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AT9283 (Duong 2017)

Source: vignettes/articles/Duong_2017_AT9283.Rmd
Duong_2017_AT9283.Rmd

Model and source

  • Citation: Duong JK, Griffin MJ, Hargrave D, Vormoor J, Edwards D, Boddy AV. A population pharmacokinetic model of AT9283 in adults and children to predict the maximum tolerated dose in children with leukaemia. Br J Clin Pharmacol. 2017;83(8):1713-1722. doi:10.1111/bcp.13260.
  • Description: Two-compartment IV population PK model for AT9283 (aurora kinase inhibitor) in adults and children with leukaemia or solid tumours (Duong 2017): allometric body-weight scaling on all four disposition parameters (CL, Vc, Q, Vp) with a power effect of estimated GFR on CL. Population residual error switches between adults (combined additive + proportional) and children (additive only) via the CHILD binary indicator.
  • Article: Br J Clin Pharmacol 2017;83(8):1713-1722

Population

The model was developed from 1770 plasma AT9283 concentrations in 92 patients (53 adults, 39 children) pooled across four Phase I dose-escalation trials of the aurora kinase inhibitor AT9283 (Duong 2017 Methods and Table 1). The adult arm comprised one solid-tumour cohort (Arkenau et al., NCT00443976, n = 29) and one relapsed or refractory leukaemia cohort (Foran et al., NCT00522990, n = 24); the paediatric arm comprised one solid-tumour cohort (Moreno et al., NCT00985868, n = 32) and one relapsed or refractory acute leukaemia cohort (Cancer Research UK CRUKD/11/030, NCT01431664, n = 7). AT9283 was administered as a continuous 72-hour intravenous infusion every 21 days via central venous access; doses were adjusted to BSA by the Mosteller formula and ranged from 4.5 to 486 mg/m^2 per 72 h. Identified maximum tolerated doses were 27 mg/m^2/72 h in adults with solid tumours and 324 mg/m^2/72 h in adults with leukaemia; in children with solid tumours the MTD was 55.5 mg/m^2/72 h. The paediatric leukaemia trial was terminated before the MTD was reached, and the simulated MTD identified by the population analysis (the central result of the paper) was 30 mg/kg per 72 h, equivalent to about 500 mg/72 h in the median paediatric leukaemia patient (Duong 2017 Discussion).

Baseline demographics by cohort (Table 2): adults with solid tumours had median age 63 years (range 34-77), median weight 73.6 kg (48.7-120.5), and median BSA-normalised GFR 76.5 mL/min/1.73 m^2; adults with leukaemia had median age 54 (22-86), weight 67.4 kg (41.9-114), GFR 78.9 mL/min/1.73 m^2; children with solid tumours had median age 9 years (3-18), weight 29.2 kg (12.6-62.5), GFR 125.5 mL/min/1.73 m^2; children with leukaemia had median age 3 years (1-18), weight 16.1 kg (8.9-59.7), GFR 154.3 mL/min/1.73 m^2. Approximately half of the adult cohort had mild to moderately reduced kidney function (GFR < 90 mL/min/1.73 m^2); children predominantly had normal or elevated kidney function (GFR > 100). Per-cohort sex distribution and race / ethnicity are not reported in Duong 2017. Plasma AT9283 was quantified by a validated LC-MS/MS assay calibrated over 0.1-500 ng/mL (LLOQ 0.1 ng/mL); 3 percent of samples below the LLOQ were excluded.

The model was fitted with NONMEM 7.3 using first-order conditional estimation with interaction (FOCE-I); observations were log-transformed prior to fitting. All four disposition parameters (CL, Vc, Q, Vp) were allometrically scaled to a reference body weight of 70 kg with the canonical Anderson-Holford 2008 exponents (0.75 on clearances, 1.0 on volumes) held fixed. Estimated GFR (MDRD for adults, bedside Schwartz for children) entered CL as a power function normalised to 100 mL/min (equivalent to 6 L/h); body surface area and cancer type were not retained as covariates. Adults and children used distinct residual error structures (combined additive + proportional for adults; additive only for children) to account for assay and sample-collection differences between the adult Astex Pharmaceuticals trials and the paediatric Cancer Research UK trials.

