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

Can we do better with Mylotarg? Model-based assessment of opportunities to improve therapeutic index

Late-stage clinical trial failures increase the overall cost and risk of bringing new drugs to market. Determining the pharmacokinetic (PK) drivers of toxicity and efficacy in preclinical studies and early clinical trials supports quantitative optimization of drug schedule and dose through computational modeling. Additionally, this approach permits prioritization of lead candidates with better PK properties early in development. Mylotarg is an antibody-drug conjugate (ADC) that attained U.S. Food and Drug Administration (FDA) approval under a fractionated dosing schedule after 17 years of clinical trials, including a 10-year period on the market resulting in hundreds of fatal adverse events. Although ADCs are often considered lower risk for toxicity due to their targeted nature, off-target activity and liberated payload can still constrain dosing and drive clinical failure. Under its original schedule, Mylotarg was dosed infrequently at high levels, which is typical for ADCs because of their long half-lives. However, our PK modeling suggests that these regimens increase maximum plasma concentration (Cmax)-related toxicities while producing suboptimal exposures to the target receptor. Our analysis demonstrates that the benefits of dose fractionation for Mylotarg tolerability should have been obvious early in the drug's clinical development and could have curtailed the proliferation of ineffective Phase III studies. We also identify schedules likely to be even more efficacious without compromising on tolerability. Alternatively, a longer-circulating Mylotarg formulation could obviate the need for dose fractionation, allowing superior patient convenience. Early-stage PK optimization through quantitative modeling methods can accelerate clinical development and prevent late-stage failures.

LC-MS/MS quantification of ropivacaine and local analgesic and adverse effects of Long-acting Ropivacaine Injection based on pharmacokinetic-pharmacodynamic modelling in Bama minipigs

The local analgesic efficacy and adverse effects of a new Long-acting Ropivacaine formulation were examined based on pharmacokinetic-pharmacodynamic (PK-PD) modelling in Bama minipigs. 24 Bama minipigs, 12 males and 12 females, were randomly and equally divided into the following treatment groups: normal saline injection, drug vehicle injection, Long-acting Ropivacaine Injection and Ropivacaine Hydrochloride Injection. After routine disinfection, a skin incision about 3 cm long and 3 cm deep was produced in the leg of each pig, and mechanical withdrawal threshold (MWT) measured at various times pre- and post-injection as an index of analgesia against incision pain. Plasma ropivacaine concentrations were also measured at the same times using a novel liquid chromatography-tandem mass spectroscopy (LC-MS/MS) method. Minipigs were sacrificed 24 h post-injection and hearts collected for drug concentration measurements by LC-MS/MS. The LC-MS/MS method demonstrated high sensitivity, linearity and precision. The Long-acting Ropivacaine formulation produced a longer analgesic effect (∼12 h) at a lower plasma concentration than Ropivacaine Hydrochloride (∼4h), suggesting a better side-effects profile. A PK-PD model revealed a direct relationship between plasma ropivacaine concentration and MWT, with peak analgesia at about 1000 ng/mL and behaved good prediction ability. Long-acting Ropivacaine Injection is a superior local anaesthetic-analgesic treatment due to longer-lasting efficacy at lower concentrations compared to Ropivacaine Hydrochloride, which will reduce the risk of side effects such as cardiotoxicity.

New Methodology for Determining Plasma Protein Binding Kinetics Using an Enzyme Reporter Assay Coupling with High-Resolution Mass Spectrometry

Determination of drug binding kinetics in plasma is important yet extremely challenging. Accordingly, we introduce "dynamic free fraction" as a new binding parameter describing drug-protein binding kinetics. We demonstrate theoretically and experimentally that the dynamic free fraction can be determined by coupling the drug binding assay with a reporter enzyme in combination with high-resolution mass spectrometry measuring the relative initial steady-state rates of enzymatic reactions in the absence and presence of matrix proteins. This novel and simple methodology circumvents a long-standing challenge inherent in existing methods for determining binding kinetics constants, such as k_on and k_off, and enables assessment of the impact of protein binding kinetics on pharmaceutical properties of drugs. As demonstrated with nine model drugs, the predicted liver extraction ratio, a measure of efficiency of drug removal by the liver, correlates significantly better to the observed extraction ratio when using the dynamic free fraction (f_D) in place of the unbound fraction (f_u) of the drug in plasma. Similarly, the in vivo hepatic clearance of these drugs, a measure of liver drug elimination, is highly comparable to the clearance values calculated with the dynamic free fraction (f_D), which is markedly better than those calculated with the unbound fraction (f_u). In contrast to the prevailing view, these results indicate that protein binding kinetics is an important pharmacokinetic property of a drug. As plasma protein binding is one of the most important drug properties, this new methodology may represent a breakthrough and could have a real impact on the field.

Pharmacodynamic model of slow reversible binding and its applications in pharmacokinetic/pharmacodynamic modeling: review and tutorial

Therapeutic responses of most drugs are initiated by the rate and degree of binding to their receptors or targets. The law of mass action describes the rate of drug-receptor complex association (k_on) and dissociation (k_off) where the ratio k_off/k_on is the equilibrium dissociation constant (K_d). Drugs with slow reversible binding (SRB) often demonstrate delayed onset and prolonged pharmacodynamic effects. This report reviews evidence for drugs with SRB features, describes previous pharmacokinetic/pharmacodynamic (PK/PD) modeling efforts of several such drugs, provides a tutorial on the mathematics and properties of SRB models, demonstrates applications of SRB models to additional compounds, and compares PK/PD fittings of SRB with other mechanistic models. We identified and summarized 52 drugs with in vitro-confirmed SRB from a PubMed literature search. Simulations with a SRB model and observed PK/PD profiles showed delayed and prolonged responses and that increasing doses/k_on or decreasing k_off led to greater expected maximum effects and a longer duration of effects. Recession slopes for return of responses to baseline after single doses were nearly linear with an inflection point that approaches a limiting value at larger doses. The SRB model newly captured literature data for the antihypertensive effects of candesartan and antiallergic effects of noberastine. Their PD profiles could also be fitted with indirect response and biophase models with minimal differences. The applicability of SRB models is probably commonplace, but underappreciated, owing to the need for in vitro confirmation of binding kinetics and the similarity of PK/PD profiles to models with other mechanistic determinants.

