Leveraging mathematical models to improve the statistical robustness of cancer immunotherapy trials

Abstract

Cancer immunotherapy is an important application area for mathematical modeling. Current modeling studies have a range of ambitious goals from dose optimization to creating “digital twins” of individual cancer patients for treatment response prediction. Here we focus on a humbler, but nonetheless important, goal: aiding with the planning and design of clinical trials. Cancer immunotherapy trials can be hard to design due to heterogeneous and time-varying treatment effects. While clinical statisticians already use computer simulations, these rarely integrate explicit pathophysiological mechanisms, such as cancer-immune interactions, to specifically adapt the design to the treatment. Encouraged by rapid progress in mathematical modeling, we here propose an “in-silico-first” approach–already common in industry–where doctors, statisticians, and modelers build knowledge-based mathematical models to examine and refine the statistical design of clinical trials. Ultimately, we hope that this collaborative effort will lead to more robust designs of future clinical trials, resulting in improved success rates.