Repurposing lapatinib as a triple antagonist of chemokine receptors 3, 4, and 5.

IF 3.2 3区 医学 Q2 PHARMACOLOGY & PHARMACY Molecular Pharmacology Pub Date : 2025-01-01 Epub Date: 2024-12-12 DOI:10.1016/j.molpha.2024.100010
Thomas R Lane, Ana C Puhl, Patricia A Vignaux, Keith R Pennypacker, Sean Ekins
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引用次数: 0

Abstract

Chemokine receptors CCR3, CCR4, and CCR5 are G protein-coupled receptors implicated in diseases like cancer, Alzheimer's, asthma, human immunodeficiency virus (HIV), and macular degeneration. Recently, CCR3 and CCR4 have emerged as potential stroke targets. Although only the CCR5 antagonist maraviroc is US Food and Drug Administration-approved (for HIV), we curated data on CCR3, CCR4, and CCR5 antagonists from ChEMBL to develop and validate machine learning models. The top 5-fold cross-validation statistics for these models were high for both classification and regression models for CCR3 (receiver operating characteristic [ROC], 0.94; R2 = 0.8), CCR4 (ROC, 0.98; R2 = 0.57), and CCR5 (ROC, 0.96; R2 = 0.78). The models for CCR3/4 were used to screen a small library of US Food and Drug Administration-approved drugs and 17 were initially tested in vitro against both CCR3/4 receptors. A promising compound lapatinib, a dual tyrosine kinase inhibitor, was identified as an antagonist for CCR3 (IC50, 0.7 μM) and CCR4 (IC50, 1.8 μM). Additional testing also identified it as an CCR5 antagonist (IC50, 0.9 μM), and it showed moderate in vitro HIV I inhibition. We demonstrated how machine learning can be used to identify molecules for repurposing as antagonists for G protein-coupled receptors such as CCR3, CCR4, and CCR5. Lapatinib may represent a new orally available chemical probe for these 3 receptors, and it provides a starting point for further chemical optimization for multiple diseases impacting human health. SIGNIFICANCE STATEMENT: We describe the building of machine learning models for the chemokine receptors CCR3, CCR4, and CCR5 trained on data from the ChEMBL database. Using these models, we identified lapatinib as a potent inhibitor of CCR3, CCR4, and CCR5. Our study illustrates the potential of machine learning in identifying molecules for repurposing as antagonists for G protein-coupled receptors, including CCR3, CCR4, and CCR5, which have various therapeutic applications.

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趋化因子受体 CCR3、CCR4 和 CCR5 是与癌症、阿尔茨海默氏症、哮喘、人类免疫缺陷病毒(HIV)和黄斑变性等疾病有关的 G 蛋白偶联受体。最近,CCR3 和 CCR4 成为潜在的中风靶点。虽然只有 CCR5 拮抗剂马拉韦罗获得了美国食品药品管理局的批准(用于治疗 HIV),但我们还是从 ChEMBL 收集了有关 CCR3、CCR4 和 CCR5 拮抗剂的数据,用于开发和验证机器学习模型。在这些模型中,CCR3(接收器操作特征[ROC],0.94;R2 = 0.8)、CCR4(ROC,0.98;R2 = 0.57)和CCR5(ROC,0.96;R2 = 0.78)的分类和回归模型的最高5倍交叉验证统计量都很高。CCR3/4 模型被用于筛选美国食品和药物管理局批准的小型药物库,其中 17 种药物最初针对两种 CCR3/4 受体进行了体外测试。一种很有前途的化合物拉帕替尼(一种双重酪氨酸激酶抑制剂)被确定为 CCR3(IC50,0.7 μM)和 CCR4(IC50,1.8 μM)的拮抗剂。额外的测试还确定它是一种 CCR5 拮抗剂(IC50,0.9 μM),并显示出适度的体外 HIV I 抑制作用。我们展示了如何利用机器学习来识别分子,并将其重新用作CCR3、CCR4和CCR5等G蛋白偶联受体的拮抗剂。拉帕替尼可能是针对这 3 种受体的一种新的口服化学探针,它为针对影响人类健康的多种疾病的进一步化学优化提供了一个起点。意义声明:我们介绍了针对趋化因子受体 CCR3、CCR4 和 CCR5 建立机器学习模型的情况,这些模型是根据 ChEMBL 数据库中的数据训练而成的。利用这些模型,我们发现拉帕替尼是 CCR3、CCR4 和 CCR5 的强效抑制剂。我们的研究说明了机器学习在鉴定分子以重新用作G蛋白偶联受体(包括CCR3、CCR4和CCR5)拮抗剂方面的潜力,这些分子具有多种治疗用途。
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来源期刊
Molecular Pharmacology
Molecular Pharmacology 医学-药学
CiteScore
7.20
自引率
2.80%
发文量
50
审稿时长
3-6 weeks
期刊介绍: Molecular Pharmacology publishes findings derived from the application of innovative structural biology, biochemistry, biophysics, physiology, genetics, and molecular biology to basic pharmacological problems that provide mechanistic insights that are broadly important for the fields of pharmacology and toxicology. Relevant topics include: Molecular Signaling / Mechanism of Drug Action Chemical Biology / Drug Discovery Structure of Drug-Receptor Complex Systems Analysis of Drug Action Drug Transport / Metabolism
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