Drug repurposing for aging research using model organisms.

Area of Science:

• Gerontology and pharmaceutical science
• Computational biology and drug discovery

Background:

• Aging is a major risk factor for prevalent diseases, yet pharmaceutical interventions targeting aging remain underexplored.
• Genetic studies reveal key aging pathways and demonstrate that genetic interventions can promote healthier aging phenotypes.

Purpose of the Study:

• To develop and validate a computational approach for ranking existing drug-like compounds based on their potential to modulate aging.
• To identify promising compounds for experimental testing in model organisms (Caenorhabditis elegans and Drosophila) to assess their effects on lifespan and aging.

Main Methods:

• Integrated diverse data sources including genetic effects on aging, gene orthology, sequence conservation, protein structures, and drug properties (binding, bioavailability).
• Developed a novel ranking system to predict the likelihood of drug-like compounds modulating aging processes.
• Applied the ranking approach to a library of 743 drug-like compounds with known mammalian targets.

Main Results:

• Successfully ranked 743 drug-like compounds based on their predicted impact on aging.
• Demonstrated enrichment of the ranking with compounds known to affect aging, despite limited validation data.
• Identified top-ranked compounds as prime candidates for in vivo lifespan studies in C. elegans and Drosophila.

Conclusions:

• The developed computational approach effectively prioritizes drug candidates for aging research.
• Top-ranked compounds represent valuable tools for investigating aging mechanisms and developing interventions for healthier human aging.