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Mechanistic analysis of oral liquid in the treatment of asthma based on network pharmacology and machine learning
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DOI：10.1016/j.lddd.2025.100243.png)
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Abstract
En 中文
Background: Asthma is a chronic inflammatory disorder characterized by airway hyperresponsiveness and remodeling. Zhikeling oral liquid (ZOL), a traditional Chinese medicine, has been reported to exhibit potential benefits in asthma management, but its underlying molecular mechanisms have yet to be fully elucidated. Methods: Differentially expressed genes (DEGs) were obtained from the GEO database. ZOL-related active components and targets were collected from TCMSP, HERB, and SwissTargetPrediction databases. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized with Cytoscape. GO and KEGG enrichment analyses were performed on the intersecting genes. Key genes were screened using LASSO, SVM-RFE, and Random Forest algorithms. Molecular docking was performed with AutoDock Vina. The therapeutic effect of atropine, a representative active compound of ZOL, was further validated in vitro using an interleukin-13 (IL-13)-induced asthma model in 16HBE airway epithelial cells. Results: A total of 1623 DEGs and 218 predicted ZOL targets were identified. Among the eight identified active compounds of ZOL, atropine was one of the core candidate compounds. Functional enrichment analysis suggested that these shared targets were mainly associated with immune and inflammatory pathways. Three key genes (SLC5A1, SLC28A2, and MGAM) were identified through a combination of three machine learning algorithms. Molecular docking demonstrated that atropine exhibited strong binding affinities with the three key genes. Further in vitro validation showed that atropine suppressed IL-13-induced SLC5A1 upregulation, reversed the IL-13-induced downregulation of SLC28A2 and MGAM, and alleviated inflammation and ROS accumulation in 16HBE cells Conclusion: This study revealed that ZOL might exert anti-asthmatic effects via a multi-target mechanism, providing a reference for further pharmacological and clinical research.
Keywords:
Zhikeling oral liquid
Asthma
Network Pharmacology
Machine Learning
Molecular Docking
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