PROComb: A Practical and Robust Design With Randomization to Optimize Dose Combinations in Drug Combination Trials.

Abstract

The US Food and Drug Administration's Project Optimus has initiated a paradigm shift in oncology drug development from "more is better" to "less is more." Methodological development for combination therapy trials remains limited due to their complex dose-combination space and the need to integrate multi-source data, including toxicity, clinical efficacy, and biological antitumor outcomes such as pharmacodynamic (PD) activity. We propose PROComb, a practical and robust design with randomization to optimize dose combinations, to address these challenges with minimal assumptions and to facilitate practical implementation. In the first stage, the complex dose-combination space is efficiently explored through the integration of multi-source data and the inherent relationships among different dose-outcomes. Patients are adaptively assigned to dose combinations to establish a therapeutic dose space (TDS), defined as the set of doses with acceptable toxicity and promising efficacy. A recommended Stage II dose set (RS2D) is constructed at the end of Stage I. In the second stage, patients are randomized to RS2D combinations for further monitoring, screening and identification of the optimal biologic dose combination (OBDC). Three PROComb versions are developed to mitigate potential between-stage drift. Simulation studies demonstrate that PROComb achieves favorable operating characteristics compared with conventional model-based and model-assisted designs.