Hua_2015_anrukinzumab
Source:vignettes/articles/Hua_2015_anrukinzumab.Rmd
Hua_2015_anrukinzumab.Rmd
library(nlmixr2lib)
library(rxode2)
#> rxode2 5.0.2 using 2 threads (see ?getRxThreads)
#> no cache: create with `rxCreateCache()`
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)
library(ggplot2)
library(PKNCA)
#>
#> Attaching package: 'PKNCA'
#> The following object is masked from 'package:stats':
#>
#> filterModel and source
- Citation: Hua F, Ribbing J, Reinisch W, Cataldi F, Martin S. A pharmacokinetic comparison of anrukinzumab, an anti-IL-13 monoclonal antibody, among healthy volunteers, asthma and ulcerative colitis patients. Br J Clin Pharmacol. 2015;80(1):101-109.
- Article: doi:10.1111/bcp.12589
- Publicly available full text: https://bpspubs.onlinelibrary.wiley.com/doi/10.1111/bcp.12589
Anrukinzumab (IMA-638) is a humanized IgG1 anti-IL-13 monoclonal antibody. Hua et al. pooled PK data from five clinical studies (N = 255) across four populations — healthy volunteers, mild-to-moderate asthma, moderate-to-severe asthma, and ulcerative colitis (UC) — to identify disease and demographic covariates on mAb PK parameters. The final model is a two-compartment structure with first-order SC absorption and linear elimination, with body weight and baseline albumin as demographic covariates and UC disease status (on CL) and moderate-to-severe asthma status (on SC bioavailability F) as disease-state covariates.
Population
Hua 2015 Table 2 summarises the pooled cohort of 255 subjects across all 5 studies:
- Sex: 65% male, 35% female.
- Race: 73% White, 12% Black, 12% Asian, 3% Other.
- Age: median 37 years (mean 38, SD 13).
- Body weight: median 81.3 kg (mean 82.6, SD 18.7).
- Disease state: healthy volunteers 17%, mild-to-moderate asthma 20%, moderate-to-severe asthma 38%, ulcerative colitis 25%.
- Dosing route: SC 72%, IV 28%.
Per-study design (Hua 2015 Table 1):
| # | Population | n | Dosing |
|---|---|---|---|
| 1 | Mild-to-moderate asthma | 37 | 3 mg/kg IV + 0.3-4 mg/kg SC single dose |
| 2 | Healthy Japanese and non-Asian | 44 | 0.3-4 mg/kg SC single dose |
| 3 | Allergen challenge, mild asthma | 14 | 2 mg/kg SC x 2 doses, 1 week apart |
| 4 | Moderate-to-severe asthma | 97 | 0.2, 0.6, 2 mg/kg SC or 200 mg SC Q2-4W |
| 5 | Ulcerative colitis | 63 | 200, 400, 600 mg IV Q2-4W x 5 doses |
The same information is available programmatically via
readModelDb("Hua_2015_anrukinzumab")$population.
Source trace
The per-parameter origin is recorded next to each ini()
entry in
inst/modeldb/specificDrugs/Hua_2015_anrukinzumab.R. The
table below collects them in one place for review.
| Element | Source | Value / form |
|---|---|---|
| CL | Hua 2015 Table 3 (Final model) | 0.00732 L/h (75-kg non-UC, ALB 4.3 g/dL) |
| Vc | Table 3 | 3.81 L |
| Vp | Table 3 | 2.17 L |
| Q | Table 3 | 0.0224 L/h |
| Ka | Table 3 | 0.0119 /h |
| F | Table 3 | 0.973 (non-moderate-to-severe-asthma) |
| WT on CL | Table 3 | Allometric power, exponent fixed at 0.75:
(WT/75)^0.75
|
| WT on Vc, Vp | Table 3 | Allometric power, estimated shared exponent 0.688:
(WT/75)^0.688
|
| ALB on CL | Table 3 | Power form, (ALB/4.3)^(-1.07)
|
| UC on CL | Table 3 | Multiplicative fractional, (1 + 0.728 * DIS_UC)
|
| Moderate-to-severe asthma on F | Table 3 | Multiplicative fractional,
(1 + (-0.309) * DIS_SASTHMA)
|
| IIV CL | Table 3 | CV 31.6% (omega^2 = log(1 + 0.316^2) = 0.0952) |
| IIV Vc, Vp (shared eta) | Table 3 | CV 26.5% (omega^2 = 0.0679) |
| IIV Ka | Table 3 | CV 54.0% (omega^2 = 0.2560) |
| Corr(CL, V) | Table 3 | 0.727 |
| Residual | Table 3 | 23.5% proportional (additive on log-transformed data) |
Covariate column naming
| Source column | Canonical column used here |
|---|---|
WT |
WT |
ALB (g/dL, per Figure 1 axis) |
ALB |
UC (binary UC indicator) |
DIS_UC |
sAsthma (binary moderate-to-severe asthma
indicator) |
DIS_SASTHMA |
DIS_UC and DIS_SASTHMA were newly ratified
in inst/references/covariate-columns.md (2026-04-21) as
scope-specific binary indicators decomposed from the paper’s four-level
disease-state categorical (healthy volunteer, mild-to-moderate asthma,
moderate-to-severe asthma, UC).
