13 = nalmefene) occupies the antagonist slot of the multi-ligand competitive mu-opioid-receptor binding model at simulation time. Uses the same integer-to-ligand mapping as `OPIOID_ID`. Mann 2022 itself uses only naloxone (10) as the antagonist; Laffont 2024 / Laffont 2025 add nalmefene (13) as the second commercially-relevant intranasal antagonist option. The wider 1..13 range is retained so downstream tasks can flip slots without code changes (e.g., partial-agonist buprenorphine `2` in the antagonist slot to model receptor occupancy without full agonist effect).
- **Units:** (categorical)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** n/a (every value selects a distinct ligand parameter set).
- **Source aliases:** none.
- **Example models:** `Mann_2022_mu_receptor_binding.R`.
- **Notes:** Scope: specific. Same row-order semantics as `OPIOID_ID` (Mann 2022 Table S2 rows 1..12 verbatim; Laffont 2024 Supp Table S3 nalmefene at row 13). Out-of-range values zero the antagonist slot rates safely. Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction; 13th-ligand extension committed by task 131 on the same day.


### L_OPIOID_pM (**canonical for time-varying opioid-agonist effect-site concentration input to the Mann 2022 binding layer**)
- **Description:** Time-varying opioid-agonist effect-site (biophase) concentration in picomolar (pM), supplied as a data covariate to the multi-ligand competitive mu-receptor binding model. The pM unit matches the per-second Kon units (pM^-n s^-1) tabulated in Mann 2022 Supplement 1 Table S2; the binding ODE inside `Mann_2022_mu_receptor_binding.R` consumes this column directly. In a composed Mann 2022 chain, the upstream IV-opioid PK layer (`Mann_2022_fentanyl_iv.R` or `Mann_2022_carfentanil_iv.R`) exposes its effect-site `Ce_pM` as this covariate; in standalone use, the operator supplies it as a time-varying data column on the subject's records.
- **Units:** pM (picomolar)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a.
- **Source aliases:** `Ce_pM` (upstream PK output name when composed in-chain).
- **Example models:** `Mann_2022_mu_receptor_binding.R`.
- **Notes:** Scope: specific because the unit choice (pM) is tied to the Mann 2022 binding-rate parameterisation. A future binding model that uses nM or mg/L should register a separately named canonical (e.g., `L_OPIOID_nM`) rather than reusing this name with a different unit, because the binding model's downstream math depends on the unit match. Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction.


### AGONIST_CODE (**canonical for vasoactive-agonist ligand selector in the Grzesk 2016 vascular-reactivity sigmoidal Emax CRC model**)
- **Description:** Integer 1..4 selecting which vasoactive agonist from the Grzesk 2016 Table I CRC panel occupies the agonist slot of the sigmoidal Emax concentration-response model at simulation time. The same compiled model can simulate any of the four agonists by varying AGONIST_CODE per record (or per simulation arm). Mapping (preserved verbatim in `Grzesk_2016_m3M3FBS.R::ini()` parameter names): 1 = phenylephrine (PHE; alpha1-adrenergic receptor agonist); 2 = arg-vasopressin (AVP; V1 vasopressin receptor agonist); 3 = mastoparan-7 (heterotrimeric G-protein direct activator); 4 = Bay K8644 (L-type voltage-gated calcium channel agonist). Out-of-range values cause every dispatch indicator to evaluate to 0, which zeros the sigmoidal Emax expression rather than throwing an error.
- **Units:** (categorical)
- **Type:** categorical
- **Scope:** specific
- **Reference category:** n/a (every value selects a distinct (EC50, Emax) parameter pair; companion `M3M3FBS_PRESENT` selects within each agonist between the control and +m-3M3FBS sub-pairs).
- **Source aliases:** none -- Grzesk 2016 Tables I and II label the four agonists in prose ("PHE", "AVP", "mastoparan-7", "Bay K8644"); the model column is the canonical `AGONIST_CODE`.
- **Example models:** `Grzesk_2016_m3M3FBS.R` (Grzesk 2016 Table I + Table II Phase 2: per-agonist EC50 (M/L) and maximal-perfusion-pressure Emax (mmHg) pairs; companion `M3M3FBS_PRESENT` flips between control and m-3M3FBS-pretreatment sub-pairs).
- **Notes:** Specific scope because the integer-to-agonist mapping is anchored to Grzesk 2016 Table I row order. The Grzesk-group sibling papers (Biomed Rep 2 / 2014 pertussis toxin; Mol Med Report 5 / 2012 calcium blockers; Exp Ther Med 4 / 2012; etc.) reuse the same isolated-perfused-tail-artery scaffold with overlapping agonist panels (PHE / AVP / mastoparan-7 are common across the series; the third "channel" probe varies); a future extraction of any of those papers can extend the integer mapping at row 5+ rather than re-introduce a parallel selector canonical. Distinct from `OPIOID_ID` / `ANTAGONIST_ID` (Mann 2022 opioid-binding panel) because the agonists here are not within a single receptor-binding family -- PHE, AVP, mastoparan-7, and Bay K8644 hit four mechanistically distinct contractile pathways (alpha1, V1, G-protein direct, L-type Ca channel), and the model uses agonist-specific (EC50, Emax) pairs rather than a shared binding-kinetic parameter set. Ratified canonically alongside the Grzesk 2016 extraction.

