Insilico Medicine Nominates Potential First-in-Class Preclinical Candidate with Novel AI-Designed Structure for Novel AI-Discovered Target in Immuno-oncology

New York, Dec. 20, 2022 (GLOBE NEWSWIRE) -- Insilico Medicine (“Insilico”), a clinical-stage end-to-end artificial intelligence (AI)-driven drug discovery company, today announced that the company has nominated ISM4312A as a preclinical candidate targeting DGKA with an AI-identified target and AI-designed structure for immuno-oncology therapeutics. It is another program fully discovered and designed by AI that Insilico has delivered from target identification to preclinical candidate nomination by leveraging its proprietary end-to-end AI platform, Pharma.AI.

Immune checkpoint blockade has proven effective in a variety of malignancies, but the response rate of existing therapeutics remains low. The diacylglycerol kinase family (DGKs) is an important player in signal transduction, phosphorylating the membrane lipid, diacylglycerol (DAG), to phosphatidic acid (PA). Research indicates that DGKA mediates T-cell dysfunction during anti-PD-1 therapy, playing a role in the development of resistance to PD-1 blockade.

“We are committed to providing novel strategies to improve therapeutic efficacy in immuno-oncology as well as addressing unmet medical needs in the world,” said Feng Ren, PhD, Co-CEO and Chief Scientific Officer of Insilico Medicine.

Powered by PandaOmics, the AI-driven target discovery engine of the Pharma.AI platform, scientists designed a project that included bulk RNA sequencing data from 4 datasets alongside 2 single-cell RNA-Seq datasets to identify novel targets for improving therapeutic efficacy. The results show that DGKA inhibitors may overcome anti-PD-1 resistance and expand the responder patient population in cancer immunotherapy. After designing and evaluating a series of compounds in 15 months, scientists nominated ISM4312A as a preclinical candidate and initiated the IND-enabling studies for the program. 

The preclinical candidate ISM4312A is a potential first-in-class DGKA inhibitor with excellent potency and high selectivity in cancer immunotherapy. The compound enhanced T-cell activity in vitro and showed robust anti-tumor activities with or without anti-PD-1 therapy in vivo. It also exhibited favorable ADME, excellent oral bioavailability, and tolerance. The deeper investigation of the biomarker and combination mechanism is ongoing and Insilico will use the findings to advance a new potential therapeutic strategy in immuno-oncology.

"This is the 7th preclinical candidate [PCC] delivered using AI in 2022 alone, bringing the total number of AI-enabled PCCs the company has nominated since 2021 to 9, further validating the strength of our end-to-end Pharma.AI platform," said Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine. "Pharma.AI is now used by many pharmaceutical companies to accelerate their pharmaceutical R&D cycles and increase the probabilities of success."

Insilico has rapidly developed its portfolio in a variety of disease areas, including fibrosis, inflammation, and cancer. Since 2021, Insilico has nominated 9 preclinical candidates discovered and designed using its AI platform. It also successfully completed a Phase 0 microdose trial and entered a Phase I clinical trial with its first internally developed program for fibrosis.

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 for novel target discovery and the generation of novel molecular structures with desired properties. Insilico Medicine is developing breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system diseases, infectious diseases, autoimmune diseases, and aging-related diseases. 




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