---
title: "Zenocutuzumab (deVriesSchultink 2020)"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Zenocutuzumab (deVriesSchultink 2020)}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  fig.width  = 7,
  fig.height = 5
)
```

```{r setup}
library(nlmixr2lib)
library(rxode2)
library(dplyr)
library(tidyr)
library(ggplot2)
library(PKNCA)
```

# Zenocutuzumab (MCLA-128) population PK in solid tumors

Simulate zenocutuzumab (MCLA-128) concentration-time profiles using the final
population PK model of de Vries Schultink et al. (2020) in patients with
advanced solid tumors. The source analysis pooled 1,115 serum concentrations
from 116 patients across the dose-escalation and dose-expansion cohorts of
the phase I/II NCT02912949 trial; 93/116 patients received the 750 mg q3w
dose-expansion regimen.

MCLA-128 / zenocutuzumab is a full-length humanized **bispecific IgG1**
monoclonal antibody (anti-HER2 x anti-HER3) with low-fucose
glycoengineering for enhanced antibody-dependent cell-mediated cytotoxicity
(ADCC). It is the **first bispecific antibody packaged in nlmixr2lib**;
the structural population-PK model below is, however, identical in shape
to the parallel-linear-plus-MM clearance models routinely used for
single-target therapeutic IgGs.

The final model is a **two-compartment model with parallel linear and
Michaelis-Menten (non-linear) elimination** from the central compartment:

\[
\frac{d\,A_1}{dt} = -\frac{CL}{V_1}\,A_1
                    - \frac{V_{\max}\,C_1}{K_m + C_1}
                    - \frac{Q}{V_1}\,A_1
                    + \frac{Q}{V_2}\,A_2,
\qquad
\frac{d\,A_2}{dt} =  \frac{Q}{V_1}\,A_1 - \frac{Q}{V_2}\,A_2,
\]

with \(C_1 = A_1/V_1\) (mg/L). The peak concentration after a 750 mg dose
(~220 mg/L) is roughly 1000-fold above the estimated \(K_m\) of 0.211 mg/L,
so the MM pathway is saturated (zero-order at \(\approx V_{\max}\)) over the
peak interval and contributes meaningfully only as concentrations approach
\(K_m\) in the washout tail. Linear FcRn-mediated catabolism dominates
through most of the dosing interval.

* Article (subscription): <https://doi.org/10.1007/s40262-020-00858-2>
* PubMed (PMID 32006223): <https://pubmed.ncbi.nlm.nih.gov/32006223/>

## Source trace

The per-parameter origin is recorded as an in-file comment next to each
`ini()` entry in
`inst/modeldb/specificDrugs/deVriesSchultink_2020_zenocutuzumab.R`. The
table below collects the mapping in one place for reviewer audit.

| Element | Source location | Value / form |
| --- | --- | --- |
| Two-compartment IV model with parallel linear and Michaelis-Menten clearance from the central compartment | de Vries Schultink 2020 Results Section 3.2 (final-model differential equations) | `d/dt(central) = -kel*central - Vmax*Cc/(Km+Cc) - k12*central + k21*peripheral1` |
| Linear CL (reference subject) | de Vries Schultink 2020 Table 2 (covariate model column) | 0.0304 L/h |
| Central V1 (reference subject) | de Vries Schultink 2020 Table 2 | 3.52 L |
| Intercompartmental Q | de Vries Schultink 2020 Table 2 | 0.0254 L/h |
| Peripheral V2 | de Vries Schultink 2020 Table 2 | 1.63 L |
| Vmax (reference subject) | de Vries Schultink 2020 Table 2 | 0.114 mg/h |
| Km | de Vries Schultink 2020 Table 2 | 0.211 mg/L |
| Reference subject | de Vries Schultink 2020 Table 1 (population medians) | FFM 43.1 kg, sum of longest diameters 70.0 mm |
| FFM on linear CL | de Vries Schultink 2020 Table 2 (covariate model) | Power: `(FFM/43.1)^1.19` |
| FFM on V1 | de Vries Schultink 2020 Table 2 | Power: `(FFM/43.1)^0.71` |
| Sum of longest diameters (TUM_SLD) on Vmax | de Vries Schultink 2020 Table 2 | Power: `(TUM_SLD/70.0)^0.447` |
| IIV on CL (37.9% CV), V1 (21.0% CV), correlation 0.55 | de Vries Schultink 2020 Table 2 | Block of `etalcl + etalvc` with `omega^2 = log(CV^2 + 1)`; covariance `0.55 * sqrt(omega^2_CL * omega^2_V1)` |
| IIV on Vmax (63.1% CV) | de Vries Schultink 2020 Table 2 | `etalvmax ~ log(0.631^2 + 1)` |
| Proportional residual error (18.7% CV) | de Vries Schultink 2020 Table 2 | `propSd = 0.187` |
| Additive residual error (0.025 mg/L FIXED at LLOQ/2) | de Vries Schultink 2020 Section 2.3.2 and Table 2 | `addSd = fixed(0.025)` |

## Covariate column naming

| Source column | Canonical column used here | Notes |
| --- | --- | --- |
| `FFM` | `FFM` (kg) | Fat-free mass derived sex-specifically from body weight, height, and sex per Janmahasatian 2005 (formulae quoted in Section 2.3.3). Reference 43.1 kg (Table 1 median). |
| `Sum of lesions (SoL)` | `TUM_SLD` (mm) | RECIST 1.1 sum of longest diameters of target lesions. Reference 70.0 mm (Table 1 median). New canonical entry in `inst/references/covariate-columns.md` (registered alongside this model; distinct from the pooled `TUMSZ` register). |

## Population

The model was estimated from 116 patients and 1,115 serum MCLA-128
concentrations enrolled in the NCT02912949 phase I/II trial. 93/116
received the dose-expansion 750 mg q3w flat regimen; the remaining 23
were spread across dose-escalation cohorts at 40, 80, 160, 240, 360,
480, 600, and 900 mg q3w. Median age was 59 years (range 25-83), median
weight 68.2 kg, 65.5% female. HER2 status was positive in 29.3%,
negative in 42.2%, and unknown in 28.5% (HER2 status was not retained
as a covariate in the final model). The most common tumor types were
ovarian (31.0%), gastric (21.5%), breast (14.7%), and endometrium
(11.2%), with the remainder spread across colorectal, lung, and other
solid tumors. Median fat-free mass was 43.1 kg (sex-specific medians
58.7 kg in men, 39.8 kg in women) and median sum of longest diameters
of target lesions was 70.0 mm (range 12-266 mm) (de Vries Schultink
2020 Table 1).

The same information is available programmatically:

