Drug repurposing in oncology: Bridging computational discovery to clinical application

Drug repurposing, the identification of new therapeutic applications for existing drugs, has emerged as a pragmatic and cost-efficient strategy to accelerate oncology drug discovery. Faced with rising development costs, protracted timelines, and high attrition rates associated with traditional de novo drug development, repurposing leverages known pharmacokinetics, safety profiles, and manufacturing processes to expedite clinical translation. This review synthesizes current advances in computational and experimental methodologies and mechanistic insights that drive drug repurposing for cancer therapy. In silico strategies, including molecular docking, machine learning, transcriptomic-proteomic signature reversal, and network-based modeling, have enabled rapid identification of repurposable agents by mining multi-omics and historical pharmacological data. Experimental pipelines spanning high-throughput screening, phenotypic assays, biochemical validations, and functional animal models remain essential to establish efficacy and delineate mechanisms of action. Notably, repurposed drugs exhibit anticancer activity by modulating key pathways, including phosphatidylinositol 3-kinase/Ak strain transforming/mechanistic target of rapamycin, mitogen-activated protein kinase/extracellular signal-regulated kinase, wingless-related integration site/β-catenin signaling, as well as redox homeostasis and DNA response. Despite their promise, repurposed candidates face barriers including limited intellectual property protections, dose optimization challenges, and regulatory uncertainty. Moreover, clinical translation is often hindered by insufficient mechanistic understanding and a lack of predictive biomarkers. Integration of multi-omics datasets, explainable artificial intelligence, patient-derived organoids, and clustered regularly interspaced short palindromic repeats-based genetic screens now offers unprecedented precision in identifying context-specific drug effects and synthetic lethal interactions. With cancer causing one in six global deaths and marked by therapeutic resistance and molecular heterogeneity, drug repurposing provides a scalable solution. This approach bridged preclinical insight with clinical application, potentially transforming cancer therapeutics through rational, data-driven innovation.