Skip to contents

Nemtabrutinib (Kemal 2026)

Source: vignettes/articles/Kemal_2026_nemtabrutinib.Rmd
Kemal_2026_nemtabrutinib.Rmd

Model and source

  • Citation: Kemal CC, Zweers TJ, Krekels EHJ, Chatterjee MS. Population Pharmacokinetic Modeling and Exposure-Response Analyses of Nemtabrutinib in Patients With Hematologic Malignancies. CPT Pharmacometrics Syst Pharmacol. 2026;15(5). doi:10.1002/psp4.70257
  • Description: Two-compartment population PK model for nemtabrutinib (oral BTK inhibitor) in adults with hematologic malignancies including CLL/SLL (Kemal 2026, full covariate model)
  • Article: https://doi.org/10.1002/psp4.70257

Population

The pooled analysis population comprised 578 adults with hematologic malignancies enrolled in two Merck-sponsored trials of the oral non-covalent BTK inhibitor nemtabrutinib (also known as MK-1026 and formerly ARQ 531):

  • BELLWAVE-001 (NCT03162536), a phase 1/2 dose-escalation study (n = 136, doses 5-75 mg qd), enrolled patients with relapsed / refractory CLL/SLL, B-cell non-Hodgkin lymphoma, and Waldenstrom’s macroglobulinemia.
  • BELLWAVE-003 (NCT04728893), a phase 2 dose-escalation and confirmation study (n = 442, doses 45 / 65 / 80 mg qd), enrolled patients with CLL/SLL, marginal zone / follicular / mantle-cell lymphomas, Richter’s transformation, and Waldenstrom’s macroglobulinemia.

Baseline demographics (Table 1 of Kemal et al. 2026): median age 68 years (range 25-89), median body weight 74 kg (range 41-147), median baseline albumin 4.14 g/dL (i.e. 41.4 g/L), 34.1% female, 85.8% White, 7.8% Asian, 2.4% Black, 2.4% Other, 1.6% Missing. Primary diagnosis: 49.8% CLL/SLL, 30.6% other hematologic malignancies, 9.7% B-cell non-Hodgkin lymphoma, 9.7% Waldenstrom’s macroglobulinemia. Renal function: 29.2% normal, 38.6% mild impairment, 30.1% moderate impairment; hepatic function: 80.3% normal, 18.3% mild impairment, 1.4% moderate impairment. Concomitant medication exposure at any observation: 27.7% weak / 3.3% moderate / 2.0% strong CYP3A4 inhibitor; 46.6% weak / 2.8% moderate CYP3A4 inducer; 25.6% PPI; 6.8% H2 antagonist; 10.8% antacid (Table S3). Model development used 5669 non-BLQ observations.

The same information is available programmatically via readModelDb("Kemal_2026_nemtabrutinib")$population.

Source trace

Every ini() value in inst/modeldb/specificDrugs/Kemal_2026_nemtabrutinib.R carries an in-file comment identifying its source location; the table below collects the audit trail in one place. All final estimates come from Table 2 of the main article; the reference values for continuous covariates and the exact form of the multiplicative covariate composition come from the NONMEM control stream in the supplement.

