The power of artificial intelligence (AI) to find out novel senolytic compounds, a class of small molecules under intense study for their potential to suppress age-related processes like inflammation, fibrosis, and cancer has been illustrated.
This study has been published in the May issue of Nature Aging by scientists from Integrated Biosciences, a biotechnology company integrating synthetic biology and machine learning to target aging.
The study was authored in partnership with scientists from the Massachusetts Institute of Technology (MIT) and the Broad Institute of MIT and Harvard and explains the AI-guided screening of over 800,000 compounds to disclose three drug candidates with equivalent efficacy and excellent medicinal chemistry properties compared to those of senolytics under investigation at present.
This research result is a significant milestone for both longevity research and the application of artificial intelligence to drug discovery. 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.
Felix Wong, Ph.D., Study First Author and Co-Founder, Integrated Biosciences
Senolytics are known as compounds that optionally induce apoptosis, or programmed cell death, in senescent cells that are not dividing anymore. A hallmark of aging, senescent cells have been implicated in an extensive spectrum of age-related diseases and conditions such as diabetes, cancer, cardiovascular disease, and Alzheimer’s disease.
Despite hopeful clinical outcomes, the majority of the senolytic compounds determined so far have been hindered by detrimental side effects and poor bioavailability.
In 2022, Integrated Biosciences was found to defeat these obstacles, target other ignored hallmarks of aging, and progress anti-aging drug development more commonly with the help of synthetic biology, artificial intelligence, 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.
Satotaka Omori, Ph.D., Joint Study First Author and Head of Aging Biology, Integrated Biosciences
Omori added, “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.”
In their new study performed, scientists from Integrated Biosciences trained deep neural networks on data that was experimentally generated to forecast the senolytic activity of any molecule. With the help of this AI model, they found 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 known to consist of favorable toxicity profiles in genotoxicity and hemolysis tests. Biochemical and structural analyses denote that all three compounds bind Bcl-2, a protein that controls apoptosis and is also known as a chemotherapy target.
Experiments that test one of the compounds in 80-week-old mice, approximately corresponding to 80-year-old humans, discovered that it cleared senescent cells and decreased the expression of senescence-associated genes present in the kidneys.
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.
James J Collins, Ph.D., Termeer Professor, Medical Engineering and Science, Massachusetts Institute of Technology
Collins added, “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.
Collins continued, “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.”
Collins is the founding chair of the Integrated Biosciences Scientific Advisory Board.
Wong, F., et al. (2023) Discovering small-molecule senolytics with deep neural networks. Nature Aging. https://doi.org/10.1038/s43587-023-00415-z.