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Brivaracetam (Schoemaker 2017)

Source: vignettes/articles/Schoemaker_2017_brivaracetam.Rmd
Schoemaker_2017_brivaracetam.Rmd

Model and source 

#> ℹ parameter labels from comments will be replaced by 'label()'
  • Citation: Schoemaker R, Wade JR, Stockis A. Brivaracetam population pharmacokinetics in children with epilepsy aged 1 month to 16 years. Eur J Clin Pharmacol. 2017 Jun;73(6):727-733. doi:10.1007/s00228-017-2230-6
  • Description: One-compartment population PK model for oral brivaracetam in paediatric patients with epilepsy aged 1 month to 16 years (Schoemaker 2017). First-order absorption, single-compartment distribution, and first-order elimination, with allometric scaling of CL/F (exponent 0.750 fixed) and V/F (exponent 1.00 fixed) on lean body weight normalised to a 50 kg adult typical value. Co-administration of phenobarbital (PB; pooled with primidone), carbamazepine (CBZ), or valproate (VPA) modify apparent oral clearance via linear-additive multiplicative factors.
  • Article: https://doi.org/10.1007/s00228-017-2230-6

The packaged model implements the Schoemaker 2017 final paediatric brivaracetam popPK model: a one-compartment structure with first-order absorption and first-order elimination, allometric scaling of CL/F (exponent 0.750 fixed) and V/F (exponent 1.00 fixed) on lean body weight normalised to a 50 kg adult typical value, and three drug-specific concomitant-AED indicators (CONMED_PB, CONMED_CBZ, CONMED_VPA) acting as linear-additive multiplicative effects on CL/F. The model was developed in NONMEM 7.2.0 using FOCE-I; covariate selection followed PsN’s stepwise covariate modelling (SCM) with forward p < 0.01 and backward p < 0.001 thresholds.

Population

Schoemaker 2017 fit the model to 600 brivaracetam plasma concentrations from 96 paediatric patients with epilepsy aged 1 month to <16 years, enrolled in the open-label, single-arm, multicentre Trial N01263 (NCT00422422). The age distribution was 29 patients aged 1 month to <2 years, 26 aged 2 to <6 years, 24 aged 6 to <12 years, and 17 aged 12 to <16 years. Patients had localisation- related, generalised, or undetermined focal/generalised epileptic syndromes per ILAE classification and were receiving 1-3 concomitant AEDs other than levetiracetam. Hepatic impairment was an exclusion criterion. Brivaracetam was administered as an oral solution in three weekly increasing-dose steps: 0.4 / 0.8 / 1.6 mg/kg bid for subjects >= 8 years and 0.5 / 1.0 / 2.0 mg/kg bid for subjects < 8 years; doses were capped at the adult 25 / 50 / 100 mg bid levels once total body weight reached 50 kg. Two scheduled plasma samples were drawn at day 7, 14, and 21 in one of three time brackets (early morning, late morning, afternoon) plus one optional sample. Race, ethnicity, sex, eGFR, non-AED CYP3A inhibitors, non-AED CYP2C19 inhibitors, age, and postconceptional age were tested as covariates and found not to be significant; only PB (pooled with primidone), CBZ, and VPA were retained as drug-specific effects on CL/F. Full population characteristics are in Schoemaker 2017 Results paragraph 1 and Supplemental Table 1.

The same metadata is available programmatically via readModelDb("Schoemaker_2017_brivaracetam")$meta$population.

Source trace

The per-parameter origin is recorded as an in-file comment next to each ini() entry in inst/modeldb/specificDrugs/Schoemaker_2017_brivaracetam.R. The table below collects them in one place for review. All point estimates are from Schoemaker 2017 Table 1 (“NONMEM estimates” column); structural-model layout and covariate parameterisation are taken from Schoemaker 2017 Methods (page 2, “Brivaracetam population pharmacokinetics model” paragraph and equation 1).

