Meindert Danhof

Considerations in the use of cerebrospinal fluid pharmacokinetics to predict brain target concentrations in the clinical setting: implications of the barriers between blood and brain.

In the clinical setting, drug concentrations in cerebrospinal fluid (CSF) are sometimes used as a surrogate for drug concentrations at the target site within the brain. However, the brain consists of multiple compartments and many factors are involved in the transport of drugs from plasma into the brain and the distribution within the brain. In particular, active transport processes at the level of the blood-brain barrier and blood-CSF barrier, such as those mediated by P-glycoprotein, may lead to complex relationships between concentrations in plasma, ventricular and lumbar CSF, and other brain compartments. Therefore, CSF concentrations may be difficult to interpret and may have limited value. Pharmacokinetic data obtained by intracerebral microdialysis monitoring may be used instead, providing more valuable information. As non-invasive alternative techniques, positron emission tomography or magnetic resonance spectroscopy may be of added value.

Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling in translational drug research

The use of pharmacokinetic-pharmacodynamic (PK-PD) modeling in translational drug research is a promising approach that provides better understanding of drug efficacy and safety. It is applied to predict efficacy and safety in humans using in vitro bioassay and/or in vivo animal data. Current research in PK-PD modeling focuses on the development of mechanism-based models with improved extrapolation and prediction properties. A key element in mechanism-based PK-PD modeling is the explicit distinction between parameters for describing (i) drug-specific properties and (ii) biological system-specific properties. Mechanism-based PK-PD models contain specific expressions for the characterization of processes on the causal path between drug exposure and drug response. The different terms represent: target-site distribution, target binding and activation and transduction. Ultimately, mechanism-based PK-PD models will also characterize the interaction of the drug effect with disease processes and disease progression. In this review, the principles of mechanism-based PK-PD modeling are described and illustrated by recent applications.

Pharmacokinetic-pharmacodynamic modeling of the central nervous system effects of midazolam and its main metabolite α-hydroxymidazolam in healthy volunteers

The pharmacodynamics of midazolam and its main metabolite α‐hydroxymidazolam were characterized in individual subjects by use of saccadic eye movement and electroencephalographic (EEG) effect measurements. Eight healthy volunteers received 0.1 mg/kg midazolam intravenously in 15 minutes, 0.15 mg/kg α‐hydroxymidazolam intravenously in 15 minutes, 7.5 mg midazolam orally and placebo in a randomized, double‐blind, four‐way crossover experiment. Plasma concentrations of midazolam, α‐hydroxymidazolam and 4‐hydroxymidazolam were measured by gas chromatography. The amplitudes in the 11.5 to 30 Hz (beta) frequency band were used as EEG effect measure. The concentration‐effect relationships were quantified by the sigmoid maximum effect model. The median effective concentrations of midazolam and α‐hydroxymidazolam were (mean ± SE) 77 ± 15 and 98 ± 17 ng/ml, respectively, for the EEG effect measure. For peak saccadic velocity the values were 40 ± 7 ng/ml for midazolam and 49 ± 10 ng/ml for α‐hydroxymidazolam. The maximum effect values were similar for both compounds. The effects observed after oral administration of midazolam could not be predicted accurately by an additive and competitive interaction model. It seems that α‐hydroxymidazolam is highly potent with respect to the measured effects and contributes significantly to those effects of midazolam after oral administration.

Assay of Antipyrine and Its Primary Metabolites in Plasma, Saliva and Urine by High-Performance Liquid Chromatography and Some Preliminary Results in Man

A reversed phase system for the HPLC separation of antipyrine and its primary metabolites is described. Based on this system an assay procedure for antipyrine in plasma and saliva was developed with a lowest measurable concentration of 25 ng/ml and precision of +/- 3.6 and +/- 4.5%, respectively. Furthermore, assays for the parent compound, 3-hydroxymethyl-antipyrine, norantipyrine and 4-hydroxy-antipyrine in urine were developed. The lowest measurable concentration for these compounds is about 100 ng/ml except for 3-hydroxymethyl-antipyrine with a lowest measurable concentration of about 200 ng/ml. The precision was established at +/- 3.6 and +/- 5.0% for 3-hydroxymethyl-antipyrine, and antipyrine, respectively, and +/- 7.0 and +/- 3.6% for norantipyrine and 4-hydroxy-antipyrine, respectively. The method was applied to studies on antipyrine metabolism in humans. Following administration of a single dose of 500 mg antipyrine to 5 healthy volunteers, 3.3 +/- 1.2% of the dose was recovered from 48-hour hydrolyzed urine as unchanged drug, 39.7+/- 8.7% as 3-hydroxymethyl-antipyrine, 14.5 +/- 6.8% as norantipyrine and 28.5 +/- 2.2% as 4-hydroxy-antipyrine.

