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Farrell_2012_farletuzumab

Source: vignettes/articles/Farrell_2012_farletuzumab.Rmd
Farrell_2012_farletuzumab.Rmd

Model and source

  • Citation: Farrell C, Schweizer C, Wustner J, Weil S, Namiki M, Nakano T, Nakai K, Phillips MD. Population pharmacokinetics of farletuzumab, a humanized monoclonal antibody against folate receptor alpha, in epithelial ovarian cancer. Cancer Chemother Pharmacol. 2012;70(5):727-734. doi:10.1007/s00280-012-1959-y
  • Description: Two-compartment population PK model for farletuzumab (humanized IgG1 anti-folate-receptor-alpha monoclonal antibody) with first-order linear elimination after IV infusion in women with advanced epithelial ovarian cancer (Farrell 2012).
  • Article: Cancer Chemother Pharmacol. 2012;70(5):727-734

Population

The analysis pooled 79 women with advanced epithelial ovarian, fallopian tube, or primary peritoneal cancer from two clinical trials (Farrell 2012 Table 1): the Phase I study MORAb-003-001 (n = 25, weekly IV farletuzumab at ascending 12.5–400 mg/m² for 4 weeks after relapse from standard chemotherapy) and the Phase II study MORAb-003-002 (n = 54, relapsed platinum-sensitive disease; 37.5, 62.5, or 100 mg/m² weekly, with a majority at 100 mg/m² and combination chemotherapy arms that later transitioned back to monotherapy). Patients were 31–81 years old (median 61); body weight ranged 44.5–118.2 kg (median 66.2); reported race was predominantly Caucasian (~77%) with smaller Asian, Hispanic, Black, and Other groups. Farletuzumab was given as a 1 mg/min infusion advanced to 5 mg/min if tolerated.

The same information is available programmatically via readModelDb("Farrell_2012_farletuzumab")$population.

Source trace

Per-parameter origin is recorded as an in-file comment next to each ini() entry in inst/modeldb/specificDrugs/Farrell_2012_farletuzumab.R. The table below collects them for review.

Equation / parameter Value Source location
Structure: two-compartment, first-order (linear) elimination, IV infusion n/a p. 729 Results, “Combined data from both studies were best described by a two-compartment model with first-order (linear, dose-independent) elimination”
Covariate form (power): TVP = θ1 × (COV / COV_ST)^θ2, COV_ST = population median n/a p. 729 Methods, Eq. TVP_i = θ1 × (COV_i / COV_ST)^θ2
Exclusion of 169 observations at 12.5 and 25 mg/m² doses for final model n/a p. 730 Results, first paragraph of “Population pharmacokinetic model”
lcl — CL log(0.00784) L/h Table 3: CL = 0.00784 L/h (5.79% RSE)
lvc — Vc log(3.00) L Table 3: Vc = 3.00 L (5.20% RSE)
lq — Q log(0.0203) L/h Table 3: Q = 0.0203 L/h (4.46% RSE)
lvp — Vp log(7.50) L Table 3: Vp = 7.50 L (20.30% RSE)
e_wt_cl — WT power on CL 0.715 Table 3: CL ~ WT = 0.715 (35.2% RSE)
e_wt_vc — WT power on Vc 0.629 Table 3: Vc ~ WT = 0.629 (30.2% RSE)
Reference weight 66.2 kg Table 1 overall median body weight
etalcl ω²(CL) 0.0616 Table 3: ω² CL = 0.0616, % CV 24.8
etalvc ω²(Vc) 0.0470 Table 3: ω² Vc = 0.0470, % CV 21.7
etalvp ω²(Vp) 1.180 Table 3: ω² Vp = 1.180, % CV 109
propSdPh1 — Phase I proportional SD sqrt(0.0420) = 0.205 Table 3 Phase I: σ² proportional = 0.0420 (20.5% CV)
propSdPh2 — Phase II proportional SD sqrt(0.122) = 0.349 Table 3 Phase II: σ² proportional = 0.122 (34.9% CV)
addSdPh2 — Phase II additive SD sqrt(63.0) = 7.94 µg/mL Table 3 Phase II: σ² additive = 63.0 (SD column = 7.94 µg/mL)
Phase I additive SD fixed to 0 0 Table 3 Phase I: σ² additive = 0 (fixed)

Virtual cohort

Original observed data are not publicly available. The cohort below approximates the Table 1 overall demographics: body weights in the 44.5–118.2 kg range drawn from a truncated distribution around the 66.2 kg median, and every patient assigned PHASE2 = 1 (Phase II residual error — the larger study and the one with combined additive + proportional error). Dosing is 100 mg/m² weekly IV infusion over 1 hour for 8 weeks, matching the primary Phase II dosing schedule.

