Division of Pharmacology, LeidenAmsterdam Center for Drug Research, Leiden, The Netherlands.
Clin Pharmacokinet. 2011 Jan;50(1):51-63. doi: 10.2165/11536750-000000000-00000.
Model validation procedures are crucial when models are to be used to develop new dosing algorithms. In this study, the predictive performance of a previously published paediatric population pharmacokinetic model for morphine and its metabolites in children younger than 3 years (original model) is studied in new datasets that were not used to develop the original model.
Six external datasets including neonates and infants up to 1 year were obtained from four different research centres. These datasets contained postoperative patients, ventilated patients and patients on extracorporeal membrane oxygenation (ECMO) treatment. Basic observed versus predicted plots, normalized prediction distribution error analysis, model refitting, bootstrap analysis, subpopulation analysis and a literature comparison of clearance predictions were performed with the new datasets to evaluate the predictive performance of the original morphine pharmacokinetic model.
The original model was found to be stable and the parameter estimates were found to be precise. The concentrations predicted by the original model were in good agreement with the observed concentrations in the four datasets from postoperative and ventilated patients, and the model-predicted clearances in these datasets were in agreement with literature values. In the datasets from patients on ECMO treatment with continuous venovenous haemofiltration (CVVH) the predictive performance of the model was good as well, whereas underprediction occurred, particularly for the metabolites, in patients on ECMO treatment without CVVH.
The predictive value of the original morphine pharmacokinetic model is demonstrated in new datasets by the use of six different validation and evaluation tools. It is herewith justified to undertake a proof-of-principle approach in the development of rational dosing recommendations - namely, performing a prospective clinical trial in which the model-based dosing algorithm is clinically evaluated.
在将模型用于开发新的剂量算法时，模型验证程序至关重要。在这项研究中，研究了先前发表的用于 3 岁以下儿童吗啡及其代谢物的儿科群体药代动力学模型（原始模型）在未用于开发原始模型的新数据集上的预测性能。
从四个不同的研究中心获得了包括新生儿和 1 岁以下婴儿在内的六个外部数据集。这些数据集包含术后患者、接受机械通气的患者和接受体外膜氧合（ECMO）治疗的患者。使用新数据集进行基本观察与预测图、归一化预测分布误差分析、模型重新拟合、引导分析、亚群分析以及清除率预测的文献比较，以评估原始吗啡药代动力学模型的预测性能。
发现原始模型稳定，参数估计精确。原始模型预测的浓度与来自术后和接受机械通气的患者的四个数据集的观察浓度吻合良好，并且这些数据集中模型预测的清除率与文献值一致。在接受 ECMO 治疗并接受连续静脉-静脉血液滤过（CVVH）的患者的数据集，模型的预测性能也很好，而在未接受 CVVH 的 ECMO 治疗的患者中，代谢物的预测值偏低。
使用六种不同的验证和评估工具，在新数据集中证明了原始吗啡药代动力学模型的预测价值。因此，在开发合理剂量建议方面进行原理验证研究是合理的，即在临床评估中进行基于模型的剂量算法的前瞻性临床试验。
