Stefanie Albers Population Pharmacokinetics and Dose Simulation of Carvedilol in
Pediatric Patients with Congestive Heart Failure
S. Albers (1), B. Meibohm (2), J. Barrett (3), TS. Mir (4), S. Laer (1)
(1) Clinical Pharmacy and Pharmacotherapy, University of Duesseldorf, Germany; (2) Department of Pharmaceutical Sciences, University of Tennessee, Memphis, USA, (3) Childrens Hospital of Philadelphia, Philadelphia, USA, (4) Department of Pediatric Cardiology, University of Hamburg,
Germany
Objectives: In-silico tools like population pharmacokinetic (Pop-PK) modeling and simulation play a pivotal role in developing safe and effective dosing strategies especially for pediatric patients.
Therefore, the aim of our work was to investigate the ontogeny of carvedilol pharmacokinetics in pediatric patients by Pop-PK analysis. Dose simulations were performed to investigate the
carvedilol dosing strategy for pediatric patients. The integration of these dosing regimens into clinical studies might increase the probability of success in future randomized controlled clinical studies aiming at efficacy.
Methods: Data were derived from a prospective, open-label study of carvedilol for the therapy of pediatric patients with congestive heart failure. Up to 13 plasma concentrations were determined from each patient during one dosing interval after 0.09 mg/kg QD and 0.35 mg/kg BID,
respectively, using a validated HPLC-assay. Total plasma concentrations were analysed using a nonlinear mixed-effects modelling approach (NONMEM, Version V 1.1). The population model was further used for simulations of different daily doses and dosing intervals. Target parameter for the simulations was the area under the plasma concentration time curve (AUC) as a measure of drug exposure.
Results: 480 carvedilol plasma concentrations of 41 patients (0.1 - 19.3 years; median 3.5) were included in the analysis. Carvedilol pharmacokinetics were best described by a two-compartment model with first order absorption and absorption lag. Allometric weight normalization was used for clearances and volume of distribution parameters. Additionally, a significant influence of age on clearance (CL) and central volume of distribution (V2) was found:
CL [L/h] = 38.1*((weight [kg]/13)**0.75)-((age [years]/3.5)**2.7) V2 [L] = 22.0*(weight [kg]/13)*(1-(0.13*age [years]/3.5))
Dose simulations revealed that for infants (28 days - 23 months), children (2 - 11 years) and
adolescents (12 - 15 years) daily doses of 3, 2 and 1 mg/kg, administered in two or three doses were necessary to reach an exposure (AUC) comparable to adults receiving 0.7 mg/kg/day.
Conclusions: Younger patients have to be treated with higher doses of carvedilol to reach the same exposure as adults. These results have to be considered for further randomized controlled trials investigating the efficacy of carvedilol in order to avoid ineffective drug exposures.
Poster: Applications- CVS
Kevin Krudys Can Bayes Prevent QTC-interval prolongation? A challenge beyond
random effects.
Kevin M. Krudys, Oscar Della-Pasqua
Clinical Pharmacology & Discovery Medicine, GlaxoSmithKline
Objectives: Early in the course of clinical development, it is important to be able to assess the propensity of non-antiarrhythmic drugs to prolong the QT/QTc interval. The current regulatory guidelines suggest using the largest time-matched mean difference between drug and placebo (baseline-adjusted) over the sampling interval[1], thereby neglecting any exposure-effect
relationship and underlying nonlinearity in physiological fluctuation of QT interval. Thus far, most modelling attempts used to characterise drug-induced QT interval prolongation do not account for the limitations in clinical data or disregard model parameterisation in terms of drug-specific and system-specific properties. The aim of this study is to use a Bayesian approach to characterise the exposure-effect relationship of three compounds known to prolong the QT/QTc interval. We show the advantage of this approach to avoid false positives/negatives and optimise the design of QT/QTc specific studies.
Methods: The database consisted of 4 studies of moxifloxacin (400 mg), one study of grepafloxacin (600 mg) and one study of d,l-sotalol (160mg) with a total of 453 subjects.
Population PK models were built for each compound to simulate individual concentration values at the times of QT measurements. The pharmacodynamic (PD) model describing QT interval
comprises three components: an individual correction factor for RR interval (heart rate), an oscillatory component describing the circadian variation and a truncated Emax model to capture drug effect[2]. Model building was performed in WinBUGS version 1.4. The posterior distributions provided by WinBUGS allowed for the simulation of scenarios to investigate the impact of different study designs.
Results: The PD model provided estimates of a heart rate correction factor of mean (95% credible interval) 0.30(0.28 - 0.32) and a 24 hour circadian component with amplitude 3.34(2.32 - 4.39) ms and phase 3.23(3.01 - 3.62) hrs. The estimated QT prolongation due to moxifloxacin, grepafloxacin and d,l-sotalol at the 75th percentile of the observed concentration range was 7.19(6.06 - 8.35), 19.43(15.83 - 23.13) and 13.68(11.52 - 15.94) ms, respectively. Simulations suggest that the use of a PK/PD model can establish the QT liability of a compound using considerably fewer subject and/or samples than the standard approach.
http://www.fda.gov/cder/guidance/6922fnl.pdf
[2] Bachman WJ, Gillespie WR. Truncated sigmoid Emax models: a reparameterization of the sigmoid Emax model for use with truncated PK/PD data. In American Society for Clinical Pharmacology and Therapeutics (ASCEPT) Meeting 1998.
