Idiopathic Pulmonary Fibrosis (IPF) and renal fibrosis are complex diseases that have challenged pharmaceutical development, as they lack efficient treatment methods. Current potential drug targets, such as TGF-β signaling pathways, have not led to viable therapies for actual use. As a result, IPF, characterized by fibroblast proliferation and extracellular matrix deposition, continues to be particularly fatal with limited treatment options like nintedanib and pirfenidone. Renal fibrosis, associated with chronic kidney disease, also lacks specific inhibitors despite its growing global prevalence.
In response to these unfulfilled clinical needs, researchers from multiple institutions, including Insilico Medicine, have harnessed AI to identify TNIK as a promising target for combating fibrosis. Through this AI-driven drug discovery process, the researchers developed INS018_055, a TNIK inhibitor, within 18 months. This compound has showed not only promising anti-fibrotic effects across various organs but also positive anti-inflammatory effects, alongside favorable drug properties. Its safety, tolerability, and pharmacokinetics have been confirmed in Phase I clinical trials involving healthy individuals.
The research study utilized a range of techniques including overexpression, knockouts, and mutations alongside the use of matrix factorization and machine learning models to optimize compounds. The study processed data, adhering strictly to HIPAA regulations and the Declaration of Helsinki, with tissues obtained through informed, written consent.
Utilizing PandaOmics, an AI-driven platform, integrated multiomics datasets, biological network analysis, and text data enabled the discovery of anti-fibrotic targets. The platform identified TNIK as a leading candidate, revealing its pivotal role in crucial fibrosis-related processes and its tight connection with IPF-associated genes.
In conclusion, this study highlights the potential of AI in advancing medical research and drug development. By identifying TNIK as a promising anti-fibrotic target using AI, the researchers have demonstrated how AI can provide swift and effective solutions to complex diseases like IPF and renal fibrosis. The small-molecule inhibitor INS018_055 developed in the study has effectively mitigated fibrosis in lung, kidney, and skin models and notably improved lung function in murine lung fibrosis. With preliminary validation and phase I trials confirming safety and tolerability, the trials are now moving into phase II. This innovative approach could potentially lead to effective treatments for not only IPF and renal fibrosis, but also COVID-19-related complications and chronic kidney disease. It is a promising step forward for leveraging AI for innovations in healthcare and pharmacology.
