Mechanism-Based Pharmacokinetic-Pharmacodynamic Modeling of 5-HT1AReceptor Agonists: Estimation of in Vivo Affinity and Intrinsic Efficacy on Body Temperature in Rats

5-HT1a Receptor Involvement in Temporal Memory and the Response to Temporal Ambiguity

It has previously been demonstrated that rats trained on the peak-interval procedure to associate two different cues with two different fixed interval schedules will generate a scalar peak function at an intermediate time when presented with the compound cue. This response pattern has been interpreted as resulting from the simultaneous retrieval of different temporal memories, and a consequential averaging process to resolve the ambiguity. In the present set of studies, we investigated the role that serotonin 1a receptors play in this process. In Experiment 1, rats were trained on a peak-interval procedure to associate the interoceptive states induced by saline and the 5-HT1a agonist, 8-OH-DPAT, with a 5 s or 20 s fixed-interval schedule signaled by the same tone cue (counter-balanced). While peak functions following administration of saline were centered at the appropriate time (5 s or 20 s), peak functions following administration of the agonist were centered around 7 s, irrespective of the reinforced time during training, suggesting agonist-induced disruption in selective temporal memory retrieval, resulting in increased ambiguity regarding the appropriate time at which to respond. In Experiment 2, rats were trained in a peak-interval procedure to associate a tone cue with a 10 s fixed interval and a light cue with a 20 s fixed interval. Administration of the 5-HT1a antagonist, WAY-100635, had no impact on timing when single cues were presented, but altered the intermediate, scalar, response to the stimulus compound, suggesting antagonist-induced disruption in the processes used to deal with temporal memory ambiguity. Together, these data suggest that manipulations of 5HT transmission at the 5-HT1a receptor cause changes in the temporal pattern of responding that are consistent with alterations in temporal memory processes and responses to temporal ambiguity.

Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: The example of luminal a breast cancer

Breast cancer (BC) is the most common cancer in women, and the second most frequent cause of cancer-related deaths in women worldwide. It is a heterogeneous disease composed of multiple subtypes with distinct morphologies and clinical implications. Quantitative systems pharmacology (QSP) is an emerging discipline bridging systems biology with pharmacokinetics (PK) and pharmacodynamics (PD) leveraging the systematic understanding of drugs' efficacy and toxicity. Despite numerous challenges in applying computational methodologies for QSP and mechanism-based PK/PD models to biological, physiological, and pharmacological data, bridging these disciplines has the potential to enhance our understanding of complex disease systems such as BC. In QSP/PK/PD models, various sources of data are combined including large, multi-scale experimental data such as -omics (i.e. genomics, transcriptomics, proteomics, and metabolomics), biomarkers (circulating and bound), PK, and PD endpoints. This offers a means for a translational application from pre-clinical mathematical models to patients, bridging the bench to bedside paradigm. Not only can these models be applied to inform and advance BC drug development, but they also could aid in optimizing combination therapies and rational dosing regimens for BC patients. Here, we review the current literature pertaining to the application of QSP and pharmacometrics-based pharmacotherapy in BC including bottom-up and top-down modeling approaches. Bottom-up modeling approaches employ mechanistic signal transduction pathways to predict the behavior of a biological system. The ones that are addressed in this review include signal transduction and homeostatic feedback modeling approaches. Alternatively, top-down modeling techniques are bioinformatics reconstruction techniques that infer static connections between molecules that make up a biological network and include (1) Bayesian networks, (2) co-expression networks, and (3) module-based approaches. This review also addresses novel techniques which utilize the principles of systems biology, synthetic lethality and tumor priming, both of which are discussed in relationship to novel drug targets and existing BC therapies. By utilizing QSP approaches, clinicians may develop a platform for improved dose individualization for subpopulation of BC patients, strengthen rationale in treatment designs, and explore mechanism elucidation for improving future treatments in BC medicine.

