Kinetic operational models of agonism for G-protein-coupled receptors

Computational modelling of dynamic cAMP responses to GPCR agonists for exploration of GLP-1R ligand effects in pancreatic β-cells and neurons

The glucagon-like peptide-1 receptor (GLP-1R) is a class B G protein-coupled receptor (GPCR) which plays important physiological roles in insulin release and promoting fullness. GLP-1R agonists initiate cellular responses by cyclic AMP (cAMP) pathway signal transduction. Understanding of the potential of GLP-1R agonists in the treatment of type 2 diabetes may be advanced by considering the cAMP dynamics for agonists at GLP-1R in both pancreatic β-cells (important in insulin release) and neurons (important in appetite regulation). Receptor desensitisation in the cAMP pathway is known to be an important regulatory mechanism, with different ligands differentially promoting G protein activation and desensitisation. Here, we use mathematical modelling to quantify and understand experimentally obtained cAMP timecourses for two GLP-1R agonists, exendin-F1 (ExF1) and exendin-D3 (ExD3), which give markedly different signals in β-cells and neurons. We formulate an ordinary differential equation (ODE) model for the dynamics of cAMP signalling in response to G protein-coupled receptor (GPCR) ligands, encompassing ligand binding, receptor activation, G protein activation, desensitisation and second messenger generation. We validate our model initially by fitting to timecourse data for HEK293 cells, then proceed to parameterise the model for β-cells and neurons. Through numerical simulation and sensitivity studies, our analysis adds support to the hypothesis that ExF1 offers more potential glucose regulation benefit than ExD3 over long timescales via signalling in pancreatic β-cells, but that there is little difference between the two ligands in the potential appetite suppression effects offered via long-time signalling in neurons on the same timescales.

Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory

Mathematical modelling has become a key tool in pharmacological analysis, towards understanding dynamics of cell signalling and quantifying ligand-receptor interactions. Ordinary differential equation (ODE) models in receptor theory may be used to parameterise such interactions using timecourse data, but attention needs to be paid to the theoretical identifiability of the parameters of interest. Identifiability analysis is an often overlooked step in many bio-modelling works. In this paper we introduce structural identifiability analysis (SIA) to the field of receptor theory by applying three classical SIA methods (transfer function, Taylor Series and similarity transformation) to ligand-receptor binding models of biological importance (single ligand and Motulsky-Mahan competition binding at monomers, and a recently presented model of a single ligand binding at receptor dimers). New results are obtained which indicate the identifiable parameters for a single timecourse for Motulsky-Mahan binding and dimerised receptor binding. Importantly, we further consider combinations of experiments which may be performed to overcome issues of non-identifiability, to ensure the practical applicability of the work. The three SIA methods are demonstrated through a tutorial-style approach, using detailed calculations, which show the methods to be tractable for the low-dimensional ODE models.

Kinetic analysis of endogenous β2 -adrenoceptor-mediated cAMP GloSensor™ responses in HEK293 cells

BACKGROUND AND AIM

Standard pharmacological analysis of agonist activity utilises measurements of receptor-mediated responses at a set time-point, or at the peak response level, to characterise ligands. However, the occurrence of non-equilibrium conditions may dramatically impact the properties of the response being measured. Here we have analysed the initial kinetic phases of cAMP responses to β2 -adrenoceptor agonists in HEK293 cells expressing the endogenous β2 -adrenoceptor at extremely low levels.

EXPERIMENTAL APPROACH

The kinetics of β2 -adrenoceptor agonist-stimulated cAMP responses were monitored in real-time, in the presence and absence of antagonists, in HEK293 cells expressing the cAMP GloSensor™ biosensor. Potency (EC50 ) and efficacy (Emax ) values were determined at the peak of the agonist GloSensor™ response and compared to kinetic parameters L50 and IRmax values derived from initial response rates.