Development of an intracellular quantitative assay to measure compound binding kinetics

Contemporary drug discovery typically quantifies the effect of a molecule on a biological target using the equilibrium-derived measurements of IC50, EC50, or KD. Kinetic descriptors of drug binding are frequently linked with the effectiveness of a molecule in modulating a disease phenotype; however, these parameters are yet to be fully adopted in early drug discovery. Nanoluciferase bioluminescence resonance energy transfer (NanoBRET) can be used to measure interactions between fluorophore-conjugated probes and luciferase fused target proteins. Here, we describe an intracellular NanoBRET competition assay that can be used to quantify cellular kinetic rates of compound binding to nanoluciferase-fused bromodomain and extra-terminal (BET) proteins. Comparative rates are generated using a cell-free NanoBRET assay and by utilizing orthogonal recombinant protein-based methodologies. A screen of known pan-BET inhibitors is used to demonstrate the value of this approach in the investigation of kinetic selectivity between closely related proteins.

Mechanism-based modeling of the effect of a novel inhibitor of vascular adhesion protein-1 on albuminuria and renal function markers in patients with diabetic kidney disease

The vascular adhesion protein-1 (VAP-1) inhibitor ASP8232 reduces albuminuria in patients with type 2 diabetes and chronic kidney disease. A mechanism-based model was developed to quantify the effects of ASP8232 on renal markers from a placebo-controlled Phase 2 study in diabetic kidney disease with 12 weeks of ASP8232 treatment. The model incorporated the available pharmacokinetic, pharmacodynamic (plasma VAP-1 concentration and activity), serum and urine creatinine, serum cystatin C, albumin excretion rate, urinary albumin-to-creatinine ratio, and urine volume information in an integrated manner. Drug-independent time-varying changes and different drug effects could be quantified for these markers using the model. Through simulations, this model provided the opportunity to dissect the relationship and longitudinal association between the estimated glomerular filtration rate and albuminuria and to quantify the pharmacological effects of ASP8232. The developed drug-independent model may be useful as a starting point for other compounds affecting the same biomarkers in a similar time scale.

Recent advances in long-acting nanoformulations for delivery of antiretroviral drugs

In spite of introduction of combination antiretroviral therapy (cART) against human immunodeficiency virus (HIV) infection; inaccessibility and poor adherence to oral cART costs 10 in 100,000 death worldwide. Failure in adherence leads to viral rebound, emergence of drug resistance and anticipated HIV infection in high risk individuals. Various Long-acting antiretroviral (LA ARV) nanoformulations including nano-prodrug, solid drug nanoparticles (SDN), nanocrystals, aspherical nanoparticles, polymeric and lipidic nanoparticles have shown plasma/tissue drug concentration in the therapeutic range for several weeks during pre-clinical evaluation. LA ARV nanoformulations therefore have replaced cART as better alternative for the treatment of HIV infection. Cabenuva™ is recently approved by Health Canada containing LA cabotegravir+LA rilpivirine nanocrystals (ViiV healthcare) for once monthly administration by intramuscular route. The LA nanoformulation due to its nanosize insist on better stability, delivery to lymphatic, slow release into systemic circulation via lymphatic-circulatory system conjoint and secondary drug depot within infiltered immune cells at site of administration and systemic circulation in contrast to conventional drugs. However, the pharmacokinetic, biodistribution and efficacy of LA nanoformulations hinge onto physicochemical properties of the drugs and route of administration. Therefore, current review emphasizes on these contradistinctive factors that affects the reproducibility, safety, efficacy and toxicity of LA anti-HIV nanoformulations. Moreover, it expatiates on application of profuse nanoformulations for long-acting effect with promising preclinical discoveries and two clinical leads. To add on, utilization of physiology-based and mechanism-based pharmacokinetic modelling and in vivo animal models which could lead to enhanced safety and efficacy of LA ARV nanoformulations in humans have been included.

Dose Predictions for Drug Design

The efficacious dose of a drug is perhaps the most holistic metric reflecting its therapeutic potential. Dose is predicted at many stages in drug discovery and development. Prior to the 1990s, dose prediction was limited to the drug "working" at a reasonable dose and dose regimen in an animal model. Through the early 2000s, dose predictions were generated at candidate nomination and then refined during clinical development. Currently, dose predictions can be made early in drug discovery to enable drug design. Dose predictions at this stage can identify critical drug properties for a viable dose regimen and provide clinically relevant context to lead optimization. In this paper, we give an overview of the opportunities and challenges associated with dose prediction for drug design. A number of general considerations, approaches, and case examples are discussed.

