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Bentham Science Publishers, Combinatorial Chemistry & High Throughput Screening, 4(26), p. 706-718, 2023

DOI: 10.2174/1386207325666220609121842

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Network Pharmacology-based Strategy to Investigate Pharmacological Mechanisms of Qingbutongluo Pill for Treatment of Brucellosis

Journal article published in 2023 by Wei-Gang Zhou, Jing Wang, Jia-Wei He, Ji-Shan Liu, Jian-E. Li, Qing-You Cui, Yi-Rui Wang
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Background and Objectives: Qingbutongluo pill (QBTLP), a Chinese herbal preparation, has been developed to treat brucellosis for many years with a good therapeutic effect. This study preliminarily explored its potential molecular mechanisms against brucellosis through network pharmacology. Methods: The active ingredients of QBTLP were screened out mainly from the Traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP), and their potential targets were predicted through the PubChem database and Swiss Target Prediction platform. GeneCards, DisGeNET, Digsee, and the Comparative Toxicogenomics Database (CTD) searched the targets corresponding to brucellosis. Then, the Venn diagram obtained intersection targets of QBTLP and diseases. Protein-protein interaction (PPI) network analysis was performed using the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized in Cytoscape software. Module analysis of the PPI network and core target identification was performed using the Molecular Complex Detection (MCODE) and the Cytohubba plugins. The Metascape data platform was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the intersection targets, and then the “active ingredientstargets- pathways” network was constructed using Cytoscape to screen key active ingredients. Results: 19 key active ingredients were identified by network pharmacological, including Baicalein, Cryptopin, etc. The core targets of QBTLP for treating brucellosis contained TNF, TLR4, MAPK3, MAPK1, MAPK8, MAPK14, MMP9, etc. And the main pathways included the Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, TNF signaling pathway, MAPK signaling pathway, Th17 cell differentiation, and IL-17 signaling pathway. Conclusions: This study explored the mechanisms of QBTLP for treating brucellosis, which may provide a scientific basis for the clinical application of QBTLP.