Virtual population
Original per-subject data are not publicly available. The virtual population below approximates the pooled Table 2 demographics and the Table 1 disease-state mixture.
set.seed(2015)
n_subj <- 400
pop <- tibble(
ID = seq_len(n_subj),
WT = rlnorm(n_subj, log(81.3), 0.22), # median 81.3, SD ~20
ALB = pmax(2.8, pmin(rnorm(n_subj, 4.3, 0.4), 5.5)) # mean 4.3, SD 0.4 g/dL
)
# Disease-state assignment matching Hua 2015 Table 2 proportions.
pop$disease <- sample(
c("HV", "mild_moderate_asthma", "moderate_severe_asthma", "UC"),
size = n_subj,
replace = TRUE,
prob = c(0.17, 0.20, 0.38, 0.25)
)
pop$DIS_UC <- as.integer(pop$disease == "UC")
pop$DIS_SASTHMA <- as.integer(pop$disease == "moderate_severe_asthma")Simulation setup: single SC dose (healthy + asthma cohort)
Replicates the profile shape of Hua 2015 Figure 4E-F (single-dose SC in healthy volunteers and mild-to-moderate asthma) and Figure 4D (single 0.6 mg/kg SC in moderate-to-severe asthma).
pop_sc <- pop %>% filter(disease != "UC")
dose_sc <- pop_sc %>%
mutate(
TIME = 0,
AMT = round(2 * WT, 1), # 2 mg/kg SC, consistent with study 1 and 3
EVID = 1,
CMT = "depot",
DV = NA_real_
) %>%
select(ID, TIME, AMT, EVID, CMT, DV, WT, ALB, DIS_UC, DIS_SASTHMA, disease)
obs_times_sc <- sort(unique(c(
seq(0, 24, by = 3),
seq(24, 24 * 7, by = 12),
seq(24 * 7, 24 * 56, by = 24)
)))
obs_sc <- pop_sc %>%
crossing(TIME = obs_times_sc) %>%
mutate(
AMT = NA_real_,
EVID = 0,
CMT = "central",
DV = NA_real_
) %>%
select(ID, TIME, AMT, EVID, CMT, DV, WT, ALB, DIS_UC, DIS_SASTHMA, disease)
d_sc <- bind_rows(dose_sc, obs_sc) %>%
arrange(ID, TIME, desc(EVID))
mod <- readModelDb("Hua_2015_anrukinzumab")
sim_sc <- rxSolve(mod, d_sc, keep = "disease", returnType = "data.frame")
#> ℹ parameter labels from comments will be replaced by 'label()'Replicates Figure 4 (SC single-dose shape)
sim_sc %>%
filter(time > 0) %>%
mutate(week = time / (24 * 7)) %>%
group_by(week, disease) %>%
summarise(
median = median(Cc, na.rm = TRUE),
lo = quantile(Cc, 0.05, na.rm = TRUE),
hi = quantile(Cc, 0.95, na.rm = TRUE),
.groups = "drop"
) %>%
ggplot(aes(x = week, color = disease, fill = disease)) +
geom_ribbon(aes(ymin = lo, ymax = hi), alpha = 0.15, linetype = 0) +
geom_line(aes(y = median), linewidth = 0.9) +
scale_y_log10() +
labs(
x = "Time since dose (weeks)",
y = "Anrukinzumab concentration (ng/mL)",
title = "Simulated 2 mg/kg SC single dose, stratified by disease state",
subtitle = "Median and 90% prediction interval; replicates the profile shape of Hua 2015 Fig. 4E,F,D",
caption = "Reduced F in moderate-to-severe asthma depresses exposure at matched dose."
