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

Cell-cell communication: new insights and clinical implications

Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.

Mechano-sensitivity of β2-adrenoceptors enhances constitutive activation of cAMP generation that is inhibited by inverse agonists

The concept of agonist-independent signalling that can be attenuated by inverse agonists is a fundamental element of the cubic ternary complex model of G protein-coupled receptor (GPCR) activation. This model shows how a GPCR can exist in two conformational states in the absence of ligands; an inactive R state and an active R* state that differ in their affinities for agonists, inverse agonists, and G-protein alpha subunits. The proportion of R* receptors that exist in the absence of agonists determines the level of constitutive receptor activity. In this study we demonstrate that mechanical stimulation can induce β2-adrenoceptor agonist-independent Gs-mediated cAMP signalling that is sensitive to inhibition by inverse agonists such as ICI-118551 and propranolol. The size of the mechano-sensitive response is dependent on the cell surface receptor expression level in HEK293G cells, is still observed in a ligand-binding deficient D113A mutant β2-adrenoceptor and can be attenuated by site-directed mutagenesis of the extracellular N-glycosylation sites on the N-terminus and second extracellular loop of the β2-adrenoceptor. Similar mechano-sensitive agonist-independent responses are observed in HEK293G cells overexpressing the A2A-adenosine receptor. These data provide new insights into how agonist-independent constitutive receptor activity can be enhanced by mechanical stimulation and regulated by inverse agonists.

Understanding the effects of oxytocin receptor variants on OXT-OXT receptor binding: A mathematical model

Approximately half of U.S. women giving birth annually receive Pitocin, the synthetic form of oxytocin (OXT), yet its effective dose can vary significantly. This variability presents safety concerns due to unpredictable responses, which may lead to adverse outcomes for both mother and baby. To address the need for improved dosing, we developed a data-driven mathematical model to predict OXT receptor (OXTR) binding. Our study focuses on five prevalent OXTR variants (V45L, P108A, L206V, V281M, and E339K) and their impact on OXT-OXTR binding dynamics in two distinct cell types: human embryonic kidney cells (HEK293T), commonly used in experimental systems, and human myometrial smooth muscle cells, containing endogenous OXTR. We parameterized the model with cell-specific OXTR surface localization measurements. To strengthen the robustness of our study, we conducted a comprehensive meta-analysis of OXT- OXTR binding, enabling parameterization of our model with cell-specific OXT-OXTR binding kinetics (myometrial OXT-OXTR K d = 1.6 nM, kon = 6.8 × 10 5 M -1 min -1 , and koff = 0.0011 min -1 ). Our meta-analysis revealed significant homogeneity in OXT-OXTR affinity across experiments and species with a K d = 0.52 - 9.32 nM and mean K d = 1.48 ± 0.36 nM. Our model achieves several valuable insights into designing dosage strategies. First, we predicted that the OXTR complex reaches maximum occupancy at 10 nM OXT in myometrial cells and at 1 µM in HEK293T cells. This information is pivotal for guiding experimental design and data interpretation when working with these distinct cell types, emphasizing the need to consider effects for specific cell types when choosing OXTR-transfected cell lines. Second, our model recapitulated the significant effects of genetic variants for both experimental and physiologically relevant systems, with V281M and E339K substantially compromising OXT-OXTR binding capacity. These findings suggest the need for personalized oxytocin dosing based on individual genetic profiles to enhance therapeutic efficacy and reduce risks, especially in the context of labor and delivery. Third, we demonstrated the potential for rescuing the attenuated cell response observed in V281M and E339K variants by increasing the OXT dosage at specific, early time points. Cellular responses to OXT, including Ca 2+ release, manifest within minutes. Our model indicates that providing V281M- and E339K-expressing cells with doubled OXT dose during the initial minute of binding can elevate OXT-OXTR complex formation to levels comparable to wild-type OXTR. In summary, our study provides a computational framework for precision oxytocin dosing strategies, paving the way for personalized medicine.

