Biomedical subjects

Y Merlé

Publications and source records attributed to Y Merlé.

13 recordsLinked to original sources

PubMed2002

Nonparametric population pharmacokinetic analysis of amikacin in neonates, infants, and children.

The therapeutic and toxic effects of amikacin are known to depend on its concentration in plasma, but the pharmacokinetics of this drug in neonates, infants, and children and the influences of clinical and biological variables have been only partially assessed. Therapeutic drug monitoring data collected from 155 patients (49 neonates, 77 infants, and 29 children) receiving amikacin were analyzed by a nonparametric population-based approach, the nonparametric maximum-likelihood method. We assessed the effects of gestational and postnatal age, weight, Apgar score, and plasma creatinine and urea concentrations on pharmacokinetic parameters. There is no specific formulation of amikacin for neonates and infants. We therefore used an error model to account for errors due to dilution during preparation of the infusion. The covariates that reduced the variance of clearance from plasma and the volume of distribution by more than 10% were postnatal age (43 and 28%, respectively) and body weight (30.4 and 17.4%, respectively). The expected reduction of clearance was about 10% for the plasma creatinine concentration. The other covariates studied (Apgar scores, plasma urea concentration, gestational age, sex) were found to have little effect. Simulations showed that a smaller percentage of patients had a maximum concentration in plasma/MIC ratio greater than 8 with a regimen of 7.5 mg/kg of body weight twice daily than with a regimen of 15 mg/kg once a day for MICs of 1 to 8 mg/liter.

Age Factors↗

PubMed2001

Impact of pharmacokinetic-pharmacodynamic model linearization on the accuracy of population information matrix and optimal design.

Influence of experimental design on hyperparameter estimates precision when performing a population pharmacokinetic-pharmacodynamic (PK-PD) analysis has been shown by several studies and various approaches have been proposed for optimizing or evaluating such designs. Some of these methods rely on the optimization of a suitable scalar function of the population information matrix. Unfortunately for the nonlinear models encountered in pharmacokinetics or pharmacodynamics the latter is particularly difficult to evaluate. Under some assumptions and after a linearization of the PK-PD model a closed form of this matrix can be obtained which considerably simplifies its calculation but leads to an approximation. The aim of this paper is to evaluate the quality of the latter and its potential impact, when comparing or optimizing population designs and to relate it to Bates and Watts curvature measures. Two models commonly used in PK-PD were considered and nominal hyperparameter values when chosen for each one. Several population designs were studied and the associated population information matrix was computed for each using the approximate procedure and also using a reference method. Design optimizations were calculated under constraints for each model from the reference and approximate population information matrix. Nonlinearity curvatures were also computed for every model and design. The impact of model linearization when calculating the population information matrix was then examined in terms of lower bound accuracies on the hyperparameter estimates, design criterion variation, as well as D-optimal population designs, these results being related to nonlinearity curvature measures. Our results emphasize the influence of the parameter effects curvature when deriving the lower bounds of the hyperparameter estimates precision for a given design from the approximate population information matrix especially for hyperparameters quantifying the PK-PD interindividual variability. No discrepancies were detected between the population D-optimal designs obtained from the approximate and reference matrix despite some minor differences in criterion variation with respect to the design. More pronounced differences were, however, observed when comparing the amplitudes of criterion variation which can lead to errors when calculating design efficiencies. From a practical point of view, a strategy easily applicable by the pharmacokineticist for avoiding such problems in the context of population design optimization or comparison is then proposed.

Humans↗

PubMed2000

Population pharmacokinetic analysis of netilmicin in neonates and infants with use of a nonparametric method.

BACKGROUND: Although the therapeutic and toxic effects of netilmicin are related to its plasma concentration, its pharmacokinetics in neonates and infants and the influence of clinical and biological variables have been only partially assessed. METHODS: Therapeutic drug monitoring data collected from 186 neonates and 95 infants receiving netilmicin were analyzed with a nonparametric population approach. The influence of gestational and postnatal age, weight, Apgar score, and creatinine and urea plasma concentrations on the pharmacokinetic parameters was assessed. The neonate and infant groups were each randomly divided into a learning sample and a validation sample. The population analysis was performed on each learning subgroup with the nonparametric maximum likelihood (NPML) method. In the validation group, the data were used to assess the concentration predictability. Because there is no specific netilmicin formulation for neonates and infants, an error model was proposed to account for errors attributable to dilution processes when preparing the infusion. RESULTS: In neonates, the covariates that reduced expected variance of plasma clearance by more than 10% were postnatal age, body weight, and plasma creatinine, as well as plasma urea and creatinine in infants. Body weight and sex played a significant role in explaining the variability of the volume of distribution. The accuracy of the concentration predictability assessed in the validation samples was satisfactory, and no significant bias was found. CONCLUSION: These findings help explain the large interindividual variability of the pharmacokinetics of netilmicin and the influence of the clinical and laboratory covariates in neonates and infants.

