Agonist-antagonist Interactions Research Articles

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

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. 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. 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. 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.

Influence of inspiratory resistive loading on expiratory muscle fatigue in healthy humans.

What is the central question of this study? This study is the first to measure objectively both inspiratory and expiratory muscle fatigue after inspiratory resistive loading to determine whether the expiratory muscles are activated to the point of fatigue when specifically loading the inspiratory muscles. What is the main finding and its importance? The absence of abdominal muscle fatigue suggests that future studies attempting to understand the neural and circulatory consequences of diaphragm fatigue can use inspiratory resistive loading without considering the confounding effects of abdominal muscle fatigue. Expiratory resistive loading elicits inspiratory as well as expiratory muscle fatigue, suggesting parallel coactivation of the inspiratory muscles during expiration. It is unknown whether the expiratory muscles are likewise coactivated to the point of fatigue during inspiratory resistive loading (IRL). The purpose of this study was to determine whether IRL elicits expiratory as well as inspiratory muscle fatigue. Healthy male subjects (n=9) underwent isocapnic IRL (60% maximal inspiratory pressure, 15 breathsmin-1 , 0.7 inspiratory duty cycle) to task failure. Abdominal and diaphragm contractile function was assessed at baseline and at 3, 15 and 30min post-IRL by measuring gastric twitch pressure (Pga,tw ) and transdiaphragmatic twitch pressure (Pdi,tw ) in response to potentiated magnetic stimulation of the thoracic and phrenic nerves, respectively. Fatigue was defined as a significant reduction from baseline in Pga,tw or Pdi,tw . Throughout IRL, there was a time-dependent increase in cardiac frequency and mean arterial blood pressure, suggesting activation of the respiratory muscle metaboreflex. The Pdi,tw was significantly lower than baseline (34.3±9.6cmH2 O) at 3 (23.2±5.7cmH2 O, P<0.001), 15 (24.2±5.1cmH2 O, P<0.001) and 30min post-IRL (26.3±6.0cmH2 O, P<0.001). The Pga,tw was not significantly different from baseline (37.6±17.1cmH2 O) at 3 (36.5±14.6cmH2 O), 15 (33.7±12.4cmH2 O) and 30min post-IRL (32.9±11.3cmH2 O). Inspiratory resistive loading elicits objective evidence of diaphragm, but not abdominal, muscle fatigue. Agonist-antagonist interactions for the respiratory muscles appear to be more important during expiratory versus inspiratory loading.

A comparison of two semi-mechanistic models for prolactin release and prediction of receptor occupancy following administration of dopamine D2 receptor antagonists in rats

We compared the model performance of two semi-mechanistic pharmacokinetic-pharmacodynamic models, the precursor pool model and the agonist-antagonist interaction model, to describe prolactin response following the administration of the dopamine D2 receptor antagonists risperidone, paliperidone or remoxipride in rats. The time course of pituitary dopamine D2 receptor occupancy was also predicted.Male Wistar rats received a single dose (risperidone, paliperidone, remoxipride) or two consecutive doses (remoxipride). Population modeling was applied to fit the pool and interaction models to the prolactin data. The pool model was modified to predict the time course of pituitary D2 receptor occupancy. Unbound plasma concentrations of the D2 receptor antagonists were considered the drivers of the prolactin response. Both models were used to predict prolactin release following multiple doses of paliperidone.Both models described the data well and model performance was comparable. Estimated unbound EC50 for risperidone and paliperidone was 35.1nM (relative standard error 51%) and for remoxipride it was 94.8nM (31%). KI values for these compounds were 11.1nM (21%) and 113nM (27%), respectively. Estimated pituitary D2 receptor occupancies for risperidone and remoxipride were comparable to literature findings. The interaction model better predicted prolactin profiles following multiple paliperidone doses, while the pool model predicted tolerance better.The performance of both models in describing the prolactin profiles was comparable. The pool model could additionally describe the time course of pituitary D2 receptor occupancy. Prolactin response following multiple paliperidone doses was better predicted by the interaction model.

