A graph model of combination therapies. Issue 5 (May 2022)

• Record Type: Journal Article
• Authors: Sang, Mengmeng; Dong, Ang; Wu, Shuang; Li, Feng; Wang, Jing; Griffin, Christopher; Wu, Rongling
• Is Part Of: Drug discovery today. Volume 27:Issue 5(2022)
• Page Start: 1210
• Page End: 1217
• Publication Date: 2022-05
• Subjects: Quasi-dynamic ordinary differential equations -- Drug–drug interaction -- Game theory -- Allometric scaling theory -- Network; Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2022.02.001
• Languages: English
• ISSNs: 1359-6446

Abstract

Highlights: Successful combination therapies are determined by drug-drug interactions (DDIs). We propose a graph theory to characterize how drugs work together in a network. It combines interdisciplinary elements to disentangle and interrogate DDIs. We develop a generic tool to infer personalized and context-specific DDI networks. 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.