### STUDY_TOMORROW (**canonical for TOMORROW (1199.30) IPF phase II study indicator**)
- **Description:** 1 = subject enrolled in the TOMORROW phase II trial (NCT00514683; Richeldi 2011 NEJM), the BI 1199.30 protocol that evaluated nintedanib 50 mg QD, 50 mg BID, 100 mg BID, or 150 mg BID vs placebo over 52 weeks in idiopathic pulmonary fibrosis (IPF); 0 = subject from any other study in the Schmid 2017 nintedanib pooled-PK analysis (NSCLC phase II, LUME-Lung 1, or LUME-Lung 2). Used as a per-subject categorical indicator for the trial-effect covariate on nintedanib first-order absorption rate ka.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-TOMORROW; in Schmid 2017 the trial-effect reference for ka is the LUME-Lung 1 + LUME-Lung 2 phase III NSCLC pair).
- **Source aliases:** derived per subject from the trial identifier.
- **Example models:** `Schmid_2017_nintedanib.R` (combined with `STUDY_NSCLC_NIN_PH2` to drive the ka phase-II trial-group effect = 2.20x; also captures the ka2 BIBF 1202 metabolite phase-II trial-group effect = 0.756x).
- **Notes:** Specific scope because the trial-effect grouping is paper-specific. Ratified canonically on 2026-06-27 alongside the Schmid 2017 nintedanib extraction.

### STUDY_NSCLC_NIN_PH2 (**canonical for nintedanib NSCLC phase II (Reck 2011) study indicator**)
- **Description:** 1 = subject enrolled in the nintedanib phase II NSCLC trial reported by Reck et al. 2011 (Annals of Oncology 22(6):1374-1381; BI 1199.4 protocol), which compared nintedanib 150 or 250 mg BID monotherapy in relapsed advanced NSCLC; 0 = subject from any other study in the Schmid 2017 pooled-PK analysis (TOMORROW IPF phase II, LUME-Lung 1, or LUME-Lung 2). Used as a per-subject categorical indicator for two distinct trial-effect covariates on F1 (relative bioavailability) and ka (absorption rate).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-NSCLC-phase-II; in Schmid 2017 the trial-effect reference for F1 is the LUME-Lung 1 + TOMORROW pair, and the reference for ka / ka2 is the LUME-Lung 1 + LUME-Lung 2 pair).
- **Source aliases:** derived per subject from the trial identifier.
- **Example models:** `Schmid_2017_nintedanib.R` (combined with `STUDY_LUMELUNG2` to drive the F1 trial-group effect = 1.30x; combined with `STUDY_TOMORROW` to drive the ka phase-II trial-group effect = 2.20x and the ka2 BIBF 1202 metabolite phase-II trial-group effect = 0.756x).
- **Notes:** Specific scope. The Schmid 2017 paper's prose refers to this trial as "NSCLC phase II [23]"; the BI protocol code (1199.4) is not explicit in the paper but is the standard BIBF 1120 NSCLC phase II Reck 2011 study. Ratified canonically on 2026-06-27 alongside the Schmid 2017 nintedanib extraction.

### STUDY_LUMELUNG1 (**canonical for LUME-Lung 1 (nintedanib + docetaxel NSCLC phase III) study indicator**)
- **Description:** 1 = subject enrolled in LUME-Lung 1 (NCT00805194; Reck 2014 Lancet Oncology 15(2):143-155; BI 1199.13 protocol), the phase III trial of nintedanib 200 mg BID + docetaxel 75 mg/m^2 vs placebo + docetaxel in second-line advanced NSCLC; 0 = subject from any other study in the Schmid 2017 pooled-PK analysis (TOMORROW IPF phase II, NSCLC phase II, or LUME-Lung 2). Used as a per-subject categorical indicator alongside the other three Schmid 2017 study indicators.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-LUME-Lung-1).
- **Source aliases:** derived per subject from the trial identifier.
- **Example models:** `Schmid_2017_nintedanib.R` (acts as the reference cohort for the F1 trial-effect and as part of the ka / ka2 reference cohort).
- **Notes:** Specific scope. Ratified canonically on 2026-06-27 alongside the Schmid 2017 nintedanib extraction.

### STUDY_LUMELUNG2 (**canonical for LUME-Lung 2 (nintedanib + pemetrexed NSCLC phase III) study indicator**)
- **Description:** 1 = subject enrolled in LUME-Lung 2 (NCT00806819; Hanna 2016 Lung Cancer 102:65-73; BI 1199.14 protocol), the phase III trial of nintedanib 200 mg BID + pemetrexed 500 mg/m^2 vs placebo + pemetrexed in second-line non-squamous NSCLC; 0 = subject from any other study in the Schmid 2017 pooled-PK analysis (TOMORROW IPF phase II, NSCLC phase II, or LUME-Lung 1). Used as a per-subject categorical indicator paired with `STUDY_NSCLC_NIN_PH2` for the F1 trial-group effect.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (non-LUME-Lung-2).
- **Source aliases:** derived per subject from the trial identifier.
- **Example models:** `Schmid_2017_nintedanib.R` (combined with `STUDY_NSCLC_NIN_PH2` to drive the F1 trial-group effect = 1.30x; acts as the reference cohort for the ka / ka2 trial-effect).
- **Notes:** Specific scope. Ratified canonically on 2026-06-27 alongside the Schmid 2017 nintedanib extraction.

