A Bayesian adaptive phase 1 design to determine the optimal dose and schedule of an adoptive T-cell therapy in a mixed patient population

We present a novel Bayesian adaptive phase 1 design to determine the optimal dosing regimen for an adoptive T-cell therapy in a mixed patient population. Our design is motivated by a B-cell Non-Hodgkin Lymphoma trial evaluating multiple dosing regimens within multiple disease subtypes. A utility score is calculated from both safety and efficacy utility functions and used to guide dose-escalation decisions. We pool safety data across disease subtypes and use a single dose-toxicity model while sharing efficacy information between disease subtypes using a hierarchical dose-response model. In addition, an adaptive randomization approach is applied to dynamically assign patients to a regimen when more than one regimen is open for enrollment. We illustrate this study design through a simulated trial example, and we investigate the operating characteristics using simulation studies.