A graph model of combination therapies

Mengmeng Sang; Ang Dong; Shuang Wu; Feng Li; Jing Wang; Christopher Griffin; Rongling Wu

doi:10.1016/j.drudis.2022.02.001

A graph model of combination therapies

Drug Discov Today. 2022 May;27(5):1210-1217. doi: 10.1016/j.drudis.2022.02.001. Epub 2022 Feb 8.

Authors

Mengmeng Sang¹, Ang Dong², Shuang Wu², Feng Li², Jing Wang², Christopher Griffin³, Rongling Wu⁴

Affiliations

¹ Institute of Reproductive Medicine, Nantong University Medical School, Nantong 226001, China.
² Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
³ Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA.
⁴ Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University College of Medicine, Hershey, PA 17033, USA. Electronic address: rwu@phs.psu.edu.

PMID: 35143962
DOI: 10.1016/j.drudis.2022.02.001

Abstract

The simultaneous use of multiple medications causes drug-drug interactions (DDI) that impact therapeutic efficacy. Here, we argue that graph theory, in conjunction with game theory and ecosystem theory, can address this issue. We treat the coexistence of multiple drugs as a system in which DDI is modeled by game theory. We develop an ordinary differential equation model to characterize how the concentration of a drug changes as a result of its independent capacity and the dependent influence of other drugs through the metabolic response of the host. We coalesce all drugs into personalized and context-specific networks, which can reveal key DDI determinants of therapeutical efficacy. Our model can quantify drug synergy and antagonism and test the translational success of combination therapies to the clinic.

Keywords: Allometric scaling theory; Drug–drug interaction; Game theory; Network; Quasi-dynamic ordinary differential equations.

MeSH terms

Drug Interactions
Ecosystem*