Development and Evaluation of a Gentamicin Pharmacokinetic Model That Facilitates Opportunistic Gentamicin Therapeutic Drug Monitoring in Neonates and Infants.
Trough gentamicin therapeutic drug monitoring (TDM) is time-consuming, disruptive to neonatal clinical care, and a patient safety issue. Bayesian models could allow TDM to be performed opportunistically at the time of routine blood tests. This study aimed to develop and prospectively evaluate a new gentamicin model and a novel Bayesian computer tool (neoGent) for TDM use in neonatal intensive care. We also evaluated model performance for predicting peak concentrations and the area under the concentration-time curve from time 0 h to time t h (AUC0- t). A pharmacokinetic meta-analysis was performed on pooled data from three studies (1,325 concentrations from 205 patients). A 3-compartment model was used with the following covariates: allometric weight scaling, postmenstrual and postnatal age, and serum creatinine concentration. Final parameter estimates (standard errors) were as follows: clearance, 6.2 (0.3) liters/h/70 kg of body weight; central volume (V), 26.5 (0.6) liters/70 kg; intercompartmental disposition (Q), 2.2 (0.3) liters/h/70 kg; peripheral volume V2, 21.2 (1.5) liters/70 kg; intercompartmental disposition (Q2), 0.3 (0.05) liters/h/70 kg; peripheral volume V3, 148 (52.0) liters/70 kg. The model's ability to predict trough concentrations from an opportunistic sample was evaluated in a prospective observational cohort study that included data from 163 patients and 483 concentrations collected in five hospitals. Unbiased trough predictions were obtained; the median (95% confidence interval [CI]) prediction error was 0.0004 (-1.07, 0.84) mg/liter. Results also showed that peaks and AUC0- t values could be predicted (from one randomly selected sample) with little bias but relative imprecision, with median (95% CI) prediction errors being 0.16 (-4.76, 5.01) mg/liter and 10.8 (-24.9, 62.2) mg · h/liter, respectively. neoGent was implemented in R/NONMEM and in the freely available TDMx software.
Inflammation, Infection and Rheumatology Section, Institute of Child Health, University College London, London, United Kingdom eva.germovsek.11@ucl.ac.uk.
Eva
E
Germovsek
Paediatric Infectious Diseases Research Group, Institute for Infection and Immunity, St. George's, University of London, Cranmer Terrace, London, United Kingdom.
Alison
A
Kent
Department of Microbiology, University of Tartu, Tartu, Estonia.
Tuuli
T
Metsvaht
Department of Microbiology, University of Tartu, Tartu, Estonia.
Irja
I
Lutsar
Inflammation, Infection and Rheumatology Section, Institute of Child Health, University College London, London, United Kingdom.
Nigel
N
Klein
Department of Women's and Children's Health, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom.
Mark A
MA
Turner
Paediatric Infectious Diseases Research Group, Institute for Infection and Immunity, St. George's, University of London, Cranmer Terrace, London, United Kingdom.
Mike
M
Sharland
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
Elisabet I
EI
Nielsen
Paediatric Infectious Diseases Research Group, Institute for Infection and Immunity, St. George's, University of London, Cranmer Terrace, London, United Kingdom.
Paul T
PT
Heath
Inflammation, Infection and Rheumatology Section, Institute of Child Health, University College London, London, United Kingdom.
Joseph F
JF
Standing
D000900:Anti-Bacterial Agents / Q000493:pharmacokinetics*; D001499:Bayes Theorem; D016903:Drug Monitoring / Q000379:methods; D005260:Female; D005839:Gentamicins / Q000493:pharmacokinetics*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D011446:Prospective Studies
D000900:Anti-Bacterial Agents; D005839:Gentamicins