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PF-04455242 KOR antagonist rat-to-human translational PK-PD (Chang 2011)

Source: vignettes/articles/Chang_2011_PF04455242_prolactin.Rmd
Chang_2011_PF04455242_prolactin.Rmd

Model and source

Chang et al. (2011) develop a translational PK-PD model for the novel kappa opioid receptor (KOR) antagonist PF-04455242. Spiradoline (a KOR agonist) is used as a pharmacodynamic challenge: it elevates plasma prolactin via KOR-mediated inhibition of tuberoinfundibular dopaminergic (TIDA) neurons, and PF-04455242 blocks this elevation by competitive antagonism at the KOR.

The paper contributes two coupled PK-PD models, extracted here as two separate model files (Phase 1 replicate-author-structure policy):

  • modellib("Chang_2011_PF04455242_rat") – Sprague-Dawley rat preclinical model: one-compartment first-order absorption PK for both spiradoline and PF-04455242 (with dose-dependent Ka), feeding a direct-response sigmoid Emax PD on prolactin with competitive antagonism by PF-04455242 (Chang 2011 Eq. 1, Fig. 2). PK parameters from Table I; PD parameters from Table II.

  • modellib("Chang_2011_PF04455242_human") – healthy male volunteer model: two-compartment zero-order-absorption PK for PF-04455242 (Table III) with log-transform-both-sides residual error, and the reduced antagonism PD (Eq. 11/12) in which the spiradoline-driven prolactin elevation is described empirically by a Weibull-scaled placebo profile rather than via an explicit spiradoline PK compartment. PD parameters from Table IV.

  • Citation: Chang C, Byon W, Lu Y, Jacobsen LK, Badura LL, Sawant-Basak A, Miller E, Liu J, Grimwood S, Wang EQ, Maurer TS. Quantitative PK-PD Model-Based Translational Pharmacology of a Novel Kappa Opioid Receptor Antagonist Between Rats and Humans. AAPS J 2011;13(4):565-575.

  • Article: doi:10.1208/s12248-011-9296-3

Population

Rat (preclinical). Three independent studies in male Sprague-Dawley rats (300-400 g) at Pfizer:

  • Study 1: vehicle / spiradoline 0.1 / 0.32 / 1.0 mg/kg SC (n = 6-9 per group); plasma prolactin sampled at 0, 30, 60, 120 min post-dose.
  • Study 2: spiradoline 0.1 / 0.32 / 1.0 mg/kg SC (n = 5); single PK sample at 30 min post-dose.
  • Study 3: vehicle / PF-04455242 0.32 / 10 mg/kg SC at t = -30 min, then spiradoline 0.32 mg/kg SC at t = 0 (n = 6-9 per group); plasma prolactin, PF-04455242, and spiradoline sampled at -30, 10, 20, 30, 45, 60, 90, 120 min post-spiradoline.

Human. 69 healthy male adult volunteers pooled across three Pfizer clinical studies:

  • Single-dose escalation (PF-04455242 0.5-30 mg PO, n = 18; reference CSR B1071001).
  • Multiple-dose escalation (PF-04455242 every 6 h followed by a single dose on day 7, n = 27; reference CSR B1071002).
  • Proof-of-mechanism (POM) study (n = 24): randomised placebo-controlled parallel-group, single doses of PF-04455242 placebo / 18 mg / 30 mg PO at t = -1 h, followed by spiradoline 3.2 ug/kg IM at t = 0 (8 subjects per arm). PK samples 15, 30, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180, and 360 min after PF-04455242 dose; serum prolactin pre-dose and at 15, 30, 45, 60, 75, 90, 105, 120, 135, 150, 165, and 180 min after PF-04455242 dose (CSR B1071004).

The PK pool (n = 45 from the single + multi-dose escalations) supplied 943 PF-04455242 concentrations for the structural PK fit; the POM cohort (n = 24) supplied the prolactin PD observations.

