Combined Analysis of Phase I and Phase II Data to Enhance the Power of Pharmacogenetic Tests

International audienceWe show through a simulation study how the joint analysis of data from phase I and phase II studies enhances the power of pharmacogenetic tests in pharmacokinetic (PK) studies. PK profiles were simulated under different designs along with 176 genetic markers. The null scenarios assumed no genetic effect, while under the alternative scenarios, drug clearance was associated to 6 genetic markers randomly sampled in each simulated dataset. We compared penalised regression Lasso and stepwise procedures to detect the associations between empirical Bayes estimates of clearance, estimated by nonlinear mixed effects models, and genetic variants. Combining data from phase I and phase II studies, even sparse, increases the power to identify the associations between genetics and PK due to the larger sample size. Design optimisation brings a further improvement, and we highlight a direct relationship between η-shrinkage and loss of genetic signal

Modélisations des relations pharmacocinétiques-pharmacodynamiques de l'effet convulsivant de la norfloxacine et étude de sa distribution centrale

PARIS-BIUP (751062107) / SudocSudocFranceF

Etude de l'intérêt de la modélisation des relations pharmacocinétique-pharmacodynamique dans la sélection de molécules candidates

PARIS-BIUP (751062107) / SudocSudocFranceF

Étude de l'action du charbon actif sur la pharmacocinétique plasmatique de la norfloxacine chez le rat

POITIERS-BU Médecine pharmacie (861942103) / SudocSudocFranceF

Description de la pharmacinétique pharmacodynamie du strontium par une approche de population

LYON1-BU Santé (693882101) / SudocSudocFranceF

Influence of covariance between random effects in design for nonlinear mixed-effect models with an illustration in pediatric pharmacokinetics.

International audienceNonlinear mixed-effect models are used increasingly during drug development. For design, an alternative to simulations is based on the Fisher information matrix. Its expression was derived using a first-order approach, was then extended to include covariance and implemented into the R function PFIM. The impact of covariance on standard errors, amount of information, and optimal designs was studied. It was also shown how standard errors can be predicted analytically within the framework of rich individual data without the model. The results were illustrated by applying this extension to the design of a pharmacokinetic study of a drug in pediatric development

Two-Stage Adaptive Designs In Nonlinear Mixed Effects Models: Application To Pharmacokinetics In Children

International audienceNonlinear mixed effects models (NLMEM) are used in pharmacokinetics to analyse concentrations of patients during drug development, particularly for pediatric studies. Approaches based on the Fisher information matrix can be used to optimize their design. Local design needs some a priori parameter values which might be difficult to guess. Therefore two-stage adaptive designs are useful to provide some flexibility. We implemented in the R function PFIM the Fisher matrix for two-stage designs in NLMEM. We evaluated, with simulations, the impact of one-stage and two-stage designs on the precision of parameter estimation when the true and a priori parameters are different

Pharmacokinetic Modeling of Free Amoxicillin Concentrations in Rat Muscle Extracellular Fluids Determined by Microdialysis

The aim of the present study was to investigate amoxicillin (AMX) distribution in muscle interstitial fluid by microdialysis in healthy, awake rats. Microdialysis probes were inserted into the jugular vein and hind leg muscle. Probe recoveries in each rat were determined by retrodialysis with cefadroxil. AMX was administered as a bolus dose of 50 mg · kg(−1), and microdialysis samples were collected during 180 min. Concentrations of unbound drug in blood and muscle were analyzed simultaneously by a population approach. Simulations were conducted using a hybrid, physiologically based pharmacokinetic model to investigate the potential impact of tissue blood flow on muscle AMX distribution. A two-compartment pharmacokinetic model described adequately the unbound amoxicillin concentration-time profiles in blood and muscle. Muscle AMX distribution equilibrium was rapidly achieved. Consequently, the best results were obtained by considering concentrations in muscle as part of the central compartment. The ratio of the concentration of unbound drug in muscle to that in blood (R(model)) was estimated to 0.80 by the model, which is close to the mean value obtained by noncompartmental data analysis (R(area) = 0.86 ± 0.29). Simulations conducted with a hybrid, physiologically based pharmacokinetic model suggest that a muscle blood flow reduction of 30% to 50%, such as could be encountered in critical care patients, has virtually no effect on muscle AMX concentration profiles. In conclusion, this study has clearly demonstrated that AMX distributes rapidly and extensively within muscle interstitial fluid, consistent with theory, and that altered muscle blood flow seems unlikely to have a major effect on these distribution characteristics