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AI first: novel drug target and novel molecule discovered

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One step closer to senescence moonshot: Insilico claims industry first by nominating preclinical candidate for IPF, with both novel drug target and novel molecule discovered by AI.

Insilico Medicine has claimed a breakthrough in AI and drug discovery. By linking together generative chemistry and biology together for the first time, they claimed to have discovered a novel preclinical candidate addressing idiopathic pulmonary fibrosis (IPF) and validated with multiple human cell and animal model experiments.

Longevity.Technology: Often found implicated in a wide range of diseases and multiple organs (lung, liver and kidney), IPF addresses a very broad medical need that affects hundreds of thousands of individuals worldwide.

To nominate a preclinical candidate, Insilico Medicine started with a set of 20 completely novel targets discovered by AI for fibrosis and narrowed down the target to specifically address IPF. Insilico then generated a set of novel compounds to selectively inhibit the novel target; the molecules had to be selective, bioavailable, metabolically stable, capable of oral administration, safe, and have many other properties of a good drug. The company also predicted high-probability of success of the phase 2 clinical trial outcome in IPF.

The molecules were first generated using Insilico’s Chemistry42 system using Structure-based Drug Design (SBDD) generative chemistry approach, tested in human cell and animal models. Then the molecules were re-designed using the Ligand-based Drug Design (LBDD) to optimise for additional properties and tested in human cells and animal models. After a review by a large team of internal and external veteran drug developers specialising in fibrosis, a preclinical candidate was nominated and IND-enabling experiments started.

Insilico
AI delivers a patentable small molecule preclinical candidate for a novel target with demonstrated efficacy in both human patient cells and animal models for Idiopathic Pulmonary Fibrosis (IPF) in <18 months. Source: Insilico Medicine

From target hypothesis to preclinical candidate selection, Insilico was able to complete target identification, molecule generation and validation through traditional laboratory experiments in less than 18 months and at a total cost of approximately $1,800,000 for IPF and $800,000 for other fibrotic disorders, with less than 80 small molecules synthesised and tested.

According to Insilico, the preclinical candidate is a first-in-class novel small molecule inhibitor of a novel biological target with unprecedented mechanism of action (MOA). It demonstrated experimentally good efficacy in both in vitro and in vivo preclinical studies for IPF, and a good safety profile. Currently progressing the candidate for IND-enabling studies and phase I clinical trials, Insilico is targeting clinical studies by early 2022 and welcomes collaborations with pharmaceutical companies to co-develop the drug candidate after phase II.

 


 

“…a major milestone for us as our ultimate moonshot is to go after senescence…”

 


 

Alex Zhavoronkov, PhD, founder and CEO of Insilico Medicine, said: “Can AI design a novel molecule for a novel target that has no known inhibitors and has not been validated in a disease? And now we have successfully linked both biology and chemistry and nominated the preclinical candidate for a novel target, with the intention of taking it into human clinical trials, which is orders of magnitude more complex and more risky problem to solve.

Elevant

Insilico
ISM001: Progress at a Glance Fibrosis Target ID, Compound Generation and Validation in <18 months. Source: Insilico Medicine

To my knowledge this is the first case where AI identified a novel target and designed a preclinical candidate for a very broad disease indication. It is a major milestone for us as our ultimate moonshot is to go after senescence and we need to have many enabling AI technologies that help us understand and manipulate human biology in other chronic diseases.”

Insilico
Potent target inhibition of ISM018_055. Source: Insilico Medicine

The company also announced the formation of a team of over 20 expert drug hunters and drug developers in Shanghai led by Dr Feng Ren, former senior VP of biology and chemistry at Medicilon, and former head of chemistry at GSK, who joined Insilico in February as chief science officer. This team will be responsible for taking the AI-discovered drugs into human clinical trials and creating a broad portfolio of preclinical assets.

 


 

“With today’s milestone of the first AI-discovered and scientifically validated PCC, Insilico has solved yet another of the biggest hurdles in drug discovery…”

 


 

Traditional drug discovery starts with the testing of thousands of small molecules, followed by further testing and synthesis of hundreds of molecules in order to get to just a few lead-like molecules appropriate for preclinical studies, of which only about one in ten of these molecules pass clinical trials in human patients. Incredibly slow and expensive, the overall process on average often totals over ten years of development and billions of dollars, with each of the process costing millions of dollars. Further compounding the hurdles in bringing a new drug to market are the massive number of R&D steps involved – each costing millions of dollars – often disconnected and conducted by different companies or different business units in the pharmaceutical ecosystem.

Insilico
Immunohistochemistry for α-SMA. Source: Insilico Medicine

Elevant

“Linking the right drug target to the right disease is the biggest challenge in pharmaceutical R&D,” said Zhavoronkov. “With today’s milestone of the first AI-discovered and scientifically validated PCC, Insilico has solved yet another of the biggest hurdles in drug discovery and removed another bottleneck in the staggeringly expensive and time-consuming traditional drug discovery process.”

Images courtesy of Insilico Medicine

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Eleanor Garth
Deputy Editor Now a science and medicine journalist, Eleanor worked as a consultant for university spin-out companies and provided research support at Imperial College London and various London hospitals in a former life.
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