The reaction between acetyl choline and muscle cells

The dynamics of agonist-β2-adrenergic receptor activation induced by binding of GDP-bound Gs protein

There is considerable uncertainty about the mechanism by which the β₂-adrenergic receptor (β₂AR) is activated. Here we use molecular metadynamics computations to predict the mechanism by which an agonist induces the activation of the β₂AR and its cognate Gs protein. We found that binding agonist alone to the inactive β₂AR does not break the ionic lock and hence does not drive the β₂AR towards the activated conformation. However, we found that attaching the inactive Gs protein to the agonist-bound inactive β₂AR (containing the ionic lock) leads to partial insertion of Gαs-α5 into the core of β₂AR, which breaks the ionic lock, leading to activation of the Gs protein coupled to β₂AR. Upon activation, the Gαs protein undergoes a remarkable opening of the GDP binding pocket, making the GDP available for exchange or release. Concomitantly, Gαs-α5 undergoes a remarkable expansion in the β₂AR cytoplasmic region after the ionic lock is broken, inducing TM6 to displace outward by ~5 Å from TM3.

Binding of Venezuelan Equine Encephalitis Virus Inhibitors to Importin-α Receptors Explored with All-Atom Replica Exchange Molecular Dynamics

Although Venezuelan equine encephalitis virus (VEEV) is a life-threatening pathogen with a capacity for epidemic outbreaks, there are no FDA-approved VEEV antivirals for humans. VEEV cytotoxicity is partially attributed to the formation of a tetrameric complex between the VEEV capsid protein, the nuclear import proteins importin-α and importin-β, and the nuclear export protein CRM1, which together block trafficking through the nuclear pore complex. Experimental studies have identified small molecules from the CL6662 scaffold as potential inhibitors of the viral nuclear localization signal (NLS) sequence binding to importin-α. However, little is known about the molecular mechanism of CL6662 inhibition. To address this issue, we employed all-atom replica exchange molecular dynamics simulations to probe, in atomistic detail, the binding mechanism of CL6662 ligands to importin-α. Three ligands, including G281-1485 and two congeners with varying hydrophobicities, were considered. We investigated the distribution of ligand binding poses, their locations, and ligand specificities measured by the strength of binding interactions. We found that G281-1485 binds nonspecifically without forming well-defined binding poses throughout the NLS binding site. Binding of the less hydrophobic congener becomes strongly on-target with respect to the NLS binding site but remains nonspecific. However, a more hydrophobic congener is a strongly specific binder and the only ligand out of three to form a well-defined binding pose, while partially overlapping with the NLS binding site. On the basis of free energy estimates, we argue that all three ligands weakly compete with the viral NLS sequence for binding to importin-α in an apparent compromise to preserve host NLS binding. We further show that all-atom replica exchange binding simulations are a viable tool for studying ligands binding nonspecifically without forming well-defined binding poses.

How clathrin-coated pits control nanoparticle avidity for cells

The paramount relevance of clathrin-coated pits (CCPs) to receptor-mediated endocytosis of nanoparticles, extracellular vesicles, and viruses has made them the focus of many studies; however, the role of CCP geometry in the ligand-receptor interactions between multivalent nanoparticles and cells has not been investigated. We hypothesized the general dependence of nanoparticle binding energy on local membrane curvature to be expandable to the specific case of ligand-functionalized nanoparticles binding cell membranes, in the sense that membrane structures whose curvature matches that of the particle (e.g., CCPs) signficantly contribute to binding avidity. We investigated this hypothesis with nanoparticles that bind multivalently to angiotensin II receptor type 1, which is subject to clathrin-mediated endocytosis. When we used cholesterol extraction to prevent the action of CCPs, we found a 67 to 100-fold loss in avidity. We created a theoretical model that predicts this decrease based on the loss of ligand-receptor interactions when CCPs, which perfectly match nanoparticle geometry, are absent. Our findings shed new light on how cells "see" nanoparticles. The presence or absence of CPPs is so influential on how cells interact with nanoparticles that the number of particles required to be visible to cells changes by two orders of magnitude depending on CCP presence.