Electromyographic Cross-Spectral Analysis of Antagonist Muscle Coactivation

The purpose of this study was two-fold: (1) to compare the responses for traditional EMG amplitude measures with those from the crossspectrum approach for assessing agonist-antagonist interaction, and (2) to examine changes in the agonist-antagonist interaction for the elbow flexors and extensors with increases in movement velocity. Eighteen healthy, college-aged subjects (mean ± SD age = 21.8 ± 2.7 yrs) volunteered to perform maximal, concentric isokinetic contractions of the dominant elbow flexors and extensors at randomly ordered velocities of 60, 180, and 300 degrees/second. The subjects also performed maximal, 6-sec isometric contractions of the dominant elbow flexors and extensors at an elbow joint angle of 120 degrees between the arm and forearm. During all contractions, bipolar surface EMG signals were detected from the biceps brachii and triceps brachii. Traditional EMG amplitude (root-mean-square, RMS) measurements were made for both the agonist and antagonist muscles during each contraction. In addition, total power and mean frequency (MNF) were calculated from the agonist-antagonist muscle cross-spectrum. The results indicated that there were no velocity-related changes in traditional EMG amplitude for either the agonist or antagonist muscles. There were also velocity related changes in cross-spectral power, but not MNF, for both elbow flexion and elbow extension. Mean crossspectral power values were similar when comparing elbow flexion versus elbow extension during isometric contractions. During the dynamic contractions, however, mean cross-spectral power values were lower (relative to the isometric values) at all velocities for elbow extension, but higher at all velocities for elbow flexion. These findings suggested that the agonist-antagonist cross-spectrum approach may provide information regarding agonist-antagonist interaction that cannot be obtained from traditional EMG amplitude measurements. In addition, the agonist-antagonist interaction may be different for elbow flexion versus elbow extension.

Antioxidants l-carnitine and d-methionine modulate neuronal activity through GABAergic inhibition

Antioxidants are well known for their neuroprotective properties against reactive oxygen species in cortical neurons and auditory cells. We recently identified L-carnitine and D-methionine to be among agents that provide such protection. Here, we investigated their neuronal modulatory actions. We used cultured neuronal networks grown on microelectrode arrays to assess the effects of L-carnitine and D-methionine on network function. Spike production and burst properties of neuronal networks were used as parameters to monitor pharmacological responses. L-Carnitine and D-methionine reduced spike activity with 100% efficacy with EC50 values of 0.22 (± 0.01) mM and 1.06 (± 0.05) mM, respectively. In the presence of 1.0-40 μM of the GABAA antagonist bicuculline, the sigmoidal concentration-response curves of both compounds exhibited stepwise shifts, without a change in efficacy. Under a maximal bicuculline concentration of 40 μM, the EC50 increased to 3.57 (± 0.26) mM for L-carnitine and to 10.52 (± 0.97) mM for D-methionine, more than a tenfold increase. The agonist-antagonist interactions with bicuculline were estimated by Lineweaver-Burk plot analyses to be competitive, corroborated by the computed dissociation constants of bicuculline. For both compounds, the effects on the network burst pattern, activity reversibility, and bicuculline antagonism resembled that elicited by the GABAA agonist muscimol. We showed that the antioxidants L-carnitine and D-methionine modulate cortical electrical spike activity primarily through GABAA receptor activation. Our findings suggest the involvement of GABAergic mechanisms that perhaps contribute to the protective actions of these compounds.

Effect of disease states on α1-adrenoceptor binding and signal transduction parameters in isolated perfused heart: quantification by pharmacokinetic-pharmacodynamic modelling

To employ a pharmacokinetic-pharmacodynamic modelling approach for analysing the effect of experimental endotoxemia and mild hypoxia on α1 -adrenoceptor (α1 AR) binding and signal transduction. In Langendorff-perfused rat hearts, phenylephrine was continuously infused, and [(3) H]-prazosin was injected as single dose (infused over 1 min). Simultaneous analysis of the time courses of prazosin outflow concentration and inotropic response (left ventricular developed pressure) using an agonist-antagonist interaction model and nonlinear regression allowed to estimate receptor affinity, as well as the parameters of the operational model of agonism. Both endotoxemia and hypoxia, significantly reduced the maximum response achievable in the system to 67% and 49% of the control group mean, respectively. In addition, endotoxemia decreased the efficiency of stimulus-response coupling and increased the steepness of the stimulus-response curve. In both disease models, no change in receptor affinity and density were found. The results revealed the causes of reduced α1 AR-mediated inotropic responsiveness in endotoxemia and hypoxia. In contrast with traditional dose-response studies, it was possible to quantify separately the underlying changes in α1 AR binding and signal transduction.