KEY RESULTS

The partial agonists salbutamol and salmeterol displayed reduced relative IRmax values (with respect to isoprenaline) when compared with their Emax values. Except for the fast dissociating bisoprolol, preincubation with β2 -adrenoceptor antagonists produced a large reduction in the isoprenaline peak response due to a state of hemi-equilibrium in this low receptor reserve system. This effect was exacerbated when IRmax parameters were measured. Furthermore, bisoprolol produced a large reduction in isoprenaline IRmax consistent with its short residence time.

CONCLUSIONS AND IMPLICATIONS

Kinetic analysis of real-time signalling data can provide valuable insights into the hemi-equilibria that can occur in low receptor reserve systems with agonist-antagonist interactions, due to incomplete dissociation of antagonist whilst the peak agonist response is developing.

A guide to enzyme kinetics in early drug discovery

Drugs interact with their target of interest to bring about the desired phenotypic outcome that results in disease alleviation. Traditionally, most lead optimization exercises were driven by affinity measures (like IC₅₀ ) to inform structure-activity relationship (SAR)-guided medicinal chemistry. However, an IC₅₀ value is a thermodynamic estimate measured under equilibrium conditions that can vary as a function of substrate concentration and/or time (the latter especially for nonequilibrium modalities). Further, like other thermodynamic estimates, it is a state-function that is indifferent to the path traversed from the initial state to the final state. This can be a cause for concern in drug discovery given the predominance of nonequilibrium interactions and the open thermodynamic nature of the human system. Under such situations, employing rates along with equilibrium constants (or IC₅₀ values) would be far more relevant to capture the time evolution of the small molecule's interaction with the target of interest. These rates are generally typified by the rate of association, rate of dissociation and the residence time of the small molecule on the target (target occupancy). These parameters, when combined with the concept of target vulnerability, therapeutic window, pharmacokinetic profile of the small molecule, estimates of endogenous ligand and target turnover, will shed critical insights into the kinetics and dynamics of a small molecule's interaction with the protein, and allow realistic modelling of the system to enable optimizations and dosing decisions. With that aim, this guide will attempt to introduce the traditional role of mechanistic enzymology within drug discovery and emphasize the importance of kinetics in guiding SAR-based optimizations. It will also present initial ideas on how kinetic investigation should be positioned relative to the temporal span of a drug-discovery pipeline to leverage maximal utility from the investment in time and effort.

Community Guidelines for GPCR Ligand Bias: IUPHAR Review XX

G protein-coupled receptors modulate a plethora of physiological processes and mediate the effects of one-third of FDA-approved drugs. Depending on which ligand activates a receptor, it can engage different intracellular transducers. This 'biased signaling' paradigm requires that we now characterize physiological signaling not just by receptors but by ligand-receptor pairs. Ligands eliciting biased signaling may constitute better drugs with higher efficacy and fewer adverse effects. However, ligand bias is very complex, making reproducibility and description challenging. Here, we provide guidelines and terminology for any scientists to design and report ligand bias experiments. The guidelines will aid consistency and clarity, as the basic receptor research and drug discovery communities continue to advance our understanding and exploitation of ligand bias. Scientific insight, biosensors, and analytical methods are still evolving and should benefit from and contribute to the implementation of the guidelines, together improving translation from in vitro to disease-relevant in vivo models.

Quantifying the Kinetics of Signaling and Arrestin Recruitment by Nervous System G-Protein Coupled Receptors

Neurons integrate inputs over different time and space scales. Fast excitatory synapses at boutons (ms and μm), and slow modulation over entire dendritic arbors (seconds and mm) are all ultimately combined to produce behavior. Understanding the timing of signaling events mediated by G-protein-coupled receptors is necessary to elucidate the mechanism of action of therapeutics targeting the nervous system. Measuring signaling kinetics in live cells has been transformed by the adoption of fluorescent biosensors and dyes that convert biological signals into optical signals that are conveniently recorded by microscopic imaging or by fluorescence plate readers. Quantifying the timing of signaling has now become routine with the application of equations in familiar curve fitting software to estimate the rates of signaling from the waveform. Here we describe examples of the application of these methods, including (1) Kinetic analysis of opioid signaling dynamics and partial agonism measured using cAMP and arrestin biosensors; (2) Quantifying the signaling activity of illicit synthetic cannabinoid receptor agonists measured using a fluorescent membrane potential dye; (3) Demonstration of multiplicity of arrestin functions from analysis of biosensor waveforms and quantification of the rates of these processes. These examples show how temporal analysis provides additional dimensions to enhance the understanding of GPCR signaling and therapeutic mechanisms in the nervous system.

