Zuojun Xu, Feng Ren, Ping Wang, Jie Cao, Chunting Tan, Dedong Ma, Li Zhao, Jinghong Dai, Yipeng Ding, Haohui Fang, Huiping Li, Hong Liu, Fengming Luo, Ying Meng, Pinhua Pan, Pingchao Xiang, Zuke Xiao, Sujata Rao, Carol Satler, Sang Liu, Yuan Lv, Heng Zhao, Shan Chen, Hui Cui, Mikhail Korzinkin, David Gennert, Alex Zhavoronkov
{"title":"A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial","authors":"Zuojun Xu, Feng Ren, Ping Wang, Jie Cao, Chunting Tan, Dedong Ma, Li Zhao, Jinghong Dai, Yipeng Ding, Haohui Fang, Huiping Li, Hong Liu, Fengming Luo, Ying Meng, Pinhua Pan, Pingchao Xiang, Zuke Xiao, Sujata Rao, Carol Satler, Sang Liu, Yuan Lv, Heng Zhao, Shan Chen, Hui Cui, Mikhail Korzinkin, David Gennert, Alex Zhavoronkov","doi":"10.1038/s41591-025-03743-2","DOIUrl":null,"url":null,"abstract":"<p>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 idiopathic pulmonary fibrosis (IPF) discovered using generative AI. IPF is an age-related progressive lung condition with no current therapies available that reverse the degenerative course of disease. Patients were randomized to 12 weeks of treatment with 30 mg rentosertib once daily (QD, <i>n</i> = 18), 30 mg rentosertib twice daily (BID, <i>n</i> = 18), 60 mg rentosertib QD (<i>n</i> = 18) or placebo (<i>n</i> = 17). The primary endpoint was the percentage of patients who have at least one treatment-emergent adverse event, which was similar across all treatment arms (72.2% in patients receiving 30 mg rentosertib QD (<i>n</i> = 13/18), 83.3% for 30 mg rentosertib BID (<i>n</i> = 15/18), 83.3% for 60 mg rentosertib QD (<i>n</i> = 15/18) and 70.6% for placebo (<i>n</i> = 12/17)). Treatment-related serious adverse event rates were low and comparable across treatment groups, with the most common events leading to treatment discontinuation related to liver toxicity or diarrhea. Secondary endpoints included pharmacokinetic dynamics (<i>C</i><sub>max</sub>, <i>C</i><sub>trough</sub>, <i>t</i><sub>max</sub>, AUC<sub>0–<i>t</i>/<i>τ</i>/∞</sub> and <i>t</i><sub>1/2</sub>), changes in lung function as measured by forced vital capacity, diffusion capacity of the lung for carbon monoxide, forced expiry in 1 s and change in the Leicester Cough Questionnaire score, change in 6-min walk distance and the number and hospitalization duration of acute exacerbations of IPF. We observed increased forced vital capacity at the highest dosage with a mean change of +98.4 ml (95% confidence interval 10.9 to 185.9) for patients in the 60 mg rentosertib QD group, compared with −20.3 ml (95% confidence interval −116.1 to 75.6) for the placebo group. These results suggest that targeting TNIK with rentosertib is safe and well tolerated and warrants further investigation in larger-scale clinical trials of longer duration. ClinicalTrials.gov registration number: NCT05938920.</p>","PeriodicalId":19037,"journal":{"name":"Nature Medicine","volume":"135 1","pages":""},"PeriodicalIF":58.7000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41591-025-03743-2","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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 idiopathic pulmonary fibrosis (IPF) discovered using generative AI. IPF is an age-related progressive lung condition with no current therapies available that reverse the degenerative course of disease. Patients were randomized to 12 weeks of treatment with 30 mg rentosertib once daily (QD, n = 18), 30 mg rentosertib twice daily (BID, n = 18), 60 mg rentosertib QD (n = 18) or placebo (n = 17). The primary endpoint was the percentage of patients who have at least one treatment-emergent adverse event, which was similar across all treatment arms (72.2% in patients receiving 30 mg rentosertib QD (n = 13/18), 83.3% for 30 mg rentosertib BID (n = 15/18), 83.3% for 60 mg rentosertib QD (n = 15/18) and 70.6% for placebo (n = 12/17)). Treatment-related serious adverse event rates were low and comparable across treatment groups, with the most common events leading to treatment discontinuation related to liver toxicity or diarrhea. Secondary endpoints included pharmacokinetic dynamics (Cmax, Ctrough, tmax, AUC0–t/τ/∞ and t1/2), changes in lung function as measured by forced vital capacity, diffusion capacity of the lung for carbon monoxide, forced expiry in 1 s and change in the Leicester Cough Questionnaire score, change in 6-min walk distance and the number and hospitalization duration of acute exacerbations of IPF. We observed increased forced vital capacity at the highest dosage with a mean change of +98.4 ml (95% confidence interval 10.9 to 185.9) for patients in the 60 mg rentosertib QD group, compared with −20.3 ml (95% confidence interval −116.1 to 75.6) for the placebo group. These results suggest that targeting TNIK with rentosertib is safe and well tolerated and warrants further investigation in larger-scale clinical trials of longer duration. ClinicalTrials.gov registration number: NCT05938920.
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