Agonist Antagonist Interactions at the Rapidly Desensitizing P2X3 Receptor

P2X3 receptors (P2XRs), as members of the purine receptor family, are deeply involved in chronic pain sensation and therefore, specific, competitive antagonists are of great interest for perspective pain management. Heretofore, Schild plot analysis has been commonly used for studying the interaction of competitive antagonists and the corresponding receptor. Unfortunately, the steady-state between antagonist and agonist, as a precondition for this kind of analysis, cannot be reached at fast desensitizing receptors like P2X3R making Schild plot analysis inappropriate. The aim of this study was to establish a new method to analyze the interaction of antagonists with their binding sites at the rapidly desensitizing human P2X3R. The patch-clamp technique was used to investigate the structurally divergent, preferential antagonists A317491, TNP-ATP and PPADS. The P2X1,3-selective α,β-methylene ATP (α,β-meATP) was used as an agonist to induce current responses at the wild-type (wt) P2X3R and several agonist binding site mutants. Afterwards a Markov model combining sequential transitions of the receptor from the closed to the open and desensitized mode in the presence or absence of associated antagonist molecules was developed according to the measured data. The P2X3R-induced currents could be fitted correctly with the help of this Markov model allowing identification of amino acids within the binding site which are important for antagonist binding. In conclusion, Markov models are suitable to simulate agonist antagonist interactions at fast desensitizing receptors such as the P2X3R. Among the antagonists investigated, TNP-ATP and A317491 acted in a competitive manner, while PPADS was identified as a (pseudo)irreversible blocker.

Predictions of In Vivo Prolactin Levels from In Vitro K i Values of D2 Receptor Antagonists Using an Agonist–Antagonist Interaction Model

Prolactin elevation is a side effect of all currently available D(2) receptor antagonists used in the treatment of schizophrenia. Prolactin elevation is the result of a direct antagonistic D(2) effect blocking the tonic inhibition of prolactin release by dopamine. The aims of this work were to assess the correlation between in vitro estimates of D(2) receptor affinity and pharmacokinetic-pharmacodynamic model-based estimates obtained from analysis of clinical data using an agonist-antagonist interaction (AAI) model and to assess the value of such a correlation in early prediction of full prolactin time profiles. A population model describing longitudinal prolactin data was fitted to clinical data from 16 clinical phases 1 and 3 trials including five different compounds. Pharmacokinetic data were modeled for each compound and the prolactin model was both fitted in per-compound fits as well as simultaneously to all prolactin data. Estimates of prolactin elevating potency were compared to corresponding in vitro values and their predictability was evaluated through model-based simulations. The model successfully described the prolactin time course for all compounds. Estimates derived from experimental preclinical data and the model fit of the clinical data were strongly correlated (p<0.001), and simulations adequately predicted the prolactin elevation in five out of six compounds. The AAI model has the potential to be used in drug development to predict prolactin response for a given exposure of D(2) antagonists using routinely produced preclinical data.

Muscle agonist–antagonist interactions in an experimental joint model

The experiments presented here and performed in anaesthetized cats aimed at studying the dynamics of interactions between antagonist muscle groups. The tendons of triceps surae muscles of both hindlimbs were connected with an artificial joint (a pulley installed on a shaft). The muscles were activated by the distributed stimulation of five filaments of cut ventral roots L7–S1 on both sides of the spinal cord; movements were evoked by the rate-modulation of the stimulation trains. The study mostly compared programs of reciprocal activation and co-activation, including different changes in stimulation rates of muscle antagonists. The most common feature of the movements in both activation modes was hysteresis of the joint angle changes in dependence on stimulus rate. Reciprocal activation appeared suitable for a precise regulation of both amplitude and velocity of the movements in direction of the agonist shortening; maximal effectiveness was achieved during full switching off the antagonist stimulation at plateaus of the movement traces. The reverse movements during decrease of the agonist’s stimulation rate demonstrated an explicit nonlinear form with pronounced initial phase of the joint angle fixation. The co-activation pattern distinctly reduced the hysteresis of joint movements and suppressed the stimulation after-effects, such as the lasting residual movements after fixation of the stimulation rates.