set.seed(20260421)
n_subj <- 100

# Truncated approximation of the Table 1 overall weight distribution:
# median 66.2, range 44.5–118.2. Use a log-normal-like sample then clip.
wt_draws <- pmin(pmax(exp(rnorm(n_subj, mean = log(66.2), sd = 0.18)), 44.5), 118.2)
# BSA derived by DuBois from weight assuming 163 cm mean height (clipped 1.37–2.24
# to match Table 1): BSA = 0.007184 * (wt^0.425) * (height_cm^0.725).
bsa_draws <- pmin(pmax(0.007184 * wt_draws^0.425 * 163^0.725, 1.37), 2.24)

cohort <- tibble::tibble(
  id = seq_len(n_subj),
  WT = wt_draws,
  BSA = bsa_draws,
  PHASE2 = 1L,
  dose_mgm2 = 100,
  dose_mg = 100 * bsa_draws  # mg/m^2 x m^2 = mg
)
cohort$treatment <- "100 mg/m2 weekly"
# Build event table: 8 weekly 1-hour IV infusions + dense sampling for NCA.
tau <- 24 * 7  # one week in hours
n_doses <- 8
dose_times <- seq(0, by = tau, length.out = n_doses)

doses <- cohort |>
  tidyr::crossing(time = dose_times) |>
  dplyr::mutate(
    amt = dose_mg,
    rate = dose_mg,      # 1-hour infusion => rate = dose (mg/h)
    cmt = "central",
    evid = 1L
  ) |>
  dplyr::select(id, time, amt, rate, cmt, evid, WT, PHASE2, treatment)

# Observation grid: dense during the first week, then per-week troughs, and
# one long post-last-dose tail for terminal-phase characterization.
obs_times <- sort(unique(c(
  seq(0, 24, by = 1),                  # first day, hourly
  seq(28, 168, by = 12),               # rest of week 1, 12-hourly
  seq(170, 8 * tau - 1, by = 24),      # dose interval troughs
  seq(8 * tau, 8 * tau + 60 * 24, by = 24)  # 60-day terminal tail
)))

obs <- cohort |>
  tidyr::crossing(time = obs_times) |>
  dplyr::mutate(amt = 0, rate = 0, cmt = NA_character_, evid = 0L) |>
  dplyr::select(id, time, amt, rate, cmt, evid, WT, PHASE2, treatment)

events <- dplyr::bind_rows(doses, obs) |>
  dplyr::arrange(id, time, dplyr::desc(evid))

Simulation 

mod <- rxode2::rxode2(readModelDb("Farrell_2012_farletuzumab"))
#> ℹ parameter labels from comments will be replaced by 'label()'

sim <- rxode2::rxSolve(
  mod,
  events = events,
  keep = c("WT", "PHASE2", "treatment")
)

Typical-value replication (zeroing the between-subject variability) is useful for a “central” prediction curve that does not carry the wide Vp IIV:

mod_typical <- mod |> rxode2::zeroRe()
#> Warning: No sigma parameters in the model

typical_cohort <- tibble::tibble(
  id = 1L, WT = 66.2, PHASE2 = 1L, treatment = "100 mg/m2 weekly (typical)"
)

typical_doses <- typical_cohort |>
  tidyr::crossing(time = dose_times) |>
  dplyr::mutate(amt = 170, rate = 170, cmt = "central", evid = 1L) |>
  dplyr::select(id, time, amt, rate, cmt, evid, WT, PHASE2, treatment)

typical_obs <- typical_cohort |>
  tidyr::crossing(time = obs_times) |>
  dplyr::mutate(amt = 0, rate = 0, cmt = NA_character_, evid = 0L) |>
  dplyr::select(id, time, amt, rate, cmt, evid, WT, PHASE2, treatment)

typical_events <- dplyr::bind_rows(typical_doses, typical_obs) |>
  dplyr::arrange(id, time, dplyr::desc(evid))

sim_typical <- rxode2::rxSolve(
  mod_typical, events = typical_events, keep = c("WT", "PHASE2", "treatment")
)
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalvc', 'etalvp'

Replicate published figures

Figure 2 — VPC of concentration-time profile

Farrell 2012 Figure 2 is a visual predictive check for the final model against the observed data. A five-fifty-ninety-five percentile summary of the simulated cohort over the weekly dosing interval and the post-last-dose terminal phase captures the same structural features (peak near end of infusion, slow biexponential decline).

