Novel Molecules from Generative AI to Phase II

There are thousands of diseases worldwide with no cure or available treatments. Traditional drug discovery and development takes decades and billions of dollars and more than 90% of these drugs fail in clinical trials. The emergence of artificial intelligence (AI) holds promise for streamlining and improving the entire process. However, ushering in a new era of AI-driven drug discovery requires costly and lengthy validation in preclinical cell, tissue, and animal models and human clinical trials.

Now, that preclinical and part of that clinical validation was published in a new study in Nature Biotechnology. In this paper, Insilico Medicine and collaborators present the journey of its lead therapeutic program with an AI-discovered target and novel molecule generated from AI algorithms to Phase II clinical trials. For the first time, the paper discloses the raw experimental data and the preclinical and clinical evaluation of the potentially first-in-class TNIK inhibitor discovered and designed through generative AI. The study underscores the benefits of AI-led drug discovery methodology to provide efficiency and speed to drug discovery and highlights the promising potential of generative AI technologies for transforming the industry.

"When our first paper in the generative AI for generation of novel molecules was published in 2016, followed by many follow-up papers, the drug discovery community was very skeptical. Even after several validation experiments and launch of our AI software platform that is now used by many biopharma companies, many questions remained. Based on the research data, especially those from the live clinical program. To date, I have not seen anything close from any other company in our field," said Alex Zhavoronkov, PhD, founder and CEO of Insilico Medicine. "From my perspective, the progress of INS018_055 has significant implications for the drug discovery field. It not only serves as a proof-of-concept for Pharma.AI, our end-to-end AI-driven drug discovery platform, but sets a precedent for the potential of generative AI to accelerate drug discovery. Using the publication as a guide, one can extrapolate how generative AI drug discovery tools may streamline early discovery efforts. We anticipate that the expanded application of this platform will address challenges facing industry R&D, including cost and efficiency, and accelerate the delivery of innovative therapies to patients."

Insilico initiated the research by focusing on fibrosis, a biological process closely associated with aging. The group first trained PandaOmics, the target identification engine of Insilico’s proprietary AI platform Pharma.AI, on the collection of omics and clinical datasets related to tissue fibrosis. Next, PandaOmics proposed a potential target list using deep feature synthesis, causality inference, and de novo pathway reconstruction. After that, the natural language processing (NLP) models of PandaOmics analyzed millions of text files, including patents, publications, grants, and clinical trial databases to further assess the novelty and disease association. TNIK was identified as the most promising anti-fibrosis target. Notably, TNIK had been indirectly linked to multiple fibrosis-driven pathways in previous research but was never pursued as a potential target for IPF. In a separate paper, Insilico scientists demonstrated that TNIK may be implicated in multiple hallmarks of aging.

After selecting TNIK as a primary target, Insilico scientists utilize Chemistry42, the Company's generative chemistry engine, to generate novel molecular structures with the desired properties using the structure-based drug design (SBDD) workflow. Chemistry42 combines over 40 generative chemistry algorithms and over 500 pre-trained reward models for de novo compound generation, and can optimize both generation and virtual screening based on expert human feedback. Following multiple iterative screens, one promising hit candidate demonstrated activity with nanomolar IC50 values. The group further optimized the compound to increase solubility, promote a good ADME safety profile, and mitigate unwanted toxicity while retaining its remarkable affinity for TNIK, which ultimately produced the lead molecule INS018_055, with less than 80 molecules synthesized and tested.

In subsequent preclinical studies, INS018_055 demonstrated significant efficacy in vitro and in vivo studies for IPF and showed promising results in pharmacokinetic and safety studies across multiple cell lines and multiple species. Furthermore, INS018_055 showed pan-fibrotic inhibitory function, attenuating skin and kidney fibrosis in two additional animal models. Based on these studies, INS018_055 achieved preclinical candidate nomination in February 2021, in less than 18 months following PandaOmics’ proposal of TNIK as a potentially novel target for IPF in 2019.

INS018_055 has exhibited excellent performance in clinical trials to date. In November 2021, 9 months after PCC nomination, the first healthy volunteers were dosed in a first-in-human (FIH) microdose trial of INS018_055 in Australia. This microdose trial exceeded expectations, delivering a favorable pharmacokinetic and safety profile that successfully demonstrated this clinical proof-of-concept and set the stage for the next step of clinical testing. In Phase I trials carried out in New Zealand and China, INS018_055 was tested in 78 and 48 healthy subjects, divided into cohorts focusing on a single ascending dose (SAD) study and multiple ascending dose (MAD) study. The international multi-site Phase I studies yielded consistent results, demonstrating favorable safety, tolerability, and pharmacokinetics (PK) profiles of INS018_055, and supporting the initiation of the Phase II studies.

