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  • 1
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Science Ltd
    Addiction 100 (2005), S. 0 
    ISSN: 1360-0443
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Medicine , Psychology
    Notes: Aims  To describe the effect of nicotine replacement therapy (NRT) on the risk of relapse as a function of time since the quit date.Data sources  Meta-analysis of 21 published, randomized, controlled clinical trials, comparing NRT to placebo.Data extraction  A total of 6644 smokers were treated with NRT and 2766 smokers treated with placebo.Data synthesis  During treatment with the medication, NRT reduced the hazard ratio (HR) significantly compared with placebo [early HR = 0.62 (95% CI: 0.58–0.67)]. At the end of the average treatment duration (145 days), the HR was 0.81 (95% CI: 0.71–0.94), showing that the benefit was still present at this time. After stopping treatment, the HR increased progressively up to a value of 1.44 (95% CI, 1.18–1.76) showing that the risk of relapse was higher after stopping NRT than after stopping placebo. If NRT and placebo had not been stopped, the HR of smoking relapse would have been established at 0.95 (95% CI: 0.76–1.18, P = 0.64), indicating a similar risk of relapse with NRT and placebo. Moreover, the observed HR of smoking relapse was significantly higher than the expected HR of smoking relapse if NRT had been continued: the difference in HR is 1.51 (95% CI: 1.16–1.98, P 〈 0.003). This suggests that if NRT had been been continued, around 50% of relapses could have been prevented.Conclusion  The protective effect of NRT against relapse slowly decreases as a function of time. After stopping NRT, the risk of relapse increases. It may be more beneficial not to stop NRT after the usual 3–6-month treatment period but to use NRT for longer periods of time.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    [s.l.] : Nature Publishing Group
    Nature 449 (2007), S. 781-781 
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] Sir Your News story 'Long-held theory is in danger of losing its nerve' described a published criticism of work we published 25 to 26 years ago and our reply (references are in ref. 1). In it, you quote unnamed experts who maintain that much of the published work ...
    Type of Medium: Electronic Resource
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  • 3
    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.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Journal of pharmacokinetics and pharmacodynamics 7 (1979), S. 579-628 
    ISSN: 1573-8744
    Keywords: lithium therapy ; linear pharmacokinetics ; steady-state pattern ; therapeutic range ; interindividual variability ; optimization of dosage regimen ; personalized dosage schedule
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract The important problem of initiation of long-term lithium treatments tackled by means of the selection of an a prioridosage regimen based on the presumed efficacy of lithium and absence of toxicity. The pharmacokinetics of Li + ion is represented by a four-compartment open model including the supposed first-order processes for the release of the active compound from the dosage form and its absorption. Experimental protocols for measurements of serum concentrations and of urinary amounts after single and multiple dosing to healthy volunteers were derived with several oral dosage forms. Estimation of the pharmacokinetic parameters for each subject made it possible to validate the model for the various dosage forms. The interindividual variability of these parameters is taken into account by estimating the characteristics of the statistical distribution for the whole population. A dosage regimen is considered optimum when serum concentration profiles at steady state range from the threshold of efficacy (0.8 mmol/liter) to the threshold of toxicity (2.0 mmol/liter). When the number of daily intakes is fixed, the search for the optimum dose for the whole population is effected by minimizing the expected value of the random variable which characterizes the risks of excursion out of the therapeutic range. By this means universal dosages are shown to be unsatisfactory. However, certain dosage regimens individualized with respect to the renal clearance value of lithium and based on two or three daily intakes can give excellent results even when conventional dosage forms are used.
    Type of Medium: Electronic Resource
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  • 5
    ISSN: 1573-8744
    Keywords: drug–drug interactions ; NPML ; experimental design ; pharmacodynamic variability ; pharmacokinetics ; entropy ; covariate ; second stage model ; controlled trial
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract 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.
    Type of Medium: Electronic Resource
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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.
    Type of Medium: Electronic Resource
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  • 7
    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.
    Type of Medium: Electronic Resource
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  • 8
    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.
    Type of Medium: Electronic Resource
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