An end-to-end artificial intelligence (AI)-driven drug discovery company recently announced that the first healthy volunteer has been dosed in a first-in-human microdose trial of ISM001-055. ISM001-055 is a potentially first-in-class small molecule inhibitor of a novel biological target discovered by Pharma.AI, the company’s end-to-end AI-powered drug discovery platform. It is being developed for the treatment of idiopathic pulmonary fibrosis (IPF), a chronic lung disease that results in a progressive and irreversible decline in lung function.
After completing IND-enabling studies, the company has initiated the micro-dose trial to begin characterising the pharmacokinetic profile in humans. The trial, administering ISM001-055 intravenously in healthy volunteers, is being conducted in Australia.
ISM001-055 demonstrated highly promising results in multiple preclinical studies including in vitro biological studies, pharmacokinetic and safety studies. The compound significantly improved myofibroblast activation which contributes to the development of fibrosis. ISM001-055’s novel target is potentially relevant to a broad range of fibrotic indications.
The CSO of the company stated that they are pleased to see the first antifibrotic drug candidate entering the clinic. This is a significant milestone in the history of AI-powered drug discovery because, currently, the drug candidate is the first-ever AI-discovered novel molecule based on an AI-discovered novel target.
The company has leveraged their end-to-end AI-powered drug discovery platform, including the usage of generative biology and generative chemistry, to discover novel biological targets and generate novel molecules with drug-like properties. ISM001-055 is the first such compound to enter the clinic, and we expect more to come in the near future.
Previously, the company demonstrated its ability to generate drug-like hit molecules using AI with the publication of the Generative Tensorial Reinforcement Learning (GENTRL) system for a well-known target in record time. It also demonstrated the target’s proof of concept by applying deep learning techniques for the identification of novel biological targets.
This novel antifibrotic program combined these target discovery and generative chemistry capabilities. Notably, Insilico Medicine completed the entire discovery process from target discovery to preclinical candidate nomination within 18 months on a budget of $2.6 million.
The Founder and CEO of the company there are very few examples of a pharmaceutical company discovering a new target for a broad range of diseases, designing a novel molecule, and initiating human clinical trials. To his knowledge, nobody has achieved this with AI to date.
The failure rates in preclinical target discovery are very high and even after the targets are validated in animal models, over half of Phase 2 clinical trials fail primarily due to the choice of target. Target discovery is the fundamental grand challenge of the pharmaceutical industry. With ISM001-055 we used end-to-end AI connecting biology, chemistry to assess activity and safety in multiple preclinical models.
In September 2020, the company officially released a part of its Pharma.ai Artificial Intelligence (AI) platform designed to empower pharmaceutical target and drug discovery pipelines. Research biologists and clinicians can use Pandomics to perform OMICS data analytics and interpretation without requiring any prior knowledge of computational biology or bioinformatics. Additionally, drug target identification and biomarker development specialists can generate powerful hypotheses and assess repositioning strategies by harnessing the power of AI.
The company started working on an engine for target identification back in 2014. Since then, the technology has been validated through several successful partnering initiatives with pharmaceutical companies and research organisations as well as through the company’s own internal drug development programs.
Pandomics aims to be the go-to platform for all biologists and clinicians, working with various OMICS datasets, and to quickly analyse, interpret and visualise data effectively to classify patient cohorts more accurately.