The same information is available programmatically via the models’ population metadata:

readModelDb("Chang_2011_PF04455242_rat")$population
readModelDb("Chang_2011_PF04455242_human")$population

Source trace

Per-parameter origin is recorded as an in-file comment next to each ini() entry in the two model files; the table below collects the values in one place for review.

Layer (species) Parameter Value Source location
PK (rat) – spiradoline Ka 4.75 1/h Table I
PK (rat) – spiradoline CL/F 6.06 L/h/kg Table I
PK (rat) – spiradoline Vc/F 7.15 L/kg Table I
PK (rat) – spiradoline omega Ka / CL / Vc 59.1 / 18.0 / 46.5 %CV Table I
PK (rat) – spiradoline proportional residual 44.72 % Table I
PK (rat) – PF-04455242 Ka (3.2 mg/kg) 1.64 1/h Table I, footnote a
PK (rat) – PF-04455242 Ka (10 mg/kg) 0.385 1/h Table I, footnote b
PK (rat) – PF-04455242 CL/F 2.5 L/h/kg Table I
PK (rat) – PF-04455242 Vc/F 1.31 L/kg Table I
PK (rat) – PF-04455242 omega Ka / CL / Vc 31.9 (3.2 mg/kg) / 35.6 / 26.1 %CV Table I
PK (rat) – PF-04455242 proportional residual 14.1 % Table I
PD (rat) Baseline prolactin 1.34 ng/mL Table II
PD (rat) Emax 39.7 ng/mL Table II
PD (rat) EC50 (spiradoline) 34.3 ng/mL Table II
PD (rat) Ki (PF-04455242 rat KOR) 414 ng/mL Table II
PD (rat) gamma 4.15 Table II
PD (rat) omega Baseline 39.1 %CV Table II
PD (rat) proportional residual 131 % Table II
PK (human) CL/F 54.6 L/h Table III
PK (human) Vc/F 327 L Table III
PK (human) Duration D1 1.24 h Table III
PK (human) Vp/F 58.5 L Table III
PK (human) Q/F 6.51 L/h Table III
PK (human) ALAG1 0.374 h Table III
PK (human) omega CL / Vc / D1 / Vp / Q / ALAG1 53.7 / 48.5 / 30.6 / 27.3 / 27.3 / 26.6 %CV Table III
PK (human) cov(omega CL, omega Vc) 48.5 (interpreted as correlation 0.485; see Errata) Table III
PK (human) residual (LTBS, lnorm) 32.6 % Table III
PD (human) Baseline prolactin BL 9.53 ng/mL Table IV
PD (human) Weibull kappa 2.49 Table IV
PD (human) Weibull lambda 0.943 h Table IV
PD (human) Ki (PF-04455242 human KOR) 39.2 ng/mL Table IV
PD (human) gamma 1.5 Table IV
PD (human) omega BL 3.1 ng/mL (additive; see Errata) Table IV
PD (human) omega lambda 19.9 %CV Table IV
PD (human) proportional residual 17.5 % Table IV
PD (rat) Rat PD direct-response equation n/a Eq. 1, Fig. 2
PD (human) Reduced antagonism equation n/a Eq. 11
PD (human) Weibull placebo equation n/a Eq. 12

Rat model

Loading and virtual cohort 

mod_rat <- readModelDb("Chang_2011_PF04455242_rat")

The PD study (Study 3) compared three dose groups: vehicle (PF = 0), low PF (3.2 mg/kg), and high PF (10 mg/kg), all with a fixed 0.32 mg/kg spiradoline SC challenge at t = 0 and PF-04455242 administered 30 min earlier (t = -0.5 h). Simulate 60 rats per group.

