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_max, C_trough, t_max, AUC_0–t/τ/∞ and t_1/2), 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.

尽管用于生成化学的人工智能 （AI） 取得了重大进展，但很少有人工智能发现或人工智能设计的新型药物进入人体临床试验。在这里，我们介绍了第一个 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（以前称为 ISM001-055）的安全性和有效性，rentosertib（以前称为 ISM001-055）是一种一流的 AI 生成的 TNIK 小分子抑制剂，TNIK 是使用生成式 AI 发现的特发性肺纤维化 （IPF） 的同类首创靶点。IPF 是一种与年龄相关的进行性肺部疾病，目前没有可用的疗法可以逆转疾病的退行性进程。患者被随机分配接受 30 mg rentosertib 每日一次 （QD， n = 18）、30 mg rentosertib 每日两次 （BID， n = 18）、60 mg rentosertib QD （n = 18） 或安慰剂 （n = 17） 治疗 12 周。主要终点是至少发生一种治疗中出现的不良事件的患者百分比，所有治疗组均相似（接受 30 mg rentosertib QD 的患者为 72.2% （n = 13/18），30 mg rentosertib BID 为 83.3% （n = 15/18），60 mg rentosertib QD 为 83.3% （n = 15/18），安慰剂为 70.6% （n= 12/17））。治疗相关的严重不良事件发生率较低且在治疗组之间具有可比性，最常见的事件导致与肝毒性或腹泻相关的治疗中断。 次要终点包括药代动力学 （C_max、C_trough、t_max、AUC_0–t/τ/∞ 和 t_1/2）、用力肺活量测量的肺功能变化、肺对一氧化碳的弥散量、1 秒用力呼气和莱斯特咳嗽问卷评分的变化、6 分钟步行距离的变化以及急性住院次数和住院时间 IPF 恶化。我们观察到最高剂量时用力肺活量增加，60 mg rentosertib QD 组患者的平均变化为 +98.4 ml （95% 置信区间 10.9 至 185.9），而安慰剂组为 -20.3 ml （95% 置信区间 -116.1 至 75.6）。这些结果表明，用 rentosertib 靶向 TNIK 是安全且耐受性良好的，值得在更长期的更大规模临床试验中进一步研究。ClinicalTrials.gov 注册号：NCT05938920。