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基于计算机模拟和体外实验的综合策略筛选一些传统埃及植物中的人芳香酶抑制剂。

Integrated in silico-in vitro strategy for screening of some traditional Egyptian plants for human aromatase inhibitors.

机构信息

Department of Pharmacognosy, Faculty of Pharmacy, Alexandria University, Egypt.

Department of Pharmacognosy, Faculty of Pharmacy, Alexandria University, Egypt.

出版信息

J Ethnopharmacol. 2018 Oct 5;224:359-372. doi: 10.1016/j.jep.2018.06.009. Epub 2018 Jun 15.

Abstract

ETHNOPHARMACOLOGICAL RELEVANCE

Aromatase enzyme (CYP19) is widely known as a critical target protein for treating hormone-dependent breast cancer. Natural products from traditional medicinal plants continue to be an active source of aromatase inhibitors. Meanwhile, high cost of experimental work and low hit rate associated with HTS have stimulated the implementation of in-silico virtual screening to resolve these pitfalls, where coupling of both classical wet lab procedure and VS may offer a more deepened access to bioactive compounds with less work and time waste.

AIM OF THE STUDY

In this work, a sequential structure-based and ligand-based virtual screening strategy was utilized for investigating an in-house database of 1720 phytochemical constituents of 29 medicinal plants and natural products used in traditional Egyptian medicine to search for compounds with the potential to be used as inhibitors of the human aromatase enzyme.

MATERIALS AND METHODS

The suggested strategy included using Glide docking with its feature 'extra precision (XP)' for carrying out structure-based virtual screening (SBVS) where the resulting hits were further promoted to ligand-based virtual screening (LBVS) through the development of two pharmacophore and QSAR models; one for steroidal and the other for non-steroidal aromatase inhibitors.

RESULTS

The combined results revealed that Artemisia annua, Zingiber officinale, Cicer arietinum, Annona muricata and Vitex agnus castus were the top scoring plants in terms of in-silico activity scores, respectively. The hydro-alcoholic extracts and different solvent fractions of the top scoring plants were subsequently tested experimentally for their aromatase inhibitory activity, by the aid of in-vitro fluorometric assay. The rank ordering of the activities for the plants agreed with the ordering predicted on the basis of SBVS and LBVS workflow implemented.

CONCLUSION

The suggested strategy provides a reliable means of prospecting in-silico screening of natural products databases in the search for new dug leads as aromatase inhibitors. The hits so obtained can then be subjected to further phytochemical studies, to isolate and identify suitable compounds for further in-vitro testing.

摘要

民族药理学相关性

芳香酶(CYP19)酶被广泛认为是治疗激素依赖性乳腺癌的关键靶标蛋白。来自传统药用植物的天然产物仍然是芳香酶抑制剂的一个活跃来源。同时,高通量筛选(HTS)相关的高实验成本和低命中率刺激了计算机虚拟筛选的实施,以解决这些问题,其中经典的湿实验室程序与 VS 的结合可能提供对具有更少工作量和时间浪费的生物活性化合物的更深入访问。

研究目的

在这项工作中,利用基于结构和基于配体的虚拟筛选策略,对 29 种用于传统埃及医学的药用植物和天然产物的 1720 种植物化学成分的内部数据库进行研究,以寻找具有成为人类芳香酶抑制剂潜力的化合物。

材料和方法

所建议的策略包括使用 Glide 对接及其“Extra Precision (XP)”功能进行基于结构的虚拟筛选(SBVS),其中所得命中进一步通过开发两个药效团和 QSAR 模型(一个用于甾体,另一个用于非甾体芳香酶抑制剂)推进到基于配体的虚拟筛选(LBVS)。

结果

综合结果表明,青蒿、生姜、鹰嘴豆、番木瓜和夏枯草分别是基于计算机活性评分的排名最高的植物。随后,借助体外荧光测定法,对排名靠前的植物的水醇提取物和不同溶剂级分进行了芳香酶抑制活性的实验测试。植物活性的排序与基于 SBVS 和 LBVS 工作流程实施的预测排序一致。

结论

该策略为寻找新的芳香酶抑制剂天然产物数据库的计算机虚拟筛选提供了一种可靠的方法。所得命中物随后可进一步进行植物化学研究,以分离和鉴定适合进一步进行体外测试的合适化合物。

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