4.6 Article

Mechanistic Models Describing Active Renal Reabsorption and Secretion: A Simulation-Based Study

期刊

AAPS JOURNAL
卷 15, 期 1, 页码 278-287

出版社

SPRINGER
DOI: 10.1208/s12248-012-9437-3

关键词

kidney transport parameters; models; reabsorption; renal clearance; secretion

资金

  1. NIH [DA-023223]
  2. Roche Inc.

向作者/读者索取更多资源

The objective of the present study was to evaluate mechanistic pharmacokinetic models describing active renal secretion and reabsorption over a range of Michaelis-Menten parameter estimates and doses. Plasma concentration and urinary excretion profiles were simulated and renal clearance (CLr) was calculated for two pharmacokinetic models describing active renal reabsorption (R1/R2), two models describing active secretion (S1/S2), and a model containing both processes. A range of doses (1-1,000 mg/kg) was evaluated, and V (max) and K (m) parameter estimates were varied over a 100-fold range. Similar CLr values were predicted for reabsorption models (R1/R2) with variations in V (max) and K (m). Tubular secretion models (S1/S2) yielded similar relationships between Michaelis-Menten parameter perturbations and CLr, but the predicted CLr values were threefold higher for model S1. For both reabsorption and secretion models, the greatest changes in CLr were observed with perturbations in V (max), suggesting the need for an accurate estimate of this parameter. When intrinsic clearance was substituted for Michaelis-Menten parameters, it failed to predict similar CLr values even within the linear range. For models S1 and S2, renal secretion was predominant at low doses, whereas renal clearance was driven by fraction unbound in plasma at high doses. Simulations demonstrated the importance of Michaelis-Menten parameter estimates (especially V (max)) for determining CLr. K (m) estimates can easily be obtained directly from in vitro studies. However, additional scaling of in vitro V (max) estimates using in vitro/in vivo extrapolation methods are required to incorporate these parameters into pharmacokinetic models.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据