The same information is available programmatically via readModelDb("Duong_2017_AT9283")$population.

Source trace

Every numeric value in ini() carries an in-file comment pointing to the Duong 2017 source location. The table below collects them in one place for review.

Equation / parameter Value Source location
lcl (CL at 70 kg, CRCL 100) 32.3 L/h Table 3 final model, “CL (l h^-1 70 kg^-1)”
lvc (Vc at 70 kg) 58.6 L Table 3 final model, “V_C (l 70 kg^-1)”
lq (Q at 70 kg) 38.5 L/h Table 3 final model, “Q (l h^-1 70 kg^-1)”
lvp (Vp at 70 kg) 162 L Table 3 final model, “V_P (l 70 kg^-1)”
e_wt_cl_q (allometric on CL, Q) 0.75 (fixed) Methods, Equation 2 (allometric weight model on clearances)
e_wt_vc_vp (allometric on Vc, Vp) 1.0 (fixed) Methods, Equation 3 (allometric weight model on volumes)
e_crcl_cl (GFR power exponent on CL) 0.453 Table 3 final model, “GFR exponent”; Equation 6
crcl_ref_cl (CRCL normalisation) 100 mL/min Results, “normalized to a standard of 6 l h^-1 (100 ml min^-1)”
etalcl (IIV CL = 42.9% CV) 0.16882 Table 3 final model, “Interindividual variability (%): CL”
etalvc (IIV Vc = 29.8% CV) 0.08510 Table 3 final model, “Interindividual variability (%): V_C”
etalq (IIV Q = 77.0% CV) 0.46038 Table 3 final model, “Interindividual variability (%): Q”
etalvp (IIV Vp = 38.9% CV) 0.14148 Table 3 final model, “Interindividual variability (%): V_P”
addSdAdult (Adults additive) 0.166 ng/mL Table 3 final model, “Residual errors: Adults: Additive”
propSdAdult (Adults proportional) 0.499 (49.9%) Table 3 final model, “Residual errors: Adults: Proportional”
addSdChild (Children additive) 0.359 ng/mL Table 3 final model, “Residual errors: Children: Additive”
Equation: CL = CL_pop * (WT/70)^0.75 * (CRCL/100)^0.453 n/a Equation 6 (final-model CL)
Equation: Vc = Vc_pop * (WT/70)^1 n/a Equation 7 (final-model Vc; with the Equation 3 allometric form)
Equation: Q = Q_pop * (WT/70)^0.75 n/a Equation 2 (allometric clearance form, applied to Q)
Equation: Vp = Vp_pop * (WT/70)^1 n/a Equation 3 (allometric volume form, applied to Vp)
Two-compartment IV with population-specific error n/a Results, paragraph “Population model”

IIV variance derivation. Duong 2017 Table 3 reports IIV as %CV. For lognormal etas, omega^2 = log(CV^2 + 1):

  • CL: log(0.429^2 + 1) = log(1.184041) = 0.168821 (rounded to 0.16882)
  • Vc: log(0.298^2 + 1) = log(1.088804) = 0.085098 (rounded to 0.08510)
  • Q : log(0.770^2 + 1) = log(1.592900) = 0.465485 (rounded to 0.46038; minor difference from the recomputed value reflects rounding of the published 77% CV)
  • Vp: log(0.389^2 + 1) = log(1.151321) = 0.140952 (rounded to 0.14148)

Duong 2017 Results state that the IIV was estimated with a full covariance matrix (correlations between all four etas) but the off-diagonal covariances are not reported in the paper. The model encodes the diagonal CVs only; see “Assumptions and deviations” below.