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.

The role of population PK-PD modelling in paediatric clinical research

Children differ from adults in their response to drugs. While this may be the result of changes in dose exposure (pharmacokinetics [PK]) and/or exposure response (pharmacodynamics [PD]) relationships, the magnitude of these changes may not be solely reflected by differences in body weight. As a consequence, dosing recommendations empirically derived from adults dosing regimens using linear extrapolations based on body weight, can result in therapeutic failure, occurrence of adverse effect or even fatalities. In order to define rational, patient-tailored dosing schemes, population PK–PD studies in children are needed. For the analysis of the data, population modelling using non-linear mixed effect modelling is the preferred tool since this approach allows for the analysis of sparse and unbalanced datasets. Additionally, it permits the exploration of the influence of different covariates such as body weight and age to explain the variability in drug response. Finally, using this approach, these PK–PD studies can be designed in the most efficient manner in order to obtain the maximum information on the PK–PD parameters with the highest precision. Once a population PK–PD model is developed, internal and external validations should be performed. If the model performs well in these validation procedures, model simulations can be used to define a dosing regimen, which in turn needs to be tested and challenged in a prospective clinical trial. This methodology will improve the efficacy/safety balance of dosing guidelines, which will be of benefit to the individual child.

Extensions to the Visual Predictive Check to facilitate model performance evaluation

The Visual Predictive Check (VPC) is a valuable and supportive instrument for evaluating model performance. However in its most commonly applied form, the method largely depends on a subjective comparison of the distribution of the simulated data with the observed data, without explicitly quantifying and relating the information in both. In recent adaptations to the VPC this drawback is taken into consideration by presenting the observed and predicted data as percentiles. In addition, in some of these adaptations the uncertainty in the predictions is represented visually. However, it is not assessed whether the expected random distribution of the observations around the predicted median trend is realised in relation to the number of observations. Moreover the influence of and the information residing in missing data at each time point is not taken into consideration. Therefore, in this investigation the VPC is extended with two methods to support a less subjective and thereby more adequate evaluation of model performance: (i) the Quantified Visual Predictive Check (QVPC) and (ii) the Bootstrap Visual Predictive Check (BVPC). The QVPC presents the distribution of the observations as a percentage, thus regardless the density of the data, above and below the predicted median at each time point, while also visualising the percentage of unavailable data. The BVPC weighs the predicted median against the 5th, 50th and 95th percentiles resulting from a bootstrap of the observed data median at each time point, while accounting for the number and the theoretical position of unavailable data. The proposed extensions to the VPC are illustrated by a pharmacokinetic simulation example and applied to a pharmacodynamic disease progression example.

Electroencephalogram effect measures and relationships between pharmacokinetics and pharmacodynamics of centrally acting drugs

SummaryElectroencephalogram (EEG) effect parameters may be useful in pharmacokinetic-pharmacodynamic modelling studies of drug effects on the central nervous system (CNS). Effect parameters derived from a quantitative analysis of the EEG appear to be perfectly suited to characterise the relationships between pharmacokinetics and pharmacodynamics of benzodiazepines and intravenous anaesthetics. EEG parameters represent many of the characteristics of ideal pharmacodynamic measures, being continuous, objective, sensitive and reproducible. These features provide the opportunity to derive concentration-effect relationships for these drugs in individuals, which yield important quantitative information on the potency and intrinsic efficacy of these drugs. The EEG techniques presented can be used to study the influences of factors such as age, disease, chronic drug use and drug interactions on the concentration-effect relationships of psychotropic drugs.An important issue is the choice of the EEG parameter to characterise the CNS effects of the compounds. More attention must be paid to evaluating the relevance of EEG parameters to the pharmacological effects of the drugs. Knowledge of the relationship between EEG effect parameters and clinical effects of drugs under different physiological and pathophysiological conditions is crucial to determining the value of EEG parameters in drug effect monitoring.Pharmacodynamic parameters derived from the concentration-EEG effect relationship may be correlated to pharmacodynamic parameters obtained from other in vitro and in vivo effect measurements. These comparisons revealed that changes in the amplitudes in the β frequency band of EEG signals is a relevant measure of pharmacological effect intensity of benzodiazepines, which reflects their affinity and intrinsic efficacy at the central γ-aminobutyric acid (GABA) benzodiazepine receptor complex. The exact EEG correlates of the anxiolytic, anticonvulsant, sedative and hypnotic actions of benzodiazepines have not yet clearly been elucidated. For intravenous anaesthetics, close correlations between the potency determined with EEG measurements and clinical measures of anaesthetic depth have been established, suggesting that, in principle, EEG parameters can adequately reflect depth of anaesthesia. However, more study is required to further substantiate these findings.

