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  • Title: Population pharmacokinetics of ciprofloxacin in patients with liver impairments analyzed by NPEM2 algorithm--a retrospective study.
    Author: Terziivanov D, Atanasova I, Dimitrova V.
    Journal: Int J Clin Pharmacol Ther; 1998 Jul; 36(7):376-82. PubMed ID: 9707352.
    Abstract:
    In order to develop a population pharmacokinetic model for ciprofloxacin after single oral dosing in patients with liver impairments, a retrospective population analysis of already published data was undertaken. The purpose of the study was to compare the population model parameter estimates for ciprofloxacin obtained with the non-parametric expectation maximization (NPEM2) algorithm based on a full data set (NPEM2-FULL) with those based on a set of 3 randomly chosen time/concentrations data (NPEM2-3RPs). Parameter values generated by the standard two-stage (STS) approach using traditional data-rich situation were used as a "gold standard" for comparative purposes. There was no significant difference between parameter means at p < 0.05 for Gauss-Newton and maximum a posteriori Bayesian (MAPB) estimators. The values of k(s) (min/ml/h) as estimated by STS and NPEM2-FULL models, on the one hand, and by STS and NPEM2-3RPs population models on the other hand (0.001, 0.00095, and 0.001, respectively), were not significantly different (p = 0.1457, respectively p = 0.6276). The population models values of k(s) suggest that good approximation between ciprofloxacin renal clearance and creatinine clearance could be expected for most of the patients and support previous observations that creatinine clearance is a meaningful predictor for ciprofloxacin elimination from the body. The 3 population models estimated Vs/F (l/kg) without significant difference. The predictive performance of these population models was subsequently assessed using internal validation approach. The 3 population models demonstrated comparable accuracy and precision in Bayesian forecasting of drug plasma levels of validation group patients based on 1 random and 2 suboptimal prior drug concentrations. There was, however, a one-order of magnitude decrease in population models bias when 2 suboptimal data points were used as Bayesian priors.
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