Insilico Medicine: Identifying Multiple Novel Therapeutic Targets for ALS AI-driven Target Discovery Engine

Insilico Medicine

Insilico Medicine is 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.

In the NEWS

Identifying Therapeutic Targets for ALS

Insilico Medicine has identified multiple unreported potential therapeutic targets for amyotrophic lateral sclerosis (ALS), using its proprietary Artificial Intelligence-driven target discovery engine, PandaOmics™. The research was in collaboration with Answer ALS, the largest and most comprehensive ALS research project in history.

The Company has been conducting research on ALS target discovery and drug repurposing with other interested parties using PandaOmics™ since 2016. This study further validates PandaOmics™ 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.

The findings were published in the June 28  issue of Frontiers in Aging Neuroscience.

Lou Gehrig’s Disease 

More than 700,000 people around the world live with ALS, also known as Lou Gehrig’s disease. ALS causes the loss of voluntary muscle movement, hence, the ability to walk, talk, eat and, eventually, breathe. ALS disease progresses rapidly in general.  The average life expectancy is 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 Study Leaders

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.

The team of researchers leveraged massive datasets to find genes relevant to disease, which could serve as potential targets for new therapeutics. PandaOmics™, 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.

The 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. 

From Insilico Medicine and Others 

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

Jeffrey D. Rothstein MD, PhD, Director, Robert Packard Center for ALS Research and Answer ALS, said, “We are truly excited to see the Answer ALS data being used to identify possible ALS disease-causing pathways and candidate drugs, The work by Insilico is exactly how this unprecedented program was envisioned to help change the course of ALS.”

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 said “It is exciting to see the power of AI to help understand ALS biology. 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.” 

Bai Lu, PhD, Professor at Tsinghua University and Founder of 4B Technologies, said, “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.  “We are very happy to be part of this international team, and very excited about the subsequent efforts to clinically validate these novel targets.”

Feng Ren, Ph.D., Co-CEO and CSO of Insilico Medicine, said, “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. 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™ to discover novel targets for other disease areas including oncology, immunology, and fibrosis.”

About PandaOmics

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. 

Prohost Observations

Working on treating severe life-threatening diseases that have yet to find treatments is the reason for being of the biotechnology world.

Our best feeling is not only hearing about but observing the efforts of biotech firms creating ways to stop these diseases from ending the lives of millions of people.

We decided to keep our eyes and mind on Insilico Medicine.

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