BACKGROUND AND OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
BACKGROUND AND OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
Authors: Christopher Brasher; Benjamin Gafsous; Sophie Dugue; Anne Thiollier; Joelle Kinderf; Yves Nivoche; Robert Grace; Souhayl Dahmani Journal: Paediatr Drugs Date: 2014-04 Impact factor: 3.022
Authors: Wei Zhao; Florentia Kaguelidou; Valérie Biran; Daolun Zhang; Karel Allegaert; Edmund V Capparelli; Nick Holford; Toshimi Kimura; Yoke-Lin Lo; José-Esteban Peris; Alison Thomson; John N van den Anker; May Fakhoury; Evelyne Jacqz-Aigrain Journal: Br J Clin Pharmacol Date: 2013-04 Impact factor: 4.335
Authors: Jeroen Diepstraten; Vidya Chidambaran; Senthilkumar Sadhasivam; Hope R Esslinger; Shareen L Cox; Thomas H Inge; Catherijne A J Knibbe; Alexander A Vinks Journal: Clin Pharmacokinet Date: 2012-08 Impact factor: 6.447
Authors: Elke H J Krekels; Dick Tibboel; Saskia N de Wildt; Ilse Ceelie; Albert Dahan; Monique van Dijk; Meindert Danhof; Catherijne A J Knibbe Journal: Clin Pharmacokinet Date: 2014-06 Impact factor: 6.447
Authors: Chenguang Wang; Senthilkumar Sadhavisvam; Elke H J Krekels; Albert Dahan; Dick Tibboel; Meindert Danhof; Alexander A Vinks; Catherijne A J Knibbe Journal: Clin Drug Investig Date: 2013-07 Impact factor: 2.859
Authors: Justine Badée; Stephen Fowler; Saskia N de Wildt; Abby C Collier; Stephan Schmidt; Neil Parrott Journal: Clin Pharmacokinet Date: 2019-02 Impact factor: 6.447
Authors: Ibrahim Ince; Saskia N de Wildt; Chenguang Wang; Chengueng Wang; Mariska Y M Peeters; Jacobus Burggraaf; Evelyne Jacqz-Aigrain; John N van den Anker; Dick Tibboel; Meindert Danhof; Catherijne A J Knibbe Journal: Clin Pharmacokinet Date: 2013-07 Impact factor: 6.447