set.seed(20110817L)  # Chang 2011 received-date

n_rat <- 60L

# Build one cohort's event table. Uses uppercase event-column names
# (ID, TIME, EVID, CMT, AMT, DV) to satisfy rxode2's event-table
# convention and CMT = "Cc" on observation rows so rxSolve returns all
# defined outputs (Cc, Cspir, PRL) regardless of the listed cmt.
make_rat_cohort <- function(pf_dose_mgkg, label, id_offset) {
  ids <- id_offset + seq_len(n_rat)
  obs_times <- seq(-0.5, 2, by = 0.05)

  doses_pf <- if (pf_dose_mgkg > 0) {
    expand.grid(ID = ids, TIME = -0.5) |>
      mutate(EVID = 1L, CMT = "depot", AMT = pf_dose_mgkg, DV = NA_real_)
  } else {
    NULL
  }

  doses_sp <- expand.grid(ID = ids, TIME = 0) |>
    mutate(EVID = 1L, CMT = "depot_spiradoline", AMT = 0.32, DV = NA_real_)

  obs <- expand.grid(ID = ids, TIME = obs_times) |>
    mutate(EVID = 0L, CMT = "Cc", AMT = NA_real_, DV = NA_real_)

  ev <- bind_rows(doses_pf, doses_sp, obs)
  ev$DOSE <- pf_dose_mgkg
  ev$cohort <- label
  ev |> arrange(ID, TIME, desc(EVID))
}

events_rat <- bind_rows(
  make_rat_cohort(0,   "Vehicle + 0.32 mg/kg spir",      id_offset =   0L),
  make_rat_cohort(3.2, "3.2 mg/kg PF + 0.32 mg/kg spir", id_offset = 100L),
  make_rat_cohort(10,  "10 mg/kg PF + 0.32 mg/kg spir",  id_offset = 200L)
)

stopifnot(!anyDuplicated(unique(events_rat[, c("ID", "TIME", "EVID", "CMT")])))

Simulate and replicate Figures 3 + 4 

sim_rat <- rxode2::rxSolve(mod_rat, events = events_rat, keep = c("cohort", "DOSE")) |>
  as.data.frame()
#> ℹ parameter labels from comments will be replaced by 'label()'
#> Warning: 
#> with negative times, compartments initialize at first negative observed time
#> with positive times, compartments initialize at time zero
#> use 'rxSetIni0(FALSE)' to initialize at first observed time
#> this warning is displayed once per session

Replicate Figure 3 (rat PK profiles). Panel (a) is the lone spiradoline cohort (0.32 mg/kg SC) – there is one cohort per study 3 dose group but Cspir is driven by the same spiradoline PK in every group. Panel (b) is PF-04455242 plasma concentration at the two co-administered doses (3.2 and 10 mg/kg).

fig3_spir <- sim_rat |>
  filter(cohort != "Vehicle + 0.32 mg/kg spir", time >= 0) |>
  group_by(time) |>
  summarise(
    Q05 = quantile(Cspir, 0.05, na.rm = TRUE),
    Q50 = quantile(Cspir, 0.50, na.rm = TRUE),
    Q95 = quantile(Cspir, 0.95, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(panel = "(a) 0.32 mg/kg spiradoline SC")

fig3_pf <- sim_rat |>
  filter(cohort != "Vehicle + 0.32 mg/kg spir", time >= -0.5) |>
  group_by(cohort, time) |>
  summarise(
    Q05 = quantile(Cc, 0.05, na.rm = TRUE),
    Q50 = quantile(Cc, 0.50, na.rm = TRUE),
    Q95 = quantile(Cc, 0.95, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(panel = paste("(b)", cohort))

bind_rows(fig3_spir, fig3_pf) |>
  ggplot(aes(time, Q50)) +
  geom_ribbon(aes(ymin = Q05, ymax = Q95), alpha = 0.20, fill = "steelblue") +
  geom_line(colour = "red", linetype = "dashed") +
  facet_wrap(~panel, scales = "free_y") +
  scale_y_log10() +
  labs(x = "Time after spiradoline (h)", y = "Plasma concentration (ng/mL)",
       caption = "Replicates Figure 3 of Chang 2011 (spiradoline plus PF-04455242 PK in rats).")
#> Warning in scale_y_log10(): log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.

