BOP2-Comb: Bayesian Optimal Phase II Design for Optimizing Doses and Assessing Contribution of Components in Drug Combinations.

Abstract

Background: Personalized cancer treatment using combination therapies offers substantial therapeutic benefits over single-agent treatments in most cancers. However, unmet clinical needs and increasing market competition pressure drug developers to quickly optimize combination doses and clearly demonstrate the contribution of each component when developing and evaluating new combination treatments.

Methods: We propose a Bayesian optimal phase II drug-combination (BOP2-Comb) design that optimizes the combination dose and evaluates the proof-of-concept as well as the contribution of each component in two seamless stages. Our optimal calibration scheme minimizes the total trial sample size while controlling incorrect decision rates at nominal levels. This calibration procedure is Monte Carlo simulation-free and provides a theoretical guarantee of false-positive control.

Results: We demonstrate the superior finite-sample operating characteristics of the proposed design through extensive simulations, achieving reduced sample sizes and improved control of both correct and incorrect decision rates compared to existing approaches. To illustrate its utility, we apply the BOP2-Comb design to redesign a real phase II trial evaluating the combination therapy of bevacizumab and lomustine.

Conclusions: The BOP2-Comb design provides a valuable framework for designing future randomized phase II trials of combination therapies, particularly when both dose optimization and assessment of component contributions are required.