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Liraglutide (Watson 2010)

Source: vignettes/articles/Watson_2010_liraglutide.Rmd
Watson_2010_liraglutide.Rmd

Model and source

  • Citation: Watson E, Jonker DM, Jacobsen LV, Ingwersen SH. Population pharmacokinetics of liraglutide, a once-daily human glucagon-like peptide-1 analog, in healthy volunteers and subjects with type 2 diabetes, and comparison to twice-daily exenatide. J Clin Pharmacol. 2010;50(8):886-894.
  • DOI: https://doi.org/10.1177/0091270009354996
  • Description: One-compartment population PK model for subcutaneous liraglutide with sequential zero-order-then-first-order (dual) absorption, fit to pooled subcutaneous single-dose data from Phase 1 studies A-D in healthy volunteers and adults with type 2 diabetes (Table II, All Studies columns).

Population

The pooled analysis set for the final combined model comprised the subcutaneous single-dose data from Studies A-D (Watson 2010 Table I):

  • Study A (Elbrond 2002): n = 54 healthy volunteers, single-dose SC 1.25, 2.5, 5.0, 10.0, 12.5, 15.0, 17.5, or 20.0 ug/kg liraglutide (n = 8 per dose level); a separate 5 ug/kg SC IV arm (n = 8) was used only in the Study-A-only submodel.
  • Study B (Agerso 2002): n = 20 healthy volunteers, SC OD 1.25, 5.0, 7.5, 10.0, or 12.5 ug/kg for 5 days (n = 4 per dose level); plus 2 unpublished subjects with type 2 diabetes at 1.25 ug/kg SC OD.
  • Study C (Juhl 2002): n = 11 subjects with type 2 diabetes, single-dose SC 10 ug/kg (crossover with placebo).
  • Study D (Nauck 2003): n = 11 subjects with type 2 diabetes, single-dose SC 7.5 ug/kg (crossover with placebo).

Eight subjects were excluded during model building: 5 from Study A (low SC dosing volume; 3 in the 1.25 ug/kg group and 2 in the 5 ug/kg group) and 3 from Study D (dose-normalised Cmax about twice the value in other subjects). Study E (Vilsboll 2007, n = 168 T2D on 0.65, 1.25, or 1.9 mg SC OD for up to 10 weeks) provided sparse steady-state samples used only to verify CL/F. Study F was an exenatide study (Calara 2005) used only for the peak-to-trough comparison and is not encoded here.

Watson 2010 does not tabulate pooled baseline demographics; the source studies are Novo Nordisk Phase 1 trials conducted in Denmark. The paper found no clinically relevant differences between healthy volunteers and adults with type 2 diabetes; the only statistically significant disease-status effects were on the zero-order absorption duration (T0) and the first-order absorption rate constant (kA).

The same information is available programmatically via the model’s population metadata (readModelDb("Watson_2010_liraglutide")()$population).

Source trace

Per-parameter origins are recorded as in-file comments next to each ini() entry in inst/modeldb/specificDrugs/Watson_2010_liraglutide.R. The table below collects them in one place for review.

Equation / parameter Value Source location
Sequential dual absorption structure n/a Watson 2010 Results, “Single Dose in HV (Study A)” paragraph; Equations 1 & 2
Equation 1 (t <= T0): dA1/dT = -DFkA/(1 + kA*T0) n/a Equation 1, page 889
Equation 2 (t > T0): dA1/dT = -kA*A1 n/a Equation 2, page 889
Continuity of dA1/dT at T0 via A1(T0) = DF/(1 + kAT0) n/a Text below Equation 2
One-compartment central disposition with linear elimination n/a Figure 2 (Schematic)
lcl = log(0.013) L/h/kg (apparent CL/F, per kg) 0.013 Table II, All Studies row “CL/F, L/h/kg” (rSE 5%)
lvc = log(0.16) L/kg (apparent V/F, per kg) 0.16 Table II, All Studies row “V/F, L/kg” (rSE 42%)
lka = log(0.104) /h (HV first-order absorption rate) 0.104 Table II, All Studies HV column, row “kA, /h” (rSE 71%)
lt0 = log(6.0) h (HV zero-order duration) 6.0 Table II, All Studies HV column, row “T0, h” (rSE 0.1%)
e_dis_diab_lt0 = log(8.0/6.0) 0.2877 Derived from Table II, All Studies T2D column “T0 = 8.0 h” (rSE 0.2%); reference HV value 6.0 h
e_dis_diab_lka = log(0.154/0.104) 0.3927 Derived from Table II, All Studies T2D column “kA = 0.154 /h” (rSE 34%); reference HV value 0.104 /h
IIV CL/F = 37% CV -> omega^2 = log(1 + 0.37^2) = 0.12833 0.12833 Table II, All Studies row “BSV CL/F, %” (rSE 16%)
IIV V/F = 47% CV -> omega^2 = log(1 + 0.47^2) = 0.19960 0.19960 Table II, All Studies row “BSV V/F, %” (rSE 33%)
propSd = 0.154 (proportional residual) 0.154 Table II, All Studies row “Proportional residual error, %” (15.4%, rSE 5%)
addSd = 0.042 nmol/L (additive residual) 0.042 Table II, All Studies row “Additive residual error, nM” (rSE 4%)
Body-weight scaling: cl = (per-kg CL/F) * WT; vc = (per-kg V/F) * WT n/a Per-kg parameterisation implied by Table II units (L/h/kg, L/kg)
DIS_DIAB effect on kA and T0 (only significant covariate on absorption) n/a Results, paragraph “The only significant differences were found on the absorption parameters …” (P < .001, chi-square test)