### DOSE_AGT_UG (**canonical for per-observation angiotensin challenge dose in ug of angiotensin II equivalents**)
- **Description:** Per-observation intravenous-bolus dose of exogenous angiotensin administered as a pharmacologic probe during an angiotensin-challenge phase I protocol, expressed as ug of angiotensin II equivalents. For an angiotensin II bolus this is the literal injected mass; for an angiotensin I bolus, multiply by Q = 0.78 (molar-weight ratio between angiotensin I and angiotensin II) before populating this column. Each row in the event table carries the dose given immediately before the observed BP peak was sampled.
- **Units:** ug (Ang II equivalents)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** none (continuous).
- **Source aliases:**
  - `D x Q` -- used in `BuchwalderCsajka_1999_angiotensin.R` (Buchwalder-Csajka 1999 Table 1 row 5 column header, where `D` is the raw angiotensin dose in ug and `Q` is the molar-weight conversion factor).
- **Example models:** `BuchwalderCsajka_1999_angiotensin.R` (drives the algebraic Emax dose-response `E = Emax * DOSE_AGT_UG / (DOSE_AGT_UG + ED50)` separately for SBP and DBP).
- **Notes:** Per-observation challenge-dose column for the algebraic Emax angiotensin-challenge PD model. Differs from the standard rxode2 `AMT` / `EVID = 1` dosing column because the model is purely algebraic (no PK, no ODEs); the dose enters the model() block as a covariate symbol rather than via a dosing event. Specific scope because the column's semantics (Q-corrected angiotensin dose) are tied to angiotensin-challenge protocols; future angiotensin-challenge extractions may reuse this canonical. Ratified canonically on 2026-06-10 alongside the Buchwalder-Csajka 1999 extraction.

### DOSE_ISOPROTERENOL_UG (**canonical for per-observation isoproterenol challenge dose in ug**)
- **Description:** Per-observation intravenous-bolus dose of isoproterenol (isoprenaline) administered as a beta-adrenergic agonist probe during an isoproterenol sensitivity test (IST), expressed in ug. Each observation row in the event table carries the isoproterenol dose given immediately before the heart-rate response was sampled; rows outside an IST challenge carry 0, which recovers the drug-free baseline heart rate.
- **Units:** ug
- **Type:** continuous
- **Scope:** specific
- **Reference category:** none (continuous). Set to 0 for observation rows with no isoproterenol challenge; the Emax term then vanishes and the readout reduces to the baseline `E0`.
- **Source aliases:**
  - `D` -- used in `Hwang_2023_carvedilol.R` (Hwang 2023 equations (3) and (4) and Fig. 1, where `D` is the isoproterenol dose driving the direct-effect Emax heart-rate response).
- **Example models:** `Hwang_2023_carvedilol.R` (drives the competitive-antagonism direct-effect Emax heart-rate model `HR = E0 + Emax * DOSE_ISOPROTERENOL_UG / (ED50 * (1 + Cc / IC50) + DOSE_ISOPROTERENOL_UG)`, in which the carvedilol plasma concentration inflates the apparent isoproterenol ED50).
- **Notes:** Per-observation challenge-dose column for algebraic Emax agonist-challenge PD models, following the `DOSE_AGT_UG` precedent (angiotensin challenge; Buchwalder-Csajka 1999). Differs from the standard rxode2 `AMT` / `EVID = 1` dosing column because the challenge agonist has no PK compartment in the model: the dose enters `model()` as a covariate symbol rather than via a dosing event. Hwang 2023 Fig. 1 draws an isoproterenol compartment with an elimination rate constant `k`, but no value for `k` is reported anywhere in the paper and the final-model equation printed in the same figure is algebraic in the dose `D`, so the dose-as-covariate encoding is the one the published parameters support. Specific scope because the semantics are tied to isoproterenol sensitivity-test protocols; future beta-blocker IST extractions should reuse this canonical. Ratified canonically on 2026-07-28 alongside the Hwang 2023 carvedilol extraction.

### AUC_EMPA (**canonical for per-subject steady-state AUC of empagliflozin**)
- **Description:** Per-subject (time-fixed) steady-state AUC of empagliflozin over the q24h dosing interval, supplied as a static drug-exposure covariate to PD models that consume the upstream popPK without instantiating a PK ODE. The source authors generate AUCss values from individual empirical Bayes estimates of an upstream empagliflozin popPK analysis (Mondick 2018 plus additional EASE-2 / EASE-3 data on file in the M-EASE-2 application) and pass them as a static covariate column to the M-EASE-2 PD model.
- **Units:** `nmol*h/L` (document per-model via `covariateData[[AUC_EMPA]]$units` if a different exposure unit is reported).
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via direct Emax form `emax_i * AUC_EMPA / (auc50 + AUC_EMPA)` on the HbA1c drug-effect term. Set AUC_EMPA = 0 to recover the placebo arm (drug-effect term vanishes; HbA1c follows baseline + placebo drift only). Reference values observed: AUC50 = 498 nmol*h/L (Johnston 2019 Table 2 typical-value AUC at half-maximal effect; corresponds approximately to the simulated median AUCss for the 2.5 mg QD arm).
- **Source aliases:**
  - `AUCss` (or `AUCSS,i`) -- the printed variable name in Johnston 2019 Equation 1 (the individual steady-state AUC entering the Emax expression).
- **Example models:** `Johnston_2019_empagliflozin.R` (M-EASE-2 exposure-response PD model for HbA1c in adults with type 1 diabetes; AUC_EMPA drives the direct Emax HbA1c-reduction term), `Johnston_2021_empagliflozin_MEASE1.R` (semi-mechanistic exposure-response PD model on TDID / MDG / HbA1c in adults with T1D; AUC_EMPA drives the direct Emax reductions on both TDID and MDG, which in turn drive HbA1c).
- **Notes:** Specific scope because the column meaning is tied to empagliflozin and to a q24h steady-state AUC convention. Sibling drug-specific AUC canonicals (`AUC_CARBO`, `AUC_GEM`, `AUC_BAST_FW`, `AUC_PAZO`, `AUC_GCV`, `AUC_RTV`) follow the same `AUC_<DRUG>` naming pattern. A future PK/PD model that uses a different empagliflozin exposure metric (trough concentration, q12h-interval AUC) should register a parallel canonical rather than overload `AUC_EMPA`. Ratified canonically alongside the Johnston 2019 M-EASE-2 extraction.

