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Sibrotuzumab (Kloft 2004)

Source: vignettes/articles/Kloft_2004_sibrotuzumab.Rmd
Kloft_2004_sibrotuzumab.Rmd

Model and source

  • Citation: Kloft C, Graefe E-U, Tanswell P, Scott AM, Hofheinz R, Amelsberg A, Karlsson MO. (2004). Population pharmacokinetics of sibrotuzumab, a novel therapeutic monoclonal antibody, in cancer patients. Invest New Drugs 22(1):39-52. doi:10.1023/B:DRUG.0000006173.72210.1c. DDMORE Foundation Model Repository: DDMODEL00000195.
  • Description: Two-compartment population PK model for sibrotuzumab in adults with metastatic FAP-positive cancer (Kloft 2004), with parallel linear and Michaelis-Menten elimination from the central compartment and a fixed linear body-weight covariate (centered at 75 kg) on linear CL, central and peripheral volumes, and Vmax.
  • Article: https://doi.org/10.1023/B:DRUG.0000006173.72210.1c
  • DDMORE Foundation Model Repository entry: DDMODEL00000195

This model was extracted from the DDMORE Foundation Model Repository bundle for DDMODEL00000195 (scraped to dpastoor/ddmore_scraping/195/). The bundle contains:

  • Executable_sibrotuzumab.mdl – the human-readable MDL control object (PARAMETERS, MODEL_PREDICTION, OBSERVATION blocks).
  • Executable_sibrotuzumab.xml – PharmML-rendered version of the same
  • Output_simulated_nca_simulation.1.lst – NMTRAN simulation listing produced by the pharmML2Nmtran v0.3.0 converter from the MDL (a 20-replicate $SIMULATION ... ONLYSIMULATION over a single 80 mg / 1-hour IV infusion per subject; not an estimation run).
  • Simulated_sibrotuzumab.csv – the corresponding simulated dataset (ID, TIME, WT, AMT, RATE, DV, MDV) with 20 subjects and WT in {70, 80, 90, 100} kg.
  • Model_Accommodations.txt, DDMODEL00000195.rdf – provenance and scenario notes.

The .mdl PARAMETERS block carries the publication-derived point values used to drive the simulation; there is no Output_real_*.lst estimation listing in the bundle, so the values cannot be cross-checked against a re-fitted set of final estimates. The original Kloft 2004 publication is not on disk in this worktree, so a side-by-side publication-table comparison is also out of scope here.

Population

Kloft 2004 reports a population PK analysis of intravenous sibrotuzumab (BIBH 1, a humanized IgG1 monoclonal antibody against fibroblast activation protein, FAP) from a Phase I dose-escalation study in adults with metastatic FAP-positive solid tumors. The DDMORE bundle does not reproduce the published demographic table, so the model’s population metadata fields for n_subjects, n_studies, weight_range, age_range, and sex_female_pct are intentionally NA – readers should consult the publication directly for those details.

The bundle ships a 20-subject smoke-test cohort (Simulated_sibrotuzumab.csv) with body weights uniformly drawn from {70, 80, 90, 100} kg and a single 80 mg / 1-hour IV infusion per subject sampled at 1, 4, 8, 16, 24, 48, 96, and 168 hours post-dose. That cohort is a regression-test artifact, not a representative clinical-trial replication. The validation in this vignette uses an analogous virtual cohort.

Source trace

Per-parameter origin (also recorded as in-file comments next to each ini() entry of inst/modeldb/ddmore/Kloft_2004_sibrotuzumab.R):

