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QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids
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 esta...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7767513/ https://www.ncbi.nlm.nih.gov/pubmed/33371501 http://dx.doi.org/10.3390/molecules25246057 |
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author | Lee, Wonyoung Park, So-Jung Hwang, Ji-Young Hur, Kwang-Hyun Lee, Yong Sup Kim, Jongmin Zhao, Xiaodi Park, Aekyung Min, Kyung Hoon Jang, Choon-Gon Park, Hyun-Ju |
author_facet | Lee, Wonyoung Park, So-Jung Hwang, Ji-Young Hur, Kwang-Hyun Lee, Yong Sup Kim, Jongmin Zhao, Xiaodi Park, Aekyung Min, Kyung Hoon Jang, Choon-Gon Park, Hyun-Ju |
author_sort | Lee, Wonyoung |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7767513 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77675132020-12-28 QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids Lee, Wonyoung Park, So-Jung Hwang, Ji-Young Hur, Kwang-Hyun Lee, Yong Sup Kim, Jongmin Zhao, Xiaodi Park, Aekyung Min, Kyung Hoon Jang, Choon-Gon Park, Hyun-Ju Molecules Article 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. MDPI 2020-12-21 /pmc/articles/PMC7767513/ /pubmed/33371501 http://dx.doi.org/10.3390/molecules25246057 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lee, Wonyoung Park, So-Jung Hwang, Ji-Young Hur, Kwang-Hyun Lee, Yong Sup Kim, Jongmin Zhao, Xiaodi Park, Aekyung Min, Kyung Hoon Jang, Choon-Gon Park, Hyun-Ju QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids |
title | QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids |
title_full | QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids |
title_fullStr | QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids |
title_full_unstemmed | QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids |
title_short | QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids |
title_sort | qsar model for predicting the cannabinoid receptor 1 binding affinity and dependence potential of synthetic cannabinoids |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7767513/ https://www.ncbi.nlm.nih.gov/pubmed/33371501 http://dx.doi.org/10.3390/molecules25246057 |
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