4.1 Article

Variance based global sensitivity analysis of physiologically based pharmacokinetic absorption models for BCS I-IV drugs

期刊

出版社

SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10928-018-9615-8

关键词

Drug absorption; Oral bioavailability; Global sensitivity analysis; Variance based GSA; PBPK; BCS

向作者/读者索取更多资源

Regulatory agencies have a strong interest in sensitivity analysis for the evaluation of physiologically-based pharmacokinetic (PBPK) models used in pharmaceutical research and drug development and regulatory submissions. One of the applications of PBPK is the prediction of fraction absorbed and bioavailability for drugs following oral administration. In this context, we performed a variance based global sensitivity analysis (GSA) on in-house PBPK models for drug absorption, with the aim of identifying key parameters that influence the predictions of the fraction absorbed and the bioavailability for neutral, acidic and basic compounds. This analysis was done for four different classes of drugs, defined according to the Biopharmaceutics Classification System, differentiating compounds by permeability and solubility. For class I compounds (highly permeable, highly soluble), the parameters that mainly influence the fraction absorbed are related to the formulation properties, for class II compounds (highly permeable, lowly soluble) to the dissolution process, for class III (lowly permeable, highly soluble) to both absorption process and formulation properties and for class IV (lowly permeable, lowly soluble) to both absorption and dissolution processes. Considering the bioavailability, the results are similar to those for the fraction absorbed, with the addition that parameters related to gut wall and liver clearance influence as well the predictions. This work aimed to give a demonstration of the GSA methodology and highlight its importance in improving our understanding of PBPK absorption models and in guiding the choice of parameters that can safely be assumed, estimated or require data generation to allow informed model prediction.

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

4.1
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