### CONMED_CYP3A4_INH_STRONG (**canonical for concomitant strong CYP3A4 inhibitor coadministration indicator**)
- **Description:** 1 = subject / dose-record with concomitant coadministration of a strong CYP3A4 inhibitor (FDA/EMA classification of "strong": AUC increase >= 5-fold for a sensitive CYP3A4 substrate; representative agents include itraconazole, ketoconazole, posaconazole, voriconazole, clarithromycin, and ritonavir-boosted regimens), 0 = no strong CYP3A4 inhibitor coadministration during the observation window (whether no CYP3A4 inhibitor at all, or a weak/moderate inhibitor only). Time-varying per record. Companion canonical to the broader `CONMED_CYP3A4_INH` that stratifies specifically by FDA/EMA inhibitor-strength tier -- distinct from `CONMED_CYP3A4_INH_HI` (which stratifies by cumulative exposure duration rather than by inhibitor-strength category).
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no strong CYP3A4 inhibitor coadministration; subject may still be on a weak or moderate CYP3A4 inhibitor).
- **Source aliases:**
  - `DDICYPSH` -- used in `Mitra_2026_ziftomenib.R` (Kura Oncology KOMET-001 + KO-MEN-003 NONMEM control-stream column; strong CYP3A4 inhibitor coadministration during a ziftomenib dose record; dominant driver in the R/R AML cohort is prophylactic antifungal azole use).
- **Example models:** `Mitra_2026_ziftomenib.R` (multiplicative effects: 0.459x on parent ziftomenib CL/F, 0.195x on KO-739 CL, 0.449x on KO-516 CL when CONMED_CYP3A4_INH_STRONG = 1; encoded as additive shifts on the log(CL) scale per the paper's NONMEM PK block), `Kemal_2026_nemtabrutinib.R` (multiplicative effect on CL/F: `(1 + -0.0119 * CONMED_CYP3A4_INH_STRONG)`, i.e. 1.2% lower CL/F under strong CYP3A4 inhibition; RSE 774%, 95% CI includes zero -- retained in the full covariate model).
- **Notes:** Companion canonical to `CONMED_CYP3A4_INH` (which pools any inhibitor strength into a single 0/1 indicator) and to `CONMED_CYP3A4_INH_HI` / `CONMED_CYP3A4_INH_LO` (which stratify by cumulative exposure duration rather than by inhibitor-strength category). Registered per the `CONMED_CYP3A4_INH` Notes explicit guidance: "Future models that need stratified encoding (separate strong / moderate / weak indicators) should register companion canonicals (e.g. `CONMED_CYP3A4_INH_STRONG`, `CONMED_CYP3A4_INH_MOD`, `CONMED_CYP3A4_INH_WEAK`) rather than overloading `CONMED_CYP3A4_INH`." Ratified canonically on 2026-07-24 alongside the Mitra 2026 ziftomenib extraction. Future extractions that need the sibling moderate- and weak-strength indicators should register `CONMED_CYP3A4_INH_MOD` and `CONMED_CYP3A4_INH_WEAK` following the same pattern.

### DOSE_PTM_MG (**canonical for administered pretomanid per-administration dose amount**)
- **Description:** Administered oral dose of the nitroimidazooxazine antituberculosis agent pretomanid (formerly PA-824), in mg, for the current dose record. Per-dose-record covariate; must equal the `amt` of the corresponding dosing record. Not a PK covariate in the usual sense -- the amount already appears on the dose record via `amt` -- but it is required as an explicit regressor because pretomanid bioavailability is saturable in dose, so the dose amount has to be readable inside `model()` to compute `f(depot)`.
- **Units:** mg
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via the saturable bioavailability `f(depot) = Fmax / (1 + DOSE_PTM_MG / ED50)` with `Fmax` assumed 1 and `ED50` = 554 mg in humans (Mehta 2023 Table 1). A 200 mg dose therefore gives F = 1 / (1 + 200/554) = 0.735.
- **Source aliases:**
  - `dose` -- used in `Mehta_2023_pretomanid_mpbpk.R` (Mehta 2023 ESM S2, which writes `doseIn = Fmax*dose/(1 + dose/ED50)` followed by `f(depot) = doseIn/dose`; the packaged model uses the algebraically identical single-expression form, which avoids dividing by the dose amount).
- **Example models:** `Mehta_2023_pretomanid_mpbpk.R` (drives the dose-dependent bioavailability of the translational lung-lesion mPBPK model; simulated regimen 200 mg once daily, clinical validation data spanning 50-1200 mg).
- **Notes:** Follows the `DOSE_<DRUG>_<UNITS>` auto-approve family (siblings: `DOSE_EMPA_MGD`, `DOSE_CIPARGAMIN_MG`, `DOSE_PHT_MGKGD`). Units suffix is `MG` because pretomanid is dosed as a per-administration amount rather than a daily total. Ratified canonically alongside the Mehta 2023 pretomanid extraction.

