A Better Approach to Screening for Existing Drugs that Slow Aging
The nature of medical regulation makes it extremely expensive to develop a new drug, and considerably less expensive to find a new use for an existing approved drug. Thus repurposing drugs receives more attention than it perhaps should, and the typical outcome is a new marginal use rather than something impressive. This will happen for treatments for aging as well, no doubt. People will find that many existing approved drugs have some small effect on aging and life span in animal studies, and some of those will be marketed and used, and none of that will make any great difference to the world. What are the odds of discovering that an existing drug has an effect size similar to that of rapamycin? Which is to say ~20% life extension in mice and maybe a few years in humans, though that remains to be seen. That is an interesting question; it may be possible for effects on the order of a few additional years of life expectancy to hide in the existing human data because no-one looked that hard. Much more than that seems unlikely, though.
Despite the thousands of genes implicated in age-related phenotypes, effective interventions for aging remain elusive, due to the multifactorial nature of longevity and the interconnectedness of molecular components involved. Here we introduce a network medicine framework to map 2,358 longevity-associated genes onto the human interactome to identify drug-repurposing candidates capable of modulating specific hallmarks of aging. We find that genes associated with each hallmark form a connected subgraph, or hallmark module, allowing us to measure the network proximity of 6,442 compounds to each hallmark.
We then introduce a transcription-based metric, pAGE, which evaluates whether drug-induced expression shifts reinforce or counteract known age-related expression changes within each hallmark module. By integrating network proximity and pAGE, we identify drug-repurposing candidates targeting specific hallmarks and provide a falsifiable framework to leverage genomic discoveries for accelerating drug repurposing in longevity. Our findings are interpretable, revealing molecular mechanisms through which drugs modulate hallmarks.