4.5 Article

Population pharmacokinetic study of gentamicin in a large cohort of premature and term neonates

期刊

BRITISH JOURNAL OF CLINICAL PHARMACOLOGY
卷 78, 期 5, 页码 1090-1101

出版社

WILEY
DOI: 10.1111/bcp.12444

关键词

aminoglycoside; dosing guidelines; neonates; population pharmacokinetics; therapeutic drug monitoring; two compartment model

资金

  1. Swiss National Science Foundation through the Nano-Tera initiative

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AimThis study aims to investigate the clinical and demographic factors influencing gentamicin pharmacokinetics in a large cohort of unselected premature and term newborns and to evaluate optimal regimens in this population. MethodsAll gentamicin concentration data, along with clinical and demographic characteristics, were retrieved from medical charts in a Neonatal Intensive Care Unit over 5 years within the frame of a routine therapeutic drug monitoring programme. Data were described using non-linear mixed-effects regression analysis ( nonmem (R)). ResultsA total of 3039 gentamicin concentrations collected in 994 preterm and 455 term newborns were included in the analysis. A two compartment model best characterized gentamicin disposition. The average parameter estimates, for a median body weight of 2170g, were clearance (CL) 0.089lh(-1) (CV 28%), central volume of distribution (V-c) 0.908l (CV 18%), intercompartmental clearance (Q) 0.157lh(-1) and peripheral volume of distribution (V-p) 0.560l. Body weight, gestational age and post-natal age positively influenced CL. Dopamine co-administration had a significant negative effect on CL, whereas the influence of indomethacin and furosemide was not significant. Both body weight and gestational age significantly influenced V-c. Model-based simulations confirmed that, compared with term neonates, preterm infants need higher doses, superior to 4mgkg(-1), at extended intervals to achieve adequate concentrations. ConclusionsThis observational study conducted in a large cohort of newborns confirms the importance of body weight and gestational age for dosage adjustment. The model will serve to set up dosing recommendations and elaborate a Bayesian tool for dosage individualization based on concentration monitoring.

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