PR96852

NEW YORK, July 8, 2022 /PRNewswire=KYODO JBN/ --

- 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.

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.

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