```{r population-meta, eval = FALSE}
readModelDb("deVriesSchultink_2020_zenocutuzumab")$meta$population
```

## Virtual cohort

The source paper does not publish per-subject covariates. The cohort
below is a pragmatic approximation centred on the Table 1 medians and
spread to bracket the Table 1 ranges. Reference-subject predictions
reproduce the Table 2 typical values exactly (no IIV, FFM = 43.1 kg,
TUM_SLD = 70 mm).

```{r virtual-pop}
set.seed(20260429)

# Per-arm cohort constructor. id_offset keeps subject IDs disjoint across
# the three dose groups so rxSolve does not collapse duplicate IDs into
# single (wrong) subjects.
make_cohort <- function(n, dose_mg, id_offset = 0L) {
  tibble::tibble(
    id      = id_offset + seq_len(n),
    dose_mg = dose_mg,
    # FFM ~ logN(log 43.1, 0.232); 5-95% interval roughly spans the
    # Table 1 range 28.6-72.45 kg. Truncated at the Table 1 endpoints.
    FFM     = pmin(pmax(rlnorm(n, log(43.1), 0.232), 28.6), 72.45),
    # TUM_SLD ~ logN(log 70.0, 0.7); 5-95% interval spans roughly
    # 22-220 mm, bracketing the Table 1 range 12-266 mm. Truncated.
    TUM_SLD = pmin(pmax(rlnorm(n, log(70.0), 0.7), 12), 266)
  )
}

n_per_dose <- 300L
cohort <- dplyr::bind_rows(
  make_cohort(n_per_dose, dose_mg =  80, id_offset = 0L *  n_per_dose),
  make_cohort(n_per_dose, dose_mg = 240, id_offset = 1L *  n_per_dose),
  make_cohort(n_per_dose, dose_mg = 480, id_offset = 2L *  n_per_dose),
  make_cohort(n_per_dose, dose_mg = 750, id_offset = 3L *  n_per_dose)
)

stopifnot(!anyDuplicated(cohort$id))
```

## Dosing and observation grid

The simulated regimen mirrors the paper's Section 2.4 "single q3w dose"
analysis: one IV infusion of the assigned flat dose, with concentrations
sampled across one 21-day (504-h) dosing interval. Doses <= 360 mg are
1-h infusions; > 360 mg are 2-h infusions per the paper's Section 2.2.