### INSDOSE_BL (**canonical for baseline total daily insulin dose per body weight**)
- **Description:** Per-subject (time-fixed) total daily exogenous insulin dose at study baseline, normalised to body weight. Captures the insulin requirement of patients on background insulin therapy (a typical input covariate in T1DM / T2DM popPK/PD models where the empagliflozin / SGLT-2 inhibitor / GLP-1 analogue effect is layered on top of pre-existing insulin treatment). Distinct from `INS_BL` (baseline fasting plasma insulin concentration, a measured biomarker in pmol/L) -- `INSDOSE_BL` is the administered dose level, not the resulting plasma level.
- **Units:** `U/kg/day` (international units of insulin per kilogram body weight per day). Document per-model via `covariateData[[INSDOSE_BL]]$units` if a paper reports total daily units without body-weight normalisation.
- **Type:** continuous
- **Scope:** specific
- **Reference category:** n/a -- enters via power form `(INSDOSE_BL / ref)^e_insdose_bl_<param>`. Reference values observed: 0.660 U/kg/day (Johnston 2019 reference-patient description).
- **Source aliases:**
  - `IDB` -- "Insulin daily dose at Baseline" abbreviation used in Johnston 2019 Table 2.
- **Example models:** `Johnston_2019_empagliflozin.R` (M-EASE-2 covariate effect on the typical baseline HbA1c and on Emax in T1DM patients with background insulin therapy; effect exponents 0.0141 on baseline HbA1c and 0.0552 on Emax, both with CIs that cross zero -- the covariate was retained as part of the full-random-effects covariate model rather than dropped), `Johnston_2021_empagliflozin_popPK.R` (Johnston 2021 empagliflozin popPK covariate on CL/F in adults with T1D; effect exponent 0.0469 with CI that spans zero -- retained as part of the full-covariate model).
- **Notes:** Specific scope because the column meaning is tied to baseline total-daily-dose accounting in insulin-treated diabetes populations; future T1DM / T2DM popPK/PD extractions can reuse this canonical and document the per-model units / dose-counting convention in `covariateData[[INSDOSE_BL]]$notes`. A model that uses time-varying insulin dose (rather than baseline only) should register a parallel canonical (e.g., `INSDOSE`). Companion concept to `INS_BL` (plasma insulin concentration) and to `HBA1C` (glycemic control). Ratified canonically alongside the Johnston 2019 M-EASE-2 extraction.

### INSDT_CSII (**canonical for binary CSII vs MDI insulin delivery type indicator**)
- **Description:** Binary indicator for the subject's insulin delivery type at study entry. 1 = continuous subcutaneous insulin infusion (CSII; insulin pump therapy); 0 = multiple daily injections (MDI; subcutaneous injection regimen) reference. Per-subject and time-fixed within the analysis window (regimen switches during the study are not represented).
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 = MDI (multiple daily injections).
- **Source aliases:**
  - `INSDT` -- "Insulin Dose Type" abbreviation used in Johnston 2019 Table 2 (the two-level categorical column decomposed into the binary CSII indicator with MDI as the reference category).
- **Example models:** `Johnston_2019_empagliflozin.R` (M-EASE-2 covariate multiplier on baseline HbA1c, Emax, and placebo rate; CSII shifts placebo drift upward by a factor of 1.47 vs MDI -- a moderate effect on the placebo-adjusted HbA1c change), `Johnston_2021_empagliflozin_MEASE1.R` (M-EASE-1 covariate multiplier on Emax_MDG in T1D; the reported CSII / MDI effect is very close to 1 (0.995) and is documented as retained-but-negligible in the full-covariate model).
- **Notes:** Specific scope because the CSII-vs-MDI contrast and reference category are study-specific. Follows the `<COLUMN>_<LEVEL>` decomposition pattern used for `REGI_BID` / `TUMTP_GLIO`. A future paper that retains the same INSDT covariate but with more than two levels (e.g., CSII vs MDI vs continuous glucose monitoring + open-loop algorithm) would register sibling canonicals (e.g., `INSDT_CGM`) rather than overload this entry. Ratified canonically alongside the Johnston 2019 M-EASE-2 extraction.

### LSCI (**canonical for amplitude of an inverse-Bateman lifestyle-change / placebo effect on energy intake in body-composition modelling**)
- **Description:** Per-subject (or per-study-arm) amplitude of the inverse-Bateman lifestyle-change (LSC) effect on energy intake (EI) in body-composition models. Drives the magnitude of the placebo / dietary-restriction-induced fractional reduction in EI; combined with onset rate Kdiet and reduction rate Kred to form the LSC effect `LSCeff(t) = 1 - LSCI * Kdiet * (exp(-Kdiet * t) - exp(-Kred * t)) / (Kred - Kdiet)` applied multiplicatively to EI. Subjects in the same study arm typically share the same LSCI value; the column is time-fixed at study entry (a single LSCI per subject per arm).
- **Units:** fraction (0..1; can be negative for arms that report a paradoxical increase in EI)
- **Type:** continuous
- **Scope:** specific
- **Reference category:** none -- LSCI = 0 corresponds to no lifestyle effect (the model collapses to pure body composition + drug effects).
- **Source aliases:**
  - `LSCI` -- used directly in `Bosch_2024_glp1ra_bodyweight.R` (Bosch 2024 supplement S10 THETA7..THETA16 are per-study fitted LSCI values).
- **Example models:** `Bosch_2024_glp1ra_bodyweight.R` (introduces the canonical; per-study fitted values 0..0.548 across diet and GLP-1RA studies, with -0.0288 reported for the Blundell 2017 arm).
- **Notes:** Specific scope because the column's semantics are tied to body-composition / energy-balance modelling. Future extractions that combine the Hall body composition framework with other drug classes or other lifestyle-intervention protocols may reuse this canonical; document the per-paper values in `covariateData[[LSCI]]$notes` so users can match the source-paper LSCI to the simulated arm. Ratified canonically on 2026-06-22 alongside the Bosch 2024 extraction.