The mechanism for ligand activation of the GPCR-G protein complex

G protein–coupled receptors (GPCRs) activate cellular responses ranging from odorants to neurotransmitters. Binding an agonist leads to activation of a heterotrimeric G protein (GP) that stimulates external signaling. Unfortunately, the mechanism remains unknown. We show for 15 class A GPCRs, including opioids, adrenergics, adenosines, chemokines, muscarinics, cannabinoids, serotonins, and dopamines, that interaction of an inactive GP, including Gs, Gi, Go, G11, and Gq, to the inactive GPCR, containing the intracellular ionic lock between transmembrane (TM) helices 3 and 6, evolves exothermically to form a precoupled GPCR-GP complex with an opened TM3-TM6 and the GP-α5 helix partially inserted into the GPCR but not activated. We show that binding of agonist to this precoupled GPCR-GP complex causes the Gα protein to open into its active form, with the guanosine diphosphate exposed for signaling. This GP-first paradigm provides a strategy for developing selective agonists for GPCRs since it is the pharmacophore for the precoupled GPCR-GP complex that should be used to design drugs.

Mathematical Modeling for an MTT Assay in Fluorine-Containing Graphene Quantum Dots

The paper reports on a new mathematical model, starting with the original Hill equation which is derived to describe cell viability (V) while testing nanomaterials (NMs). Key information on the sample's morphology, such as mean size (⟨s⟩) and size dispersity (σ) is included in the new model via the lognormal distribution function. The new Hill-inspired equation is successfully used to fit MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) data from assays performed with the HepG2 cell line challenged by fluorine-containing graphene quantum dots (F:GQDs) under light (400-700 nm wavelength) and dark conditions. The extracted "biological polydispersity" (light: ⟨sMTT⟩=1.77±0.02 nm and σMTT=0.21±0.02); dark: ⟨sMTT⟩=1.87±0.02 nm and σMTT=0.22±0.01) is compared with the "morphological polydispersity" (⟨sTEM⟩=1.98±0.06 nm and σTEM=0.19±0.03), the latter obtained from TEM (transmission electron microscopy). The fitted data are then used to simulate a series of V responses. Two aspects are emphasized in the simulations: (i) fixing σ, one simulates V versus ⟨s⟩ and (ii) fixing ⟨s⟩, one simulates V versus σ. Trends observed in the simulations are supported by a phenomenological model picture describing the monotonic reduction in V as ⟨s⟩ increases (V~pa/(s)p-a; p and a are fitting parameters) and accounting for two opposite trends of V versus σ: under light (V~σ) and under dark (V~1/σ).

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.

High Dose of Caffeine Mouth Rinse Increases Resistance Training Performance in Men

Caffeine mouth rinsing (CMR) has been shown to enhance exercise performance. However, no studies have analyzed the effects of different dosages of CMR on muscular performance. Therefore, the purpose of this study was to examine the effects of different dosages of CMR on strength (bench press 1 repetition maximum (1-RM)) and muscular endurance (60% of 1-RM repetitions to failure) in resistance-trained males. Fourteen resistance-trained males (age: 23 ± 2 years, height: 179 ± 3 cm, body mass: 83 ± 4 kg, BMI: 17 ± 2 kg/m2) completed four conditions in random order. The four conditions consisted of a mouth rinse with 25 mL solutions containing either 1% (250 mg) of CMR (low dose of CMR: LCMR), 2% (500 mg) of CMR (moderate dose of CMR: MCMR), 3% (750 mg) of CMR (high dose of CMR: HCMR) and sweetened water (placebo: PLA) for 5 s prior to a bench press strength and muscular endurance test. Maximal strength, muscular endurance, heart rate (HR) and ratings of perceived exertion (RPE) were recorded for each condition. There were no significant differences in strength (p = 0.30) and HR (p = 0.83) between conditions. HCMR significantly increased muscular endurance performance (p = 0.01) and decreased RPE values (p = 0.01). In conclusion, CMR did not affect bench press 1-RM strength performance, but muscular endurance responses to CMR seems to be dose-dependent.

Predicted Structure of Fully Activated Tas1R3/1R3' Homodimer Bound to G Protein and Natural Sugars: Structural Insights into G Protein Activation by a Class C Sweet Taste Homodimer with Natural Sugars

The Tas1R3 G protein-coupled receptor constitutes the main component of sweet taste sensory response in humans via forming a heterodimer with Tas1R2 or a homodimer with Tas1R3. The Tas1R3/1R3' homodimer serves as a low-affinity sweet taste receptor, stimulating gustducin G protein (G_Gust) signaling in the presence of a high concentration of natural sugars. This provides an additional means to detect the taste of natural sugars, thereby differentiating the flavors between natural sugars and artificial sweeteners. We report here the predicted 3D structure of active state Tas1R3/1R3' homodimer complexed with heterotrimeric G_Gust and sucrose. We discovered that the G_Gust makes ionic anchors to intracellular loops 1 and 2 of Tas1R3 while the Gα-α5 helix engages the cytoplasmic region extensively through salt bridge and hydrophobic interactions. We show that in the activation of this complex the Venus flytrap domains of the homodimer undergo a remarkable twist up to ∼100° rotation around the vertical axis to adopt a closed-closed conformation while the intracellular region relaxes to an open-open conformation. We find that binding of sucrose to the homodimer stabilizes a preactivated conformation with a largely open intracellular region that recruits and activates the G_Gust. Upon activation, the Gα subunit spontaneously opens up the nucleotide-binding site, making nucleotide exchange facile for signaling. This activation of G_Gust promotes the interdomain twist of the Venus flytrap domains. These structures and transformations could potentially be a basis for the design of new sweeteners with higher activity and less unpleasant flavors.

