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通过药效团建模合理发现新型角鲨烯合酶抑制剂

Rational Discovery of Novel Squalene Synthase Inhibitors through Pharmacophore Modelling.

作者信息

Chokshi Avani B, Chhabria Mahesh T, Desai Pritesh R

机构信息

Department of Pharmaceutical Chemistry and Analysis, Ramanbhai Patel College of Pharmacy, CHARUSAT, Changa 388 421, Gujarat, India.

Department of Pharmaceutical Chemistry, L.M. College of Pharmacy, Navrangpura, Ahmedabad 380 009, Gujarat, India.

出版信息

Curr Comput Aided Drug Des. 2018;14(3):221-233. doi: 10.2174/1573409914666180507143024.

Abstract

INTRODUCTION

In the present research work, a pharmacophore based virtual screening was performed using Discovery Studio 2.1 for the discovery of some novel molecules as inhibitors of Squalene Synthase Enzyme, a key enzyme in cholesterol biosynthetic pathway.

METHODS

A quantitative pharmacophore HypoGen was generated and the best HypoGen had two ring aromatic and one hydrogen bond acceptor lipid features. The best HypoGen showed a very good correlation coefficient (r = 0.901) with satisfactory cost analysis. Furthermore, the HypoGen was validated externally by predicting the activity of test set. The developed model was found to be predictive as it showed low error of prediction for test set molecules. The developed model was used as a search query for virtually screening two chemical databases: sample database from catalyst and minimaybridge.

RESULTS AND DISCUSSION

The best hit with good fit value and low predicted activity was further modified to design novel drug-like molecules, which were able to bind to Squalene synthase enzyme active site.

CONCLUSION

The best scoring molecule, compound 67 showed 53% inhibition of the human Squalene synthase enzyme, isolated from the cell lysates of Human Hepatoma Cell Line, at a dose of 10 mcg with an IC50 value of 9.43 µm.

摘要

引言

在本研究工作中,使用Discovery Studio 2.1进行了基于药效团的虚拟筛选,以发现一些新型分子作为角鲨烯合酶的抑制剂,角鲨烯合酶是胆固醇生物合成途径中的关键酶。

方法

生成了定量药效团HypoGen,最佳的HypoGen具有两个环状芳香族和一个氢键受体脂质特征。最佳的HypoGen显示出非常好的相关系数(r = 0.901)以及令人满意的成本分析。此外,通过预测测试集的活性对HypoGen进行了外部验证。发现所开发的模型具有预测性,因为它对测试集分子显示出较低的预测误差。所开发的模型用作搜索查询,对两个化学数据库进行虚拟筛选:来自催化剂的样本数据库和最小桥数据库。

结果与讨论

对具有良好拟合值和低预测活性的最佳命中物进行了进一步修饰,以设计能够结合角鲨烯合酶活性位点的新型类药物分子。

结论

得分最高的分子化合物67在剂量为10 mcg时,对从人肝癌细胞系细胞裂解物中分离出的人角鲨烯合酶显示出53%的抑制作用,IC50值为9.43 µm。

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