### WM_IBT (**canonical for weight-management + intensive-behavioural-treatment intervention indicator**)
- **Description:** Binary indicator that the subject is enrolled in a study arm with weight management and intensive behavioural treatment (e.g., the STEP-trial-family lifestyle-intervention protocols described in Wadden 2021 / Garvey 2022 / Rubino 2022). Used in QSP body-composition models to gate a body-weight-dependent activity effect on the exercise component of physical activity energy expenditure. Subjects in arms without an explicit weight-management + IBT protocol carry WM_IBT = 0.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (no weight management + intensive behavioural treatment).
- **Source aliases:**
  - `IFLAG` -- used in `Bosch_2024_glp1ra_bodyweight.R` (Bosch 2024 supplement S10 IFLAG > 0 gates the activity effect).
- **Example models:** `Bosch_2024_glp1ra_bodyweight.R` (introduces the canonical; gates the body-weight-dependent activity effect on PAE for STEP-family arms in the Bosch 2024 dataset).
- **Notes:** Specific scope because the canonical name is tied to body-composition QSP modelling where the activity effect is empirically estimated for IBT-instrumented arms. Future extractions of similar lifestyle-intervention-aware body-composition or weight-loss-trajectory models may reuse this canonical; mark the per-paper IBT-intensity definition in `covariateData[[WM_IBT]]$notes` so users can distinguish "weekly clinic counselling + diary review" intensity (STEP 3) from "minimal-counselling -500 kcal/d" intensity (STEP 5 / STEP 8). Ratified canonically on 2026-06-22 alongside the Bosch 2024 extraction.

### STRAIN_C57BI6 (**canonical for C57BI/6 mouse-strain indicator**)
- **Description:** Binary within-species mouse-strain indicator: 1 = subject is a C57BI/6 mouse, 0 = subject is a mouse of the reference strain (in the founding example, NMRI). Used to gate strain-specific fractional multipliers on structural PK parameters within a mouse popPK model when the source paper pools two strains and reports a fractional strain effect.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (NMRI mouse in Larsen 2018; other reference strains permitted for future extractions provided the per-paper reference is documented in `covariateData[[STRAIN_C57BI6]]$notes`).
- **Source aliases:** none.
- **Example models:** `Larsen_2018_factorviia_mouse.R` (Larsen 2018 pooled C57BI/6 + NMRI mice with fractional C57BI/6 effect on V and CL for rFVIIa: `V_C57BI6 = V * 0.588`, `CL_C57BI6 = CL * 0.87` per Table 2).
- **Notes:** Analogous to the `RACE_<GROUP>` naming family for human race indicators: `STRAIN_<GROUP>` gives each within-species strain-of-interest its own binary indicator, with the reference strain implicitly encoded as `0`. A future mouse popPK model that pools additional strains (e.g., BALB/c, C3H) should register a parallel canonical (`STRAIN_BALBC`, `STRAIN_C3H`, ...) rather than overload this one. Ratified alongside the Larsen 2018 rFVIIa mouse extraction.

### IP_FA (**canonical for tablet-transit inflection-point time from fundus to antrum**)
- **Description:** Individual inflection-point time (h) at which the sigmoid step function governing tablet movement from the fundus to the antrum equals 0.5 (paper Equation 1 form: `STEP(t) = 1 / (1 + exp(-SIG * (t - IP)))`). Used in the Gastro-Intestinal Transit Time (GITT) absorption model of Henin 2012 to drive per-subject tablet residence time in the fundus. The paper samples IP per subject from a fixed log-normal distribution `IP = MRT * exp(eta)` with `eta ~ N(0, VRT)` and MRT / VRT taken from the upstream Bergstrand 2009 Markov-chain fit (Table II of Henin 2012): MRT_fundus = 0.4 h (fasted) / 1.04 h (fed), VRT_fundus = 0.46 / 1.09 h^2 (CV 100%).
- **Units:** h
- **Type:** continuous
- **Scope:** specific
- **Reference category:** NULL -- IP_FA is a continuous per-subject residence time; the sigmoid step is centred at IP_FA regardless of whether the subject is fed or fasted, but the sampling distribution is fed / fasted stratified.
- **Source aliases:**
  - `IP_F_A` -- used directly in the Henin 2012 model file annotations for the fundus-to-antrum inflection point.
- **Example models:** `Henin_2012_felodipine.R` (founding example; used in the felodipine GITT extraction for the extended-release tablet transit).
- **Notes:** Specific scope because the canonical name is tied to the GITT / MMM-derived semi-mechanistic absorption modelling framework where the tablet position is modelled by sigmoid STEP functions with per-subject inflection points. Only the "no return to fundus" subpopulation is encoded in the Henin 2012 extraction (7/12 felodipine subjects per the paper); the paper's 3-component mixture with 0 / 1 / 2 antrum-to-fundus returns is documented as a deviation in the vignette. Future GITT-family extractions (Bergstrand 2009 upstream, or 2020+ mechanistic-absorption papers extending the same STEP-function idiom) should reuse this canonical. Ratified canonically alongside the Henin 2012 extraction.

