系统药理学与基因芯片技术预测中药复方治疗原发性肝癌靶点的比较研究

A Comparative Study of Systems Pharmacology and Gene Chip Technology for Predicting Targets of a Traditional Chinese Medicine Formula in Primary Liver Cancer Treatment.

作者信息

Li Songzhe, Sun Yang, Sun Yue

机构信息

Department of Biology, College of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, China.

出版信息

Front Pharmacol. 2022 Mar 2;13:768862. doi: 10.3389/fphar.2022.768862. eCollection 2022.

Abstract

The systems pharmacology approach is a target prediction model for traditional Chinese medicine and has been used increasingly in recent years. However, the accuracy of this model to other prediction models is yet to be established. To compare the systems pharmacology modelwithexperimental gene chip technology by using these models to predict targets of a traditional Chinese medicine formulain the treatment of primary liver cancer. Systems pharmacology and gene chip target predictions were performed for the traditional Chinese medicine formula (ZZXJT). A third square alignment was performed with molecular docking. Identification of systems pharmacology accounted for 17% of targets, whilegene chip-predicted outcomes accounted for 19%.Molecular docking showed that the top ten targets (excludingcommon targets) of the system pharmacology model had better binding free energies than the gene chip model using twocommon targets as a benchmark. For both models, the core drugs predictions were more consistent than the core small molecules predictions. In this study, the identified targets of systems pharmacology weredissimilar to those identified by gene chip technology; whereas the core drug and small molecule predictions were similar.

摘要

系统药理学方法是一种针对中药的靶点预测模型,近年来其应用日益广泛。然而,该模型与其他预测模型相比的准确性尚未确立。为了通过使用这些模型预测一种中药方剂治疗原发性肝癌的靶点,将系统药理学模型与实验性基因芯片技术进行比较。对中药方剂(ZZXJT)进行了系统药理学和基因芯片靶点预测。使用分子对接进行了第三方阵比对。系统药理学鉴定的靶点占17%,而基因芯片预测的结果占19%。以两个共同靶点为基准,分子对接显示系统药理学模型的前十个靶点(不包括共同靶点)比基因芯片模型具有更好的结合自由能。对于这两种模型,核心药物预测比核心小分子预测更一致。在本研究中,系统药理学鉴定的靶点与基因芯片技术鉴定的靶点不同;而核心药物和小分子预测相似。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f19c/8926147/1a1d035f82c7/fphar-13-768862-g001.jpg

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