San Carlos, Calif. – 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 target aging, reveals that artificial intelligence (AI) is the new face of aging. It demonstrates the power to discover cell-degrading compounds. A class of small molecules that have been intensively studied for their ability to inhibit age-related processes such as fibrosis, inflammation, and cancer. In his paper, “Discovering small-molecule senolytics with deep neuron network,” co-authored with researchers at the Massachusetts Institute of Technology (MIT) and his MIT and Harvard Broad Institute, he described his AI-guided screening of over 800,000 compounds. I’m explaining. We reveal three of his drug candidates with similar potency and superior medicinal chemistry properties to the current investigational senolytics.
“This study result is an important milestone for both longevity research and the application of artificial intelligence to drug discovery,” said Felix Wong, PhD, co-founder of Integrated Biosciences and lead author of the publication. I’m here. “These data show that the chemical space can be explored in silico and compared to the most promising examples being studied today, revealing multiple candidate anti-aging compounds that are more likely to be successful in the clinic. I have.”
Senolytics are compounds that selectively induce apoptosis or programmed cell death in senescent cells that are no longer dividing. A hallmark of aging, senescent cells are implicated in a wide range of age-related diseases and conditions, including cancer, diabetes, cardiovascular disease, and Alzheimer’s disease. Despite promising clinical results, most senolytic compounds identified to date have been hampered by poor bioavailability and adverse side effects. Integrated Biosciences, founded in 2022, aims to overcome these obstacles, target other overlooked hallmarks of aging, and use artificial intelligence, synthetic biology, and other next-generation tools to make it more popular. We will advance the development of anti-aging drugs.
“One of the most promising ways to treat 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 have discovered exhibit the high selectivity and favorable medicinal chemistry properties necessary to create successful drugs,” said Head of Aging Biology at Integrated Biosciences, who commented on this publication. Toshitaka Omori, Ph.D., co-first author of the book, said: “We believe compounds discovered using our platform will improve prospects for clinical trials and ultimately help restore health to older adults.”
In a new study, researchers at Integrated Biosciences trained a deep neural network based 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 her chemical space of over 800,000 molecules. All three were found to exhibit chemical properties suggestive of high oral bioavailability and to have favorable toxicity profiles in hemolytic and genotoxicity studies. Structural and biochemical analyzes indicate that all three compounds bind to Bcl-2, a protein that regulates apoptosis and is also a chemotherapeutic target. Experiments testing one of the compounds in 80-week-old mice, roughly equivalent to 80-year-old humans, found that it eliminated senescent cells and reduced the expression of senescence-related genes in the kidneys.
“This study shows how AI can be used to bring medicine one step closer to therapeutics that address one of the fundamental challenges in biology: aging.” Integrated Biosciences Scientific Advisory Board is also the founder of “Integrated Biosciences builds on the fundamental research my academic lab has been doing over the past decade or so, showing that systems and synthetic biology can be used to target cellular stress responses. This experimental feat, and the excellent platform from which it was generated, will make this work stand out in the field of drug discovery and facilitate substantial advances in longevity research.”
Senior author of the Nature Aging paper, Dr. Collins led the team that discovered the first antibiotic identified by machine learning in 2020.