### IP_APSI (**canonical for tablet-transit inflection-point time from antrum to proximal small intestine**)
- **Description:** Individual inflection-point time (h) at which the sigmoid step function governing tablet movement from the antrum (or the enteric-coated-tablet whole-stomach exit) to the proximal small intestine equals 0.5. Central variable in the Gastro-Intestinal Transit Time (GITT) model (Henin 2012 Equation 1). For the extended-release felodipine model the population distribution is fed / fasted stratified: MRT_antrum = 0.32 h (fasted) / 1.58 h (fed), VRT_antrum = 0.15 / 2.50 h^2 (CV 100%). For enteric-coated diclofenac the same covariate represents the combined stomach transit (mean ~ 2 h ranging 1.5-3 h per paper Results).
- **Units:** h
- **Type:** continuous
- **Scope:** specific
- **Reference category:** NULL -- continuous per-subject residence time.
- **Source aliases:**
  - `IP_A_PSI` -- used in the Henin 2012 model file annotations for the antrum-to-proximal-small-intestine inflection point.
- **Example models:** `Henin_2012_felodipine.R` (extended-release with drug release in fundus + antrum + PSI + DSI + colon), `Henin_2012_diclofenac.R` (enteric-coated with stomach transit only, no drug release in stomach; IP_APSI represents the gastric emptying event).
- **Notes:** Specific scope. IP_APSI's exact operational meaning differs slightly between the two Henin 2012 models: felodipine's IP_APSI is the antrum -> PSI transition (fundus and antrum are separate compartments), while diclofenac's IP_APSI is a lumped stomach -> PSI transition (fundus and antrum are not distinguished because the enteric coating prevents any drug release in the stomach). Both use the same STEP-function machinery and the same "no return to fundus" subpopulation simplification. Ratified canonically alongside the Henin 2012 extraction.

### IP_PSI_DSI (**canonical for tablet-transit inflection-point time from proximal to distal small intestine**)
- **Description:** Individual inflection-point time (h) at which the sigmoid step function governing tablet movement from the proximal small intestine to the distal small intestine equals 0.5. Not fed / fasted stratified in the Henin 2012 GITT framework. Sampled per subject as `IP_PSI_DSI = MRT_psi * exp(eta)` with MRT_psi = 1.17 h, VRT_psi = 1.37 h^2 (CV 50%) per Table II.
- **Units:** h
- **Type:** continuous
- **Scope:** specific
- **Reference category:** NULL -- continuous per-subject residence time.
- **Source aliases:** none.
- **Example models:** `Henin_2012_felodipine.R`, `Henin_2012_diclofenac.R`.
- **Notes:** Specific scope. Time-zero-referenced: IP_PSI_DSI is the absolute clock time (relative to dose administration) at which the sigmoid switch crosses 0.5. The effective PSI residence time is IP_PSI_DSI - IP_APSI. Ratified canonically alongside the Henin 2012 extraction.

### IP_DSI_C (**canonical for tablet-transit inflection-point time from distal small intestine to colon**)
- **Description:** Individual inflection-point time (h) at which the sigmoid step function governing tablet movement from the distal small intestine to the colon equals 0.5. Not fed / fasted stratified in the Henin 2012 GITT framework. Sampled per subject as `IP_DSI_C = MRT_dsi * exp(eta)` with MRT_dsi = 1.22 h, VRT_dsi = 1.48 h^2 (CV 58%) per Table II.
- **Units:** h
- **Type:** continuous
- **Scope:** specific
- **Reference category:** NULL -- continuous per-subject residence time.
- **Source aliases:** none.
- **Example models:** `Henin_2012_felodipine.R`, `Henin_2012_diclofenac.R`.
- **Notes:** Specific scope. Time-zero-referenced; effective DSI residence time is IP_DSI_C - IP_PSI_DSI. Ratified canonically alongside the Henin 2012 extraction.

## Molecule-level physicochemical / developability attributes

Covariates whose value is a property of the **administered molecule** rather than of the subject. They arise in inter-molecule-variability analyses (mAb developability panels, MBMA-style multi-compound PBPK fits) where many compounds are dosed under one protocol and a measured in-vitro attribute of each compound explains the between-compound spread in PK. Operationally they behave like any other covariate column: every record for a given subject carries the attribute of the compound that subject received, and the value is constant within subject.

### CONMED_VENLAFAXINE (**canonical for concomitant venlafaxine coadministration indicator**)
- **Description:** 1 = subject is coadministered venlafaxine (serotonin-norepinephrine reuptake inhibitor, cleared predominantly by CYP2D6 to O-desmethylvenlafaxine with a CYP3A4 minor route) at the pharmacokinetic observation, 0 = no concomitant venlafaxine. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant venlafaxine).
- **Source aliases:** none.
- **Example models:** `Zhang_2024_sertraline.R` (screened on sertraline CL/F and V/F via the paper's categorical-covariate form and not retained; declared in `covariatesDataExcluded` to preserve the screen -- founding example).
- **Notes:** Named-drug member of the `CONMED_<INN>` family. Relevant to psychotropic popPK models both as a coadministered antidepressant and as a CYP2D6 substrate that can compete with other CYP2D6-cleared comedications. Register sibling named canonicals for other antidepressants screened by name rather than reusing this one; reserve a future class-level `CONMED_ANTIDEPRESSANT` for sources that pool the class.