Pharmacological Characterization of Low Molecular Weight Biased Agonists at the Follicle Stimulating Hormone Receptor

Follicle-stimulating hormone receptor (FSHR) plays a key role in reproduction through the activation of multiple signaling pathways. Low molecular weight (LMW) ligands composed of biased agonist properties are highly valuable tools to decipher complex signaling mechanisms as they allow selective activation of discrete signaling cascades. However, available LMW FSHR ligands have not been fully characterized yet. In this context, we explored the pharmacological diversity of three benzamide and two thiazolidinone derivatives compared to FSH. Concentration/activity curves were generated for Gαs, Gαq, Gαi, β-arrestin 2 recruitment, and cAMP production, using BRET assays in living cells. ERK phosphorylation was analyzed by Western blotting, and CRE-dependent transcription was assessed using a luciferase reporter assay. All assays were done in either wild-type, Gαs or β-arrestin 1/2 CRISPR knockout HEK293 cells. Bias factors were calculated for each pair of read-outs by using the operational model. Our results show that each ligand presented a discrete pharmacological efficacy compared to FSH, ranging from super-agonist for β-arrestin 2 recruitment to pure Gαs bias. Interestingly, LMW ligands generated kinetic profiles distinct from FSH (i.e., faster, slower or transient, depending on the ligand) and correlated with CRE-dependent transcription. In addition, clear system biases were observed in cells depleted of either Gαs or β-arrestin genes. Such LMW properties are useful pharmacological tools to better dissect the multiple signaling pathways activated by FSHR and assess their relative contributions at the cellular and physio-pathological levels.

Partial agonism improves the anti-hyperglycaemic efficacy of an oxyntomodulin-derived GLP-1R/GCGR co-agonist

OBJECTIVE

Glucagon-like peptide-1 and glucagon receptor (GLP-1R/GCGR) co-agonism can maximise weight loss and improve glycaemic control in type 2 diabetes and obesity. In this study, we investigated the cellular and metabolic effects of modulating the balance between G protein and β-arrestin-2 recruitment at GLP-1R and GCGR using oxyntomodulin (OXM)-derived co-agonists. This strategy has been previously shown to improve the duration of action of GLP-1R mono-agonists by reducing target desensitisation and downregulation.

METHODS

Dipeptidyl dipeptidase-4 (DPP-4)-resistant OXM analogues were generated and assessed for a variety of cellular readouts. Molecular dynamic simulations were used to gain insights into the molecular interactions involved. In vivo studies were performed in mice to identify the effects on glucose homeostasis and weight loss.

RESULTS

Ligand-specific reductions in β-arrestin-2 recruitment were associated with slower GLP-1R internalisation and prolonged glucose-lowering action in vivo. The putative benefits of GCGR agonism were retained, with equivalent weight loss compared to the GLP-1R mono-agonist liraglutide despite a lesser degree of food intake suppression. The compounds tested showed only a minor degree of biased agonism between G protein and β-arrestin-2 recruitment at both receptors and were best classified as partial agonists for the two pathways measured.

CONCLUSIONS

Diminishing β-arrestin-2 recruitment may be an effective way to increase the therapeutic efficacy of GLP-1R/GCGR co-agonists. These benefits can be achieved by partial rather than biased agonism.