The contractile sponge epitheliumsensu lato– body contraction of the demospongeTethya wilhelmais mediated by the pinacoderm

Sponges constitute one of the two metazoan phyla that are able to contract their bodies despite a complete lack of muscle cells. Two competing hypotheses on the mechanisms behind this have been postulated to date: (1) mesohyl-mediated contraction originating from fusiform smooth muscle-like actinocytes ('myocytes') and (2) epidermal contraction originating in pinacocytes. No direct support exists for either hypothesis. The question of agonist-antagonist interaction in sponge contraction seems to have been completely neglected so far. In the present study we addressed this by studying sponge contraction kinetics. We also tested both hypotheses by carrying out volumetric studies of 3D synchrotron radiation-based x-ray microtomography data obtained from contracted and expanded specimens of Tethya wilhelma. Our results support the pinacoderm contraction hypothesis. Should mesohyl contraction be present, it is likely to be part of the antagonist system. We conclude that epithelial contraction plays a major role in sponges. Contractile epithelia sensu lato may be regarded as part of the ground pattern of the Metazoa.

Muscarinic receptor agonist-antagonist interaction in the rat rectum: are there ways of activating the same receptors?

The interaction between the muscarinic receptor agonists, carbachol, acetylcholine (ACh) and methacholine, and antagonists, atropine, gallamine, 4-DAMP and pirenzepine, was studied on the rat isolated rectum preparation. ACh (1.93 X 10(-8)-1.95 X 10(-6) M), methacholine (8.7 X 10(-8)-1.1 X 10(-6) M) and carbachol (1.1 X 10(-7)-3.5 X 10(-6) M) induced contractions that were reversibly antagonized by atropine (1.9 X 10(-9)-4.8 X 10(-8) M), 4-DAMP (1.5 X 10(-8)-2.86 X 10(-7) M) gallamine (1.12 X 10(-6)-1.12 X 10(-4) M) and pirenzepine (2.8 X 10(-7)-7.0 X 10(-6) M). The pA2 values were atropine: 8.99 +/- 0.28, 9.29 +/- 0.14 and 8.86 +/- 0.05; 4-DAMP: 8.39 +/- 0.10, 8.66 +/- 0.15 and 8.26 +/- 0.30, gallamine: 5.85 +/- 0.23, 5.73 +/- 0.25 and 5.96 +/- 0.10 and pirenzepine: 6.85 +/- 0.44, 7.17 +/- 0.13 and 7.21 +/- 0.03 against ACh, methacholine and carbachol, respectively. The experimental dose-ratio (atropine + gallamine) was greater than the expected dose-ratio (as predicted by the Paton & Rang rule) for ACh and methacholine while the experimental dose-ratio closely approximates the expected dose-ratio for carbachol. It is suggested that atropine, 4-DAMP pirenzepine and gallamine act on the same receptors but gallamine allosterically altered the binding of the agonists and antagonists to varying extents.

Comparison of the agonist-antagonist interaction model and the pool model for the effect of remoxipride on prolactin

The tolerance to the prolactin response following administration of antipsychotic drugs has been modelled as a depletion of a prolactin pool (pool model) and a model where the tolerance is explained by a feedback loop including the dopamine interaction of prolactin release (agonist-antagonist interaction model, (AAI model)). The AAI model was superior to the pool model when analyzing data from clinical trials of risperidone and paliperidone. Here we evaluated the two models using the remoxipride data, designed to challenge the short-term prolactin response, from which the original pool model was built. The remoxipride data were collected from a study where eight healthy male subjects received two remoxipride infusions on five occasions. The intervals between the first and second dose on each occasion were 2, 8, 12, 24 and 48 h, respectively. The pool and AAI models were fitted using NONMEM. According to the objective function values the pool model with a circadian rhythm function fitted the data slightly better, while the AAI model was better in describing the circadian rhythm of prolactin. Visual predictive checks revealed that the models predicted the prolactin profiles equally well. According to the analysis performed here, a previous analysis of several clinical studies and literature reports on prolactin concentrations, it appears that the dopamine feedback mechanism included in the AAI model is better than the storage depletion mechanism in the pool model to estimate the bio-rhythm of prolactin time-course and the tolerance development across different populations, drugs, treatment schedules and time.

