The generative artificial intelligence platform
Pharma.AI is at the center of ISM001-055’s history. The team at Insilico chose to point its multi-pronged Pharma.AI toolset at IPF because of the enormous unmet need for patients: current approved therapies for IPF do not reverse disease progression, only slow it, with patients typically dying 2-5 years after diagnosis, and the number of IPF patients continue to grow from today’s estimate of 30,000-40,000 new cases per year in the United States as the population ages.
Fibrotic disease is a broad indication that is estimated to be the cause of death in as much as 18% of all deaths globally. Fibrosis is the result of complicated biological processes that may involve many types of cells in many types of tissues throughout the body, and the ultimate cause is not always identifiable, such as in IPF. Identifying a disease-associated molecular target is an important step in tackling this poorly understood age-related disease and would enable development of a safe and effective treatment to resolve fibrosis at the cellular level.
Insilico first identified TNIK as a priority molecular target for treating IPF in 2019 using the Biology AI module of the Pharma.AI platform. The AI module integrated huge repositories of published data – “omics” data sets like gene expression and genome profiles, publication texts, patents, grants, and more – to identify genes most likely to be responsible for IPF pathology and disease resolution when treated with a targeted therapy.
With a target in sight, the Insilico team went to work developing a small-molecule inhibitor that could interrupt TNIK’s functions. Within a year, the Chemistry AI module of the Pharma.AI platform aided our medicinal chemists and biologists in designing, optimizing, and synthesizing ISM001-055. Our group rigorously tested ISM001-055’s safety, toxicity, and efficacy, resulting in its preclinical candidate nomination in February 2021, just 18 months following its initial identification by Pharma.AI. This rapid timeline is a testament to the power of generative AI in streamlining the drug discovery process.