Integrated PharmacoMetrics, PharmacoGenomics and PharmacoKinetics, Louvain Drug Research Institute, Université catholique de Louvain, Avenue E. Mounier 72, B01.72.0, Brussels, Belgium.
Louvain Centre for Toxicology and Applied Pharmacology, Institut de recherche expérimentale et clinique, Université catholique de Louvain, Brussels, Belgium.
Eur J Clin Pharmacol. 2021 Apr;77(4):607-616. doi: 10.1007/s00228-020-03036-2. Epub 2020 Nov 11.
A variety of diagnostic methods are available to validate the performance of population pharmacokinetic models. Internal validation, which applies these methods to the model building dataset and to additional data generated through Monte Carlo simulations, is often sufficient, but external validation, which requires a new dataset, is considered a more rigorous approach, especially if the model is to be used for predictive purposes. Our first objective was to validate a previously published population pharmacokinetic model of darunavir, an HIV protease inhibitor boosted with ritonavir or cobicistat. Our second objective was to use this model to derive optimal sampling strategies that maximize the amount of information collected with as few pharmacokinetic samples as possible.
A validation dataset comprising 164 sparsely sampled individuals using ritonavir-boosted darunavir was used for validation. Standard plots of predictions and residuals, NPDE, visual predictive check, and bootstrapping were applied to both the validation set and the combined learning/validation set in NONMEM to assess model performance. D-optimal designs for darunavir were then calculated in PopED and further evaluated in NONMEM through simulations.
External validation confirmed model robustness and accuracy in most scenarios but also highlighted several limitations. The best one-, two-, and three-point sampling strategies were determined to be pre-dose (0 h); 0 and 4 h; and 1, 4, and 19 h, respectively. A combination of samples at 0, 1, and 4 h was comparable to the optimal three-point strategy. These could be used to reliably estimate individual pharmacokinetic parameters, although with fewer samples, precision decreased and the number of outliers increased significantly.
Optimal sampling strategies derived from this model could be used in clinical practice to enhance therapeutic drug monitoring or to conduct additional pharmacokinetic studies.
有多种诊断方法可用于验证群体药代动力学模型的性能。内部验证将这些方法应用于模型构建数据集和通过蒙特卡罗模拟生成的其他数据，通常就足够了，但外部验证需要新数据集，被认为是一种更严格的方法，特别是如果模型将用于预测目的。我们的首要目标是验证先前发表的 HIV 蛋白酶抑制剂达芦那韦的群体药代动力学模型，该模型与利托那韦或考比司他联合增强。我们的第二个目标是使用该模型得出最佳采样策略，以尽可能少的药代动力学样本收集尽可能多的信息。
使用包含 164 名使用利托那韦增强的达芦那韦进行稀疏采样的个体的验证数据集进行验证。标准预测和残差图、NPDE、可视化预测检查和自举法均应用于 NONMEM 中的验证集和组合学习/验证集中，以评估模型性能。然后在 PopED 中计算达芦那韦的 D-最优设计，并通过模拟在 NONMEM 中进一步评估。
外部验证在大多数情况下证实了模型的稳健性和准确性，但也突出了一些局限性。确定的最佳单点、两点和三点采样策略分别为：预剂量（0 小时）；0 小时和 4 小时；1 小时、4 小时和 19 小时。在 0、1 和 4 小时采集样本的组合与最佳三点策略相当。这些可以用于可靠地估计个体药代动力学参数，尽管采样较少，精度降低，异常值数量显著增加。
从该模型得出的最佳采样策略可用于临床实践，以增强治疗药物监测或进行额外的药代动力学研究。