Current status and future perspective on preclinical pharmacokinetic and pharmacodynamic (PK/PD) analysis: Survey in Japan pharmaceutical manufacturers association (JPMA)

Preclinical pharmacokinetic/pharmacodynamic (PK/PD) analysis is an efficient tool for the translational research and proof of mechanism/concept in animals. The questionnaire survey on the practice of preclinical PK/PD analysis was conducted in the member companies of the Japan Pharmaceutical Manufacturers Association (JPMA). According to the survey, 60% of companies conducted preclinical PK/PD analysis and its impact for drug development was different between each of the companies. The frequently analyzed therapeutic areas of preclinical PK/PD analysis were neurology, inflammation and metabolic disease, and those are different from the therapeutic area (infectious disease and oncology) in which PK/PD analysis was considered as effective by the present survey. Many companies which have used preclinical PK/PD analysis for the translation to human PK/PD and for the prediction of dose/regimen had good communication with other research & development (R&D) departments (e.g. pharmacology/clinical pharmacology). The increase in resources for preclinical PK/PD analysis including education was highly demanded. As a future perspective, the closer collaboration between pharmacokinetics scientists, pharmacologists, toxicologists and clinical pharmacologists and the increase in resources including upskilling and the comprehension of preclinical PK/PD analysis by the project team are considered to lead to efficient contributions to improve the success ratio of drug discovery and development.

Analysis of Biased Agonism

Agonists and most natural ligands bind to receptors in their inactive state and quickly induce an active receptor conformation that initiates cell signaling. The active receptor state initiates signaling because of its structural complementariness with coupling proteins that activate signaling pathways, such as G proteins and G protein-coupled receptor kinases. Agonist bias refers to the propensity of an agonist to direct receptor signaling through one pathway relative to another. Thus, if the agonist exhibits much higher affinity for active state 1 compared to active state 2, it will cause a robust activation of receptor coupling protein 1 but not 2, and ultimately, a preferential stimulation of signaling pathway 1. Biased agonists are potentially more selective therapeutic agents because there are numerous cases where the therapeutic and adverse effects of an agonist are mediated by distinct pathways involving G proteins and β-arrestin. Given the mechanism for agonist bias, the most straightforward approach for quantifying bias involves the estimation of agonist affinity for the inactive receptor state and the active receptor states involved in signaling through different pathways. The approach provides quantitative estimates of the sensitivities of different signaling pathways, enabling one to determine to what extent the observed selectivity is caused by agonist or system bias. In addition, the approach is a powerful adjunct to in silico docking studies and can be applied to in vivo assays, structure-activity relationships, and the analysis of published agonist concentration-response curves.

Prediction of non-linear pharmacokinetics in humans of an antibody-drug conjugate (ADC) when evaluation of higher doses in animals is limited by tolerability: Case study with an anti-CD33 ADC

For antibody-drug conjugates (ADCs) that carry a cytotoxic drug, doses that can be administered in preclinical studies are typically limited by tolerability, leading to a narrow dose range that can be tested. For molecules with non-linear pharmacokinetics (PK), this limited dose range may be insufficient to fully characterize the PK of the ADC and limits translation to humans. Mathematical PK models are frequently used for molecule selection during preclinical drug development and for translational predictions to guide clinical study design. Here, we present a practical approach that uses limited PK and receptor occupancy (RO) data of the corresponding unconjugated antibody to predict ADC PK when conjugation does not alter the non-specific clearance or the antibody-target interaction. We used a 2-compartment model incorporating non-specific and specific (target mediated) clearances, where the latter is a function of RO, to describe the PK of anti-CD33 ADC with dose-limiting neutropenia in cynomolgus monkeys. We tested our model by comparing PK predictions based on the unconjugated antibody to observed ADC PK data that was not utilized for model development. Prospective prediction of human PK was performed by incorporating in vitro binding affinity differences between species for varying levels of CD33 target expression. Additionally, this approach was used to predict human PK of other previously tested anti-CD33 molecules with published clinical data. The findings showed that, for a cytotoxic ADC with non-linear PK and limited preclinical PK data, incorporating RO in the PK model and using data from the corresponding unconjugated antibody at higher doses allowed the identification of parameters to characterize monkey PK and enabled human PK predictions.

Modelling the delay between pharmacokinetics and EEG effects of morphine in rats: binding kinetic versus effect compartment models

Drug–target binding kinetics (as determined by association and dissociation rate constants, k_on and k_off) can be an important determinant of the kinetics of drug action. However, the effect compartment model is used most frequently instead of a target binding model to describe hysteresis. Here we investigate when the drug–target binding model should be used in lieu of the effect compartment model. The utility of the effect compartment (EC), the target binding kinetics (TB) and the combined effect compartment–target binding kinetics (EC–TB) model were tested on either plasma (EC_PL, TB_PL and EC–TB_PL) or brain extracellular fluid (ECF) (EC_ECF, TB_ECF and EC–TB_ECF) morphine concentrations and EEG amplitude in rats. It was also analyzed when a significant shift in the time to maximal target occupancy (Tmax_TO) with increasing dose, the discriminating feature between the TB and EC model, occurs in the TB model. All TB models assumed a linear relationship between target occupancy and drug effect on the EEG amplitude. All three model types performed similarly in describing the morphine pharmacodynamics data, although the EC model provided the best statistical result. The analysis of the shift in Tmax_TO (∆Tmax_TO) as a result of increasing dose revealed that ∆Tmax_TO is decreasing towards zero if the k_off is much smaller than the elimination rate constant or if the target concentration is larger than the initial morphine concentration. The results for the morphine PKPD modelling and the analysis of ∆Tmax_TO indicate that the EC and TB models do not necessarily lead to different drug effect versus time curves for different doses if a delay between drug concentrations and drug effect (hysteresis) is described. Drawing mechanistic conclusions from successfully fitting one of these two models should therefore be avoided. Since the TB model can be informed by in vitro measurements of k_on and k_off, a target binding model should be considered more often for mechanistic modelling purposes.

