PK/PD-integrated Bayesian platform design for phase II dose regimen optimization

2026-09-24

AI Summary

This study addresses the limitation of conventional dose-only approaches in accurately characterizing differences among dosing regimens. To overcome this, we propose PROP, a Bayesian phase II adaptive platform design that integrates population pharmacokinetic/pharmacodynamic (PK/PD) models. By incorporating discrete-time survival analysis and Bayesian model averaging, PROP represents the first framework to embed population PK/PD modeling within an adaptive trial structure, enabling dynamic addition, graduation, and termination of regimens. Simulation studies demonstrate that, compared with traditional dose-oriented methods, PROP substantially enhances decision-making accuracy. It achieves more precise estimation of toxicity and efficacy profiles while facilitating the reliable selection of optimal dosing regimens.

Abstract

Early-phase dose-finding methods increasingly assess toxicity and efficacy jointly, but comparisons based only on administered dose may inadequately characterize regimens differing in schedule. We developed a Bayesian phase II adaptive platform design for regimen optimization that integrates pharmacokinetic/pharmacodynamic (PK/PD) modelling into toxicity, efficacy, regimen selection and adaptation decisions. The proposed PK/PD-informed Regimen Optimization Platform (PROP) design uses a population PK/PD model to generate patient- and population-level predictions of exposure and biological activity. Acute and cumulative toxicities are analysed using a discrete-time time-to-event model informed by PK exposure. Efficacy is evaluated through Bayesian model averaging of exposure-driven and biomarker-driven time-to-event models. The design supports regimen graduation, discontinuation for futility or safety, and addition of unexplored regimens. Performance was evaluated through simulations motivated by an influenza intensive-care setting. Across six scenarios, PROP generally improved graduation and futility decisions, reduced inappropriate graduation, and supported the addition of promising regimens compared with dose-based alternatives. It also more accurately estimated regimen-specific toxicity and arm-specific efficacy, while the model-averaging framework favored the efficacy model consistent with the data-generating mechanism. Dose-based approaches performed better for safety stopping in some scenarios, despite less accurate characterization of the regimen--toxicity relationship. PK/PD-informed platform designs can improve adaptive regimen selection and knowledge generation when dose alone cannot adequately characterize treatment regimens.

Problem

• dose regimen optimization
• PK/PD modelling
• Bayesian adaptive platform
• phase II clinical trial
• toxicity and efficacy

Innovation

• Bayesian adaptive platform design
• PK/PD modeling
• Regimen optimization
• Bayesian model averaging
• Time-to-event model