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基于中药的药物发现的高内涵筛选

High content screening for drug discovery from traditional Chinese medicine.

作者信息

Wang Jing, Wu Ming-Yue, Tan Jie-Qiong, Li Min, Lu Jia-Hong

机构信息

State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao SAR, China.

2Key Laboratory of Medical Genetics, Xiangya Medical School, Central South University, Changsha, Hunan China.

出版信息

Chin Med. 2019 Feb 28;14:5. doi: 10.1186/s13020-019-0228-y. eCollection 2019.

Abstract

Traditional Chinese medicine (TCM) represents the crystallization of Chinese wisdom and civilization. It has been valued as the renewable source for the discovery of novel drugs, owing to its long-term proved efficacy in human diseases and abundant biologically active components pools. To dissect the mystery of TCM, modern technologies such as omics approaches (proteomics, genomics, metabolomics) and drug screening technologies (high through-put screening, high content screening and virtual screening) have been widely applied to either identify the drug target of TCM or identify the active component with certain bio-activity. The advent of high content screening technology has absolutely contributed to a breakthrough in compounds discovery and influenced the evolution of technology in screening field. The review introduces the concept and principle of high content screening, lists and compares the currently used HCS instruments, and summarizes the examples from ours and others research work which applied HCS in TCM-derived compounds screening. Meanwhile, this article also discusses the advantages and limitations of HSC technology in drug discovery from TCM libraries.

摘要

中医(TCM)是中国智慧和文明的结晶。由于其在人类疾病方面长期被证明的疗效以及丰富的生物活性成分库,它一直被视为发现新药的可再生资源。为了解开中医的奥秘,诸如组学方法(蛋白质组学、基因组学、代谢组学)和药物筛选技术(高通量筛选、高内涵筛选和虚拟筛选)等现代技术已被广泛应用于识别中药的药物靶点或鉴定具有特定生物活性的活性成分。高内涵筛选技术的出现绝对促成了化合物发现方面的突破,并影响了筛选领域技术的发展。本文综述介绍了高内涵筛选的概念和原理,列举并比较了目前使用的高内涵筛选仪器,并总结了我们自己以及其他研究工作中应用高内涵筛选进行中药衍生化合物筛选的实例。同时,本文还讨论了高内涵筛选技术在从中药库中发现药物方面的优势和局限性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fc2/6394041/a1b4faa7b85c/13020_2019_228_Fig1_HTML.jpg

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