ABSTRACT
Objective::To explore the mechanism of Xiao Banxiatang in preventing and treating chemotherapy-induced nausea and vomiting by using network pharmacology. Method::The targets of chemotherapy-related nausea and vomiting were collected by therapeutic target database (TTD), Drugbank database and DisGeNET database. The target genes were normalized by Uniprot database. The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was selected according to oral bioavailabilityc (OB) ≥ 30%, drug-likeness (DL) ≥ 0.14 and the literature research. The active constituents of pinellia ternata and ginger were collected through the PubChem database, the ALOGPS2.1 database and the Swiss Target Prediction database, and the target of ginger was collected and standardized through the Uniprot database, the molecular inverse docking of the important component 6-gingerol was carried out through the DRAR-CPI database, gene ontology (GO) analysis and kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed through DAVID 6.8 database, and relationship diagrams were drawn by Cytoscape 3.2.1 software and network topology parameters were analyzed, GO and KEGG bubble maps were drawn by ImageGP tool. Result::A total of 148 targets for chemotherapeutic nausea and vomiting, and 27 active ingredients of Xiao Banxiatang were collected, including 22 associated with chemotherapeutic nausea and vomiting, 38 control targets, 67 biological processes based on GO analysis, 11 cell components, 18 molecular functions, 21 KEGG pathways, involving cyclic Adenosine monophosphate (cAMP) signaling pathway, calcium signaling pathway, Rap1 signaling pathway. Conclusion::Based on network pharmacology, chemotherapy-related nausea and vomiting and Xiao Banxiatang were analyzed to provide ideas for the prevention and treatment of chemotherapy-induced nausea and vomiting.
ABSTRACT
Drugs play the pharmacological effects by combining with target proteins. Identification of drug-target interactions is important for discovering new functions of drugs. In this paper, the target fingerprints based on molecular substructure and the drug-target similarity based on fingerprints are proposed to a random forest-based classification method, in order to identify the drug-target interactions. Experiments on enzymes, ion channels, G protein-coupled receptors and nuclear receptors proved the effectiveness of the proposed method. In addition, the proposed method is applied to predict the interactions between ingredients and targets of traditional Chinese medicines.