Predictive performance of a recently developed population pharmacokinetic model for morphine and its metabolites in new datasets of (preterm) neonates, infants and children.
ABSTRACT 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.
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ABSTRACT: From a previously validated paediatric population pharmacokinetic model, it was derived that non-linear morphine maintenance doses of 5 μg/kg(1.5)/h, with a 50 % dose reduction in neonates with a postnatal age (PNA) <10 days, yield similar morphine and metabolite concentrations across patients younger than 3 years. Compared with traditional dosing, this model-derived dosing regimen yields significantly reduced doses in neonates aged <10 days. Concentration predictions of the population model were prospectively evaluated in postoperative term neonates and infants up to the age of 1 year who received morphine doses according to the model-derived algorithm. The efficacy of this dosing algorithm was evaluated using morphine rescue medication and actual average infusion rates. Morphine and metabolite concentrations were accurately predicted by the paediatric pharmacokinetic morphine model. With regard to efficacy, 5 out of 18 neonates (27.8 %) with a PNA of <10 days needed rescue medication versus 18 of the 20 older patients (90 %) (p = 0.06). The median (interquartile range [IQR]) total morphine rescue dose was 0 (0-20) μg/kg in younger patients versus 193 (19-362) μg/kg in older patients (p = 0.003). The median (IQR) actual average morphine infusion rate was 4.4 (4.0-4.8) μg/kg/h in younger patients versus 14.4 (11.3-23.4) μg/kg/h in older patients (p < 0.001). Morphine paediatric dosing algorithms corrected for pharmacokinetic differences alone yield effective doses that prevent over-dosing for neonates with a PNA <10 days. The fact that many neonates and infants with a PNA ≥10 days still required rescue medication warrants pharmacodynamic studies to further optimize the dosing algorithm for these patients.Clinical Pharmacokinetics 02/2014; DOI:10.1007/s40262-014-0135-4 · 5.49 Impact Factor
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ABSTRACT: Introduction: Pediatric physiology and disease states are often complex and are distinct from adults. Pediatric clinicians and scientists, as well as governmental regulators, are increasingly concerned with the necessity to optimize clinical pharmacological study design to achieve appropriate drug-dosing practices for various pediatric populations. Areas covered: There are significant challenges in clinical trial design, implementation and analysis that are unique to pediatric populations. Innovative techniques of sample preparation and analysis of pharmacokinetic (PK) studies as well as modern methods of data analysis, including PK modeling and simulation, that address some of these challenges are reviewed along with recent examples from the literature. Expert opinion: There have been clear and exciting advances in the understanding of pediatric clinical pharmacology in the areas of clinical trial design and sample analysis, as well as PK and pharmacodynamic modeling and simulation. Further advances require collaboration and interdisciplinary efforts from multiple specialties in both academia and industry. Ideally, these efforts will then not only provide informed and individualized dosing for pediatric patients but will also provide new methods and algorithms for broader use of new pharmacotherapeutic agents or applications.Expert Opinion on Drug Metabolism & Toxicology 02/2014; DOI:10.1517/17425255.2014.885502 · 2.93 Impact Factor
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ABSTRACT: Paediatric clinical pharmacology is the scientific study of medicines in children and is a relatively new subspecialty in paediatrics in the UK. Training encompasses both the study of the effectiveness of drugs in children (clinical trials) and aspects of drug toxicity (pharmacovigilance). Ethical issues in relation to clinical trials and also studies of the pharmacokinetics and drug metabolism in children are crucial. Paediatric patients require formulations that young children in particular are able to take. The scientific evidence generated from clinical trials, pharmacokinetic studies and studies of drug toxicity all need to be applied in order to ensure that medicines are used rationally in children.Archives of Disease in Childhood 09/2014; 99(12). DOI:10.1136/archdischild-2014-306853 · 2.91 Impact Factor