Novel application of neural network modelling for multicomponent herbal medicine optimization

Abstract The conventional method for effective or toxic chemical substance identification of multicomponent herbal medicine is based on single component separation, which is time-consuming, labor intensive, inefficient, and neglects the interaction and integrity among the components; therefore, it i...

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Autores principales:	Yong-Shen Ren, Lei Lei, Xin Deng, Yao Zheng, Yan Li, Jun Li, Zhi-Nan Mei
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c60c8f784d02487a991d241f3a15c37a
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spelling	oai:doaj.org-article:c60c8f784d02487a991d241f3a15c37a2021-12-02T15:09:34ZNovel application of neural network modelling for multicomponent herbal medicine optimization10.1038/s41598-019-51956-62045-2322https://doaj.org/article/c60c8f784d02487a991d241f3a15c37a2019-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-51956-6https://doaj.org/toc/2045-2322Abstract The conventional method for effective or toxic chemical substance identification of multicomponent herbal medicine is based on single component separation, which is time-consuming, labor intensive, inefficient, and neglects the interaction and integrity among the components; therefore, it is necessary to find an alternative routine to evaluate the components more efficiently and scientifically. In this study, sodium aescinate injection (SAI), obtained from different manufacturers and prepared as “components knockout” samples, was chosen as the case study. The chemical fingerprints of SAI were obtained by high-performance liquid chromatography to provide the chemical information. The effectiveness and irritation of each sample were evaluated using anti-inflammatory and irritation tests, and then “Gray correlation” analysis (GCA) was applied to rank the effectiveness and irritability of each component to provide a preliminary judgment for product optimization. The prediction model of the proportions of the expected components was constructed using the artificial neural network. The results of the GCA showed that the irritation sorting of each SAI component was in the order of B > A > G > J > I > H > D > F > E > C and the effectiveness sorting of SAI components was in the order of D > C > B > A > F > E > H > I > G > J; the predictive proportion of SAI was optimized by the BP neural network as A: B: C: D: E: F = 0.7526: 0.5005: 5.4565: 1.4149: 0.8113: 1.0642. This study provided a scientific, accurate, reliable, and efficient approach for the proportion optimization of multicomponent drugs, which has a good prospect of popularization and application in product upgrading and development of herbal medicine.Yong-Shen RenLei LeiXin DengYao ZhengYan LiJun LiZhi-Nan MeiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-11 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yong-Shen Ren Lei Lei Xin Deng Yao Zheng Yan Li Jun Li Zhi-Nan Mei Novel application of neural network modelling for multicomponent herbal medicine optimization
description	Abstract The conventional method for effective or toxic chemical substance identification of multicomponent herbal medicine is based on single component separation, which is time-consuming, labor intensive, inefficient, and neglects the interaction and integrity among the components; therefore, it is necessary to find an alternative routine to evaluate the components more efficiently and scientifically. In this study, sodium aescinate injection (SAI), obtained from different manufacturers and prepared as “components knockout” samples, was chosen as the case study. The chemical fingerprints of SAI were obtained by high-performance liquid chromatography to provide the chemical information. The effectiveness and irritation of each sample were evaluated using anti-inflammatory and irritation tests, and then “Gray correlation” analysis (GCA) was applied to rank the effectiveness and irritability of each component to provide a preliminary judgment for product optimization. The prediction model of the proportions of the expected components was constructed using the artificial neural network. The results of the GCA showed that the irritation sorting of each SAI component was in the order of B > A > G > J > I > H > D > F > E > C and the effectiveness sorting of SAI components was in the order of D > C > B > A > F > E > H > I > G > J; the predictive proportion of SAI was optimized by the BP neural network as A: B: C: D: E: F = 0.7526: 0.5005: 5.4565: 1.4149: 0.8113: 1.0642. This study provided a scientific, accurate, reliable, and efficient approach for the proportion optimization of multicomponent drugs, which has a good prospect of popularization and application in product upgrading and development of herbal medicine.
format	article
author	Yong-Shen Ren Lei Lei Xin Deng Yao Zheng Yan Li Jun Li Zhi-Nan Mei
author_facet	Yong-Shen Ren Lei Lei Xin Deng Yao Zheng Yan Li Jun Li Zhi-Nan Mei
author_sort	Yong-Shen Ren
title	Novel application of neural network modelling for multicomponent herbal medicine optimization
title_short	Novel application of neural network modelling for multicomponent herbal medicine optimization
title_full	Novel application of neural network modelling for multicomponent herbal medicine optimization
title_fullStr	Novel application of neural network modelling for multicomponent herbal medicine optimization
title_full_unstemmed	Novel application of neural network modelling for multicomponent herbal medicine optimization
title_sort	novel application of neural network modelling for multicomponent herbal medicine optimization
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/c60c8f784d02487a991d241f3a15c37a
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Novel application of neural network modelling for multicomponent herbal medicine optimization

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