School of Pharmacy, Sungkyunkwan University, Suwon 16419, Korea.
Department of Pharmacy, College of Pharmacy, Kyung Hee University, Seoul 02447, Korea.
Molecules. 2020 Dec 21;25(24):6057. doi: 10.3390/molecules25246057.
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.
近年来，有关合成大麻素（SC）滥用的不良影响的报道频繁出现。SC 通过与中枢神经系统中的大麻素受体 1（CB1R）结合并激活它，引起与大麻相似的精神活性作用。本研究旨在建立一个可靠的定量构效关系（QSAR）模型，以将各种 SC 的结构和物理化学性质与其 CB1R 结合亲和力相关联。我们制备了四氢大麻酚（THC）和 14 种 SC 及其衍生物（萘酰基吲哚、萘酰基萘、苯甲酰基吲哚和环己基苯酚），并测定了它们与 CB1R 的结合亲和力，CB1R 是一种与依赖性相关的靶标。我们使用 R/CDK（R 包与 CDK 集成，版本 3.5.0）工具包为数据集化合物计算了分子描述符，以构建 QSAR 回归模型。我们使用 R 软件中的 mlr 和 plsr 包建立并进行了统计评估。通过 Y 随机化测试和外部验证，从偏最小二乘回归方法中获得了最可靠的 QSAR 模型。该模型可在体内用于预测非法新型 SC 的成瘾特性。通过使用有限数量的数据集化合物和我们自己的实验活性数据，我们为 SC 建立了一个具有良好可预测性的 QSAR 模型。这种 QSAR 建模方法为建立一种有效的工具提供了一种新策略，以预测各种 SC 的滥用潜力并控制其非法使用。
