Drug repurposing in oncology: Bridging computational discovery to clinical application.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not applicable — no quantitative effect sizes reported; this is a narrative review without meta-analytic synthesis

• Primary Outcome: Narrative synthesis of computational and experimental drug repurposing methodologies and their application to oncology, including identification of anticancer mechanisms across key signalling pathways

• Nnt Or Sensitivity: Not applicable — no NNT, sensitivity, specificity, or hazard ratios reported; the review does not synthesise clinical trial data in a quantitative format

• Confidence Interval: Not reported — no statistical analysis performed

Clinical Application

Computational repurposing strategies described (molecular docking, machine learning, network modelling) are feasible in well-resourced academic and pharmaceutical research settings. High-throughput screening and patient-derived organoid platforms require significant infrastructure investment. Clinical translation of repurposed agents faces real-world barriers including off-label prescribing regulations, dose optimisation requirements, and limited commercial incentives due to intellectual property constraints on generic drugs. In Australia, drug repurposing in oncology intersects with several regulatory and funding frameworks. The TGA regulates off-label use of repurposed agents, and prescribers must navigate informed consent obligations for non-approved indications. The PBS does not currently fund most repurposed non-oncology drugs for cancer indications, creating access inequities. The RACGP and COSA (Clinical Oncology Society of Australia) have not issued specific guidelines on repurposing frameworks. The ReDO (Repurposing Drugs in Oncology) project and CARE (Cancer Adjuvant Research and Education) trial platform in Australia represent relevant domestic initiatives. Metformin, statins, aspirin, and ivermectin — commonly discussed repurposing candidates — are PBS-listed for their primary indications but require prospective RCT evidence before oncology-specific PBS listing could be considered. Australian researchers at institutions such as the Peter MacCallum Cancer Centre and WEHI are active in translational repurposing research, providing domestic relevance to the computational and mechanistic frameworks described. Broadly applicable to oncology patients across tumour types where repurposed agents targeting PI3K/AKT/mTOR, MAPK/ERK, Wnt/β-catenin, redox, and DNA damage response pathways are under investigation. Most directly relevant to oncologists, medical researchers, and translational scientists evaluating repurposing candidates in early-phase clinical trials or preclinical pipelines. Not directly applicable to individual patient management decisions without supporting RCT-level evidence.

Abstract

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.

References

1. 1.Baskar, S., & Prasad, N. R. (2026). Drug repurposing in oncology: Bridging computational discovery to clinical application. International Journal of Cancer. https://doi.org/10.1002/ijc.70429