Equation / parameter Value Source location
lka (Ka) log(1.84) -> 1.84 1/h Table 1; 95% CI 0.91/2.78, bootstrap median 1.83
lcl (CL/F at 50 kg LBW) log(3.63) -> 3.63 L/h Table 1; 95% CI 3.42/3.85, bootstrap median 3.62
lvc (V/F at 50 kg LBW) log(47.8) -> 47.8 L Table 1; 95% CI 43.1/52.5, bootstrap median 47.6
e_lbm_cl fixed(0.750) Table 1; “Allometric scaling CL/F 0.750 fixed”
e_lbm_vc fixed(1.00) Table 1; “Allometric scaling V/F 1.00 fixed”
e_conmed_pb_cl +0.408 Table 1; CL change with PB = +40.8% (95% CI +19.9%/+65.2%)
e_conmed_cbz_cl +0.479 Table 1; CL change with CBZ = +47.9% (95% CI +27.8%/+71.2%)
e_conmed_vpa_cl -0.101 Table 1; CL change with VPA = -10.1% (95% CI -18.5%/-0.8%)
etalcl variance 0.228^2 = 0.0520 Table 1; IIV CL = 22.8% (shrinkage 6.1%)
etalvc variance 0.167^2 = 0.0279 Table 1; IIV V = 16.7% (shrinkage 45.6%)
etalka variance 0.319^2 = 0.1018 Table 1; IIV Ka = 31.9% (shrinkage 73.4%)
propSd 0.234 Table 1; Residual error CV = 23.4% (95% CI 19.6%/27.1%)
Allometric model (LBM/50)^e_lbm_cl on CL/F Equation 1, page 2 Methods
Allometric model (LBM/50)^e_lbm_vc on V/F Equation 1, page 2 Methods
AED effect on CL/F (1 + theta * indicator) product across PB / CBZ / VPA PsN SCM “linear” categorical form, Methods page 2
ODE d/dt(depot) -ka * depot Standard 1-cmt oral structure; Methods page 2
ODE d/dt(central) ka * depot - kel * central Standard 1-cmt oral structure; Methods page 2

Virtual cohort

Original observed data from Trial N01263 are not publicly available. The figures below use a virtual paediatric cohort whose lean-body-weight distribution approximates the published age strata. Lean body weight for each virtual subject is sampled from a log-normal distribution spanning roughly 4 - 50 kg, matching the 1 month - 16 year age range and the paper’s LBW-based allometric scaling. AED comedication status is assigned as a factorial design across four cohorts (no AED, +PB, +CBZ, +VPA) so the covariate effect on Css can be inspected directly.

set.seed(20170309)

mod <- rxode2::rxode(readModelDb("Schoemaker_2017_brivaracetam"))
#> ℹ parameter labels from comments will be replaced by 'label()'

n_per_arm <- 100
lbm_min   <- 4
lbm_max   <- 55

dose_mg_per_kg_lbm <- function(lbm) {
  # Trial N01263 maximum scheduled dose: 2.0 mg/kg bid for subjects <8 y and
  # 1.6 mg/kg bid for subjects >=8 y, capped at adult 100 mg bid for total
  # body weight >= 50 kg. We approximate the age-vs-LBM split at 25 kg LBM
  # (~8 y) and apply the 50 kg LBM cap analogous to the WT cap.
  per_dose <- ifelse(lbm < 25, 2.0 * lbm, 1.6 * lbm)
  pmin(per_dose, 100)
}

make_aed_cohort <- function(n, aed_name, pb, cbz, vpa, id_offset = 0L) {
  lbm <- pmin(pmax(exp(rnorm(n, mean = log(20), sd = 0.6)), lbm_min), lbm_max)
  per_dose <- dose_mg_per_kg_lbm(lbm)
  base <- tibble(
    id           = id_offset + seq_len(n),
    LBM          = lbm,
    CONMED_PB    = pb,
    CONMED_CBZ   = cbz,
    CONMED_VPA   = vpa,
    aed_cohort   = aed_name,
    amt_per_dose = per_dose
  )
  # Steady-state simulation: 20 doses at tau = 12 h, observe the last interval.
  n_doses <- 20L
  tau     <- 12
  doses <- tidyr::crossing(id = base$id, dose_idx = seq_len(n_doses)) |>
    mutate(time = (dose_idx - 1) * tau, evid = 1L, cmt = "depot") |>
    left_join(base, by = "id") |>
    mutate(amt = amt_per_dose)
  obs_grid <- seq((n_doses - 1) * tau + 0.25, n_doses * tau, by = 0.5)
  obs <- tidyr::crossing(id = base$id, time = obs_grid) |>
    mutate(evid = 0L, cmt = "Cc", amt = NA_real_) |>
    left_join(base, by = "id")
  bind_rows(doses, obs) |>
    arrange(id, time, desc(evid))
}

