Functional studies cast light on receptor states

Quantitative receptor model for responses that are left- or right-shifted versus occupancy (are more or less concentration sensitive): the SABRE approach

Simple one-to three-parameter models routinely used to fit typical dose-response curves and calculate EC50 values using the Hill or Clark equation cannot provide the full picture connecting measured response to receptor occupancy, which can be quite complex due to the interplay between partial agonism and (pathway-dependent) signal amplification. The recently introduced SABRE quantitative receptor model is the first one that explicitly includes a parameter for signal amplification (γ) in addition to those for binding affinity (K d), receptor-activation efficacy (ε), constitutive activity (ε R0), and steepness of response (Hill slope, n). It can provide a unified framework to fit complex cases, where fractional response and occupancy do not match, as well as simple ones, where parameters constrained to specific values can be used (e.g., ε R0 = 0, γ = 1, or n = 1). Here, it is shown for the first time that SABRE can fit not only typical cases where response curves are left-shifted compared to occupancy (κ = K d/EC50 > 1) due to signal amplification (γ > 1), but also less common ones where they are right-shifted (i.e., less concentration-sensitive; κ = K d/EC50 < 1) by modeling them as apparent signal attenuation/loss (γ < 1). Illustrations are provided with μ-opioid receptor (MOPr) data from three different experiments with one left- and one right-shifted response (G protein activation and β-arrestin2 recruitment, respectively; EC50,Gprt < K d < EC50,βArr). For such cases of diverging pathways with differently shifted responses, partial agonists can cause very weak responses in the less concentration-sensitive pathway without having to be biased ligands due to the combination of low ligand efficacy and signal attenuation/loss-an illustration with SABRE-fitted oliceridine data is included.

A brief overview about the adipokine: Isthmin-1

Isthmin-1 is a secreted protein with multiple capability; however, it truly attracts our attention since the definition as an adipokine in 2021, which exerts indispensable roles in various pathophysiological processes through the endocrine or autocrine manners. In this review, we summarize recent knowledge of isthmin-1, including its distribution, structure, receptor and potential function.

Low Intrinsic Efficacy Alone Cannot Explain the Improved Side Effect Profiles of New Opioid Agonists

In a recent report in Science Signaling (Gillis, A., et al. Low intrinsic efficacy for G protein activation can explain the improved side effect profiles of new opioid agonists. Sci. Signaling 2020, 13, eaaz3140 10.1126/scisignal.aaz3140), it was suggested that low intrinsic agonism, and not biased agonism, leads to an improvement in the separation of potency in opioid-induced respiratory suppression versus antinociception. Although many of the compounds that were tested have been shown to display G protein signaling bias in prior publications, the authors conclude that because they cannot detect biased agonism in their cellular signaling studies the compounds are therefore not biased agonists. Rather, they conclude that it is low intrinsic efficacy that leads to the therapeutic window improvement. Intrinsic efficacy is the extent to which an agonist can stimulate a G protein-coupled receptor response in a system, while biased agonism takes into consideration not only the intrinsic efficacy but also the potency of an agonist in an assay. Herein, we have reanalyzed the data presented in the published work (10.1126/scisignal.aaz3140) [including the recent Erratum (10.1126/scisignal.abf9803)] to derive intrinsic efficacy and bias factors as ΔΔlog(τ/K_A) and ΔΔlog(Emax/EC₅₀), respectively. On the basis of this reanalysis, the data support the conclusion that biased agonism, favoring G protein signaling, was observed. Moreover, a conservation of rank order intrinsic efficacy was not observed upon comparing responses in each assay, further suggesting that multiple active receptor states were present. These observations agree with prior studies in which oliceridine, PZM21, and SR-17018 were first described as biased agonists with improvement in antinociception over respiratory suppression in mice. Therefore, the data in the Science Signaling paper provide strong corroborating evidence that G protein signaling bias may be a means of improving opioid analgesia while avoiding certain undesirable side effects.