Age Factors↗

PubMed1999

Optimal sampling times for Bayesian estimation of the pharmacokinetic parameters of nortriptyline during therapeutic drug monitoring.

Sampling times for Bayesian estimation of the pharmacokinetic parameters of an antidepressant drug, nortriptyline, during its therapeutic drug monitoring were optimized. Our attention was focused on designs including a limited number of measurements: one, two, and three sample designs in which sampling times had to be chosen between 0 and 24 hr after the last intake of a test-dose study. The optimization was conducted in four groups of patients defined by their gender and the administration or not of concomitant drugs inhibiting the metabolism of nortriptyline. The Bayesian design criterion was defined as the expected information provided by an experiment. A stochastic approximation algorithm, the Kiefer-Wolfowitz algorithm, was used for the criterion maximization under experimental constraints. Results showed that optimal Bayesian sampling times differ between patients in monotherapy and polytherapy. For one-sample designs the measurements have to be performed either at the lower (0 hr) or at the upper (24 hr) bound of the admissible interval. Replications were often found for 2- and 3-point designs. Other sampling designs can lead to criterion close to the optimum and can therefore be performed without great loss of information. In contrast, we found that several designs lead to low values of the information criterion, which justifies the approach.

Absorption↗

PubMed1999

Drug-drug pharmacodynamic interaction detection by a nonparametric population approach. Influence of design and of interindividual variability.

Population approaches are appealing methods for detecting then assessing drug-drug interactions mainly because they can cope with sparse data and quantify the interindividual pharmacokinetic (PK) and pharmacodynamic (PD) variability. Unfortunately these methods sometime fail to detect interactions expected on biochemical and/or pharmacological basis and the reasons of these false negatives are somewhat unclear. The aim of this paper is firstly to propose a strategy to detect and assess PD drug-drug interactions when performing the analysis with a nonparametric population approach, then to evaluate the influence of some design variates (i.e., number of subjects, individual measurements) and of the PD interindividual variability level on the performances of the suggested strategy. Two interacting drugs A and B are considered, the drug B being supposed to exhibit by itself a pharmacological action of no interest in this work but increasing the A effect. Concentrations of A and B after concomitant administration are simulated as well as the effect under various combinations of design variates and PD variability levels in the context of a controlled trial. Replications of simulated data are then analyzed by the NPML method, the concentration of the drug B being included as a covariate. In a first step, no model relating the latter to each PD parameter is specified and the NPML results are then proceeded graphically, and also by examining the expected reductions of variance and entropy of the estimated PD parameter distribution provided by the covariate. In a further step, a simple second stage model suggested by the graphic approach is introduced, the fixed effect and its associated variance are estimated and a statistical test is then performed to compare this fixed effect to a given value. The performances of our strategy are also compared to those of a non-population-based approach method commonly used for detecting interactions. Our results illustrate the relevance of our strategy in a case where the concentration of one of the two drugs can be included as a covariate and show that an existing interaction can be detected more often than with a usual approach. The prominent role of the interindividual PD variability level and of the two controlled factors is also shown.

Algorithms↗

PubMed1998

Robust optimal design for the estimation of hyperparameters in population pharmacokinetics.

The expectation of the determinant of the inverse of the population Fisher information matrix is proposed as a criterion to evaluate and optimize designs for the estimation of population pharmacokinetic (PK) parameters. Given a PK model, a measurement error model, a parametric distribution of the parameters and a prior distribution representing the belief about the hyperparameters to be estimated, the EID criterion is minimized in order to find the optimal population design. In this approach, a group is defined as a number of subjects to whom the same sampling schedule (i.e., the number of samples and their timing) is applied. The constraints, which are defined a priori, are the number of groups, the size of each group and the number of samples per subject in each group. The goal of the optimization is to determine the optimal sampling times in each group. This criterion is applied to a one-compartment open model with first-order absorption. The error model is either homoscedastic or heteroscedastic with constant coefficient of variation. Individual parameters are assumed to arise from a lognormal distribution with mean vector M and covariance matrix C. Uncertainties about the M and C are accounted for by a prior distribution which is normal for M and Wishart for C. Sampling times are optimized by using a stochastic gradient algorithm. Influence of the number of different sampling schemes, the number of subjects per sampling schedule, the number of samples per subject in each sampling scheme, the uncertainties on M and C and the assumption about the error model and the dose have been investigated.

