Pitfalls of using numerical predictive checks for population physiologically-based pharmacokinetic model evaluation.
Pitfalls of using numerical predictive checks for population physiologically-based pharmacokinetic model evaluation.
复制标题
使用数值预测检查进行基于群体生理学的药代动力学模型评估的缺陷。
DOI：
10.1007/s10928-019-09636-5
复制
发表时间：
2019
影响因子：
2.5
通讯作者：
Cohen-Wolkowiez,Michael
中科院分区：
文献类型：
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作者：
Maharaj,AnilR;Wu,Huali;Hornik,ChristophP;Cohen-Wolkowiez,Michael
观察数据和模型模拟之间的比较是建立基于群体生理学的药代动力学（Pop-PBPK）模型置信度的关键组成部分。数值预测检验（NPC）评估对应于Pop-PBPK模型预测区间（PI）的观察数据的比例，经常用于验证此类模型。我们评估了三个组成部分对NPC性能的影响，以鉴定Pop-PBPK模型浓度-时间预测：（1）相关性（每例受试者多个样本），（2）残差，和（3）观察受试者和虚拟受试者之间人口统计学分布的差异。使用基于模拟的研究设计，我们人工拟合了观察到的药代动力学（PK）数据集，并将其与在相同Pop-PBPK模型下生成的模型模拟进行比较。此外，我们还观察了受试者体重分布与用于生成模型模拟的虚拟人群不同的数据集。根据Pop-PBPK模型的90%PI计算每个数据集的NPC。对于包含相关观测值、残差或两者的数据集，NPC与膨胀的I类错误率（> 0.10）相关。此外，NPC的表现对受试者的人口学分布敏感。NPC的可接受使用仅在理想情况下得到证明，其中虚拟数据不相关，无残留误差，虚拟受试者的人口统计学分布与虚拟受试者的人口统计学分布相匹配。考虑到NPC用于Pop-PBPK模型评价的适用性有限，应谨慎解释其在此背景下的使用。
Comparisons between observed data and model simulations represent a critical component for establishing confidence in population physiologically-based pharmacokinetic (Pop-PBPK) models. Numerical predictive checks (NPC) that assess the proportion of observed data that correspond to Pop-PBPK model prediction intervals (PIs) are frequently used to qualify such models. We evaluated the effects of three components on the performance of NPC for qualifying Pop-PBPK model concentration–time predictions: (1) correlations (multiple samples per subject), (2) residual error, and (3) discrepancies in the distribution of demographics between observed and virtual subjects. Using a simulation-based study design, we artificially createdobservedpharmacokinetic (PK) datasets and compared them to model simulations generated under the same Pop-PBPK model.Observeddatasets containing uncorrelated and correlatedobservations(± residual error) were formulated using different random-sampling techniques. In addition, we createdobserveddatasets where the distribution of subject body weights differed from that of the virtual population used to generate model simulations. NPC for eachobserveddataset were computed based on the Pop-PBPK model’s 90% PI. NPC were associated with inflated type-I-error rates (> 0.10) forobserveddatasets that contained correlatedobservations, residual error, or both. Additionally, the performance of NPC were sensitive to the demographic distribution ofobservedsubjects. Acceptable use of NPC was only demonstrated for the idealistic case whereobserveddata were uncorrelated, free of residual error, and the demographic distribution of virtual subjects matched that ofobservedsubjects. Considering the restricted applicability of NPC for Pop-PBPK model evaluation, their use in this context should be interpreted with caution.