A generalized Bayesian optimal interval design for dose optimization in immunotherapy.

Abstract

For novel immuno-oncology therapies, the primary purpose of a dose-finding trial is to identify an optimal dose (OD), defined as the tolerable dose having adequate efficacy and immune response under the unpredictable dose-outcome (toxicity, efficacy, and immune response) relationships. In addition, the multiple low or moderate-grade toxicities rather than dose-limiting toxicities (DLTs) and multiple levels of efficacy should be evaluated differently in dose-finding to determine true OD for developing novel immuno-oncology therapies. We proposed a generalized Bayesian optimal interval design for immunotherapy, simultaneously considering efficacy and toxicity grades and immune response outcomes. The proposed design, named gBOIN-ETI design, is model-assisted and easy to implement to develop immunotherapy efficiently. The operating characteristics of the gBOIN-ETI are compared with other dose-finding trial designs in oncology by simulation across various realistic settings. Our simulations show that the gBOIN-ETI design could outperform the other available approaches in terms of both the percentage of correct OD selection and the average number of patients allocated to the OD across various realistic trial settings.