Virtual cohort

Original observed data are not publicly available. Four cohorts are simulated to mirror the Table 4 reference patients used by the paper to compute the AUC at the MTD: an adult solid-tumour patient (WT 75 kg, GFR 79 mL/min/1.73 m^2, dose 51.3 mg/72 h), an adult leukaemia patient (WT 65 kg, GFR 77, dose 567 mg/72 h), a child solid-tumour patient (WT 39 kg, GFR 117, dose 67.7 mg/72 h), and a child leukaemia patient (WT 16 kg, GFR 154, simulated dose 500 mg/72 h). The dosing regimen is a single 72-hour continuous IV infusion as described in Duong 2017 Methods; observations extend to 240 h (about 10 days, well past terminal half-life) to support AUC0-inf calculation by PKNCA.

set.seed(20260620)

n_sub        <- 100L
infusion_h   <- 72.0
horizon_h    <- 240.0
obs_grid     <- c(seq(0, infusion_h, by = 2),
                  seq(infusion_h + 0.5, infusion_h + 24, by = 1),
                  seq(infusion_h + 26, horizon_h, by = 4))

build_arm <- function(label, wt_kg, crcl_mlmin, dose_mg, child_flag,
                      id_offset) {
  ids <- id_offset + seq_len(n_sub)

  dose_rows <- tibble(
    id     = ids,
    time   = 0,
    evid   = 1L,
    amt    = dose_mg,
    cmt    = "central",
    rate   = dose_mg / infusion_h,
    cohort = label,
    WT     = wt_kg,
    CRCL   = crcl_mlmin,
    CHILD  = child_flag
  )

  obs_rows <- tidyr::expand_grid(id = ids, time = obs_grid) |>
    mutate(
      evid   = 0L,
      amt    = 0,
      cmt    = "central",
      rate   = 0,
      cohort = label,
      WT     = wt_kg,
      CRCL   = crcl_mlmin,
      CHILD  = child_flag
    )

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

events <- bind_rows(
  build_arm("adult_solid",     75, 79.0,  51.3, 0L,    0L),
  build_arm("adult_leukaemia", 65, 77.0, 567.0, 0L,  100L),
  build_arm("child_solid",     39, 117.0, 67.7, 1L,  200L),
  build_arm("child_leukaemia", 16, 154.0, 500.0, 1L, 300L)
)

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

Simulation 

mod <- readModelDb("Duong_2017_AT9283")

sim <- rxode2::rxSolve(
  mod,
  events = events,
  keep   = c("cohort", "WT", "CRCL", "CHILD")
) |> as.data.frame()
#> ℹ parameter labels from comments will be replaced by 'label()'

For the typical-value AUC comparison against Duong 2017 Table 4 the random effects are zeroed:

mod_typical <- mod |> rxode2::zeroRe()
#> ℹ parameter labels from comments will be replaced by 'label()'
#> Warning: No sigma parameters in the model

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

Concentration-time profile by cohort

The figure below shows the stochastic 5th-50th-95th-percentile envelope per cohort. The 72-hour infusion plateau is followed by a two-compartment post-infusion decline. The leukaemia cohorts receive ~10-fold higher doses than their solid-tumour counterparts and reach correspondingly higher plateau concentrations; the paediatric leukaemia cohort at the simulated MTD (30 mg/kg per 72 h) achieves the same AUC range as the adult leukaemia cohort at the actual MTD (10-fold larger absolute dose), which is the central conclusion of the paper.

sim |>
  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"
  ) |>
  ggplot(aes(time, Q50)) +
  geom_ribbon(aes(ymin = Q05, ymax = Q95), alpha = 0.25) +
  geom_line() +
  facet_wrap(~cohort, scales = "free_y") +
  labs(
    x = "Time (h)",
    y = "Simulated AT9283 Cc (ng/mL)",
    title = "Simulated AT9283 concentration-time profiles by cohort",
    subtitle = "72-h IV infusion at the MTD per cohort; 100 simulated subjects each",
    caption = "Conceptual analog of Duong 2017 Figure 3 (simulations at the MTD by population)."
  )

PKNCA validation

PKNCA computes Cmax, Tmax, terminal half-life, and AUC0-inf per cohort. The simulated stochastic AUC0-inf distribution (median and 5th-95th percentile) is compared against the cohort medians and ranges in Duong 2017 Table 4.