The G protein-first activation mechanism of opioid receptors by Gi protein and agonists

We report the G protein-first mechanism for activation of G protein-coupled receptors (GPCR) for the three closest subtypes of the opioid receptors (OR), μOR, κOR and δOR. We find that they couple to the inactive Gi protein-bound guanosine diphosphate (GDP) prior to agonist binding. The inactive Gi protein forms anchors to the intracellular loops of the inactive apo-μOR, apo-κOR and apo-δOR, inducing opening of the cytoplasmic region to form a pre-activated state that holds Gi protein in place until agonist binds. Then, agonist binds to μOR, κOR and δOR already complexed with Gi protein, to trigger the Gαi to open up the tightly coupled GDP binding site, making GDP accessible for GTP exchange, an essential step for Gi signalling. We show that the agonist alone cannot open the intracellular region of μOR and κOR, requiring Gi protein to open the cytoplasmic region by itself. We consider that this G protein-first mechanism may apply to activation of other Class A GPCRs. However, for δOR, agonist binding can open up the intracellular region to encourage Gi protein recruitment. Thus, activation of Gi protein mediated by δOR favourably may proceed with either ligand-first or G protein-first activation mechanisms.

The Problems of Applying Classical Pharmacology Analysis to Modern In Vitro Drug Discovery Assays: Slow Binding Kinetics and High Target Concentration

The analysis framework used to quantify drug potency in vitro (e.g., K_d or K_i) was initially developed for classical pharmacology bioassays, for example, organ bath experiments testing moderate-affinity natural products. Modern drug discovery can infringe the assumptions of the classical pharmacology analysis equations, owing to the reduction of assay volume in miniaturization, target overexpression, and the increase of compound-target affinity in medicinal chemistry. These assumptions are that (1) the compound concentration greatly exceeds the target concentration (i.e., minimal ligand depletion), and (2) the compound is at equilibrium with the receptor (i.e., rapid ligand binding kinetics). Unappreciated infringement of these assumptions can lead to substantial underestimation of compound affinity, which negatively impacts the drug discovery process, from early-stage lead optimization to prediction of human dosing. This study evaluates the real-world impact of these factors on the target interaction assays used in drug discovery using literature examples, database searches, and simulations. The ranges of compound affinity and the assay types that are prone to depletion and equilibration artifacts are identified. Importantly, the highest-affinity compounds, usually the highest value chemical matter in drug discovery, are the most affected. Methods and simulation tools are provided to enable investigators to evaluate, manage, and minimize depletion or equilibration artifacts. This study enables the correct application of pharmacological data analysis to accurately quantify affinity using modern drug discovery assay technology.

Partial agonists go molecular

Single-channel analysis previously revealed a key role for a short-lived 'flipped' state during glycine receptor activation by partial agonists. Structures solved by Yu and colleagues now reveal a surprising mechanism involving a partially activated agonist-bound closed state that is too long-lived to be considered the flipped state.

New analysis pipeline for high-throughput domain-peptide affinity experiments improves SH2 interaction data

Protein domain interactions with short linear peptides, such as those of the Src homology 2 (SH2) domain with phosphotyrosine-containing peptide motifs (pTyr), are ubiquitous and important to many biochemical processes of the cell. The desire to map and quantify these interactions has resulted in the development of high-throughput (HTP) quantitative measurement techniques, such as microarray or fluorescence polarization assays. For example, in the last 15 years, experiments have progressed from measuring single interactions to covering 500,000 of the 5.5 million possible SH2-pTyr interactions in the human proteome. However, high variability in affinity measurements and disagreements about positive interactions between published data sets led us here to reevaluate the analysis methods and raw data of published SH2-pTyr HTP experiments. We identified several opportunities for improving the identification of positive and negative interactions and the accuracy of affinity measurements. We implemented model-fitting techniques that are more statistically appropriate for the nonlinear SH2-pTyr interaction data. We also developed a method to account for protein concentration errors due to impurities and degradation or protein inactivity and aggregation. Our revised analysis increases the reported affinity accuracy, reduces the false-negative rate, and increases the amount of useful data by adding reliable true-negative results. We demonstrate improvement in classification of binding versus nonbinding when using machine-learning techniques, suggesting improved coherence in the reanalyzed data sets. We present revised SH2-pTyr affinity results and propose a new analysis pipeline for future HTP measurements of domain-peptide interactions.