Component Value Source
Two-compartment PK with first-order absorption + lag, first-order elimination – Kemal 2026 Methods 2.2.1 and NONMEM $SUBROUTINES ADVAN4 TRANS4
CL/F (typical) 3.33 L/h Table 2
Vc/F (typical) 120 L Table 2
Ka 2.83 1/h Table 2
Q/F 0.681 L/h Table 2
Vp/F 66.9 L Table 2
Absorption lag 0.494 h Table 2
Weight exponent on CL (fixed) 0.331 Table 2 (fixed after WT-only fit; Methods 2.2.2)
Weight exponent on Vc (fixed) 0.807 Table 2 (fixed after WT-only fit; Methods 2.2.2)
Reference weight 73.5 kg NONMEM supplement $PK (WT = 73.5 if missing)
Reference age 68 years NONMEM supplement $PK (AGEM = 68 if missing)
Reference albumin 41.2 g/L NONMEM supplement $PK (BALBM = 41.2 if missing)
Age power exponent on CL -0.503 Table 2
Albumin power exponent on CL -0.395 Table 2
Sex effect on CL (female vs male ref) -0.133 Table 2
Sex effect on Vc -0.0901 Table 2
Race effect on CL (Black vs White ref) 0.0533 Table 2
Race effect on CL (Asian vs White ref) -0.117 Table 2
Race effect on Vc (Black) 0.102 Table 2
Race effect on Vc (Asian) -0.116 Table 2
Disease effect on CL (B-cell NHL vs CLL/SLL ref) -0.166 Table 2
Disease effect on CL (WM vs CLL/SLL) 0.0718 Table 2
Disease effect on CL (Other vs CLL/SLL) -0.0244 Table 2
Disease effect on Vc (B-cell NHL) 0.00224 Table 2
Disease effect on Vc (WM) 0.152 Table 2
Disease effect on Vc (Other) -0.0200 Table 2
Mild renal impairment effect on CL 0.0537 Table 2
Moderate renal impairment effect on CL -0.00186 Table 2
Mild hepatic impairment effect on CL 0.00401 Table 2
Moderate CYP3A4 inducer effect on CL 0.0220 Table 2
Strong CYP3A4 inhibitor effect on CL -0.0119 Table 2
Low-dose (<30 mg) effect on F -0.151 Table 2
PPI effect on F 0.00232 Table 2
H2 antagonist effect on F 0.0264 Table 2
Antacid effect on F -0.0360 Table 2
CL IIV 39.8% CV -> omega^2 = 0.14710 Table 2 (log(1 + 0.398^2))
Vc IIV 17.1% CV -> omega^2 = 0.02884 Table 2 (log(1 + 0.171^2))
Proportional residual SD 0.222 Table 2
Additive residual SD 3.19 ng/mL Table 2
S2 = V2/1000 -> Cc = 1000 * central / vc – NONMEM $PK S2 = V2 / 1000 (dose in mg, Vc in L, Cc in ng/mL)
Terminal half-life derived from parameter estimates 85 h Kemal 2026 Section 3.2
Tmax (observed median) ~2 h Kemal 2026 Introduction
Accumulation ratio (AUC0-24) 1.8 to 3.4 Kemal 2026 Introduction
Accumulation ratio (Cmax) 1.53 to 2.85 Kemal 2026 Introduction

Virtual cohort

Original observed data are not publicly available. The figures below use small virtual populations (n = 50 per dose arm across the three BELLWAVE-003 dose levels of 45, 65, and 80 mg qd; 150 subjects total, well under the 200 per arm cap) whose covariate distributions approximate the pooled analysis population (Kemal 2026 Table 1). All covariates are set to the median / reference-category typical individual to keep the simulation deterministic-typical; between-arm differences reflect dose only. Reproducing the paper’s forest-plot covariate simulations (Figure 1) would require the full covariate distributions and is out of scope for this per-model validation.

set.seed(19260517)

typical_covs <- list(
  WT                       = 73.5,   # reference weight (NONMEM $PK)
  AGE                      = 68,     # reference age
  ALB                      = 41.2,   # reference albumin, g/L
  SEXF                     = 0L,     # male reference
  RACE_ASIAN               = 0L,     # White reference
  RACE_BLACK               = 0L,
  DIS_BCELLNHL             = 0L,     # CLL/SLL reference
  DIS_WM                   = 0L,
  DIS_OTHER_HEME           = 0L,
  RENALIMP_MILD            = 0L,     # normal renal function
  RENALIMP_MOD             = 0L,
  HEPIMP_MILD              = 0L,     # normal hepatic function
  CONMED_CYP3A4_IND_MOD    = 0L,     # no comedication
  CONMED_CYP3A4_INH_STRONG = 0L,
  CONMED_PPI               = 0L,
  CONMED_H2RA              = 0L,
  CONMED_ANTACID           = 0L
)

make_cohort <- function(dose_mg, n_id, id_offset) {
  # 28 daily doses at 0, 24, ..., 24*27 h; dense observation grid over
  # first 24 h and around each trough, plus post-dose-28 half-life
  # window to expose the terminal phase.
  dose_times   <- seq(0, by = 24, length.out = 28)
  obs_times    <- sort(unique(c(
    seq(0, 24, by = 0.5),                        # first-dose PK profile
    dose_times,                                  # pre-dose troughs
    dose_times + 2,                              # near-Tmax at each dose
    seq(24 * 27, 24 * 27 + 24, by = 1),          # last-dose PK profile
    seq(24 * 28, 24 * 28 + 24 * 10, by = 6)      # terminal-phase tail
  )))