Development of a mechanism-based pharmacokinetic/pharmacodynamic model to characterize the thermoregulatory effects of serotonergic drugs in mice

We have shown recently that concurrent harmaline, a monoamine oxidase-A inhibitor (MAOI), potentiates serotonin (5-HT) receptor agonist 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT)-induced hyperthermia. The objective of this study was to develop an integrated pharmacokinetic/pharmacodynamic (PK/PD) model to characterize and predict the thermoregulatory effects of such serotonergic drugs in mice. Physiological thermoregulation was described by a mechanism-based indirect-response model with adaptive feedback control. Harmaline-induced hypothermia and 5-MeO-DMT–elicited hyperthermia were attributable to the loss of heat through the activation of 5-HT_1A receptor and thermogenesis via the stimulation of 5-HT_2A receptor, respectively. Thus serotonergic 5-MeO-DMT–induced hyperthermia was readily distinguished from handling/injection stress-provoked hyperthermic effects. This PK/PD model was able to simultaneously describe all experimental data including the impact of drug-metabolizing enzyme status on 5-MeO-DMT and harmaline PK properties, and drug- and stress-induced simple hypo/hyperthermic and complex biphasic effects. Furthermore, the modeling results revealed a 4-fold decrease of apparent SC₅₀ value (1.88–0.496 µmol/L) for 5-MeO-DMT when harmaline was co-administered, providing a quantitative assessment for the impact of concurrent MAOI harmaline on 5-MeO-DMT–induced hyperthermia. In addition, the hyperpyrexia caused by toxic dose combinations of harmaline and 5-MeO-DMT were linked to the increased systemic exposure to harmaline rather than 5-MeO-DMT, although the body temperature profiles were mispredicted by the model. The results indicate that current PK/PD model may be used as a new conceptual framework to define the impact of serotonergic agents and stress factors on thermoregulation.

Systems pharmacology - Towards the modeling of network interactions

Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior.

Kinetics of drug action in disease states: towards physiology-based pharmacodynamic (PBPD) models

Gerhard Levy started his investigations on the "Kinetics of Drug Action in Disease States" in the fall of 1980. The objective of his research was to study inter-individual variation in pharmacodynamics. To this end, theoretical concepts and experimental approaches were introduced, which enabled assessment of the changes in pharmacodynamics per se, while excluding or accounting for the cofounding effects of concomitant changes in pharmacokinetics. These concepts were applied in several studies. The results, which were published in 45 papers in the years 1984-1994, showed considerable variation in pharmacodynamics. These initial studies on kinetics of drug action in disease states triggered further experimental research on the relations between pharmacokinetics and pharmacodynamics. Together with the concepts in Levy's earlier publications "Kinetics of Pharmacologic Effects" (Clin Pharmacol Ther 7(3): 362-372, 1966) and "Kinetics of pharmacologic effects in man: the anticoagulant action of warfarin" (Clin Pharmacol Ther 10(1): 22-35, 1969), they form a significant impulse to the development of physiology-based pharmacodynamic (PBPD) modeling as novel discipline in the pharmaceutical sciences. This paper reviews Levy's research on the "Kinetics of Drug Action in Disease States". Next it addresses the significance of his research for the evolution of PBPD modeling as a scientific discipline. PBPD models contain specific expressions to characterize in a strictly quantitative manner processes on the causal path between exposure (in terms of concentration at the target site) and the drug effect (in terms of the change in biological function). Pertinent processes on the causal path are: (1) target site distribution, (2) target binding and activation and (3) transduction and homeostatic feedback.

Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability

This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

Incorporating receptor theory in mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling

Pharmacokinetic-Pharmacodynamic (PK-PD) modeling helps to better understand drug efficacy and safety and has, therefore, become a powerful tool in the learning-confirming cycles of drug-development. In translational drug research, mechanism-based PK-PD modeling has been recognized as a tool for bringing forward early insights in drug efficacy and safety into the clinical development. These models differ from descriptive PK-PD models in that they quantitatively characterize specific processes in the causal chain between drug administration and effect. This includes target site distribution, binding and activation, pharmacodynamic interactions, transduction and homeostatic feedback mechanisms. Compared to descriptive models mechanism-based PK-PD models that utilize receptor theory concepts for characterization of target binding and target activation processes have improved properties for extrapolation and prediction. In this respect, receptor theory constitutes the basis for 1) prediction of in vivo drug concentration-effect relationships and 2) characterization of target association-dissociation kinetics as determinants of hysteresis in the time course of the drug effect. This approach intrinsically distinguishes drug- and system specific parameters explicitly, allowing accurate extrapolation from in vitro to in vivo and across species. This review provides an overview of recent developments in incorporating receptor theory in PK-PD modeling with a specific focus on the identifiability of these models.

A receptor theory-based semimechanistic PD model for the CCR5 noncompetitive antagonist maraviroc

AIM

To develop a novel combined viral dynamics/operational model of (ant-)agonism that describes the pharmacodynamic effects of maraviroc, a noncompetitive CCR5 inhibitor, on viral load.

METHODS

A common theoretical framework based on receptor theory and the operational model of (ant-)agonism has been developed to describe the binding of maraviroc to the CCR5 receptor and the subsequent decrease in viral load. The anchor point of the operational model in the differential equations of the viral dynamic model is the infection rate constant; this is assumed to be dependent on the number of free activated receptors on each target cell.

RESULTS

The new model provides one explanation for the apparent discrepancy between the in vivo binding of maraviroc to the CCR5 receptor (K(D) = 0.089 ng ml(-1)) and the estimated in vivo inhibition (IC(50) = 8 ng ml(-1)) of the infection rate. The estimated K(E) value of the operational model indicates that only 1.2% of free activated receptors are utilized to elicit 50% of the maximum infection rate.

CONCLUSIONS

The developed model suggests that the target cells, when activated, express more receptors (spare receptors) than needed. In the presence of maraviroc these spare receptors first require blocking before any decrease in the infection rate, and consequently in the viral load at equilibrium, can be detected. The model allows the simultaneous simulation of the binding of maraviroc to the CCR5 receptor and the change in viral load after both short- and long-term treatment.

Allometric Scaling of Pharmacodynamic Responses: Application to 5-Ht1A Receptor Mediated Responses from Rat to Man

PURPOSE

The aim of the present study was to assess whether two widely used biomarkers for 5-HT(1A)-receptor mediated responses in the rat (hypothermia and corticosterone increase) could be scaled to man using allometric principles.

MATERIALS AND METHODS

Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) models were developed and characterized in rats for the standard 5-HT(1A)-receptor agonists, buspirone and flesinoxan. Allometric scaling was investigated on the basis of simulation taking into account the inter-individual variability and clinical study design. The model-predicted effects of both flesinoxan and buspirone were compared to those published in the literature.

RESULTS

The main finding of this analysis was that for both hypothermia and cortisol increase, the model could predict the extent of the pharmacological response in man adequately. For the hypothermic response, the time course of the response was also predicted with a high degree of accuracy. In contrast, in the case of the cortisol response, the observed time lag was, despite the fact that it fell within the model uncertainty, not predicted.

CONCLUSIONS

Based on these analyses, it is concluded that allometrically scaled mechanism based PK-PD models are promising as a means of predicting the pharmacodynamic responses in man. This approach provides for a novel way of interpreting and scaling pre-clinical pharmacological responses and ultimately facilitates the understanding and prediction of pharmacological responses in man.

Pharmacokinetic-pharmacodynamic modelling of S(-)-atenolol in rats: reduction of isoprenaline-induced tachycardia as a continuous pharmacodynamic endpoint

BACKGROUND AND PURPOSE

For development of mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) models, continuous recording of drug effects is essential. We therefore explored the use of isoprenaline in the continuous measurement of the cardiovascular effects of antagonists of beta-adrenoceptors (beta-blockers). The aim was to validate heart rate as a pharmacodynamic endpoint under continuous isoprenaline-induced tachycardia by means of PK-PD modelling of S(-)-atenolol.

