From complexity to clarity: development of CHM-FIEFP for predicting effective components in Chinese herbal formulas by using big data.

IF 5.6 2区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Boyu Pan, Han Zhu, Jiaqi Yang, Liangjiao Wang, Zizhen Chen, Jian Ma, Bo Zhang, Zhanyu Pan, Guoguang Ying, Shao Li, Liren Liu
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引用次数: 0

Abstract

Objective: The presence of complex components in Chinese herbal medicine (CHM) hinders identification of the primary active substances and understanding of pharmacological principles. This study was aimed at developing a big-data-based, knowledge-driven in silico algorithm for predicting central components in complex CHM formulas.

Methods: Network pharmacology (TCMSP) and clinical (GEO) databases were searched to retrieve gene targets corresponding to the formula ingredients, herbal components, and specific disease being treated. Intersections were determined to obtain disease-specific core targets, which underwent further GO and KEGG enrichment analyses to generate non-redundant biological processes and molecular targets for the formula and each component. The ratios of the numbers of biological and molecular events associated with a component were calculated with a formula, and entropy weighting was performed to obtain a fitting score to facilitate ranking and improve identification of the key components. The established method was tested on the traditional CHM formula Danggui Sini Decoction (DSD) for gastric cancer. Finally, the effects of the predicted critical component were experimentally validated in gastric cancer cells.

Results: An algorithm called Chinese Herb Medicine-Formula vs. Ingredients Efficacy Fitting & Prediction (CHM-FIEFP) was developed. Ferulic acid was identified as having the highest fitting score among all tested DSD components. The pharmacological effects of ferulic acid alone were similar to those of DSD.

Conclusions: CHM-FIEFP is a promising in silico method for identifying pharmacological components of CHM formulas with activity against specific diseases. This approach may also be practical for solving other similarly complex problems. The algorithm is available at http://chm-fiefp.net/.

从复杂到清晰:利用大数据开发用于预测中草药配方中有效成分的 CHM-FIEFP。
目的:中草药(CHM)中存在的复杂成分阻碍了对主要活性物质的鉴定和对药理原理的理解。本研究旨在开发一种基于大数据、知识驱动的硅学算法,用于预测复杂中药配方中的中心成分:方法:搜索网络药理学(TCMSP)和临床(GEO)数据库,检索与配方成分、中药成分和治疗的特定疾病相对应的基因靶点。确定交叉点以获得疾病特异性核心靶点,并对其进行进一步的 GO 和 KEGG 富集分析,以生成非冗余的生物过程以及配方和每种成分的分子靶点。通过公式计算与某一成分相关的生物事件和分子事件的数量比,并进行熵加权以获得拟合得分,从而便于排序和改进关键成分的识别。在治疗胃癌的传统中药方剂当归四逆汤(DSD)上对所建立的方法进行了测试。最后,在胃癌细胞中对预测的关键成分的作用进行了实验验证:结果:开发出了一种名为 "中草药配方与成分功效拟合预测"(CHM-FIEFP)的算法。阿魏酸在所有测试的 DSD 成分中拟合得分最高。阿魏酸单独使用的药理作用与 DSD 相似:结论:CHM-FIEFP 是一种很有前景的硅学方法,可用于鉴定 CHM 配方中对特定疾病具有活性的药理成分。这种方法也可用于解决其他类似的复杂问题。该算法可在 http://chm-fiefp.net/ 上查阅。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Biology & Medicine
Cancer Biology & Medicine Medicine-Oncology
CiteScore
9.80
自引率
3.60%
发文量
1143
审稿时长
12 weeks
期刊介绍: Cancer Biology & Medicine (ISSN 2095-3941) is a peer-reviewed open-access journal of Chinese Anti-cancer Association (CACA), which is the leading professional society of oncology in China. The journal quarterly provides innovative and significant information on biological basis of cancer, cancer microenvironment, translational cancer research, and all aspects of clinical cancer research. The journal also publishes significant perspectives on indigenous cancer types in China.
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