A robust Bayesian dose optimization design with backfill and randomization for phase I/II clinical trials

The integration of backfill cohorts into Phase I clinical trials has garnered increasing interest within the clinical community, particularly following the "Project Optimus" initiative by the U.S. Food and Drug Administration, as detailed in their final guidance of August 2024. This approach allows for the collection of additional clinical data to assess safety and activity before initiating trials that compare multiple dosages. For novel cancer treatments such as targeted therapies, immunotherapies, antibody-drug conjugates, and chimeric antigen receptor T-cell therapies, the efficacy of a drug may not necessarily increase with dose levels. Backfill strategies are especially beneficial as they enable the continuation of patient enrollment at lower doses while higher doses are being explored. We propose a robust Bayesian design framework that borrows information across dose levels without imposing stringent parametric assumptions on dose-response curves. This framework minimizes the risk of administering subtherapeutic doses by jointly evaluating toxicity and efficacy, and by effectively addressing the challenge of delayed outcomes. Simulation studies demonstrate that our design not only generates additional data for late stage studies but also enhances the accuracy of optimal dose selection, improves patient safety, reduces the number of patients receiving subtherapeutic doses, and shortens trial duration across various realistic trial settings.