Drug repurposing in oncology: Bridging computational discovery to clinical application

Area of Science:

• Oncology
• Pharmacology
• Drug Discovery

Background:

• Traditional de novo drug development faces high costs, long timelines, and frequent failures.
• Drug repurposing offers a pragmatic and cost-efficient alternative by utilizing existing drugs with known safety and pharmacokinetic profiles.

Purpose of the Study:

• To review current computational and experimental methods driving drug repurposing in cancer therapy.
• To synthesize mechanistic insights into how repurposed drugs target key cancer pathways.

Main Methods:

• In silico strategies: molecular docking, machine learning, transcriptomic-proteomic signature reversal, network-based modeling.
• Experimental pipelines: high-throughput screening, phenotypic assays, biochemical validation, animal models.
• Integration of multi-omics data, explainable AI, patient-derived organoids, and CRISPR-based screens.

Main Results:

• Repurposed drugs modulate critical cancer pathways like PI3K/Akt/mTOR, MAPK/ERK, and Wnt/β-catenin.
• Computational and experimental approaches rapidly identify potential repurposable agents.
• Advanced techniques enhance precision in identifying context-specific drug effects and synthetic lethality.

Conclusions:

• Drug repurposing is a scalable solution to combat cancer's therapeutic resistance and heterogeneity.
• Overcoming barriers like IP protection and regulatory uncertainty is crucial for clinical translation.
• Data-driven innovation through drug repurposing promises to transform cancer therapeutics.