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  • 1
    ISSN: 1546-170X
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Medicine
    Notes: [Auszug] To the editor: It has been proposed that nuclear magnetic resonance (NMR) plasma analysis can improve lipoprotein subclass discrimination and predict coronary artery disease (CAD). In the recent paper by Kirschenlohr et al., it was concluded that proton NMR spectroscopy shows only weak ...
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 16 (1988), S. 311-327 
    ISSN: 1573-8744
    Keywords: population pharmacokinetics ; random regression ; distribution estimation ; nonparametric estimation ; maximum likelihood ; cyclosporine
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract A new method, nonparametric maximum likelihood (NPML), for statistical analysis of population kinetic data is proposed. NPML provides a discrete estimate of the whole probability density function of the pharmacokinetic parameters. This permits a straightforward derivation of usual population characteristics. To illustrate the application of the NPML method, a population analysis of cyclosporine RIA measured plasma levels in 188 bone marrow transplant patients after intravenous infusion, is presented. The capability of NPML to extract population information from sparse individual data is also outlined.
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  • 3
    ISSN: 1573-8744
    Keywords: mizolastine ; pharmacokinetics ; population analysis ; zero-order absorption ; heteroscedastic variance ; NPML ; validation ; predictive distributions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract A population analysis of the kinetics of mizolastine was performed from concentrations on 449 allergic patients, using the nonparametric maximum likelihood method (NPML). A two-compartment open model with zero-order absorption was used to describe the kinetics of mizolastine after oral administration. A heteroscedastic variance model was assumed for the error. To explain the kinetic variability, eight covariates were introduced in the analysis: gender, pharmaceutical dosage form, age, body weight, serum creatinine concentration, creatinine renal clearance, plasma levels of hepatic transaminases ASAT and ALAT. Their relationships to the kinetic parameters were studied by means of the estimated distribution of each kinetic parameter conditional on different levels of each covariate. An important interindividual kinetic variability was found for all parameters. Moreover, several kinetic parameters among which the duration of absorption were found to be influenced by pharmaceutical dosage form and gender. Body weight and creatinine renal clearance were found to have a little influence on the oral clearance and the smallest disposition rate constant. This population analysis was validated on a separate group of 247 other patients. For each observed concentration of this sample, a predictive distribution was computed using the individual covariates. Predicted concentrations and standardized prediction errors were deduced. The mean and variance of the standardized prediction errors were, respectively, 0.21 and 2.79. Moreover, in the validation sample, the predicted cumulative distribution function of each observed concentration was computed. Empirical distribution of these values was not significantly different from a uniform distribution, as expected under the assumption that the population model estimated by NPML is adequate.
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  • 4
    ISSN: 1573-8744
    Keywords: mizolastine ; noncompartmental approach ; pharmacokinetic model ; bioavailability estimation ; nonlinear regression ; heteroscedastic variance ; S-PLUS library
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract This paper presents the analysis of the kinetics of a new antihistamine, mizolastine, in 18 healthy volunteers, from concentrations measured after an intravenous infusion and two different oral administrations: tablet and capsule. Two approaches were used to analyze these data: (i) a noncompartmental approach implemented in PHARM-NCA: (ii) a compartmental modeling approach implemented in a new S-PLUS library. NLS2, 5 which allows the estimation of variance parameters simultaneously with the kinetic parameters. For the compartmental modeling approach, two-compartment open models were used. According to the Akaike criterion, the best model describing the kinetics of mizolastine after oral administration was the zero-order absorption model. The kinetic parameters obtained with PHARM-NCA and NLS2 were similar. The estimated duration of absorption was greater for the tablets than for the capsules (with means equal to 1.13 hr and 0.84 hr respectively). After an intravenous infusion, the mean estimated clearance was 4.9 L/hr, the mean λ 2 -phase apparent volume of distribution was 89.6 L and the mean terminal half-life was 12.9 hr.
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 23 (1995), S. 101-125 
    ISSN: 1573-8744
    Keywords: Bayesian designs ; Bayesian estimation ; prior distribution ; pharmacokinetics ; pharmacodynamics ; E max model ; nonlinear models
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract 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 preposterior 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 theE max 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 theE max 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 theE max 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.
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 26 (1998), S. 689-716 
    ISSN: 1573-8744
    Keywords: experimental design ; population pharmacokinetics ; D-optimality
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract 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.
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 27 (1999), S. 85-101 
    ISSN: 1573-8744
    Keywords: nortriptyline ; Bayesian design ; Bayesian estimation ; information criterion ; optimal sampling times ; therapeutic drug monitoring
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract 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.
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  • 8
    ISSN: 1573-8744
    Keywords: prediction interval ; pharmacokinetics ; population analysis ; NONMEM ; inverse regression ; immunosuppressives
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract Basiliximab is an immunosuppressant chimeric monoclonal antibody directed to the human interleukin-2 receptor α-chain used for prevention of acute rejection episodes in organ transplantation. The minimally effective serum concentration necessary to saturate receptor epitopes in kidney transplant patients is 0.2 μg/ml. To guide dose selection for Phase 3 efficacy trials, a population pharmacostatistical model was fitted to intensively sampled Phase 2 pharmacokinetic data. This served as a basis from which to examine candidate dose regimens with respect to the duration over which receptor-saturating concentrations would be achieved posttransplant. Three prediction methods were assessed: one based on simulations, and two others based on first-order approximation using either inverse regression or inversion of confidence intervals. An 80% prediction interval was generated by each method to evaluate its predictive performance against prospectively collected Phase 3 data in 39 renal transplant patients who received two injections of 20mg basiliximab, one prior to surgery and one on Day 4 posttransplant. All methods provided correct prediction of the duration of receptor-saturating concentration. As anticipated, the best performance was obtained from the simulation method which predicted 30 values in the 80% prediction interval, 19.7–52.7 days. The actually observed 80% interval from the Phase 3 data was 23.7–58.3 days.
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  • 9
    Publication Date: 2006-08-12
    Print ISSN: 0724-8741
    Electronic ISSN: 1573-904X
    Topics: Chemistry and Pharmacology
    Published by Springer
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  • 10
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