Adjustment of endogenous concentrations in pharmacokinetic modeling.

Purpose: Estimating pharmacokinetic parameters in the presence of an endogenous concentration is not straightforward as cross-reactivity in the analytical methodology prevents differentiation between endogenous and dose-related exogenous concentrations. This article proposes a novel intuitive modeli...

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Publicado en:European Journal of Clinical Pharmacology Vol. 70; no. 12; pp. 1465 - 1471
Autores principales: Bauer, Alexander, Wolfsegger, Martin
Formato: equations & formulas research tables/charts Journal Article
Publicado: Springer Nature Dec2014
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Dec2014
      vid: 70
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      pub: Springer Nature
      place: New York, New York
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        atl: Adjustment of endogenous concentrations in pharmacokinetic modeling.
      aug:
        au:
          Bauer, Alexander
          Wolfsegger, Martin
        affil: Baxter Innovations GmbH, Vienna Austria
      sug:
        subj:
          Pharmacokinetics
          Models, Statistical
          Human
          Simulations
          Descriptive Statistics
          Measurement Error
          Bias (Research)
          Regression
          Data Analysis Software
      ab: Purpose: Estimating pharmacokinetic parameters in the presence of an endogenous concentration is not straightforward as cross-reactivity in the analytical methodology prevents differentiation between endogenous and dose-related exogenous concentrations. This article proposes a novel intuitive modeling approach which adequately adjusts for the endogenous concentration. Methods: Monte Carlo simulations were carried out based on a two-compartment population pharmacokinetic (PK) model fitted to real data following intravenous administration. A constant and a proportional error model were assumed. The performance of the novel model and the method of straightforward subtraction of the observed baseline concentration from post-dose concentrations were compared in terms of terminal half-life, area under the curve from 0 to infinity, and mean residence time. Results: Mean bias in PK parameters was up to 4.5 times better with the novel model assuming a constant error model and up to 6.5 times better assuming a proportional error model. Conclusions: The simulation study indicates that this novel modeling approach results in less biased and more accurate PK estimates than straightforward subtraction of the observed baseline concentration and overcomes the limitations of previously published approaches.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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