Naloxone Reversal of Morphine- and Morphine-6-Glucuronide-induced Respiratory Depression in Healthy Volunteers

Opioid-induced respiratory depression is antagonized effectively by the competitive opioid receptor antagonist naloxone. However, to fully understand the complex opioid agonist-antagonist interaction, the effects of various naloxone doses on morphine and morphine-6-glucuronide (M6G)-induced respiratory depression were studied in healthy volunteers. Twenty-four subjects received 0.15 mg/kg morphine intravenously at t = 0 followed by placebo, 200 or 400 microg naloxone at t = 30 min. Thirty-two subjects received 0.3 mg/kg M6G intravenously at t = 0 followed by placebo, 25, 100, or 400 microg naloxone at t = 55 min. There were a total of 8 subjects per treatment group. Respiration was measured on a breath-to-breath basis at constant end-tidal Pco2. A mechanism-based pharmacokinetic-pharmacodynamic model consisting of a part describing biophase equilibration and a part describing receptor association-dissociation kinetics was used to analyze the data. Naloxone reversal of M6G-induced respiratory depression developed more slowly than reversal of the respiratory effect of morphine. A simulation study revealed that this was related to the slower receptor association-dissociation kinetics of M6G (koff M6G = 0.0327 +/- 0.00455 min versus morphine 0.138 +/- 0.0148 min; values are typical +/-SE). Duration of naloxone reversal was longer for M6G. This was related to the three- to fourfold greater potency of naloxone as an antagonist against M6G compared with morphine. Increasing the naloxone dose had no effect on the speed of reversal, but it did extend reversal duration. Naloxone reversal of the opioid effect is dependent on the receptor association-dissociation kinetics of the opioid that needs reversal with respect to the rate of reversal. The pharmacodynamics of naloxone determines reversal magnitude and duration.

Influence of Plasma Protein Binding on Pharmacodynamics: Estimation of In Vivo Receptor Affinities of β Blockers Using a New Mechanism-Based PK–PD Modelling Approach

The objective of this investigation was to examine in a systematic manner the influence of plasma protein binding on in vivo pharmacodynamics. Comparative pharmacokinetic–pharmacodynamic studies with four β blockers were performed in conscious rats, using heart rate under isoprenaline-induced tachycardia as a pharmacodynamic endpoint. A recently proposed mechanism-based agonist–antagonist interaction model was used to obtain in vivo estimates of receptor affinities (KB,vivo). These values were compared with in vitro affinities (KB,vitro) on the basis of both total and free drug concentrations. For the total drug concentrations, the KB,vivo estimates were 26, 13, 6.5 and 0.89 nM for S(−)-atenolol, S(−)-propranolol, S(−)-metoprolol and timolol. The KB,vivo estimates on the basis of the free concentrations were 25, 2.0, 5.2 and 0.56 nM, respectively. The KB,vivo–KB,vitro correlation for total drug concentrations clearly deviated from the line of identity, especially for the most highly bound drug S(−)-propranolol (ratio KB,vivo/KB,vitro ∼6.8). For the free drug, the correlation approximated the line of identity. Using this model, for β-blockers the free plasma concentration appears to be the best predictor of in vivo pharmacodynamics. © 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:3816–3828, 2009

Pharmacokinetic–pharmacodynamic modeling of the effect of propofol on α 1-adrenoceptor-mediated positive inotropy in rat heart

Since it is unclear whether and how propofol affects the α 1-mediated inotropic response, we used a pharmacokinetic–pharmacodynamic modeling approach in isolated rat hearts to analyze the effect of propofol on receptor binding and signal transduction. In Langendorff rat hearts perfused with buffer containing 12.3 μM phenylephrine, 1.27 nmol doses of [ 3H]-prazosin were infused (over 1 min), in the absence and presence of propofol (28 μM). Simultaneous analysis of prazosin outflow concentration and inotropic response (left ventricular developed pressure) using an agonist–antagonist interaction model allowed to estimate receptor affinity, as well as the parameters of the operational model of agonism. Propofol significantly ( P < 0.05) increased the negative inotropic effect of prazosin. Modeling suggested that propofol increased the Hill coefficient, which determines the steepness of the stimulus–response curve for the positive inotropic effect of phenylephrine, from 1 to 2.6 ± 0.1 and decreased the maximum achievable inotropic effect from 121.2 ± 12 to 91.8 ± 6 mmHg. Thus, propofol may attenuate the positive inotropic effect of α 1-agonists by decreasing the transduction efficiency of α 1-adrenergic receptor signaling primarily at lower receptor occupancy.

