Pharmacokinetic-pharmacodynamic analysis of the EEG effect of alfentanil in rats following beta-funaltrexamine-induced mu-opioid receptor "knockdown" in vivo

Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics

INTRODUCTION

CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

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.

Efficacy and ligand bias at the μ-opioid receptor

In order to describe drug action at a GPCR, a full understanding of the pharmacological terms affinity, efficacy and potency is necessary. This is true whether comparing the ability of different agonists to produce a measurable response in a cell or tissue, or determining the relative ability of an agonist to activate a single receptor subtype and produce multiple responses. There is a great deal of interest in the μ-opioid receptor (MOP receptor) and the ligands that act at this GPCR not only because of the clinically important analgesic effects produced by MOP agonists but also because of their liability to induce adverse effects such as respiratory depression and dependence. Our understanding of the mechanisms underlying these effects, as well as the ability to develop new, more effective MOP receptor drugs, depends upon the accurate determination of the efficacy with which these ligands induce coupling of MOP receptors to downstream signalling events. In this review, which is written with the minimum of mathematical content, the basic meaning of terms including efficacy, intrinsic activity and intrinsic efficacy is discussed, along with their relevance to the field of MOP receptor pharmacology, and in particular in relation to biased agonism at this important GPCR.

The mastermind approach to CNS drug therapy: translational prediction of human brain distribution, target site kinetics, and therapeutic effects

Despite enormous advances in CNS research, CNS disorders remain the world's leading cause of disability. This accounts for more hospitalizations and prolonged care than almost all other diseases combined, and indicates a high unmet need for good CNS drugs and drug therapies.Following dosing, not only the chemical properties of the drug and blood-brain barrier (BBB) transport, but also many other processes will ultimately determine brain target site kinetics and consequently the CNS effects. The rate and extent of all these processes are regulated dynamically, and thus condition dependent. Therefore, heterogenious conditions such as species, gender, genetic background, tissue, age, diet, disease, drug treatment etc., result in considerable inter-individual and intra-individual variation, often encountered in CNS drug therapy.For effective therapy, drugs should access the CNS "at the right place, at the right time, and at the right concentration". To improve CNS therapies and drug development, details of inter-species and inter-condition variations are needed to enable target site pharmacokinetics and associated CNS effects to be translated between species and between disease states. Specifically, such studies need to include information about unbound drug concentrations which drive the effects. To date the only technique that can obtain unbound drug concentrations in brain is microdialysis. This (minimally) invasive technique cannot be readily applied to humans, and we need to rely on translational approaches to predict human brain distribution, target site kinetics, and therapeutic effects of CNS drugs.In this review the term "Mastermind approach" is introduced, for strategic and systematic CNS drug research using advanced preclinical experimental designs and mathematical modeling. In this way, knowledge can be obtained about the contributions and variability of individual processes on the causal path between drug dosing and CNS effect in animals that can be translated to the human situation. On the basis of a few advanced preclinical microdialysis based investigations it will be shown that the "Mastermind approach" has a high potential for the prediction of human CNS drug effects.

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.

Impact of efflux transporters and of seizures on the pharmacokinetics of oxcarbazepine metabolite in the rat brain

BACKGROUND AND PURPOSE

Accurate prediction of biophase pharmacokinetics (PK) is essential to optimize pharmacotherapy in epilepsy. Here, we characterized the PK of the active metabolite of oxcarbazepine, 10,11-dihydro-10-hydroxy-carbamazepine (MHD) in plasma and in the hippocampus. Simultaneously, the impact of acute seizures and efflux transport mechanisms on brain distribution was quantified.

EXPERIMENTAL APPROACH

Rats received subtherapeutic and anticonvulsant doses of MHD in non-epileptic conditions and during focal pilocarpine-induced limbic seizures. To evaluate the effect of efflux transport blockade, a separate group received subtherapeutic doses combined with intrahippocampal perfusion of verapamil. Free plasma and extracellular hippocampal MHD concentrations were determined using microdialysis and liquid chromatography techniques. An integrated PK model describing simultaneously the PK of MHD in plasma and brain was developed using nonlinear mixed effects modelling. A bootstrap procedure and a visual predictive check were performed to assess model performance.

KEY RESULTS

A compartmental model with combined zero- and first-order absorption, including lag time and biophase distribution best described the PK of MHD. A distributional process appeared to underlie the increased brain MHD concentrations observed following seizure activity and efflux transport inhibition, as reflected by changes in the volume of distribution of the biophase compartment. In contrast, no changes were observed in plasma PK.

CONCLUSIONS AND IMPLICATIONS

Simultaneous PK modelling of plasma and brain concentrations has not been used previously in the evaluation of antiepileptic drugs (AEDs). Characterisation of biophase PK is critical to assess the impact of efflux transport mechanisms and acute seizures on brain disposition and, consequently, on AED effects.

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.

Buprenorphine induces ceiling in respiratory depression but not in analgesia

BACKGROUND

We measured the effect of two weight adjusted i.v. doses (0.2 mg per 70 kg and 0.4 mg per 70 kg) of the potent opioid buprenorphine on analgesia and respiratory depression in healthy volunteers. The aim of the study was to compare buprenorphine's behaviour with respect to the occurrence of ceiling (or apparent maximum) in these typical micro-opioid protein-(MOP) receptor effects.

METHODS

Ten subjects (5 males) received 0.2 mg per 70 kg, 10 others (5 males) 0.4 mg per 70 kg i.v. buprenorphine. Steady-state inspired minute ventilation at a fixed end-tidal Pco(2) of 7 kPa was measured before drug infusion and at regular intervals after drug infusion. Experimental pain was induced using transcutaneous electrical stimulation and a gradually increasing current. Pain tolerance was measured at regular intervals before and after drug infusion. The studies lasted 8 h.