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.

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.

Logistic Models for Simulating the Growth of Plants by Defining the Maximum Plant Size as the Limit of Information Flow

Today, the Logistic equations are widely applied to simulate the population growth across a range of fields, chiefly, demography and ecology. Based on an assumption that growth-regulating factors within the Logistic model, namely, the rate of increase (r) and carrying capacity (K), can be considered as the functions reflecting the combination of the organism- and environment-specific parameters, here, we discussed the possible application of modified synthetic Logistic equations to the simulation of the changes in (1) population (density per volume) of photosynthetically growing free-living algae and (2) size (mass per individual) of higher plants, by newly composing r value as a function reflecting the photosynthetic activities. Since higher plants are multi-cellular organisms, a novel concept for the carrying capacity K must also be introduced. We brought the a priori assumption that information sharing amongst cells strongly influences the physiology of multi-cellular structures eventually defining the maximum size of plants, into view. A simplest form of 'synthetic organism' conformed to test this assumption is a linear chain of cells, and the first physiological phenomenon, modeled in this way, is growth. This combination of information flow along a chain, with exponential growth, produces a simple allotropic relationship. This relationship is compared with results for plants and is found to have excellent predictive power. This theory shows that fast-growing organisms, or multicellular structures, remain small, because of their inability to share information sufficiently quickly and, also, predicts determinate growth. The success of this simple model suggests, firstly, that the inclusion of information flows in theoretical physiology models, which have been, to date, dominated by energetic or metabolic assumptions, will be improved by incorporating information flows. Secondly, the application of more complex information theories, such as those of Shannon, to biological systems will offer deep insights into the mechanisms and control of intercellular communication.

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).

Hill equation and Hatze's muscle activation dynamics complement each other: enhanced pharmacological and physiological interpretability of modelled activity-pCa curves

In pharmacology, particularly receptor theory, the drug dose-effect relation of bio-active substances is frequently described by a sigmoidal function formulated by A.V. Hill. In biomechanics and muscle physiology then again, H. Hatze had elaborated a mathematical model for the stimulation- and length-dependent dynamics of the calcium-induced activation of mammalian skeletal muscle. Here, we prove that muscular activity-pCa curves described by the Hill equation and the equilibrium state predicted by Hatze's activation dynamics are equivalent. Thus, the exponent introduced by Hatze can be directly identified with its counterpart in the Hill equation, by which the former model gains further physiological interpretability. Conversely, the Hill constant can now be interpreted as a function of the fibre length, generally allowing for advanced Hill plots based on model ideas. We derive and examine the complementary relation of both model approaches, highlight the benefits of mutually viewing one approach from the perspective of the other, and address the physiology behind sigmoidal curves.

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.

No Dose-Response Effect of Carbohydrate Mouth Rinse Concentration on 5-km Running Performance in Recreational Athletes

Clarke, ND, Thomas, JR, Kagka, M, Ramsbottom, R, and Delextrat, A. No dose-response effect of carbohydrate mouth rinse concentration on 5-km running performance in recreational athletes. J Strength Cond Res 31(3): 715-720, 2017-Oral carbohydrate rinsing has been demonstrated to provide beneficial effects on exercise performance of durations of up to 1 hour, albeit predominately in a laboratory setting. The aim of the present study was to investigate the effects of different concentrations of carbohydrate solution mouth rinse on 5-km running performance. Fifteen healthy men (n = 9; mean ± SD age; 42 ± 10 years; height, 177.6 ± 6.1 cm; body mass, 73.9 ± 8.9 kg) and women (n = 6; mean ± SD age, 43 ± 9 years; height, 166.5 ± 4.1 cm; body mass, 65.7 ± 6.8 kg) performed a 5-km running time trial on a track on 4 separate occasions. Immediately before starting the time trial and then after each 1 km, subjects rinsed 25 ml of 0, 3, 6, or 12% maltodextrin for 10 seconds. Mouth rinsing with 0, 3, 6, or 12% maltodextrin did not have a significant effect on the time to complete the time trial (0%, 26:34 ± 4:07 minutes:seconds; 3%, 27:17 ± 4:33 minutes:seconds; 6%, 27:05 ± 3:52 minutes:seconds; 12%, 26:47 ± 4.31 minutes:seconds; p = 0.071; (Equation is included in full-text article.)= 0.15), heart rate (p = 0.095; (Equation is included in full-text article.)= 0.16), rating of perceived exertion (p = 0.195; (Equation is included in full-text article.)= 0.11), blood glucose (p = 0.920; (Equation is included in full-text article.)= 0.01), and blood lactate concentration (p = 0.831; (Equation is included in full-text article.)= 0.02), with only nonsignificant trivial to small differences between concentrations. Results of this study suggest that carbohydrate mouth rinsing provides no ergogenic advantage over an acaloric placebo (0%) and that there is no dose-response relationship between carbohydrate solution concentration and 5-km track running performance.

