Background and objective

Phenytoin is extensively protein bound with a narrow therapeutic range. The unbound phenytoin is pharmacologically active, but total concentrations are routinely measured in clinical practice. The relationship between free and total phenytoin has been described by various binding models with inconsistent findings. Systematic comparison of these binding models in a single experimental setting is warranted to determine the optimal binding behaviors.

Methods

Non-linear mixed-effects modeling was conducted on retrospectively collected data (n = 37 adults receiving oral or intravenous phenytoin) using a stochastic approximation expectation–maximization algorithm in MonolixSuite-2019R2. The optimal base structural model was initially developed and utilized to compare four binding models: Winter–Tozer, linear binding, non-linear single-binding site, and non-linear multiple-binding site. Each binding model was subjected to error and covariate modeling. The final model was evaluated using relative standard errors (RSEs), goodness-of-fit plots, visual predictive check, and bootstrapping.

Results

A one-compartment, first-order absorption, Michaelis–Menten elimination, and linear protein-binding model best described the population pharmacokinetics of free phenytoin at typical clinical concentrations. The non-linear single-binding-site model also adequately described phenytoin binding but generated larger RSEs. The non-linear multiple-binding-site model performed the worst, with no identified covariates. The optimal linear binding model suggested a relatively high binding capacity using a single albumin site. Covariate modeling indicated a positive relationship between albumin concentration and the binding proportionality constant.

Conclusions

The linear binding model best described the population pharmacokinetics of unbound phenytoin in adult subjects and may be used to improve the prediction of free phenytoin concentrations.

中文翻译：

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使用非线性混合效应模型比较四种蛋白质结合模型，以表征成人患者中未结合苯妥英的药代动力学

 背景和目标

苯妥英与蛋白质广泛结合，治疗范围较窄。未结合的苯妥英具有药理学活性，但在临床实践中常规测量总浓度。游离苯妥英和总苯妥英之间的关系已通过各种结合模型进行了描述，但结果不一致。有必要在单个实验环境中对这些结合模型进行系统比较，以确定最佳的结合行为。

 方法

使用 MonolixSuite-2019R2 中的随机近似期望最大化算法对回顾性收集的数据（ n = 37 名接受口服或静脉注射苯妥英的成人）进行非线性混合效应建模。最初开发了最佳基础结构模型并用于比较四种结合模型：Winter-Tozer、线性结合、非线性单结合位点和非线性多结合位点。每个结合模型都经过误差和协变量建模。使用相对标准误差 (RSE)、拟合优度图、视觉预测检查和引导法对最终模型进行评估。

 结果

单室、一级吸收、米氏消除和线性蛋白质结合模型最好地描述了游离苯妥英在典型临床浓度下的群体药代动力学。非线性单结合位点模型也充分描述了苯妥英结合，但产生了更大的 RSE。非线性多结合位点模型表现最差，没有确定的协变量。最佳线性结合模型表明使用单个白蛋白位点具有相对较高的结合能力。协变量模型表明白蛋白浓度与结合比例常数之间存在正相关关系。

 结论

线性结合模型最好地描述了成年受试者中未结合的苯妥英的群体药代动力学，并且可用于改进游离苯妥英浓度的预测。