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Войти в исследования Frontiers in pharmacology · 2025
Understanding the molecular basis of herbal medicines for cough variant asthma under the guidance of traditional herbal theories.
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Liu Rui, Yao Jiaqi, Sui Yihang, Zhang Yinnan, Kho Boon Seng, Zhu Yingli, Tan Ninghua, Wang Yinyin
Аннотация
INTRODUCTION: Despite the clear clinical efficacy of the herbal formula Suhuang in treating cough variant asthma (CVA), its underlying mechanisms of action (MOAs) remain poorly understood. Traditional Chinese Medicine (TCM) offers a unique framework for disease treatment based on traditional herbal theories. However, the molecular basis of these theories remains largely unexplored.
METHODS: To address this gap, we proposed a novel computational paradigm to understand how herbal medicines exert therapeutic effects on CVA under the guidance of TCM theories. Our approach integrates transcriptional perturbation data, graph neural network (GNN) models, and network proximity analysis, enabling the interpretation of herbal actions within a network pharmacology context.
RESULTS: We found that traditional herbal theories show strong molecular-level associations with therapeutic mechanisms: 1) Meridian classifications of herbs align with their gene perturbation profiles across different organs; 2) Herbal combinations and their therapeutic efficacy correlate with the network proximity of their targets to disease-specific genes. Notably, network proximity analysis revealed mechanistic support for key TCM concepts such as the JUN-CHEN-ZUO-SHI hierarchy and the Lung-Large Intestine Theory; 3) By incorporating features derived from traditional herbal theory, we developed two GNN-based models to predict herb-disease associations and herb-herb combinations, which identified potential active ingredients and synergistic formulations for CVA.
DISCUSSION: This study presents a novel framework for interpreting the molecular basis of herbal medicines and their combinations under TCM theory guidance.
METHODS: To address this gap, we proposed a novel computational paradigm to understand how herbal medicines exert therapeutic effects on CVA under the guidance of TCM theories. Our approach integrates transcriptional perturbation data, graph neural network (GNN) models, and network proximity analysis, enabling the interpretation of herbal actions within a network pharmacology context.
RESULTS: We found that traditional herbal theories show strong molecular-level associations with therapeutic mechanisms: 1) Meridian classifications of herbs align with their gene perturbation profiles across different organs; 2) Herbal combinations and their therapeutic efficacy correlate with the network proximity of their targets to disease-specific genes. Notably, network proximity analysis revealed mechanistic support for key TCM concepts such as the JUN-CHEN-ZUO-SHI hierarchy and the Lung-Large Intestine Theory; 3) By incorporating features derived from traditional herbal theory, we developed two GNN-based models to predict herb-disease associations and herb-herb combinations, which identified potential active ingredients and synergistic formulations for CVA.
DISCUSSION: This study presents a novel framework for interpreting the molecular basis of herbal medicines and their combinations under TCM theory guidance.