Biomarkers in epilepsy-A modelling perspective

Biomarkers can be categorised from type 0 (genotype or phenotype), through 6 (clinical scales), each level representing a part of the processes involved in the biological system and drug treatment. This classification facilitates the identification and connection of information required to fully (mathematically) model a disease and its treatment using integrated information from biomarkers. Two recent reviews thoroughly discussed the current status and development of biomarkers for epilepsy, but a path towards the integration of such biomarkers for the personalisation of anti-epileptic drug treatment is lacking. Here we aim to 1) briefly categorise the available epilepsy biomarkers and identify gaps, and 2) provide a modelling perspective on approaches to fill such gaps. There is mainly a lack of biomarker types 2 (target occupancy) and 3 (target activation). Current literature typically focuses on qualitative biomarkers for diagnosis and prediction of treatment response or failure, leaving a need for biomarkers that help to quantitatively understand the overall system to explain and predict differences in disease and treatment outcome. Due to the complexity of epilepsy, filling the biomarker gaps will require collaboration and expertise from the fields of systems biology and systems pharmacology.

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.

Data Sharing in the Pharmaceutical Enterprise: The Genie's Out of the Bottle

OBJECTIVE

This Commentary shows that the present emphasis on the sharing of data from clinical trials can be extended to the entire pharmaceutical enterprise.

METHODS

The authors constructed a Data Sharing Dashboard that shows the relationship between all of the life-cycle domains of the pharmaceutical enterprise from discovery to obsolescence and the domain-bridging disciplines, such as target credentialing, structure-activity relationships, and exposure-effect relationships.

FINDINGS

The published literature encompassing the pharmaceutical enterprise is expansive, covering the major domains of discovery, translation, clinical development, and post-marketing outcomes research, all of which have even larger, though generally inaccessible, troves of legacy data bases. Notable exceptions include the fields of genomics and bioinformatics.

IMPLICATIONS

We have the opportunity to broaden the present momentum of interest in data sharing to the entire pharmaceutical enterprise, beginning with discovery and extending into health technology assessment and post-patent expiry generic use with the plan of integrating new levels and disciplines of knowledge and with the ultimate goal of improving the care of our patients.

Drug-disease modeling in the pharmaceutical industry - where mechanistic systems pharmacology and statistical pharmacometrics meet

Modeling & simulation (M&S) methodologies are established quantitative tools, which have proven to be useful in supporting the research, development (R&D), regulatory approval, and marketing of novel therapeutics. Applications of M&S help design efficient studies and interpret their results in context of all available data and knowledge to enable effective decision-making during the R&D process. In this mini-review, we focus on two sets of modeling approaches: population-based models, which are well-established within the pharmaceutical industry today, and fall under the discipline of clinical pharmacometrics (PMX); and systems dynamics models, which encompass a range of models of (patho-)physiology amenable to pharmacological intervention, of signaling pathways in biology, and of substance distribution in the body (today known as physiologically-based pharmacokinetic models) - which today may be collectively referred to as quantitative systems pharmacology models (QSP). We next describe the convergence - or rather selected integration - of PMX and QSP approaches into 'middle-out' drug-disease models, which retain selected mechanistic aspects, while remaining parsimonious, fit-for-purpose, and able to address variability and the testing of covariates. We further propose development opportunities for drug-disease systems models, to increase their utility and applicability throughout the preclinical and clinical spectrum of pharmaceutical R&D.

Parameter Identifiability of Fundamental Pharmacodynamic Models

Issues of parameter identifiability of routinely used pharmacodynamics models are considered in this paper. The structural identifiability of 16 commonly applied pharmacodynamic model structures was analyzed analytically, using the input-output approach. Both fixed-effects versions (non-population, no between-subject variability) and mixed-effects versions (population, including between-subject variability) of each model structure were analyzed. All models were found to be structurally globally identifiable under conditions of fixing either one of two particular parameters. Furthermore, an example was constructed to illustrate the importance of sufficient data quality and show that structural identifiability is a prerequisite, but not a guarantee, for successful parameter estimation and practical parameter identifiability. This analysis was performed by generating artificial data of varying quality to a structurally identifiable model with known true parameter values, followed by re-estimation of the parameter values. In addition, to show the benefit of including structural identifiability as part of model development, a case study was performed applying an unidentifiable model to real experimental data. This case study shows how performing such an analysis prior to parameter estimation can improve the parameter estimation process and model performance. Finally, an unidentifiable model was fitted to simulated data using multiple initial parameter values, resulting in highly different estimated uncertainties. This example shows that although the standard errors of the parameter estimates often indicate a structural identifiability issue, reasonably “good” standard errors may sometimes mask unidentifiability issues.

Pharmacotherapy in pediatric epilepsy: from trial and error to rational drug and dose selection - a long way to go

INTRODUCTION

Whereas ongoing efforts in epilepsy research focus on the underlying disease processes, the lack of a physiologically based rationale for drug and dose selection contributes to inadequate treatment response in children. In fact, limited information on the interindividual variation in pharmacokinetics and pharmacodynamics of anti-epileptic drugs (AEDs) in children drive prescription practice, which relies primarily on dose regimens according to a mg/kg basis. Such practice has evolved despite advancements in pediatric pharmacology showing that growth and maturation processes do not correlate linearly with changes in body size.

AREAS COVERED

In this review we aim to provide 1) a comprehensive overview of the sources of variability in the response to AEDs, 2) insight into novel methodologies to characterise such variation and 3) recommendations for treatment personalisation.

