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      A Biased View of μ-Opioid Receptors?

      ,
      Molecular Pharmacology
      American Society for Pharmacology & Experimental Therapeutics (ASPET)

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          Abstract

          The field of biased agonism has grown substantially in recent years and the μ-opioid receptor has been one of the most intensively studied receptor targets for developing biased agonists. Yet, despite extensive research efforts, the development of analgesics with reduced adverse effects remains a significant challenge. In this review we discuss the evidence to support the prevailing hypothesis that a G protein-biased agonist at the μ-opioid receptor would be an effective analgesic without the accompanying adverse effects associated with conventional μ-opioid agonists. We also assess the current status of established and novel μ-opioid-receptor ligands that are proposed to be biased ligands. SIGNIFICANCE STATEMENT: The idea that biased agonists at the μ-opioid receptor might provide a therapeutic advantage in terms of producing effective analgesia with fewer adverse effects has driven the design of novel G protein-biased agonists. However, is the desirability of G protein-biased agonists at μ-opioid receptor substantiated by what we know of the physiology and pharmacology of the receptor? Also, do any of the novel biased agonists live up to their initial promise? Here we address these issues by critically examining the evidence that G protein bias really is desirable and also by discussing whether the ligands so far developed are clearly biased in vitro and whether this produces responses in vivo that might be commensurate with such bias.

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          Molecular mechanisms of opioid receptor-dependent signaling and behavior.

          Opioid receptors have been targeted for the treatment of pain and related disorders for thousands of years and remain the most widely used analgesics in the clinic. Mu (μ), kappa (κ), and delta (δ) opioid receptors represent the originally classified receptor subtypes, with opioid receptor like-1 (ORL1) being the least characterized. All four receptors are G-protein coupled and activate inhibitory G proteins. These receptors form homo- and heterodimeric complexes and signal to kinase cascades and scaffold a variety of proteins.The authors discuss classic mechanisms and developments in understanding opioid tolerance and opioid receptor signaling and highlight advances in opioid molecular pharmacology, behavioral pharmacology, and human genetics. The authors put into context how opioid receptor signaling leads to the modulation of behavior with the potential for therapeutic intervention. Finally, the authors conclude there is a continued need for more translational work on opioid receptors in vivo.
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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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              Operational models of pharmacological agonism.

              The traditional receptor-stimulus model of agonism began with a description of drug action based on the law of mass action and has developed by a series of modifications, each accounting for new experimental evidence. By contrast, in this paper an approach to modelling agonism is taken that begins with the observation that experimental agonist-concentration effect, E/[A], curves are commonly hyperbolic and develops using the deduction that the relation between occupancy and effect must be hyperbolic if the law of mass action applies at the agonist-receptor level. The result is a general model that explicitly describes agonism by three parameters: an agonist-receptor dissociation constant, KA; the total receptor concentration, [R0]; and a parameter, KE, defining the transduction of agonist-receptor complex, AR, into pharmacological effect. The ratio, [R0]/KE, described here as the 'transducer ratio', tau, is a logical definition for the efficacy of an agonist in a system. The model may be extended to account for non-hyperbolic E/[A] curves with no loss of meaning. Analysis shows that an explicit formulation of the traditional receptor-stimulus model is one particular form of the general model but that it is not the simplest. An alternative model is proposed, representing the cognitive and transducer functions of a receptor, that describes agonist action with one fewer parameter than the traditional model. In addition, this model provides a chemical definition of intrinsic efficacy making this parameter experimentally accessible in principle. The alternative models are compared and contrasted with regard to their practical and conceptual utilities in experimental pharmacology.
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                Author and article information

                Journal
                Molecular Pharmacology
                Mol Pharmacol
                American Society for Pharmacology & Experimental Therapeutics (ASPET)
                0026-895X
                1521-0111
                September 30 2019
                November 2019
                November 2019
                June 07 2019
                : 96
                : 5
                : 542-549
                Article
                10.1124/mol.119.115956
                6784500
                31175184
                9be29650-20a7-4e34-b15a-c7aebb81c247
                © 2019
                History

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