How New Developments in Pharmacology Receptor Theory Are Changing (Our Understanding of) Hypertension Therapy

BACKGROUND

Many hypertension therapeutics were developed prior to major advances in drug receptor theory. Moreover, newer drugs may take advantage of some of the newly understood modalities of receptor function.

GOAL

The goal of this review is to provide an up-to-date summary of drug receptor theory. This is followed by a discussion of the drug classes recognized for treating hypertension to which new concepts in receptor theory apply.

RESULTS

We raise ideas for mechanisms of potential new antihypertensive drugs and whether they may take advantage of new theories in drug-receptor interaction.

Discovery of Nivasorexant (ACT-539313): The First Selective Orexin-1 Receptor Antagonist (SO1RA) Investigated in Clinical Trials

The orexin system consists of two neuropeptides (orexins A and B) and two receptors (OX1 and OX2). Selective OX1 receptor antagonists (SO1RA) are gaining interest for their potential use in the treatment of CNS disorders, including substance abuse, eating, obsessive compulsive, or anxiety disorders. While blocking OX2 reduces wakefulness, the expected advantage of selectively antagonizing OX1 is the ability to achieve clinical efficacy without the promotion of sleep. Herein we report our discovery efforts starting from a dual orexin receptor antagonist and describe a serendipitous finding that triggered a medicinal chemistry program that culminated in the identification of the potent SO1RA ACT-539313. Efficacy in a rat model of schedule-induced polydipsia supported the decision to select the compound as a preclinical candidate. Nivasorexant (20) represents the first SO1RA to enter clinical development and completed a first proof of concept phase II clinical trial in binge eating disorder in 2022.

A pleiotropic-epistatic entangelement model of drug response

Because drug response is multifactorial, graph models are uniquely powerful for comprehending its genetic architecture. We deconstruct drug response into many different and interdependent sub-traits, with each sub-trait controlled by multiple genes that act and interact in a complicated manner. The outcome of drug response is the consequence of multileveled intertwined interactions between pleiotropic effects and epistatic effects. Here, we propose a general statistical physics framework to chart the 3D geometric network that codes how epistasis pleiotropically influences a complete set of sub-traits to shape body-drug interactions. This model can dissect the topological architecture of epistatically induced pleiotropic networks (EiPN) and pleiotropically influenced epistatic networks (PiEN). We analyze and interpret the practical implications of the pleiotropic-epistatic entanglement model for pharmacogenomic studies.

Structural basis of efficacy-driven ligand selectivity at GPCRs

A drug's selectivity for target receptors is essential to its therapeutic utility, but achieving selectivity between similar receptors is challenging. The serendipitous discovery of ligands that stimulate target receptors more strongly than closely related receptors, despite binding with similar affinities, suggests a solution. The molecular mechanism of such 'efficacy-driven selectivity' has remained unclear, however, hindering design of such ligands. Here, using atomic-level simulations, we reveal the structural basis for the efficacy-driven selectivity of a long-studied clinical drug candidate, xanomeline, between closely related muscarinic acetylcholine receptors (mAChRs). Xanomeline's binding mode is similar across mAChRs in their inactive states but differs between mAChRs in their active states, with divergent effects on active-state stability. We validate this mechanism experimentally and use it to design ligands with altered efficacy-driven selectivity. Our results suggest strategies for the rational design of ligands that achieve efficacy-driven selectivity for many pharmaceutically important G-protein-coupled receptors.

Ligands and agonists of the aryl hydrocarbon receptor AhR: Facts and myths

The aryl hydrocarbon receptor (AhR) belongs to the essential helix-loop-helix transcription factors family. This receptor has a central role in determining host physiology and a variety of pathophysiologies ranging from inflammation and metabolism to cancer. AhR is a ligand-driven receptor with intricate pharmacology of activation depending on the type and quantity of ligand present. Therefore, a better understanding of AhR ligands per se is critical to move the field forward. In this minireview, we clarify some facts and myths about AhR ligands and how further studies could shed light on the true nature of AhR activation by these ligands. The review covers select chemical classes and explores parameters that qualify them as true receptor ligands.

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.