An Agonist–Antagonist Interaction Model for Prolactin Release Following Risperidone and Paliperidone Treatment

A mechanistic pharmacokinetic/pharmacodynamic model is presented, characterizing the time course of prolactin in healthy as well as schizophrenic subjects following the administration of various doses and formulations of the antipsychotic drugs risperidone and paliperidone. Prolactin concentrations from nine studies (1,462 subjects) were analyzed in NONMEM. A competitive agonist-antagonist interaction model described the competition between these drugs and dopamine for the D(2) receptors that regulate prolactin release. Tolerance development was explained by a feedback loop with prolactin stimulating dopamine release, whereas models wherein tolerance is described in terms of depletion of a prolactin pool did not explain the data well. The diurnal prolactin rhythm was described by a two-period cosine function. Baseline prolactin was health-status dependent and higher in women than in men, although the drug-induced release was less than proportional to baseline. This quantitative mechanism-based model is the first to describe prolactin release in patients, and it confirms that paliperidone and risperidone have similar potencies for prolactin release.

Mechanism-Based Pharmacodynamic Modeling ofS(–)-Atenolol: Estimation of in Vivo Affinity for the β1-Adrenoceptor with an Agonist-Antagonist Interaction Model

The aim of this study was the development of an agonist-antagonist interaction model to estimate the in vivo affinity of S(-)-atenolol for the beta(1)-adrenoreceptor. Male Wistar-Kyoto (WKY) rats were used to characterize the interaction between the model drugs isoprenaline (to induce tachycardia) and S(-)-atenolol. Blood samples were taken to determine plasma pharmacokinetics. Reduction of isoprenaline-induced tachycardia was used as a pharmacodynamic endpoint. The pharmacokinetic-pharmacodynamic relationship of isoprenaline was first characterized with the operational model of agonism using the literature value for the affinity (K(A)) of isoprenaline (3.2 x 10(-8) M; left atria WKY rats). Resulting estimates for baseline (E(0)), maximal effect (E(max)), and efficacy (tau) were 374 (1.9%), 130 (5.9%), and 247 (33%) beats per minute, respectively. In addition, the interaction between isoprenaline and S(-)-atenolol was characterized using a pharmacodynamic interaction model based on the operational model of agonism that describes the heart rate response based on the affinity of the agonist (K(A)), the affinity of the antagonist (K(B)), the efficacy (tau), the maximal effect (E(max)), the Hill coefficient (n(H)), the concentrations of isoprenaline and atenolol, and the displacement of the endogenous agonist adrenaline. The estimated in vivo affinity (K(B)) of S(-)-atenolol for the beta(1) -receptor was 4.6 x 10(-8) M. The obtained estimate for in vivo affinity of S(-)-atenolol (4.6 x 10(-8) M) is comparable to literature values for the in vitro affinity in functional assays. In conclusion, a meaningful estimate of in vivo affinity for S(-)-atenolol could be obtained using a mechanism-based pharmacodynamic modeling approach.

From 'captive' agonism to insurmountable antagonism: demonstrating the power of analytical pharmacology.

