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Computational Drug Design of Novel Agonists of the μ -Opioid Receptor to Inhibit Pain Signaling

Opioids such as Morphine, Codeine, Hydrocodone, and Oxycodone target the μ-opioid receptor, a G-protein-coupled receptor (GPCR), blocking the transmission of nociceptive signals. In this study, four opioids were analyzed for ADMET properties and molecular interactions with a GPCR crystal structure (...

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Autores principales: Daoud, Nancy, Mateos, Diego Lopez, Riley, Mary A., Siegel, Justin B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473735/
https://www.ncbi.nlm.nih.gov/pubmed/37662242
http://dx.doi.org/10.1101/2023.08.25.554876
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author Daoud, Nancy
Mateos, Diego Lopez
Riley, Mary A.
Siegel, Justin B.
author_facet Daoud, Nancy
Mateos, Diego Lopez
Riley, Mary A.
Siegel, Justin B.
author_sort Daoud, Nancy
collection PubMed
description Opioids such as Morphine, Codeine, Hydrocodone, and Oxycodone target the μ-opioid receptor, a G-protein-coupled receptor (GPCR), blocking the transmission of nociceptive signals. In this study, four opioids were analyzed for ADMET properties and molecular interactions with a GPCR crystal structure (PDB ID: 8EF6). This aided in the computational design of two novel drug candidates with improved docking scores and ADMET properties when compared to Hydrocodone. Homology analysis indicated that a Mus musculus (house mouse) animal model could be used in the preclinical studies of these drug candidates in the development of safer and more effective opioid drugs for pain management with reduced side effects.
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spelling pubmed-104737352023-09-02 Computational Drug Design of Novel Agonists of the μ -Opioid Receptor to Inhibit Pain Signaling Daoud, Nancy Mateos, Diego Lopez Riley, Mary A. Siegel, Justin B. bioRxiv Article Opioids such as Morphine, Codeine, Hydrocodone, and Oxycodone target the μ-opioid receptor, a G-protein-coupled receptor (GPCR), blocking the transmission of nociceptive signals. In this study, four opioids were analyzed for ADMET properties and molecular interactions with a GPCR crystal structure (PDB ID: 8EF6). This aided in the computational design of two novel drug candidates with improved docking scores and ADMET properties when compared to Hydrocodone. Homology analysis indicated that a Mus musculus (house mouse) animal model could be used in the preclinical studies of these drug candidates in the development of safer and more effective opioid drugs for pain management with reduced side effects. Cold Spring Harbor Laboratory 2023-08-26 /pmc/articles/PMC10473735/ /pubmed/37662242 http://dx.doi.org/10.1101/2023.08.25.554876 Text en https://creativecommons.org/licenses/by-nc/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Daoud, Nancy
Mateos, Diego Lopez
Riley, Mary A.
Siegel, Justin B.
Computational Drug Design of Novel Agonists of the μ -Opioid Receptor to Inhibit Pain Signaling
title Computational Drug Design of Novel Agonists of the μ -Opioid Receptor to Inhibit Pain Signaling
title_full Computational Drug Design of Novel Agonists of the μ -Opioid Receptor to Inhibit Pain Signaling
title_fullStr Computational Drug Design of Novel Agonists of the μ -Opioid Receptor to Inhibit Pain Signaling
title_full_unstemmed Computational Drug Design of Novel Agonists of the μ -Opioid Receptor to Inhibit Pain Signaling
title_short Computational Drug Design of Novel Agonists of the μ -Opioid Receptor to Inhibit Pain Signaling
title_sort computational drug design of novel agonists of the μ -opioid receptor to inhibit pain signaling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473735/
https://www.ncbi.nlm.nih.gov/pubmed/37662242
http://dx.doi.org/10.1101/2023.08.25.554876
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