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A new publication in the May issue of Nature Aging by researchers at Integrated Biosciences, a biotechnology company that combines synthetic biology and machine learning to counteract aging, demonstrates the power of artificial intelligence (AI) to discover new senolytic compounds, a class of small molecules under intense study for the their ability to suppress age-related processes such as fibrosis, inflammation and cancer.
The paper, “Discovering small-molecule sinolytics with deep neural networks,” co-authored with researchers at the Massachusetts Institute of Technology (MIT) and the Broad Institute of MIT and Harvard, describes the AI-guided screening of more than 800,000 compounds to reveal three drug candidates with comparable efficacy and medicinal chemical properties superior to those of senolytics currently under investigation.
“This research achievement is a significant milestone for both longevity research and the application of artificial intelligence to drug discovery,” said Felix Wong, Ph.D., co-founder of Integrated Biosciences and first author of the publication. “These data demonstrate that we can explore the in silico chemical space and emerge with more candidate anti-aging compounds that are more likely to be successful in the clinic, than even the most promising examples of their kind studied today.”
Senolytics are compounds that selectively induce apoptosis, or programmed cell death, in senescent cells that no longer divide. A hallmark of aging, senescent cells have been implicated in a broad spectrum of age-related diseases and conditions including cancer, diabetes, cardiovascular disease and Alzheimer’s disease. Despite promising clinical results, most of the senolytic compounds identified to date have been hampered by poor bioavailability and adverse side effects. Integrated Biosciences was founded in 2022 to overcome these barriers, target other overlooked hallmarks of aging, and advance antiaging drug development more broadly using artificial intelligence, synthetic biology, and other next-generation tools.
‘One of the most promising avenues for treating age-related diseases is to identify therapeutic interventions that selectively remove these cells from the body similar to how antibiotics kill bacteria without harming host cells. The compounds we discovered show a high selectivity, as well as the favorable medicinal chemical properties required to produce a successful drug,” said Satotaka Omori, Ph.D., head of aging biology at Integrated Biosciences and joint first author of the publication. “We believe compounds discovered using our platform will have better prospects in clinical trials and ultimately help restore the health of aging people.”
In their new study, Integrated Biosciences researchers trained deep neural networks on experimentally generated data to predict the senolytic activity of any molecule. Using this AI model, they discovered three highly selective and potent senolytic compounds from a chemical space of more than 800,000 molecules. All three exhibited chemical properties indicative of high oral bioavailability and were found to have favorable toxicity profiles in hemolysis and genotoxicity tests.
Structural and biochemical analyzes indicate that all three compounds bind Bcl-2, a protein that regulates apoptosis and is also a target of chemotherapy. Experiments testing one of the compounds in 80-week-old mice, roughly corresponding to 80-year-old humans, found that it cleared senescent cells and reduced the expression of senescence-associated genes in the kidneys.
“This work illustrates how artificial intelligence can be used to take medicine one step closer to therapies that address aging, one of the fundamental challenges in biology,” said James J. Collins, Ph.D., professor of medical engineering and science at MIT. and founding chair of the Integrated Biosciences Scientific Advisory Board. Dr. Collins, who is senior author of Nature Aging paper, led the team that discovered the first antibiotic identified by machine learning in 2020.
“Integrated Biosciences is building on the basic research my academic lab has conducted over 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 has product make this work stand out in the field of drug discovery and will drive substantial advances in longevity research.”
Felix Wong et al, Discovering Small Molecule Senolytics with Deep Neural Networks, Nature Aging (2023). DOI: 10.1038/s43587-023-00415-z
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