) +
theme_bw()
Simulation setup: 200 mg IV repeat dosing (UC vs matched non-UC)
Replicates Figure 5 of Hua 2015: five 200 mg IV doses on day 0, weeks 2, 4, 8, and 12, comparing UC patients to covariate-matched non-UC subjects. The paper holds WT and ALB distributions matched so that the only difference is UC disease status; we do the same here.
n_iv <- 100
pop_uc <- tibble(
ID = seq_len(n_iv),
WT = rlnorm(n_iv, log(75), 0.18),
ALB = pmax(2.8, pmin(rnorm(n_iv, 4.3, 0.3), 5.5))
) %>%
crossing(group = c("non-UC", "UC")) %>%
mutate(
ID = (as.integer(factor(group)) - 1L) * n_iv + ID,
DIS_UC = as.integer(group == "UC"),
DIS_SASTHMA = 0L
)
dose_weeks_iv <- c(0, 2, 4, 8, 12)
dose_times_iv <- dose_weeks_iv * 7 * 24 # hours
dose_iv <- pop_uc %>%
crossing(TIME = dose_times_iv) %>%
mutate(
AMT = 200, # 200 mg IV
EVID = 1,
CMT = "central",
DV = NA_real_
) %>%
select(ID, TIME, AMT, EVID, CMT, DV, WT, ALB, DIS_UC, DIS_SASTHMA, group)
obs_times_iv <- sort(unique(c(
seq(0, 24, by = 4),
seq(24, 24 * 7 * 16, by = 24)
)))
obs_iv <- pop_uc %>%
crossing(TIME = obs_times_iv) %>%
mutate(
AMT = NA_real_,
EVID = 0,
CMT = "central",
DV = NA_real_
) %>%
select(ID, TIME, AMT, EVID, CMT, DV, WT, ALB, DIS_UC, DIS_SASTHMA, group)
d_iv <- bind_rows(dose_iv, obs_iv) %>%
arrange(ID, TIME, desc(EVID))
sim_iv <- rxSolve(mod, d_iv, keep = "group", returnType = "data.frame")
#> ℹ parameter labels from comments will be replaced by 'label()'Replicates Figure 5 (200 mg IV, UC vs non-UC)
sim_iv %>%
filter(time > 0) %>%
mutate(week = time / (24 * 7)) %>%
group_by(week, group) %>%
summarise(
median = median(Cc, na.rm = TRUE),
lo = quantile(Cc, 0.025, na.rm = TRUE),
hi = quantile(Cc, 0.975, na.rm = TRUE),
.groups = "drop"
) %>%
ggplot(aes(x = week, color = group, fill = group)) +
geom_ribbon(aes(ymin = lo, ymax = hi), alpha = 0.15, linetype = 0) +
geom_line(aes(y = median), linewidth = 0.9) +
scale_y_log10() +
scale_color_manual(values = c("non-UC" = "#2f7f4f", "UC" = "#c25252")) +
scale_fill_manual(values = c("non-UC" = "#2f7f4f", "UC" = "#c25252")) +
labs(
x = "Time since first dose (weeks)",
y = "Anrukinzumab concentration (ng/mL)",
title = "Simulated 200 mg IV (day 0, wk 2, 4, 8, 12): UC vs non-UC",
subtitle = "Median and 95% prediction interval; replicates Hua 2015 Fig. 5.",
caption = "UC patients show +72.8% CL and lower exposure at matched WT/ALB."
