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In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine
BACKGROUNDS AND AIMS: Recently, a growing number of hepatotoxicity cases aroused by Traditional Chinese Medicine (TCM) have been reported, causing increasing concern. To date, the reported predictive models for drug induced liver injury show low prediction accuracy and there are still no related rep...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6509242/ https://www.ncbi.nlm.nih.gov/pubmed/31130860 http://dx.doi.org/10.3389/fphar.2019.00458 |
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author | Wu, Qihui Cai, Chuipu Guo, Pengfei Chen, Meiling Wu, Xiaoqin Zhou, Jingwei Luo, Yunxia Zou, Yidan Liu, Ai-lin Wang, Qi Kuang, Zaoyuan Fang, Jiansong |
author_facet | Wu, Qihui Cai, Chuipu Guo, Pengfei Chen, Meiling Wu, Xiaoqin Zhou, Jingwei Luo, Yunxia Zou, Yidan Liu, Ai-lin Wang, Qi Kuang, Zaoyuan Fang, Jiansong |
author_sort | Wu, Qihui |
collection | PubMed |
description | BACKGROUNDS AND AIMS: Recently, a growing number of hepatotoxicity cases aroused by Traditional Chinese Medicine (TCM) have been reported, causing increasing concern. To date, the reported predictive models for drug induced liver injury show low prediction accuracy and there are still no related reports for hepatotoxicity evaluation of TCM systematically. Additionally, the mechanism of herb induced liver injury (HILI) still remains unknown. The aim of the study was to identify potential hepatotoxic ingredients in TCM and explore the molecular mechanism of TCM against HILI. MATERIALS AND METHODS: In this study, we developed consensus models for HILI prediction by integrating the best single classifiers. The consensus model with best performance was applied to identify the potential hepatotoxic ingredients from the Traditional Chinese Medicine Systems Pharmacology database (TCMSP). Systems pharmacology analyses, including multiple network construction and KEGG pathway enrichment, were performed to further explore the hepatotoxicity mechanism of TCM. RESULTS: 16 single classifiers were built by combining four machine learning methods with four different sets of fingerprints. After systematic evaluation, the best four single classifiers were selected, which achieved a Matthews correlation coefficient (MCC) value of 0.702, 0.691, 0.659, and 0.717, respectively. To improve the predictive capacity of single models, consensus prediction method was used to integrate the best four single classifiers. Results showed that the consensus model C-3 (MCC = 0.78) outperformed the four single classifiers and other consensus models. Subsequently, 5,666 potential hepatotoxic compounds were identified by C-3 model. We integrated the top 10 hepatotoxic herbs and discussed the hepatotoxicity mechanism of TCM via systems pharmacology approach. Finally, Chaihu was selected as the case study for exploring the molecular mechanism of hepatotoxicity. CONCLUSION: Overall, this study provides a high accurate approach to predict HILI and an in silico perspective into understanding the hepatotoxicity mechanism of TCM, which might facilitate the discovery and development of new drugs. |
format | Online Article Text |
id | pubmed-6509242 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65092422019-05-24 In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine Wu, Qihui Cai, Chuipu Guo, Pengfei Chen, Meiling Wu, Xiaoqin Zhou, Jingwei Luo, Yunxia Zou, Yidan Liu, Ai-lin Wang, Qi Kuang, Zaoyuan Fang, Jiansong Front Pharmacol Pharmacology BACKGROUNDS AND AIMS: Recently, a growing number of hepatotoxicity cases aroused by Traditional Chinese Medicine (TCM) have been reported, causing increasing concern. To date, the reported predictive models for drug induced liver injury show low prediction accuracy and there are still no related reports for hepatotoxicity evaluation of TCM systematically. Additionally, the mechanism of herb induced liver injury (HILI) still remains unknown. The aim of the study was to identify potential hepatotoxic ingredients in TCM and explore the molecular mechanism of TCM against HILI. MATERIALS AND METHODS: In this study, we developed consensus models for HILI prediction by integrating the best single classifiers. The consensus model with best performance was applied to identify the potential hepatotoxic ingredients from the Traditional Chinese Medicine Systems Pharmacology database (TCMSP). Systems pharmacology analyses, including multiple network construction and KEGG pathway enrichment, were performed to further explore the hepatotoxicity mechanism of TCM. RESULTS: 16 single classifiers were built by combining four machine learning methods with four different sets of fingerprints. After systematic evaluation, the best four single classifiers were selected, which achieved a Matthews correlation coefficient (MCC) value of 0.702, 0.691, 0.659, and 0.717, respectively. To improve the predictive capacity of single models, consensus prediction method was used to integrate the best four single classifiers. Results showed that the consensus model C-3 (MCC = 0.78) outperformed the four single classifiers and other consensus models. Subsequently, 5,666 potential hepatotoxic compounds were identified by C-3 model. We integrated the top 10 hepatotoxic herbs and discussed the hepatotoxicity mechanism of TCM via systems pharmacology approach. Finally, Chaihu was selected as the case study for exploring the molecular mechanism of hepatotoxicity. CONCLUSION: Overall, this study provides a high accurate approach to predict HILI and an in silico perspective into understanding the hepatotoxicity mechanism of TCM, which might facilitate the discovery and development of new drugs. Frontiers Media S.A. 2019-05-03 /pmc/articles/PMC6509242/ /pubmed/31130860 http://dx.doi.org/10.3389/fphar.2019.00458 Text en Copyright © 2019 Wu, Cai, Guo, Chen, Wu, Zhou, Luo, Zou, Liu, Wang, Kuang and Fang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Wu, Qihui Cai, Chuipu Guo, Pengfei Chen, Meiling Wu, Xiaoqin Zhou, Jingwei Luo, Yunxia Zou, Yidan Liu, Ai-lin Wang, Qi Kuang, Zaoyuan Fang, Jiansong In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine |
title | In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine |
title_full | In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine |
title_fullStr | In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine |
title_full_unstemmed | In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine |
title_short | In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine |
title_sort | in silico identification and mechanism exploration of hepatotoxic ingredients in traditional chinese medicine |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6509242/ https://www.ncbi.nlm.nih.gov/pubmed/31130860 http://dx.doi.org/10.3389/fphar.2019.00458 |
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