Humans↗

PubMed1997

Stochastic optimization algorithms of a Bayesian design criterion for Bayesian parameter estimation of nonlinear regression models: application in pharmacokinetics.

This article proposes three stochastic algorithms to optimize a Bayesian design criterion for Bayesian estimation of the parameters of nonlinear regression models; this criterion is the information expected from an experiment. The first algorithm is based on a stochastic version of the simplex with an adaptive sampling procedure. The others are stochastic approximation algorithms: the Kiefer-Wolfowitz and the pseudogradient algorithms. We first present the information criterion and the optimization algorithms. The efficiency of each algorithm for optimizing this Bayesian design criterion is then assessed by a simulation study for a nonlinear model assuming a discrete prior distribution. An application for designing an experiment to estimate the kinetics of radioiodine thyroid uptake is then proposed.

Algorithms↗

PubMed1997

Population pharmacokinetics of clozapine evaluated with the nonparametric maximum likelihood method.

AIMS: To evaluate the distribution of population kinetic parameters for clozapine and their relationship to age and gender in patients on continuous treatment with the drug. METHODS: Retrospective therapeutic drug monitoring data (391 samples from 241 patients) were evaluated using the nonparametric maximum likelihood method. Patients treated concomitantly with drugs known to interact with clozapine were not included. The distribution of clozapine clearance was compared with the distribution of the activity of the drug metabolic enzyme CYP1A2 found in other populations, as recent studies indicate that CYP1A2 is a major determinant for clozapine elimination. The kinetic linearity for clozapine was studied in 41 patients who each provided data from more than one dose level. RESULTS: Clozapine clearance was highly variable in the population and skewed towards high values. Men had higher clearances CL/F (median with 25% and 75% quartiles 38.2 (22.0, 60.0) vs 28.3 (15.2, 48.6) l h(-1)) and a larger volume of distribution V/F (694 (224, 970) vs 401 (189, 932) l) than women. Clearance did not decrease with age in any gender. Clozapine clearance was similarly distributed as the indices of CYP1A2-activity found in other populations by other authors. Evidence of nonlinear kinetics was not found. CONCLUSION: The large kinetic variability for clozapine found in this study implies that the dose of clozapine needs to be individualised over a wide dose range. The similarity of the distribution of clozapine clearance in this study and the CYP1A2-activity in other populations support the assumption that CYP1A2 is a major determinant for clozapine elimination.

Administration, Oral↗

PubMed1995

Bayesian design criteria: computation, comparison, and application to a pharmacokinetic and a pharmacodynamic model.

In this paper 3 criteria to design experiments for Bayesian estimation of the parameters of nonlinear models with respect to their parameters, when a prior distribution is available, are presented: the determinant of the Bayesian information matrix, the determinant of the pre-posterior covariance matrix, and the expected information provided by an experiment. A procedure to simplify the computation of these criteria is proposed in the case of continuous prior distributions and is compared with the criterion obtained from a linearization of the model about the mean of the prior distribution for the parameters. This procedure is applied to two models commonly encountered in the area of pharmacokinetics and pharmacodynamics: the one-compartment open model with bolus intravenous single-dose injection and the Emax model. They both involve two parameters. Additive as well as multiplicative gaussian measurement errors are considered with normal prior distributions. Various combinations of the variances of the prior distribution and of the measurement error are studied. Our attention is restricted to designs with limited numbers of measurements (1 or 2 measurements). This situation often occurs in practice when Bayesian estimation is performed. The optimal Bayesian designs that result vary with the variances of the parameter distribution and with the measurement error. The two-point optimal designs sometimes differ from the D-optimal designs for the mean of the prior distribution and may consist of replicating measurements. For the studied cases, the determinant of the Bayesian information matrix and its linearized form lead to the same optimal designs. In some cases, the pre-posterior covariance matrix can be far from its lower bound, namely, the inverse of the Bayesian information matrix, especially for the Emax model and a multiplicative measurement error. The expected information provided by the experiment and the determinant of the pre-posterior covariance matrix generally lead to the same designs except for the Emax model and the multiplicative measurement error. Results show that these criteria can be easily computed and that they could be incorporated in modules for designing experiments.

