– Insilico and its collaborators scoured massive datasets and found genes relevant to ALS through PandaOmics(TM), Insilico’s proprietary AI-driven target identification engine.
– 28 targets were identified from CNS and diMN samples; for 18 targets (64%), suppression moderately or strongly rescued neurodegeneration.
– The collaborative study was led by Insilico with support from Answer ALS and researchers at Johns Hopkins University School of Medicine, Massachusetts General Hospital and Harvard Medical School, Mayo Clinic, University of Zurich, 4B Technologies, Limited, Tsinghua University, and the Buck Center for Aging Research.
Insilico Medicine, a clinical-stage end-to-end artificial intelligence (AI)-driven drug discovery company, announced today that the company has identified multiple unreported potential therapeutic targets for amyotrophic lateral sclerosis (ALS), using its proprietary AI-driven target discovery engine, PandaOmics(TM). The research was in collaboration with Answer ALS, the largest and most comprehensive ALS research project in history. The findings were published in the June 28 issue of Frontiers in Aging Neuroscience.
Globally, more than 700,000 people live with ALS, also known as Lou Gehrig’s disease. People with ALS lose voluntary muscle movement and therefore the ability to walk, talk, eat and, eventually, breathe. Progression of ALS disease is generally rapid, with patients facing an average life expectancy of between two and five years from the onset of symptoms. Unfortunately, existing approved drugs for ALS do not halt or reverse the loss of function.
The team of researchers leveraged massive datasets to find genes relevant to disease, which could serve as potential targets for new therapeutics. PandaOmics(TM), Insilico’s proprietary AI-driven target discovery engine, helped analyze the expression profiles of central nervous system (CNS) samples from public datasets, and direct iPSC-derived motor neurons (diMN) from Answer ALS.
As a result of the study, 17 high-confidence and 11 novel therapeutic targets were identified from CNS and diMN samples. These targets were further validated in c9ALS Drosophila model, mimicking the most common genetic cause of ALS, of which 18 targets (64%) have been validated to have functional correlations to ALS. Notably, eight unreported genes, including KCNB2, KCNS3, ADRA2B, NR3C1, P2RY14, PPP3CB, PTPRC, and RARA, rescue neurodegeneration through their suppression strongly. All the potential therapeutic targets were disclosed in the paper and at ALS.AI. The paper is available here: DOI:10.3389/fnagi.2022.914017
“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, PhD, Founder and CEO of Insilico Medicine. “This is only the beginning.”
The study was led by Frank Pun, Ph.D., head of Insilico’s Greater Bay Area team. Other co-authors from Insilico include Dr. Zhavoronkov, Feng Ren, Ph.D., co-CEO and Chief Scientific Officer, and Ju Wang, Ph.D., head of biology. Researchers from Mayo Clinic, University of Zurich, Tsinghua University, 4B Technologies, Johns Hopkins School of Medicine, Harvard Medical School and Buck Institute for Research on Aging also contributed to this study.
“We are truly excited to see the Answer ALS data being used to identify possible ALS disease-causing pathways and candidate drugs,” said Jeffrey D. Rothstein MD, PhD, Director, Robert Packard Center for ALS Research and Answer ALS. “The work by Insilico is exactly how this unprecedented program was envisioned to help change the course of ALS.”
“It is exciting to see the power of AI to help understand ALS biology,” said Merit Cudkowicz, MD, Chief of Neurology and Director of the Healey & AMG Center for ALS at Mass General Hospital and Harvard Medical School and corresponding author. “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 to turn this knowledge into new targets for treatments for people living with ALS.”
“From AI-powered target discovery based on massive datasets, to biological validation by multiple model systems (fly, mouse, human iPS cells), to rapid clinical testing through investigator-initiated trials (IIT), the represents a new trend that may dramatically reduce the costs and duration and more importantly the success rate of developing medicines, especially for neurodegenerative diseases” said Bai Lu, PhD, Professor at Tsinghua University and Founder of 4B Technologies. “We are very happy to be part of this international team, and very excited about the subsequent efforts to clinically validate these novel targets.”
“This demonstrates the power of our biology AI platform, PandaOmics, in target discovery. It is impressive that around 70% (18 out of 28) targets identified by AI were validated in a preclinical animal model,” said Feng Ren, Ph.D., Co-CEO and CSO of Insilico Medicine. “We are working with collaborators to progress some targets toward clinical trials for ALS. At the same time, we are also further expanding the utilization of PandaOmics(TM) to discover novel targets for other disease areas including oncology, immunology, and fibrosis.”
Insilico Medicine has been conducting research on ALS target discovery and drug repurposing with other interested parties using PandaOmics(TM) since 2016. This study further validates PandaOmics(TM) as an AI tool capable of identifying therapeutic targets with potential roles on ALS neurodegeneration, and to create new avenues for drug discovery and a better understanding of this rare and fatal neuromuscular disease.
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, financial 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 diseases and aging-related diseases.
Source: Insilico Medicine