Correlated Drug Action as a Baseline Additivity Model for Combination Cancer Therapy in Patient Cohorts and Cell Cultures

Identifying and characterizing the effect of combination cancer therapies is of paramount importance in cancer research. The benefit of a combination can originate in the inherent heterogeneity in the response across patients, the heterogeneity in the clonal composition of tumors in each patient, or in molecular synergy between the compounds given in combination. The effect of drug combinations is usually studied in cell culture, in patient populations or both. To shed light and help characterize combinations and their enhanced benefits over single therapies, we introduce Correlated Drug Action (CDA) as a baseline additivity model. We formulate the CDA theory and propose to model it using a closed-form expression both in the temporal domain (tCDA) to explain survival curves, and in the dose domain (dCDA), to explain dose-response curves. CDA can be used in clinical trials and cell culture experiments. At the level of clinical trials, we demonstrate tCDA’s utility in identifying non-additive combinations, and cases where the combination can be explained in terms of the monotherapies. At the level of cells in culture, dCDA naturally embeds null models such as Bliss additivity, the Highest Single Agent model, the dose equivalence principle and sham combinations. We demonstrate the applicability of dCDA in assessing non-additive combinations in experimental data by introducing a new metric, the Excess over CDA (EOCDA). CDA is a general framework to model additivity at the cell line and patient population levels and can be used to characterize and quantify the degree of non-additivity in drug combination therapy. ### Competing Interest Statement The authors have declared no competing interest.