Allosteric Modulation of Neurotransmitter Transporters as a Therapeutic Strategy

Neurotransmitter transporters (NTTs) are involved in the fine-tuning of brain neurotransmitter homeostasis. As such, they are implicated in a plethora of complex behaviors, including reward, movement, and cognition. During recent decades, compounds that modulate NTT functions have been developed. Some of them are in clinical use for the management of different neuropsychiatric conditions. The majority of these compounds have been found to selectively interact with the orthosteric site of NTTs. Recently, diverse allosteric sites have been described in a number of NTTs, modulating their function. A more complex NTT pharmacology may be useful in the development of novel therapeutics. Here, we summarize current knowledge on such modulatory allosteric sites, with specific focus on their pharmacological and therapeutic potential.

Unveiling functional motions based on point mutations in biased signaling systems: A normal mode study on nerve growth factor bound to TrkA

Many receptors elicit signal transduction by activating multiple intracellular pathways. This transduction can be triggered by a non-specific ligand, which simultaneously activates all the signaling pathways of the receptors. However, the binding of one biased ligand preferentially trigger one pathway over another, in a process called biased signaling. The identification the functional motions related to each of these distinct pathways has a direct impact on the development of new effective and specific drugs. We show here how to detect specific functional motions by considering the case of the NGF/TrkA-Ig2 complex. NGF-mediated TrkA receptor activation is dependent on specific structural motions that trigger the neuronal growth, development, and survival of neurons in nervous system. The R221W mutation in the ngf gene impairs nociceptive signaling. We discuss how the large-scale structural effects of this mutation lead to the suppression of collective motions necessary to induce TrkA activation of nociceptive signaling. Our results suggest that subtle changes in the NGF interaction network due to the point mutation are sufficient to inhibit the motions of TrkA receptors putatively linked to nociception. The methodological approach presented in this article, based jointly on the normal mode analysis and the experimentally observed functional alterations due to point mutations provides an essential tool to reveal the structural changes and motions linked to the disease, which in turn could be necessary for a drug design study.

The Endocannabinoid System and Synthetic Cannabinoids in Preclinical Models of Seizure and Epilepsy

Cannabinoids are compounds that are structurally and/or functionally related to the primary psychoactive constituent of Cannabis sativa, [INCREMENT]-tetrahydrocannabinol (THC). Cannabinoids can be divided into three broad categories: endogenous cannabinoids, plant-derived cannabinoids, and synthetic cannabinoids (SCs). Recently, there has been an unprecedented surge of interest into the pharmacological and medicinal properties of cannabinoids for the treatment of epilepsies. This surge has been stimulated by an ongoing shift in societal opinions about cannabinoid-based medicines and evidence that cannabidiol, a nonintoxicating plant cannabinoid, has demonstrable anticonvulsant activity in children with treatment-refractory epilepsy. The major receptors of the endogenous cannabinoid system (ECS)-the type 1 and 2 cannabinoid receptors (CB1R, CB2R)-have critical roles in the modulation of neurotransmitter release and inflammation, respectively; so, it is not surprising therefore that the ECS is being considered as a target for the treatment of epilepsy. SCs were developed as potential new drug candidates and tool compounds for studying the ECS. Beyond the plant cannabinoids, an extensive research effort is underway to determine whether SCs that directly target CB1R, CB2R, or the enzymes that breakdown endogenous cannabinoids have anticonvulsant effects in preclinical rodent models of epilepsy and seizure. This research demonstrates that many SCs do reduce seizure severity in rodent models and may have both positive and negative pharmacodynamic and pharmacokinetic interactions with clinically used antiepilepsy drugs. Here, we provide a comprehensive review of the preclinical evidence for and against SC modulation of seizure and discuss the important questions that need to be addressed in future studies.

A Receptor Model With Binding Affinity, Activation Efficacy, and Signal Amplification Parameters for Complex Fractional Response Versus Occupancy Data

