Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities

Area of Science:

• Pharmacology
• Drug Development
• Computational Biology

Background:

• Drug repurposing offers an efficient approach to developing treatments for rare diseases, stratified populations, and public health emergencies.
• Identifying optimal drug candidates from numerous possibilities remains a significant challenge in drug development.
• Recent advancements in genomic profiling, database curation, and machine learning have enhanced the accuracy of identifying drug repurposing candidates.

Purpose of the Study:

• To review and categorize major methodologic classes for drug repurposing.
• To highlight the advantages and limitations of different drug repurposing approaches in clinical practice.

Main Methods:

• The review categorizes drug repurposing methods based on four main approaches:
• Utilizing protein structure data.
• Analyzing genomic signatures.
• Leveraging biological networks.
• Incorporating real-world clinical data.

Main Results:

• Drug repurposing methodologies are broadly classified into four categories: protein structure-based, genomic signature-based, biological network-based, and real-world clinical data-based.
• Each method offers distinct advantages and faces specific limitations when applied to clinical scenarios.

Conclusions:

• Effective drug repurposing requires a comprehensive understanding of the strengths and weaknesses of various methodologies.
• Integrating insights from diverse approaches is crucial for maximizing the impact of drug repurposing in clinical practice.