A novel network based linear model for prioritization of synergistic drug combinations

• Published in: PloS one Vol. 17; no. 4; p. e0266382
• Main Authors: Li, Jiaqi, Xu, Hongyan, McIndoe, Richard A
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.04.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Antineoplastic drugs; Antitumor agents; Biology and Life Sciences; Computational Biology - methods; Computer and Information Sciences; Computer applications; Data processing; Drug Combinations; Drug dosages; Drug resistance; Drug screening; Drug Synergism; Drug therapy, Combination; Drugs; Empirical analysis; Gene expression; Genomics; Laboratories; Linear Models; Medical research; Medicine and Health Sciences; Pharmaceutical industry; Physical Sciences; Research and Analysis Methods; Synergistic effect; Testing; United States; United States > US; Georgia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0266382

Drug combination therapies can improve drug efficacy, reduce drug dosage, and overcome drug resistance in cancer treatments. Current research strategies to determine which drug combinations have a synergistic effect rely mainly on clinical or empirical experience and screening predefined pools of drugs. Given the number of possible drug combinations, the speed, and scope to find new drug combinations are very limited using these methods. Due to the exponential growth in the number of drug combinations, it is difficult to test all possible combinations in the lab. There are several large-scale public genomic and phenotypic resources that provide data from single drug-treated cells as well as data from small molecule treated cells. These databases provide a wealth of information regarding cellular responses to drugs and offer an opportunity to overcome the limitations of the current methods. Developing a new advanced data processing and analysis strategy is imperative and a computational prediction algorithm is highly desirable. In this paper, we developed a computational algorithm for the enrichment of synergistic drug combinations using gene regulatory network knowledge and an operational module unit (OMU) system which we generate from single drug genomic and phenotypic data. As a proof of principle, we applied the pipeline to a group of anticancer drugs and demonstrate how the algorithm could help researchers efficiently find possible synergistic drug combinations using single drug data to evaluate all possible drug pairs.