In quantitative pharmacology, multi-parameter receptor models are needed to account for the complex nonlinear relationship between fractional occupancy and response that can occur due to the intermixing of the effects of partial receptor activation and post-receptor signal amplification. Here, a general two-state receptor model and corresponding quantitative forms are proposed that unify three distinct processes, each characterized with its own parameter: 1) receptor binding, characterized by K_d, the equilibrium dissociation constant used for binding affinity; 2) receptor activation, characterized by an (intrinsic) efficacy parameter ε; and 3) post-activation signal transduction (amplification), characterized by a gain parameter γ. Constitutive activity is accommodated via an additional ε_R0 parameter quantifying the activation of the ligand-free receptor. Receptors can be active or inactive in both their ligand-free and ligand-bound states (two-state receptor theory), but ligand binding alters the likelihood of activation (induced fit). Because structural data now confirm that for most receptors in their active conformation, the small-molecule ligand-binding site is buried inside, straightforward binding to the active form (direct conformational selection) is unlikely. The proposed general equation has parameters that are more intuitive and better suited for optimization by nonlinear regression than those of the operational (Black and Leff) or del Castillo–Katz model. The model provides a unified framework for fitting complex data including a) fractional responses that do not match independently measured fractional occupancies, b) responses measured after partial irreversible inactivation of the “receptor reserve” (Furchgott method), c) fractional responses that are different along distinct downstream pathways (biased agonism), and d) responses with constitutive receptor activity. Furthermore, unlike previous models, the present one can be reduced back for special cases of its parameters to consecutively nested simplified forms that can be used on their own when adequate (e.g., ε_R0 = 0, no constitutive activity; γ = 1: E_max model for partial agonism; ε = 1: Clark equation).

Analysis of Biased Agonism

Agonists and most natural ligands bind to receptors in their inactive state and quickly induce an active receptor conformation that initiates cell signaling. The active receptor state initiates signaling because of its structural complementariness with coupling proteins that activate signaling pathways, such as G proteins and G protein-coupled receptor kinases. Agonist bias refers to the propensity of an agonist to direct receptor signaling through one pathway relative to another. Thus, if the agonist exhibits much higher affinity for active state 1 compared to active state 2, it will cause a robust activation of receptor coupling protein 1 but not 2, and ultimately, a preferential stimulation of signaling pathway 1. Biased agonists are potentially more selective therapeutic agents because there are numerous cases where the therapeutic and adverse effects of an agonist are mediated by distinct pathways involving G proteins and β-arrestin. Given the mechanism for agonist bias, the most straightforward approach for quantifying bias involves the estimation of agonist affinity for the inactive receptor state and the active receptor states involved in signaling through different pathways. The approach provides quantitative estimates of the sensitivities of different signaling pathways, enabling one to determine to what extent the observed selectivity is caused by agonist or system bias. In addition, the approach is a powerful adjunct to in silico docking studies and can be applied to in vivo assays, structure-activity relationships, and the analysis of published agonist concentration-response curves.

A method for the quantification of biased signalling at constitutively active receptors

BACKGROUND AND PURPOSE

Biased agonism, the ability of an agonist to differentially activate one of several signal transduction pathways when acting at a given receptor, is an increasingly recognized phenomenon at many receptors. The Black and Leff operational model lacks a way to describe constitutive receptor activity and hence inverse agonism. Thus, it is impossible to analyse the biased signalling of inverse agonists using this model. In this theoretical work, we develop and illustrate methods for the analysis of biased inverse agonism.

EXPERIMENTAL APPROACH

Methods were derived for quantifying biased signalling in systems that demonstrate constitutive activity using the modified operational model proposed by Slack and Hall. The methods were illustrated using Monte Carlo simulations.

KEY RESULTS

The Monte Carlo simulations demonstrated that, with an appropriate experimental design, the model parameters are 'identifiable'. The method is consistent with methods based on the measurement of intrinsic relative activity (RA_i ) (ΔΔlogR or ΔΔlog(τ/K_a )) proposed by Ehlert and Kenakin and their co-workers but has some advantages. In particular, it allows the quantification of ligand bias independently of 'system bias' removing the requirement to normalize to a standard ligand.

CONCLUSIONS AND IMPLICATIONS

In systems with constitutive activity, the Slack and Hall model provides methods for quantifying the absolute bias of agonists and inverse agonists. This provides an alternative to methods based on RA_i and is complementary to the ΔΔlog(τ/K_a ) method of Kenakin et al. in systems where use of that method is inappropriate due to the presence of constitutive activity.