1. Mathematical modelling is useful in pharmacology, allowing the investigator to obtain insights into the biological processes under study that may not always be intuitively obvious. Examples are presented in this review using the pharmacology of the muscarinic acetylcholine receptor (mAChR) agonist xanomeline. 2. Xanomeline possesses a novel mode of action that involves persistent binding to the M1 mAChR, yielding a fraction of agonist in the receptor compartment that continually activates the receptor, despite extensive washout, as assessed in functional assays measuring the cumulative production of M1 mAChR-mediated L-[3H]-citrulline. This persistent effect was reversed by the antagonist atropine, but re-established upon the removal of atropine. Thus, xanomeline may represent the first 'captive' agonist of the mAChR. 3. Atropine was equally potent at reversing the effect of persistently bound xanomeline and preventing the effect of added xanomeline. Application of standard quantitative equilibrium models of agonist-antagonist interaction to these data suggested that the interaction between xanomeline and atropine satisfied the criteria of competitivity in each case. 4. Subsequent real-time assays of M1 mAChR-mediated intracellular calcium mobilization found that atropine inhibited the effects of xanomeline in an insurmountable manner. 5. The discrepancy between the modes of antagonism in the various functional assays could be reconciled in a dynamic receptor model of antagonism within a transient response system and subsequent Monte Carlo simulations allowed for the development of an optimized analytical procedure to quantify antagonist potency under such conditions of response fade. 6. These types of studies exemplify the diagnostic and integrative features of analytical pharmacology.

Pharmacological analysis of cannabinoid receptor activity in the rat vas deferens.

1. The interaction between the cannabinoid agonists, WIN 55,212-2 or CP 55,940 with the CB(1) receptor-selective antagonists, SR141716A or LY320135 was investigated using the rat electrically-stimulated vas deferens bioassay. 2. Tissues were stimulated by single-field pulses (150 V, 0.5 ms) delivered every 30 mins. In the presence of nifedipine (3 microM), agonists elicited a concentration-dependent inhibition of the contractile response, with pEC(50) values of 7.93 and 6.84 for WIN 55,212-2 and CP 55,940, respectively. 3. SR141716A and LY320135 caused parallel dextral displacements of the agonist concentration-response curves. However, the shift of the agonist curves by either antagonist was accompanied by a concentration-dependent enhancement of basal (agonist-independent) tissue contraction. 4. Addition of the amidase inhibitor, phenylmethylsulphonylfluoride (200 microM), resulted in a significant reduction of the basal twitch response, an effect consistent with the presence of tonic receptor activation mediated by the endogenous cannabinoid, anandamide. 5. In light of these findings, we propose a theoretical model of competitive agonist-antagonist interaction in the presence of endogenous agonist tone that was used to derive an optimized analytical approach for the determination of antagonist potency estimates under conditions of tonic receptor activation. 6. This approach yielded pK(B) estimates for SR141716A and LY320135 that were in good agreement with their activity at cannabinoid CB(1) receptors. 7. It is concluded that the rat vas deferens contains prejunctional cannabinoid CB(1) receptors that are under tonic activation from endogenous substances; under these conditions our analytical approach is preferable to the standard methods for the determination of antagonist potency.

Effects of different expiratory maneuvers on inspiratory muscle force output.

We assessed the effects of two different expiratory maneuvers (fast [F] or slow [S]) on the ability of normal subjects (n = 12, age 35 +/- 6 yr) to generate maximal inspiratory pressures and maximal inspiratory flows near residual volume (RV). With the F maneuver, the subject exhaled rapidly to RV and immediately performed a maximal inspiratory effort, whereas with the S maneuver the subject exhaled slowly to RV, paused for 4 to 6 s at RV, and then inspired forcefully. Maximal static inspiratory pressure against an occluded airway (PImax), and maximal dynamic inspiratory pressure (PIdyn) and maximal inspiratory flow (V Imax) with no added resistance, as well as the electromyographic activity of the parasternal muscles, were measured during each maneuver. Both maneuvers were initiated from TLC and were performed randomly. In comparison with the S maneuver, the F maneuver yielded values of higher (mean +/- SE) PImax (148 +/- 5 cm H2O versus 135 +/- 7 cm H2O, p < 0.05), PIdyn (33 +/- 2 cm H2O versus 28 +/- 2 cm H2O, p < 0.05), and V Imax (12.3 +/- 0.4 L/s versus 11.4 +/- 0.6 L/s, p < 0.05). In addition, the rate of rise of PImax, the rate of rise of PIdyn, and the integrated peak electromyographic activity of the parasternal muscles were significantly greater with the F than with the S maneuver, suggesting greater inspiratory muscle (IM) activation. The enhanced IM activation may be related to a specific inspiratory-expiratory muscle interaction similar to the agonist-antagonist interactions described for a pair of skeletal muscles.