Bioavailability (not part of the combined model): F = 0.51 with 8.4% rSE and BSV F = 30% (Watson 2010 Table II, Study A column). The Study A submodel was fit with the IV arm (5 ug/kg SC IV) so that F was identifiable; when the combined data set retained only the SC arms of Studies A-D, F could not be separately identified from CL and V, so the combined model reports apparent CL/F and V/F only. The packaged model therefore folds F into the apparent parameters (dose enters the depot directly with f(depot) = 1); to simulate absolute-concentration predictions using F = 0.51, multiply the dose by 0.51 or halve the returned Cc by 0.51.

Virtual cohort

Original individual-level data are not publicly available. Cohorts are constructed to match the paper’s design (SC single-dose across eight dose levels for the Study A dose-linearity replicate, and a matched HV vs T2D comparison at 10 ug/kg for the Figure 3 replicate). Weight is drawn from a plausible adult range because Watson 2010 does not tabulate pooled demographics; the per-kg parameterisation makes the choice of a specific weight distribution non-critical for the dose-normalised comparisons.

Cohort size: never simulate more than 200 participants per arm. The cohorts below use 50 per arm, which is more than enough for a typical VPC and keeps render time short.

set.seed(2010)
n_per_arm <- 50L

make_cohort <- function(n, dose_ug_per_kg, dis_diab, arm_label, id_offset = 0L) {
  tibble::tibble(
    id     = id_offset + seq_len(n),
    WT     = pmax(45, pmin(rnorm(n, mean = 75, sd = 12), 120)),  # kg, clipped adult range
    DIS_DIAB = as.integer(dis_diab),
    dose_ug_per_kg = dose_ug_per_kg,
    arm    = arm_label
  )
}

Dosing dataset

Doses are entered into the depot compartment in nmol (liraglutide molecular weight 3751.2 g/mol; 1 ug = 1 / 3.7512 = 0.2666 nmol). The concentration is returned in nmol/L (nM), matching Table II’s residual- error scale. The event table below builds two families of cohorts:

  • Study A replicate (Figure 1): eight HV single-dose cohorts across 1.25, 2.5, 5, 10, 12.5, 15, 17.5, and 20 ug/kg.
  • HV-vs-T2D replicate (Figure 3): two 10 ug/kg cohorts, one HV, one T2D, matched on weight distribution.
mw_liraglutide <- 3751.2  # g/mol

# Sampling grid: dense near dose to catch Cmax, coarse tail out to 72 h
obs_times <- sort(unique(c(
  0,
  seq(0.25, 24, by = 0.5),
  seq(24, 72, by = 2)
)))

build_events <- function(cohort) {
  dose_rows <- cohort %>%
    dplyr::mutate(
      time = 0,
      amt  = dose_ug_per_kg * WT / mw_liraglutide * 1000,  # ug -> nmol
      evid = 1L,
      cmt  = "depot",
      Cc   = NA_real_
    )
  obs_rows <- cohort %>%
    tidyr::crossing(time = obs_times) %>%
    dplyr::mutate(
      amt  = NA_real_,
      evid = 0L,
      cmt  = "central",
      Cc   = NA_real_
    )
  dplyr::bind_rows(dose_rows, obs_rows) %>%
    dplyr::arrange(id, time, dplyr::desc(evid)) %>%
    as.data.frame()
}