RESULTS

After infusion of the drug ventilation showed a rapid decline and reached peak depression between 150 and 180 min after drug administration. This effect was dose-independent with respect to timing and magnitude. At peak respiratory depression minute ventilation was 13.1 (sd 1.8) litre min(-1) in the 0.2 mg group vs 12.0 (sd 1.3) litre min(-1) in the 0.4 mg group (n.s.). At buprenorphine 0.2 mg a small short-lived analgesic effect was observed with a maximum increase in pain tolerance current of 6.7 (sd 2.8) mA occurring at 75 min after drug administration. Peak analgesic effect was 29% above baseline current. In contrast, buprenorphine 0.4 mg caused a large and long-lived analgesic effect with a maximum increase in pain tolerance current of 23.8 (sd 7.4) mA occurring at 130 min after drug administration. Peak analgesic effect was 160% above baseline current (0.4 vs 0.2 mg, P<0.01).

CONCLUSIONS

While buprenorphine's analgesic effect increased significantly, respiratory depression was similar in magnitude and timing for the two doses tested. We conclude that over the dose range tested buprenorphine displays ceiling in respiratory effect but none in analgesic effect.

Toward the prediction of CNS drug-effect profiles in physiological and pathological conditions using microdialysis and mechanism-based pharmacokinetic-pharmacodynamic modeling

Our ultimate goal is to develop mechanism-based pharmacokinetic (PK)-pharmacodynamic (PD) models to characterize and to predict CNS drug responses in both physiologic and pathologic conditions. To this end, it is essential to have information on the biophase pharmacokinetics, because these may significantly differ from plasma pharmacokinetics. It is anticipated that biophase kinetics of CNS drugs are strongly influenced by transport across the blood-brain barrier (BBB). The special role of microdialysis in PK/PD modeling of CNS drugs lies in the fact that it enables the determination of free-drug concentrations as a function of time in plasma and in extracellular fluid of the brain, thereby providing important data to determine BBB transport characteristics of drugs. Also, the concentrations of (potential) extracellular biomarkers of drug effects or disease can be monitored with this technique. Here we describe our studies including microdialysis on the following: (1) the evaluation of the free drug hypothesis; (2) the role of BBB transport on the central effects of opioids; (3) changes in BBB transport and biophase equilibration of anti-epileptic drugs; and (4) the relation among neurodegeneration, BBB transport, and drug effects in Parkinson's disease progression.

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.

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

The pharmacokinetic-pharmacodynamic (PK-PD) correlations of seven prototypical 5-HT(1A) agonists were analyzed on the basis of a recently proposed semi-mechanistic PK-PD model for the effect on body temperature. The resulting concentration-effect relationships were subsequently analyzed on the basis of the operational model of agonism to estimate the operational affinity (pK(A)) and efficacy (log tau) at the 5-HT(1A) receptor in vivo. The values obtained in this manner were compared with estimates of the affinity (pK(i)) and intrinsic efficacy (log[agonist ratio]) in a receptor-binding assay. Between 5-HT(1A) agonists wide differences in in vivo affinity and efficacy were observed, with values of the pK(A) ranging from 5.67 for flesinoxan to 8.63 for WAY-100,635 [N-(2-(4-(2-methoxyphenyl)-1-piperazinyl)ethyl)-N-2-pyridinyl-cyclohexanecarboxamide] and of the log tau ranging from -1.27 for WAY-100,135 [N-(1,1-dimethylethyl)-4-(2-methoxyphenyl)-alpha-phenyl-1-piperazine-propanamide] to 0.62 for R-(+)-8-hydroxy-2-[di-n-propylamino)tetralin. Poor correlations were observed between the in vivo receptor affinity (pK(A)) and the affinity estimates in the in vitro receptor binding assay (pK(i); r(2) = 0.55, P > 0.05), which could in part be explained by differences in blood-brain distribution. In contrast, a highly significant correlation was observed between the efficacy parameters in vivo (log tau) and in vitro (log [agonist ratio]; r(2) = 0.76, P < 0.05). Thus by combining the previously proposed semi-mechanistic PK-PD model for the effect on body temperature with the operational model of agonism, a full mechanistic PK-PD model for 5-HT(1A) receptor agonists has been obtained, which is highly predictive of the in vivo intrinsic efficacy.

Endogenous opiates: 2000

This paper is the twenty-third installment of the annual review of research concerning the opiate system. It summarizes papers published during 2000 that studied the behavioral effects of the opiate peptides and antagonists, excluding the purely analgesic effects, although stress-induced analgesia is included. The specific topics covered this year include stress; tolerance and dependence; learning, memory, and reward; eating and drinking; alcohol and other drugs of abuse; sexual activity, pregnancy, and development; mental illness and mood; seizures and other neurological disorders; electrical-related activity; general activity and locomotion; gastrointestinal, renal, and hepatic function; cardiovascular responses; respiration and thermoregulation; and immunological responses.

Synthesis and use of FSCPX, an irreversible adenosine A1 antagonist, as a 'receptor knock-down' tool

A new preparative synthetic route for the irreversible adenosine A1 antagonist 8-cyclopentyl-3-N-[3-((3-(4-fluorosulphonyl)benzoyl)-oxy)-propyl]-1-N-propyl-xanthine (FSCPX, 1) is described. The availability of ample amounts of the irreversible antagonist FSCPX allowed us to use FSCPX as a research tool for adenosine A1 receptors in in vivo experiments. After verification of the irreversible antagonistic function of FSCPX in in vitro experiments, FSCPX was used successfully as a 'receptor knock-down' tool in in vivo experiments on conscious rats.