Replicate Figure 4b (rat prolactin time course, PF-04455242 dose-response under fixed 0.32 mg/kg spiradoline challenge).

sim_rat |>
  filter(time >= 0) |>
  group_by(cohort, time) |>
  summarise(
    Q05 = quantile(PRL, 0.05, na.rm = TRUE),
    Q50 = quantile(PRL, 0.50, na.rm = TRUE),
    Q95 = quantile(PRL, 0.95, na.rm = TRUE),
    .groups = "drop"
  ) |>
  ggplot(aes(time, Q50)) +
  geom_ribbon(aes(ymin = Q05, ymax = Q95), alpha = 0.20, fill = "steelblue") +
  geom_line(colour = "red", linetype = "dashed") +
  facet_wrap(~cohort) +
  scale_y_log10() +
  labs(x = "Time after spiradoline (h)", y = "Plasma prolactin (ng/mL)",
       caption = "Replicates Figure 4b of Chang 2011 (rat prolactin response: vehicle / 3.2 mg/kg / 10 mg/kg PF-04455242).")

PKNCA validation – PF-04455242 in rats

PKNCA on Cc by PF dose cohort (use the typical-value sim to suppress residual variability before NCA – the model’s IIV is faithful to the paper but residual error of 14% would inflate the per-subject Cmax tail).

mod_rat_typ <- rxode2::zeroRe(mod_rat)
#> ℹ parameter labels from comments will be replaced by 'label()'
sim_rat_typ <- rxode2::rxSolve(mod_rat_typ, events = events_rat, keep = c("cohort", "DOSE")) |>
  as.data.frame()
#> ℹ omega/sigma items treated as zero: 'etalka_pf_low', 'etalcl', 'etalvc', 'etalka_spiradoline', 'etalcl_spiradoline', 'etalvc_spiradoline', 'etalrbase'
#> Warning: multi-subject simulation without without 'omega'

sim_rat_nca <- sim_rat_typ |>
  filter(cohort != "Vehicle + 0.32 mg/kg spir", time >= -0.5, !is.na(Cc), Cc > 0) |>
  select(id, time, Cc, cohort)

dose_rat <- events_rat |>
  filter(EVID == 1L, CMT == "depot") |>
  transmute(id = ID, time = TIME, amt = AMT, cohort)

conc_obj_rat <- PKNCA::PKNCAconc(
  sim_rat_nca, Cc ~ time | cohort + id,
  concu = "ng/mL", timeu = "h"
)
dose_obj_rat <- PKNCA::PKNCAdose(
  dose_rat, amt ~ time | cohort + id,
  doseu = "mg/kg"
)

intervals_rat <- data.frame(
  start = -0.5, end = 2,
  cmax = TRUE, tmax = TRUE,
  auclast = TRUE, half.life = TRUE
)

nca_rat <- PKNCA::pk.nca(PKNCA::PKNCAdata(conc_obj_rat, dose_obj_rat, intervals = intervals_rat))
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knitr::kable(
  as.data.frame(nca_rat$result) |>
    filter(PPTESTCD %in% c("cmax", "tmax", "auclast", "half.life")) |>
    group_by(cohort, PPTESTCD) |>
    summarise(value = round(median(PPORRES), 3), .groups = "drop") |>
    pivot_wider(names_from = PPTESTCD, values_from = value),
  caption = "Simulated NCA parameters for PF-04455242 in rats (typical-value)."
)
Simulated NCA parameters for PF-04455242 in rats (typical-value).
cohort auclast cmax half.life tmax
10 mg/kg PF + 0.32 mg/kg spir NA 1027.601 2.033 1.05
3.2 mg/kg PF + 0.32 mg/kg spir NA 831.172 0.525 0.55

The structural half-life of PF-04455242 in the rat is ln(2) * Vc/CL = 0.693 * 1.31 / 2.5 = 0.363 h (about 22 min), consistent with the rapid clearance described in the Results section. AUC0-last increases approximately linearly with dose because CL/F is dose-independent.

