Modeling combination therapies in patient cohorts and cell cultures using correlated drug action

Adith S Arun; Sung-Cheol Kim; Mehmet Eren Ahsen; Gustavo Stolovitzky

doi:10.1016/j.isci.2024.108905

Modeling combination therapies in patient cohorts and cell cultures using correlated drug action

iScience. 2024 Jan 15;27(3):108905. doi: 10.1016/j.isci.2024.108905. eCollection 2024 Mar 15.

Authors

Adith S Arun^{1

2}, Sung-Cheol Kim³, Mehmet Eren Ahsen^{4

5}, Gustavo Stolovitzky⁶

Affiliations

¹ Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.
² Yale School of Medicine, New Haven, CT 06510, USA.
³ Psychogenics, Inc, Paramus, NJ 07652, USA.
⁴ Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.
⁵ Carle-Illinois School of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.
⁶ DREAM Challenges, NY, NY, 10471, USA.

Abstract

Characterizing the effect of combination therapies is vital for treating diseases like cancer. We introduce correlated drug action (CDA), a baseline model for the study of drug combinations in both cell cultures and patient populations, which assumes that the efficacy of drugs in a combination may be correlated. We apply temporal CDA (tCDA) to clinical trial data, and demonstrate the utility of this approach in identifying possible synergistic combinations and others that can be explained in terms of monotherapies. Using MCF7 cell line data, we assess combinations with dose CDA (dCDA), a model that generalizes other proposed models (e.g., Bliss response-additivity, the dose equivalence principle), and introduce Excess over CDA (EOCDA), a new metric for identifying possible synergistic combinations in cell culture.

Keywords: Applied computing; Computational chemistry.