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The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity

Designer benzodiazepines (DBZDs) represent a serious health concern and are increasingly reported in polydrug consumption-related fatalities. When new DBZDs are identified, very limited information is available on their pharmacodynamics. Here, computational models (i.e., quantitative structure-activ...

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Autores principales: Catalani, Valeria, Botha, Michelle, Corkery, John Martin, Guirguis, Amira, Vento, Alessandro, Scherbaum, Norbert, Schifano, Fabrizio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8398354/
https://www.ncbi.nlm.nih.gov/pubmed/34451817
http://dx.doi.org/10.3390/ph14080720
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author Catalani, Valeria
Botha, Michelle
Corkery, John Martin
Guirguis, Amira
Vento, Alessandro
Scherbaum, Norbert
Schifano, Fabrizio
author_facet Catalani, Valeria
Botha, Michelle
Corkery, John Martin
Guirguis, Amira
Vento, Alessandro
Scherbaum, Norbert
Schifano, Fabrizio
author_sort Catalani, Valeria
collection PubMed
description Designer benzodiazepines (DBZDs) represent a serious health concern and are increasingly reported in polydrug consumption-related fatalities. When new DBZDs are identified, very limited information is available on their pharmacodynamics. Here, computational models (i.e., quantitative structure-activity relationship/QSAR and Molecular Docking) were used to analyse DBZDs identified online by an automated web crawler (NPSfinder(®)) and to predict their possible activity/affinity on the gamma-aminobutyric acid A receptors (GABA-ARs). The computational software MOE was used to calculate 2D QSAR models, perform docking studies on crystallised GABA-A receptors (6HUO, 6HUP) and generate pharmacophore queries from the docking conformational results. 101 DBZDs were identified online by NPSfinder(®). The validated QSAR model predicted high biological activity values for 41% of these DBDZs. These predictions were supported by the docking studies (good binding affinity) and the pharmacophore modelling confirmed the importance of the presence and location of hydrophobic and polar functions identified by QSAR. This study confirms once again the importance of web-based analysis in the assessment of drug scenarios (DBZDs), and how computational models could be used to acquire fast and reliable information on biological activity for index novel DBZDs, as preliminary data for further investigations.
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spelling pubmed-83983542021-08-29 The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity Catalani, Valeria Botha, Michelle Corkery, John Martin Guirguis, Amira Vento, Alessandro Scherbaum, Norbert Schifano, Fabrizio Pharmaceuticals (Basel) Article Designer benzodiazepines (DBZDs) represent a serious health concern and are increasingly reported in polydrug consumption-related fatalities. When new DBZDs are identified, very limited information is available on their pharmacodynamics. Here, computational models (i.e., quantitative structure-activity relationship/QSAR and Molecular Docking) were used to analyse DBZDs identified online by an automated web crawler (NPSfinder(®)) and to predict their possible activity/affinity on the gamma-aminobutyric acid A receptors (GABA-ARs). The computational software MOE was used to calculate 2D QSAR models, perform docking studies on crystallised GABA-A receptors (6HUO, 6HUP) and generate pharmacophore queries from the docking conformational results. 101 DBZDs were identified online by NPSfinder(®). The validated QSAR model predicted high biological activity values for 41% of these DBDZs. These predictions were supported by the docking studies (good binding affinity) and the pharmacophore modelling confirmed the importance of the presence and location of hydrophobic and polar functions identified by QSAR. This study confirms once again the importance of web-based analysis in the assessment of drug scenarios (DBZDs), and how computational models could be used to acquire fast and reliable information on biological activity for index novel DBZDs, as preliminary data for further investigations. MDPI 2021-07-26 /pmc/articles/PMC8398354/ /pubmed/34451817 http://dx.doi.org/10.3390/ph14080720 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Catalani, Valeria
Botha, Michelle
Corkery, John Martin
Guirguis, Amira
Vento, Alessandro
Scherbaum, Norbert
Schifano, Fabrizio
The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity
title The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity
title_full The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity
title_fullStr The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity
title_full_unstemmed The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity
title_short The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity
title_sort psychonauts’ benzodiazepines; quantitative structure-activity relationship (qsar) analysis and docking prediction of their biological activity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8398354/
https://www.ncbi.nlm.nih.gov/pubmed/34451817
http://dx.doi.org/10.3390/ph14080720
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