Adaptive biosensing platform using immune cell-based nanovesicles for food allergen detection

Inspired by an adaptive immune system, we have developed a bioelectronic sensing platform which relies on nanovesicles for a signal amplification and can be easily adapted for the detection of new food allergens. In this work, nanovesicles with anti-immunoglobulin E (anti-IgE) antibody receptors were extracted from immune cells and immobilized on a carbon nanotube-based transistor to build a highly sensitive and selective biosensing platform. Our sensor could detect peanut allergen, arachis hypogaea 2 (Ara h 2), down to 0.1 fM and selectively discriminate target allergens in real food samples such as peanut and egg white. As a proof of concept, we demonstrated the detection of different target molecules using the same nanovesicles linked with different antibodies. Our sensor platform was also utilized to quantitatively evaluate the effect of allergy drug such as cromolyn. In this regard, our strategy can be utilized for basic research and versatile applications in food and pharmacological industries.

Translational aspect in peptide drug discovery and development: An emerging therapeutic candidate

In the last two decades, protein-protein interactions (PPIs) have been used as the main target for drug development. However, with larger or superficial binding sites, it has been extremely difficult to disrupt PPIs with small molecules. On the other hand, intracellular PPIs cannot be targeted by antibodies that cannot penetrate the cell membrane. Peptides that have a combination of conformational rigidity and flexibility can be used to target difficult binding interfaces with appropriate binding affinity and specificity. Since the introduction of insulin nearly a century ago, more than 80 peptide drugs have been approved to treat a variety of diseases. These include deadly diseases such as cancer and human immunodeficiency virus infection. It is also useful against diabetes, chronic pain, and osteoporosis. Today, more research is being done on these drugs as lessons learned from earlier approaches, which are still valid today, complement newer approaches such as peptide display libraries. At the same time, integrated genomics and peptide display libraries are new strategies that open new avenues for peptide drug discovery. The purpose of this review is to examine the problems in elucidating the peptide-protein recognition mechanism. This is important to develop peptide-based interventions that interfere with endogenous protein interactions. New approaches are being developed to improve the binding affinity and specificity of existing approaches and to develop peptide agents as potentially useful drugs. We also highlight the key challenges that must be overcome in peptide drug development to realize their potential and provide an overview of recent trends in peptide drug development. In addition, we take an in-depth look at early efforts in human hormone discovery, smart medicinal chemistry and design, natural peptide drugs, and breakthrough advances in molecular biology and peptide chemistry.

Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy

The advent of single-cell RNA sequencing (scRNA-seq) has provided insight into the tumour immune microenvironment (TIME). This review focuses on the application of scRNA-seq in investigation of the TIME. Over time, scRNA-seq methods have evolved, and components of the TIME have been deciphered with high resolution. In this review, we first introduced the principle of scRNA-seq and compared different sequencing approaches. Novel cell types in the TIME, a continuous transitional state, and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer. Thus, we concluded novel cell clusters of cancer-associated fibroblasts (CAFs), T cells, tumour-associated macrophages (TAMs) and dendritic cells (DCs) discovered after the application of scRNA-seq in TIME. We also proposed the development of TAMs and exhausted T cells, as well as the possible targets to interrupt the process. In addition, the therapeutic interventions based on cellular interactions in TIME were also summarized. For decades, quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer. Summarizing the current findings, we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy, which can subsequently be implemented in the clinic. Finally, we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.

Free Drug Theory - No Longer Just a Hypothesis?

The Free Drug Hypothesis is a well-established concept within the scientific lexicon pervading many areas of Drug Discovery and Development, and yet it is poorly defined by virtue of many variations appearing in the literature. Clearly, unbound drug is in dynamic equilibrium with respect to absorption, distribution, metabolism, elimination, and indeed, interaction with the desired pharmacological target. Binding interactions be they specific (e.g. high affinity) or nonspecific (e.g. lower affinity/higher capacity) are governed by the same fundamental physicochemical tenets including Hill-Langmuir Isotherms, the Law of Mass Action and Drug Receptor Theory. With this in mind, it is time to recognise a more coherent version and consider it the Free Drug Theory and a hypothesis no longer. Today, we have the experimental and modelling capabilities, pharmacological knowledge, and an improved understanding of unbound drug distribution (e.g. Kp_uu) to raise the bar on our understanding and analysis of experimental data. The burden of proof should be to rule out mechanistic possibilities and/or experimental error before jumping to the conclusion that any observations contradict these fundamentals.

Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs

Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, virtual screening (VS) has gained much attention due to several advantages, including timesaving, reduced reagent and consumable costs and the performance of selective analyses regarding the affinity between test molecules and pharmacological targets. Currently, VS is based mainly on algorithms that apply physical and chemistry principles and quantum mechanics to estimate molecule affinities and conformations, among others. Nevertheless, VS has not reached the expected results concerning the improvement of market-approved drugs, comprising less than twenty drugs that have reached this goal to date. In this context, graph neural networks (GNN), a recent deep-learning subtype, may comprise a powerful tool to improve VS results concerning natural products that may be used both simultaneously with standard algorithms or isolated. This review discusses the pros and cons of GNN applied to VS and the future perspectives of this learnable algorithm, which may revolutionize drug discovery if certain obstacles concerning spatial coordinates and adequate datasets, among others, can be overcome.

Ultrasensitive Bioelectronic Tongue Based on the Venus Flytrap Domain of a Human Sweet Taste Receptor

Sweet taste is an important factor that regulates calorie intake and contributes to food preferences in humans and animals. Therefore, the evaluation of sweet substances is essential for various fields such as healthcare, food, and pharmaceutical industries. Sweet tastants are detected by sweet taste receptors which are class C G-protein-coupled receptors. T1R2 venus flytrap (VFT) of the sweet taste receptor is known as a primary ligand-binding domain for sweet tastants. In this study, we developed an ultrasensitive artificial sweet taste bioelectronic tongue based on the T1R2 VFT of a human sweet taste receptor. Here, the T1R2 VFT of a human sweet taste receptor was successfully overexpressed in a bacterial expression system. A T1R2 VFT-immobilized carbon nanotube field-effect transistor with floating electrodes was exploited as an artificial sweet taste sensory system. Significantly, our T1R2 VFT-functionalized bioelectronic tongue could be used to detect solutions of sweet tastants down to 0.1 fM and selectively discriminate sweet substances from other taste substances. Furthermore, our device could be used to monitor the response of the T1R2 VFT domain of a sweet taste receptor to sweet substances in real food environments such as apple juice and chamomile herb tea. Moreover, our device was used to evaluate the inhibition and enhancement effects on sweet taste receptors by zinc ions and chamomile tea, respectively. In addition, our device demonstrated long-term storability and reusability. In this respect, our sweet taste bioelectronic tongue could be a promising tool for various basic research and industrial applications.

Exploring determinants of agonist efficacy at the CB1 cannabinoid receptor: Analogues of the synthetic cannabinoid receptor agonist EG-018

Neutral antagonists of GPCRs remain relatively rare—indeed, a large majority of GPCR antagonists are actually inverse agonists. The synthetic cannabinoid receptor agonist (SCRA) EG‐018 was recently reported as a low efficacy cannabinoid receptor agonist. Here we report a comparative characterization of EG‐018 and 13 analogues along with extant putative neutral antagonists of CB₁. In HEK cells stably expressing human CB₁, assays for inhibition of cAMP were performed by real‐time BRET biosensor (CAMYEL), G protein cycling was quantified by [³⁵S]GTPγS binding, and stimulation of pERK was characterized by AlphaLISA (PerkinElmer). Signaling outcomes for the EG‐018 analogues were highly variable, ranging from moderate efficacy agonism with high potency, to marginal agonism at lower potency. As predicted by differing pathway sensitivities to differences in ligand efficacy, most EG‐018‐based compounds were completely inactive in pERK alone. The lowest efficacy analogue in cAMP assays, 157, had utility in antagonism assay paradigms. Developing neutral antagonists of the CB₁ receptor has been a long‐standing research goal, and such compounds would have utility both as research tools and in therapeutics. Although these results emphasize again the importance of system factors in determining signaling outcomes, some compounds characterized in this study appear among the lowest efficacy agonists described to date and therefore suggest that development of neutral antagonists is an achievable goal for CB₁.

