QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids

Molecules

Jongmin Kim¹  KyungHoon Min¹  Aekyung Park²  YongSup Lee³  Xiaodi Zhao⁴  So-Jung Park⁴  Choon-Gon Jang⁴  Hyun-Ju Park⁴  Ji-Young Hwang⁴  Wonyoung Lee⁴  Kwang-Hyun Hur⁴ 

[1] College of Pharmacy, Chung-Ang University, Seoul 06974, Korea;College of Pharmacy, Sunchon National University, Suncheon 57922, Korea;Department of Pharmacy, College of Pharmacy, Kyung Hee University, Seoul 02447, Korea;School of Pharmacy, Sungkyunkwan University, Suwon 16419, Korea;

关键词: cannabinoid receptor 1;    synthetic cannabinoids;    quantitative structure-activity relationship;    multiple linear regression;    partial least squares regression;    dependence and abuse potential;   

DOI  :  10.3390/molecules25246057

来源: DOAJ

【 摘 要 】

In recent years, there have been frequent reports on the adverse effects of synthetic cannabinoid (SC) abuse. SCs cause psychoactive effects, similar to those caused by marijuana, by binding and activating cannabinoid receptor 1 (CB1R) in the central nervous system. The aim of this study was to establish a reliable quantitative structure–activity relationship (QSAR) model to correlate the structures and physicochemical properties of various SCs with their CB1R-binding affinities. We prepared tetrahydrocannabinol (THC) and 14 SCs and their derivatives (naphthoylindoles, naphthoylnaphthalenes, benzoylindoles, and cyclohexylphenols) and determined their binding affinity to CB1R, which is known as a dependence-related target. We calculated the molecular descriptors for dataset compounds using an R/CDK (R package integrated with CDK, version 3.5.0) toolkit to build QSAR regression models. These models were established, and statistical evaluations were performed using the mlr and plsr packages in R software. The most reliable QSAR model was obtained from the partial least squares regression method via Y-randomization test and external validation. This model can be applied in vivo to predict the addictive properties of illicit new SCs. Using a limited number of dataset compounds and our own experimental activity data, we built a QSAR model for SCs with good predictability. This QSAR modeling approach provides a novel strategy for establishing an efficient tool to predict the abuse potential of various SCs and to control their illicit use.

【 授权许可】

Unknown