EXPERT OPINION

The use of pharmacokinetic-pharmacodynamic principles in clinical practice is hindered by the lack of biomarkers and by practical constraints in the evaluation of polytherapy. The identification of biomarkers and their validation as tools for drug development and therapeutics will require some time. Meanwhile, one should not miss the opportunity to integrate the available pharmacokinetic data with modeling and simulation concepts to prevent further delays in the development of personalised treatments for pediatric patients.

Elvitegravir/cobicistat-associated toxic optical neuropathy in an HIV-infected patient: a call for caution?

Ocular toxicity may not only be caused by medication overdoses and drug-drug interactions, but also by chronic administration of medications at recommended doses. We describe a case of an HIV-infected patient who experienced significant and sustained bilateral visual loss 2 months after starting treatment with elvitegravir/cobicistat/tenofovir/emtricitabine. Given the absence of any evidence of tenofovir- or emtricitabine-induced optical neuropathy after several years of clinical use, the antiretroviral therapy was promptly changed to tenofovir/emtricitabine plus atazanavir/ritonavir, which led to a progressive improvement in visual acuity. However, visual evoked potentials never returned to normal amplitudes. This is the first report of toxic optical neuropathy associated with the use of elvitegravir/cobicistat. It is imperative to recognize any signs of possible eye toxicity as rapidly as possible, and refer affected patients to an ophthalmologist promptly because early detection and the withdrawal of the offending agent are crucial in reversing this adverse ocular event.

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.

Approaching Pharmacometrics as a Paleontologist Would: Recovering the Links Between Drugs and the Body Through Reconstruction

Our knowledge of dinosaurs comes primarily from the fossil record. Notwithstanding the condition of these vestiges, paleontologists reconstruct early reptilian life by comparison to previously discovered specimens. When relics are missing, reasonable deductions are used to fill in the gaps.

Optimization of human dose prediction by using quantitative and translational pharmacology in drug discovery

In this perspective article, we explain how quantitative and translational pharmacology, when well-implemented, is believed to lead to improved clinical candidates and drug targets that are differentiated from current treatment options. Quantitative and translational pharmacology aims to build and continuously improve the quantitative relationship between drug exposure, target engagement, efficacy, safety and its interspecies relationship at every phase of drug discovery. Drug hunters should consider and apply these concepts to develop compounds with a higher probability of interrogating the clinical biological hypothesis. We offer different approaches to set an initial effective concentration or pharmacokinetic-pharmacodynamic target in man and to predict human pharmacokinetics that determine together the predicted human dose and dose schedule. All concepts are illustrated with ample literature examples.

Utility of free and total target measurements as target engagement and efficacy biomarkers in biotherapeutic development--opportunities and challenges

For biotherapeutics directed against soluble targets, most often monoclonal antibodies (mAbs), their therapeutic efficacy theoretically is driven by the magnitude and duration of free target suppression. However, for soluble targets of rapid turnover and low abundance, it can be technically challenging to directly measure the lowering of free target following treatment with biologics. The opportunities, challenges, and practical approaches to assess free and bound soluble targets and the utility of free and bound target measurements as biomarkers for target engagement and efficacy are covered in this review. In particular, case examples are presented to illustrate the interplay between drug and free/bound target, and how an integrated bioanalytical and pharmacokinetic/target engagement/pharmacodynamic (PK/TE/PD) modeling approach can be used to assess the target engagement for biologics directed against soluble targets with rapid turnover. Important caveats of the modeling approach in the absence of free target measurements are also discussed.

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.

Determination of in Vivo Enzyme Occupancy Utilizing Inhibitor Dissociation Kinetics

During drug discovery, assessment of in vivo target occupancy by therapeutic candidates is often required for predicting clinical efficacy. Current strategies for determining target occupancy include using radiolabeled or irreversible surrogates, which can be technically challenging, and the results are often not sufficiently quantitative. We developed a straightforward method by applying slow-dissociation kinetics to quantitatively determine enzyme occupancy without using specialized reagents. We applied this method to determine occupancy of Cathepsin K inhibitors in bone tissues harvested from rabbit femurs. Tissues from dosed animals were harvested, flash frozen, lysed, then analyzed by a jump-dilution assay with substrate. The rate of substrate turnover was monitored continuously until reaching steady state and progress curves were fit with the equation [product] = vst + ((vi - vs)/kobs)(1 - exp(-kobst)). The initial rate vi represents the residual activity of the enzyme before inhibitor dissociation; vs is the reaction rate after dissociation of the inhibitor. Occupancy is derived from the ratio of vi/vs. A significant benefit of the method is that data from both the occupied and unoccupied states are obtained in the same assay under identical conditions, which provides greater consistency between studies. The Cat K inhibitor MK-0674 (in vitro IC50 1 nM) was tested in young rabbits (<6 month old) and showed a dose-dependent increase in occupancy, reaching essentially complete occupancy at 1.0 mg/kg. In addition the method enables measurement of the total Cat K in the target tissue. Results confirmed complete occupancy even as the osteoclasts responded to higher doses with increased enzyme production.

