Insilico Medicine, which uses AI for drug discovery, has identified potential dual-purpose therapeutic targets that are implicated in aging and age-associated diseases using artificial intelligence and a Hallmarks of Aging framework. The full press release is included here.
March 29, 2022, New York (11AM ET) — Insilico Medicine, a clinical stage end-to-end artificial intelligence (AI)-driven drug discovery company, today announced that it has successfully established a unique approach to identify potential dual-purpose targets for therapeutics of aging and age-associated diseases with PandaOmics, its proprietary AI-enabled biological target discovery platform. The research was published on Aging.
People worldwide are living longer. According to WHO, one in six people in the world will be aged 60 years or over by 2030. However, aging increases vulnerability to a wide range of human disorders, including cancers, diabetes, cardiovascular diseases, and neurodegenerative diseases. Roughly two-thirds of 150,000 people who die each day globally suffer from age-associated diseases.
Recent aging research suggests that targeting the aging process itself could ameliorate many age-related pathologies. The research proposed by Insilico Medicine’s scientists aims to utilize AI to identify potential targets that are implicated in multiple age-associated diseases and also play a role in the basic biology of aging, which may have substantial benefits for the discovery and development of therapeutics for both aging and age-associated diseases.
Insilico Medicine deployed PandaOmics to perform target identification for 14 age-associated diseases (AADs) and 19 non-age-associated diseases (NAADs) across multiple disease areas to identify targets of age-associated diseases targets. Upon the comprehensive assessment, 145 genes were considered as potential aging-related targets and mapped into corresponding aging hallmark(s), including 69 high confidence targets with high druggability, 48 medium novel targets with high or medium druggability, and 28 highly novel targets with medium druggability.
“Developing interventions that target multiple age-associated diseases and aging itself could result in unprecedented health benefits by not only treating disease but also extending healthspan and providing for more fresh drug repurposing candidates,” said Alex Zhavoronkov, PhD, CEO of Insilico Medicine. “The current study also demonstrated the power of PandaOmics AI-powered target discovery platform to identify novel dual-purpose targets not only for specific disorders but across multiple types of diseases in a cost-saving and time-efficient manner.”
A list of potential therapeutic dual-purpose aging targets for drug discovery was disclosed in the paper.
PandaOmics is an AI-enabled biological target discovery platform. It utilizes advanced deep learning models and AI approaches to predict the target genes associated with a given disease through a combination of Omics AI scores, Text-based AI scores, Finance scores, and Key opinion leader (KOL) scores, and is currently being employed in both academic and industry settings. The algorithm also allows the prioritization of protein targets for novelty, confidence, commercial tractability, druggability, safety, and other key properties that drive target selection decisions.
About Insilico Medicine
Insilico Medicine, a clinical stage end-to-end artificial intelligence (AI)-driven drug discovery company, is connecting biology, chemistry, and clinical trials analysis using next-generation AI systems. The company has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques to discover novel targets and to design novel molecular structures with desired properties. Insilico Medicine is delivering breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system (CNS) diseases and aging-related diseases.
For more information, visit www.insilico.com.
For media inquiry, please contact [email protected].