Equation / parameter Value Source location
lcl log(0.0221) Executable_sibrotuzumab.mdl PARAMETERS / STRUCTURAL POP_CLL (mirrored as $THETA(1) 0.0221 in Output_simulated_*.lst line 72)
lvc log(4.13) .mdl PARAMETERS POP_V1
lvp log(3.19) .mdl PARAMETERS POP_V2
lq log(0.0376) .mdl PARAMETERS POP_Q
lvmax log(0.0338) .mdl PARAMETERS POP_Vmax
lkm log(8.0) .mdl PARAMETERS POP_Km
e_wt_cl fixed(0.0182) .mdl PARAMETERS BETA_CLL_WT (fix = true)
e_wt_vc fixed(0.0125) .mdl PARAMETERS BETA_V1_WT (fix = true)
e_wt_vp fixed(0.0105) .mdl PARAMETERS BETA_V2_WT (fix = true)
e_wt_vmax fixed(0.00934) .mdl PARAMETERS BETA_Vmax_WT (fix = true)
etalcl 0.3249 (= 0.57^2) .mdl VARIABILITY PPV_CLL SD = 0.57 (squared to log-scale variance)
etalvc 0.04 (= 0.20^2) .mdl VARIABILITY PPV_V1 SD = 0.20
etalvp 0.04 (= 0.20^2) .mdl VARIABILITY PPV_V2 SD = 0.20
etalvmax fixed(0.0841 = 0.29^2) .mdl VARIABILITY PPV_Vmax SD = 0.29 (fix = true)
addSd 0.093 (mg/L) .mdl PARAMETERS RUV_ADD
propSd 0.0491 (frac) .mdl PARAMETERS RUV_PROP
Cc = central / vc n/a .mdl MODEL_PREDICTION DEQ block
Cp = peripheral1 / vp n/a .mdl MODEL_PREDICTION DEQ block
d/dt(central) n/a .mdl MODEL_PREDICTION DEQ: dAC = Q*(CP-CC) - CLL*CC - Vmax*CC/(Km+CC) (rendered NMTRAN Output_simulated_*.lst $DES line 58)
d/dt(peripheral1) n/a .mdl MODEL_PREDICTION DEQ: dAP = Q*(CC-CP) (rendered NMTRAN $DES line 59) | | `(WT - 75) / 1` covariate centering | reference WT = 75 kg | `.mdl` GROUP_VARIABLES block (`(WT-75)` literal) | | `Cc ~ add + prop + combined1()` | linear-SD combined model | `.mdl` OBSERVATION `combinedError1(additive=RUV_ADD, proportional=RUV_PROP, eps=EPS_Y, prediction=CC)` (rendered NMTRAN: `W = RUV_ADD + RUV_PROP*IPRED; Y = IPRED + W*EPS(1)` with `$SIGMA 1.0 FIX`)

Virtual cohort 

set.seed(20260506L)

obs_times <- c(1, 4, 8, 16, 24, 48, 96, 168)
n_per_wt  <- 25L
wt_values <- c(70, 80, 90, 100)
dose_mg   <- 80
infusion_h <- 1

events <- expand.grid(
  WT       = wt_values,
  rep_id   = seq_len(n_per_wt),
  KEEP.OUT.ATTRS = FALSE,
  stringsAsFactors = FALSE
) |>
  mutate(id = seq_len(n())) |>
  rowwise() |>
  do({
    rec <- .
    dose_row <- tibble::tibble(
      id = rec$id, time = 0,
      amt = dose_mg, rate = dose_mg / infusion_h,
      evid = 1L, WT = rec$WT,
      treatment = "80 mg single 1-h IV"
    )
    obs_rows <- tibble::tibble(
      id = rec$id, time = obs_times,
      amt = 0, rate = 0,
      evid = 0L, WT = rec$WT,
      treatment = "80 mg single 1-h IV"
    )
    dplyr::bind_rows(dose_row, obs_rows)
  }) |>
  ungroup() |>
  arrange(id, time, desc(evid))

stopifnot(!anyDuplicated(unique(events[, c("id", "time", "evid")])))

Simulation 

mod <- rxode2::rxode2(readModelDb("Kloft_2004_sibrotuzumab"))

sim <- rxode2::rxSolve(
  mod,
  events = events,
  keep   = c("treatment", "WT")
) |>
  as.data.frame()

For the typical-value trajectory used in the figure below, zero out the random effects so the prediction is deterministic per WT bin:

mod_typical <- mod |> rxode2::zeroRe()
sim_typical <- rxode2::rxSolve(
  mod_typical,
  events = events,
  keep   = c("treatment", "WT")
) |>
  as.data.frame()
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalvc', 'etalvp', 'etalvmax'
#> Warning: multi-subject simulation without without 'omega'