Clinical dose-response for a broad set of biological products: A model-based meta-analysis

Characterizing clinical dose-response is a critical step in drug development. Uncertainty in the dose-response model when planning a dose-ranging study can often undermine efficiency in both the design and analysis of the trial. Results of a previous meta-analysis on a portfolio of small molecule compounds from a large pharmaceutical company demonstrated a consistent dose-response relationship that was well described by the maximal effect model. Biologics are different from small molecules due to their large molecular sizes and their potential to induce immunogenicity. A model-based meta-analysis was conducted on the clinical efficacy of 71 distinct biologics evaluated in 91 placebo-controlled dose-response studies published between 1995 and 2014. The maximal effect model, arising from receptor occupancy theory, described the clinical dose-response data for the majority of the biologics (81.7%, n = 58). Five biologics (7%) with data showing non-monotonic trend assuming the maximal effect model were identified and discussed. A Bayesian model-based hierarchical approach using different joint specifications of prior densities for the maximal effect model parameters was used to meta-analyze the whole set of biologics excluding these five biologics ( n = 66). Posterior predictive distributions of the maximal effect model parameters were reported and they could be used to aid the design of future dose-ranging studies. Compared to the meta-analysis of small molecules, the combination of fewer doses, narrower dosing ranges, and small sample sizes further limited the information available to estimate clinical dose-response among biologics.

An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice

The study of the magnitude and variation of drug response is defined as pharmacodynamics (PDs). PD models examine plasma concentration and effect relationship. It can predict the archetypal effect ([Formula: see text]) of a drug as a function of the drug concentration ([Formula: see text]) and estimate an unknown PD parameter ([Formula: see text]). The PD models have been described as fixed, linear, log-linear, [Formula: see text], sigmoid [Formula: see text], and indirect PD response. Ligand binding model is an example of a PD model that works on the underpinning PD principle of a drug, eliciting its pharmacological effect at the receptor site. The pharmacological effect is produced by the drug binding to the receptor to either activate or antagonise the receptor. Ligand binding models describe a system of interacting components, i.e. the interaction of one or more ligands with one or more binding sites. The [Formula: see text] model is the central method that provides an empirical justification for the concentration/dose-effect relationship. However, for ligand binding models justification is provided by theory of receptor occupancy. In essence, for ligand binding models, the term [Formula: see text] is best used to describe the fraction of receptors occupied at a particular ligand concentration. It is stated that the [Formula: see text], which means the effect of a drug should depend on the fraction of receptors that are occupied. In the future, network-based systems pharmacology models using ligand binding principles could be an effective way of understanding drug-related adverse effects. This will facilitate and strengthen the development of rational drug therapy in clinical practice.

Choline on the Move: Perspectives on the Molecular Physiology and Pharmacology of the Presynaptic Choline Transporter

Genetic, biochemical, physiological, and pharmacological approaches have advanced our understanding of cholinergic biology for over 100 years. High-affinity choline uptake (HACU) was one of the last features of cholinergic signaling to be defined at a molecular level, achieved through the cloning of the choline transporter (CHT, SLC5A7). In retrospect, the molecular era of CHT studies initiated with the identification of hemicholinium-3 (HC-3), a potent, competitive CHT antagonist, though it would take another 30 years before HC-3, in radiolabeled form, was used by Joseph Coyle's laboratory to identify and monitor the dynamics of CHT proteins. Though HC-3 studies provided important insights into CHT distribution and regulation, another 15 years would pass before the structure of CHT genes and proteins were identified, a full decade after the cloning of most other neurotransmitter-associated transporters. The availability of CHT gene and protein probes propelled the development of cell and animal models as well as efforts to gain insights into how human CHT gene variation affects the risk for brain and neuromuscular disorders. Most recently, our group has pursued a broadening of CHT pharmacology, elucidating novel chemical structures that may serve to advance cholinergic diagnostics and medication development. Here we provide a short review of the transformation that has occurred in HACU research and how such advances may promote the development of novel therapeutics.