events_ss <- bind_rows(
  make_aed_cohort(n_per_arm, "no AED", pb = 0L, cbz = 0L, vpa = 0L,
                  id_offset = 0L),
  make_aed_cohort(n_per_arm, "+PB",    pb = 1L, cbz = 0L, vpa = 0L,
                  id_offset = n_per_arm),
  make_aed_cohort(n_per_arm, "+CBZ",   pb = 0L, cbz = 1L, vpa = 0L,
                  id_offset = 2L * n_per_arm),
  make_aed_cohort(n_per_arm, "+VPA",   pb = 0L, cbz = 0L, vpa = 1L,
                  id_offset = 3L * n_per_arm)
)
stopifnot(!anyDuplicated(unique(events_ss[, c("id", "time", "evid")])))

Simulation 

sim_ss <- rxode2::rxSolve(
  object     = mod,
  events     = events_ss,
  keep       = c("LBM", "CONMED_PB", "CONMED_CBZ", "CONMED_VPA",
                 "aed_cohort", "amt_per_dose"),
  returnType = "data.frame"
) |>
  dplyr::filter(time > 0)

For deterministic typical-value replication of the population mean profile, zero out the random effects:

sim_ss_typical <- rxode2::rxSolve(
  object     = rxode2::zeroRe(mod),
  events     = events_ss,
  keep       = c("LBM", "CONMED_PB", "CONMED_CBZ", "CONMED_VPA",
                 "aed_cohort", "amt_per_dose"),
  returnType = "data.frame"
) |>
  dplyr::filter(time > 0)
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalvc', 'etalka'
#> Warning: multi-subject simulation without without 'omega'

Replicate published figures

Steady-state Css distribution by AED comedication (Schoemaker 2017 Figure 2 context)

Schoemaker 2017 Figure 2 shows the simulated paediatric Css distribution stratified by AED background (PB, CBZ, VPA, or none) and overlays an adult reference Css range. The chunk below reproduces the same stratification using the packaged model: per-subject average steady-state concentration (computed as AUC0-tau / tau over the last dosing interval) is summarised by AED cohort.

tau <- 12
last_interval_start <- 19 * tau

trapz_auc <- function(t, y) {
  ord <- order(t)
  t   <- t[ord]
  y   <- y[ord]
  sum(0.5 * (y[-1] + y[-length(y)]) * diff(t))
}

ss_css <- sim_ss |>
  dplyr::filter(time >= last_interval_start) |>
  group_by(id, aed_cohort, LBM) |>
  summarise(
    auc_tau = trapz_auc(time - last_interval_start, Cc),
    Css     = auc_tau / tau,
    .groups = "drop"
  )

ggplot(ss_css, aes(aed_cohort, Css)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(width = 0.15, alpha = 0.25, size = 0.8) +
  geom_hline(yintercept = 2.10, linetype = "dashed", colour = "tomato") +
  annotate("text", x = 0.6, y = 2.50, hjust = 0, colour = "tomato",
           label = "Paper Css = 2.10 mg/L (AED-naive subgroup, n=71)") +
  labs(x = "Background AED",
       y = "Steady-state Css (mg/L)",
       title = "Paediatric brivaracetam Css at maximum scheduled dose, by AED cohort",
       caption = paste("Simulated from ", 4L * n_per_arm,
                       "virtual subjects (n = ", n_per_arm,
                       "per AED cohort).", sep = " "))

Typical-value Css vs LBM (Schoemaker 2017 Figure 1 context)

Schoemaker 2017 Figure 1 shows predicted Css against body weight (left) and age (right) for the trial dosing scheme. The chunk below shows the typical-value Css trajectory across the paediatric LBM range for the trial maximum scheduled dose, separated by AED background.

ss_typ_css <- sim_ss_typical |>
  dplyr::filter(time >= last_interval_start) |>
  group_by(id, aed_cohort, LBM, amt_per_dose) |>
  summarise(
    auc_tau = trapz_auc(time - last_interval_start, Cc),
    Css     = auc_tau / tau,
    .groups = "drop"
  )

ggplot(ss_typ_css, aes(LBM, Css, colour = aed_cohort)) +
  geom_point(alpha = 0.6, size = 1) +
  geom_smooth(se = FALSE, linewidth = 0.7, method = "loess",
              formula = y ~ x) +
  geom_hline(yintercept = 2.10, linetype = "dashed", colour = "grey40") +
  labs(x = "Lean body weight (kg)",
       y = "Typical-value Css (mg/L)",
       colour = "Background AED",
       title  = "Typical-value Css vs LBM at trial maximum scheduled dose",
       caption = "Random effects zeroed; deviations reflect the per-cohort AED multiplier and the dose cap.")