Molecular mechanism matters: Benefits of mechanistic computational models for drug development

Making drug development a more efficient and cost-effective process will have a transformative effect on human health. A key, yet underutilized, tool to aid in this transformation is mechanistic computational modeling. By incorporating decades of hard-won prior knowledge of molecular interactions, cellular signaling, and cellular behavior, mechanistic models can achieve a level of predictiveness that is not feasible using solely empirical characterization of drug pharmacodynamics. These models can integrate diverse types of data from cell culture and animal experiments, including high-throughput systems biology experiments, and translate the results into the context of human disease. This provides a framework for identification of new drug targets, measurable biomarkers for drug action in target tissues, and patient populations for which a drug is likely to be effective or ineffective. Additionally, mechanistic models are valuable in virtual screening of new therapeutic strategies, such as gene or cell therapy and tissue regeneration, identifying the key requirements for these approaches to succeed in a heterogeneous patient population. These capabilities, which are distinct from and complementary to those of existing drug development strategies, demonstrate the opportunity to improve success rates in the drug development pipeline through the use of mechanistic computational models.

OARSI Clinical Trials Recommendations: Soluble biomarker assessments in clinical trials in osteoarthritis

The objective of this work was to describe requirements for inclusion of soluble biomarkers in osteoarthritis (OA) clinical trials and progress toward OA-related biomarker qualification. The Guidelines for Biomarkers Working Group, representing experts in the field of OA biomarker research from both academia and industry, convened to discuss issues related to soluble biomarkers and to make recommendations for their use in OA clinical trials based on current knowledge and anticipated benefits. This document summarizes current guidance on use of biomarkers in OA clinical trials and their utility at five stages, including preclinical development and phase I to phase IV trials. As demonstrated by this summary, biomarkers can provide value at all stages of therapeutics development. When resources permit, we recommend collection of biospecimens in all OA clinical trials for a wide variety of reasons but in particular, to determine whether biomarkers are useful in identifying those individuals most likely to receive clinically important benefits from an intervention; and to determine whether biomarkers are useful for identifying individuals at earlier stages of OA in order to institute treatment at a time more amenable to disease modification.

Drug effects on the CVS in conscious rats: separating cardiac output into heart rate and stroke volume using PKPD modelling

BACKGROUND AND PURPOSE

Previously, a systems pharmacology model was developed characterizing drug effects on the interrelationship between mean arterial pressure (MAP), cardiac output (CO) and total peripheral resistance (TPR). The present investigation aims to (i) extend the previously developed model by parsing CO into heart rate (HR) and stroke volume (SV) and (ii) evaluate if the mechanism of action (MoA) of new compounds can be elucidated using only HR and MAP measurements.

EXPERIMENTAL APPROACH

Cardiovascular effects of eight drugs with diverse MoAs (amiloride, amlodipine, atropine, enalapril, fasudil, hydrochlorothiazide, prazosin and propranolol) were characterized in spontaneously hypertensive rats (SHR) and normotensive Wistar-Kyoto (WKY) rats following single administrations of a range of doses. Rats were instrumented with ascending aortic flow probes and aortic catheters/radiotransmitters for continuous recording of MAP, HR and CO throughout the experiments. Data were analysed in conjunction with independent information on the time course of the drug concentration following a mechanism-based pharmacokinetic-pharmacodynamic modelling approach.

KEY RESULTS

The extended model, which quantified changes in TPR, HR and SV with negative feedback through MAP, adequately described the cardiovascular effects of the drugs while accounting for circadian variations and handling effects.

CONCLUSIONS AND IMPLICATIONS

A systems pharmacology model characterizing the interrelationship between MAP, CO, HR, SV and TPR was obtained in hypertensive and normotensive rats. This extended model can quantify dynamic changes in the CVS and elucidate the MoA for novel compounds, with one site of action, using only HR and MAP measurements. Whether the model can be applied for compounds with a more complex MoA remains to be established.

PKPD modelling of the interrelationship between mean arterial BP, cardiac output and total peripheral resistance in conscious rats

BACKGROUND AND PURPOSE

The homeostatic control of arterial BP is well understood with changes in BP resulting from changes in cardiac output (CO) and/or total peripheral resistance (TPR). A mechanism-based and quantitative analysis of drug effects on this interrelationship could provide a basis for the prediction of drug effects on BP. Hence, we aimed to develop a mechanism-based pharmacokinetic-pharmacodynamic (PKPD) model in rats that could be used to characterize the effects of cardiovascular drugs with different mechanisms of action (MoA) on the interrelationship between BP, CO and TPR.

EXPERIMENTAL APPROACH

The cardiovascular effects of six drugs with diverse MoA, (amlodipine, fasudil, enalapril, propranolol, hydrochlorothiazide and prazosin) were characterized in spontaneously hypertensive rats. The rats were chronically instrumented with ascending aortic flow probes and/or aortic catheters/radiotransmitters for continuous recording of CO and/or BP. Data were analysed in conjunction with independent information on the time course of drug concentration using a mechanism-based PKPD modelling approach.

KEY RESULTS

By simultaneous analysis of the effects of six different compounds, the dynamics of the interrelationship between BP, CO and TPR were quantified. System-specific parameters could be distinguished from drug-specific parameters indicating that the model developed is drug-independent.

CONCLUSIONS AND IMPLICATIONS

A system-specific model characterizing the interrelationship between BP, CO and TPR was obtained, which can be used to quantify and predict the cardiovascular effects of a drug and to elucidate the MoA for novel compounds. Ultimately, the proposed PKPD model could be used to predict the effects of a particular drug on BP in humans based on preclinical data.

