A Neonatal Amikacin Covariate Model Can Be Used to Predict Ontogeny of Other Drugs Eliminated Through Glomerular Filtration in Neonates
Recently, a covariate model characterizing developmental changes in clearance of amikacin in neonates has been developed using birth bodyweight and postnatal age. The aim of this study was to evaluate whether this covariate model can be used to predict maturation in clearance of other renally excreted drugs.
Five different neonatal datasets were available on netilmicin, vancomycin, tobramycin and gentamicin. The extensively validated covariate model for amikacin clearance was used to predict clearance of these drugs. In addition, independent reference models were developed based on a systematic covariate analysis.
The descriptive and predictive properties of the models developed using the amikacin covariate model were good, and fairly similar to the independent reference models (goodness-of-fit plots, NPDE). Moreover, similar clearance values were obtained for both approaches. Finally, the same covariates as in the covariate model of amikacin, i.e. birth bodyweight and postnatal age, were identified on clearance in the independent reference models.
This study shows that pediatric covariate models may contain physiological information since information derived from one drug can be used to describe other drugs. This semi-physiological approach may be used to optimize sparse data analysis and to derive individualized dosing algorithms for drugs in children.
KEY WORDSdevelopmental changes extrapolation glomerular filtration rate neonates pharmacokinetics
Glomerular filtration rate
Normalized prediction distribution error method
ACKNOWLEDGMENTS AND DISCLOSURES
This study was performed within the framework of Top Institute Pharma project number D2-104. The clinical research of Karel Allegaert is supported by the Fund for Scientific Research, Flanders (Clinical Fellowship 1800214N) and has been supported by an IWT-SBO project (130033). The clinical research of J. van den Anker is supported by NIH grants (R01HD060543, K24DA027992, R01HD048689, U54HD071601) and FP7 grants TINN (223614), TINN2 (260908), NEUROSIS (223060), and GRIP (261060). The authors also would like to thank LAP&P Consultants for their technical support with NONMEM.
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