Piet H. van der Graaf

Systems Pharmacology: Bridging Systems Biology and Pharmacokinetics-Pharmacodynamics (PKPD) in Drug Discovery and Development

ABSTRACTMechanistic PKPD models are now advocated not only by academic and industrial researchers, but also by regulators. A recent development in this area is based on the growing realisation that innovation could be dramatically catalysed by creating synergy at the interface between Systems Biology and PKPD, two disciplines which until now have largely existed in ‘parallel universes’ with a limited track record of impactful collaboration. This has led to the emergence of systems pharmacology. Broadly speaking, this is the quantitative analysis of the dynamic interactions between drug(s) and a biological system to understand the behaviour of the system as a whole, as opposed to the behaviour of its individual constituents; thus, it has become the interface between PKPD and systems biology. It applies the concepts of Systems Engineering, Systems Biology, and PKPD to the study of complex biological systems through iteration between computational and/or mathematical modelling and experimentation. Application of systems pharmacology can now impact across all stages of drug research and development, ranging from very early discovery programs to large-scale Phase 3/4 patient studies, and has the potential to become an integral component of a new ‘enhanced quantitative drug discovery and development’ (EQD3) R&D paradigm.

Drug Disposition in Obesity: Toward Evidence-Based Dosing

Obesity and morbid obesity are associated with many physiological changes affecting pharmacokinetics, such as increased blood volume, cardiac output, splanchnic blood flow, and hepatic blood flow. In obesity, drug absorption appears unaltered, although recent evidence suggests that this conclusion may be premature. Volume of distribution may vary largely, but the magnitude and direction of changes seem difficult to predict, with extrapolation on the basis of total body weight being the best approach to date. Changes in clearance may be smaller than in distribution, whereas there is growing evidence that the influence of obesity on clearance can be predicted on the basis of reported changes in the metabolic or elimination pathways involved. For obese children, we propose two methods to distinguish between developmental and obesity-related changes. Future research should focus on the characterization of physiological concepts to predict the optimal dose for each drug in the obese population.

In vivo Target Residence Time and Kinetic Selectivity: The Association Rate Constant as Determinant

It is generally accepted that, in conjunction with pharmacokinetics, the first-order rate constant of target dissociation is a major determinant of the time course and duration of in vivo target occupancy. Here we show that the second-order rate constant of target association can be equally important. On the basis of the commonly used mathematical models for drug-target binding, it is shown that a high target association rate constant can increase the (local) concentration of the drug, which decreases the rate of decline of target occupancy. The increased drug concentration can also lead to increased off-target binding and decreased selectivity. Therefore, the kinetics of both target association and dissociation need to be taken into account in the selection of drug candidates with optimal pharmacodynamic properties.

Pharmacokinetic-Pharmacodynamic Modeling of Buspirone and Its Metabolite 1-(2-Pyrimidinyl)-piperazine in Rats

The objective of this investigation was to compare the in vivo potency and intrinsic activity of buspirone and its metabolite 1-(2-pyrimidinyl)-piperazine (1-PP) in rats by pharmacokinetic-pharmacodynamic modeling. Following intravenous administration of buspirone (5 or 15 mg/kg in 15 min) or 1-PP (10 mg/kg in 15 min), the time course of the concentrations in blood were determined in conjunction with the effect on body temperature. The pharmacokinetics of buspirone and 1-PP were analyzed based on a two-compartment model with metabolite formation. Differences in the pharmacokinetics of buspirone and 1-PP were observed with values for clearance of 13.1 and 8.2 ml/min and for terminal elimination half-life of 25 and 79 min, respectively. At least 26% of the administered dose of buspirone was converted into 1-PP. Complex hypothermic effects versus time profiles were observed, which were successfully analyzed on the basis of a physiological indirect response model with set-point control. Both buspirone and 1-PP behaved as partial agonists relative to R-(+)-8-hydroxy-2-(di-n-propylamino)tetralin (R-8-OH-DPAT) with values of the intrinsic activity of 0.465 and 0.312, respectively. Differences in the potency were observed with values of 17.6 and 304 ng/ml for buspirone and 1-PP, respectively. The results of this analysis show that buspirone and 1-PP behave as partial 5-hydroxytryptamine1A agonists in vivo and that following intravenous administration the amount of 1-PP formed is too small to contribute to the hypothermic effect.

Pharmacokinetic-Pharmacodynamic Analysis of the EEG Effect of Alfentanil in Rats Followingβ-Funaltrexamine-Induced μOpioid Receptor “Knockdown”In Vivo

AbstractPurpose. The objective of this investigation was to determine theinfluence of pre-treatment with the irreversible μ-opioid receptorantagonist β-funaltrexamine (β-FNA) on thepharmacokinetic-pharmacodynamic (PK/PD) relationship of alfentanil in rats.nMethods. The PK/PD correlation of alfentanil (2 mg.kg−1intravenously in 20 min) was determined in chronically instrumented ratsusing amplitudes in the 0.5–4.5 Hz frequency band of the EEG aspharmacodynamic endpoint. β-FNA was administered intravenously(10 mg.kg−1) either 35 min or 24 h prior to the PK/PD experiments.nResults. Pre-treatment with β-FNA had no influence on thepharmacokinetics of alfentanil. The in vivo concentration-EEG effectrelationships, however, were steeper and shifted towards higher concentrationswith no difference between the 35-min and the 24-h pre-treatmentgroups. Analysis of the data on basis of the operational model agonismrevealed that the observed changes could be explained by a 70–80%reduction in alfentanil efficacy in β-FNA pre-treated rats. This isconsistent with results from an in vitro receptor bioassay showing a40–60% reduction in the number of specific μ-opioid binding sites inthe brain.nConclusions. This investigation confirms the validity of a previouslypostulated mechanism-based PK/PD model for the effect of syntheticopiates in rats.

