CPT: pharmacometrics & systems pharmacology · 2026
Latent Variable Indirect Response Modeling of Cendakimab Exposure-Response for Longitudinal Dysphagia Days Using a Combined Uniform-Binomial Likelihood Framework.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод в очереди
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Du Shengnan, Wojciechowski Jessica, Zhang Peijin, Aras Urvi, Murthy Bindu, Shen Jun, Kondic Anna, Hu Chuanpu
Аннотация
To characterize the relationship between cendakimab exposure and the longitudinal efficacy endpoint dysphagia days (DD), E-R analyses were performed using data from the EE-001 study (N = 427) with eosinophilic esophagitis. DD-a bounded, discrete endpoint assessed over 14-day period via modified daily symptom diary (mDSD)-was modeled using a latent variable indirect response (IDR) model coupled with a combined uniform-binomial (CUB) distribution. The latent variable, representing the underlying disease status, was dynamically modulated by placebo and drug effects (a function of individual-predicted exposure) to govern the binomial probability of DD, while the uniform component captured the residual variability in patient-reported outcomes. Inter-individual variability was estimated for baseline DD, maximum placebo effect, and maximum drug effect. Covariates, including steroid inadequate response or intolerance (Steroid IR/I) status and baseline DD, were incorporated in the final model based on the clinical relevance. The estimated placebo half-life was ~28 weeks, estimated EC50 was 76.5 μg/mL, corresponding to an EC90 of ~688 μg/mL, indicating steepness of the Emax curve. Model-based simulations showed that both 360 mg QW and QW-to-Q2W regimens reduced DD compared to placebo at Week 48, with mean reductions of ~1.65 and ~1.36 days, respectively. Covariate-stratified simulations suggested consistent responses across sex, age, and race. Steroid IR/I and baseline DD influenced treatment response magnitude but did not warrant dose modification. These findings support QW-to-Q2W as an effective maintenance posology and the utility of latent variable IDR models with appropriate likelihoods for modeling bounded, discrete longitudinal endpoints in E-R analyses.