EXPERIMENTAL APPROACH

Groups of WKY rats received a 15 min i.v. infusion of 5 mg kg(-1) S(-)-atenolol, with or without i.v. infusion of 5 microg kg(-1) h(-1) isoprenaline. Heart rate was continuously monitored and blood samples were taken.

KEY RESULTS

A three-compartment model best described the pharmacokinetics of S(-)-atenolol. The PK-PD relationship was described by a sigmoid Emax model and an effect compartment was used to resolve the observed hysteresis. In the group without isoprenaline, the variability in heart rate (30 b.p.m.) approximated the maximal effect (Emax=43+/-18 b.p.m.), leaving the parameter estimate of potency (EC50=28+/-27 ng ml(-1)) unreliable. Both precise and reliable parameter estimates were obtained during isoprenaline-induced tachycardia: 517+/-13 b.p.m. (E0), 168+/-15 b.p.m. (Emax), 49+/-14 ng ml(-1) (EC50), 0.042+/-0.012 min(-1) (k(eo)) and 0.95+/-0.34 (n).

CONCLUSIONS AND IMPLICATIONS

Reduction of heart rate during isoprenaline-induced tachycardia is a reliable pharmacodynamic endpoint for beta-blockers in vivo in rats. Consequently this experimental approach will be used to investigate the relationship between drug characteristics and in vivo effects of different beta-blockers.

Mechanism-Based Pharmacokinetic-Pharmacodynamic Modeling: Biophase Distribution, Receptor Theory, and Dynamical Systems Analysis

Mechanism-based PK-PD models differ from conventional PK-PD models in that they contain specific expressions to characterize, in a quantitative manner, processes on the causal path between drug administration and effect. This includes target site distribution, target binding and activation, pharmacodynamic interactions, transduction, and homeostatic feedback mechanisms. As the final step, the effects on disease processes and disease progression are considered. Particularly through the incorporation of concepts from receptor theory and dynamical systems analysis, important progress has been made in the field of mechanism-based PK-PD modeling. This has yielded models with much-improved properties for extrapolation and prediction. These models constitute a theoretical basis for rational drug discovery and development.

Animal-to-Human Extrapolation of the Pharmacokinetic and Pharmacodynamic Properties of Buprenorphine

OBJECTIVES

This investigation describes the interspecies scaling of the pharmacokinetics and pharmacodynamics of buprenorphine.

METHODS

Data on the time course of the antinociceptive and respiratory depressant effects of buprenorphine in rats and in humans were simultaneously analysed on the basis of a mechanism-based pharmacokinetic-pharmacodynamic model.

RESULTS

An allometric three-compartment pharmacokinetic model described the time course of the concentration in plasma. The value of the allometric coefficient for clearance was 35.2 mL/min (relative standard error [RSE] = 5.6%) and the value of the allometric exponent was 0.76 (RSE 5.61%). A combined biophase distribution-receptor association/dissociation model with a linear transduction function described hysteresis between plasma concentration and effect. The values of the drug-specific pharmacodynamic parameters were identical in rats and in humans. For the respiratory depressant effect, the values of the second-order rate constant of receptor association (k(on)) and the first-order rate constant of receptor dissociation (k(off)) were 0.23 mL/ng/min (RSE = 15.8%) and 0.014 min(-1) (RSE = 27.7%), respectively, and the value of the equilibrium dissociation constant (K(diss)) was 0.13 nmol/L. The value of the intrinsic activity alpha was 0.52 (RSE = 3.4%). For the antinociceptive effect, the values of the k(on) and k(off) were 0.015 mL/ng/min (RSE = 18.3%) and 0.053 min(-1) (RSE = 23.1%), respectively. The value of the K(diss) was 7.5 nmol/L. An allometric equation described the scaling of the system-specific parameter, the first-order distribution rate constant (k(e0)). The value of the allometric coefficient for the k(e0) was 0.0303 min(-1) (RSE = 11.3%) and the value of the exponent was -0.28 (RSE = 9.6%).