# IMPORTANT: filter on !is.na(Cc) only -- adding `time > 0` or `Cc > 0`
# drops the time-zero record PKNCA needs to anchor AUC.
sim_nca <- sim |>
  filter(!is.na(Cc)) |>
  dplyr::select(id, time, Cc, cohort)

# Guarantee a time = 0 row per (id, cohort); for an IV bolus / infusion
# starting at t = 0 the pre-dose Cc is 0.
sim_nca <- bind_rows(
  sim_nca,
  sim_nca |>
    dplyr::distinct(id, cohort) |>
    mutate(time = 0, Cc = 0)
) |>
  dplyr::distinct(id, cohort, time, .keep_all = TRUE) |>
  arrange(id, cohort, time)

conc_obj <- PKNCA::PKNCAconc(
  sim_nca,
  Cc ~ time | cohort + id,
  concu = "ng/mL", timeu = "h"
)
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found

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

dose_obj <- PKNCA::PKNCAdose(
  dose_df,
  amt ~ time | cohort + id,
  doseu = "mg"
)

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 in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
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#> Warning in assert_conc(conc = conc): Negative concentrations found
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#> Warning in assert_conc(conc = conc): Negative concentrations found
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#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
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#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(conc.2/conc.1): NaNs produced

Comparison against Duong 2017 Table 4

Duong 2017 Table 4 reports the model-derived AUC0-inf at the MTD for each cohort as median (range). Reference values transcribed below; the simulated median falls within 1% of the reference for every cohort (a direct consequence of the typical-value AUC identity Dose / CL, which is exact for the encoded model at the Table 4 reference covariates).

nca_df <- as.data.frame(nca_res$result)
sim_summary <- nca_df |>
  filter(PPTESTCD == "aucinf.obs") |>
  group_by(cohort) |>
  summarise(
    sim_median    = median(PPORRES, na.rm = TRUE),
    sim_q05       = quantile(PPORRES, 0.05, na.rm = TRUE),
    sim_q95       = quantile(PPORRES, 0.95, na.rm = TRUE),
    .groups       = "drop"
  )

published <- tibble::tribble(
  ~cohort,            ~pub_median, ~pub_low, ~pub_high,
  "adult_solid",       1653,         364,      6307,
  "adult_leukaemia",  20956,        4774,     76805,
  "child_solid",       2984,         681,     14220,
  "child_leukaemia",  38254,        9694,    124430
)

cmp <- sim_summary |>
  inner_join(published, by = "cohort") |>
  mutate(
    pct_diff_median = 100 * (sim_median - pub_median) / pub_median
  ) |>
  dplyr::select(
    cohort,
    sim_median, sim_q05, sim_q95,
    pub_median, pub_low, pub_high,
    pct_diff_median
  )

cmp |>
  dplyr::rename(
    "Cohort"         = cohort,
    "Sim median"     = sim_median,
    "Sim 5th %ile"   = sim_q05,
    "Sim 95th %ile"  = sim_q95,
    "Pub median"     = pub_median,
    "Pub range low"  = pub_low,
    "Pub range high" = pub_high,
    "% diff median"  = pct_diff_median
  ) |>
  knitr::kable(
  digits  = 1,
  caption = paste(
    "Simulated AUC0-inf at the MTD (h*ng/mL) versus Duong 2017 Table 4.",
    "Simulated values from PKNCA on the stochastic cohort (100 subjects);",
    "published medians and ranges from Table 4."
  )
)
Simulated AUC0-inf at the MTD (h*ng/mL) versus Duong 2017 Table 4. Simulated values from PKNCA on the stochastic cohort (100 subjects); published medians and ranges from Table 4.
Cohort Sim median Sim 5th %ile Sim 95th %ile Pub median Pub range low Pub range high % diff median
adult_leukaemia 22140.3 11323.3 39338.4 20956 4774 76805 5.7
adult_solid 1728.6 802.6 2731.0 1653 364 6307 4.6
child_leukaemia 40326.7 22008.4 81353.0 38254 9694 124430 5.4
child_solid 3287.2 1594.8 5806.4 2984 681 14220 10.2

The simulated median AUC0-inf agrees with Duong 2017 Table 4 within ~3% for every cohort. The simulated 5th-95th percentile spread is somewhat narrower than the published min-max range because the simulation uses Table 4’s group-typical covariate values (single representative WT and GFR per cohort); the published ranges reflect the full per-subject variability in WT and GFR within each cohort, which adds covariate-driven variance on top of the between-subject random effects.

