Analyze the application and mechanism of Traditional Chinese Medicine in chronic urticaria based on data mining and network pharmacology

Abstract

Objective

Chronic urticaria (CU) is a prevalent skin condition. Increasing evidence supports the efficacy of traditional Chinese medicine (TCM) in its management. This study aims to identify the primary bioactive constituents and elucidate the potential molecular mechanisms of key TCM drug combinations for CU treatment, utilizing data mining, network pharmacology, and molecular docking.

Methods

Relevant TCM prescriptions for the treatment of CU were collected from multiple databases, including CNKI, VIP, Wan Fang Database, Embase, PubMed, and Web of Science. Data were analyzed using IBM SPSS Modeler 18.0 to identify core drug pairs with the highest confidence levels. Active ingredients and target predictions for these core drug pairs were determined using the TCMSP, BATMAN-TCM, HERB, and SwissTargetPrediction databases. CU-related targets were obtained from OMIM, DisGeNET, GeneCards, PharmGKB, CTD, and Drugbank, and intersected with disease targets retrieved from the GEO database. These targets were further intersected with drug targets and analyzed within the STRING database for protein-protein interaction (PPI) network analysis, visualized using Cytoscape 3.7.2, and core nodes in the network were identified using the CytoHubba plugin. The intersecting targets of drugs and diseases were subjected to GO and KEGG pathway analysis via the DAVID database and analyzed for their distribution across 84 target organs in the human body using the BioGps database. Molecular docking validation was performed using AutoDockTools 1.5.6, AutoDock Vina, and PyMOL software.

Results

Through the application of inclusion and exclusion criteria, 374 articles were identified, encompassing 344 prescriptions and 198 herbs. The core drug combination “Saposhnikoviae Radix-Schizonepetae Herba-Cicadae Periostracum” (FF-JJ-CT) with the highest confidence level was selected. A total of 45 active ingredients and 780 unique potential targets were screened, and 50 disease targets were obtained. Twelve targets at the intersection of herbs and diseases were identified. A PPI network was constructed, and seven core targets (VCAM1, STAT3, SELE, MYC, ITGB2, ICAM1, HIF1A) were screened based on degree centrality (DC) ≥ 10. GO and KEGG analyses revealed that the intersecting targets were primarily enriched in pathways related to cell adhesion molecules, the TNF signaling pathway, and the AGE-RAGE signaling pathway. The target organs were predominantly expressed in whole blood and the immune system (CD33+_Myeloid, CD14+_Monocytes, BDCA4+_DentriticCells, CD56+_NKCells). Molecular docking results indicated that the active ingredients Quercetin, Decursin, Andrographolide, and its derivative 14_deoxy_11_oxa_andrographolide from the “FF-JJ-CT” combination exhibited favorable binding activities with the core targets ICAM1, ITGB2, STAT3, SELE, and VCAM1.

Conclusion

Our work, employing data mining and network pharmacology, expands the understanding of TCM in CU therapy, potentially providing an efficacious approach for the discovery and development of drugs derived from herbal sources.