Exploring group size for statistical analysis of real-time signalling experiments

BACKGROUND AND PURPOSE

Classical pharmacological bioassays generally use observed effects from a concentration series, at a single equilibrium time point to construct a concentration-effect curve, representing one experiment. However, if the full kinetic profile of the effect data for each concentration was evaluated simultaneously, then the analysis would be more powerful. In this work, we explore if more precise parameters can be achieved by using the full kinetic method.

EXPERIMENTAL APPROACH

We used a simulation estimation study to explore the influence of kinetic analysis on the precision of the E_max model parameter estimates (E_max and C₅₀ ). We compared a full kinetic approach in which all effect versus time data from a theoretical real-time signalling experiment were analysed simultaneously with a 'reference' approach. The theoretical real-time signalling experiment was based on a previously published CB₂ receptor-binding experiment.

KEY RESULTS

The reference method with a group size (n) of 5 provided highly precise parameter estimates (coefficient of variation [CV] 3.4% for E_max and 0.72% for C₅₀ ). A full kinetic method provided more precise estimates than the reference with equal or smaller group sizes. Note that group size 'n' here refers to the number of technical replicates rather than the number of biological replicates.

CONCLUSION AND IMPLICATIONS

A full kinetic method can yield more precise parameter estimates than the equilibrium method. Such an approach may be more useful for researchers.

Pharmacological selection of cannabinoid receptor effectors: Signalling, allosteric modulation and bias

The type-1 cannabinoid receptor (CB₁) is a promising drug target for a wide range of diseases. However, many existing and novel candidate ligands for CB₁ have shown only limited therapeutic potential. Indeed, no ligands are currently approved for the clinic except formulations of the phytocannabinoids Δ⁹-THC and CBD and a small number of analogues. A key limitation of many promising CB₁ ligands are their on-target adverse effects, notably including psychoactivity (agonists) and depression/suicidal ideation (inverse agonists). Recent drug development attempts have therefore focussed on altering CB₁ signalling profiles in two ways. Firstly, with compounds that enhance or reduce the signalling of endogenous (endo-) cannabinoids, namely allosteric modulators. Secondly, with compounds that probe the capability of selectively targeting specific cellular signalling pathways that may mediate therapeutic effects using biased ligands. This review will summarise the current paradigm of CB₁ signalling in terms of the intracellular transduction pathways acted on by the receptor. The development of compounds that selectively activate CB₁ signalling pathways, whether allosterically or via orthosteric agonist bias, will also be addressed.

Luciferase Complementation Approaches to Measure GPCR Signaling Kinetics and Bias

An understanding of the kinetic contributions to G protein-coupled receptor pharmacology and signaling is increasingly important in compound profiling. Nonequilibrium conditions are commonly present in vivo, for example, as the drug competes with dynamic changes in hormone or neurotransmitter concentration for the receptor. Under such conditions individual binding kinetic properties of the ligands can influence duration of action, local ligand concentration, and functional properties such as the degree of insurmountable inhibition. Mapping the kinetic patterns of GPCR signaling events elicited by agonists, rather than a peak response at a single timepoint, is often key to predicting their functional impact. This is also a path to a better understanding of the origins of ligand bias, and whether such ligands demonstrate their effects through selection of distinct GPCR conformations, or via their kinetic properties. Recent developments in complementation approaches, based on a small bright shrimp luciferase Nanoluc, provide a new route to kinetic analysis of GPCR signaling in living cells that is amenable to the throughput required for compound profiling. In the NanoBiT luciferase complementation system, GPCRs and effector proteins are tagged with Nanoluc fragments optimized for their low interacting affinity and stability. The interactions brought about by GPCR recruitment of the effector are reproduced by a rapid and reversible increase in NanoBiT luminescence, in the presence of its substrate furimazine. Here we discuss the methods for optimizing and validating the GPCR NanoBiT assays, and protocols for their application to study endpoint and kinetic aspects of agonist and antagonist pharmacology. We also describe how timecourse families of agonist concentration response curves, derived from a single NanoBiT assay experiment, can be used to evaluate the kinetic components in operational model derived parameters of ligand bias.

