Panacea: Predicting anti-aging combinations from expression analysis

Abstract: Identifying interventions, such as drugs, that can counteract the effects of aging is crucial due to the complex nature of the aging process, which involves multiple biological processes. By targeting these processes, interventions have the potential to promote healthy aging. Utilizing pairs of drugs that exhibit synergistic effects becomes particularly effective as they can simultaneously impact multiple pathways associated with aging and reprogramming, enhancing their anti-aging potential. The Panacea (predicting anti-aging combinations from expression analysis) framework was developed to facilitate the discovery of such drug combinations. Deep generative models were incorporated into the Panacea framework to effectively capture complex patterns in gene expression data, leveraging their non-linear nature for an accurate representation of relationships and interactions. This makes them ideal for predicting drug combinations. The trained models, using the CMap dataset, demonstrated an improved performance to predict the effect of drugs. The age effect of these drug combinations was evaluated using an age-predictive model, revealing that synergistic anti-aging combinations mainly comprised reprogramming (the process of transforming one type of cell into another by altering its gene expression and properties), apoptosis (programmed cell death mechanism), and chemotherapy drugs, while pro-aging combinations involved cellular growth-limiting, longevity-extending, and chemotherapy drugs. These results emphasize the capability of deep generative models in predicting potent drug combinations for anti-aging and anti-cancer interventions.