PKNCA validation

PKNCA is run on the typical-value steady-state simulation, restricted to the final dosing interval (time in [19tau, 20tau] h, with the dose at the start of the interval re-anchored to time = 0). The treatment grouping is the AED cohort so each background can be compared against the others and the paper’s reported Css.

# Reanchor the final dosing interval to time = 0 for NCA.
last_dose_time <- last_interval_start
ss_nca_conc <- sim_ss_typical |>
  dplyr::filter(time >= last_dose_time, time <= last_dose_time + tau) |>
  dplyr::mutate(time = time - last_dose_time) |>
  dplyr::select(id, time, Cc, aed_cohort, LBM, amt_per_dose) |>
  dplyr::filter(!is.na(Cc))

ss_nca_dose <- ss_nca_conc |>
  dplyr::distinct(id, aed_cohort, amt_per_dose) |>
  dplyr::mutate(time = 0, amt = amt_per_dose)

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

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

nca_ss <- PKNCA::pk.nca(
  PKNCA::PKNCAdata(conc_obj, dose_obj, intervals = intervals_ss)
)
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
nca_ss_summary <- summary(nca_ss)
knitr::kable(
  nca_ss_summary,
  caption = paste("Steady-state NCA over the last 12-hour dosing interval,",
                  "by AED cohort. Typical-value simulation",
                  "(no random effects); per-subject heterogeneity comes",
                  "entirely from the LBM distribution and the dose cap.")
)
Steady-state NCA over the last 12-hour dosing interval, by AED cohort. Typical-value simulation (no random effects); per-subject heterogeneity comes entirely from the LBM distribution and the dose cap.
Interval Start Interval End aed_cohort N AUClast (hr*mg/L) Cmax (mg/L) Cmin (mg/L) Tmax (hr) Cav (mg/L)
0 12 +CBZ 100 NC 1.92 [6.91] 0.478 [23.5] 1.25 [1.25, 1.25] NC
0 12 +PB 100 NC 2.00 [6.59] 0.535 [18.9] 1.25 [1.25, 1.25] NC
0 12 +VPA 100 NC 2.63 [6.64] 1.14 [15.3] 1.75 [1.25, 1.75] NC
0 12 no AED 100 NC 2.46 [6.91] 0.988 [17.4] 1.75 [1.25, 1.75] NC

Comparison against published Css

Schoemaker 2017 Results paragraph 7 reports a predicted mean (SD) Css of 2.10 (0.51) mg/L for the n = 71 paediatric patients NOT taking enzyme inducers and receiving the maximum scheduled dose. The simulated “no AED” cohort above represents the analogous subgroup. The Css boxplot above overlays the paper’s mean as a dashed line; the simulated and paper means agree to within the published SD across the cohort weight range. The relative AED effects (+PB and +CBZ reduce Css by ~29% and ~32% respectively, +VPA increases Css by ~11%) are reproduced.

The mean-Css ratio between the no-AED and +CBZ cohorts is a direct check of the e_conmed_cbz_cl parameter: an increase in CL by (1 + 0.479) divides exposure by the same factor, so

ratios <- ss_css |>
  group_by(aed_cohort) |>
  summarise(mean_Css = mean(Css), .groups = "drop")

ratio_check <- ratios |>
  dplyr::filter(aed_cohort %in% c("no AED", "+PB", "+CBZ", "+VPA")) |>
  tidyr::pivot_wider(names_from = aed_cohort, values_from = mean_Css) |>
  dplyr::mutate(
    pred_no_aed_vs_pb_pct  = round(100 * (1 - `+PB`  / `no AED`), 1),
    pred_no_aed_vs_cbz_pct = round(100 * (1 - `+CBZ` / `no AED`), 1),
    pred_no_aed_vs_vpa_pct = round(100 * (`+VPA` / `no AED` - 1), 1)
  )
knitr::kable(
  ratio_check,
  digits = 2,
  caption = "Simulated AED effects on mean Css vs no-AED reference. Paper-reported decreases in exposure: PB 29% (95% CI 17/39%), CBZ 32% (22/42%); VPA increase 11% (1/23%)."
)
Simulated AED effects on mean Css vs no-AED reference. Paper-reported decreases in exposure: PB 29% (95% CI 17/39%), CBZ 32% (22/42%); VPA increase 11% (1/23%).
+CBZ +PB +VPA no AED pred_no_aed_vs_pb_pct pred_no_aed_vs_cbz_pct pred_no_aed_vs_vpa_pct
1.12 1.24 1.87 1.7 27.5 34.2 9.8

The simulated reductions / increase match the paper’s reported exposure changes (Schoemaker 2017 Abstract and Results paragraph 5) within Monte-Carlo sampling noise.

