Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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
_version_ 1783628976536682496
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
work_keys_str_mv AT leewonyoung qsarmodelforpredictingthecannabinoidreceptor1bindingaffinityanddependencepotentialofsyntheticcannabinoids
AT parksojung qsarmodelforpredictingthecannabinoidreceptor1bindingaffinityanddependencepotentialofsyntheticcannabinoids
AT hwangjiyoung qsarmodelforpredictingthecannabinoidreceptor1bindingaffinityanddependencepotentialofsyntheticcannabinoids
AT hurkwanghyun qsarmodelforpredictingthecannabinoidreceptor1bindingaffinityanddependencepotentialofsyntheticcannabinoids
AT leeyongsup qsarmodelforpredictingthecannabinoidreceptor1bindingaffinityanddependencepotentialofsyntheticcannabinoids
AT kimjongmin qsarmodelforpredictingthecannabinoidreceptor1bindingaffinityanddependencepotentialofsyntheticcannabinoids
AT zhaoxiaodi qsarmodelforpredictingthecannabinoidreceptor1bindingaffinityanddependencepotentialofsyntheticcannabinoids
AT parkaekyung qsarmodelforpredictingthecannabinoidreceptor1bindingaffinityanddependencepotentialofsyntheticcannabinoids
AT minkyunghoon qsarmodelforpredictingthecannabinoidreceptor1bindingaffinityanddependencepotentialofsyntheticcannabinoids
AT jangchoongon qsarmodelforpredictingthecannabinoidreceptor1bindingaffinityanddependencepotentialofsyntheticcannabinoids
AT parkhyunju qsarmodelforpredictingthecannabinoidreceptor1bindingaffinityanddependencepotentialofsyntheticcannabinoids