Merck Healthcare KGaA, Frankfurter Str. 250, 64293, Darmstadt, Germany.
Clin Pharmacokinet. 2019 Nov;58(11):1355-1371. doi: 10.1007/s40262-019-00790-0.
When scientifically well-founded, the mechanistic basis of physiologically based pharmacokinetic (PBPK) models can help reduce the uncertainty and increase confidence in extrapolations outside the studied scenarios or studied populations. However, it is not always possible to establish mechanistically credible PBPK models. Requirements to establishing confidence in PBPK models, and challenges to meeting these requirements, are presented in this article. Parameter non-identifiability is the most challenging among the barriers to establishing confidence in PBPK models. Using case examples of small molecule drugs, this article examines the use of hypothesis testing to overcome parameter non-identifiability issues, with the objective of enhancing confidence in the mechanistic basis of PBPK models and thereby improving the quality of predictions that are meant for internal decisions and regulatory submissions. When the mechanistic basis of a PBPK model cannot be established, we propose the use of simpler models or evidence-based approaches.
当基于科学的机制基础能够为生理药代动力学（PBPK）模型提供支持时，这有助于减少不确定性，并提高对研究场景或研究人群之外的外推的信心。然而，并非总是能够建立具有机制可信度的 PBPK 模型。本文介绍了建立 PBPK 模型信心的要求，以及满足这些要求所面临的挑战。参数不可识别性是建立 PBPK 模型信心的最大障碍之一。本文使用小分子药物的案例研究，探讨了使用假设检验来克服参数不可识别性问题，目的是增强对 PBPK 模型机制基础的信心，从而提高旨在用于内部决策和监管提交的预测质量。当无法建立 PBPK 模型的机制基础时，我们建议使用更简单的模型或基于证据的方法。
