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      Identification and characterization of plant-derived alkaloids, corydine and corydaline, as novel mu opioid receptor agonists

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          Abstract

          Pain remains a key therapeutic area with intensive efforts directed toward finding effective and safer analgesics in light of the ongoing opioid crisis. Amongst the neurotransmitter systems involved in pain perception and modulation, the mu-opioid receptor (MOR), a G protein-coupled receptor, represents one of the most important targets for achieving effective pain relief. Most clinically used opioid analgesics are agonists to the MOR, but they can also cause severe side effects. Medicinal plants represent important sources of new drug candidates, with morphine and its semisynthetic analogues as well-known examples as analgesic drugs. In this study, combining in silico (pharmacophore-based virtual screening and docking) and pharmacological (in vitro binding and functional assays, and behavioral tests) approaches, we report on the discovery of two naturally occurring plant alkaloids, corydine and corydaline, as new MOR agonists that produce antinociceptive effects in mice after subcutaneous administration via a MOR-dependent mechanism. Furthermore, corydine and corydaline were identified as G protein-biased agonists to the MOR without inducing β-arrestin2 recruitment upon receptor activation. Thus, these new scaffolds represent valuable starting points for future chemical optimization towards the development of novel opioid analgesics, which may exhibit improved therapeutic profiles.

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          Most cited references54

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          Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database

          Here, we present the algorithm and validation for OMEGA, a systematic, knowledge-based conformer generator. The algorithm consists of three phases: assembly of an initial 3D structure from a library of fragments; exhaustive enumeration of all rotatable torsions using values drawn from a knowledge-based list of angles, thereby generating a large set of conformations; and sampling of this set by geometric and energy criteria. Validation of conformer generators like OMEGA has often been undertaken by comparing computed conformer sets to experimental molecular conformations from crystallography, usually from the Protein Databank (PDB). Such an approach is fraught with difficulty due to the systematic problems with small molecule structures in the PDB. Methods are presented to identify a diverse set of small molecule structures from cocomplexes in the PDB that has maximal reliability. A challenging set of 197 high quality, carefully selected ligand structures from well-solved models was obtained using these methods. This set will provide a sound basis for comparison and validation of conformer generators in the future. Validation results from this set are compared to the results using structures of a set of druglike molecules extracted from the Cambridge Structural Database (CSD). OMEGA is found to perform very well in reproducing the crystallographic conformations from both these data sets using two complementary metrics of success.
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            Crystal structure of the μ-opioid receptor bound to a morphinan antagonist

            Summary Opium is one of the world’s oldest drugs, and its derivatives morphine and codeine are among the most used clinical drugs to relieve severe pain. These prototypical opioids produce analgesia as well as many of their undesirable side effects (sedation, apnea and dependence) by binding to and activating the G-protein-coupled μ-opioid receptor (μOR) in the central nervous system. Here we describe the 2.8 Å crystal structure of the μOR in complex with an irreversible morphinan antagonist. Compared to the buried binding pocket observed in most GPCRs published to date, the morphinan ligand binds deeply within a large solvent-exposed pocket. Of particular interest, the μOR crystallizes as a two-fold symmetric dimer through a four-helix bundle motif formed by transmembrane segments 5 and 6. These high-resolution insights into opioid receptor structure will enable the application of structure-based approaches to develop better drugs for the management of pain and addiction.
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              Signalling bias in new drug discovery: detection, quantification and therapeutic impact.

              Agonists of seven-transmembrane receptors, also known as G protein-coupled receptors (GPCRs), do not uniformly activate all cellular signalling pathways linked to a given seven-transmembrane receptor (a phenomenon termed ligand or agonist bias); this discovery has changed how high-throughput screens are designed and how lead compounds are optimized for therapeutic activity. The ability to experimentally detect ligand bias has necessitated the development of methods for quantifying agonist bias in a way that can be used to guide structure-activity studies and the selection of drug candidates. Here, we provide a viewpoint on which methods are appropriate for quantifying bias, based on knowledge of how cellular and intracellular signalling proteins control the conformation of seven-transmembrane receptors. We also discuss possible predictions of how biased molecules may perform in vivo, and what potential therapeutic advantages they may provide.
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                Author and article information

                Contributors
                Birgit.Waltenberger@uibk.ac.at
                Daniela.Schuster@pmu.ac.at
                Mariana.Spetea@uibk.ac.at
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                14 August 2020
                14 August 2020
                2020
                : 10
                : 13804
                Affiliations
                [1 ]GRID grid.5771.4, ISNI 0000 0001 2151 8122, Department of Pharmaceutical Chemistry, Institute of Pharmacy and Center for Molecular Biosciences Innsbruck (CMBI), , University of Innsbruck, ; Innrain 80-82, 6020 Innsbruck, Austria
                [2 ]GRID grid.5771.4, ISNI 0000 0001 2151 8122, Department of Pharmacognosy, Institute of Pharmacy and Center for Molecular Biosciences Innsbruck (CMBI), , University of Innsbruck, ; Innrain 80-82, 6020 Innsbruck, Austria
                [3 ]GRID grid.21604.31, ISNI 0000 0004 0523 5263, Department of Medicinal and Pharmaceutical Chemistry, Institute of Pharmacy, , Paracelsus Medical University, ; Strubergasse 22, 5020 Salzburg, Austria
                Article
                70493
                10.1038/s41598-020-70493-1
                7427800
                32796875
                1b0b79d9-f3d6-4b62-bc38-c97ed10e7d93
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 June 2020
                : 27 July 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002428, Austrian Science Fund;
                Award ID: TRP19-B18
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                drug discovery,medicinal chemistry,pharmacology,computational chemistry,natural products,small molecules

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