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.

Why Taste Is Pharmacology

The chapter presents an argument supporting the view that taste, defined as the receptor-mediated signaling of taste cells and consequent sensory events, is proper subject matter for the field of pharmacology. The argument develops through a consideration of how the field of pharmacology itself is to be defined. Though its application toward the discovery and development of therapeutics is of obvious value, pharmacology nevertheless is a basic science committed to examining biological phenomena controlled by the selective interactions between chemicals - regardless of their sources or uses - and receptors. The basic science of pharmacology is founded on the theory of receptor occupancy, detailed here in the context of taste. The discussion then will turn to consideration of the measurement of human taste and how well the results agree with the predictions of receptor theory.

A novel β2-AR agonist, Higenamine, induces β-arrestin-biased signaling

The biased ligands in G protein-coupled receptors (GPCRs) have opened new avenues for developing safer and more effective drugs. However, the identification of such biased ligands as drug candidates is highly desirable. Here, we report that Higenamine, a compound isolated from a Chinese herb, functions as a novel β-arrestin-biased ligand of the β₂-adrenergic receptor (β₂-AR). The radioligand binding assays demonstrated that Higenamine was the ligand of β₂-AR. Higenamine induced phosphorylation of extracellular signal-regulated kinase 1/2 (ERK1/2), which can be blocked by propranolol, an inhibitor of β₂-AR. The Gi protein inhibitor, pertussis toxin, had no effect on the phosphorylation of ERK1/2 induced by Higenamine. Furthermore, Higenamine induced ERK1/2 phosphorylation through transactivation of Epithelial growth factor receptor (EGFR). We also found that Higenamine-induced-ERK1/2 phosphorylation is dependent on β-arrestin1/2, and HG inhibits Doxorubicin-induced cardiomyocyte apoptosis. Our results identify Higenamine as a novel biased ligand via the β-arrestin-dependent pathway. These findings give us a better understanding of Higenamine's potential role in designing diagnostic and therapeutic strategies.

Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level

BACKGROUND

Cellular communication is an essential feature of multicellular organisms. Binding of ligands to their homologous receptors, which activate specific cell signaling pathways, is a basic type of cellular communication and intimately linked to many degeneration processes leading to diseases.

MAIN BODY

This study reviewed the history of ligand-receptor and presents the databases which store ligand-receptor pairs. The recently applications and research tools of ligand-receptor interactions for cell communication at single cell level by using single cell RNA sequencing have been sorted out.

CONCLUSION

The summary of the advantages and disadvantages of analysis tools will greatly help researchers analyze cell communication at the single cell level. Learning cell communication based on ligand-receptor interactions by single cell RNA sequencing gives way to developing new target drugs and personalizing treatment.

Antisense technology: A review

Antisense technology is beginning to deliver on the broad promise of the technology. Ten RNA-targeted drugs including eight single-strand antisense drugs (ASOs) and two double-strand ASOs (siRNAs) have now been approved for commercial use, and the ASOs in phase 2/3 trials are innovative, delivered by multiple routes of administration and focused on both rare and common diseases. In fact, two ASOs are used in cardiovascular outcome studies and several others in very large trials. Interest in the technology continues to grow, and the field has been subject to a significant number of reviews. In this review, we focus on the molecular events that result in the effects observed and use recent clinical results involving several different ASOs to exemplify specific molecular mechanisms and specific issues. We conclude with the prospective on the technology.