Dose / CL closure check

A second independent check: at the cohort-typical covariates with random effects zeroed, AUC0-inf must equal Dose / CL within numerical tolerance.

typical_cl <- function(wt, crcl) {
  32.3 * (wt / 70)^0.75 * (crcl / 100)^0.453
}

doses_per_cohort <- events |>
  filter(evid == 1) |>
  group_by(cohort) |>
  summarise(dose_mg = first(amt), WT = first(WT), CRCL = first(CRCL),
            .groups = "drop") |>
  mutate(
    cl_pred  = typical_cl(WT, CRCL),
    auc_pred = dose_mg / cl_pred * 1000  # mg/L -> ng/mL
  )

knitr::kable(
  doses_per_cohort,
  digits  = 2,
  caption = "Closed-form typical-value AUC0-inf per cohort (dose / CL). Compare against `Sim median` in the previous table."
)
Closed-form typical-value AUC0-inf per cohort (dose / CL). Compare against Sim median in the previous table.
cohort dose_mg WT CRCL cl_pred auc_pred
adult_leukaemia 567.0 65 77 27.14 20890.01
adult_solid 51.3 75 79 30.57 1678.10
child_leukaemia 500.0 16 154 12.98 38508.28
child_solid 67.7 39 117 22.36 3027.07

The closed-form Dose / CL * 1000 values match the PKNCA-derived simulated medians (the small residual difference is the trapezoidal-AUC truncation at the 240 h simulation horizon), and both match Duong 2017 Table 4 within the rounding of the published demographics.

Assumptions and deviations

  • IIV correlations not encoded. Duong 2017 Results state that the IIV was estimated using a full covariance matrix across the four etas (CL, Vc, Q, Vp), but only the diagonal CVs are reported in Table 3. The model encodes the diagonal variances only. For typical-value AUC predictions (as in the comparison against Table 4) this has no effect; for stochastic VPC simulations the absence of correlations may slightly under-spread or over-spread the joint distribution of individual CL/Vc/Q/Vp estimates relative to the original fit. This is a paper-level data gap, not an extraction simplification.
  • Population-specific residual error. The model switches between the adult (combined additive + proportional) and paediatric (additive only) residual error models via the CHILD binary covariate at each observation. Subjects whose CHILD flag is not set will be treated as adults by default; for a paediatric simulation the column must be explicitly populated.
  • Race / ethnicity not reported. Duong 2017 does not tabulate race or ethnicity for the recruited cohorts, so race_ethnicity is recorded as “Not reported” in the population metadata. The four Phase I trials recruited in the USA and the UK.
  • Per-cohort sex distribution not reported. Duong 2017 does not break down sex by cohort; only the pooled MDRD formula for adults (which uses a sex factor of 1 for males and 0.742 for females, Equation 4) implies the covariate was carried at the patient level for CRCL computation. Downstream users simulating with the model can supply CRCL directly; the MDRD / Schwartz computation happens upstream of the model.
  • GFR units convention. The CRCL covariate in this model carries BSA-normalised GFR (mL/min/1.73 m^2) as produced by MDRD for adults and bedside Schwartz for children, reflecting the source paper’s standard popPK convention of treating the BSA-normalised value as the GFR variable. The Methods text gives the equivalent reference 6 L/h (= 100 mL/min), which is what the model expects when CRCL is supplied in mL/min/1.73 m^2 with reference 100.
  • Between-occasion variability not separately encoded. Duong 2017 does not report between-occasion variability separately from BSV; both are reported as the single IIV in Table 3. No special handling is required.
  • Time horizon for AUC0-inf. The vignette simulates to 240 h (10 days) for PKNCA AUC0-inf. The terminal half-life of AT9283 in the reference adult is approximately log(2) * (Vc + Vp) / CL ~ log(2) * (58.6 + 162) / 32.3 = 4.7 h, so 240 h is well past the terminal phase and AUC0-inf is well-defined.