Methods to Quantify Cell Signaling and GPCR Receptor Ligand Bias: Characterization of Drugs that Target the Endocannabinoid Receptors in Huntington's Disease

G protein-coupled receptors (GPCRs) interact with multiple intracellular effector proteins such that different ligands may preferentially activate one signal pathway over others, a phenomenon known as signaling bias. Signaling bias can be quantified to optimize drug selection for preclinical research. Here, we describe moderate-throughput methods to quantify signaling bias of known and novel compounds. In the example provided, we describe a method to define cannabinoid-signaling bias in a cell culture model of Huntington's disease (HD). Decreasing type 1 cannabinoid receptor (CB₁) levels is correlated with chorea and cognitive deficits in HD. There is evidence that elevating CB₁ levels and/or signaling may be beneficial for HD patients while decreasing CB₁ levels and/or signaling may be detrimental. Recent studies have found that Gα_i/o-biased CB₁ agonists activate extracellular signal-regulated kinase (ERK), increase CB₁ protein levels, and improve viability of cells expressing mutant huntingtin. In contrast, CB₁ agonists that are β-arrestin1-biased were found to reduce CB₁ protein levels and cell viability. Measuring agonist bias of known and novel CB₁ agonists will provide important data that predict CB₁-specific agonists that might be beneficial in animal models of HD and, following animal testing, in HD patients. This method can also be applied to study signaling bias for other GPCRs.

A Complementary Scale of Biased Agonism for Agonists with Differing Maximal Responses

Compelling data in the literature from the recent years leave no doubt about the pluridimensional nature of G protein-coupled receptor function and the fact that some ligands can couple with different efficacies to the multiple pathways that a receptor can signal through, a phenomenon most commonly known as functional selectivity or biased agonism. Nowadays, transduction coefficients (log(τ/K_A)), based on the Black and Leff operational model of agonism, are widely used to calculate bias. Nevertheless, combining both affinity and efficacy in a single parameter can result in compounds showing a defined calculated bias of one pathway over other though displaying varying experimental bias preferences. In this paper, we present a novel scale (log(τ)), that attempts to give extra substance to different compound profiles in order to better classify compounds and quantify their bias. The efficacy-driven log(τ) scale is not proposed as an alternative to the affinity&efficacy-driven log(τ/K_A) scale but as a complement in those situations where partial agonism is present. Both theoretical and practical approaches using μ-opioid receptor agonists are presented.

Quantifying the allosteric interactions within a G-protein-coupled receptor heterodimer

G-protein-coupled receptors are central to signal transduction and cell communication. The possibility that cells use receptor heteromerization to modulate individual receptor pathways is a surmise that cannot be precluded. Given the complexity of these processes, mathematical models contribute to understanding how receptors and their respective ligands regulate signaling. Here, a mathematical model is presented that quantifies the allosteric interactions within a receptor heterodimer. The model is based on the operational model of allosterism including constitutive receptor activity, which provides the pharmacological analysis of heteromerization with well-established and widely used modeling and fitting procedures.

Approaches to Assess Biased Signaling at the CB1R Receptor

G protein-coupled receptors, such as the cannabinoid type 1 receptor (CB1R), have been shown to interact with multiple binding partners to transmit signals. In both transfected cell systems and in endogenously expressing cell lines, CB1R signaling has been described as multifaceted. The question remains as to how this highly widely expressed receptor signals in a given cell at a given time in vivo. The concept of functional selectivity, or biased agonism, describes the ability of an agonist to engage the receptor in a manner that preferentially engages certain signaling interactions (e.g., G proteins) over others (e.g., β-arrestins), presumably by stabilizing certain receptor conformations. There is growing interest in using such properties of ligands to direct signaling downstream of CB1R toward desirable therapeutic outcomes and to avoid adverse side effects. While it is not currently clear what pathways should be engaged and which should be avoided, the development of biased agonist tool compounds will aid in answering these questions. In this chapter, we discuss the approaches and caveats to assessing biased agonism at the CB1R.

A three-parameter two-state model of receptor function that incorporates affinity, efficacy, and signal amplification

A generalized model of receptor function is proposed that relies on the essential assumptions of the minimal two-state receptor theory (i.e., ligand binding followed by receptor activation), but uses a different parametrization and allows nonlinear response (transduction) for possible signal amplification. For the most general case, three parameters are used: Kd, the classic equilibrium dissociation constant to characterize binding affinity; ε, an intrinsic efficacy to characterize the ability of the bound ligand to activate the receptor (ranging from 0 for an antagonist to 1 for a full agonist); and γ, a gain (amplification) parameter to characterize the nonlinearity of postactivation signal transduction (ranging from 1 for no amplification to infinity). The obtained equation, E/Emax=εγLεγ+1-εL+Kd, resembles that of the operational (Black and Leff) or minimal two-state (del Castillo-Katz) models, E/Emax=τLτ+1L+Kd, with εγ playing a role somewhat similar to that of the τ efficacy parameter of those models, but has several advantages. Its parameters are more intuitive as they are conceptually clearly related to the different steps of binding, activation, and signal transduction (amplification), and they are also better suited for optimization by nonlinear regression. It allows fitting of complex data where receptor binding and response are measured separately and the fractional occupancy and response are mismatched. Unlike the previous models, it is a true generalized model as simplified forms can be reproduced with special cases of its parameters. Such simplified forms can be used on their own to characterize partial agonism, competing partial and full agonists, or signal amplification.

