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Model-Informed Bayesian Estimation Improves the Prediction of Morphine Exposure in Neonates and Infants
Joshua C Euteneuer1,2, Tomoyuki Mizuno3,4, Tsuyoshi Fukuda3,4
1Division of Neonatology, Cincinnati Children's Hospital Medical Center, Perinatal Institute, Cincinnati, Ohio.
Insights
Model-informed Bayesian estimation accurately predicts morphine exposure in infants, outperforming standard pharmacokinetic models. This approach helps manage significant variability in morphine clearance for better pain control.
Area of Science:
- Pharmacology
- Clinical Pharmacy
- Pediatric Critical Care
Background:
- Pain management in infants is crucial due to potential neurodevelopmental impacts.
- Intravenous morphine is standard for postoperative pain but lacks characterized dose-concentration-response in neonates and infants.
- Existing guidelines don't account for individual variability in morphine clearance and response.
Purpose of the Study:
- To evaluate morphine pharmacokinetics (PKs) and exposure in critically ill neonates and infants.
- To compare population-based PK models with model-informed Bayesian techniques for predicting morphine exposure.
Main Methods:
- Prospective opportunistic PK study involving 221 discarded blood samples from 57 infants.
- Evaluation of morphine and its active metabolites' PKs and exposure.
- Comparison of population PK model predictions against Bayesian adaptive control strategy predictions.
Main Results:
- Substantial variability in morphine clearance observed (40-fold range).
- Bayesian predictions showed a significantly higher correlation (R=0.61) with observed concentrations compared to population models (R=0.13).
Conclusions:
- Model-informed Bayesian estimation is superior for predicting morphine exposure in critically ill infants.
- Significant variability in morphine clearance necessitates further research.
- Future studies should identify covariates and precision dosing strategies using morphine concentration and pain scores.
Background:
Pain control in infants is an important clinical concern, with potential long-term adverse neurodevelopmental effects. Intravenous morphine is routinely administered for postoperative pain management; however, its dose-concentration-response relationship in neonates and infants has not been well characterized. Although the current literature provides dosing guidelines for the average infant, it fails to control for the large unexplained variability in morphine clearance and response in individual patients. Bayesian estimation can be used to control for some of this variability. The authors aimed to evaluate morphine pharmacokinetics (PKs) and exposure in critically ill neonates and infants receiving standard-of-care morphine therapy and compare a population-based approach to the model-informed Bayesian techniques.
Methods:
The PKs and exposure of morphine and its active metabolites were evaluated in a prospective opportunistic PK study using 221 discarded blood samples from 57 critically ill neonates and infants in the neonatal intensive care unit. Thereafter, a population-based PK model was compared with a Bayesian adaptive control strategy to predict an individual's PK profile and morphine exposure over time.
Results:
Among the critically ill neonates and infants, morphine clearance showed substantial variability with a 40-fold range (ie, 2.2 to 87.1, mean 23.7 L/h/70 kg). Compared with the observed morphine concentrations, the population-model based predictions had an R of 0.13, whereas the model-based Bayesian predictions had an R of 0.61.
Conclusions:
Model-informed Bayesian estimation is a better predictor of morphine exposure than PK models alone in critically ill neonates and infants. A large variability was also identified in morphine clearance. A further study is warranted to elucidate the predictive covariates and precision dosing strategies that use morphine concentration and pain scores as feedbacks.
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