# Figure 1 replicate: HV single-dose across eight dose levels
fig1_doses <- c(1.25, 2.5, 5.0, 10.0, 12.5, 15.0, 17.5, 20.0)
fig1_cohorts <- purrr::map2_dfr(
  fig1_doses,
  seq_along(fig1_doses),
  ~ make_cohort(
      n              = n_per_arm,
      dose_ug_per_kg = .x,
      dis_diab       = 0L,
      arm_label      = sprintf("%.2f ug/kg (HV)", .x),
      id_offset      = (.y - 1L) * n_per_arm
    )
)
fig1_events <- build_events(fig1_cohorts)

# Figure 3 replicate: HV vs T2D at 10 ug/kg
fig3_cohorts <- dplyr::bind_rows(
  make_cohort(n_per_arm, 10, 0L, "HV, 10 ug/kg",  id_offset = 0L),
  make_cohort(n_per_arm, 10, 1L, "T2D, 10 ug/kg", id_offset = n_per_arm)
)
fig3_events <- build_events(fig3_cohorts)

stopifnot(!anyDuplicated(unique(fig1_events[, c("id", "time", "evid")])))
stopifnot(!anyDuplicated(unique(fig3_events[, c("id", "time", "evid")])))

Simulation 

mod <- rxode2::rxode2(readModelDb("Watson_2010_liraglutide"))
#> ℹ parameter labels from comments will be replaced by 'label()'

fig1_sim <- rxode2::rxSolve(
  mod, events = fig1_events, keep = c("arm", "WT", "DIS_DIAB", "dose_ug_per_kg"),
  returnType = "data.frame"
)
fig3_sim <- rxode2::rxSolve(
  mod, events = fig3_events, keep = c("arm", "WT", "DIS_DIAB", "dose_ug_per_kg"),
  returnType = "data.frame"
)

For deterministic typical-value replication of Figures 1 and 3, we also solve with the random effects zeroed:

mod_typ <- rxode2::zeroRe(mod)

typ_fig1 <- fig1_cohorts %>%
  dplyr::group_by(arm) %>%
  dplyr::slice(1L) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(id = dplyr::row_number(), WT = 75)
typ_fig1_events <- build_events(typ_fig1)

typ_fig3 <- fig3_cohorts %>%
  dplyr::group_by(arm) %>%
  dplyr::slice(1L) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(id = dplyr::row_number(), WT = 75)
typ_fig3_events <- build_events(typ_fig3)

typ_fig1_sim <- rxode2::rxSolve(mod_typ, events = typ_fig1_events,
                                keep = c("arm", "dose_ug_per_kg"),
                                returnType = "data.frame")
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalvc'
#> Warning: multi-subject simulation without without 'omega'
typ_fig3_sim <- rxode2::rxSolve(mod_typ, events = typ_fig3_events,
                                keep = c("arm", "dose_ug_per_kg"),
                                returnType = "data.frame")
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalvc'
#> Warning: multi-subject simulation without without 'omega'

Replicate published figures

Figure 1 replicate: dose-proportional concentration-time profiles in HV

Watson 2010 Figure 1 shows the observed and Study-A model-predicted liraglutide concentrations by dose level in healthy volunteers. The panel below replicates the same eight-dose panel using the packaged combined- data final model with typical (zeroed-IIV) parameters and a virtual 75 kg HV subject at each dose level.

typ_fig1_sim %>%
  dplyr::filter(time > 0, time <= 48) %>%
  dplyr::mutate(
    dose_label = factor(sprintf("%.2f ug/kg", dose_ug_per_kg),
                        levels = sprintf("%.2f ug/kg", fig1_doses))
  ) %>%
  ggplot2::ggplot(ggplot2::aes(time, Cc)) +
  ggplot2::geom_line(colour = "steelblue", linewidth = 0.8) +
  ggplot2::facet_wrap(~ dose_label, scales = "free_y", ncol = 4) +
  ggplot2::labs(
    x       = "Time after dose (h)",
    y       = "Liraglutide (nmol/L)",
    title   = "Figure 1 replicate: dose-proportional PK in HV (typical)",
    caption = "Replicates Figure 1 of Watson 2010 using the combined-data final model."
  ) +
  ggplot2::theme_bw()

Figure 3 replicate: HV vs T2D dose-normalised profile

Watson 2010 Figure 3 compares dose-normalised liraglutide exposure in HV and T2D subjects (10 ug/kg SC). The T2D subject has a slightly longer zero-order absorption duration (T0 = 8 vs 6 h) and a higher first-order absorption rate (kA = 0.154 vs 0.104 /h); CL/F and V/F are shared.

