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综合虚拟查方法识别了新的CYP19A1抑制剂

Sijie Liu^1,2, Jie Wu³, Ya Chen²

¹Pharmaceutical and Medicinal Chemistry (Computer-Aided Drug Design), Institute of Pharmacy, Freie Universität Berlin, 14195 Berlin, Germany.

Journal of chemical information and modeling

|March 19, 2025

概括

研究人员开发了一种新的计算工作流程,以发现人类细胞染色体P450 19A1 (CYP19A1) 的新抑制剂,这是雌激素生物合成中的关键酶. 化合物9是一种强大的非共价抑制剂,被确定用于治疗与激素有关的疾病.

科学领域:

生物化学生物化学
药用化学医学化学
计算生物学计算生物学

背景情况:

人类细胞染色体P450 19A1 (CYP19A1),也称为芳酶,对于合成类固醇激素17β-雌激醇至关重要.
由于其在将雄激素转化为雌激素中的作用,CYP19A1是性激素依赖性疾病的主要治疗标.

研究的目的:

开发和应用一个虚拟查工作流程,整合机器学习和基于结构的建模来识别新型CYP19A1抑制剂.
为了发现潜在的治疗应用,强效和结构多样化的CYP19A1抑制剂.

主要方法:

机器学习模型是使用来自ChEMBL和PubChem生物试验数据库的广泛CYP19A1数据构建的,随后进行了严格的验证.
执行了一个虚拟选活动,并通过实验评估了有前途的命中,使用酵素测定与酵母中异构表达的人类CYP19A1进行了实验性评估.

主要成果:

虚拟查工作流成功地从十种测试化合物中识别出七种结构上不同的新型CYP19A1抑制剂.
化合物9是一种新型非对应性抑制剂,含有库马林和伊米达部分,具有271 ± 51nM的IC50值,显示出高强度.

结论:

开发的计算方法对于发现新型CYP19A1抑制剂是有效的.
化合物9是开发针对激素相关疾病中CYP19A1介导途径的新疗法的有前途的化合物.

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