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QSAR 分析和新型含喹唑啉的羟肟酸作为组蛋白去乙酰化酶 6 抑制剂的实验评估。

QSAR analysis and experimental evaluation of new quinazoline-containing hydroxamic acids as histone deacetylase 6 inhibitors.

机构信息

Department of Pharmacology and Pharmaceutical Chemistry, Medical Faculty, Shevchenko Transnistria State University, Tiraspol, Moldova.

Molecular Design, Institute of Physiologically Active Compounds of the Russian Academy of SciencesDepartment of Computer-aided, Chernogolovka, Russia.

出版信息

SAR QSAR Environ Res. 2022 Jul;33(7):513-532. doi: 10.1080/1062936X.2022.2092210. Epub 2022 Jul 4.

Abstract

Histone deacetylase inhibitors represent the most important class of drugs for the treatment of human cancer and other diseases due to their influence on cell growth, differentiation, and apoptosis. Among the well-known eighteen histone deacetylases, histone deacetylase 6 (HDAC6), which is involved in oncogenesis, cell survival, and cancer cell metastasis, is of great importance. Using the CDK and alvaDesc molecular descriptors and the Random Forest and EXtreme Gradient Boosting methods, we propose a number of adequate QSAR classification models, which are integrated into a consensus model and are freely available on the OCHEM web platform (https://ochem.eu). The consensus QSAR model is used for virtual screening of a series of seven new compounds, the derivatives of -((hydroxyamino)-oxoalkyl)-2-(quinazoline-4-ilamino)-benzamides, the synthesis schemes of which are also presented in this work. In vitro evaluation of the inhibitory activity (IC) of this series of compounds against HDAC6 allowed us to confirm the results of virtual screening and to reveal promising compounds V-2 and V-4, IC of which is 3.25 nM and 0.04 nM, respectively. The subsequent in silico evaluation of the main ADMET properties of active compounds V-2 and V-4 allowed us to find that they have acceptable pharmacokinetic parameters and level of acute toxicity.

摘要

组蛋白去乙酰化酶抑制剂由于其对细胞生长、分化和凋亡的影响,成为治疗人类癌症和其他疾病的最重要药物类别之一。在已知的十八种组蛋白去乙酰化酶中,与致癌、细胞存活和癌细胞转移有关的组蛋白去乙酰化酶 6(HDAC6)非常重要。我们使用 CDK 和 alvaDesc 分子描述符以及随机森林和极端梯度提升方法,提出了许多合适的 QSAR 分类模型,这些模型被整合到一个共识模型中,并在 OCHEM 网络平台(https://ochem.eu)上免费提供。共识 QSAR 模型用于对一系列七种新化合物进行虚拟筛选,这些化合物是-((羟基氨基)-氧代烷基)-2-(喹唑啉-4-基氨基)-苯甲酰胺的衍生物,其合成方案也在本工作中提出。对该系列化合物对 HDAC6 的抑制活性(IC)的体外评估使我们能够确认虚拟筛选的结果,并揭示出具有前景的化合物 V-2 和 V-4,其 IC 分别为 3.25 nM 和 0.04 nM。随后对活性化合物 V-2 和 V-4 的主要 ADMET 性质进行的计算评估使我们发现它们具有可接受的药代动力学参数和急性毒性水平。

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