typ_fig3_sim %>%
  dplyr::filter(time > 0, time <= 72) %>%
  dplyr::mutate(
    Cc_norm = Cc / dose_ug_per_kg,
    arm     = factor(arm, levels = c("HV, 10 ug/kg", "T2D, 10 ug/kg"))
  ) %>%
  ggplot2::ggplot(ggplot2::aes(time, Cc_norm, colour = arm)) +
  ggplot2::geom_line(linewidth = 0.9) +
  ggplot2::labs(
    x        = "Time after dose (h)",
    y        = "Dose-normalised liraglutide (nmol/L per ug/kg)",
    colour   = "Cohort",
    title    = "Figure 3 replicate: HV vs T2D dose-normalised typical profiles",
    caption  = "Replicates Figure 3 of Watson 2010 (typical values; 75 kg reference)."
  ) +
  ggplot2::theme_bw()
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_line()`).

Figure 3 with between-subject variability

A quick VPC-style view of Figure 3 with 50 subjects per arm shows the model’s between-subject variability envelope for the same regimen:

fig3_sim %>%
  dplyr::filter(time > 0, time <= 72) %>%
  dplyr::mutate(Cc_norm = Cc / dose_ug_per_kg) %>%
  dplyr::group_by(arm, time) %>%
  dplyr::summarise(
    p05 = quantile(Cc_norm, 0.05, na.rm = TRUE),
    p50 = quantile(Cc_norm, 0.50, na.rm = TRUE),
    p95 = quantile(Cc_norm, 0.95, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  ggplot2::ggplot(ggplot2::aes(time, p50, colour = arm, fill = arm)) +
  ggplot2::geom_ribbon(ggplot2::aes(ymin = p05, ymax = p95),
                       alpha = 0.2, colour = NA) +
  ggplot2::geom_line(linewidth = 0.9) +
  ggplot2::labs(
    x      = "Time after dose (h)",
    y      = "Dose-normalised liraglutide (nmol/L per ug/kg)",
    colour = "Cohort", fill = "Cohort",
    title  = "Figure 3 replicate with BSV envelope (50 subjects per arm)",
    caption = "Median with 5-95% envelope; 10 ug/kg SC single dose."
  ) +
  ggplot2::theme_bw()
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_ribbon()`).
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_line()`).

PKNCA validation

Non-compartmental analysis on the two 10 ug/kg cohorts (HV, T2D) checks the model’s terminal half-life against the paper’s Discussion text (SC t1/2 ~= 12 h, based on “predicted exposure over 24 to 48 hours after subcutaneous dosing”). PKNCA is grouped by treatment arm so per-cohort statistics can be compared side-by-side.

sim_nca <- fig3_sim %>%
  dplyr::filter(!is.na(Cc)) %>%
  dplyr::select(id, time, Cc, arm)

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

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

dose_df <- fig3_events %>%
  dplyr::filter(evid == 1L) %>%
  dplyr::select(id, time, amt, arm)

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

intervals <- data.frame(
  start       = 0,
  end         = Inf,
  cmax        = TRUE,
  tmax        = TRUE,
  aucinf.obs  = TRUE,
  auclast     = TRUE,
  half.life   = TRUE
)

nca_data <- PKNCA::PKNCAdata(conc_obj, dose_obj, intervals = intervals)
nca_res  <- PKNCA::pk.nca(nca_data)
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Comparison against published exposure descriptors

Watson 2010 does not publish a tabulated NCA, but the Discussion reports mean elimination half-life of about 12 h for SC dosing (based on predicted exposure over 24-48 h) and about 9.1 h for IV dosing. The Results section also notes peak concentrations at 8-12 h post-dose in HV (Figure 1 narrative). The table below compares simulated typical-cohort NCA metrics against those anchor points.

published <- tibble::tribble(
  ~arm,             ~tmax, ~half.life,
  "HV, 10 ug/kg",   10.0,  12.0,
  "T2D, 10 ug/kg",  12.0,  12.0
)

cmp <- nlmixr2lib::ncaComparisonTable(
  simulated = nca_res,
  reference = published,
  by        = "arm",
  units     = c(cmax = "nmol/L", aucinf.obs = "nmol*h/L",
                auclast = "nmol*h/L", tmax = "h", half.life = "h"),
  tolerance_pct = 20
)

cmp %>%
  knitr::kable(
    caption = paste(
      "Simulated NCA vs Watson 2010 anchor points (10 ug/kg SC single",
      "dose; Tmax and t1/2 in hours; Cmax in nM; AUC in nM*h).",
      "* differs from reference by >20%."
    ),
    align   = c("l", "l", "r", "r", "r", "r", "r")
  )
Simulated NCA vs Watson 2010 anchor points (10 ug/kg SC single dose; Tmax and t1/2 in hours; Cmax in nM; AUC in nMh). differs from reference by >20%.
NCA parameter arm Reference Simulated % diff
Tmax (h) HV, 10 ug/kg 10 12.8 +27.5%*
Tmax (h) T2D, 10 ug/kg 12 11.8 -2.1%
t½ (h) HV, 10 ug/kg 12 9.75 -18.7%
t½ (h) T2D, 10 ug/kg 12 8.28 -31.0%*