Drug Combinations: Tests and Analysis with Isoboles

Described in this unit are experimental and computational methods to detect and classify drug interactions. In most cases, this relates to two drugs or compounds with overtly similar effects, e.g., two analgesics or two anti-hypertensives. From the dose-response data of the individual drugs, it is possible to generate a curve, the isobole, which defines all dose combinations that are expected to yield a specified effect. The theory underlying the isobole involves the calculation of doses of drug A that are effectively equivalent to doses of drug B with that equivalence determining whether the isobole is linear or nonlinear. In either case, the isobole allows for a comparison with actual combination effects making it possible to determine whether the interaction is synergistic, additive, or sub-additive. Actual as well as illustrative data are employed to demonstrate experimental design and data analysis.

Cato Guldberg and Peter Waage, the history of the Law of Mass Action, and its relevance to clinical pharmacology

We have traced the historical link between the Law of Mass Action and clinical pharmacology. The Law evolved from the work of the French chemist Claude Louis Berthollet, was first formulated by Cato Guldberg and Peter Waage in 1864 and later clarified by the Dutch chemist Jacobus van 't Hoff in 1877. It has profoundly influenced our qualitative and quantitative understanding of a number of physiological and pharmacological phenomena. According to the Law of Mass Action, the velocity of a chemical reaction depends on the concentrations of the reactants. At equilibrium the concentrations of the chemicals involved bear a constant relation to each other, described by the equilibrium constant, K. The Law of Mass Action is relevant to various physiological and pharmacological concepts, including concentration-effect curves, dose-response curves, and ligand-receptor binding curves, all of which are important in describing the pharmacological actions of medications, the Langmuir adsorption isotherm, which describes the binding of medications to proteins, activation curves for transmembrane ion transport, enzyme inhibition and the Henderson-Hasselbalch equation, which describes the relation between pH, as a measure of acidity and the concentrations of the contributory acids and bases. Guldberg and Waage recognized the importance of dynamic equilibrium, while others failed to do so. Their ideas, over 150 years old, are embedded in and still relevant to clinical pharmacology. Here we explain the ideas and in a subsequent paper show how they are relevant to understanding adverse drug reactions.

Harms from medicines: inevitable, in error or intentional

Rational therapeutics requires a balance between benefits and harms. (i) Harm may be inevitable. Some adverse drug reactions cannot be predicted or prevented. (ii) Some harm occurs in error when a medicine is wrongly formulated, prescribed, dispensed or administered. Adverse drug reactions that might have been prevented, for example, by monitoring, fall into this category. (iii) Rarely, harm is inflicted deliberately, for example, in murder by poisoning. Here I consider adverse drug reactions, errors and deliberate drug-induced harm from the perspective of a clinical pharmacologist.

Quantitative versus qualitative data: the numerical dimensions of drug action

The application of detailed quantitative analyses of the concentration dependence of the biological responses mediated by endogenous hormones and other mediators, drugs, and related compounds has been the foundation of pharmacology for the past century or more. This approach has been remarkably successful in identifying the specific molecular targets for these mediators and drugs, in establishing the mechanisms for those effects at both the cellular and whole organismal levels, and in the development of new chemical entities (NCEs) with great selectivity for individual molecular targets. The availability of such compounds has unfortunately led to a mindset that detailed quantitative analyses are no longer necessary to use such compounds in understanding biological system function and to draw valid conclusions in regard to the utility of NCEs selective for putative drug targets in the potential treatment of human disease states. This lack of appreciation for quantitative approaches has contributed significantly to the all-too-frequent failures of new drug candidates in early-stage clinical trials. The present article reviews basic drug/receptor concepts together with the mathematical relationships that underlie the quantitative analysis of dose-response and concentration-effect relationships for individual compounds and for more complex systems, such as the comparative analysis of multiple compounds at a single receptor. A thorough understanding of these concepts and their associated analyses, along with their proper and rigorous application in all pre-clinical drug development studies, is an essential component of an integrated approach toward improving drug development.

