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Insilico Medicine Identifies Therapeutic Targets for ALS With AI

Image Credit: Insilico Medicine

Drug discovery startup Insilico Medicine—alongside researchers from Harvard Medical School, Johns Hopkins School of Medicine, the Mayo Clinic, and others—used AI to identify…

Drug discovery startup Insilico Medicine—alongside researchers from Harvard Medical School, Johns Hopkins School of Medicine, the Mayo Clinic, and others—used AI to identify more than two dozen gene targets related to amyotrophic lateral sclerosis (ALS). The research findings, which included 17 high-confidence and 11 novel therapeutic targets, were recently published in Frontiers in Aging Neuroscience.

Using Insilico’s AI-driven target discovery engine, called PandaOmics, the researchers analyzed massive datasets to discover genes that new drugs could target to improve outcomes for ALS,  also known as Lou Gehrig’s disease. Today, patients typically face an average life expectancy of between two and five years after symptom onset. 

The research team used NVIDIA GPUs to train the deep learning models for target identification. The PandaOmics AI engine uses a combination of omics AI scores, text-based AI scores, financial scores, and more to rank gene targets. 

ALS is a debilitating disease. Patients rapidly lose voluntary muscle movement, affecting the ability to walk, talk, eat, and breathe. The five existing FDA-approved therapies for the disease are unable to halt or reverse this loss of function, which affects more than 700,000 people around the world. 

“The results of this collaborative research effort show what is possible when we bring together human expertise with AI tools to discover new targets for diseases where there is a high unmet need,” said Alex Zhavoronkov, founder and CEO of Insilico Medicine, in a press release. “This is only the beginning.”

Insilico Medicine is a Premier member of NVIDIA Inception, a global program designed to support cutting-edge startups with co-marketing, expertise, and technology. 

AI uncovers new paths to treat untreatable diseases

The research team used Quiver, a distributed graph learning library, to accelerate its AI models on multiple NVIDIA GPUs. They used natural language processing models including BioBERT, GPT, and OPT, as well as text recognition models including PaddleOCR and docTR. 

Figure 1. The PandaOmics AI platform analyzed ALS patient brain samples and other ALS data to identify new gene targets and existing drugs that could be repurposed to treat the disease.

To help identify the genes related to ALS, the researchers used public datasets as well as data from Answer ALS, a global project with clinical data consisting of 2.6 trillion data points from around 1,000 ALS patients. In a preclinical animal model, the team validated that 18 of the 28 identified gene targets were functionally correlated to ALS—and that in eight of them, suppression would strongly reduce neurodegeneration. 

The researchers are now working to advance some of these targets toward clinical trials for ALS. The targets will be shared on ALS.AI to help accelerate drug discovery.

Earlier this year, Insilico began a Phase 1 clinical trial for an AI-discovered, AI-designed drug to treat pulmonary fibrosis, another fast-progressing, hard-to-treat disease. 

Read more in Insilico Medicine’s press release and Frontiers in Aging Neuroscience article. 

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Acknowledgments

Featured image courtesy of Insilico Medicine.

Source:: NVIDIA