Drug combinations screening using a Bayesian ranking approach based on dose–response models

Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose–response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank‐based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4‐parameter log‐logistic (4PLL) model was used to estimate dose–response curves of dose–candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose–response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia.