【AI详解】A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.

文献信息

一句话小结

本研究通过预测性人工智能方法发现TRAF2和NCK相互作用激酶（TNIK）作为特发性肺纤维化的抗纤维化靶点，并研发了小分子TNIK抑制剂INS018_055，展示出良好的药物特性和抗纤维化、抗炎作用。此研究不仅验证了该药物的安全性和药代动力学，还展示了基于生成性AI的药物发现管线的高效性，为IPF的治疗提供了新的潜在方案。

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小分子TNIK抑制剂 · 特发性肺纤维化 · 抗纤维化 · 人工智能 · 临床试验

摘要

特发性肺纤维化（IPF）是一种具有高死亡率的侵袭性间质性肺疾病。IPF中的潜在药物靶点在临床层面上未能转化为有效的治疗方法。我们通过预测性人工智能（AI）方法，确定了TRAF2和NCK相互作用激酶（TNIK）作为抗纤维化的靶点。利用AI驱动的方法，我们研发了INS018_055，这是一种小分子TNIK抑制剂，具有良好的药物特性，并在体内通过口服、吸入或局部给药方式展现出抗纤维化活性，且适用于不同器官。INS018_055除了具有抗纤维化特性外，亦展现出抗炎作用，经过多项体内研究验证。其安全性与耐受性以及药代动力学在一项随机、双盲、安慰剂对照的I期临床试验（NCT05154240）中得到了验证，参与者为78名健康志愿者。在中国进行的另一项I期试验（CTR20221542）也显示出相似的安全性和药代动力学特征。这项工作从靶点发现到前临床候选药物提名大约用了18个月，展示了我们基于生成性AI的药物发现管线的能力。

英文摘要

Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline.

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主要研究问题

1. TNIK抑制剂在其他类型的纤维化疾病中是否也显示出类似的效果？
2. INS018_055在不同给药途径下的药效差异是什么？
3. 在临床试验中，TNIK抑制剂的安全性和耐受性具体表现如何？
4. 使用AI驱动的方法在药物发现中相比传统方法有哪些优势和局限性？
5. TNIK抑制剂的作用机制是否涉及其他生物标志物或信号通路？

核心洞察

研究背景和目的

特发性肺纤维化（IPF）是一种具有高死亡率的侵袭性间质性肺病，目前针对IPF的药物研发进展缓慢，已有的药物未能有效改善患者预后。本研究旨在利用人工智能（AI）技术识别新的抗纤维化靶点，并开发小分子药物INS018_055，以期为IPF和其他纤维化相关疾病提供有效的治疗选择。

主要方法/材料/实验设计

本研究采用了AI驱动的药物发现平台PandaOmics，结合多组学数据进行靶点识别。以下是研究的主要步骤：

• 研究设计：采用随机对照的I期临床试验，评估INS018_055的安全性和耐受性。
• 入排标准：纳入78名健康志愿者，排除有严重合并症者。
• 分组与随机化：随机分为干预组和对照组，采用双盲设计。
• 干预措施：干预组接受不同剂量的INS018_055，控制组接受安慰剂。
• 主要与次要结局指标：主要指标为药物的安全性和耐受性，次要指标包括药物的药代动力学特征。
• 统计分析方法：使用ANOVA和t检验分析组间差异，统计显著性设定为P<0.05。

关键结果和发现

• 靶点识别：TNIK被确定为抗纤维化靶点，其在多种纤维化相关通路中发挥关键作用。
• 化合物开发：INS018_055显示出良好的药物样特性，能够有效抑制TGF-β诱导的纤维化标志物（如α-SMA和纤维连接蛋白）表达。
• 动物实验：在小鼠模型中，INS018_055显著改善了肺功能，减少了肺纤维化面积，并降低了炎症细胞浸润。
• 临床试验：I期临床试验结果表明，INS018_055在健康志愿者中安全耐受，且药代动力学特征良好。

主要结论/意义/创新性

本研究首次利用AI技术成功识别TNIK作为抗纤维化靶点，并开发了小分子抑制剂INS018_055。该化合物在多种体内外模型中表现出显著的抗纤维化效果，具有良好的安全性和耐受性，为IPF及其他纤维化疾病的治疗提供了新的思路和方法。

研究局限性和未来方向

尽管本研究取得了积极成果，但仍存在一些局限性，包括：

• 目前仅在I期临床试验中验证了安全性，尚需大规模的II期和III期临床试验来评估疗效。
• TNIK的作用机制尚需进一步研究，以全面理解其在纤维化过程中的角色。

未来的研究方向包括：

• 继续开展大规模临床试验，以验证INS018_055在IPF患者中的疗效。
• 探索TNIK在其他纤维化相关疾病（如肾纤维化、皮肤纤维化）中的应用潜力。

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