Biased Opioid Antagonists as Modulators of Opioid Dependence: Opportunities to Improve Pain Therapy and Opioid Use Management

Opioid analgesics are effective pain therapeutics but they cause various adverse effects and addiction. For safer pain therapy, biased opioid agonists selectively target distinct μ opioid receptor (MOR) conformations, while the potential of biased opioid antagonists has been neglected. Agonists convert a dormant receptor form (MOR-μ) to a ligand-free active form (MOR-μ*), which mediates MOR signaling. Moreover, MOR-μ converts spontaneously to MOR-μ* (basal signaling). Persistent upregulation of MOR-μ* has been invoked as a hallmark of opioid dependence. Contrasting interactions with both MOR-μ and MOR-μ* can account for distinct pharmacological characteristics of inverse agonists (naltrexone), neutral antagonists (6β-naltrexol), and mixed opioid agonist-antagonists (buprenorphine). Upon binding to MOR-μ*, naltrexone but not 6β-naltrexol suppresses MOR-μ*signaling. Naltrexone blocks opioid analgesia non-competitively at MOR-μ*with high potency, whereas 6β-naltrexol must compete with agonists at MOR-μ, accounting for ~100-fold lower in vivo potency. Buprenorphine's bell-shaped dose-response curve may also result from opposing effects on MOR-μ and MOR-μ*. In contrast, we find that 6β-naltrexol potently prevents dependence, below doses affecting analgesia or causing withdrawal, possibly binding to MOR conformations relevant to opioid dependence. We propose that 6β-naltrexol is a biased opioid antagonist modulating opioid dependence at low doses, opening novel avenues for opioid pain therapy and use management.

Analyzing kinetic signaling data for G-protein-coupled receptors

In classical pharmacology, bioassay data are fit to general equations (e.g. the dose response equation) to determine empirical drug parameters (e.g. EC50 and Emax), which are then used to calculate chemical parameters such as affinity and efficacy. Here we used a similar approach for kinetic, time course signaling data, to allow empirical and chemical definition of signaling by G-protein-coupled receptors in kinetic terms. Experimental data are analyzed using general time course equations (model-free approach) and mechanistic model equations (mechanistic approach) in the commonly-used curve-fitting program, GraphPad Prism. A literature survey indicated signaling time course data usually conform to one of four curve shapes: the straight line, association exponential curve, rise-and-fall to zero curve, and rise-and-fall to steady-state curve. In the model-free approach, the initial rate of signaling is quantified and this is done by curve-fitting to the whole time course, avoiding the need to select the linear part of the curve. It is shown that the four shapes are consistent with a mechanistic model of signaling, based on enzyme kinetics, with the shape defined by the regulation of signaling mechanisms (e.g. receptor desensitization, signal degradation). Signaling efficacy is the initial rate of signaling by agonist-occupied receptor (kτ), simply the rate of signal generation before it becomes affected by regulation mechanisms, measurable using the model-free analysis. Regulation of signaling parameters such as the receptor desensitization rate constant can be estimated if the mechanism is known. This study extends the empirical and mechanistic approach used in classical pharmacology to kinetic signaling data, facilitating optimization of new therapeutics in kinetic terms.

Intrinsic Efficacy of Opioid Ligands and Its Importance for Apparent Bias, Operational Analysis, and Therapeutic Window