Bayes Theorem↗

PubMed1995

Critical influence of resistance to streptogramin B-type antibiotics on activity of RP 59500 (quinupristin-dalfopristin) in experimental endocarditis due to Staphylococcus aureus.

In order to determine the microbiological and pharmacokinetic parameters that best predicted the in vivo antistaphylococcal activity of the streptogramin RP 59500 (quinupristin-dalfopristin), we evaluated the activity in rabbit aortic endocarditis of three regimens of quinupristin-dalfopristin against five strains of Staphylococcus aureus with various streptogramin B-type antibiotic resistance phenotypes and susceptible to streptogramin A-type antibiotics. Quinupristin-dalfopristin was as active as vancomycin against three strains that were susceptible to its streptogramin B component quinupristin, including one strain that was inducibly resistant to erythromycin, but had a significantly decreased activity against two strains that were resistant to quinupristin, for all quinupristin-dalfopristin regimens tested (P < 0.05). The area under the concentration-time curve for quinupristin-dalfopristin in plasma divided by the MIC of quinupristin was the only parameter retained by multilinear regression that predicted the in vivo activity of quinupristin-dalfopristin (P = 0.0001), emphasizing the importance of determining the susceptibility to quinupristin in order to predict the in vivo activity of quinupristin-dalfopristin against S. aureus.

Animals↗

PubMed1994

Designing an optimal experiment for Bayesian estimation: application to the kinetics of iodine thyroid uptake.

We consider the problem of designing an optimal experiment for Bayesian estimation of the parameters of a non-linear model. When their distribution is known, the Bayesian approach allows individual estimation from a small number of measurements; the design determines the accuracy of the estimates. We propose to optimize this design by maximizing a general criterion: the expectation of the information supplied by the experiment. This approach is applied to optimize the two sampling times for Bayesian estimation of the kinetics of radioiodine thyroid uptake from an estimated non-parametric prior distribution.

Bayes Theorem↗

PubMed1994

Population pharmacokinetics of nortriptyline during monotherapy and during concomitant treatment with drugs that inhibit CYP2D6--an evaluation with the nonparametric maximum likelihood method.

Therapeutic drug monitoring data for nortriptyline (674 analyses from 578 patients) were evaluated with the nonparametric maximum likelihood (NPML) method in order to determine the population kinetic parameters of this drug and their relation to age, body weight and duration of treatment. Clearance of nortriptyline during monotherapy exhibited a large interindividual variability and a skewed distribution. A small, separate fraction with a very high clearance, constituting between 0.5% and 2% of the population, was seen in both men and women. This may be explained by the recent discovery of subjects with multiple copies of the gene encoding the cytochrome-P450-enzyme CYP2D6, which catalyses the hydroxylation of nortriptyline. However, erratic compliance with the prescription may also add to this finding. A separate distribution of low clearance values with a frequency corresponding to that of poor metabolizers of CYP2D6 (circa 7% in Caucasian populations) could not be detected. Concomitant therapy with drugs that inhibit CYP2D6 resulted in a major increase in the plasma nortriptyline concentrations. This was caused by a decrease in nortriptyline clearance, whereas the volume of distribution was unchanged. The demographic factors age and body weight had a minor influence on the clearance of nortriptyline which was also unaffected by the duration of treatment.

Administration, Oral↗

PubMed1993

Computer-assisted individual estimation of radioiodine thyroid uptake in Grave's disease.

A computer-assisted Bayesian individual estimation of radioiodine thyroid uptake kinetics for patients suffering from Grave's disease is proposed. The program provides a fast computation of the activity to be administered to a given patient to achieve a target thyroid absorbed dose. This determination relies upon the patient biological covariates and upon a small number of measurements performed during a preliminar kinetic study of radioiodine thyroid uptake. Our results indicate that a two-sample Bayesian approach is reliable when external thyroid counts are performed at 2 h and 168 h after a test dose and has advantages over conventional kinetic experiments in terms of patient acceptability. This method is implemented on widespread computers and interfaced with a patient database. An interactive user interface with in-line data checking is provided. The program could be also a tool to better study the relationship between the absorbed dose and the clinical effect.

Adult↗