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DDMODEL00000238: Population PK model for gentamicin

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Executable file for a MAXEVAL=0 run with simulated dataset
Original code
  • Development and Evaluation of a Gentamicin Pharmacokinetic Model That Facilitates Opportunistic Gentamicin Therapeutic Drug Monitoring in Neonates and Infants.
  • Germovsek E, Kent A, Metsvaht T, Lutsar I, Klein N, Turner MA, Sharland M, Nielsen EI, Heath PT, Standing JF
  • Antimicrobial agents and chemotherapy, 8/2016, Volume 60, Issue 8, pages: 4869-4877
  • Inflammation, Infection and Rheumatology Section, Institute of Child Health, University College London, London, United Kingdom
  • 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.
Eva Germovsek
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  • Model owner: Eva Germovsek
  • Submitted: Mar 28, 2017 1:51:59 PM
  • Last Modified: Mar 28, 2017 1:59:42 PM
  • Version: 18 public model Download this version
    • Submitted on: Mar 28, 2017 1:59:42 PM
    • Submitted by: Eva Germovsek
    • With comment: Edited model metadata online.
  • Version: 16 public model Download this version
    • Submitted on: Mar 28, 2017 1:51:59 PM
    • Submitted by: Eva Germovsek
    • With comment: Edited model metadata online.