Compatibility optimization of the traditional Chinese medicines ‘Eczema mixture’ based on back-propagation artificial neural network and non-dominated sorting genetic algorithm

IntroductionChinese medicine formulas (CMF) are an important aspect of traditional Chinese medicine (TCM) and are formulated based on strict compatibility proportions guided by TCM theory. Due to the complex chemical constituents of TCM and the diversity of evaluation indicators for a certain diseas...

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Main Authors: Xin He, Zhijie Song, Yanqun Yang, Siqi Wu, Shuo Meng, Huanyu E, Hongfei Li, Guoyu Ding
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Pharmacology
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Online Access:https://www.frontiersin.org/articles/10.3389/fphar.2025.1593783/full
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author Xin He
Zhijie Song
Yanqun Yang
Siqi Wu
Shuo Meng
Huanyu E
Hongfei Li
Guoyu Ding
author_facet Xin He
Zhijie Song
Yanqun Yang
Siqi Wu
Shuo Meng
Huanyu E
Hongfei Li
Guoyu Ding
author_sort Xin He
collection DOAJ
description IntroductionChinese medicine formulas (CMF) are an important aspect of traditional Chinese medicine (TCM) and are formulated based on strict compatibility proportions guided by TCM theory. Due to the complex chemical constituents of TCM and the diversity of evaluation indicators for a certain disease, the research strategy on how to obtain the optimal combination of these crude extracts, homologous compounds or even the specific compounds mixture becomes the key step in the study of compatibility proportion research. Therefore, in this research, the “Eczema mixture” (EM) which includes six kinds of Chinese medicinal materials for the treatment of atopic dermatitis was cited as an example to illustrate the proposed compatibility optimization strategy.MethodsUltra-performance liquid chromatography-quadrupole/time-of-flight (UPLC-Q/TOF) technology was used to analyze the chemical components in the EM formula, and a total of 136 chemical compounds were identified. 76 formulas with different compatibility ratios were generated with the simplex centroid mixture design (SCMD). Two defined objective functions, the maximum of the anti-inflammatory and anti-allergic activity were used to evaluate the bioactivities of all the formulas. The 6-n-2 three-layers of back-propagation artificial neural network (BP-ANN) was employed to model the two defined objective functions. With the predictive models, the Pareto front was determined by a variant of non-dominated sorting genetic algorithm II(VNSGAII) to provide the optimal prescription set.ResultsThe 6-n-2 three-layers of artificial neural networks demonstrated a satisfactory fitting effect for the nonlinear activity relationship. In the EM formula, Huangbai and Kushen were identified as the main botanical drugs with anti-inflammatory and anti-allergic roles. The results were consistent with the clinical application of the 113 prescriptions involving 230 botanical drugs for the treatment of AD from the ‘Dictionary of Traditional Chinese Medicine Prescription’.ConclusionThe proposed SCMD-ANN-VNSGAII is a powerful approach that may facilitate future compatibility optimization of homologous compounds or specific component mixtures.
