2024/12/07 by Arindam Ghosh, Vittorio Fortino · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · Pharmacology, Toxicology and Pharmaceutics · #Bioinformatics and Genomic Networks #Computational Drug Discovery Methods #Pharmacogenetics and Drug Metabolism
paper · doi:10.1016/j.csbj.2024.12.003
openalex publication_date 2024/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
methods, on the other hand, can enable rapid screening of drug pairs to prioritise for experimental validation. Here we present a novel network medicine approach that systematically models the proximity of drug targets to disease-associated genes and adverse effect-associated genes, through the combination of network propagation algorithm and gene set enrichment analysis. The proposed approach is applied in the context of identifying effective drug combinations for cancer treatment starting from a training set of drug combinations curated from DrugComb and DrugBank databases. We observed that effective drug combinations usually enrich disease-related gene sets while adverse drug combinations enrich adverse-effect gene sets. We use this observation to systematically train classifiers distinguishing drug combinations with higher therapeutic effects and no known adverse reaction from combinations with lower therapeutic effects and potential adverse reactions in six cancer types. The approach is tested and validated using drug combinations curated from in vitro screening data and clinical reports. Trained classification models are also used to identify novel potential anti-cancer drug combinations for experimental validation. We believe our framework would be a key addition to the anti-cancer drug combination identification pipeline by enabling rapid yet robust estimation of therapeutic efficacy or adverse reaction potential.