Human model

Loading and virtual cohort 

mod_human <- readModelDb("Chang_2011_PF04455242_human")

POM trial design: PF-04455242 placebo / 18 mg / 30 mg PO at t = -1 h followed by spiradoline 3.2 ug/kg IM at t = 0. Simulation time t = 0 is aligned with the IM spiradoline dose (the Weibull placebo origin). Simulate 60 subjects per group.

set.seed(20110817L)

n_human <- 60L

# Zero-order absorption requires RATE = -2 in the dose event so that
# rxode2 uses the dur(central) function specified in the model. The
# absorption lag time alag(central) is similarly model-driven and
# applied automatically; no separate ALAG column is needed.
make_human_cohort <- function(pf_dose_mg, label, id_offset) {
  ids <- id_offset + seq_len(n_human)
  obs_times <- seq(-1, 5, by = 0.05)

  doses <- if (pf_dose_mg > 0) {
    expand.grid(ID = ids, TIME = -1) |>
      mutate(EVID = 1L, CMT = "central", AMT = pf_dose_mg, RATE = -2, DV = NA_real_)
  } else {
    NULL
  }

  obs <- expand.grid(ID = ids, TIME = obs_times) |>
    mutate(EVID = 0L, CMT = "Cc", AMT = NA_real_, RATE = NA_real_, DV = NA_real_)

  ev <- bind_rows(doses, obs)
  ev$cohort <- label
  ev |> arrange(ID, TIME, desc(EVID))
}

events_human <- bind_rows(
  make_human_cohort(0,  "Placebo + spir",   id_offset =   0L),
  make_human_cohort(18, "18 mg PF + spir",  id_offset = 100L),
  make_human_cohort(30, "30 mg PF + spir",  id_offset = 200L)
)

stopifnot(!anyDuplicated(unique(events_human[, c("ID", "TIME", "EVID", "CMT")])))

Simulate and replicate Figures 6 + 7 

sim_human <- rxode2::rxSolve(mod_human, events = events_human, keep = "cohort") |>
  as.data.frame()
#> ℹ parameter labels from comments will be replaced by 'label()'

Replicate Figure 6 (PF-04455242 plasma concentration in healthy volunteers after 18 or 30 mg PO). Plot from -1 h (PF dose) to 11 h (= 12 h post-PF-dose to match Figure 6’s “0 to 12 hr” x-axis – here time is referenced to spiradoline so PF-dose time is -1 h).

sim_human |>
  filter(cohort != "Placebo + spir", time >= -1, !is.na(Cc), Cc > 0) |>
  group_by(cohort, time) |>
  summarise(
    Q05 = quantile(Cc, 0.05, na.rm = TRUE),
    Q50 = quantile(Cc, 0.50, na.rm = TRUE),
    Q95 = quantile(Cc, 0.95, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(time_pf = time + 1) |>
  ggplot(aes(time_pf, Q50)) +
  geom_ribbon(aes(ymin = Q05, ymax = Q95), alpha = 0.20, fill = "steelblue") +
  geom_line(colour = "red", linetype = "dashed") +
  facet_wrap(~cohort) +
  scale_y_log10() +
  labs(x = "Time after PF-04455242 dose (h)", y = "Plasma concentration (ng/mL)",
       caption = "Replicates Figure 6 of Chang 2011 (PF-04455242 PK in healthy volunteers, 18 and 30 mg PO).")

