George Sutphin, PhD
University of Arizona
Competition Sponsor: US National Academy of Medicine
We are on the precipice of real anti-aging medicine. Over the past several decades, research into the biology of aging has identified nearly a thousand genetic changes and hundreds of drugs capable of extending lifespan in eukaryotic organisms. The discovery of new pro-longevity drugs is growing at an unprecedented rate. The first clinical trial designed to evaluate the anti-aging potential of a drug—Targeting Aging with Metformin (TAME)—was approved in 2019. The biology of aging is complex, encompassing the progressive deterioration of various molecular systems across cell and tissue types. No single intervention is likely to simultaneously correct the full range of these changes, which is reflected by the fact that only a few interventions extend average lifespan beyond 20-30%. The most impressive longevity enhancement achieved results from targeted combination of two genetic interventions, resulting in lifespan extension in excess of 400% in the roundworm Caenorhabditis elegans. This and other studies suggest that substantial synergy may be achieved through combined therapy. To date, no systematic effort has been made to assess interactions between pro-longevity compounds, largely because standard methods for measuring lifespan are costly in terms of labor, resources, and time. Recent advances in automated data collection and analysis using robotic tools and machine learning have substantially reduced these barriers in invertebrate models of aging. Here we propose a proof-of-concept study using one of these systems to systematically screen drug combinations to identify beneficial additive and synergistic interactions in the context of healthy C. elegans longevity.