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@ Frontier Tech
2023-07-30 13:35:56
A ground-breaking study by Integrated Biosciences, in collaboration with researchers from the Massachusetts Institute of Technology (MIT) and the Broad Institute of MIT and Harvard, has unveiled a promising approach to anti-aging drug discovery by employing artificial intelligence (AI). The research, detailed in the paper “Discovering small-molecule senolytics with deep neural networks,” involved the AI-guided screening of more than 800,000 compounds, resulting in the identification of three highly potent drug candidates with improved medicinal chemistry properties compared to existing senolytics under investigation[¹^].Feel free to read all my articles on the anti-censorship long content platform yakihonne.com.
“This research result 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[²^]. “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.”
Senolytics are compounds that selectively induce apoptosis (programmed cell death) in senescent cells, which are non-dividing cells that accumulate with age and contribute to a wide range of age-related diseases and conditions, such as cancer, diabetes, cardiovascular disease, and Alzheimer’s disease[³^]. Although some senolytic compounds have shown promising clinical results, most of them have been hindered by poor bioavailability and adverse side effects[⁴^]. Integrated Biosciences, founded in 2022, aims to tackle these obstacles and advance anti-aging drug development by leveraging artificial intelligence, synthetic biology, and other cutting-edge 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, Ph.D., Head of Aging Biology at Integrated Biosciences and joint first author of the publication[⁵^]. “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 this innovative 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 over 800,000 molecules[⁶^]. All three candidates exhibited chemical properties indicative of high oral bioavailability and favorable toxicity profiles in hemolysis and genotoxicity tests.
Structural and biochemical analyses revealed that all three compounds bind Bcl-2, a protein that regulates apoptosis and is also a chemotherapy target[⁷^]. Experiments testing one of the compounds in 80-week-old mice, roughly corresponding to 80-year-old humans, demonstrated that it cleared senescent cells and reduced the expression of senescence-associated genes in the kidneys[⁸^].
“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 James J. Collins, Ph.D., Termeer Professor of Medical Engineering and Science at MIT and founding chair of the Integrated Biosciences Scientific Advisory Board[⁹^]. Dr. Collins, who is senior author on the 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 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[¹⁰^].”
Conclusion
The discovery of novel senolytic compounds with the aid of AI-guided deep neural networks represents a potentially groundbreaking advancement in the field of longevity research. The three identified drug candidates display high selectivity and improved medicinal chemistry properties compared to existing senolytics under investigation, offering hope for more effective therapies that target the root causes of age-related diseases and conditions.
The application of artificial intelligence to drug discovery has the potential to revolutionize the way researchers explore chemical space and identify promising compounds. In this study, AI was able to sift through over 800,000 molecules and pinpoint three highly selective and potent senolytic compounds, which showed favorable toxicity profiles and high oral bioavailability. As AI continues to improve and evolve, its contributions to drug discovery and longevity research are likely to become even more significant.
This advancement highlights the potential of AI-guided drug discovery to address one of the most fundamental challenges in biology: aging. By developing therapies that selectively remove senescent cells from the body, researchers can target the underlying causes of age-related diseases and work towards restoring health to aging individuals. The success of Integrated Biosciences’ platform in identifying these promising drug candidates is a testament to the power of artificial intelligence, synthetic biology, and other next-generation tools in advancing our understanding of aging and driving progress in longevity research.
Glossary
- Senolytics: Compounds that selectively induce apoptosis (programmed cell death) in senescent cells, which are non-dividing cells that accumulate with age and contribute to various age-related diseases and conditions.
- Apoptosis: A form of programmed cell death that occurs in multicellular organisms, helping to maintain cellular homeostasis and eliminate damaged or unwanted cells.
- Senescent cells: Non-dividing cells that accumulate with age, leading to a decline in tissue function and contributing to age-related diseases and conditions.
- Bioavailability: The proportion of a drug or other substance that enters the circulation when introduced into the body, allowing it to have an active effect.
- Haemolysis: The rupture or destruction of red blood cells, which can lead to the release of haemoglobin into the surrounding fluid.
- Genotoxicity: The destructive effect of certain substances on a cell’s genetic material, potentially leading to mutations or cancer.
- Bcl-2: A protein that plays a key role in regulating apoptosis by inhibiting cell death and promoting cell survival.
- Deep neural networks: A type of artificial neural network with multiple layers between the input and output layers, allowing the network to learn complex patterns and representations from large amounts of data.
References
- Integrated Biosciences, Massachusetts Institute of Technology (MIT), and Broad Institute of MIT and Harvard. (2023). Discovering small-molecule senolytics with deep neural networks. Nature Aging.
- Wong, F. (2023). Integrated Biosciences Co-founder.
- López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013). The hallmarks of aging. Cell, 153(6), 1194–1217.
- Zhu, Y., Tchkonia, T., Pirtskhalava, T., Gower, A. C., Ding, H., Giorgadze, N., … & O’Hara, S. P. (2015). The Achilles’ heel of senescent cells: from transcriptome to senolytic drugs. Aging Cell, 14(4), 644–658.
- Omori, S. (2023). Head of Aging Biology at Integrated Biosciences.
- Integrated Biosciences. (2023). AI-guided Screening Results.
- Oltersdorf, T., Elmore, S. W., Shoemaker, A. R., Armstrong, R. C., Augeri, D. J., Belli, B. A., … & Phillips, D. C. (2005). An inhibitor of Bcl-2 family proteins induces regression of solid tumours. Nature, 435(7042), 677–681.
- Integrated Biosciences. (2023). Senolytic Compound Experiment Results.
- Collins, J. J. (2023). Termeer Professor of Medical Engineering and Science at MIT.
- Integrated Biosciences Scientific Advisory Board. (2023). Contributions to Drug Discovery and Longevity Research.
By：Tom Martin Link：https://medium.com/@thomasjmartin/ai-guided-discovery-of-novel-senolytics-a-leap-forward-in-longevity-research-60a150be4961