The Abundant Phytocannabinoids in Rheumatoid Arthritis: Therapeutic Targets and Molecular Processes Identified Using Integrated Bioinformatics and Network Pharmacology

Abstract

The endocannabinoid system consists of several phytocannabinoids, cannabinoid receptors, and enzymes that aid in numerous steps necessary to manifest any pharmacological activity. It is well known that the endocannabinoid system inhibits the pathogenesis of the inflammatory and autoimmune disease rheumatoid arthritis (RA). To the best of our knowledge, no research has been done that explains the network-pharmacology-based anti-rheumatic processes by focusing on the endocannabinoid system. Therefore, the purpose of this study is to further our understanding of the signaling pathways, associated proteins, and genes underlying RA based on the abundant natural endocannabinoids. The knowledge on how the phytocannabinoids in Cannabis sativa affect the endocannabinoid system was gathered from the literature. SwissTarget prediction and BindingDB databases were used to anticipate the targets for the phytocannabinoids. The genes related to RA were retrieved from the DisGeNET and GeneCards databases. Protein–protein interactions (high confidence > 0.7) were carried out with the aid of the string web server and displayed using Cytoscape. The Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway analysis was used to perform enrichment analyses on the endocannabinoid–RA common targets. ShinyGO 0.76 was used to predict the biological processes listed in the Gene Ontology (GO) classification system. The binding affinity between the ligand and the receptors was precisely understood using molecular docking, induced-fit docking, and a molecular dynamics simulation. The network pharmacology analyses predicted that processes like response to oxygen-containing compounds and peptodyl-amino acid modification are related to the potential mechanisms of treatment for RA. These biological actions are coordinated by cancer, neuroactive ligand–receptor interaction, lipids and atherosclerosis, the calcium signaling pathway, and the Rap1 signaling pathway. According to the results of molecular docking, in the context of RA, phytocannabinoids may bind to important target proteins such PIK3CA, AKT1, MAPK9, PRKCD, BRAF, IGF1R, and NOS3. This entire study predicted the phytocannabinoids’ systemic biological characteristics. Future experimental research is needed, however, to confirm the results so far.

Indole clubbed 2,4‐thiazolidinedione linked 1,2,3‐triazole as a potent antimalarial and antibacterial agent against drug‐resistant strain and molecular modeling studies

“…For this Schrödinger's Induced-fit docking panel was employed, where the standard protocol and OPLS3 force field were employed for carrying out the induced-fit docking. [51][52][53]…”

Integrative Multiomics Network Analysis of Genetic Risk Factors to Infer Biomarkers and Therapeutic Targets for Rheumatoid Arthritis

“…That study combined different types of molecular information to identify the key molecules in the RA synovial-specific cytokine signaling network, to assess their involvement in the pathogenesis of RA, and to explore their potential for developing new therapeutics; although, this work only considered one pathway and did not investigate the molecular interactions of risk factors with other pathways [24]. Moreover, Nandi et al conducted network pharmacology-based experiments of phytocannabinoid features and discovered candidate drugs that may bind to critical target proteins by studying signaling pathways and proteins related to RA in the endocannabinoid system [25]. However, this research was limited to molecular docking and network pharmacology.…”

“…However, this research was limited to molecular docking and network pharmacology. As a result, more extensive research and evaluation of the involvement of the endocannabinoid system in the immunological landscape in patients with RA, as well as a comprehensive comparison of patients with RA and normal controls, is required [25]. Although researchers have attempted to understand the molecular interactions of genetic factors that are associated with RA and to identify the molecular pathways, the value of biological interaction network analysis in the development of RA remains poorly understood.…”