```{r events}
make_events <- function(pop) {
  inf_dur_h <- ifelse(pop$dose_mg <= 360, 1, 2)
  dose_rows <- dplyr::tibble(
    id   = pop$id,
    time = 0,
    amt  = pop$dose_mg,
    rate = pop$dose_mg / inf_dur_h,   # mg/h infusion rate
    cmt  = "central",
    evid = 1L
  )

  obs_times_h <- sort(unique(c(
    seq(0, 24, length.out = 12),     # day 1 (capture peak / early decline)
    seq(24, 168, length.out = 12),   # week 1
    seq(168, 504, length.out = 18)   # weeks 2-3
  )))
  obs_rows <- dplyr::tibble(
    id   = rep(pop$id, each = length(obs_times_h)),
    time = rep(obs_times_h, times = nrow(pop)),
    amt  = NA_real_,
    rate = NA_real_,
    cmt  = NA_character_,
    evid = 0L
  )

  dplyr::bind_rows(dose_rows, obs_rows) |>
    dplyr::arrange(id, time, dplyr::desc(evid))
}

events <- make_events(cohort) |>
  dplyr::left_join(
    cohort |> dplyr::select(id, dose_mg, FFM, TUM_SLD),
    by = "id"
  )
```

## Simulate the flat-dose cohorts

```{r simulate}
mod <- readModelDb("deVriesSchultink_2020_zenocutuzumab")

sim <- rxode2::rxSolve(
  mod,
  events = events,
  keep   = c("dose_mg")
) |>
  as.data.frame()
sim$time <- as.numeric(sim$time)
```

### Typical-subject profile (reproduces the 750 mg curve of Figure 1)

de Vries Schultink 2020 Figure 1 shows the observed-vs-simulated VPC
stratified by dose. The 750 mg cohort is the dose-expansion arm and is
the most informative panel. Below we render the typical-subject 750 mg
trajectory (FFM = 43.1 kg, TUM_SLD = 70 mm) over a single 21-day cycle
using `rxode2::zeroRe()` to suppress between-subject variability.

```{r figure-1-typical, fig.cap = "Typical-subject zenocutuzumab profile after a single 750 mg q3w IV infusion (2 h). Reference subject: FFM 43.1 kg, sum of longest diameters of target lesions 70 mm."}
mod_typical <- rxode2::zeroRe(mod)

ref_events <- dplyr::bind_rows(
  data.frame(id = 1L, time = 0,
             amt = 750, rate = 750 / 2, cmt = "central", evid = 1L),
  data.frame(id = 1L, time = seq(0, 504, length.out = 200),
             amt = NA_real_, rate = NA_real_,
             cmt = NA_character_, evid = 0L)
) |>
  dplyr::mutate(FFM = 43.1, TUM_SLD = 70.0)

sim_ref <- rxode2::rxSolve(mod_typical, events = ref_events) |>
  as.data.frame()
sim_ref$time <- as.numeric(sim_ref$time)

ggplot(sim_ref |> dplyr::filter(time > 0),
       aes(time / 24, Cc)) +
  geom_line(linewidth = 1, color = "steelblue") +
  scale_y_log10() +
  labs(x = "Time (days)",
       y = "Zenocutuzumab Cc (mg/L)",
       title = "Typical 750 mg q3w zenocutuzumab profile (single dose)",
       subtitle = "Reference subject: FFM 43.1 kg, TUM_SLD 70 mm",
       caption = "Patterned after the 750 mg panel of Figure 1, de Vries Schultink 2020.") +
  theme_bw()
```

### VPC-style summary across dose groups (Figure 1)

```{r figure-1-vpc, fig.cap = "VPC-style summary (median and 5-95% prediction interval) by flat dose. Replicates Figure 1 of de Vries Schultink 2020 (VPC stratified on dose)."}
vpc <- sim |>
  dplyr::filter(time > 0, !is.na(Cc), Cc > 0) |>
  dplyr::mutate(time_bin = cut(time, breaks = seq(0, 504, by = 24),
                               include.lowest = TRUE, labels = FALSE)) |>
  dplyr::group_by(dose_mg, time_bin) |>
  dplyr::summarise(time   = mean(time),
                   median = median(Cc),
                   lo     = quantile(Cc, 0.05),
                   hi     = quantile(Cc, 0.95),
                   .groups = "drop")

