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Chinese Herbal Medicine Hepatotoxicity: The Evaluation and Recognization Based on Large-scale Evidence Database

BACKGROUND: Due to the special nature of Chinese Herbal medicine and the complexity of its clinical use, it is difficult to identify and evaluate its toxicity and resulting herb induced liver injury (HILI). METHODS: First, the database would provide full profile of HILI from the basic ingredients to...

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Autores principales: Liu, Zhi, He, Xin, Wang, Lili, Zhang, Yunhua, Hai, Yue, Gao, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bentham Science Publishers 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635764/
https://www.ncbi.nlm.nih.gov/pubmed/30101702
http://dx.doi.org/10.2174/1389200219666180813144114
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author Liu, Zhi
He, Xin
Wang, Lili
Zhang, Yunhua
Hai, Yue
Gao, Rui
author_facet Liu, Zhi
He, Xin
Wang, Lili
Zhang, Yunhua
Hai, Yue
Gao, Rui
author_sort Liu, Zhi
collection PubMed
description BACKGROUND: Due to the special nature of Chinese Herbal medicine and the complexity of its clinical use, it is difficult to identify and evaluate its toxicity and resulting herb induced liver injury (HILI). METHODS: First, the database would provide full profile of HILI from the basic ingredients to clinical out-comes by the most advanced algorithms of artificial intelligence, and it is also possible that we can predict possibilities of HILI after patients taking Chinese herbs by individual patient evaluation and prediction. Second, the database would solve the chaos and lack of the relevant data faced by the current basic re-search and clinical practice of Chinese Herbal Medicine. Third, we can also screen the susceptible patients from the database and thus prevent the accidents of HILI from the very beginning. RESULTS: The Roussel Uclaf Causality Assessment Method (RUCAM) is the most accepted method to evalu-ate DILI, but at present before using the RUCAM evaluation method, data resource collection and analysis are yet to be perfected. Based on existing research on drug-metabolizing enzymes mediating reactive me-tabolites (RMs), the aim of this study is to explore the possibilities and methods of building multidimen-sional hierarchical database composing of RMs evidence library, Chinese herbal evidence library, and indi-vidualized reports evidence library of herb induced liver injury HILI. CONCLUSION: The potential benefits lie in its ability to organize, use vast amounts of evidence and use big data mining techniques at the center for Chinese herbal medicine liver toxicity research, which is the most difficult key point of scientific research to be investigated in the next few years.
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spelling pubmed-66357642019-08-09 Chinese Herbal Medicine Hepatotoxicity: The Evaluation and Recognization Based on Large-scale Evidence Database Liu, Zhi He, Xin Wang, Lili Zhang, Yunhua Hai, Yue Gao, Rui Curr Drug Metab Article BACKGROUND: Due to the special nature of Chinese Herbal medicine and the complexity of its clinical use, it is difficult to identify and evaluate its toxicity and resulting herb induced liver injury (HILI). METHODS: First, the database would provide full profile of HILI from the basic ingredients to clinical out-comes by the most advanced algorithms of artificial intelligence, and it is also possible that we can predict possibilities of HILI after patients taking Chinese herbs by individual patient evaluation and prediction. Second, the database would solve the chaos and lack of the relevant data faced by the current basic re-search and clinical practice of Chinese Herbal Medicine. Third, we can also screen the susceptible patients from the database and thus prevent the accidents of HILI from the very beginning. RESULTS: The Roussel Uclaf Causality Assessment Method (RUCAM) is the most accepted method to evalu-ate DILI, but at present before using the RUCAM evaluation method, data resource collection and analysis are yet to be perfected. Based on existing research on drug-metabolizing enzymes mediating reactive me-tabolites (RMs), the aim of this study is to explore the possibilities and methods of building multidimen-sional hierarchical database composing of RMs evidence library, Chinese herbal evidence library, and indi-vidualized reports evidence library of herb induced liver injury HILI. CONCLUSION: The potential benefits lie in its ability to organize, use vast amounts of evidence and use big data mining techniques at the center for Chinese herbal medicine liver toxicity research, which is the most difficult key point of scientific research to be investigated in the next few years. Bentham Science Publishers 2018-12 2018-12 /pmc/articles/PMC6635764/ /pubmed/30101702 http://dx.doi.org/10.2174/1389200219666180813144114 Text en © 2018 Bentham Science Publishers https://creativecommons.org/licenses/by-nc/4.0/legalcode This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
spellingShingle Article
Liu, Zhi
He, Xin
Wang, Lili
Zhang, Yunhua
Hai, Yue
Gao, Rui
Chinese Herbal Medicine Hepatotoxicity: The Evaluation and Recognization Based on Large-scale Evidence Database
title Chinese Herbal Medicine Hepatotoxicity: The Evaluation and Recognization Based on Large-scale Evidence Database
title_full Chinese Herbal Medicine Hepatotoxicity: The Evaluation and Recognization Based on Large-scale Evidence Database
title_fullStr Chinese Herbal Medicine Hepatotoxicity: The Evaluation and Recognization Based on Large-scale Evidence Database
title_full_unstemmed Chinese Herbal Medicine Hepatotoxicity: The Evaluation and Recognization Based on Large-scale Evidence Database
title_short Chinese Herbal Medicine Hepatotoxicity: The Evaluation and Recognization Based on Large-scale Evidence Database
title_sort chinese herbal medicine hepatotoxicity: the evaluation and recognization based on large-scale evidence database
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635764/
https://www.ncbi.nlm.nih.gov/pubmed/30101702
http://dx.doi.org/10.2174/1389200219666180813144114
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