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spelling doaj-art-2d93d1c20fe74451a76eecf2bb18d5f82025-08-20T02:13:31ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-05-011610.3389/fphar.2025.15937831593783Compatibility optimization of the traditional Chinese medicines ‘Eczema mixture’ based on back-propagation artificial neural network and non-dominated sorting genetic algorithmXin He0Zhijie Song1Yanqun Yang2Siqi Wu3Shuo Meng4Huanyu E5Hongfei Li6Guoyu Ding7Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, ChinaSchool of Pharmacy, Shenyang Medical College, Shenyang, ChinaSchool of Pharmacy, Shenyang Medical College, Shenyang, ChinaSchool of Pharmacy, Shenyang Medical College, Shenyang, ChinaSchool of Pharmacy, Shenyang Medical College, Shenyang, ChinaSchool of Pharmacy, Shenyang Medical College, Shenyang, ChinaShenyang 15th Retired Cadres’ Center, Liaoning Province Military Command, Shenyang, ChinaSchool of Pharmacy, Shenyang Medical College, Shenyang, ChinaIntroductionChinese medicine formulas (CMF) are an important aspect of traditional Chinese medicine (TCM) and are formulated based on strict compatibility proportions guided by TCM theory. Due to the complex chemical constituents of TCM and the diversity of evaluation indicators for a certain disease, the research strategy on how to obtain the optimal combination of these crude extracts, homologous compounds or even the specific compounds mixture becomes the key step in the study of compatibility proportion research. Therefore, in this research, the “Eczema mixture” (EM) which includes six kinds of Chinese medicinal materials for the treatment of atopic dermatitis was cited as an example to illustrate the proposed compatibility optimization strategy.MethodsUltra-performance liquid chromatography-quadrupole/time-of-flight (UPLC-Q/TOF) technology was used to analyze the chemical components in the EM formula, and a total of 136 chemical compounds were identified. 76 formulas with different compatibility ratios were generated with the simplex centroid mixture design (SCMD). Two defined objective functions, the maximum of the anti-inflammatory and anti-allergic activity were used to evaluate the bioactivities of all the formulas. The 6-n-2 three-layers of back-propagation artificial neural network (BP-ANN) was employed to model the two defined objective functions. With the predictive models, the Pareto front was determined by a variant of non-dominated sorting genetic algorithm II(VNSGAII) to provide the optimal prescription set.ResultsThe 6-n-2 three-layers of artificial neural networks demonstrated a satisfactory fitting effect for the nonlinear activity relationship. In the EM formula, Huangbai and Kushen were identified as the main botanical drugs with anti-inflammatory and anti-allergic roles. The results were consistent with the clinical application of the 113 prescriptions involving 230 botanical drugs for the treatment of AD from the ‘Dictionary of Traditional Chinese Medicine Prescription’.ConclusionThe proposed SCMD-ANN-VNSGAII is a powerful approach that may facilitate future compatibility optimization of homologous compounds or specific component mixtures.https://www.frontiersin.org/articles/10.3389/fphar.2025.1593783/fullChinese medicine formulaTCM compatibilitymulti-objective optimizationartificial neural networknon-dominated sorting genetic algorithm
spellingShingle Xin He
Zhijie Song
Yanqun Yang
Siqi Wu
Shuo Meng
Huanyu E
Hongfei Li
Guoyu Ding
Compatibility optimization of the traditional Chinese medicines ‘Eczema mixture’ based on back-propagation artificial neural network and non-dominated sorting genetic algorithm
Frontiers in Pharmacology
Chinese medicine formula
TCM compatibility
multi-objective optimization
artificial neural network
non-dominated sorting genetic algorithm
title Compatibility optimization of the traditional Chinese medicines ‘Eczema mixture’ based on back-propagation artificial neural network and non-dominated sorting genetic algorithm
title_full Compatibility optimization of the traditional Chinese medicines ‘Eczema mixture’ based on back-propagation artificial neural network and non-dominated sorting genetic algorithm
title_fullStr Compatibility optimization of the traditional Chinese medicines ‘Eczema mixture’ based on back-propagation artificial neural network and non-dominated sorting genetic algorithm
title_full_unstemmed Compatibility optimization of the traditional Chinese medicines ‘Eczema mixture’ based on back-propagation artificial neural network and non-dominated sorting genetic algorithm
title_short Compatibility optimization of the traditional Chinese medicines ‘Eczema mixture’ based on back-propagation artificial neural network and non-dominated sorting genetic algorithm
title_sort compatibility optimization of the traditional chinese medicines eczema mixture based on back propagation artificial neural network and non dominated sorting genetic algorithm
topic Chinese medicine formula
TCM compatibility
multi-objective optimization
artificial neural network
non-dominated sorting genetic algorithm
url https://www.frontiersin.org/articles/10.3389/fphar.2025.1593783/full
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