AI identifies three new antiaging senolytic candidates

Integrated Biosciences’ new platform has potential to fuel advances in senotherapeutic compounds and longevity research.

New research by biotech Integrated Biosciences and scientists from MIT and the Broad Institute of MIT and Harvard has demonstrated the potential of AI in discovering novel senolytic compounds.

Longevity.Technology: Senolytics are small molecules that suppress age-related processes such as fibrosis, inflammation and cancer. They target senescent cells – the so-called ‘zombie’ cells that are no longer dividing, emit toxic chemicals and are a hallmark of aging. Senescent cells have been linked to various age-related diseases, including cancer, cardiovascular disease, diabetes and Alzheimer’s disease, but senolytic compounds can tackle them by selectively inducing apoptosis or programmed cell death in these zombie cells. This new research reduced the number of senescent cells and lowered the expression of senescence-associated genes in aged mice, results which, the authors say “underscore the promise of leveraging deep learning to discover senotherapeutics[1].

The AI-guided screening of more than 800,000 compounds led to the identification of three drug candidates, which, when compared with senolytics currently under investigation, were found to have comparable efficacy and superior medicinal chemistry properties [1].

The research result is important both for longevity research and the use of AI in drug discovery, according to the researchers.

“This research result is a significant milestone for both longevity research and the application of artificial intelligence to drug discovery,” said Felix Wong, PhD, co-founder of Integrated Biosciences and first author of the publication. “These data demonstrate that we can explore chemical space in silico and emerge with multiple candidate anti-aging compounds that are more likely to succeed in the clinic, compared to even the most promising examples of their kind being studied today [2].”

The AI platform developed by Integrated Biosciences is designed to overcome obstacles, target neglected hallmarks of aging, and advance antiaging drug development using synthetic biology and other next-generation tools.

“One of the most promising routes to treat age-related diseases is to identify therapeutic interventions that selectively remove these cells from the body similarly to how antibiotics kill bacteria without harming host cells. The compounds we discovered display high selectivity, as well as the favorable medicinal chemistry properties needed to yield a successful drug,” said Satotaka Omori, PhD, Head of Aging Biology at Integrated Biosciences and joint first author of the publication.

AI identifies three new antiaging senolytic candidates
Photograph: Senolytics are an emerging class of investigational drug compounds that selectively kill aging-associated senescent cells (left, with red stain) without affecting other cells (right). Using artificial intelligence, researchers from Integrated Biosciences have, for the first time, identified three senolytics with comparable efficacy and superior drug-like properties relative to leading investigational compounds
Credit: Integrated Biosciences

“We believe that the compounds discovered using our platform will have improved prospects in clinical trials and will eventually help restore health to aging individuals [2].”

The researchers used deep neural networks to predict the senolytic activity of any molecule, using experimentally generated data. The AI model discovered three highly selective and potent senolytic compounds from a chemical space of over 800,000 molecules; all three compounds had chemical properties suggestive of high oral bioavailability and were found to have favorable toxicity profiles in hemolysis and genotoxicity tests. Structural and biochemical analyses showed that all three compounds bind Bcl-2, a protein that regulates apoptosis and is also a chemotherapy target. In experiments testing one of the compounds in 80-week-old mice, roughly corresponding to 80-year-old humans, it cleared senescent cells and reduced expression of senescence-associated genes in the kidneys.

James J Collins, PhD, Termeer Professor of Medical Engineering and Science at MIT and founding chair of the Integrated Biosciences Scientific Advisory Board is a senior author on the Nature Aging paper; he led the team that discovered the first antibiotic identified by machine learning in 2020.

“This work illustrates how AI can be used to bring medicine a step closer to therapies that address aging, one of the fundamental challenges in biology,” said Collins.

“Integrated Biosciences is building on the basic research that my academic lab has done for the last decade or so, showing that we can target cellular stress responses using systems and synthetic biology. This experimental tour de force and the stellar platform that produced it make this work stand out in the field of drug discovery and will drive substantial progress in longevity research [2].”