Researchers from Integrated Biosciences, a biotechnology company combining synthetic biology and machine learning, have showcased the potential of artificial intelligence (AI) in discovering innovative senolytic compounds, which are being studied for their ability to combat age-related processes such as fibrosis, inflammation, and cancer. The recently published paper titled “Discovering small-molecule senolytics with deep neural networks” in the May issue of Nature Aging outlines the AI-guided screening of over 800,000 compounds, leading to the identification of three drug candidates with superior medicinal chemistry properties compared to existing senolytics.
Described as a significant milestone in both longevity research and AI-driven drug discovery, the study has received collaboration from researchers at the Massachusetts Institute of Technology (MIT) and the Broad Institute of MIT and Harvard. Dr. Felix Wong, co-founder of Integrated Biosciences and the publication’s first author, emphasized the importance of these findings, stating that the research demonstrates the potential to explore chemical space virtually and uncover multiple promising anti-aging compounds with higher chances of success in clinical trials than current options.
Senolytics are compounds that induce programmed cell death in senescent cells, which are no longer dividing. These cells play a key role in various age-related diseases and conditions, including cancer, diabetes, cardiovascular disease, and Alzheimer’s disease. However, the development of senolytic compounds has been hindered by limited bioavailability and unwanted side effects. Integrated Biosciences, established in 2022, aims to overcome these challenges, target neglected aging mechanisms, and advance anti-aging drug development using AI, synthetic biology, and other cutting-edge tools.
According to Dr. Satotaka Omori, Head of Aging Biology at Integrated Biosciences and joint first author of the publication, selectively removing senescent cells from the body, akin to how antibiotics eliminate bacteria without harming host cells, holds promise for treating age-related diseases. The newly discovered compounds exhibit high selectivity and favorable medicinal chemistry properties, raising their potential for successful drug development and eventual restoration of health in aging individuals.
In their study, Integrated Biosciences researchers trained deep neural networks using experimental data to predict the senolytic activity of any molecule. Leveraging this AI model, they identified three potent and highly selective senolytic compounds from a vast chemical space. Further analyses revealed that all three compounds interact with Bcl-2, a protein regulating apoptosis and a target for chemotherapy. Testing one of the compounds on 80-week-old mice, roughly equivalent to 80-year-old humans, demonstrated its ability to clear senescent cells and reduce the expression of senescence-associated genes in the kidneys.
Dr. James J. Collins, Termeer Professor of Medical Engineering and Science at MIT and founding chair of the Integrated Biosciences Scientific Advisory Board, praised the study’s impact, highlighting how AI brings medicine closer to addressing aging, a fundamental challenge in biology. Dr. Collins, senior author of the Nature Aging paper, also acknowledged the work as a significant advancement in longevity research, building upon the foundational research conducted by his academic lab over the past decade. The groundbreaking study and the exceptional platform behind it are expected to drive substantial progress in drug discovery and longevity research.
Overall, this research underscores the potential of AI in identifying novel senolytic compounds and advancing our understanding of age-related diseases, opening doors to innovative therapeutic interventions targeting aging processes.
By Impact Lab