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首页> 外文期刊>Regulatory Toxicology and Pharmacology: RTP >Pharmacokinetic-pharmacodynamic models for categorical toxicity data.
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Pharmacokinetic-pharmacodynamic models for categorical toxicity data.

机译:分类毒性数据的药代动力学-药效学模型。

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摘要

We propose a pharmacokinetic-pharmacodynamic (PK/PD) model (with possibly different choices for the PD link) for categorical toxicity data analysis. This is extension of the one-comportment model that applies to toxic endpoints categorised by grades (e.g., benign, mild, severe, and very severe). The model assumes that the area under the curve (AUC) of the internal quantity of the chemical substance is the critical dose-metric that drives the acute toxic phenomenon. That model handles time-varying concentrations and takes into account follow-up time, i.e., time at which effects are observed. Moreover the model bridges mechanistically based dose-response models and standard dose-response models, retaining the advantages of both. We use Markov chain-Monte Carlo (MCMC) simulations to fit the model to mortality data for mice exposed to chlorine, rats exposed to ammonia, and categorical data (different severity levels) from acute exposures of rats and humans to hydrogen sulfide.
机译:我们提出了用于分类毒性数据分析的药代动力学-药效学(PK / PD)模型(PD链接可能具有不同的选择)。这是适用于按等级(例如,良性,轻度,严重和非常严重)分类的毒性终点的一室模型的扩展。该模型假定化学物质内部量的曲线下面积(AUC)是驱动急性毒性现象的关键剂量指标。该模型处理随时间变化的浓度并考虑了随访时间,即观察到影响的时间。此外,该模型将基于机械的剂量反应模型与标准剂量反应模型联系起来,保留了两者的优点。我们使用马尔可夫链-蒙特卡罗(MCMC)模拟将模型拟合到暴露于氯的小鼠,暴露于氨的大鼠的死亡率数据以及来自大鼠和人类急性暴露于硫化氢的分类数据(不同严重性水平)。

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