Assumptions and deviations

  • Interindividual-variability scale. Schoemaker 2017 Methods (page 2) states “Exponential models were used to describe the interindividual variability … IIV was calculated as the square root of the diagonal element in the omega matrix.” The Table 1 IIV column is therefore the log-scale omega (SD), not a linear-scale CV; the packaged ini() squares each reported value to obtain the variance required by nlmixr2’s eta ~ var syntax (e.g., etalcl ~ 0.0520 == 0.228^2). For small values of omega (< 0.30) this is numerically indistinguishable from the lognormal CV; for the Ka IIV (0.319) the lognormal-CV approximation diverges slightly, but the source convention is unambiguous.
  • AED-effect functional form. Schoemaker 2017 Methods (page 2) states “Categorical covariates were investigated in the SCM approach using a linear model”; PsN SCM “linear” categorical corresponds to CL = TVCL * (1 + theta * indicator). The packaged model applies the three AED indicators as a product of (1 + theta * indicator) terms. For patients on a single AED this matches the paper’s univariate reporting exactly; for patients on multiple AEDs the product captures the joint effect, but the paper notes (Results paragraph 5) that only a single trial subject was on both PB and CBZ, so no empirical cross-validation of the multiplicative joint form is possible from this dataset.
  • Lean body weight is taken as an input covariate. The paper computes LBW from total body weight and BMI (Schoemaker 2017 Methods page 2, citing Janmahasatian 2005 / reference 17). The packaged model takes LBM directly as a covariate column and does not implement the Janmahasatian formula internally; downstream users must precompute LBM from WT, height, and sex before simulation. The reference LBM value of 50 kg corresponds to a typical adult.
  • Allometric exponents fixed. The 0.750 / 1.00 theoretical allometric exponents on CL/F and V/F are wrapped in fixed(), matching the paper’s “fixed” notation in Table 1 (the paper does not test estimating them in the final-model SCM run; they are held at the theoretical Anderson & Holford values throughout).
  • Bioavailability F is implicit. Trial N01263 used oral solution without an IV reference arm, so absolute F is not identifiable; all clearance and volume terms are apparent (X/F). The packaged model does not declare a lfdepot and lets f(depot) default to 1; downstream users should treat exposure outputs as relative to the unknown F.
  • Phenytoin (PHT) effect not modelled. Schoemaker 2017 Methods page 2 explicitly excludes PHT from SCM testing because only a single trial subject was on PHT at entry. The packaged model therefore has no CONMED_PHT covariate; users simulating PHT-comedicated patients should either fall back to the broader CONMED_EIAED framework used by other models in the library, or treat such patients as outside the fitted covariate range.
  • Residual-error model. The proportional-only residual model was retained because adding an additive component yielded no OFV improvement (Schoemaker 2017 Results paragraph 2). The packaged model reflects this with a single Cc ~ prop(propSd) term.
  • Adult-reference simulations. The paper compares paediatric Css predictions against an adult reference range derived from a separate popPK analysis in Phase 3 efficacy trial patients (Schoemaker 2017 reference 25, an upstream paper not used by this packaged model). The vignette’s no-AED simulation reproduces only the paediatric Css range in the same patients; the adult reference range is not re-simulated here.
  • Errata. No erratum or corrigendum to Schoemaker 2017 was located on disk for this extraction. A search of PubMed and the Springer corrections feed for "Schoemaker" + "brivaracetam" + 2017 + erratum returned no hits; operators should reconfirm against the journal’s corrections listing if a re-extraction is undertaken.
  • Population race / ethnicity. Race and ethnicity were tested as covariates and found not to be significant (no effect retained). Schoemaker 2017 does not publish the race / ethnicity distribution of the cohort in detail; the packaged population$race_ethnicity is recorded as “Not detected as significant covariate,” and the virtual cohort therefore does not stratify on race.