Anh Thi Van Nguyen 1, Simon J Carter 2, Frank Kloprogge 3, Joseph F Standing 3,4, Veronique Stove 1,5, Zhiyuan Tan 1, Sarah Desmet 6, Evelyn Dhont 1,5, Daphne Vens 7, Peter De Paepe 1, Pieter A. De Cock 1,5
1 Ghent University (Ghent, Belgium), 2 AstraZeneca (Gothenburg, Sweden), 3 University College London (London, UK), 4 Great Ormond Street Hospital for Children (London, UK), 5 Ghent University Hospital (Ghent, Belgium), 6 AZ Sint-Lucas Gent (Ghent, Belgium), 7 Hôpital Universitaire des Enfants Reine Fabiola (Brussels, Belgium)
Introduction
Vancomycin is a moderately plasma protein-bound glycopeptide antibiotic used to treat severe infections caused by Gram-positive bacteria [1]. Sub- and supratherapeutic exposures are frequently observed in critically ill children due to pharmacokinetic (PK) variability and vancomycin’s narrow therapeutic index [2, 3]. Lower plasma protein binding as compared to adults.
Objectives
This study aimed to (i) quantify vancomycin pharmacokinetics (PK) using both total and unbound plasma concentrations (ii) identify predictors of interpatient PK variability and (iii) evaluate PK/PD target attainment in early and steady-state dose conditions for current dosing regimens using PK/PD targets based on total and unbound concentrations.
Methods
A multicentric, observational PK study was conducted in the intensive care unit. Children aged 1 day to 16 years old who received intravenous vancomycin therapy were included in the study. Blood samples were collected at the first dose and assumed steady-state conditions, and the concentrations of both total and unbound vancomycin were quantified. Population PK analysis was performed using non-linear mixed-effects modelling in NONMEM 7.4 (ICON Plc, Dublin, Ireland). Pearl-speaks-NONMEM (PsN) 5.6.0, Pirana 25.7.1 (Certara USA, Inc., Radnor, USA), R 4.5.1 (R Foundation, Austria) were used for pre- and post-processing of the data and for graphical and statistical summaries [4, 5]. Plasma concentrations below the lower limit of quantification were retained as is. Allometric scaling and maturation functions were a priori included in the clearance. Serum creatinine and cystatin C were tested as potential covariates on clearance. Additionally, albumin and total protein were evaluated for their effect on the fraction unbound. Other covariates with sufficient data, such as CRP, PELOD score, total bilirubin, urea, mechanical ventilation, and the use of comedications, were also tested. The plausibility and acceptable precision of parameter estimates, goodness-of-fit plots, visual predictive checks (VPC), bootstrap, and sensitivity analyses were used to evaluate the model. Monte Carlo simulations were performed to evaluate the probability of PK/PD target attainment (PTA) under early (0-24h) and steady-state (24-48h) conditions. Currently used intermittent dosing regimens (10-15 mg/kg Q6h-8h) and continuous infusion strategies (40-60 mg/kg/day) were evaluated. PK/PD targets were defined as total AUC/MIC > 400 and free AUC/MIC > 200, assuming a MIC of 1 mg/L for treatment of methicillin-resistant Staphylococci infections [3].
Results
A total of 395 samples from 75 critically ill children (median age 1.01 years [range 0.013-15.66], median weight 10 kg [range 1.1-64]) were used for the analysis. The data were best described by a two-compartment model with allometric scaling and a maturation function to capture the effects of growth and development. Serum creatinine and use of inotropic agents were significant covariates on clearance, while total protein was on the fraction unbound. The typical values for clearance, central volume of distribution, inter-compartment clearance, and peripheral volume of distribution were 8.5 L/h/70 kg, 14.8 L/70 kg, 11.7 L/h/70 kg, and 23.1 L/70 kg, respectively. Large inter-individual variability was observed in central and peripheral volumes of distribution, with 64.5% CV and 38.3% CV, respectively. The diagnostic and VPC plots indicate adequate characterization of the data, with no model misspecification. PTA for the most commonly used PK/PD target (AUC/MIC>400) ranged from 61 to 91%, while for free concentrations, PTA were substantially higher (87-99%) in both early and steady-state conditions.
Conclusions
A robust population PK model describing both free and total vancomycin concentrations was successfully developed, demonstrating substantial interpatient variability in critically ill children. Model-based simulations showed that a dosing regimen of 60 mg/kg/day maximized the PTAs for both total and free concentrations. Notably, higher PTAs were observed when PK/PD indices were based on free concentrations rather than total concentrations. These findings underscore the clinical relevance of accounting for protein binding in vancomycin dose optimization and highlight the need to establish pediatric-specific PK/PD targets.
References:
[1] R. S. Watson, E. D. Carrol, M. J. Carter, N. Kissoon, S. Ranjit, and L. J. Schlapbach, “The burden and contemporary epidemiology of sepsis in children,” The Lancet Child & Adolescent Health, vol. 8, no. 9, pp. 670–681, 2024, doi: 10.1016/S2352-4642(24)00140-8.
[2] K. Matsumoto et al., “Clinical Practice Guidelines for Therapeutic Drug Monitoring of Vancomycin in the Framework of Model-Informed Precision Dosing: A Consensus Review by the Japanese Society of Chemotherapy and the Japanese Society of Therapeutic Drug Monitoring,” (in eng), Pharmaceutics, vol. 14, no. 3, Feb 23 2022, doi: 10.3390/pharmaceutics14030489.
[3] M. J. Rybak et al., “Therapeutic monitoring of vancomycin for serious methicillin-resistant Staphylococcus aureus infections: A revised consensus guideline and review by the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, the Pediatric Infectious Diseases Society, and the Society of Infectious Diseases Pharmacists,” American Journal of Health-System Pharmacy, vol. 77, no. 11, pp. 835–864, 2020, doi: 10.1093/ajhp/zxaa036.
[4] L. Lindbom, P. Pihlgren, and E. N. Jonsson, “PsN-Toolkit–a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM,” (in eng), Comput Methods Programs Biomed, vol. 79, no. 3, pp. 241–57, Sep 2005, doi: 10.1016/j.cmpb.2005.04.005.
[5] R. J. Keizer, M. O. Karlsson, and A. Hooker, “Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose,” (in eng), CPT Pharmacometrics Syst Pharmacol, vol. 2, no. 6, p. e50, Jun 26 2013, doi: 10.1038/psp.2013.24.
Reference: PAGE 34 (2026) Abstr 11897 [www.page-meeting.org/?abstract=11897]
Poster: Clinical Applications