Biased agonism at the cannabinoid receptors - Evidence from synthetic cannabinoid receptor agonists

The type 1 and type 2 cannabinoid receptors are G protein-coupled receptors implicated in a variety of physiological processes and diseases. Synthetic cannabinoid receptor agonists (SCRAs) were originally developed to explore the therapeutic benefits of cannabinoid receptor activation, although more recently, these compounds have been diverted to the recreational drug market and are increasingly associated with incidences of toxicity. A prominent concept in contemporary pharmacology is functional selectivity or biased agonism, which describes the ability of ligands to elicit differential activation of signalling pathways through stabilisation of distinct receptor conformations. Biased agonists may maximise drug effectiveness by reducing on-target adverse effects if they are mediated by signalling pathways distinct from those that drive the therapeutic effects. For the cannabinoid receptors, it remains unclear as to which signalling pathways mediate desirable and adverse effects. However, given their structural diversity and potential to induce a plethora of signalling effects, SCRAs provide the most promising prospect for detecting and studying bias at the cannabinoid receptors. This review summarises the emerging evidence of SCRA bias at the cannabinoid receptors.

Critical Assessment of G Protein-Biased Agonism at the μ-Opioid Receptor

G protein-biased agonists of the μ-opioid receptor (MOPr) have been proposed as an improved class of opioid analgesics. Recent studies have been unable to reproduce the original experiments in the β-arrestin2-knockout mouse that led to this proposal, and alternative genetic models do not support the G protein-biased MOPr agonist hypothesis. Furthermore, assessment of putatively biased ligands has been confounded by several factors, including assay amplification. As such, the extent to which current lead compounds represent mechanistically novel, extremely G protein-biased agonists is in question, as is the underlying assumption that β-arrestin2 mediates deleterious opioid effects. Addressing these current challenges represents a pressing issue to successfully advance drug development at this receptor and improve upon current opioid analgesics.

The neuropeptide calcitonin gene-related peptide alpha is essential for bone healing

Background

Impaired fracture healing represents an ongoing clinical challenge, as treatment options remain limited. Calcitonin gene-related peptide (CGRP), a neuropeptide targeted by emerging anti-migraine drugs, is also expressed in sensory nerve fibres innervating bone tissue.

Method

Bone healing following a femoral osteotomy stabilized with an external fixator was analysed over 21 days in αCGRP-deficient and WT mice. Bone regeneration was evaluated by serum analysis, µCT analysis, histomorphometry and genome-wide expression analysis. Bone-marrow-derived osteoblasts and osteoclasts, as well as the CGRP antagonist olcegepant were employed for mechanistic studies.

Findings

WT mice with a femoral fracture display increased CGRP serum levels. αCGRP mRNA expression after skeletal injury is exclusively induced in callus tissue, but not in other organs. On protein level, CGRP and its receptor, calcitonin receptor-like receptor (CRLR) complexing with RAMP1, are differentially expressed in the callus during bone regeneration. On the other hand, αCGRP-deficient mice display profoundly impaired bone regeneration characterised by a striking reduction in the number of bone-forming osteoblasts and a high rate of incomplete callus bridging and non-union. As assessed by genome-wide expression analysis, CGRP induces the expression of specific genes linked to ossification, bone remodeling and adipogenesis. This suggests that CGRP receptor-dependent PPARγ signaling plays a central role in fracture healing.

Interpretation

This study demonstrates an essential role of αCGRP in orchestrating callus formation and identifies CGRP receptor agonism as a potential approach to stimulate bone regeneration. Moreover, as novel agents blocking CGRP or its receptor CRLR are currently introduced clinically for the treatment of migraine disorders, their potential negative impact on bone regeneration warrants clinical investigation.

Funding

This work was funded by grants from the Else-Kröner-Fresenius-Stiftung (EKFS), the Deutsche Forschungsgemeinschaft (DFG), and the Berlin Institute of Health (BIH).

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.

Molecular Targets and Related Biologic Activities of Fucoidan: A Review

Fucoidan-a marine natural active polysaccharide derived from brown algae with a variety of medicinal activities and low toxicity-has been used as clinical drug for renal diseases for nearly 20 years. The pharmacological mechanism of fucoidan has been well-investigated, based on target molecules and downstream signaling pathways. This review summarizes some important molecular targets of fucoidan and its related biologic activities, including scavenger receptor (SR), Toll-like receptors (TLRs), C-type lectin (CLEC) and some newly found target molecules, which may be beneficial for further understanding the pharmacological mechanism of fucoidan and discovering its new functions, as well as developing related clinical or adjuvant drugs and functional preparations.

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.