Multiple Targets, One Goal: Compounding life-extending effects through Polypharmacology

Area of Science:

• Gerontology and pharmacology
• Computational drug discovery
• Molecular biology

Background:

• The most effective lifespan-extending compounds, or geroprotectors, often interact with multiple biogenic amine receptors.
• Understanding polypharmacology is key to developing highly effective anti-aging interventions.

Purpose of the Study:

• To test the hypothesis that polypharmacological compounds targeting multiple biogenic amine receptors are superior geroprotectors.
• To develop a predictive model for identifying such compounds using graph neural networks.
• To experimentally validate the efficacy of predicted compounds in extending lifespan.

Main Methods:

• Utilized graph neural networks (GNNs) for in silico prediction of polypharmacological compounds.
• Selected compounds based on GNN predictions targeting multiple biogenic amine receptors.
• Evaluated the lifespan-extending effects of selected compounds in the model organism *C. elegans*.
• Compared the efficacy of identified geroprotectors against existing data in the DrugAge database.

Main Results:

• Over 70% of the GNN-selected compounds demonstrated significant lifespan extension in *C. elegans*.
• The observed effect sizes for lifespan extension were among the top 5% in comparison to the DrugAge database.
• This validates the predictive power of GNNs for identifying potent geroprotective agents.

Conclusions:

• Rationally designing polypharmacological compounds is a viable strategy for developing highly effective geroprotectors.
• Graph neural network-based prediction offers a powerful approach for discovering novel anti-aging compounds.
• This study highlights the potential of targeting multiple biogenic amine receptors for significant lifespan extension.