AI finds potential drug targets for amyotrophic lateral sclerosis

Scientists have identified several potential therapeutic targets for amyotrophic lateral sclerosis (ALS) using an artificial intelligence (AI)-powered target discovery engine to analyze dysregulated gene expression in large, publicly available ALS datasets.

The researchers used a proprietary AI platform called PandaOmics from Insilico Medicine, an end-to-end AI-driven clinical-stage drug discovery company. PandaOmics uses advanced deep learning models to predict target genes associated with a particular disease by combining omics and text-based AI scores, financial scores and key opinion leader scores. The algorithm can prioritize protein targets for novelty, trust, commercial traceability, drug use, safety, and other attributes that drive target selection decisions.

Alex Zhavoronkov, PhD, founder and CEO of Insilico Medicine, is a senior author of the study.

The findings were published in an article in the journal Frontiers in aging neuroscience titled “Identification of therapeutic targets for amyotrophic lateral sclerosis using PandaOmics – an AI-activated biological target detection platform,” On July 7, 2022. Merit Cudkowicz, MD, chief of neurology and director of the Healey & AMG Center for ALS at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS), and Alex Zhavoronkov, PhD, founder and CEO of Insilico Medicine , are the corresponding authors, and Frank Pun, PhD, head of Insilico’s Greater Bay Area team is the lead author of the study.

“Our study provides new insights into the pathophysiology of ALS and demonstrates how AI speeds up the target discovery process and opens up new possibilities for therapeutic interventions,” the authors noted.

ALS, also known as Lou Gehrig’s disease, is a serious progressive neurodegenerative disease that affects more than 700,000 people worldwide. Patients lose muscle movements that rapidly impair their ability to walk, talk, eat and eventually breathe. Life expectancy ranges from two to five years from the onset of symptoms. Currently available drugs for ALS cannot reverse or stop this progressive loss of function, requiring the urgent development of new targeted therapies.

In the current study, the international team analyzed gene expression profiles in central nervous system samples from 237 patients and 91 controls from public datasets, and motor neurons derived directly from induced pluripotent stem cells from 135 patients and 31 controls, from Answer ALS, the largest and most widely used. extensive ALS research project to date.

The researchers identified 17 highly reliable and 11 novel therapeutic targets. They validated 18 of these targets in an established fruit fly model of ALS (c9ALS drosophile) to demonstrate functional correlations with ALS. The researchers also conducted rescue experiments and showed that suppression of eight new targetsKCNB2KCNS3ADRA2BNR3C1P2RY14PPP3CBPTPRCand RARA) rescued neurodegeneration of the eye and restored a normal phenotype. The team disclosed all potential therapeutic targets at: AS.AI

“The results of this collaborative research effort show what is possible when we bring human expertise together with AI tools to discover new targets for diseases that have a high unmet need,” said Zhavoronkov. “This is just the beginning.”

Merit Cudkowicz, MD, chief of neurology and director of the Healey & AMG Center for ALS at Massachusetts General Hospital and Harvard Medical School is a senior author of the study.

“It’s exciting to see the power of AI to help understand the biology of ALS,” Cudkowicz said. “Through Sean Healey and his friends, I was introduced to Dr. Zhavoronkov and the Insilico team. We immediately saw the potential to connect the Insilico team with the multidisciplinary Answer ALS team. We look forward to the next steps in turning this knowledge into new treatment targets for people with ALS.”

“From AI-powered target discovery based on massive data sets, to biological validation through multiple model systems (fly, mouse, human iPS cells), to rapid clinical testing through investigator-initiated trials, the study represents a new trend that will reduce costs and expensive and, more importantly, the success rate of drug development, especially for neurodegenerative diseases,” said Bai Lu, PhD, professor at Tsinghua University and founder of 4B Technologies.

“This demonstrates the power of our biology AI platform, PandaOmics, in target discovery. It is impressive that approximately 70% (18 out of 28) targets identified by AI were validated in a preclinical animal model,” said Feng Ren, PhD, Co-CEO and CSO of Insilico Medicine. “We are working with collaborators to achieve a number of goals toward clinical trials for ALS. At the same time, we are also expanding the use of PandaOmics to discover new targets for other areas of disease, including oncology, immunology and fibrosis.”

“We are really excited to see the Answer ALS data being used to identify potential ALS disease-causing pathways and drug candidates,” said Jeffrey Rothstein MD, PhD, director of the Robert Packard Center for ALS Research and Answer ALS. “Insilico’s work is exactly how this unprecedented program was designed to help change the course of ALS.”

The study was the result of a collaboration between scientists from Insilico, Johns Hopkins University School of Medicine, MGH and HMS, Mayo Clinic, University of Zurich, 4B Technologies Limited, Tsinghua University and the Buck Center for Aging Research with support from Answer ALS. In future studies, the team plans to further define the pathogenic role and therapeutic potential of the ALS targets identified in this study using motor neurons derived directly from induced pluripotent stem cells and mammalian models.

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