Evidence from several novel opioid agonists and knockout animals suggests that improved opioid therapeutic window, notably for analgesia versus respiratory depression, is a result of ligand bias downstream of activation of the µ-opioid receptor (MOR) toward G protein signaling and away from other pathways, such as arrestin recruitment. Here, we argue that published claims of opioid bias based on application of the operational model of agonism are frequently confounded by failure to consider the assumptions of the model. These include failure to account for intrinsic efficacy and ceiling effects in different pathways, distortions introduced by analysis of amplified (G protein) versus linear (arrestin) signaling mechanisms, and nonequilibrium effects in a dynamic signaling cascade. We show on both theoretical and experimental grounds that reduced intrinsic efficacy that is unbiased across different downstream pathways, when analyzed without due considerations, does produce apparent but erroneous MOR ligand bias toward G protein signaling, and the weaker the G protein partial agonism is the greater the apparent bias. Experimentally, such apparently G protein-biased opioids have been shown to exhibit low intrinsic efficacy for G protein signaling when ceiling effects are properly accounted for. Nevertheless, such agonists do display an improved therapeutic window for analgesia versus respiratory depression. Reduced intrinsic efficacy for G proteins rather than any supposed G protein bias provides a more plausible, sufficient explanation for the improved safety. Moreover, genetic models of G protein-biased opioid receptors and replication of previous knockout experiments suggest that reduced or abolished arrestin recruitment does not improve therapeutic window for MOR-induced analgesia versus respiratory depression. SIGNIFICANCE STATEMENT: Efforts to improve safety of µ-opioid analgesics have focused on agonists that show signaling bias for the G protein pathway versus other signaling pathways. This review provides theoretical and experimental evidence showing that failure to consider the assumptions of the operational model can lead to large distortions and overestimation of actual bias. We show that low intrinsic efficacy is a major determinant of these distortions, and pursuit of appropriately reduced intrinsic efficacy should guide development of safer opioids.

100 years of modelling ligand-receptor binding and response: A focus on GPCRs

Experimental pharmacologists rely on the application of models to describe biological observations in order to learn about a drug's effective concentration, the strength with which it binds its target and drives a response (at either molecular or system level), and the nature of more complex drug actions (allosterism/functional selectivity). Models in current use build upon decades of basic principles, going back to the beginning of the last century. Yet often, researchers are only partially familiar with these underlying principles, creating the potential for confusion due to failure to recognise the underpinning assumptions of the models that are used. Here, we describe the history of receptor theory as it underpins receptor stimulus-response models in use today, emphasising particularly attributes and models relevant to GPCRs-and point to some current aims of model development.

A kinetic method for measuring agonist efficacy and ligand bias using high resolution biosensors and a kinetic data analysis framework

The kinetics/dynamics of signaling are of increasing value for G-protein-coupled receptor therapeutic development, including spatiotemporal signaling and the kinetic context of biased agonism. Effective application of signaling kinetics to developing new therapeutics requires reliable kinetic assays and an analysis framework to extract kinetic pharmacological parameters. Here we describe a platform for measuring arrestin recruitment kinetics to GPCRs using a high quantum yield, genetically encoded fluorescent biosensor, and a data analysis framework to quantify the recruitment kinetics. The sensor enabled high temporal resolution measurement of arrestin recruitment to the angiotensin AT1 and vasopressin V2 receptors. The analysis quantified the initial rate of arrestin recruitment (kτ), a biologically-meaningful kinetic drug efficacy parameter, by fitting time course data using routine curve-fitting methods. Biased agonism was assessed by comparing kτ values for arrestin recruitment with those for Gq signaling via the AT1 receptor. The kτ ratio values were in good agreement with bias estimates from existing methods. This platform potentially improves and simplifies assessment of biased agonism because the same assay modality is used to compare pathways (potentially in the same cells), the analysis method is parsimonious and intuitive, and kinetic context is factored into the bias measurement.

The nature of efficacy at G protein-coupled receptors

G protein-coupled receptors (GPCRs) participate in many pathophysiological processes as well as almost all aspects of normal physiology. They are present at the surface of all cell types making them amenable and attractive targets for pharmaceutical therapeutics. GPCRs possess complex pharmacology with the ability to be turned on to various extents, have their constitutive activity suppressed and even switch between signaling pathways to which they couple. Underlying this complex pharmacology is GPCR signaling efficacy, and differences in efficacy promoted by alternative ligands and in different tissues is of great interest to biology in general and also the pharmaceutical industry. In this review we hope to discuss what the molecular foundations of efficacy are and whether a new approach utilizing a rate-dependent model may provide new insights into this phenomenon.