Implementation of pharmacokinetic and pharmacodynamic strategies in early research phases of drug discovery and development at Novartis Institute of Biomedical Research

Characterizing the relationship between the pharmacokinetics (PK, concentration vs. time) and pharmacodynamics (PD, effect vs. time) is an important tool in the discovery and development of new drugs in the pharmaceutical industry. The purpose of this publication is to serve as a guide for drug discovery scientists toward optimal design and conduct of PK/PD studies in the research phase. This review is a result of the collaborative efforts of DMPK scientists from various Metabolism and Pharmacokinetic (MAP) departments of the global organization Novartis Institute of Biomedical Research (NIBR). We recommend that PK/PD strategies be implemented in early research phases of drug discovery projects to enable successful transition to drug development. Effective PK/PD study design, analysis, and interpretation can help scientists elucidate the relationship between PK and PD, understand the mechanism of drug action, and identify PK properties for further improvement and optimal compound design. Additionally, PK/PD modeling can help increase the translation of in vitro compound potency to the in vivo setting, reduce the number of in vivo animal studies, and improve translation of findings from preclinical species into the clinical setting. This review focuses on three important elements of successful PK/PD studies, namely partnership among key scientists involved in the study execution; parameters that influence study designs; and data analysis and interpretation. Specific examples and case studies are highlighted to help demonstrate key points for consideration. The intent is to provide a broad PK/PD foundation for colleagues in the pharmaceutical industry and serve as a tool to promote appropriate discussions on early research project teams with key scientists involved in PK/PD studies.

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

OBJECTIVES

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.

METHODS

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.

KEY FINDINGS

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.

CONCLUSIONS

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.

Overview of experimental models of the blood-brain barrier in CNS drug discovery

The blood-brain barrier (BBB) is a physical and metabolic entity that isolates the brain from the systemic circulation. The barrier consists of tight junctions between endothelial cells that contain egress transporters and catabolic enzymes. To cross the BBB, a drug must possess the appropriate physicochemical properties to achieve a sufficient time-concentration profile in brain interstitial fluid (ISF). In this overview, we review techniques to measure BBB permeation, which is evidenced by the free concentration of compound in brain ISF over time. We consider a number of measurement techniques, including in vivo microdialysis and brain receptor occupancy following perfusion. Consideration is also given to the endothelial and nonendothelial cell systems used to assess both the BBB permeation of a test compound and its interactions with egress transporters, and computer models employed for predicting passive permeation and the probability of interactions with BBB transporters.

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.

The dynamics of drug-target interactions: drug-target residence time and its impact on efficacy and safety

The extent and duration of pharmacological action is determined by the lifetime of drug occupancy on a molecular target. This lifetime is defined by dynamic processes that control the rates of drug association and dissociation from the target. Recently, the term residence time has been coined to describe experimental measurements that can be related to the lifetime of the binary drug-target complex, and this in turn to durable, pharmacodynamic activity. The residence time concept and its impact on drug optimization are reviewed here. Examples are provided that demonstrate how a long residence time can improve drug efficacy in vivo. Additionally, optimization of drug-target residence time can help to mitigate off-target mediated toxicity, hence, improving drug safety and tolerability. Recent applications of the residence time concept to both drug discovery and development are also presented.

Compartmental tissue distribution of antibody therapeutics: experimental approaches and interpretations

Monoclonal antibodies have provided many validated and potential new therapeutic candidates for various diseases encompassing the realms of neurology, ophthalmology, immunology, and especially oncology. The mechanism of action for these biological molecules typically involves specific binding to a soluble ligand or cell surface protein in order to block or alter a molecular pathway, induce a desired cellular response, or deplete a target cell. Many antigens reside within the interstitial space, the fluid-filled compartment that lies between the outer endothelial vessel wall and the plasma membranes of cells. This mini-review examines the concepts relevant to the kinetics and behavior of antibodies within the interstitium with a special emphasis on radiometric measurement of quantitative pharmacology. Molecular probes are discussed to outline chemical techniques, selection criteria, data interpretation, and relevance to the study of antibody pharmacokinetics. The importance of studying the tissue uptake of antibodies at a compartmental level is highlighted, including a brief overview of receptor occupancy and its interpretation in radiotracer studies. Experimental methods for measuring the spatial composition of tissues are examined in terms of relative vascular, interstitial, and cellular volumes using solid tumors as a representative example. Experimental methods and physiologically based pharmacokinetic modeling are introduced as distinct approaches to distinguish between free and bound fractions of interstitial antibody. Overall, the review outlines the available methods for pharmacokinetic measurements of antibodies and physiological measurements of the compartments that they occupy, while emphasizing that such approaches may not fully capture the complexities of dynamic, heterogeneous tumors and other tissues.

High glucose enhances responsiveness of human airways smooth muscle via the Rho/ROCK pathway

Glucose moves into airway secretions after a glucose load. Therefore people with diabetes or hyperglycemia spend a significant proportion of each day with glucose in their airways secretions. This study investigated the effects of glucose on isolated human airways and on cultured airway smooth muscle (ASM) cells. Human isolated bronchi were stimulated with acetylcholine, histamine, and transmural stimulation and treated with the selective ROCK inhibitors Y27632 and SB772077B under high-glucose conditions. The effect of high glucose concentrations on intracellular calcium flux and the phosphorylation of MYPT1 in ASM cells was also investigated. High (44 mM for 6 h) glucose, but not mannitol, concentrations led to an enhanced responsiveness of ASM to contractile agents. Y27632 and SB772077B completely abolished (P < 0.05) the enhanced contractile effects with a high-concentration glucose solution, compared with control tissues. In cultured ASM cells, incubation with high glucose concentrations significantly (P < 0.05) enhanced bradykinin-induced intracellular calcium flux and the levels of pMYPT1, which were inhibited by Y27632 (P < 0.05). Our study has demonstrated that high glucose concentrations leads to hyperresponsiveness of human isolated bronchi and enhances intracellular calcium release in cultured ASM cells via a Rho/ROCK- and pMYPT1-dependent pathway, suggesting that this crucial pathway may contribute to the reduced lung function observed in patients with diabetes. These data propose novel targets for the treatment of patients with respiratory diseases that also suffer from diabetes mellitus.