Hormesis: its impact on medicine and health

This article offers a broad assessment of the hormetic dose response and its relevance to biomedical researchers, physicians, the pharmaceutical industry, and public health scientists. This article contains a series of 61 questions followed by relatively brief but referenced responses that provides support for the conclusion that hormesis is a reproducible phenomenon, commonly observed, with a frequency far greater than other dose-response models such as the threshold and linear nonthreshold dose-response models. The article provides a detailed background information on the historical foundations of hormesis, its quantitative features, mechanistic foundations, as well as how hormesis is currently being used within medicine and identifying how this concept could be further applied in the development of new therapeutic advances and in improved public health practices.

Toxicology rewrites its history and rethinks its future: giving equal focus to both harmful and beneficial effects

This paper assesses how medicine adopted the threshold dose-response to evaluate health effects of drugs and chemicals throughout the 20th century to the present. Homeopathy first adopted the biphasic dose-response, making it an explanatory principle. Medicine used its influence to discredit the biphasic dose-response model to harm homeopathy and to promote its alternative, the threshold dose-response. However, it failed to validate the capacity of its model to make accurate predictions in the low-dose zone. Recent attempts to validate the threshold dose-response indicate that it poorly predicts responses below the threshold. The long marginalized biphasic/hormetic dose-response model made accurate predictions in these validation studies. The failure to accept the possibility of the hormetic-biphasic dose-response during toxicology's dose-response concept formative period, while adopting the threshold model, and later the linear no-threshold model for carcinogens, led toxicology to adopt a hazard assessment process that involved testing only a few very high doses. This created the framework that toxicology was a discipline that only studied harmful responses, ignoring the possibility of benefit at low doses by the induction of adaptive mechanisms. Toxicology needs to assess the entire dose-response continuum, incorporating both harmful and beneficial effects into the risk assessment process.

Reflections on the theory of "silver bullet" octreotide tracers: implications for ligand-receptor interactions in the age of peptides, heterodimers, receptor mosaics, truncated receptors, and multifractal analysis

The classical attitude of Nuclear Medicine practitioners on matters of peptide-receptor interactions has maintained an intrinsic monogamic character since many years. New advances in the field of biochemistry and even in clinical Nuclear Medicine have challenged this type of thinking, which prompted me to work on this review. The central issue of this paper will be the use of somatostatin analogs, i.e., octreotide, in clinical imaging procedures as well as in relation to neuroendocirne tumors. Newly described characteristics of G-protein coupled receptors such as the formation of receptor mosaics will be discussed. A small section will enumerate the regulatory processes found in the cell membrane. Possible new interpretations, other than tumor detection, based on imaging procedures with somatostatin analogs will be presented. The readers will be taken to situations such as inflammation, nociception, mechanosensing, chemosensing, fibrosis, taste, and vascularity where somatostatin is involved. Thyroid-associated orbitopathy will be used as a model for the development of multi-agent therapeutics. The final graphical summary depicts the multifactorial properties of ligand binding.

Antigen potency and maximal efficacy reveal a mechanism of efficient T cell activation

T cell activation, a critical event in adaptive immune responses, depends on productive interactions between T cell receptors (TCRs) and antigens presented as peptide-bound major histocompatibility complexes (pMHCs). Activated T cells lyse infected cells, secrete cytokines, and perform other effector functions with various efficiencies, which depend on the binding parameters of the TCR-pMHC complex. The mechanism through which binding parameters are translated to the efficiency of T cell activation, however, remains controversial. The "affinity model" suggests that the dissociation constant (KD) of the TCR-pMHC complex determines the response, whereas the "productive hit rate model" suggests that the off-rate (koff) is critical. Here, we used mathematical modeling to show that antigen potency, as determined by the EC50 (half-maximal effective concentration), which is used to support KD-based models, could not discriminate between the affinity and the productive hit rate models. Both models predicted a correlation between EC50 and KD, but only the productive hit rate model predicted a correlation between maximal efficacy (Emax), the maximal T cell response induced by pMHC, and koff. We confirmed the predictions made by the productive hit rate model in experiments with cytotoxic T cell clones and a panel of pMHC variants. Thus, we propose that the activity of an antigen is determined by both its potency (EC50) and maximal efficacy (Emax).

Kinase cascades and ligand-directed signaling at the kappa opioid receptor

BACKGROUND AND RATIONALE

The dynorphin/kappa opioid receptor (KOR) system has been implicated as a critical component of the stress response. Stress-induced activation of dynorphin-KOR is well known to produce analgesia, and more recently, it has been implicated as a mediator of stress-induced responses including anxiety, depression, and reinstatement of drug seeking.

OBJECTIVE

Drugs selectively targeting specific KOR signaling pathways may prove potentially useful as therapeutic treatments for mood and addiction disorders.

