Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomics-knowledgebase network analysis and integrated in silico modeling and simulation

Authors: Yue-min Bian, Xi-bing He, Yan-kang Jing, Li-rong Wang, Jun-mei Wang, Xiang-Qun Xie
DOI: 10.1038/s41401-018-0071-1
Received: 11 January 2018
Accepted: 10 June 2018
Advance online: 10 September 2018


With treatment benefits in both the central nervous system and the peripheral system, the medical use of cannabidiol (CBD) has gained increasing popularity. Given that the therapeutic mechanisms of CBD are still vague, the systematic identification of its potential targets, signaling pathways, and their associations with corresponding diseases is of great interest for researchers. In the present work, chemogenomics-knowledgebase systems pharmacology analysis was applied for systematic network studies to generate CBD-target, target-pathway, and target-disease networks by combining both the results from the in silico analysis and the reported experimental validations. Based on the network analysis, three human neuro-related rhodopsin-like GPCRs, i.e., 5-hydroxytryptamine receptor 1 A (5HT1A), delta-type opioid receptor (OPRD) and G protein-coupled receptor 55 (GPR55), were selected for close evaluation. Integrated computational methodologies, including homology modeling, molecular docking, and molecular dynamics simulation, were used to evaluate the protein-CBD binding modes. A CBD-preferred pocket consisting of a hydrophobic cavity and backbone hinges was proposed and tested for CBD-class A GPCR binding. Finally, the neurophysiological effects of CBD were illustrated at the molecular level, and dopamine receptor 3 (DRD3) was further predicted to be an active target for CBD.
Keywords: cannabinoid; cannabidiol (CBD); systems pharmacology; homology modeling; molecular docking; molecular dynamics simulation; 5HT1A; D3

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