@article{APS9934,
author = {Yue-min Bian and Xi-bing He and Yan-kang Jing and Li-rong Wang and Jun-mei Wang and Xiang-Qun Xie},
title = {Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomics-knowledgebase network analysis and integrated in silico modeling and simulation},
journal = {Acta Pharmacologica Sinica},
volume = {40},
number = {3},
year = {2019},
keywords = {},
abstract = {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.},
issn = {1745-7254}, url = {http://www.chinaphar.com/article/view/9934}
}