Clozapine, atypical antipsychotics, and the benefits of fast-off D2 dopamine receptor antagonism

Drug-receptor interactions are traditionally quantified in terms of affinity and efficacy, but there is increasing awareness that the drug-on-receptor residence time also affects clinical performance. While most interest has hitherto been focused on slow-dissociating drugs, D(2) dopamine receptor antagonists show less extrapyramidal side effects but still have excellent antipsychotic activity when they dissociate swiftly. Fast dissociation of clozapine, the prototype of the "atypical antipsychotics", has been evidenced by distinct radioligand binding approaches both on cell membranes and intact cells. The surmountable nature of clozapine in functional assays with fast-emerging responses like calcium transients is confirmatory. Potential advantages and pitfalls of the hitherto used techniques are discussed, and recommendations are given to obtain more precise dissociation rates for such drugs. Surmountable antagonism is necessary to allow sufficient D(2) receptor stimulation by endogenous dopamine in the striatum. Simulations are presented to find out whether this can be achieved during sub-second bursts in dopamine concentration or rather during much slower, activity-related increases thereof. While the antagonist's dissociation rate is important to distinguish between both mechanisms, this becomes much less so when contemplating time intervals between successive drug intakes, i.e., when pharmacokinetic considerations prevail. Attention is also drawn to the divergent residence times of hydrophobic antagonists like haloperidol when comparing radioligand binding data on cell membranes with those on intact cells and clinical data.

Long-lasting target binding and rebinding as mechanisms to prolong in vivo drug action

An increasing number of examples in the literature suggest that the in vivo duration of drug action not only depends on macroscopic pharmacokinetic properties like plasma half-life and the time needed to equilibrate between the plasma and the effect compartments, but is also influenced by long-lasting target binding and rebinding. The present review combines information from different research areas and simulations to explore the nature of these mechanisms and the conditions in which they are most prevalent. Simulations reveal that these latter phenomena become especially influential when there is no longer sufficient free drug around to maintain high levels of receptor occupancy. There is not always a direct link between slow dissociation and long-lasting in vivo target protection, as the rate of free drug elimination from the effect compartment is also a key influencing factor. Local phenomena that hinder the diffusion of free drug molecules away from their target may allow them to consecutively bind to the same target and/or targets nearby (denoted as 'rebinding') even when their concentration in the bulk phase has already dropped to insignificant levels. The micro-anatomic properties of many effect compartments are likely to intensify this phenomenon. By mimicking the complexity of tissues, intact cells offer the opportunity to investigate both mechanisms under the same, physiologically relevant conditions.

Rebinding: or why drugs may act longer in vivo than expected from their in vitro target residence time

IMPORTANCE OF THE FIELD

It is well established that the in vivo duration of drug action not only depends on macroscopic pharmacokinetic properties like its plasma half-life, but also on the residence time of the drug-target complexes. However, drug 'rebinding' (i.e., the consecutive binding of dissociated drug molecules to the original target and/or targets nearby) can be influential in vivo as well.

AREAS COVERED IN THIS REVIEW

Information about rebinding is available since the 1980s but it is dispersed in the life sciences literature. This review compiles this information. In this respect, neurochemists and biopohysicians advance the same equations to describe drug rebinding.

WHAT THE READER WILL GAIN

The rebinding mechanism is explained according to the prevailing viewpoint in different life science disciplines. There is a general consensus that high target densities, high association rates and local phenomena that hinder the diffusion of free drug molecules away from their target all promote rebinding.

TAKE HOME MESSAGE

Simulations presented here for the first time suggest that rebinding may increase the duration and even the constancy of the drug's clinical action. Intact cell radioligand dissociation and related ex vivo experiments offer useful indications about a drug's aptitude to experience target rebinding.

Estimation of binding rate constants using a simultaneous mixed-effects method: application to monoamine transporter reuptake inhibitor reboxetine

BACKGROUND AND PURPOSE

Reboxetine is a clinically used antidepressant and is a racemic mixture of two enantiomers, SS- and RR-reboxetine. The aim of the work described in this manuscript was to determine the kinetics of binding of the RR- and SS-reboxetine to the human noradrenaline transporter (hNET).

EXPERIMENTAL APPROACH

We have applied a simultaneous mixed-effects method to the analysis of the transient kinetics of binding of SS-, RR- and racemic reboxetine to hNET. This method allowed simultaneous modelling of multiple datasets, taking into account inter-experiment variability, thereby facilitating robust parameter estimation and minimizing the assumptions made.

KEY RESULTS

The mixed-effects method proved simple and robust. SS-reboxetine bound to hNET according to a one-step binding model with the SS-enantiomer having 130-fold higher steady state affinity than the RR-enantiomer (K(d)= 0.076 +/- 0.009 nM vs. 9.7 +/- 0.8 nM respectively). The k(on) for SS-reboxetine was c. 1.4 x 10(5) M(-1).s(-1) and k(off) 1.05 x 10(-5) s(-1) (t(1/2) approximately 18 h). The k(on) for RR-reboxetine was c. 4.3 x 10(5) M(-1).s(-1) and k(off) 4.2 x 10(-3) s(-1) (t(1/2) approximately 3 min). The racemate behaved as expected for an equimolar mixture of RR- and SS-reboxetine, assuming mutually exclusive binding.

CONCLUSIONS AND IMPLICATIONS

These data will be useful for the interpretation of the behaviour of reboxetine and its enantiomers in man and the method used could be applied to other candidate drugs.