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

作　　者：Boyu Pan Han Zhu Jiaqi Yang Liangjiao Wang Zizhen Chen Jian Ma Bo Zhang Zhanyu Pan Guoguang Ying Shao Li Liren Liu 

机构地区：[1]Department of Molecular Pharmacology,Tianjin Medical University Cancer Institute&Hospital,Tianjin 300060,China [2]National Clinical Research Center for Cancer,Key Laboratory of Cancer Prevention and Therapy,Tianjin,Tianjin's Clinical Research Center for Cancer,Tianjin 300060,China [3]College of Electronic and Information Engineering,Shanghai Research Institute for Intelligent Autonomous Systems,Tongji University,Shanghai 200092,China [4]Department of General Surgery.Tianjin Haihe Hospital,Tianjin 300350,China [5]Beijing Intelligent Medicine and Network Pharmacology Co.,Ltd,Beijing 100020,China [6]Institute for TCM-X,MOE Key Laboratory of Bioinformatics,Bioinformatics Division,BNRist,Department of Automation,Tsinghua University,Beijing 100084,China [7]Department of Integrative Oncology,Tianjin Medical University Cancer Institute and Hospital,Tianjin 300060,China [8]Laboratory of Cancer Cell Biology,Tianjin Medical University Cancer Institute&Hospital,Tianjin 300060,China

出　　处：《Cancer Biology & Medicine》2024年第11期1067-1077,共11页癌症生物学与医学(英文版)

基　　金：funded by the National Natural Science Foundation of China(Grant Nos.82204694 and 81572416);the Tianjin Health and Family Planning Commission Program(Grant No.ZC20169);the Tianjin Key Medical Discipline(Specialty)Construction Project(Grant No.TJYXZDXK-009A)。

摘　　要：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,knowledgedriven 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 diseasespecific 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/.

关 键 词：Chinese herbal medicine(CHM) CHM-FIEFP network pharmacology Danggui Sini Decoction ferulic acid 

分 类 号：R28[医药卫生—中药学]