An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer

Cancer is a leading cause of death worldwide, and advanced cancer is often treated with combinations of multiple chemotherapy drugs. In this work, we develop models to predict the outcomes of clinical trials testing combination chemotherapy regimens before they are run and to select the combination chemotherapy regimens to be tested in new phase II and phase III clinical trials, with the primary objective of improving the quality of regimens tested in phase III trials compared to current practice. We built a database of 414 clinical trials for advanced gastric cancer and use it to build statistical models that attain an out-of-sample R 2 of 0.56 when predicting a trial’s median overall survival (OS) and an out-of-sample area under the curve (AUC) of 0.83 when predicting if a trial has unacceptably high toxicity. We propose models that use machine learning and optimization to suggest regimens to be tested in phase II and phase III trials. Though it is inherently challenging to evaluate the performance of such models without actually running clinical trials, we use two techniques to obtain estimates for the quality of regimens selected by our models compared with those actually tested in current clinical practice. Both techniques indicate that the models might improve the efficacy of the regimens selected for testing in phase III clinical trials without changing toxicity outcomes. This evaluation of the proposed models suggests that they merit further testing in a clinical trial setting. This paper was accepted by Noah Gans, stochastic models and systems .