On this same day one year ago, Insilico Medicine announced the nomination of a preclinical candidate
in Idiopathic Pulmonary Fibrosis (IPF) for a novel antifibrotic target, both discovered using our artificial intelligence platform Pharma.AI™
, in under 18 months. It was a precedent among any existing AI systems at that time to achieve simultaneous major success in target discovery and drug candidate generation for a broad indication, such as fibrosis, and a historical proof of concept for the ability of deep learning to link biology and chemistry in an integral workflow. Around 9 months later, after successful results in preclinical studies, the company initiated the first-in-human (FiH) study in healthy volunteers
to establish dose and basic safety for the discovered molecule. With the results of the FiH study having exceeded our expectations, today we announce the start of a Phase I clinical trial evaluating ISM001_055
, an anti-fibrotic small molecule inhibitor generated by our AI-powered drug discovery platform for the treatment of idiopathic pulmonary fibrosis.
The entire drug discovery path from the initial concept to novel target discovery and preclinical drug candidate nomination took Insilico Medicine a small fraction of cost and time for a typical preclinical program which is estimated to be around $430 million
out-of-pocket expenses and above $1 billion capitalized, and takes anywhere from three to six
years to finalize. The antifibrotic targets were picked using AI with two main criteria: the target must be an important regulator of pathways implicated in fibrosis and the target must be important in aging. The AI-powered target discovery tools now available in the PandaOmics ™platform
were used for target selection and prioritization. Once the antifibrotic targets were discovered and prioritized, Insilico utilized its Chemistry42™engine
. This extraordinary drug development sprint has proved to be possible owing to Insilico Medicine's end-to-end Artificial Intelligence-powered platform Pharma.AI
. The highly integrated architecture of the AI platform allowed for linking Biology and Chemistry into a smooth, well-orchestrated research workflow, resembling an industrial conveyor line.
To the best of our knowledge, this achievement sets a historical precedent for a company to be able to bring to clinical trials an AI-discovered molecule based on an AI-discovered novel target -- rapidly and at a low cost. A number of drug candidates for other indications were also designed using Pharma.AI
— for Insilico's own pipeline (e.g. a recent drug candidate nomination for kidney fibrosis
), and for the company's clients (e.g. a recent success story with immunotherapy drug candidate for Fosun Pharmaceuticals
) — illustrating the system's ability to replicate success and generalize to other therapeutic areas.