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一种用于特发性肺纤维化的生成式人工智能发现的TNIK抑制剂:一项随机2a期试验。

A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial.

作者信息

Xu Zuojun, Ren Feng, Wang Ping, Cao Jie, Tan Chunting, Ma Dedong, Zhao Li, Dai Jinghong, Ding Yipeng, Fang Haohui, Li Huiping, Liu Hong, Luo Fengming, Meng Ying, Pan Pinhua, Xiang Pingchao, Xiao Zuke, Rao Sujata, Satler Carol, Liu Sang, Lv Yuan, Zhao Heng, Chen Shan, Cui Hui, Korzinkin Mikhail, Gennert David, Zhavoronkov Alex

机构信息

Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Beijing, China.

Insilico Medicine Shanghai, Shanghai, China.

出版信息

Nat Med. 2025 Jun 3. doi: 10.1038/s41591-025-03743-2.

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 (C, C, t, AUC and t), 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 .

摘要

尽管生成化学领域的人工智能(AI)取得了重大进展,但很少有新的由AI发现或设计的药物进入人体临床试验阶段。在此,我们展示了第一项2a期多中心、双盲、随机、安慰剂对照试验的结果,该试验测试了rentosertib(原ISM001 - 055)的安全性和有效性。rentosertib是一种一流的AI生成的小分子TNIK抑制剂,TNIK是使用生成式AI发现的特发性肺纤维化(IPF)中的一流靶点。IPF是一种与年龄相关的进行性肺部疾病,目前尚无可用疗法能逆转疾病的退行性病程。患者被随机分为四组,分别接受为期12周的治疗:每日一次30mg rentosertib(QD,n = 18)、每日两次30mg rentosertib(BID,n = 18)、每日一次60mg rentosertib(n = 18)或安慰剂(n = 17)。主要终点是至少发生一次治疗中出现的不良事件的患者百分比,所有治疗组的这一比例相似(接受每日一次30mg rentosertib的患者中为72.2%(n = 13/18),每日两次30mg rentosertib为83.3%(n = 15/18),每日一次60mg rentosertib为83.3%(n = 15/18),安慰剂组为70.6%(n = 12/17))。治疗相关的严重不良事件发生率较低,且各治疗组相当,最常见的导致治疗中断的事件与肝毒性或腹泻有关。次要终点包括药代动力学参数(C、C、t、AUC和t)、通过用力肺活量、肺一氧化碳弥散量、1秒用力呼气量测量的肺功能变化、莱斯特咳嗽问卷评分变化、6分钟步行距离变化以及IPF急性加重的次数和住院时长。我们观察到,60mg rentosertib QD组患者在最高剂量下用力肺活量增加,平均变化为 +98.4ml(95%置信区间10.9至185.9),而安慰剂组为 -20.3ml(95%置信区间 -116.1至75.6)。这些结果表明,使用rentosertib靶向TNIK是安全且耐受性良好的,值得在更大规模、更长疗程的临床试验中进一步研究。ClinicalTrials.gov注册号:NCT05938920 。

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