CONCLUSIONS

The different values of the drug-specific pharmacodynamic parameters are consistent with the different opioid mu receptor subtypes involved in the antinociceptive and respiratory depressant effects.

Mechanism-based pharmacokinetic-pharmacodynamic modeling of the respiratory-depressant effect of buprenorphine and fentanyl in rats

The purpose of this investigation was to develop a mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model to predict the time course of respiratory depression following administration of opioids in rats. The proposed model is based on receptor theory and aims at the separate characterization of biophase distribution and receptor association/dissociation kinetics as determinants of hysteresis between plasma concentration and effect. Individual concentration time courses of buprenorphine and fentanyl were determined in conjunction with continuous monitoring of respiratory depression. Buprenorphine and fentanyl were administered intravenously in various doses. For buprenorphine hysteresis was best described by a combined biophase distribution-receptor association/dissociation model with a linear transducer function. The values of the parameter estimates of the rate constants for biophase distribution (k(eo)), receptor association (k(on)), and dissociation (k(off)) were 0.0348 min(-1) [95% confidence interval (CI), 0.0193-0.0503 min(-1)], 0.57 ml/ng/min (95% CI, 0.38-0.76 ml/ng/min), and 0.0903 min(-1) (95% CI, 0.035-0.196 min(-1)), respectively. The values of the equilibrium dissociation constant and intrinsic activity were 0.16 ng/ml and 0.48 (95% CI, 0.45-0.51), respectively. The value of the K(d) is close to reported estimates of receptor affinity in vitro confirming the validity of the mechanism-based PK/PD model. For fentanyl, unrealistically high estimates of the rate constants for receptor association and dissociation were obtained, indicating that hysteresis is caused solely by biophase distribution kinetics. This is consistent with fentanyl's fast receptor association/dissociation kinetics in vitro. As a result, the mechanism-based PK/PD model of fentanyl could be reduced to a biophase distribution model with fractional sigmoid E(max) pharmacodynamic model.

Differential effects of 5-hydroxytryptamine4 receptor agonists at gastric versus cardiac receptors: an operational framework to explain and quantify organ-specific behavior

Quantification of different levels of 5-hydroxytryptamine4 (5-HT4) receptor agonism expression across animal species as well as across organs within the same animal species offers substantial potential for the separation of desired gastrointestinal versus undesired cardiac pharmacological activity of compounds in development. Since a detailed investigation of such properties is lacking to date, we set out to quantify gastric and cardiac effects of 5-HT4 receptor ligands in the pig, a model considered to be representative for the human situation. An in vitro test was developed to study the potentiating effect of 5-HT, prucalopride, tegaserod, R149402 (4-amino-5-chloro-2,2-dimethyl-2,3-dihydro-benzofuran-7-carboxylic acid [3-hydroxy-1-(3-methoxy-propyl)-piperidin-4ylmethyl]-amide), and R199715 (4-amino-5-chloro-2,3-dihydro-benzofuran-7-carboxylic acid [3-hydroxy-1-(3-methoxy-propyl)-piperidin-4ylmethyl]-amide) on electrically induced cholinergic contractions in longitudinal muscle strips of the proximal stomach. The results were compared with inotropic and chronotropic effects of these compounds in the electrically paced left atrium and spontaneously beating right atrium, respectively. To quantify the observed tissue-dependent responses, a nonlinear mixed-effects model based on the operational model of agonism was developed and successfully fitted to the data. The model quantified the tissue-dependent partial agonism of the selective 5-HT4 receptor agonists prucalopride, R149402, and R199715, whereas tegaserod and 5-HT were equiefficacious. The model was further extended to incorporate the responses to prucalopride in the presence of the 5-HT4 receptor antagonist GR113808 ([1-[2-[(methylsulphonyl)amino]ethyl]-4-piperidinyl-]methyl 1-methyl-1H-indole-3-carboxylate). The results indicate that these interactions do not follow a simple competitive pattern and that they differ between stomach and left atrium.

