Insilico Medicine has started a Phase III clinical trial for Rentosertib, a drug for idiopathic pulmonary fibrosis whose target and molecular design were both identified and optimized using AI. The earlier Phase IIa trial showed the 60 mg daily dose improved lung function by a mean of 98.4 mL at 12 weeks. This trial of 320 patients will test whether the drug can meaningfully alter the course of a severe, progressive disease with limited current treatment options.
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Insilico Medicine initiated a Phase III clinical trial for Rentosertib, an oral small-molecule inhibitor targeting TNIK for idiopathic pulmonary fibrosis (IPF). The drug was discovered using Insilico's AI platforms—PandaOmics identified TNIK as a high-priority fibrosis target, and Chemistry42 designed the molecule. The Phase IIa trial showed manageable safety and tolerability, with the 60 mg once-daily arm demonstrating mean forced vital capacity improvement of +98.4 mL at 12 weeks.
Why it matters
IPF is a progressive lung-scarring disease with median survival commonly reported at approximately two to four years after diagnosis, and current approved antifibrotic therapies can only slow progression but do not reverse the disease. Rentosertib represents a potentially first-in-class medicine whose target was identified by AI, whose chemical structure was designed by generative AI, and whose development is aimed at this severe age-related disease with high unmet medical need. This marks a major late-stage milestone for AI-driven drug discovery moving from research into late-stage clinical validation.
What to watch
The Phase III trial is expected to enroll 320 patients with IPF and is designed to systematically evaluate efficacy and safety of once-daily Rentosertib administered over 52 weeks. The trial will be led by Professor Zuojun Xu of Peking Union Medical College Hospital as Leading Principal Investigator, with Academician Nanshan Zhong and President Chang Chen as Co-Leading Principal Investigators.
Rentosertib's path from discovery to late-stage clinical trial illustrates how AI-driven target identification and chemistry design can accelerate drug development for age-related diseases. Insilico used its PandaOmics platform to integrate multi-omics data from fibrotic tissues, biological network analysis, causal inference, and pathway analysis to rank TNIK as a top-priority fibrosis target. The company then applied Chemistry42 to generate and optimize the small molecule, embedding this workflow in a framework that connects hallmarks of aging—fibrosis, chronic inflammation, extracellular matrix remodeling, and cellular senescence—to disease mechanisms. Earlier work published in peer-reviewed journals (Nature Biotechnology for discovery-to-clinic rationale and Nature Medicine for Phase IIa results) provided the scientific foundation for this advancement.
IPF remains a severe clinical challenge with limited disease-modifying options: median survival after diagnosis is approximately two to four years, and existing antifibrotic drugs can only slow progression. The Phase IIa dose-dependent efficacy signal—most notably the +98.4 mL improvement in forced vital capacity at the 60 mg dose—has generated interest from research institutions globally. The Phase III trial, enrolling 320 patients over 52 weeks, will now test whether this early signal translates to clinically meaningful outcomes in a larger, longer-term setting under rigorous double-blind, placebo-controlled conditions led by specialists at leading Chinese respiratory medicine institutions.
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