Self-consistency vs the bundle’s simulated dataset

Because the original publication is not on disk, the validation here is the F.2 self-consistency check from the extraction skill: the typical- value trajectory of this rxode2-translated model should match the shape of the per-subject DV cloud shipped in the bundle’s Simulated_sibrotuzumab.csv. (Per-subject exact matches are not expected – each NMTRAN simulation subject draws its own ETAs and the combined-error EPS, which differ from the seeds drawn here.)

bundle_csv <- system.file(
  "extdata", "ddmore", "DDMODEL00000195_Simulated_sibrotuzumab.csv",
  package = "nlmixr2lib"
)

bundle_obs <- if (nzchar(bundle_csv)) {
  read.csv(bundle_csv) |>
    dplyr::filter(MDV == 0) |>
    dplyr::transmute(id = ID, time = TIME, Cc = DV, WT = WT,
                     source = "DDMORE bundle (NONMEM simulation)")
} else {
  NULL
}

typical_lines <- sim_typical |>
  dplyr::filter(time > 0) |>
  dplyr::distinct(WT, time, Cc) |>
  dplyr::mutate(source = "rxode2 typical value (this model, zero IIV / EPS)")

stoch_quantiles <- sim |>
  dplyr::filter(time > 0) |>
  dplyr::group_by(WT, time) |>
  dplyr::summarise(
    Q05 = stats::quantile(Cc, 0.05, na.rm = TRUE),
    Q50 = stats::quantile(Cc, 0.50, na.rm = TRUE),
    Q95 = stats::quantile(Cc, 0.95, na.rm = TRUE),
    .groups = "drop"
  )

p <- ggplot() +
  geom_ribbon(
    data = stoch_quantiles,
    aes(time, ymin = Q05, ymax = Q95, fill = "rxode2 5th-95th percentile"),
    alpha = 0.20
  ) +
  geom_line(
    data = typical_lines,
    aes(time, Cc, colour = "rxode2 typical value")
  )

if (!is.null(bundle_obs)) {
  p <- p + geom_point(
    data = bundle_obs,
    aes(time, Cc, shape = "DDMORE bundle observation"),
    alpha = 0.7
  )
}

p +
  facet_wrap(~ WT, labeller = label_both) +
  scale_y_log10() +
  scale_colour_manual(values = c("rxode2 typical value" = "black")) +
  scale_fill_manual(values = c("rxode2 5th-95th percentile" = "steelblue")) +
  labs(
    x = "Time after dose (h)",
    y = "Cc (mg/L)",
    colour = NULL, fill = NULL, shape = NULL,
    title = "Self-consistency check: rxode2 simulation vs DDMORE bundle",
    caption = "If the bundle CSV is not yet installed under inst/extdata/ddmore/, only the rxode2 cohort is shown."
  ) +
  theme(legend.position = "bottom")

PKNCA validation

Single 80 mg / 1-hour IV infusion: compute Cmax, Tmax, AUCinf, and half-life by WT bin.

sim_nca <- sim |>
  dplyr::filter(!is.na(Cc), time > 0) |>
  dplyr::transmute(id, time, Cc, treatment, WT)

dose_df <- events |>
  dplyr::filter(evid == 1) |>
  dplyr::transmute(id, time, amt, treatment, WT,
                   duration = pmin(amt / rate, amt / 80))

conc_obj <- PKNCA::PKNCAconc(sim_nca, Cc ~ time | treatment + id)
dose_obj <- PKNCA::PKNCAdose(dose_df, amt ~ time | treatment + id,
                             route = "intravascular",
                             duration = "duration")

intervals <- data.frame(
  start      = 0,
  end        = Inf,
  cmax       = TRUE,
  tmax       = TRUE,
  aucinf.obs = TRUE,
  half.life  = TRUE
)

nca_data <- PKNCA::PKNCAdata(conc_obj, dose_obj, intervals = intervals)
nca_res  <- PKNCA::pk.nca(nca_data)
#> Warning: Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (1) is not allowed

