Virtual Screening Technique Used to Estimate the Mechanism of Adhatoda vasica Nees for the Treatment of Rheumatoid Arthritis Based on Network Pharmacology and Molecular Docking

Abstract

Adhatoda vasica Nees (AVN) is commonly used to treat joint diseases such as rheumatoid arthritis (RA) in ethnic minority areas of China, especially in Tibetan and Dai areas, and its molecular mechanisms on RA still remain unclear. Network pharmacology, a novel strategy, utilizes bioinformatics to predict and evaluate drug targets and interactions in disease. Here, network pharmacology was used to investigate the mechanism by which AVN acts in RA. The chemical compositions and functional targets of AVN were retrieved using the systematic pharmacological analysis platform PharmMapper. The targets of RA were queried through the DrugBank database. The protein-protein interaction network (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of key targets were constructed in the STRING database, and the network visualization analysis was performed in Cytoscape. Maestro 11.1, a type of professional software, was used for verifying prediction and analysis based on network pharmacology. By comparing the predicted target information with the targets of RA-related drugs, 25 potential targets may be related to the treatment of RA, among which MAPK1, TNF, DHODH, IL2, PTGS2, and JAK2 may be the main potential targets for the treatment of RA. Finally, the chemical components and potential target proteins were scored by molecular docking, and compared with the ligands of the protein, the prediction results of network pharmacology were preliminarily verified. The active ingredients and mechanism of AVN against RA were firstly investigated using network pharmacology. Additionally, this research provided a solid foundation for further experimental studies.

Figure 1

A flow chart of this study.

Figure 2

GO enrichment analysis of biological processes, cell composition, and molecular function annotation of the selected 25 target proteins.

Figure 3

KEGG analysis of potential targets related to occurrence and development of rheumatoid arthritis: (a) Bubble chart and (b) Histogram.

Figure 4

Protein-protein interaction network. (a) Constructed by Cytoscape 3.7.0; the color of the node is positively related to the degree of contribution of the node in the network. (b) Constructed by String 11.0; the color of the node is representative to the different signaling pathway as is shown above.

Figure 5

Component-target-pathway network (The color of the node is positively related to the degree of contribution of the node.).

Figure 6

Procedure of molecular docking.

Figure 7

Molecular docking of active compounds and key targets: (a) kaempferol-3-O-rutinoside to MAPK1; (b) quercetin-3-O-sophoroside to TNF; (c) N-demethyl adhatodine to DHODH; (d) vasicine to IL2; (e) quercetin-3-O-rutinoside to PTGS2; and (f) quercetin-3-O-sophoroside to JAK2.