Discovery Medicine and Clinical Pharmacology, Bristol Myers Squibb Company, Lawrenceville, New Jersey, USA.
CPT Pharmacometrics Syst Pharmacol. 2014 Jun 25;3(6):e121. doi: 10.1038/psp.2014.19.
Mechanism-based pharmacokinetic/pharmacodynamic models have a fundamental basis in biology and pharmacology and, thus, are useful for hypothesis generation and extrapolation beyond the conditions of the original analysis data. The complexity of these models necessitates the incorporation of prior knowledge to inform many of the model parameters. Markov chain Monte Carlo Bayesian estimation offers a robust and statistically rigorous approach for incorporation of prior information into mechanism-based models. This article provides a perspective on the utility of this approach.
基于机制的药代动力学/药效动力学模型在生物学和药理学方面具有根本基础，因此可用于生成假设并推断原始分析数据条件之外的情况。这些模型的复杂性需要纳入先验知识以告知许多模型参数。马尔可夫链蒙特卡罗贝叶斯估计为将先验信息纳入基于机制的模型提供了一种强大且严格的统计方法。本文对这种方法的实用性提供了一个视角。