sim |>
  dplyr::filter(!is.na(Cc), time > 0) |>
  dplyr::mutate(day = time / 24) |>
  dplyr::group_by(day) |>
  dplyr::summarise(
    Q05 = quantile(Cc, 0.05),
    Q50 = quantile(Cc, 0.50),
    Q95 = quantile(Cc, 0.95),
    .groups = "drop"
  ) |>
  ggplot(aes(day, Q50)) +
  geom_ribbon(aes(ymin = Q05, ymax = Q95), alpha = 0.25) +
  geom_line() +
  scale_y_log10() +
  labs(
    x = "Time (days)", y = "Farletuzumab concentration (µg/mL)",
    title = "Simulated 5-50-95 percentiles — 100 mg/m² weekly × 8",
    caption = "Replicates the structure of Figure 2 (VPC) of Farrell 2012."
  ) +
  theme_minimal()

Figure 3 — weekly vs. 3-weekly dosing at matched mg/kg/week

Farrell 2012 Figure 3 shows that steady-state exposure is similar with weekly vs. 3-weekly dosing when the weekly-equivalent mg/kg dose is maintained. The block below reproduces that comparison using typical-value predictions.

wt_ref <- 66.2
bsa_ref <- 0.007184 * wt_ref^0.425 * 163^0.725  # ~1.70 m^2

weekly_mg  <- 100 * bsa_ref                      # 100 mg/m^2 weekly
q3w_mg     <- 3 * weekly_mg                      # same mg/kg/week, given every 3 weeks

tau_w  <- 24 * 7
tau_q3 <- 24 * 21

make_regimen <- function(amt, tau, n_doses, label) {
  dt <- seq(0, by = tau, length.out = n_doses)
  obs_t <- sort(unique(c(
    seq(0, max(dt) + tau, by = 6),
    seq(max(dt) + tau, max(dt) + tau + 60 * 24, by = 24)
  )))
  dplyr::bind_rows(
    tibble::tibble(id = 1L, time = dt,   amt = amt, rate = amt,
      cmt = "central", evid = 1L, WT = wt_ref, PHASE2 = 1L, treatment = label),
    tibble::tibble(id = 1L, time = obs_t, amt = 0, rate = 0,
      cmt = NA_character_, evid = 0L, WT = wt_ref, PHASE2 = 1L, treatment = label)
  ) |> dplyr::arrange(time, dplyr::desc(evid))
}

ev_weekly <- make_regimen(weekly_mg, tau_w,  12, "100 mg/m² weekly")
ev_q3w    <- make_regimen(q3w_mg,    tau_q3,  4, "300 mg/m² every 3 weeks")

regimen_events <- dplyr::bind_rows(
  ev_weekly,
  ev_q3w |> dplyr::mutate(id = 2L)
)

sim_regimens <- rxode2::rxSolve(
  mod_typical, events = regimen_events, keep = c("WT", "PHASE2", "treatment")
)
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalvc', 'etalvp'
#> Warning: multi-subject simulation without without 'omega'

sim_regimens |>
  dplyr::filter(!is.na(Cc), time > 0) |>
  dplyr::mutate(day = time / 24) |>
  ggplot(aes(day, Cc, colour = treatment)) +
  geom_line(linewidth = 0.8) +
  labs(
    x = "Time (days)", y = "Farletuzumab concentration (µg/mL)",
    colour = NULL,
    title = "Weekly vs. every-3-week dosing at matched mg/m²/week (typical)",
    caption = "Replicates Figure 3 of Farrell 2012."
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")

PKNCA validation

Compute Cmax, Tmax, AUC(0-τ) (one-week interval after the first dose), and the terminal half-life using PKNCA. The terminal half-life is estimated from the 60-day post-last-dose tail, where the β-phase dominates.

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

conc_obj <- PKNCA::PKNCAconc(sim_nca, Cc ~ time | treatment + id)

dose_df <- events |>
  dplyr::filter(evid == 1) |>
  dplyr::select(id, time, amt, treatment)

dose_obj <- PKNCA::PKNCAdose(dose_df, amt ~ time | treatment + id)