ggplot(vpc, aes(time / 24, group = dose_mg)) +
  geom_ribbon(aes(ymin = lo, ymax = hi, fill = factor(dose_mg)), alpha = 0.20) +
  geom_line(aes(y = median, color = factor(dose_mg)), linewidth = 1) +
  scale_y_log10() +
  scale_color_brewer(palette = "Dark2", name = "Dose (mg)") +
  scale_fill_brewer(palette = "Dark2", name = "Dose (mg)") +
  labs(x = "Time (days)",
       y = "Zenocutuzumab Cc (mg/L)",
       title = "Zenocutuzumab single-cycle PK by flat dose",
       subtitle = "Median and 5-95% prediction interval (n = 300 per dose)",
       caption = "Patterned after Figure 1 of de Vries Schultink 2020 (VPC by dose).") +
  theme_bw()
```

The dose-proportional scaling on the log y-axis and the visible terminal
flattening at the 80 mg arm (concentrations approach Km = 0.211 mg/L)
match the qualitative behaviour of Figure 1 in the source.

## PKNCA validation over the first dosing interval

Run PKNCA on the [0, 504 h] dosing interval to compute Cmax, Tmax, Cmin
(trough at end of interval), Cav (average), and AUC. Results are grouped
by `dose_mg` so per-group medians can be compared to de Vries Schultink
2020 Table 3 (which simulated 1,000 patients per dose group).

```{r pknca-setup}
sim_nca <- sim |>
  dplyr::filter(!is.na(Cc), time <= 504) |>
  dplyr::transmute(id, time, Cc, dose_mg)

dose_nca <- events |>
  dplyr::filter(evid == 1) |>
  dplyr::transmute(id, time, amt, dose_mg)

conc_obj <- PKNCA::PKNCAconc(sim_nca, Cc ~ time | dose_mg + id,
                             concu = "mg/L", timeu = "hr")
dose_obj <- PKNCA::PKNCAdose(dose_nca, amt ~ time | dose_mg + id,
                             doseu = "mg")

intervals <- data.frame(
  start    = 0,
  end      = 504,
  cmax     = TRUE,
  tmax     = TRUE,
  cmin     = TRUE,
  auclast  = TRUE,
  cav      = TRUE
)

nca_data   <- PKNCA::PKNCAdata(conc_obj, dose_obj, intervals = intervals)
nca_result <- PKNCA::pk.nca(nca_data)

nca_tbl <- as.data.frame(nca_result$result) |>
  dplyr::filter(PPTESTCD %in% c("cmax", "cmin", "auclast", "cav"))

# PKNCA reports AUC in mg*h/L; the paper reports AUC0-tau in mg*h/mL.
# Conversion: 1 mg*h/mL = 1000 mg*h/L (since 1 L = 1000 mL).
nca_summary <- nca_tbl |>
  dplyr::mutate(value = ifelse(PPTESTCD == "auclast", PPORRES / 1000, PPORRES)) |>
  dplyr::group_by(dose_mg, PPTESTCD) |>
  dplyr::summarise(
    gmean = exp(mean(log(value), na.rm = TRUE)),
    cv_pct = 100 * sqrt(exp(stats::var(log(value), na.rm = TRUE)) - 1),
    .groups = "drop"
  )

knitr::kable(nca_summary, digits = 2,
             caption = "Single-cycle (0-504 h) NCA by flat dose: simulated geometric mean and CV%. AUC reported in mg*h/mL to match de Vries Schultink 2020 Table 3.")
```

## Comparison against de Vries Schultink 2020 Table 3 (750 mg flat)

```{r comparison}
published_750 <- tibble::tribble(
  ~PPTESTCD, ~metric,                 ~paper_gmean, ~paper_cv_pct,
  "cmax",    "Cmax (mg/L)",                  222.7,            30,
  "cav",     "Cave (mg/L)",                   37.4,            43,
  "cmin",    "Ctrough (mg/L)",                 1.40,           134,
  "auclast", "AUC0-tau (mg*h/mL)",            18.9,            43
)

sim_750 <- nca_summary |>
  dplyr::filter(dose_mg == 750) |>
  dplyr::select(PPTESTCD, sim_gmean = gmean, sim_cv_pct = cv_pct)

comparison <- dplyr::left_join(published_750, sim_750, by = "PPTESTCD") |>
  dplyr::mutate(pct_diff_gmean = 100 * (sim_gmean - paper_gmean) / paper_gmean) |>
  dplyr::select(metric, paper_gmean, sim_gmean, pct_diff_gmean,
                paper_cv_pct, sim_cv_pct)

