Pharmacokinetic models of morphine and its metabolites in neonates systematic comparisons of models from the literature, and development of a new meta-model
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Morphine is commonly used for pain management in preterm neonates. The aims of this study were to compare published models of neonatal pharmacokinetics of morphine and its metabolites with a new dataset, and to combine the characteristics of the best predictive models to design a meta-model for morphine and its metabolites in preterm neonates. Moreover, the concentration-analgesia relationship for morphine in this clinical setting was also investigated. A population of 30 preterm neonates (gestational age: 23-32weeks) received a loading dose of morphine (50-100μg/kg), followed by a continuous infusion (5-10μg/kg/h) until analgesia was no longer required. Pain was assessed using the Premature Infant Pain Profile. Five published population models were compared using numerical and graphical tests of goodness-of-fit and predictive performance. Population modelling was conducted using NONMEM® and the $PRIOR subroutine to describe the time-course of plasma concentrations of morphine, morphine-3-glucuronide, and morphine-6-glucuronide, and the concentration-analgesia relationship for morphine. No published model adequately described morphine concentrations in this new dataset. Previously published population pharmacokinetic models of morphine, morphine-3-glucuronide, and morphine-6-glucuronide were combined into a meta-model. The meta-model provided an adequate description of the time-course of morphine and the concentrations of its metabolites in preterm neonates. Allometric weight scaling was applied to all clearance and volume terms. Maturation of morphine clearance was described as a function of postmenstrual age, while maturation of metabolite elimination was described as a function of postnatal age. A clear relationship between morphine concentrations and pain score was not established.
Original language | English |
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Journal | European Journal of Pharmaceutical Sciences |
ISSN | 0928-0987 |
DOIs | |
Publication status | Published - 30 Jun 2016 |
Externally published | Yes |
ID: 163755570