RESULTS

KOR is a member of the seven transmembrane spanning (7TM) G-protein coupled receptor (GPCR) superfamily. KOR activation of pertussis toxin-sensitive G proteins leads to Galphai/o inhibition of adenylyl cyclase production of cAMP and releases Gbetagamma, which modulates the conductances of Ca(+2) and K(+) channels. In addition, KOR agonists activate kinase cascades including G-protein coupled Receptor Kinases (GRK) and members of the mitogen-activated protein kinase (MAPK) family: ERK1/2, p38 and JNK. Recent pharmacological data suggests that GPCRs exist as dynamic, multi-conformational protein complexes that can be directed by specific ligands towards distinct signaling pathways. Ligand-induced conformations of KOR that evoke beta-arrestin-dependent p38 MAPK activation result in aversion; whereas ligand-induced conformations that activate JNK without activating arrestin produce long-lasting inactivation of KOR signaling.

CONCLUSIONS

In this review, we discuss the current status of KOR signal transduction research and the data that support two novel hypotheses: (1) KOR selective partial agonists that do not efficiently activate p38 MAPK may be useful analgesics without producing the dysphoric or hallucinogenic effects of selective, highly efficacious KOR agonists and (2) KOR antagonists that do not activate JNK may be effective short-acting drugs that may promote stress-resilience.

Defining hormesis: evaluation of a complex concentration response phenomenon

Hormesis describes dose-response relationships characterized by a reversal of response between low and high doses of chemicals, biological molecules, physical stressors, or other initiators of a response. Acceptance of hormesis as a viable dose-response theory has been limited until recently, in part, because of poor conceptual understanding, ad hoc and inappropriate use, and lack of a defined mechanism. By examining the history of this dose-response theory, it is clear that both pharmacological and toxicological studies provide evidence for hormetic dose responses, but retrospective examination of studies can be problematic at best. Limited scientific evidence and lack of a common lexicon with which to describe these responses have left hormesis open to inappropriate application to unrelated dose-response relationships. Future studies should examine low-dose effects using unbiased, descriptive criteria to further the scientific understanding of this dose response. A clear, concise definition is required to further the limited scientific evidence for hormetic dose responses.

Functionomics: the analysis of a postgenomic concept on the basis of pregenomic pharmacological studies in smooth muscle

The term functionomics (Amin 2003, Neumann et al. 2004) refers to a postgenomic integrated Systems Biology (Attur et al. 2002) using a multidimensional approach for cells, tissues and organs. It considers current or future involvement among genomics, proteomics or metabolomics, including the main factors that cause biological responses and modulation under different conditions. Our objective in the present review is to summarize the contemporary understanding of functionomics of smooth muscle pharmacology, based on the results obtained on the pregenomic era during several years in our laboratory. The present approach is based on the knowledge of the dynamics of the receptor system, which comprises a cascade of phenomena, leading from the drug administration to the final biological response. We will describe several conditions in which the final effect is modified, based on perturbations induced on drug absorption, distribution, metabolism, interaction with receptors and mobilization of second messengers, as well as by interactions with a second receptor system. We will also discuss the gaps that need to be fulfilled in order to obtain a clear and better understanding of the receptor system in smooth muscle, and to narrow the bridge between ourknowledge of the function of biological systems, genomics, and other recently introduced areas.

Drug discovery paradigms: past, present, future - a centennial symposium of the American Society for Pharmacology and Experimental Therapeutics

BACKGROUND

The American Society for Pharmacology and Experimental Therapeutics (ASPET) celebrated its centennial during the April 2008 Experimental Biology meeting held in San Diego, CA, USA.

OBJECTIVES

This report summarizes a centennial symposium on past, present and future paradigms in drug discovery. The John Langley (1905) concept of 'receptive substances' initiated a cascade of cardinal discoveries for pharmacology. During the following 100 years, receptors achieved the status of well-defined multifunctional cellular proteins mediating all drug effects. The G-protein-coupled receptors (GPCRs) were discussed in relation to multiple targets they represent for clinically used or new medicines. The development of inbred and transgenic animal strains is a fundamental twentieth century achievement for biological research activities. Progress in treating CNS disorders still awaits breakthrough treatments. Drug development remains a risky and expensive venture; improvements are expected from emerging biomarker and translational medicine technologies. Future clinical research has to document the benefits that new medications provide to concerned stakeholders.

CONCLUSIONS

We wish ASPET a new century of great achievements and acknowledge the dedication of several generations of colleagues, many of whom are our 'unsung heroes'. They have left us a precious heritage to be pursued with passion in the quest for better medicines.