Shotgun: A Bayesian seamless phase I-II design to accelerate the development of targeted therapies and immunotherapy.

Drug development of novel antitumor agents is conventionally divided by phase and cancer indication. With the advent of new molecularly targeted therapies and immunotherapies, this approach has become inefficient and dysfunctional. We propose a Bayesian seamless phase I-II "shotgun" design to evaluate the safety and antitumor efficacy of a new drug in multiple cancer indications simultaneously. "Shotgun" is used to describe the design feature that the trial begins with an all-comer dose finding phase to identify the maximum tolerated dose (MTD) or recommended phase II dose (RP2D), and then is seamlessly split to multiple indication-specific cohort expansions. Patients treated during dose finding are rolled over to the cohort expansion for more efficient evaluation of efficacy, while patients enrolled in cohort expansion contribute to the continuous learning of the safety and tolerability of the new drug. During cohort expansion, interim analyses are performed to discontinue ineffective or unsafe expansion cohorts early. To improve the efficiency of such interim analyses, we propose a clustered Bayesian hierarchical model (CBHM) to adaptively borrow information across indications. A simulation study shows that compared to conventional approaches and the standard Bayesian hierarchical model, the shotgun design has substantially higher probabilities to discover indications that are responsive to the treatment in question, and is associated with a reasonable false discovery rate. The shotgun provides a phase I-II trial design for accelerating drug development and to build a more robust foundation for subsequent phase III trials. The proposed CBHM methodology also provides an efficient design for basket trials.