Data-driven strategies for drug repurposing: insights, recommendations, and case studies

Area of Science:

• Pharmacology and Drug Discovery
• Computational Biology
• Medicinal Chemistry

Background:

• Traditional drug discovery is lengthy, expensive, and has a high failure rate.
• Drug repurposing presents a viable strategy to accelerate therapeutic development and address unmet medical needs.
• Systematic analysis of drug-target interactions is crucial for effective drug repurposing.

Purpose of the Study:

• To comparatively analyze drug-target interaction databases (ChEMBL, BindingDB, GtoPdb).
• To develop a structured framework for therapeutic interpretation of drug and target data.
• To establish a computational pipeline for predicting drug repurposing opportunities.

Main Methods:

• Comparative analysis of ChEMBL, BindingDB, and GtoPdb databases.
• Manual classification of targets and mapping of drug indications into broader categories.
• Profiling of physicochemical properties and examination of cross-indication drug approvals.
• Implementation of a pathway-based computational pipeline for predicting drug repositioning.

Main Results:

• Established a structured framework for drug and target data, linking physicochemical properties to therapeutic groups.
• Identified associations between drug properties and therapeutic categories, guiding compound prioritization.
• Revealed high repurposing potential in specific areas through analysis of cross-indication approvals.
• Demonstrated the utility of a computational pipeline for predicting drug repurposing in oncology.

Conclusions:

• Consolidated drug-target data and computational methods into a data-driven framework for drug discovery.
• Provided practical guidance for indication-specific compound prioritization and refining repurposing studies.
• Advanced drug discovery and translational applications through a systematic approach to drug repurposing.