The Power of AI in Identifying Therapeutic Targets Involved in Aging

Insilico identifies therapeutic target implicated in aging by using AI and the hallmarks of aging Framework

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In Phase II of human clinical trials (including those that evaluate investigational anti-aging drug), where the efficacy is tested, a significant percentage of them fail. In part, this poor success can be attributed to the failure to choose an appropriate target and to determine which patients are most likely to respond to a specific agent. The biological age of patients makes this challenge even more difficult, since the therapeutic targets are different for each age group. Unfortunately, the majority of targets are not tested on older patients and instead are found in younger populations (the average age at phase I was 24). Finding potential targets that play a part in both age-associated diseases and the biology of aging may be beneficial.

Finding dual-purpose targets involved in both aging and diseases will prolong healthspan, and delay age-related issues. Even if the target was not the most significant in a particular patient, the drug could still be beneficial to that patient.

Zhavoronkov said that when it comes to identifying targets in chronic diseases it is important prioritizing targets implicated in age associated diseases, implicated more than one hallmarks of aging and are safe. \”The drug will treat the disease and ageing at the same time – this is an added bonus.\”

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Insilico identifies therapeutic targets implicated in aging using AI and hallmarks of aging framework