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients’ (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.

Towards integrative systems pharmacology models in oncology drug development.

Quantitative systems pharmacology (QSP) modeling represents an emerging area of value to further streamline knowledge integration and to better inform decision making processes in drug development. QSP models reside at the interface between systems biology models and pharmacological models, yet their concrete implementation still needs to be established further. This review outlines key modeling techniques in both of these areas and to subsequently discuss challenges and opportunities for further integration, in oncology drug development.

Mechanism-based modeling of functional adaptation upon chronic treatment with midazolam.

AbstractPurpose. A mechanism-based model is applied to analyse adaptivechanges in the pharmacodynamics of benzodiazepines upon chronictreatment in rats.nMethods. The pharmacodynamics of midazolam was studied in ratswhich received a constant rate infusion of the drug for 14 days, resultingin a steady-state concentration of 102 ± 8 ng·ml−1. Vehicle treated ratswere used as controls. Concentration-EEG effect data were analysed onbasis of the operational model of agonism. The results were comparedto data obtained in vitro in a brain synaptoneurosomal preparation.nResults. The relationship between midazolam concentration and EEGeffect was non-linear. In midazolam pre-treated rats the maximum EEGeffect was reduced by 51 ± 23 μV from the original value of 109 ±15 μV in vehicle treated group. Analysis of this change on basis ofthe operational model of agonism showed that it can be explained bya change in the parameter tissue maximum (Em) rather than efficacy(τ). In the in vitro studies no changes in density, affinity or functionalityof the benzodiazepine receptor were observed.nConclusions. It is concluded that the observed changes in theconcentration-EEG effect relationship of midazolam upon chronic treatmentare unrelated to changes in benzodiazepine receptor function.

Integrating clinical metabolomics-based biomarker discovery and clinical pharmacology to enable precision medicine

Novel developments in biomarkers discovery are essential in modern health care, notably in treatment individualization and precision medicine. Clinical metabolomics, which aims to identify small molecule metabolites present in patient-derived samples, has attracted much attention to support discovery of novel biomarkers. However, the step from discriminatory features of disease states towards biomarkers that can truly individualize treatments is challenging. Biomarkers used for treatment individualization can either be dynamic or static prognostic biomarkers. Dynamic biomarkers are relevant for describing the clinical response, including dynamical disease progression and associated treatment response. Static (prognostic) biomarkers do not describe but rather predict a clinical response, and typically reflect aspects of the physiological state of a patient related to drug treatment response or disease progression dynamics. Pharmacokinetic-pharmacodynamic (PK-PD) modeling represents an established approach for drug treatment individualization based on drug exposure or treatment response biomarkers, as well as for the description of disease progression dynamics. Here, we discuss how novel treatment individualization biomarkers can be identified using a clinical metabolomics-based approach, and how concepts inspired from the field of PK-PD modeling can be integrated in this process in order to increase the clinical relevance of identified biomarkers and precision medicine.

Pooled population pharmacokinetic model of imipenem in plasma and the lung epithelial lining fluid

AIMSnSeveral clinical trials have confirmed the therapeutic benefit of imipenem for treatment of lung infections. There is however no knowledge of the penetration of imipenem into the lung epithelial lining fluid (ELF), the site of action relevant for lung infections. Furthermore, although the plasma pharmacokinetics (PK) of imipenem has been widely studied, most studies have been based on selected patient groups. The aim of this analysis was to characterize imipenem plasma PK across populations and to quantify imipenem ELF penetration.nnnMETHODSnA population model for imipenem plasma PK was developed using data obtained from healthy volunteers, elderly subjects and subjects with renal impairment, in order to identify predictors for inter-individual variability (IIV) of imipenem PK. Subsequently, a clinical study which measured plasma and ELF concentrations of imipenem was included in order to quantify lung penetration.nnnRESULTSnA two compartmental model best described the plasma PK of imipenem. Creatinine clearance and body weight were included as subject characteristics predictive for IIV on clearance. Typical estimates for clearance, central and peripheral volume, and inter-compartmental clearance were 11.5 lxa0h(-1) , 9.37 l, 6.41 l, 13.7 lxa0h(-1) , respectively (relative standard error (RSE) <8%). The distribution of imipenem into ELF was described using a time-independent penetration coefficient of 0.44 (RSE 14%).nnnCONCLUSIONnThe identified lung penetration coefficient confirms the clinical relevance of imipenem for treatment of lung infections, while the population PK model provided insights into predictors of IIV for imipenem PK and may be of relevance to support dose optimization in various subject groups.