M-DODII: Bayesian dose optimization design for randomized phase II study with multiple indications

The landscape of oncology drug development is transitioning from traditional cytotoxic chemotherapy drugs to novel agents, such as molecularly targeted therapies (MTA) or immunotherapies. Conventional dose optimization methods based on chemotherapy that assume a monotone dose-response relationship might not be ideal for the development of these novel therapies. Recognizing these limitations, the US FDA has introduced Project Optimus, an initiative aimed to reform the current paradigm of dose optimization. In addition to dose optimization, another critical objective for early phase proof-of-concept clinical trials is indication selection. However, there are limited methodologies that can address dose optimization and indication selection simultaneously. In this paper, we propose a Bayesian Dose Optimization Design for Randomized Phase II trials with Multiple Indications (M-DODII) that integrates Bayesian continuous monitoring and Bayesian pick-the-winner approach, utilizing efficacy and toxicity endpoints to inform dose selection for multiple indications simultaneously. Through simulation studies, we demonstrate that M-DODII has favorable operating characteristics with controlled selection error. Compared to other adaptive designs, M-DODII shows a lower probability of choosing a suboptimal dose, a higher probability of selecting the optimal dose, and reduced total sample size.