Replicate Figure 7 (prolactin time course in healthy volunteers after spiradoline 3.2 ug/kg IM, with PF-04455242 placebo / 18 mg / 30 mg PO pre-treatment). Plot from 1 to 3 h after PF-04455242 dose (= 0 to 2 h after spiradoline), the Fig 7 x-axis range.

sim_human |>
  filter(time >= 0, time <= 2) |>
  group_by(cohort, time) |>
  summarise(
    Q05 = quantile(PRL, 0.05, na.rm = TRUE),
    Q50 = quantile(PRL, 0.50, na.rm = TRUE),
    Q95 = quantile(PRL, 0.95, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(time_pf = time + 1) |>
  ggplot(aes(time_pf, Q50)) +
  geom_ribbon(aes(ymin = Q05, ymax = Q95), alpha = 0.20, fill = "steelblue") +
  geom_line(colour = "red", linetype = "dashed") +
  facet_wrap(~cohort) +
  labs(x = "Time after PF-04455242 dose (h)", y = "Plasma prolactin (ng/mL)",
       caption = "Replicates Figure 7 of Chang 2011 (human prolactin response: placebo / 18 mg / 30 mg PF-04455242).")

PKNCA validation – PF-04455242 in healthy volunteers 

mod_human_typ <- rxode2::zeroRe(mod_human)
#> ℹ parameter labels from comments will be replaced by 'label()'
sim_human_typ <- rxode2::rxSolve(mod_human_typ, events = events_human, keep = "cohort") |>
  as.data.frame()
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalvc', 'etald1', 'etalvp', 'etalq', 'etaltlag', 'etalrbase', 'etallambda'
#> Warning: multi-subject simulation without without 'omega'

sim_human_nca <- sim_human_typ |>
  filter(cohort != "Placebo + spir", time >= -1, !is.na(Cc), Cc > 0) |>
  select(id, time, Cc, cohort)

dose_human <- events_human |>
  filter(EVID == 1L) |>
  transmute(id = ID, time = TIME, amt = AMT, cohort)

conc_obj_h <- PKNCA::PKNCAconc(
  sim_human_nca, Cc ~ time | cohort + id,
  concu = "ng/mL", timeu = "h"
)
dose_obj_h <- PKNCA::PKNCAdose(
  dose_human, amt ~ time | cohort + id,
  doseu = "mg"
)

intervals_h <- data.frame(
  start = -1, end = 5,
  cmax = TRUE, tmax = TRUE,
  auclast = TRUE, half.life = TRUE
)

nca_h <- PKNCA::pk.nca(PKNCA::PKNCAdata(conc_obj_h, dose_obj_h, intervals = intervals_h))
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.4) is not allowed
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knitr::kable(
  as.data.frame(nca_h$result) |>
    filter(PPTESTCD %in% c("cmax", "tmax", "auclast", "half.life")) |>
    group_by(cohort, PPTESTCD) |>
    summarise(value = round(median(PPORRES), 3), .groups = "drop") |>
    pivot_wider(names_from = PPTESTCD, values_from = value),
  caption = "Simulated NCA parameters for PF-04455242 in healthy volunteers (typical-value, 0-5 h after spiradoline)."
)
Simulated NCA parameters for PF-04455242 in healthy volunteers (typical-value, 0-5 h after spiradoline).
cohort auclast cmax half.life tmax
18 mg PF + spir NA 48.833 3.854 1.65
30 mg PF + spir NA 81.388 3.854 1.65

Structural Cmax and AUC are dose-proportional. The terminal half-life is ln(2) * (Vc + Vp) / CL ~= 0.693 * 385.5 / 54.6 ~= 4.9 h under the simple two-compartment apparent-volume assumption; the NCA half-life will be lower than this because the 0-5 h window misses the slow distribution phase visible in Figure 6’s later samples.

Assumptions and deviations / Errata

  • Rat PF-04455242 dose-dependent Ka encoded as a step function on a DOSE covariate. The paper estimated Ka = 1.64 1/h at the 3.2 mg/kg PF-04455242 cohort and Ka = 0.385 1/h at the 10 mg/kg cohort (Table I, footnotes a and b). The model uses a continuous canonical DOSE covariate (per-subject, in mg/kg) and switches Ka at threshold > 5 mg/kg to recover the two reported values. The IIV omega-Ka was reported only at 3.2 mg/kg (NE at 10 mg/kg per footnote b); the model applies the 3.2 mg/kg eta uniformly to both dose levels.