Modeling drug- and system-related changes in body temperature: application to clomethiazole-induced hypothermia, long-lasting tolerance development, and circadian rhythm in rats

The aim of the present investigation was to develop a pharmacokinetic-pharmacodynamic model for the characterization of clomethiazole (CMZ)-induced hypothermia and the rapid development of long-lasting tolerance in rats while taking into account circadian rhythm in baseline and the influence of handling. CMZ-induced hypothermia and tolerance was measured using body temperature telemetry in male Sprague-Dawley rats, which were given s.c. bolus injections of 0, 15, 150, 300, and 600 micromol kg(-1) and 24-h s.c. continuous infusions of 0, 20, and 40 micromol kg(-1) h(-1) using osmotic pumps. The duration of tolerance was studied by repeated injections of 300 micromol kg(-1) at 3- to 32-day intervals. Plasma exposure to CMZ was obtained in satellite groups of catheterized rats. Fitted population concentration-time profiles served as input for the pharmacodynamic analysis. The asymmetric circadian rhythm in baseline body temperature was successfully described by a novel negative feedback model incorporating external light-dark conditions. An empirical function characterized the transient increase in temperature upon handling of the animal. A feedback model for temperature regulation and tolerance development allowed estimation of CMZ potency at 30 +/- 1 microM. The delay in onset of tolerance was estimated via a series of four transit compartments at 7.6 +/- 2 h. The long-lasting tolerance was assumed to be caused by inactivation of a mediator with an estimated turnover time of 46 +/- 3 days. This multicomponent turnover model was able to quantify the CMZ-induced hypothermia, circadian rhythm in baseline, and rapid onset of a long-lasting tolerance to CMZ in rats.

Mechanism-Based Pharmacokinetic–Pharmacodynamic Modeling—A New Classification of Biomarkers

In recent years, pharmacokinetic/pharmacodynamic (PK/PD) modeling has developed from an empirical descriptive discipline into a mechanistic science that can be applied at all stages of drug development. Mechanism-based PK/PD models differ from empirical descriptive models in that they contain specific expressions to characterize processes on the causal path between drug administration and effect. Mechanism-based PK/PD models have much improved properties for extrapolation and prediction. As such, they constitute a scientific basis for rational drug discovery and development. In this report, a novel classification of biomarkers is proposed. Within the context of mechanism-based PK/PD modeling, a biomarker is defined as a measure that characterizes, in a strictly quantitative manner, a process, which is on the causal path between drug administration and effect. The new classification system distinguishes seven types of biomarkers: type 0, genotype/phenotype determining drug response; type 1, concentration of drug or drug metabolite; type 2, molecular target occupancy; type 3, molecular target activation; type 4, physiological measures; type 5, pathophysiological measures; and type 6, clinical ratings. In this paper, the use of the new biomarker classification is discussed in the context of the application of mechanism-based PK/PD analysis in drug discovery and development.

Fromin vivotoin vitro/in silicoADME: progress and challenges

High-throughput screening technologies in biological sciences of large libraries of compounds obtained via combinatorial or parallel chemistry approaches, as well as the application of design rules for drug-likeness, have resulted in more hits to be evaluated with respect to their ADME or drug metabolism and pharmacokinetic properties. The traditional in vivo methods using preclinical species, such as rat, dog or monkey, are no longer sufficient to cope with this demand. This editorial discusses the changes towards medium- to high-throughput in vitro and in silico ADME screening. In addition, much more attention is now put on early safety and risk assessment of promising lead series and potential clinical candidates.