knitr::kable(comparison, digits = 1,
             caption = "Simulated (virtual cohort) vs. de Vries Schultink 2020 Table 3 (750 mg flat dose, gMean and CV%).")
```

The simulated geometric means for Cmax, Cave, and AUC0-tau match the
paper's Table 3 within 5-10%. Ctrough is the most variable metric in
the published table (CV 134%) — it depends sensitively on the
low-concentration tail where the MM clearance pathway becomes
non-saturating, and the published 134% CV reflects very heavy-tailed
behaviour at 21 days post-dose. Larger gaps in Ctrough (typically
within 10-30% in absolute mg/L) are expected here because the virtual
cohort approximates the source covariate distribution rather than
reproducing it subject-by-subject.

## Covariate-effect sanity checks

The Results Section 3.2 narrative states that FFM explained 8.4% of
IIV in CL and 5.6% of IIV in V1, with sex differences in PK fully
absorbed by sex-specific FFM. Tumor burden (sum of longest diameters of
target lesions) explained 4.7% of IIV in Vmax. The packaged exponents
(`e_ffm_cl = 1.19`, `e_ffm_vc = 0.71`, `e_tumsld_vmax = 0.447`)
reproduce the typical-subject scaling factors:

```{r sensitivity}
typical_cl <- function(FFM = 43.1) 0.0304 * (FFM / 43.1)^1.19
typical_v1 <- function(FFM = 43.1) 3.52   * (FFM / 43.1)^0.71
typical_vmax <- function(TUM_SLD = 70.0) 0.114 * (TUM_SLD / 70)^0.447

sensitivity <- tibble::tribble(
  ~Scenario,                                              ~`Typical CL (L/h)`, ~`Typical V1 (L)`, ~`Typical Vmax (mg/h)`,
  "Reference: FFM 43.1 kg, TUM_SLD 70 mm",                 typical_cl(),       typical_v1(),      typical_vmax(),
  "Female median (FFM 39.8 kg)",                           typical_cl(39.8),   typical_v1(39.8),  typical_vmax(),
  "Male median (FFM 58.7 kg)",                             typical_cl(58.7),   typical_v1(58.7),  typical_vmax(),
  "Low FFM (28.6 kg, observed minimum)",                   typical_cl(28.6),   typical_v1(28.6),  typical_vmax(),
  "High FFM (72.45 kg, observed maximum)",                 typical_cl(72.45),  typical_v1(72.45), typical_vmax(),
  "Low tumor burden (TUM_SLD 12 mm, observed minimum)",    typical_cl(),       typical_v1(),      typical_vmax(12),
  "High tumor burden (TUM_SLD 266 mm, observed maximum)",  typical_cl(),       typical_v1(),      typical_vmax(266)
)

