Novel application of neural network modelling for multicomponent herbal medicine optimization
Yong-Shen Ren1, Lei Lei2, Xin Deng2
1School of Pharmaceutical Science, South-Central University for Nationalities, Wuhan, Hubei, China. godreny@mail.scuec.edu.cn.
Scientific Reports
|October 30, 2019
Summary
This study introduces a new method for analyzing multicomponent herbal medicines, moving beyond single-component analysis. It uses chemical fingerprints and artificial neural networks to optimize drug proportions for better effectiveness and safety.
Area of Science:
- Pharmacology
- Analytical Chemistry
- Computational Chemistry
Background:
- Traditional methods for identifying effective or toxic substances in multicomponent herbal medicine are inefficient and neglect component interactions.
- A more scientific and efficient approach is needed for evaluating complex herbal formulations.
Purpose of the Study:
- To develop and validate a novel method for optimizing the component proportions of multicomponent herbal drugs.
- To evaluate the effectiveness and irritation of sodium aescinate injection (SAI) components using a "components knockout" strategy.
Main Methods:
- High-performance liquid chromatography (HPLC) was used to obtain chemical fingerprints of SAI samples.
- Anti-inflammatory and irritation tests were conducted to assess sample efficacy and safety.
- Gray correlation analysis (GCA) was applied to rank component contributions to effectiveness and irritation.
- Artificial neural networks (ANNs), specifically a BP neural network, were employed to build a predictive model for optimizing component proportions.
Main Results:
- GCA revealed distinct rankings for irritation (B>A>G>J>I>H>D>F>E>C) and effectiveness (D>C>B>A>F>E>H>I>G>J) of SAI components.
- A predictive model using BP neural network optimized the proportions of SAI components to A:B:C:D:E:F = 0.7526:0.5005:5.4565:1.4149:0.8113:1.0642.
Conclusions:
- The developed approach offers a scientific, accurate, reliable, and efficient method for optimizing multicomponent drug proportions.
- This methodology shows significant potential for application in the upgrading and development of herbal medicines.
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