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

Danhof, Meindert; Jongh, Joost de; Lange, Elizabeth C. M. de; Pasqua, Oscar Della; Ploeger, Bart; Voskuyl, Rob A.

doi:10.1146/annurev.pharmtox.47.120505.105154

Cited by 226 publications

(183 citation statements)

References 94 publications

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“…This extension was successfully established as (i) all drug effects of compounds with different MoAs were adequately described, (ii) all system-specific parameters were estimated with good precision and (iii) drug-and system-specific parameters were not correlated. Distinguishing drug-from system-specific properties is essential for mechanism-based pharmacokineticpharmacodynamic modelling (Danhof et al, 2007;Ploeger et al, 2009) and enables the prediction of treatment effects to later stages of development using a translational modelling approach (Danhof et al, 2008), which is an ultimate application for the quantitative systems pharmacology model developed. The system-specific parameters of the extended CVS model were comparable with the systemspecific parameters of the basic CVS model (Snelder et al, 2013a) except for k out_TPR, which was about 10-fold higher in the extended CVS model.…”

Section: Discussionmentioning

confidence: 99%

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

Snelder

Ploeger

Luttringer

et al. 2014

British J Pharmacology

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BACKGROUND AND PURPOSEPreviously, 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 APPROACHCardiovascular 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 RESULTSThe 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 IMPLICATIONSA 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. Abbreviations amp, amplitude; BSL_CO, baseline value of cardiac output; BSL_HR, baseline value of heart rate; BSL_MAP, baseline value of mean arterial pressure; BSL_SV, baseline value of stroke volume; BSL_TPR, baseline value of total peripheral resistance; C, drug concentration in plasma; CO, cardiac output; Emax, maximum effect; FB, negative feedback of mean arterial pressure; FB0, feedback of a typical subject; FB0_MAP, exponent of the power relationship between FB and the individual BSL_MAP; HCTZ, hydrochlorothiazide; hor, horizontal displacement; HR, heart rate; Kin_HR, zero-order production rate constant of HR; Kin_SV, zero-order production rate constant of stroke volume; Kin_TPR, zero-order production rate constant of total peripheral resistance; kout_HR, first-order dissipation rate constant of HR; kout_SV, first-order dissipation rate constant of stroke volume; kout_TPR, first-order dissipation rate constant of total peripheral resistance; LVFT, left ventricular filling time; MAP, mean arterial pressure; MoA, mechanisms of action; MVOF, minimum value of the objective function;

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Section: Discussionmentioning

confidence: 99%

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

Snelder

Ploeger

Luttringer

et al. 2014

British J Pharmacology

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“…Mechanism-based PK/PD models differ from conventional PK/PD models in that they contain specific expressions to characterize, in a quantitative manner, processes in the causal path between drug administration and effect [14] . In addition, mechanism-based PK/PD models can resolve drugspecific and biological system-specific parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Because the system-specific parameters (ie, k in , k out , τ, k syn , k deg , α, and β) characterize the physiological functioning of CYP3A1/2 mRNA, protein and enzyme activity in the rat, they should remain generally consistent within the Sprague-Dawley rat population. These values of system-specific parameters can only be estimated by in vivo analysis [14] . In contrast, drugspecific parameters (ie, E max and EC 50 ) can often be predicted based on in vitro bioassays [43,44] .…”

Section: Discussionmentioning

confidence: 99%

“…The key element in mechanism-based PK/PD modeling is the explicit distinction between parameters that describe drugspecific properties and those that describe biological systemspecific properties [14] . As a result, the potential of unknown compounds to induce CYP3A1/2 could be predicted and simulated by this mechanism-based PK/PD model solely by utilizing knowledge of the in vitro potency (S max and SC 50 ) and single-dose in vivo pharmacokinetics of these compounds.…”

Section: Introductionmentioning

confidence: 99%

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A mechanism-based pharmacokinetic/pharmacodynamic model for CYP3A1/2 induction by dexamethasone in rats

Deng

et al. 2012

Acta Pharmacol Sin

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Aim: To develop a pharmacokinetic/pharmacodynamic (PK/PD) model describing the receptor/gene-mediated induction of CYP3A1/2 by dexamethasone (DEX) in rats. Methods: A group of male Sprague-Dawley rats receiving DEX (100 mg/kg, ip) were sacrificed at various time points up to 60 h posttreatment. Their blood sample and liver were collected. The plasma concentration of DEX was determined with a reverse phase HPLC method. CYP3A1/2 mRNA, protein levels and enzyme activity were measured using RT-PCR, ELISA and the testosterone substrate assay, respectively. Data analyses were performed using a first-order conditional estimate (FOCE) with INTERACTION method in NON-MEM version 7.1.2. Results: A two-compartment model with zero-order absorption was applied to describe the pharmacokinetic characteristics of DEX. Systemic clearance, the apparent volume of distribution and the duration of zero-order absorption were calculated to be 172.7 mL·kg --1 ·h -1 , 657.4 mL/kg and 10.47 h, respectively. An indirect response model with a series of transit compartments was developed to describe the induction of CYP3A1/2 via PXR transactivation by DEX. The maximum induction of CYP3A1 and CYP3A2 mRNA levels was achieved, showing nearly 21.29-and 8.67-fold increases relative to the basal levels, respectively. The CYP3A1 and CYP3A2 protein levels were increased by 8.02-fold and 2.49-fold, respectively. The total enzyme activities of CYP3A1/2 were shown to increase by up to 2.79-fold, with a lag time of 40 h from the Tmax of the DEX plasma concentration. The final PK/PD model was able to recapitulate the delayed induction of CYP3A1/2 mRNA, protein and enzyme activity by DEX. Conclusion: A mechanism-based PK/PD model was developed to characterize the complex concentration-induction response relationship between DEX and CYP3A1/2 and to resolve the drug-and system-specific PK/PD parameters for the course of induction.

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“…Such parameterization also allows one to quantify the impact of influential factors on parameter values and describe them as covariates. The incorporation of covariates into a PKPD or disease model has an important advantage in that it enhances the prediction of response for specific groups of patients [94][95][96]. In conjunction with clinical trial simulations, model-based techniques offer an excellent opportunity for the evaluation of novel therapies [97] as well as personalization of the dosing regimen for children [98].…”

Section: Understanding and Predicting Variabilitymentioning

confidence: 99%

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

Dijkman

Álvarez-Jiménez

Danhof

et al. 2016

Expert Opinion on Drug Metabolism & Toxicology

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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.

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

Cited by 226 publications

References 94 publications

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

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

A mechanism-based pharmacokinetic/pharmacodynamic model for CYP3A1/2 induction by dexamethasone in rats

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

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