# After-first-dose NCA over tau = 1 week, plus terminal-phase half-life over
# the 60-day post-last-dose tail.
start_last <- max(dose_df$time)  # last dose time
intervals <- data.frame(
  start      = c(0,   start_last + 1,    start_last + 1),
  end        = c(tau, start_last + tau,  start_last + 60 * 24),
  cmax       = c(TRUE,  TRUE,  FALSE),
  tmax       = c(TRUE,  TRUE,  FALSE),
  auclast    = c(TRUE,  TRUE,  FALSE),
  ctrough    = c(TRUE,  TRUE,  FALSE),
  cmin       = c(FALSE, TRUE,  FALSE),
  half.life  = c(FALSE, FALSE, TRUE)
)

nca_res <- PKNCA::pk.nca(PKNCA::PKNCAdata(conc_obj, dose_obj, intervals = intervals))
#> Warning: Requesting an AUC range starting (0) before the first measurement (1) is not allowed
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#> 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
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#> 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
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#> 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
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nca_summary <- summary(nca_res)
knitr::kable(nca_summary,
  caption = "Simulated NCA summaries for the 100 mg/m² weekly Phase II cohort.")
Simulated NCA summaries for the 100 mg/m² weekly Phase II cohort.
start end treatment N auclast cmax cmin tmax ctrough half.life
0 168 100 mg/m2 weekly 100 NC 58.7 [22.6] . 1.00 [1.00, 1.00] NC .
1177 1344 100 mg/m2 weekly 100 NC 112 [22.5] 55.4 [43.6] 1.00 [1.00, 1.00] NC .
1177 2616 100 mg/m2 weekly 100 . . . . . 1930 [2350]

Comparison against the analytical terminal half-life

Farrell 2012 does not tabulate NCA-derived Cmax/AUC. The dominant biexponential rate constant (β) can be computed analytically from Table 3 parameters and is the most direct numerical check.

CL <- 0.00784; Vc <- 3.00; Q <- 0.0203; Vp <- 7.50
k10 <- CL / Vc
k12 <- Q  / Vc
k21 <- Q  / Vp
a <- k10 + k12 + k21
b <- k10 * k21
alpha <- (a + sqrt(a^2 - 4 * b)) / 2
beta  <- (a - sqrt(a^2 - 4 * b)) / 2
thalf_alpha_h <- log(2) / alpha
thalf_beta_h  <- log(2) / beta

tibble::tibble(
  quantity = c("Distribution half-life (α-phase)",
               "Terminal half-life (β-phase)"),
  value_h    = round(c(thalf_alpha_h, thalf_beta_h), 1),
  value_days = round(c(thalf_alpha_h, thalf_beta_h) / 24, 1)
) |>
  knitr::kable(caption = "Analytical two-compartment half-lives from Table 3 point estimates.")
Analytical two-compartment half-lives from Table 3 point estimates.
quantity value_h value_days
Distribution half-life (α-phase) 60.4 2.5
Terminal half-life (β-phase) 1124.0 46.8

The terminal β half-life computed from Table 3 is consistent with published IgG-class monoclonal antibody half-lives (multi-week).

Assumptions and deviations

  • IIV correlation. The paper’s Methods section states “Initial model building used a diagonal covariance matrix of inter-individual variability and a correlation between clearance and volume of the central compartment was included in the starting model.” Table 3, however, reports only diagonal ω² values for CL, Vc, and Vp without a covariance entry. The model is implemented with diagonal IIV to match what Table 3 tabulates; the narrative hint at a starting CL–Vc correlation is not carried over because no final covariance value is published.
  • Per-study residual error. The paper reports separate residual-error estimates for the Phase I and Phase II studies (Table 3). A new binary PHASE2 indicator (0 = Phase I, 1 = Phase II) selects between them in the model, following the same pattern used by Cirincione_2017_exenatide.R with STUDY1 / STUDY5. The virtual cohort sets PHASE2 = 1 because the Phase II study is the larger cohort (1,750 of 2,472 samples) and drives the weekly weekly / Q3W comparison explored in Figure 3.
  • Reference weight. The paper specifies COV_ST as the median covariate value in the study population. The Table 1 overall median weight of 66.2 kg is used; a within-final-dataset median (after excluding 169 low-dose samples) is not separately reported.
  • BSA for dose scaling. Doses were administered as mg/m². The virtual cohort computes BSA by DuBois from each subject’s weight assuming a 163 cm mean height, clipped to the Table 1 BSA range (1.37–2.24 m²). The paper does not publish per-subject heights. BSA is used only to convert mg/m² doses into mg amounts; it is not a covariate in the model itself (the weight effect on CL and Vc is the relevant PK scaling).
  • Dose exclusion. The final model was built on 2,303 samples after excluding 169 samples at 12.5 and 25 mg/m² due to apparent low-dose nonlinearity. The packaged model reproduces the final-model parameters and therefore should not be used to simulate exposure at those two lowest doses; the implemented structure is strictly linear.
  • Residual-error interpretation. Table 3’s column heading “σ² proportional (% CV)” reports the variance and the SD% is the last column’s % CV or SD. This model uses the SDs (20.5% / 34.9% / 7.94 µg/mL) as the nlmixr2 propSd and addSd magnitudes.