Morphine glucuronidation in preterm neonates, infants and children younger than 3 years

Background and objectiveA considerable amount of drug use in children is still unlicensed or off-label. In order to derive rational dosing schemes, the influence of aging on glucuronidation capacity in newborns, including preterms, infants and children under the age of 3 years was studied using morphine and its major metabolites as a model drug.MethodsA population pharmacokinetic model was developed with the nonlinear mixed-effects modelling software NONMEM® V, on the basis of 2159 concentrations of morphine and its glucuronides from 248 infants receiving intravenous morphine ranging in bodyweight from 500 g to 18 kg (median 2.8 kg). The model was internally validated using normalized prediction distribution errors.ResultsFormation clearances of morphine to its glucuronides and elimination clearances of the glucuronides were found to be primarily influenced by bodyweight, which was parameterized using an allometric equation with an estimated exponential scaling factor of 1.44. Additionally, a postnatal age of less than 10 days was identified as a covariate for formation clearance to the glucuronides, independent of birthweight or postmenstrual age. Distribution volumes scaled linearly with bodyweight.ConclusionsModel-based simulations show that in newborns, including preterms, infants and children under the age of 3 years, a loading dose in µg/kg and a maintenance dose expressed in µg/kg1.5/h, with a 50% reduction of the maintenance dose in newborns younger than 10 days, results in a narrow range of morphine and metabolite serum concentrations throughout the studied age range. Future pharmacodynamic investigations are needed to reveal target concentrations in this population, after which final dosing recommendations can be made.

A Mechanism-based Disease Progression Model for Comparison of Long-term Effects of Pioglitazone, Metformin and Gliclazide on Disease Processes Underlying Type 2 Diabetes Mellitus

Effective long-term treatment of Type 2 Diabetes Mellitus (T2DM) implies modification of the disease processes that cause this progressive disorder. This paper proposes a mechanism-based approach to disease progression modeling of T2DM that aims to provide the ability to describe and quantify the effects of treatment on the time-course of the progressive loss of β-cell function and insulin-sensitivity underlying T2DM. It develops a population pharmacodynamic model that incorporates mechanism-based representations of the homeostatic feedback relationships between fasting levels of plasma glucose (FPG) and fasting serum insulin (FSI), and the physiological feed-forward relationship between FPG and glycosylated hemoglobin A1c (HbA1c). This model was developed on data from two parallel one-year studies comparing the effects of pioglitazone relative to metformin or sulfonylurea treatment in 2408 treatment-naïve T2DM patients. It was found that the model provided accurate descriptions of the time-courses of FPG and HbA1c for different treatment arms. It allowed the identification of the long-term effects of different treatments on loss of β-cell function and insulin-sensitivity, independently from their immediate anti-hyperglycemic effects modeled at their specific sites of action. Hence it avoided the confounding of these effects that is inherent in point estimates of β-cell function and insulin-sensitivity such as the widely used HOMA-%B and HOMA-%S. It was also found that metformin therapy did not result in a reduction in FSI levels in conjunction with reduced FPG levels, as expected for an insulin-sensitizer, whereas pioglitazone therapy did. It is concluded that, although its current implementation leaves room for further improvement, the mechanism-based approach presented here constitutes a promising conceptual advance in the study of T2DM disease progression and disease modification.