"Combining AI methods with human intelligence, we have successfully nominated INS018_055, a potentially first-in-class antifibrotic inhibitor, with significant reductions in time and costs," said Feng Ren, PhD, co-CEO and Chief Scientific Officer of Insilico Medicine. "Encouraged by positive preclinical and available clinical data, we look forward to favorable performance of INS018_055 in Phase 2 clinical trials, which would provide innovative options for patients while bringing more solid evidence for the AI-driven drug discovery industry."

At the time of this publication, two Phase 2a clinical trials of INS018_055 for the treatment of IPF are being conducted in parallel in the United States and China. The studies are randomized, double-blind, placebo-controlled trials designed to evaluate the safety, tolerability and pharmacokinetics of the lead drug. In addition, the trials will assess the preliminary efficacy of INS018_055 on lung function in IPF patients. As this drug continues to advance, it drives hope for the roughly five million people worldwide suffering from this deadly disease.

Insilico's drug discovery efforts are driven by its validated and commercially viable AI drug discovery platform, Pharma.AI, which works across biology, chemistry, and clinical medicine, providing the biotechnology and the pharmaceutical industry with advanced generative AI tools to accelerate their internal research and development. Powered by Pharma.AI, Insilico is delivering breakthroughs for healthcare in multiple disease areas, including fibrosis, cancer, immunology and aging-related disease. Since 2021, Insilico has nominated 18 preclinical candidates in its comprehensive portfolio of over 30 assets and has advanced six pipelines to the clinical stage.

Ren F, Aliper A, Chen J, Zhao H, Rao S, Kuppe C, Ozerov IV, Zhang M, Witte K, Kruse C, Aladinskiy V, Ivanenkov Y, Polykovskiy D, Fu Y, Babin E, Qiao J, Liang X, Mou Z, Wang H, Pun FW, Ayuso PT, Veviorskiy A, Song D, Liu S, Zhang B, Naumov V, Ding X, Kukharenko A, Izumchenko E, Zhavoronkov A.
A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.
Nat Biotechnol. 2024 Mar 8. doi: 10.1038/s41587-024-02143-0

Most Popular Now

Transforming Drug Discovery with AI

A new AI-powered program will allow researchers to level up their drug discovery efforts. The program, called TopoFormer, was developed by an interdisciplinary team led by Guowei Wei, a Michigan...

We may Soon be Able to Detect Cancer wit…

A new paper in Biology Methods & Protocols, published by Oxford University Press, indicates that it may soon be possible for doctors to use artificial intelligence (AI) to detect and...

Maternity Tech Launched to Help NHS Meas…

Health tech provider C2-Ai has formally launched a new 'observatory' system to help hospitals gain a better understanding of risks, outcomes and safety within maternity and neonatal services. Announced at the...

Large Language Models Illuminate a Progr…

This study is led by Prof. Bin Dong (Beijing International Center for Mathematical Research, Peking University) and Prof. Lin Shen (Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational...

Health Innovation East Partners with Cog…

Health Innovation East, the innovation arm of the NHS in the East of England and Cogniss, a no-code ecosystem for digital health solutions, have announced a strategic partnership to launch...

An AI-Powered Wearable System Tracks the…

Scientists at the University of Southern California have developed an artificial intelligence (AI)-powered system to track tiny devices that monitor markers of disease in the gut. Devices using the novel...

"Self-Taught" AI Tool Helps to…

A computer program based on data from nearly a half-million tissue images and powered by artificial intelligence (AI) can accurately diagnose cases of adenocarcinoma, the most common form of lung...

New Computational Model of Real Neurons …

Nearly all the neural networks that power modern artificial intelligence (AI) tools such as ChatGPT are based on a 1960s-era computational model of a living neuron. A new model developed...

Meet CARMEN, a Robot that Helps People w…

Meet CARMEN, short for Cognitively Assistive Robot for Motivation and Neurorehabilitation - a small, tabletop robot designed to help people with mild cognitive impairment (MCI) learn skills to improve memory...

AI Matches Protein Interaction Partners

Proteins are the building blocks of life, involved in virtually every biological process. Understanding how proteins interact with each other is crucial for deciphering the complexities of cellular functions, and...

AI Model to Improve Patient Response to …

A new artificial intelligence (AI) tool that can help to select the most suitable treatment for cancer patients has been developed by researchers at The Australian National University (ANU). DeepPT, developed...

Mobile Phone Data Helps Track Pathogen S…

A new way to map the spread and evolution of pathogens, and their responses to vaccines and antibiotics, will provide key insights to help predict and prevent future outbreaks. The...