Computational framework for predictive PBPK-PD-Tox simulations of opioids and antidotes

The primary goal of this work was to develop a computational tool to enable personalized prediction of pharmacological disposition and associated responses for opioids and antidotes. Here we present a computational framework for physiologically-based pharmacokinetic (PBPK) modeling of an opioid (morphine) and an antidote (naloxone). At present, the model is solely personalized according to an individual's mass. These PK models are integrated with a minimal pharmacodynamic model of respiratory depression induction (associated with opioid administration) and reversal (associated with antidote administration). The model was developed and validated on human data for IV administration of morphine and naloxone. The model can be further extended to consider different routes of administration, as well as to study different combinations of opioid receptor agonists and antagonists. This work provides the framework for a tool that could be used in model-based management of pain, pharmacological treatment of opioid addiction, appropriate use of antidotes for opioid overdose and evaluation of abuse deterrent formulations.

Model-free and kinetic modelling approaches for characterising non-equilibrium pharmacological pathway activity: Internalisation of cannabinoid CB1 receptors

BACKGROUND AND PURPOSE

Receptor internalisation is by nature kinetic. Application of a standard equilibrium dose response model to describe the properties of a ligand inducing internalisation, while commonly used, are therefore problematic. Here, we propose two quantitative approaches to address this issue-(a) a model-free method and (b) a kinetic modelling approach-and systematically evaluate the performance of these methods against traditional equilibrium methods to characterise the internalisation profiles of cannabinoid CB1 receptor agonists.

EXPERIMENTAL APPROACH

Kinetic internalisation assays were conducted using a concentration series of six CB1 receptor ligands. Internalisation rate analysis and snapshot equilibrium analysis were performed. A model-free method was developed based on the mean residence time of internalisation. A kinetic internalisation model was developed under the quasi-steady state assumption.

KEY RESULTS

Rates of receptor internalisation depended on both agonist and concentration. Agonist potencies from snapshot equilibrium analysis increased with stimulation time, and there was no single time point at which internalisation profiles could infer agonist properties in a comparative manner. The model-free method yielded a time-invariant measure of potency/efficacy for internalisation. The kinetic model adequately described the internalisation of CB1 receptors over time and provided robust estimates of both potency and efficacy.

CONCLUSION AND IMPLICATIONS

Applying equilibrium analysis to a non-equilibrium pathway cannot provide a reliable estimate of agonist potency. Both the model-free and kinetic modelling approaches characterised the internalisation profiles of CB1 receptor agonists. The kinetic model provides additional advantages as a method to capture changes in receptor number during other functional assays.

Biased Signaling and Allosteric Modulation at the FSHR

Knowledge on G protein-coupled receptor (GPCRs) structure and mechanism of activation has profoundly evolved over the past years. The way drugs targeting this family of receptors are discovered and used has also changed. Ligands appear to bind a growing number of GPCRs in a competitive or allosteric manner to elicit balanced signaling or biased signaling (i.e., differential efficacy in activating or inhibiting selective signaling pathway(s) compared to the reference ligand). These novel concepts and developments transform our understanding of the follicle-stimulating hormone (FSH) receptor (FSHR) biology and the way it could be pharmacologically modulated in the future. The FSHR is expressed in somatic cells of the gonads and plays a major role in reproduction. When compared to classical GPCRs, the FSHR exhibits intrinsic peculiarities, such as a very large NH2-terminal extracellular domain that binds a naturally heterogeneous, large heterodimeric glycoprotein, namely FSH. Once activated, the FSHR couples to Gαs and, in some instances, to other Gα subunits. G protein-coupled receptor kinases and β-arrestins are also recruited to this receptor and account for its desensitization, trafficking, and intracellular signaling. Different classes of pharmacological tools capable of biasing FSHR signaling have been reported and open promising prospects both in basic research and for therapeutic applications. Here we provide an updated review of the most salient peculiarities of FSHR signaling and its selective modulation.