Redefining Breast Cancer Care by Harnessing Computational Drug Repositioning

Area of Science:

• Oncology
• Pharmacology
• Bioinformatics

Background:

• Breast cancer, particularly triple-negative breast cancer (TNBC), presents significant therapeutic challenges due to limited targeted options and drug resistance.
• Drug repositioning provides a cost-effective alternative to de novo drug discovery by utilizing existing drug safety and pharmacokinetic data.

Purpose of the Study:

• To critically review recent advancements in computational drug repositioning strategies for breast cancer.
• To assess the effectiveness and challenges of various in silico approaches in identifying new therapeutic indications.

Main Methods:

• Network-based methods
• Computer-aided drug design (CADD)
• Artificial intelligence (AI) and machine learning (ML)
• Transcriptomic signature matching
• Multi-omics integration

Main Results:

• Key case studies demonstrating progression to preclinical or clinical evaluation were highlighted.
• Comparative performance metrics, validation outcomes, and real-world success rates were assessed.
• Methodological challenges including data heterogeneity, bias, and reproducibility were identified.

Conclusions:

• In silico drug repositioning holds significant potential for addressing unmet needs in breast cancer therapy.
• Standardized data sharing, reproducible pipelines, and window-of-opportunity trials are crucial for advancing the field.