Cannabinoid receptor ligand bias: implications in the central nervous system

The G protein-coupled cannabinoid receptors CB₁, CB₂, GPR18, and GPR55 regulate neurotransmission, pain, and inflammation and have been intensively investigated as potential drug targets. Each of these GPCRs is coupled to multiple effector proteins mediating divergent cellular signals. The ligand bias of cannabinoid-targeted compounds is only beginning to be quantified. Research into cannabinoid bias is now revealing correlations between bias in cell culture and functional outcomes in vivo. We present an example study of cannabinoid bias in the context of Huntington disease. In future, an understanding of cannabinoid receptor structure and quantification of ligand bias will optimize drug selection matched to patient population and disease.

Estimation of the receptor-state affinity constants of ligands in functional studies using wild type and constitutively active mutant receptors: Implications for estimation of agonist bias

We describe a method for estimating the affinities of ligands for active and inactive states of a G protein-coupled receptor (GPCR). Our protocol involves measuring agonist-induced signaling responses of a wild type GPCR and a constitutively active mutant of it under control conditions and after partial receptor inactivation or reduced receptor expression. Our subsequent analysis is based on the assumption that the activating mutation increases receptor isomerization into the active state without affecting the affinities of ligands for receptor states. A means of confirming this assumption is provided. Global nonlinear regression analysis yields estimates of 1) the active (K_act) and inactive (K_inact) receptor-state affinity constants, 2) the isomerization constant of the unoccupied receptor (K_q-obs), and 3) the sensitivity constant of the signaling pathway (K_E-obs). The latter two parameters define the output response of the receptor, and hence, their ratio (K_q-obs/K_E) is a useful measure of system bias. If the cellular system is reasonably stable and the K_q-obs and K_E-obs values of the signaling pathway are known, the K_act and K_inact values of additional agonists can be estimated in subsequent experiments on cells expressing the wild type receptor. We validated our method through computer simulation, an analytical proof, and analysis of previously published data. Our approach provides 1) a more meaningful analysis of structure-activity relationships, 2) a means of validating in silico docking experiments on active and inactive receptor structures and 3) an absolute, in contrast to relative, measure of agonist bias.

Leveraging allostery to improve G protein-coupled receptor (GPCR)-directed therapeutics: cannabinoid receptor 1 as discovery target

INTRODUCTION

Allosteric modulators of G-protein coupled receptors (GPCRs) hold the promise of improved pharmacology and safety over typical orthosteric GPCR ligands. These features are particularly relevant to the cannabinoid receptor 1 (CB1R) GPCR, since typical orthosteric CB1R ligands are associated with adverse events that limit their translational potential. Areas covered: The contextual basis for applying allostery to CB1R is considered from pharmacological, drug-discovery, and medicinal standpoints. Rational design of small-molecule CB1R allosteric modulators as potential pharmacotherapeutics would be greatly facilitated by direct experimental characterization of structure-function correlates underlying the biological activity of chemically-diverse CB1R allosteric modulators, CB1R allosteric ligand-binding binding pockets, and amino acid contact residues critical to allosteric ligand engagement and activity. In these regards, designer covalent probes exhibiting well-characterized molecular pharmacology as CB1R allosteric modulators are emerging as valuable molecular reporters enabling experimental interrogation of CB1R allosteric site(s) and informing the design of new CB1R agents as drugs. Expert opinion: Synthesis and pharmacological profiling of CB1R allosteric ligands will continue to provide valuable insights into CB1R structure-function correlates. The resulting data should expand the repertoire of novel agents capable of exerting therapeutic benefit by modulating CB1R-dependent signaling.