Involvement of serotoninergic receptors in the anteroventral preoptic region on hypoxia-induced hypothermia

Hypoxia causes a regulated decrease in body temperature (Tb). There is circumstantial evidence that the neurotransmitter serotonin (5-HT) in the anteroventral preoptic region (AVPO) mediates this response. However, which 5-HT receptor(s) is (are) involved in this response has not been assessed. Thus, we investigated the participation of the 5-HT receptors (5-HT1, 5-HT2, and 5-HT7) in the AVPO in hypoxic hypothermia. To this end, Tb of conscious Wistar rats was monitored by biotelemetry before and after intra-AVPO microinjection of methysergide (a 5-HT1 and 5-HT2 receptor antagonist, 0.2 and 2 microg/100 nL), WAY-100635 (a 5-HT(1A) receptor antagonist, 0.3 and 3 microg/100 nL), and SB-269970 (a 5-HT7 receptor antagonist, 0.4 and 4 micro/100 nL), followed by 60 min of hypoxia exposure (7% O2). During the experiments, the mean chamber temperature was 24.6 +/- 0.7 degrees C (mean +/- SE) and the mean room temperature was 23.5 +/- 0.8 degrees C (mean +/- SE). Intra-AVPO microinjection of vehicle or 5-HT antagonists did not change Tb during normoxic conditions. Exposure of rats to 7% of inspired oxygen evoked typical hypoxia-induced hypothermia after vehicle microinjection, which was not affected by both doses of methysergide. However, WAY-100635 and SB-269970 treatment attenuated the drop in Tb in response to hypoxia. The effect was more pronounced with the 5-HT7 antagonist since both doses (0.4 and 4 microg/0.1 microL) were capable of attenuating the hypothermic response. As to the 5-HT(1A) antagonist, the attenuation of hypoxia-induced hypothermia was only observed at the higher dose. Therefore, the present results are consistent with the notion that 5-HT acts on both 5-HT(1A) and 5-HT7 receptors in the AVPO to induce hypothermia, during hypoxia.

Pharmacokinetic-pharmacodynamic modeling of the antinociceptive effect of buprenorphine and fentanyl in rats: role of receptor equilibration kinetics

The objective of this investigation was to characterize the pharmacokinetic/pharmacodynamic correlation of buprenorphine and fentanyl for the antinociceptive effect in rats. Data on the time course of the antinociceptive effect following intravenous administration of buprenorphine or fentanyl was analyzed in conjunction with plasma concentrations by nonlinear mixed-effects analysis. For fentanyl, the pharmacokinetics was described on the basis of a two-compartment pharmacokinetic model. For buprenorphine, a three-compartment pharmacokinetic model best described the concentration time course. To explain time dependencies in pharmacodynamics of buprenorphine and fentanyl, a combined effect compartment/receptor binding model was applied. A log logistic probability distribution model is proposed to account for censored tail-flick latencies. The model converged, yielding precise estimates of the parameters characterizing hysteresis. The results show that onset and offset of the antinociceptive effect of both buprenorphine and fentanyl is mainly determined by biophase distribution. The k(eo) was 0.024 min(-1) [95% confidence interval (CI): 0.018-0.030 min(-1)] and 0.123 min(-1) (95% CI: 0.095-0.151 min(-1)) for buprenorphine and fentanyl, respectively. On the other hand, part of the hysteresis in the buprenorphine pharmacodynamics could be explained by slow receptor association/dissociation kinetics. The k(off) was 0.073 min(-1) (95% CI: 0.042-0.104 min(-1)) and k(on) was 0.023 ml/ng/min (95% CI: 0.013-0.033 ml/ng/min). Fentanyl binds instantaneously to the OP3 receptor because no reasonable values for k(on) and k(off) were obtained with the dynamical receptor model. In contrast to earlier reports in the literature, the findings of this study show that the rate-limiting step in the onset and offset of buprenorphine's antinociceptive effect is distribution to the brain.