nca_res$result |>
  dplyr::filter(PPTESTCD %in% c("cmax", "tmax", "aucinf.obs", "half.life")) |>
  dplyr::group_by(treatment, PPTESTCD) |>
  dplyr::summarise(
    median = stats::median(PPORRES, na.rm = TRUE),
    p05    = stats::quantile(PPORRES, 0.05, na.rm = TRUE),
    p95    = stats::quantile(PPORRES, 0.95, na.rm = TRUE),
    .groups = "drop"
  ) |>
  knitr::kable(
    caption = "Simulated NCA parameters across the virtual cohort (single 80 mg / 1-h IV infusion)."
  )
Simulated NCA parameters across the virtual cohort (single 80 mg / 1-h IV infusion).
treatment PPTESTCD median p05 p95
80 mg single 1-h IV aucinf.obs NA NA NA
80 mg single 1-h IV cmax 17.84390 11.06654 22.54965
80 mg single 1-h IV half.life 90.54811 43.96467 163.46668
80 mg single 1-h IV tmax 1.00000 1.00000 1.00000

Assumptions and deviations

  • Inter-occasion variability on bioavailability was dropped from the encoding. The original Kloft 2004 publication used IOV on F to account for IV dose-level uncertainty between occasions; the DDMORE framework available at the time of DDMODEL00000195’s submission only supported bioavailability via depot compartments or PK macros, neither of which fits a 2-compartment model with combined linear and Michaelis-Menten elimination dosed IV. The DDMORE encoder therefore removed the IOV-on-F term entirely – see Model_Accommodations.txt and the model-implementation-source-discrepancies-freetext field of DDMODEL00000195.rdf. This vignette and the packaged model both inherit that simplification.
  • Source values are publication-derived point estimates, not re-fitted final estimates. The bundle ships only a $SIMULATION listing (Output_simulated_nca_simulation.1.lst); there is no Output_real_*.lst from a $ESTIMATION step. The values used by the packaged model are the values declared in Executable_sibrotuzumab.mdl PARAMETERS / STRUCTURAL and VARIABILITY blocks – i.e., the publication’s reported numbers as transcribed into the DDMORE encoding, not numbers obtained from a re-fit.
  • The Kloft 2004 publication is not on disk in this worktree. The package metadata (description, units, citation, DOI) reflects the publication, but a side-by-side comparison against the published parameter table or NCA summary is not part of this vignette. The validation here is restricted to the F.2 self-consistency check against the bundle’s own simulated dataset.
  • Population demographics are intentionally NA in population. n_subjects, n_studies, weight_range, age_range, and sex_female_pct are not in the DDMORE bundle and have not been back-filled from external sources. Consumers needing those details should consult Kloft 2004 directly (DOI in the model’s reference).
  • Combined-error parameterisation is combined1() (linear SD), not the nlmixr2lib default combined2() (Pythagorean SD). The MDL source uses the combinedError1 form (combinedError1(additive=..., proportional=..., eps=..., prediction=...)); the rendered NMTRAN Output_simulated_*.lst confirms this with W = RUV_ADD + RUV_PROP*IPRED; Y = IPRED + W*EPS(1) and $SIGMA 1.0 FIX. The nlmixr2 syntax Cc ~ add(addSd) + prop(propSd) + combined1() preserves this parameterisation; do not “simplify” to Cc ~ add(addSd) + prop(propSd) – that defaults to combined2 and changes the residual SD function.
  • Reference WT = 75 kg, linear (not allometric) covariate form. Each affected parameter is multiplied by (1 + e_wt_<param> * (WT - 75)) rather than the more common (WT/75)^e_wt_<param> allometric form. The four e_wt_<param> coefficients are declared fix = true in the .mdl PARAMETERS block, so they are fixed at the values reported and not estimated.
  • Bundle simulated dataset is a smoke-test cohort. The 20-subject cohort with WT in {70, 80, 90, 100} kg and a single 80 mg / 1-h IV infusion is a regression-test artifact, not a recreation of the Kloft 2004 trial. The virtual cohort built in this vignette mirrors the bundle’s covariate structure for the self-consistency overlay and is not an attempt to reproduce the actual study population.