) +
theme_bw()
PKNCA validation
Run PKNCA on the first 28-day single-dose interval of the 200 mg IV
cohort, stratified by group (UC vs non-UC). The PKNCA formula includes
the group treatment variable so Cmax, AUClast, and
half-life are computed per-group.
single_dose_obs <- sim_iv %>%
filter(time >= 0, time <= 28 * 24, Cc > 0) %>%
transmute(
ID = id,
time,
Cc,
treatment = group
)
single_dose_df <- pop_uc %>%
mutate(TIME = 0, AMT = 200) %>%
transmute(ID, time = TIME, amt = AMT, treatment = group)
conc_obj <- PKNCAconc(single_dose_obs, Cc ~ time | treatment + ID)
dose_obj <- PKNCAdose(single_dose_df, amt ~ time | treatment + ID)
data_obj <- PKNCAdata(
conc_obj,
dose_obj,
intervals = data.frame(
start = 0,
end = 28 * 24, # 28 days in hours
cmax = TRUE,
tmax = TRUE,
auclast = TRUE,
half.life = TRUE
)
)
nca_results <- pk.nca(data_obj)
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nca_summary <- summary(nca_results)
knitr::kable(
nca_summary,
digits = 3,
caption = "PKNCA summary after a single 200 mg IV dose (first 28 days), stratified by UC vs non-UC."
)| start | end | treatment | N | auclast | cmax | tmax | half.life |
|---|---|---|---|---|---|---|---|
| 0 | 672 | non-UC | 100 | 2.69e7 [28.1] | 88900 [29.6] | 672 [672, 672] | NC |
| 0 | 672 | UC | 100 | 2.12e7 [31.5] | 74000 [32.6] | 672 [672, 672] | NC |
Comparison against published values
Hua 2015 reports a median half-life for non-UC subjects of 567 h (95%
CI 548-586) and for UC subjects of 328 h (95% CI 301-352) (page 105 /
Figure 5 caption). These are computed from the model’s terminal-phase
parameters CL and Vss = Vc + Vp, so at matched
WT and ALB:
vss_nonuc <- 3.81 + 2.17
t_half_nonuc <- log(2) * vss_nonuc / 0.00732
t_half_uc <- log(2) * vss_nonuc / (0.00732 * (1 + 0.728))
tibble(
group = c("non-UC", "UC"),
t_half_h = c(t_half_nonuc, t_half_uc),
paper_h = c(567, 328)
) %>%
knitr::kable(
digits = 0,
caption = "Analytic terminal-phase half-life from the model's typical-value parameters vs Hua 2015 reported medians."
)| group | t_half_h | paper_h |
|---|---|---|
| non-UC | 566 | 567 |
| UC | 328 | 328 |
The analytic values differ from the sparse-sampling NCA output above because NCA estimates the half-life from whatever time window the simulated concentrations span; the paper’s 567 h / 328 h values come from the population model itself and are what the model-file parameters are supposed to reproduce.
Assumptions and deviations
- Subject-level covariates: Hua 2015 does not publish per-subject WT, ALB, or disease-state values. WT is sampled log-normal around the reported Table 2 mean 82.6 kg / SD 18.7 kg; ALB sampled normal around mean 4.3 g/dL (the Table 3 reference value), clipped to a clinical range [2.8, 5.5] g/dL.
-
Albumin units: the paper does not state units
explicitly in Table 3; the value 4.3 and the Figure 1 x-axis range
(3.0-5.0) are consistent with g/dL (US convention). This is recorded in
covariateData[[ALB]]$unitsand the per-parameter comment. - Full-dose regimen for UC: we simulate the 200 mg arm (day 0, weeks 2, 4, 8, 12) that Hua 2015 Figure 5 plots. The paper’s study-5 protocol also tested 400 mg and 600 mg doses; the concentration-time shape is dose-proportional for this linear model, so a single dose level demonstrates the UC-vs-non-UC contrast.
- Time-varying covariates: WT and ALB are treated as time-invariant baseline values, consistent with typical IgG1 PK modelling and Hua 2015’s “baseline” covariate terminology.
- NCA half-life vs typical-value half-life: the PKNCA sparse-sampling output differs from the analytic CL/V-based half-life; the “Comparison against published values” table above reports the analytic value, which is what the model-file parameters are meant to reproduce.
Reference
- Hua F, Ribbing J, Reinisch W, Cataldi F, Martin S. A pharmacokinetic comparison of anrukinzumab, an anti-IL-13 monoclonal antibody, among healthy volunteers, asthma and ulcerative colitis patients. Br J Clin Pharmacol. 2015;80(1):101-109. doi:10.1111/bcp.12589