Bayesian Design for Optimizing Doses and Assessing Contribution of Components in Drug Combinations

Music City Center 

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. 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. We demonstrate the superior finite-sample operating characteristics of our design through extensive simulations. For illustration, we apply the proposed design to a real phase II trial evaluating the combination therapy of bevacizumab and lomustine.

Combination therapy

two-stage design

multi-arm randomized trial

phase II

dose optimization 

Biopharmaceutical Section