### STUDY_VANCO_CENTER2 (**canonical for Shen 2024 vancomycin second-study-center indicator**)
- **Description:** 1 = subject enrolled at the second of the two study centers pooled in the Shen 2024 Southern Chinese pediatric vancomycin analysis (Table 1 column N2, n = 176 patients / 249 concentrations); 0 = subject enrolled at the first center (Table 1 column N1, n = 210 patients / 272 concentrations). Selects between the two additive residual-error magnitudes the paper estimated per center.
- **Units:** (binary)
- **Type:** binary
- **Scope:** specific
- **Reference category:** 0 (center N1).
- **Source aliases:** derived per subject from the recruiting-hospital identifier. Shen 2024 does not state which of Baoan Women's and Children's Hospital / Shenzhen Children's Hospital is N1 and which is N2, so the indicator is anchored to the Table 1 column label rather than to a hospital name.
- **Example models:** `Shen_2024_vancomycin.R` (switches the additive residual SD between `addSdCenter1 = 4.64 mg/L` and `addSdCenter2 = 4.53 mg/L`, per Shen 2024 Table 3 rows RV1 / RV2 and Results "RV was best characterized by an additive error model for each of the two study centers").
- **Notes:** Specific scope; tied to the two-hospital Shen 2024 vancomycin cohort. Member of the auto-approved `STUDY_<id>` family, and the first member keyed on a recruiting *center* rather than on a trial phase or protocol number -- the `STUDY_<DRUG>_PHASE<N>` naming of `STUDY_POSA_PHASE3` / `STUDY_NIPOCALIMAB_PHASE1` / `STUDY_SULDUR_PHASE2` does not apply because both Shen 2024 centers contributed the same kind of routine-TDM observations under one protocol. Subject-level (time-fixed). Distinct from the model's age strata: center N1 has median age 0.95 y and N2 median 4.32 y, but both centers contribute patients on both sides of the 2-year clearance cutoff, so this indicator must not be conflated with the `AGE <= 2` stratum switch. A future multi-center analysis of a different drug should register its own `STUDY_<drug>_CENTER<N>` sibling rather than reuse this entry, because the center numbering is source-table-specific.

### CONMED_MEROPENEM (**canonical for concomitant meropenem coadministration indicator**)
- **Description:** 1 = subject is coadministered meropenem (carbapenem beta-lactam, eliminated predominantly by renal filtration and tubular secretion with partial hydrolysis by dehydropeptidase-I) at the pharmacokinetic observation, 0 = no concomitant meropenem. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant meropenem).
- **Source aliases:** none.
- **Example models:** `Shen_2024_vancomycin.R` (screened on vancomycin CL because it was coadministered in more than 10% of patients -- 43.76% of records -- and not retained; declared in `covariatesDataExcluded` to preserve the screen -- founding example).
- **Notes:** Named-drug member of the `CONMED_<INN>` family. Relevant to renally cleared antibacterial popPK models as a potential competitor for tubular secretion. Register sibling named canonicals for other antibacterials screened by name rather than reusing this one; reserve a future class-level `CONMED_CARBAPENEM` or `CONMED_ABX` for sources that pool the class.

### CONMED_CEFOPERAZONE (**canonical for concomitant cefoperazone coadministration indicator**)
- **Description:** 1 = subject is coadministered cefoperazone (third-generation cephalosporin, eliminated predominantly by biliary excretion) at the pharmacokinetic observation, 0 = no concomitant cefoperazone. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant cefoperazone).
- **Source aliases:** none.
- **Example models:** `Shen_2024_vancomycin.R` (screened on vancomycin CL because it was coadministered in more than 10% of patients -- 25.91% of records -- and not retained; declared in `covariatesDataExcluded` to preserve the screen -- founding example).
- **Notes:** Named-drug member of the `CONMED_<INN>` family. Cefoperazone is unusual among cephalosporins in being cleared mainly by the biliary rather than the renal route, so it should not be folded into a renal-competition class indicator. Register sibling named canonicals for other cephalosporins screened by name rather than reusing this one.

### CONMED_CEFTRIAXONE (**canonical for concomitant ceftriaxone coadministration indicator**)
- **Description:** 1 = subject is coadministered ceftriaxone (third-generation cephalosporin, highly protein-bound, eliminated by both renal and biliary routes) at the pharmacokinetic observation, 0 = no concomitant ceftriaxone. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant ceftriaxone).
- **Source aliases:** none.
- **Example models:** `Shen_2024_vancomycin.R` (screened on vancomycin CL because it was coadministered in more than 10% of patients -- 25.72% of records -- and not retained; declared in `covariatesDataExcluded` to preserve the screen -- founding example).
- **Notes:** Named-drug member of the `CONMED_<INN>` family. Ceftriaxone's high albumin binding makes it a plausible displacement interactant as well as a coeliminated antibacterial, which is why sources screen it by name rather than pooling it into a class indicator.

### CONMED_MANNITOL (**canonical for concomitant mannitol coadministration indicator**)
- **Description:** 1 = subject is coadministered mannitol (osmotic diuretic, freely filtered at the glomerulus and not reabsorbed) at the pharmacokinetic observation, 0 = no concomitant mannitol. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
- **Units:** (binary)
- **Type:** binary
- **Scope:** general
- **Reference category:** 0 (no concomitant mannitol).
- **Source aliases:** none.
- **Example models:** `Shen_2024_vancomycin.R` (screened on vancomycin CL because it was coadministered in more than 10% of patients -- 12.67% of records -- and not retained; declared in `covariatesDataExcluded` to preserve the screen -- founding example).
- **Notes:** Named-drug member of the `CONMED_<INN>` family. Mechanistically distinct from the antibacterial `CONMED_<INN>` entries: mannitol raises urine flow and can transiently increase the renal clearance of filtered drugs, so it is screened in renally cleared popPK models as a renal-haemodynamic perturbation rather than as a metabolic interactant. Register `CONMED_FUROSEMIDE` and other named diuretics separately rather than reusing this entry; reserve a future class-level `CONMED_DIURETIC` for sources that pool the class.
