Exploiting drug-disease relationships for computational drug repositioning

Area of Science:

• Pharmacology
• Bioinformatics
• Drug Discovery

Background:

• Drug repositioning is a long-standing strategy to expand therapeutic applications of existing medications.
• Advancements in high-throughput molecular measurement technologies have enabled computational approaches for drug repositioning.
• Computational predictions have shown promise in cellular models, but require further validation in animal and clinical studies.

Purpose of the Study:

• To review and classify computational methods for drug repositioning.
• To discuss the integration of diverse molecular data into drug repositioning algorithms.
• To highlight the future role of computational drug repositioning in addressing global health challenges.

Main Methods:

• Classification of computational drug repositioning methods along two axes: drug-based (chemical perspective) and disease-based (clinical/pathological perspective).
• Discussion of emerging algorithms that integrate both axes and leverage novel molecular measurements.
• Review of the validation landscape for computational drug repositioning predictions.

Main Results:

• Computational drug repositioning methods can be categorized based on their starting point: chemical properties of drugs or clinical aspects of diseases.
• Newer algorithms are expected to be more comprehensive, spanning both drug-based and disease-based approaches.
• The potential for computational methods to accelerate the identification of new drug indications is significant.

Conclusions:

• Computational drug repositioning is a vital strategy for identifying new therapeutic uses of existing drugs.
• Future computational methods will likely integrate diverse data types and span multiple classification axes.
• These advanced computational approaches hold promise for reducing the global burden of disease.