TY - JOUR T1 - Network Pharmacology Study of Boosting effect from Aiye Herb on Anti-covid Properties of Cangzhu AU - Zhang , Ying-Chen AU - Zhang , Xia-Nan AU - Qiu , Zhen-Zhong AU - Wu , Hong-Yan AU - Zhang , Qing-Song AU - Zhang , Zhi-Ru AU - Li , Jia-Hao AU - Zhao , Jia-Qing AU - Pan , Meng-Yao JO - Journal of Fiber Bioengineering and Informatics VL - 2 SP - 131 EP - 144 PY - 2022 DA - 2022/08 SN - 15 DO - http://doi.org/10.3993/jfbim00387 UR - https://global-sci.org/intro/article_detail/jfbi/20943.html KW - Cangzhu KW - Aiye KW - Covid-19 KW - Network pharmacology KW - molecular docking KW - symptoms KW - Efficacy boost AB -

Introduction: Cangzhu, an herbal medicine used to treat symptoms of respiratory pneumonia in traditional Chinese medical system, has shown its effectiveness in combating fever, cough, and fatigue of current pandemic while no specialty drugs are available. Latest research in network pharmacology has confirmed the theoretical mechanism behind, the drug itself is commonly prescribed alone side another herb Aiye, which believed to be able to improve the effectiveness of Cangzhu. In this study, network pharmacology will be applied in search of potential mechanism behind.

Method: The Traditional Chinese Medicine Systems Pharmacology (TCMSP) is used to filter the active compounds and the target of the prescription compound. The Genecard and OMIM database are applied to identify the target related to our aim symptom fever, cough, and fatigue. The STRING database is used to analyse the intercepted targets. Compound-target interaction and protein-protein interaction networks are constructed using the Cytoscape between target disease Covid and our medicine mixture Cangzhu and Aiye. The Kyoto Encyclopaedia of Genes and Genome (KEGG) pathway and Gene Ontology (GO) enrichment analysis are performed for investigation of the molecular mechanisms. Finally, the interaction probability between the targets and the active compounds can be determined by molecular docking technology.

Results: A total of 14 target are identified, in which are 10 most important targets and 2 key compounds. Besides, 216 biological processes items are obtained (P<0.05). Two hundred and seventyone pathways are obtained (P<0.05). The result of molecular docking shows a stable binding between the active compounds and the target.