Cooperativity Has Empirical and Ultimate Levels of Explanation

Controversy over the meaning of pharmacological parameters often arises because of a lack of appreciation of different hierarchical levels of analysis. In a recent letter in Trends in Pharmacological Sciences, Zhang and Kavana [1] concluded that my two-state model for allosterism lacks cooperativity, even though Figures 5 and 6 in my review [2] illustrate examples of how the two-state model yields specific cooperativity values. Here, I explain how the two-state model (receptor-state analysis) gives rise to the cooperativity parameter (α) of the allosteric ternary complex model (receptor-population analysis).

Complex Pharmacology of Free Fatty Acid Receptors

G protein-coupled receptors (GPCRs) are historically the most successful family of drug targets. In recent times it has become clear that the pharmacology of these receptors is far more complex than previously imagined. Understanding of the pharmacological regulation of GPCRs now extends beyond simple competitive agonism or antagonism by ligands interacting with the orthosteric binding site of the receptor to incorporate concepts of allosteric agonism, allosteric modulation, signaling bias, constitutive activity, and inverse agonism. Herein, we consider how evolving concepts of GPCR pharmacology have shaped understanding of the complex pharmacology of receptors that recognize and are activated by nonesterified or "free" fatty acids (FFAs). The FFA family of receptors is a recently deorphanized set of GPCRs, the members of which are now receiving substantial interest as novel targets for the treatment of metabolic and inflammatory diseases. Further understanding of the complex pharmacology of these receptors will be critical to unlocking their ultimate therapeutic potential.

Quantitative Measure of Receptor Agonist and Modulator Equi-Response and Equi-Occupancy Selectivity

G protein-coupled receptors (GPCRs) are an important class of drug targets. Quantitative analysis by global curve fitting of properly designed dose-dependent GPCR agonism and allosterism data permits the determination of all affinity and efficacy parameters based on a general operational model. We report here a quantitative and panoramic measure of receptor agonist and modulator equi-response and equi-occupancy selectivity calculated from these parameters. The selectivity values help to differentiate not only one agonist or modulator from another, but on-target from off-target receptor or functional pathway as well. Furthermore, in conjunction with target site free drug concentrations and endogenous agonist tones, the allosterism parameters and selectivity values may be used to predict in vivo efficacy and safety margins.

Biased Type 1 Cannabinoid Receptor Signaling Influences Neuronal Viability in a Cell Culture Model of Huntington Disease

Huntington disease (HD) is an inherited, autosomal dominant, neurodegenerative disorder with limited treatment options. Prior to motor symptom onset or neuronal cell loss in HD, levels of the type 1 cannabinoid receptor (CB1) decrease in the basal ganglia. Decreasing CB1 levels are strongly correlated with chorea and cognitive deficit. CB1 agonists are functionally selective (biased) for divergent signaling pathways. In this study, six cannabinoids were tested for signaling bias in in vitro models of medium spiny projection neurons expressing wild-type (STHdh(Q7/Q7)) or mutant huntingtin protein (STHdh(Q111/Q111)). Signaling bias was assessed using the Black and Leff operational model. Relative activity [ΔlogR (τ/KA)] and system bias (ΔΔlogR) were calculated relative to the reference compound WIN55,212-2 for Gαi/o, Gαs, Gαq, Gβγ, and β-arrestin1 signaling following treatment with 2-arachidonoylglycerol (2-AG), anandamide (AEA), CP55,940, Δ(9)-tetrahydrocannabinol (THC), cannabidiol (CBD), and THC+CBD (1:1), and compared between wild-type and HD cells. The Emax of Gαi/o-dependent extracellular signal-regulated kinase (ERK) signaling was 50% lower in HD cells compared with wild-type cells. 2-AG and AEA displayed Gαi/o/Gβγ bias and normalized CB1 protein levels and improved cell viability, whereas CP55,940 and THC displayed β-arrestin1 bias and reduced CB1 protein levels and cell viability in HD cells. CBD was not a CB1 agonist but inhibited THC-dependent signaling (THC+CBD). Therefore, enhancing Gαi/o-biased endocannabinoid signaling may be therapeutically beneficial in HD. In contrast, cannabinoids that are β-arrestin-biased--such as THC found at high levels in modern varieties of marijuana--may be detrimental to CB1 signaling, particularly in HD where CB1 levels are already reduced.