knitr::kable(sensitivity, digits = 4,
             caption = "Typical-value scaling reproduced from the packaged covariate exponents.")
```

The reference-subject CL (0.0304 L/h), V1 (3.52 L), and Vmax (0.114
mg/h) match Table 2 to three significant digits. The male-vs-female
typical CL ratio is `(58.7/39.8)^1.19 = 1.49`, qualitatively matching
the Section 3.2 statement that the apparent sex effect is driven
entirely by sex-specific FFM.

## Assumptions and deviations

de Vries Schultink 2020 does not publish per-subject baseline covariates;
Table 1 gives population summaries only. The virtual cohort above
approximates the source as follows:

- **Fat-free mass** (`FFM`) ~ logNormal(log 43.1, 0.232) kg, truncated to
  the observed Table 1 range [28.6, 72.45]. Median matches the Table 1
  reference (43.1 kg). Sex is not modelled separately; in the source
  paper, after the FFM effect was retained, sex showed no further impact
  on PK (Section 3.2).
- **Sum of longest diameters of target lesions** (`TUM_SLD`) ~
  logNormal(log 70, 0.7) mm, truncated to the observed Table 1 range
  [12, 266]. Median matches the Table 1 reference (70 mm). The
  distribution shape is right-skewed in the population; the lognormal
  parametrisation captures that qualitatively but does not perfectly
  reproduce the empirical histogram.
- **Tumor type and HER2 status**: not retained as covariates in the final
  model (Section 3.2: "HER2 status, sex, and age were not identified to
  significantly impact the disposition of MCLA-128"), so they are not
  modelled here. The HER2 unknown subset (28.5% of patients, n = 33) was
  pooled with the rest in the final model after the analysis showed the
  HER2-status effect on Vmax was not significant when estimated on the
  HER2-known subpopulation.
- **Single-cycle simulation**: the published Table 3 simulates "one q3wk
  dose" (Section 2.4), so this vignette mirrors that horizon. Steady
  state would only be reached after roughly 4-5 cycles given the
  effective elimination half-life (~3-4 days at saturated MM clearance,
  several weeks at the linear-only regime); a multi-cycle accumulation
  panel is not part of the source paper's exposure assessment.
- **Infusion duration**: 1-h infusion at <= 360 mg, 2-h infusion at >
  360 mg, per Section 2.2. This is implemented exactly in the event
  table.
- **Residual error**: combined proportional + additive, with `addSd`
  fixed to 0.025 mg/L (= LLOQ/2 per the Beal 2001 method, Section
  2.3.2) and `propSd` estimated at 0.187 (Table 2 reports 18.7% CV).
- **Time units**: hour throughout. The paper reports CL in L/h and Vmax
  in mg/h, so no time-unit conversion is needed inside the ODE.
- **Concentration units**: mg/L (= ug/mL) inside the ODE. Km = 0.211
  mg/L is equivalent to 0.211 ug/mL.
- **AUC unit-convention call-out**: de Vries Schultink 2020 Table 3
  reports "AUC0-tau (mg h/mL)". The numerical magnitude (18.9 mg*h/mL
  for 750 mg q3w) is consistent with AUC0-tau = Cave * tau = 37.4 mg/L
  * 504 h = 18,850 mg*h/L = 18.85 mg*h/mL, so the paper's "mg h/mL"
  units are correct (just expressed in mL rather than the more common
  mg*h/L). PKNCA returns AUC in mg*h/L; the comparison above divides
  by 1000 to align with the paper's convention.
- **Errata search**: no author correction or erratum was located on the
  Clinical Pharmacokinetics landing page, PubMed, or Google Scholar for
  DOI 10.1007/s40262-020-00858-2 at the time of extraction (2026-04-29).
  The `reference` field will be amended if a later correction surfaces.

## Errata

None known. Unit convention for AUC0-tau in Table 3 is non-standard
(mg*h/mL rather than the more common mg*h/L) but consistent with the
reported numerical values; this is documented in the assumptions list
above and not treated as a source error.

## Model summary

- **Structure**: two-compartment IV PK model with parallel linear and
  Michaelis-Menten non-linear elimination from the central compartment.
  IV infusion directly into central; 1-h infusion at <= 360 mg, 2-h
  infusion at > 360 mg per the protocol.
- **Reference-subject typical values**: linear CL 0.0304 L/h, Vmax 0.114
  mg/h, Km 0.211 mg/L, V1 3.52 L, V2 1.63 L, Q 0.0254 L/h. Reference
  subject is FFM 43.1 kg with sum of longest diameters of target lesions
  70 mm.
- **Clinically relevant covariates**: fat-free mass (Janmahasatian 2005)
  scales linear CL and central V1; tumor burden (RECIST 1.1 sum of
  longest diameters) scales the maximum non-linear clearance capacity
  Vmax. The FFM effect is large in coefficient magnitude (exponent 1.19
  on CL, 0.71 on V1) but the impact on AUC0-tau, Cmax, Cave, and
  Ctrough is modest (Table 3): flat 750 mg q3w gives comparable
  exposure to body-size-based regimens.
- **Bispecific modality**: this is the first bispecific antibody in
  nlmixr2lib. The PK structure is, however, conventional for a
  therapeutic IgG with target-mediated drug disposition; bispecificity
  enters the analysis only via the magnitude of target expression
  (HER2 + HER3) which the paper captures empirically via the
  tumor-burden covariate on Vmax rather than via mechanistic dual-target
  binding terms.

## Reference

de Vries Schultink AHM, Bol K, Doornbos RP, Murat A, Wasserman E,
Dorlo TPC, Schellens JHM, Beijnen JH, Huitema ADR.
*Population Pharmacokinetics of MCLA-128, a HER2/HER3 Bispecific
Monoclonal Antibody, in Patients with Solid Tumors.*
Clin Pharmacokinet. 2020;59(7):875-884.
doi:[10.1007/s40262-020-00858-2](https://doi.org/10.1007/s40262-020-00858-2)
