Clinical Pharmacology, Advanced PK-PD Modeling and Simulation, Johnson & Johnson Pharmaceutical R&D, Raritan, NJ, USA.
J Pharmacokinet Pharmacodyn. 2011 Aug;38(4):423-32. doi: 10.1007/s10928-011-9201-9. Epub 2011 May 31.
The objectives of the simulation study were to evaluate the impact of BQL data on pharmacokinetic (PK) parameter estimates when the incidence of BQL data is low (e.g. ≤10%), and to compare the performance of commonly used modeling methods for handling BQL data such as data exclusion (M1) and likelihood-based method (M3). Simulations were performed by adapting the method of a recent publication by Ahn et al. (J Phamacokinet Pharmacodyn 35(4):401-421, 2008). The BQL data in the terminal elimination phase were created at frequencies of 1, 2.5, 5, 7.5, and 10% based on a one- and a two-compartment model. The impact of BQL data on model parameter estimates was evaluated based on bias and imprecision. The simulations demonstrated that for the one-compartment model, the impact of ignoring the low percentages of BQL data (≤10%) in the elimination phase was minimal. For the two-compartment model, when the BQL incidence was less than 5%, omission of the BQL data generally did not inflate the bias in the fixed-effect parameters, whereas more pronounced bias in the estimates of inter-individual variability (IIV) was observed. The BQL data in the elimination phase had the greatest impact on the volume of distribution estimate of the peripheral compartment of the two-compartment model. The M3 method generally provided better parameter estimates for both PK models than the M1 method. However, the advantages of the M3 over the M1 method varied depending on different BQL censoring levels, PK models and parameters. As the BQL percentages decreased, the relative gain of the M3 method based on more complex likelihood approaches diminished when compared to the M1 method. Therefore, it is important to balance the trade-off between model complexity and relative gain in model improvement when the incidence of BQL data is low. Understanding the model structure and the distribution of BQL data (percentage and location of BQL data) allows selection of an appropriate and effective modeling approach for handling low percentages of BQL data.
本模拟研究的目的在于评估当 BQL 数据的发生率较低（例如，≤10%）时，BQL 数据对药代动力学（PK）参数估计的影响，并比较常用于处理 BQL 数据的常用建模方法的性能，如数据剔除（M1）和基于似然的方法（M3）。该模拟通过改编 Ahn 等人最近发表的方法进行（J Pharmacokinet Pharmacodyn 35(4):401-421, 2008）。基于一房室和二房室模型，在终末消除相创建了 BQL 数据的频率为 1%、2.5%、5%、7.5%和 10%。基于偏差和不精确性，评估了 BQL 数据对模型参数估计的影响。模拟结果表明，对于单房室模型，忽略消除相中低比例的 BQL 数据（≤10%）的影响最小。对于二房室模型，当 BQL 发生率小于 5%时，通常不会使固定效应参数的偏差增大，而观察到个体间变异（IIV）估计值的偏差更为明显。消除相的 BQL 数据对二房室模型外周室分布容积的估计影响最大。M3 方法通常比 M1 方法为两种 PK 模型提供更好的参数估计。然而，M3 方法相对于 M1 方法的优势取决于不同的 BQL 截尾水平、PK 模型和参数。随着 BQL 百分比的降低，与 M1 方法相比，基于更复杂似然方法的 M3 方法的相对增益减小。因此，当 BQL 数据的发生率较低时，平衡模型复杂性和模型改进的相对增益之间的权衡非常重要。了解模型结构和 BQL 数据的分布（BQL 数据的百分比和位置）允许选择一种适当和有效的建模方法来处理低百分比的 BQL 数据。