  cov_cols <- as.data.frame(typical_covs)

  ids <- id_offset + seq_len(n_id)

  doses <- tidyr::crossing(id = ids, time = dose_times) |>
    dplyr::mutate(evid = 1L, amt = dose_mg, cmt = "depot", DOSE = dose_mg)

  obs <- tidyr::crossing(id = ids, time = obs_times) |>
    dplyr::mutate(evid = 0L, amt = 0, cmt = "central", DOSE = dose_mg)

  ev <- dplyr::bind_rows(doses, obs) |>
    dplyr::arrange(id, time, dplyr::desc(evid)) |>
    dplyr::mutate(treatment = paste0(dose_mg, " mg qd"))

  cbind(ev, cov_cols)
}

events <- dplyr::bind_rows(
  make_cohort(45, n_id = 50, id_offset =   0L),
  make_cohort(65, n_id = 50, id_offset = 100L),
  make_cohort(80, n_id = 50, id_offset = 200L)
)

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

Simulation 

mod <- rxode2::rxode2(readModelDb("Kemal_2026_nemtabrutinib"))
#> ℹ parameter labels from comments will be replaced by 'label()'

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

Replicate published patterns

The paper does not publish observed concentration-time data (the BELLWAVE trials are ongoing) and does not publish a summary NCA table. Instead the Introduction reports narrative descriptors (median Tmax ~ 2 h, dose-proportional exposure at >= 30 mg, accumulation ratios 1.8-3.4 for AUC0-24 and 1.53-2.85 for Cmax) and Section 3.2 reports a derived terminal half-life of 85 h. Figure 2 plots per-subject Cavg and Cmax across dose levels but the underlying values are not tabulated. The chunks below reproduce the exposure patterns implied by the packaged full-covariate model at the three BELLWAVE-003 doses (45, 65, 80 mg qd).

First-dose PK profile 

sim |>
  dplyr::filter(time <= 24) |>
  ggplot(aes(time, Cc, group = interaction(id, treatment), colour = treatment)) +
  geom_line(alpha = 0.35) +
  facet_wrap(~treatment) +
  labs(
    x = "Time after first dose (h)",
    y = "Nemtabrutinib plasma concentration (ng/mL)",
    title = "First-dose PK profile by dose level",
    caption = "Per-subject simulations (n = 50 per arm, typical covariates)."
  ) +
  theme_bw() +
  theme(legend.position = "none")

Steady-state trough progression across the first 28 days 

dose_times_grid <- seq(0, by = 24, length.out = 28)

sim |>
  dplyr::filter(time %in% dose_times_grid, time > 0) |>
  dplyr::mutate(day = round(time / 24)) |>
  dplyr::group_by(treatment, day) |>
  dplyr::summarise(
    median_pre = median(Cc, na.rm = TRUE),
    p25_pre    = quantile(Cc, 0.25, na.rm = TRUE),
    p75_pre    = quantile(Cc, 0.75, na.rm = TRUE),
    .groups    = "drop"
  ) |>
  ggplot(aes(day, median_pre, colour = treatment, fill = treatment)) +
  geom_ribbon(aes(ymin = p25_pre, ymax = p75_pre), alpha = 0.2, colour = NA) +
  geom_line(size = 0.7) +
  labs(
    x = "Study day (pre-dose)",
    y = "Nemtabrutinib pre-dose concentration (ng/mL)",
    title = "Pre-dose (trough) concentrations approaching steady state",
    caption = "Ribbons show IQR; lines show medians (n = 50 per arm). The paper reports 'plateau after ~15 days' (Introduction)."
  ) +
  theme_bw()
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

PKNCA validation

Compute Cmax, Tmax, AUC0-24 on day 1, and derived terminal half-life from the terminal-phase window after the 28th dose (last dose at t = 648 h). PKNCA formulas include the treatment grouping variable so per-dose values are separately available.

sim_day1 <- sim |>
  dplyr::filter(time <= 24, !is.na(Cc)) |>
  dplyr::select(id, time, Cc, treatment)

