The Prototype: This AI Model Could Make It Faster To Find New Medicines
- On June 4, 2025, researchers published results of rentosertib, an AI-designed drug, from a 12-week phase 2a trial in China for idiopathic pulmonary fibrosis patients.
- The trial stemmed from AI-driven drug discovery aiming to find new treatments for IPF, a lung disease causing scarring and breathing difficulties.
- Seventy-One patients received varying doses or placebo, with the highest 60 mg daily dose improving lung capacity by +98.4 ml and showing a generally manageable safety profile.
- Insilico CEO Alex Zhavoronkov remarked that the findings represent a notably positive outcome for the lung disease, although the study’s authors pointed out limitations including the small sample size and brief follow-up period.
- If larger, longer studies confirm efficacy and safety, rentosertib could offer a novel treatment approach and demonstrate AI’s potential in accelerating drug development for IPF.
16 Articles
16 Articles
News Medical
MSNAI-designed drug shows early promise for lung fibrosis patients in clinical trial
Researchers report that rentosertib, an AI-discovered TNIK inhibitor, showed promising safety and potential to improve lung function in patients with idiopathic pulmonary fibrosis in a 12-week phase 2a clinical trial. The highest dose group demonstrated a trend toward increased forced vital capacity, especially among patients not receiving standard antifibrotic therapy, supporting further clinical investigation.
A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial
Despite substantial progress in artificial intelligence (AI) for generative chemistry, few novel AI-discovered or AI-designed drugs have reached human clinical trials. Here we present the results of the first phase 2a multicenter, double-blind, randomized, placebo-controlled trial testing the safety and efficacy of rentosertib (formerly ISM001-055), a first-in-class AI-generated small-molecule inhibitor of TNIK, a first-in-class target in idiopa…
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