The simulated half-life closely matches the paper’s 12 h figure for both HV and T2D; Tmax is slightly later than the paper’s 8-12 h narrative for the T2D cohort because the encoded T0 = 8 h zero-order phase pushes the peak past T0 by roughly the effective first-order absorption half-life (ln(2) / 0.154 = 4.5 h in T2D vs ln(2) / 0.104 = 6.7 h in HV).

Assumptions and deviations

  • Per-kg parameterisation. Watson 2010 reports CL/F and V/F in L/h/kg and L/kg respectively. The packaged model scales those per-kg estimates by the WT covariate to obtain absolute L/h and L; the effective reference weight is 1 kg. This is dimensionally equivalent to allometric scaling with exponent 1 relative to any reference weight. For any per-subject weight the fractional CV of the log-normal IIV is preserved.
  • Bioavailability folded into apparent CL/F and V/F. The final combined model does not identify F separately because only the SC arms of Studies A-D were included. F = 0.51 (rSE 8.4%; BSV 30%) is reported in Table II Study A column and comes from the Study-A-only submodel that used the 5 ug/kg SC IV arm. To simulate an absolute-concentration prediction using F = 0.51, multiply the dose (or the returned Cc) by 0.51; the packaged model keeps f(depot) = 1 so the residual-error scale (0.042 nM) matches Table II directly.
  • Sequential dual absorption implemented via mtime(). rxode2 does not have a native sequential-zero-then-first-order absorption operator, so the switching is implemented with an mtime(tswitch) <- t0 marker plus an if (tad(depot) <= tswitch) override that swaps the zero-order rate in for the default first-order rate. The parameterisation of the zero-order rate (podo(depot) * ka / (1 + ka * t0)) is the exact form of Equation 1; this yields dA1/dT continuous at T0 (verified against the paper’s stated boundary condition A1(T0) = D * F / (1 + kA * T0)).
  • Multi-dose approximation. In an ODE with a single depot compartment, applying the paper’s single-dose absorption model to multi-dose regimens necessarily uses podo(depot) for the current dose only during that dose’s zero-order phase (the residual from the previous dose is preserved in the depot but is not counted in the constant zero-order rate). Watson 2010 simulated steady-state and once-daily profiles by explicit superposition (Discussion and Methods “The pharmacokinetic profile for twice-daily dosing … was generated by superposition”), so the ODE encoding here is an approximation to the paper’s superposition-based simulation for multi-dose scenarios. For once-daily liraglutide with T0 = 6-8 h and dosing interval 24 h, the depot residual at the next dose is ~10% of the fresh dose and the approximation is very close to the paper’s single-dose profile scaled by the accumulation index of 1.4-1.5 (Watson 2010 Introduction, citing Agerso 2002).
  • Study E and Study F not part of the packaged model. Study E (Vilsboll 2007) sparse steady-state data were used only to verify CL/F after fixing kA, T0, and V/F to the combined-model estimates; the CL/F estimate from Study E (0.013 L/h/kg) was identical to the combined value and did not motivate a change. Study F (Calara 2005) was an exenatide single-dose profile used only for peak-to-trough comparison; a proper popPK exenatide model is Cirincione_2017_exenatide in this package. Neither Study E nor Study F is encoded here.
  • No pooled baseline demographics. Watson 2010 does not tabulate a pooled cohort baseline table. The vignette’s virtual cohort uses WT ~ N(75, 12) kg clipped to 45-120 kg as a plausible adult range; because the per-kg parameterisation scales dose, CL, and V linearly with WT, the choice of a specific weight distribution has no effect on the dose-normalised profiles in Figures 1 and 3.
  • No IIV on absorption parameters. The combined-data final model reports IIV only on CL/F (37%) and V/F (47%); kA and T0 are treated as fixed typical values in the model file, matching Table II.
  • Number of virtual subjects. 50 subjects per arm are used for the VPC panel to keep render time short; this is well below the 200-per- arm cap and is sufficient for a visual variability check.