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DOI 10.1016/j.jpba.2025.117313

Медицинская библиотека · Journal of pharmaceutical and biomedical analysis · 2025

SuHeXiang Wan in the treatment of stroke: Prediction potentially active metabolites using a combination of in silico analysis and experimental viability assessment.

Shen Lingyu, Li Wenxing, Chen Yupeng, Guan Daogang, Lu Aiping, Xu Anqi

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Аннотация

SuHeXiang Wan (SHXW) is a renowned traditional Chinese medicine (TCM) prescription for treating stroke, but its active components remain largely unidentified. This study aimed to screen potential active compounds of SHXW and reveal their underlying mechanisms in stroke treatment. Ingredients and compounds in SHXW were obtained from TCM databases and subjected to initial screening based on ADMET and physicochemical properties, followed by target gene prediction for each filtered compound. A comprehensive network of filtered compound-target gene-stroke pathogenic gene was constructed and optimized using a multi-objective optimization algorithm. Seventeen potential active compounds were identified, which primarily influenced neuroactive ligand-receptor interactions, arachidonic acid metabolism, and several signaling pathways including PI3K-Akt, calcium, and cAMP. Using an oxygen-glucose deprivation and reoxygenation (OGD/R) model, cirsiliol was identified as the lead compound, significantly enhancing cell viability and morphology, decreasing apoptosis, and reducing oxidative stress and inflammatory responses. Molecular docking and dynamics simulations revealed that cirsiliol stably binds to the NADPH binding pocket of CBR1 protein. Further experiments demonstrated that cirsiliol decreased 4-hydroxynonenal (4-HNE, a substrate of CBR1) levels. This study provides a methodological framework for screening active compounds in TCM prescriptions. The neuroprotective effects of cirsiliol against ischemic stroke merit further investigation.

Для цитирования: Shen Lingyu, Li Wenxing, Chen Yupeng, Guan Daogang, Lu Aiping, Xu Anqi. SuHeXiang Wan in the treatment of stroke: Prediction potentially active metabolites using a combination of in silico analysis and experimental viability assessment.. Journal of pharmaceutical and biomedical analysis. 2025. DOI: 10.1016/j.jpba.2025.117313.

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