# Guarantee a time=0 row per (id, treatment); for extravascular pre-dose Cc=0.
sim_day1 <- dplyr::bind_rows(
  sim_day1,
  sim_day1 |> dplyr::distinct(id, treatment) |>
    dplyr::mutate(time = 0, Cc = 0)
) |>
  dplyr::distinct(id, treatment, time, .keep_all = TRUE) |>
  dplyr::arrange(id, treatment, time)

conc_day1 <- PKNCA::PKNCAconc(sim_day1, Cc ~ time | treatment + id)

dose_day1 <- events |>
  dplyr::filter(evid == 1, time == 0) |>
  dplyr::select(id, time, amt, treatment)

pk_dose_day1 <- PKNCA::PKNCAdose(dose_day1, amt ~ time | treatment + id)

intervals_day1 <- data.frame(
  start    = 0,
  end      = 24,
  cmax     = TRUE,
  tmax     = TRUE,
  auclast  = TRUE
)

nca_day1 <- PKNCA::pk.nca(
  PKNCA::PKNCAdata(conc_day1, pk_dose_day1, intervals = intervals_day1)
)
# Terminal-phase profile after the last (28th) dose: obs times
# 648 h ... 648 + 240 h. Use the paper's "85 h" claim as the check.
sim_terminal <- sim |>
  dplyr::filter(time >= 24 * 27, time <= 24 * 27 + 24 * 10, !is.na(Cc)) |>
  dplyr::mutate(time_after_last_dose = time - 24 * 27) |>
  dplyr::select(id, time = time_after_last_dose, Cc, treatment)

# Guarantee a time=0 row per (id, treatment) so PKNCA can anchor the
# terminal slope. Set Cc = 0 as the anchor -- half.life ignores the
# time=0 point when it falls below the lambda-z window.
sim_terminal <- dplyr::bind_rows(
  sim_terminal,
  sim_terminal |> dplyr::distinct(id, treatment) |>
    dplyr::mutate(time = 0, Cc = 0)
) |>
  dplyr::distinct(id, treatment, time, .keep_all = TRUE) |>
  dplyr::arrange(id, treatment, time)

conc_terminal <- PKNCA::PKNCAconc(sim_terminal, Cc ~ time | treatment + id)

# For the terminal window, no doses within the window -- use a synthetic
# "dose at t=0" placeholder so PKNCA has a dose reference for half.life.
dose_terminal <- events |>
  dplyr::filter(evid == 1) |>
  dplyr::group_by(id, treatment) |>
  dplyr::summarise(time = 0, amt = dplyr::last(amt), .groups = "drop")

pk_dose_terminal <- PKNCA::PKNCAdose(dose_terminal, amt ~ time | treatment + id)

intervals_terminal <- data.frame(
  start     = 0,
  end       = 24 * 10,
  half.life = TRUE
)

nca_terminal <- PKNCA::pk.nca(
  PKNCA::PKNCAdata(conc_terminal, pk_dose_terminal, intervals = intervals_terminal)
)
# Assemble a per-treatment simulated summary alongside the paper's
# narrative reference values. The paper gives an accumulation-ratio
# range across dose levels rather than per-dose Cmax / AUC values;
# the derived-half-life value is the single testable claim (85 h,
# Kemal 2026 Section 3.2). Tmax and dose proportionality of exposure
# are qualitative expectations.

sim_day1_summary <- as.data.frame(nca_day1$result) |>
  dplyr::group_by(treatment, PPTESTCD) |>
  dplyr::summarise(sim_median = median(PPORRES, na.rm = TRUE), .groups = "drop") |>
  tidyr::pivot_wider(names_from = PPTESTCD, values_from = sim_median) |>
  dplyr::rename(
    "Treatment"                 = treatment,
    "Simulated Cmax (ng/mL)"    = cmax,
    "Simulated Tmax (h)"        = tmax,
    "Simulated AUC0-24 (ng*h/mL)" = auclast
  )

sim_terminal_summary <- as.data.frame(nca_terminal$result) |>
  dplyr::filter(PPTESTCD == "half.life") |>
  dplyr::group_by(treatment) |>
  dplyr::summarise(
    "Simulated t1/2 (h, terminal)" = median(PPORRES, na.rm = TRUE),
    .groups = "drop"
  ) |>
  dplyr::rename("Treatment" = treatment)

summary_tbl <- dplyr::left_join(sim_day1_summary, sim_terminal_summary, by = "Treatment") |>
  dplyr::mutate(
    "Paper Tmax (h, observed median)" = "~ 2 (Introduction)",
    "Paper t1/2 (h, derived)"         = "85 (Section 3.2)"
  )