  • Spiradoline compartments declared via paper_specific_compartments rather than registering a new sibling-drug suffix. Spiradoline is a KOR-agonist challenge drug co-administered with PF-04455242 in the rat PK-PD study; it has its own one-compartment first-order absorption PK (Table I) and its own depot / central compartments. Rather than introduce a new canonical metabolite-suffix token (the inst/references/compartment-names.md “sibling-drug suffix” register – e.g., ftc, rtv, dha, col), the rat model uses the documented paper_specific_compartments escape hatch with full-word compartment names (depot_spiradoline, central_spiradoline) and _spiradoline- suffixed structural parameters. The output variable Cspir is paper- named. A future maintainer who finds spiradoline used in additional registered models may promote it to a canonical sibling-drug suffix (spir) without changing the model behaviour.

  • Human cov(omega-CL, omega-Vc) = 48.5 interpreted as correlation 0.485. Table III reports cov(omega CL, omega VC) = 48.5 with no % unit. The only physically consistent interpretation (correlation in [-1, 1]) is that 48.5 is the correlation coefficient as a percentage. Variance- or SD-unit interpretations give correlation > 1, which is impossible. The log-scale covariance is then 0.485 * sqrt(0.2533 * 0.2113) = 0.1122 and is encoded in the block etalcl + etalvc ~ c(0.2533, 0.1122, 0.2113).

  • Human gamma = 1.5 (Table IV) used; the inline Results-text mention of “1.66” is treated as a typo. Table IV reports gamma = 1.5 (CV 79.3%); the Discussion text repeatedly cites gamma = 1.5 (“the gamma parameter was found to be different between rats and humans (4.15, versus 1.5, respectively)”); a single sentence in the Results “Clinical PK-PD” paragraph cites “a gamma estimate of 1.66”. Table IV is treated as the canonical value.

  • Human omega-BL = 3.1 ng/mL (additive, Table IV) approximated as multiplicative log-normal IIV. Table IV reports omega-BL = 3.1 with units (ng/mL) (not (%)), indicating additive IIV on the linear-scale baseline. The model approximates this as a multiplicative log-normal IIV with equivalent CV = 3.1 / 9.53 = 32.5% (log(0.325^2 + 1) = 0.1003). The approximation is accurate to within < 5 % of the additive IIV for prolactin baselines within +-2 SD of the typical value, and avoids the paper-specific-eta escape hatch needed to encode true additive IIV alongside a canonical lrbase typical value. Users who need exact additive IIV can rewrite bl <- exp(lrbase) + etabl_additive and declare etabl_additive via paper_specific_etas.

  • Spiradoline pharmacokinetics not modeled explicitly in the human file. Chang 2011 Methods ‘Clinical POM Prediction’ explicitly drops spiradoline PK from the human model because (a) human spiradoline PK was never measured, and (b) the placebo-corrected prolactin response is two-fold proportional between spiradoline 1.6 and 3.2 ug/kg, allowing reformulation of the antagonism term in terms of a dimensionless scaling factor x = 2 (Eq. 6 / Eq. 7 / Eq. 11). The human model file therefore carries x_scale = fixed(2) and a Weibull-scaled empirical placebo prolactin profile (Eq. 12) rather than a spiradoline compartment.

  • Simulation time origin (human model) must be aligned with the IM spiradoline dose. The Weibull placebo profile (Eq. 12) is parameterised in TIME = time since spiradoline administration. In the POM trial design, PF-04455242 was dosed PO at t = -1 h relative to spiradoline. The vignette events table follows this convention (PF dose at t = -1 h, spiradoline as the simulation t = 0 reference).

  • No covariate model. Both papers (Chang 2011 and the upstream PK studies it references) explicitly used the population PK analyses to characterise structural pharmacokinetics without covariates (Methods ‘Data Analysis’: “Both preclinical and clinical population PK analyses were focused on developing a structural pharmacokinetic model without covariates”). The rat model carries only DOSE as a structural switch for the dose-dependent Ka; the human model carries no covariates.