Poster: Applications- CVS
Céline LAFFONT Population pharmacokinetic analysis of perindoprilat in
hypertensive paediatric patients
Laffont CM (1), Mentré F (2) and Foos-Gilbert E (1)
(1) Servier Research Group, Courbevoie, France (2) INSERM U738, Paris, France; University Paris 7, Paris, France
Introduction: The use of angiotensin-converting enzyme (ACE) inhibitors in the treatment of children suffering from hypertension or congestive heart failure has been widely recognized as useful. However, little is known about the pharmacokinetics of ACE inhibitors in these patients.
Objective: To develop a population pharmacokinetic model for perindoprilat in paediatric patients using both paediatric and adult data.
Material and Methods: An orodispersible formulation of perindopril (prodrug) was developed for paediatric use. This formulation was administered once per day to 60 hypertensive children and adolescents [median age (range) = 6 (2-15) years)] in a 4-month Phase II study. It was also given once per day to 24 healthy adult volunteers for one week in a Phase I study. Sparse (n=3-5) or extensive (n=12) blood sampling was performed at steady-state in the Phase II and Phase I studies, respectively. Perindoprilat plasma concentrations in paediatric patients (254 observations) were analysed together with adult data (286 observations) in order to better evaluate the impact of body weight on perindoprilat pharmacokinetic parameters. The population pharmacokinetic analysis was performed using NONMEM V.
Results: Perindoprilat plasma concentrations were analysed as the sum of unbound perindoprilat concentrations and concentrations of perindoprilat bound to circulating ACE. Unbound
concentrations were best described by a one-compartment model with first-order formation and lag-time. Perindoprilat binding to ACE was modelled using a Michaelis-Menten relationship, assuming a single saturable binding site. Since it was not possible to estimate perindoprilat binding
parameters from the present data, those were fixed to parameter estimates found in a previous population analysis in adults, for which adequate blood sampling was performed. Perindoprilat apparent clearance (CL/F) and volume of distribution (V/F) were related to body weight using general allometric equations (power model). Estimation of power model parameters revealed that CL/F and V/F increased proportionally with body weight. Creatinine clearance was estimated from serum creatinine using Schwartz formula. It was found to significantly influence CL/F and V/F,
Poster: Applications- CVS
Divya Menon Pharmacokinetics / Pharmacodynamics of Intravenous Bolus
Nicardipine in Adults Undergoing Cardiovascular Surgery
Divya Menon (1), John T. Mondick (1), Bhuvana Jayaraman (1), Albert T. Cheung (2), Jeffrey S.
Barrett (1)
(1) Laboratory for Applied PK/PD, Division of Clinical Pharmacology and Therapeutics, The Children’s Hospital of Philadelphia, PA. (2) Department of Anesthesia, University of Pennsylvania,
Philadelphia, PA.
Background: Nicardipine is a dihydropyridine calcium channel antagonist with selectivity for the coronary and peripheral vasculature, and minimal negative inotropic effects. The antihypertensive effect of nicardipine is characterized by a rapid onset of action and a short duration of effect, making it suitable for use in perioperative conditions. IV bolus administration of nicardipine in anesthetized patients has been shown to result in an acute decrease in arterial blood pressure without reflex tachycardia. Previous studies have concluded that nicardipine obeys linear pharmacokinetics.
Purpose: The objectives of this investigation were the following: (1) Construct a population pharmacokinetic / pharmacodynamic (PK/PD) model to describe the effect of an intravenous bolus nicardipine dose on the mean arterial blood pressure in anesthetized patients undergoing
cardiovascular surgery. (2) Identify covariates that are predictors of variability. (3) Utilize the population PK/PD model to plan a multiple dose trial from which drug labeling can be defined.
Methods: A population PK/PD model was constructed from data collected from 40 anesthetized patients undergoing cardiovascular surgery, randomized to receive 0.25, 0.5, 1 or 2 mg of
nicardipine. Nicardipine was administered directly into the right atrial port of the pulmonary artery catheter. PK samples were collected at predose, 2, 5, 7, 10, 20, 30, 90, 120, 180 and 240 min.
Systolic and diastolic blood pressure was measured predose and every 15 seconds for up to 240s after nicardipine administration and every 60 s thereafter for up to 30 minutes. The data was analyzed using nonlinear mixed effects modeling with NONMEM. The predictive performance of the final model was evaluated by the predictive check method.
Results: The pharmacokinetics of nicardipine following IV bolus administration was well described by a two compartment model. The effects of nicardipine on mean arterial pressure were modeled using a slow receptor-binding model. The population mean (%CV) PK parameter estimates for CL, V1, Q and V2 were 23.6 (27.2) L/h, 0.22 (30.8) L, 10.3 (30.7) L/h and 3.08 (23.1) L. A graded increase in clearance was observed with increasing doses.
Conclusions: Contrary to the results reported following administration of an infusion, an effect mediated increase in the clearance of nicardipine was observed. We are exploring design elements in the planned multiple dose trial to aid us in discriminating equally plausible models. All possible models and approaches are discussed.
Reference:
Cheung et al, Anesth Analg 1999, 89:1116-23
Poster: Applications- Endocrine
Silke Dittberner Determination of the absolute bioavailability of BI 1356, a