knitr::kable(
  summary_tbl,
  caption = "Simulated day-1 Cmax / Tmax / AUC0-24 and terminal-phase half-life per dose arm alongside the paper's reported values. Simulated Tmax should sit near 2 h and simulated t1/2 should be near 85 h; the paper does not tabulate per-dose Cmax / AUC values.",
  digits  = 2
)
Simulated day-1 Cmax / Tmax / AUC0-24 and terminal-phase half-life per dose arm alongside the paper’s reported values. Simulated Tmax should sit near 2 h and simulated t1/2 should be near 85 h; the paper does not tabulate per-dose Cmax / AUC values.
Treatment Simulated AUC0-24 (ng*h/mL) Simulated Cmax (ng/mL) Simulated Tmax (h) Simulated t1/2 (h, terminal) Paper Tmax (h, observed median) Paper t1/2 (h, derived)
45 mg qd 5950.79 360.36 2 84.69 ~ 2 (Introduction) 85 (Section 3.2)
65 mg qd 8959.41 513.16 2 88.92 ~ 2 (Introduction) 85 (Section 3.2)
80 mg qd 10888.23 626.23 2 84.22 ~ 2 (Introduction) 85 (Section 3.2)

Dose proportionality check

The paper states nemtabrutinib bioavailability is “linear at high concentrations” and the -0.151 low-dose effect on F applies only for doses < 30 mg (Table 2). All three BELLWAVE-003 doses (45, 65, 80 mg) sit above the 30 mg threshold, so simulated AUC0-24 should scale proportionally with dose. Ratios of median AUC0-24 relative to the 45 mg arm:

sim_day1_summary |>
  dplyr::mutate(
    dose_mg = as.numeric(sub(" mg qd", "", `Treatment`)),
    expected_ratio = dose_mg / 45
  ) |>
  dplyr::mutate(
    simulated_ratio = `Simulated AUC0-24 (ng*h/mL)` / `Simulated AUC0-24 (ng*h/mL)`[dose_mg == 45]
  ) |>
  dplyr::select(`Treatment`, dose_mg, simulated_ratio, expected_ratio) |>
  dplyr::rename(
    "Dose (mg)"               = dose_mg,
    "Simulated AUC0-24 ratio" = simulated_ratio,
    "Expected (dose/45)"      = expected_ratio
  ) |>
  knitr::kable(
    caption = "Dose proportionality check across the >= 30 mg linear-F regime.",
    digits  = 3
  )
Dose proportionality check across the >= 30 mg linear-F regime.
Treatment Dose (mg) Simulated AUC0-24 ratio Expected (dose/45)
45 mg qd 45 1.000 1.000
65 mg qd 65 1.506 1.444
80 mg qd 80 1.830 1.778

Assumptions and deviations

  • Covariate distributions in the virtual cohort were collapsed to the reference / typical individual (73.5 kg, 68 y, 41.2 g/L albumin, White male with CLL/SLL, no organ impairment, no CYP3A4 modulators, no acid-reducing agents) to keep the simulation deterministic across the three dose arms. Reproducing the paper’s forest-plot covariate simulations (Figure 1) would require the full observed covariate distributions from BELLWAVE-001 and -003, which are not published.
  • The paper does not publish observed concentration-time data or a summary NCA table. The comparison chunk above therefore benchmarks simulated Tmax (~ 2 h) and terminal half-life (~ 85 h) against the paper’s narrative values; per-dose Cmax and AUC are shown as reference values only.
  • Additive residual error (3.19 ng/mL, RSE 101%, 95% CI includes zero) was retained in the final model per the paper’s full-model approach even though its identifiability is very poor.
  • IIV variance was computed from the paper’s reported CV% via omega^2 = log(1 + CV^2), which is the exact log-normal transformation matching NONMEM’s $OMEGA in the supplement code (initial estimate 0.164 for CL / 0.0692 for Vc; the final parameterization corresponds to the CV% values reported in the main-article Table 2).
  • The 30 mg dose threshold for the low-dose bioavailability effect is encoded as if (DOSE < 30) dose_lt30 <- 1 using the DOSE regressor carried on the event table; this matches the NONMEM supplement’s IF (DOSE < 30) THEN DOSE_F = 1 + THETA(7) logic (dose-record level, not subject level).