Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
AAPS J. 2012 Jun;14(2):155-63. doi: 10.1208/s12248-012-9324-y.
Absorption models used in the estimation of pharmacokinetic drug characteristics from plasma concentration data are generally empirical and simple, utilizing no prior information on gastro-intestinal (GI) transit patterns. Our aim was to develop and evaluate an estimation strategy based on a mechanism-based model for drug absorption, which takes into account the tablet movement through the GI transit. This work is an extension of a previous model utilizing tablet movement characteristics derived from magnetic marker monitoring (MMM) and pharmacokinetic data. The new approach, which replaces MMM data with a GI transit model, was evaluated in data sets where MMM data were available (felodipine) or not available (diclofenac). Pharmacokinetic profiles in both datasets were well described by the model according to goodness-of-fit plots. Visual predictive checks showed the model to give superior simulation properties compared with a standard empirical approach (first-order absorption rate + lag-time). This model represents a step towards an integrated mechanism-based NLME model, where the use of physiological knowledge and in vitro–in vivo correlation helps fully characterize PK and generate hypotheses for new formulations or specific populations.
用于从血浆浓度数据估算药代动力学药物特征的吸收模型通常是经验性和简单的,不利用关于胃肠道(GI)转运模式的先验信息。我们的目的是开发和评估一种基于药物吸收的基于机制的模型的估算策略,该模型考虑了片剂通过 GI 转运的运动。这项工作是先前利用从磁性标记监测(MMM)和药代动力学数据得出的片剂运动特征的模型的扩展。新方法用 GI 转运模型代替 MMM 数据,在有 MMM 数据(非洛地平)或没有 MMM 数据(双氯芬酸)的数据集进行了评估。根据拟合优度图,模型很好地描述了两个数据集的药代动力学曲线。视觉预测检查表明,与标准经验方法(一级吸收速率+滞后时间)相比,该模型具有更好的模拟特性。该模型代表了向基于机制的 NLME 模型的集成迈进的一步,其中利用生理学知识和体内外相关性有助于充分描述 PK 并为新配方或特定人群生成假设。