A Network Pharmacology of Camellia sinensis (Green Tea)

Network pharmacology focuses on the therapeutic concept of one-target-one-drug to network-target-components to combat complex diseases. This research uses bioinformatics and high-throughput screening methods to facilitate the prediction of various drug target networks based on the establishment of biological models and becomes more important in uncovering the underlying mechanisms of drug action. Tea (Camelia sinensis) is one of the most ancient and popular therapeutic drinks consumed throughout the world and prepared as a drink that can have many health effects. This research aims to determine the pharmacological network analysis of C. sinensis. The list of C. sinensis secondary metabolite compounds was obtained from the Dr. database Duke. Protein predictions associated with C. sinensis were obtained from SwissTargetPrediction. Pharmacological network analysis was performed with StringDB and KEGG enrichment. From the search results, 57 compounds were obtained. From network pharmacology analysis, 15 biomolecular pathways were obtained that were closely related to secondary metabolite compounds in C. sinensis. From the results of further analysis, it was found that C. sinensis has a role in the treatment of hypertension, cancer, and anti-inflammation.