Publication: Population pharmacokinetics analysis of vancomycin in critically ill patients
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Issued Date
2018-01-01
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19054637
01254685
01254685
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2-s2.0-85047405986
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Mahidol University
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SCOPUS
Bibliographic Citation
Thai Journal of Pharmaceutical Sciences. Vol.42, No.2 (2018), 102-109
Suggested Citation
Phumprut Gawmahanil, Prawat Chantharit, Pakwan Bunupuradah, Supasil Sra-Ium, Nantaporn Lekpittaya, Wanchai Treyaprasert Population pharmacokinetics analysis of vancomycin in critically ill patients. Thai Journal of Pharmaceutical Sciences. Vol.42, No.2 (2018), 102-109. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/47335
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Title
Population pharmacokinetics analysis of vancomycin in critically ill patients
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Abstract
© 2018, Faculty of Pharmaceutical Sciences, Chulalongkorn University. All rights reserved. Objectives: The aim of this study was to determine population pharmacokinetic parameters of vancomycin in adult critically ill patients and to investigate the covariates affecting population pharmacokinetic parameters. Methods: Plasma concentration data from therapeutic drug monitoring were collected from a retrospective study. Vancomycin population pharmacokinetic modeling was analyzed using nonlinear mixed effect model (NONMEM) program. Patient characteristics that could potentially influence vancomycin pharmacokinetic parameters were tested in the pharmacokinetic model. The covariates were analyzed by the forward inclusion and backward elimination method to identify their potential influence on vancomycin parameter. To assess the robustness of the estimated parameter, bootstrap analysis was performed. Results: A total of 171 patients with 398 concentrations during clinical routine therapeutic monitoring were analysis. Vancomycin serum concentration-time profiles were best described by a one-compartmental model with first-order elimination. Creatinine clearance calculated by Cockcroft-Gault equation, diabetes mellitus and sepsis were found significantly influence CL whereas V of vancomycin showed the significant dependence on patient serum creatinine and gender. The mean population parameters were CL = 3.63 L/h and V = 118 L. The inter-individual variability for CL and V was 32.90 and 29.12 %, respectively. A comparison of the population pharmacokinetic parameters of vancomycin in the final model estimated in NONMEM with original data and 1000 bootstrap samples show that both sets of estimates were comparable, thereby indicating the robustness of the proposed model. Conclusions: A population pharmacokinetic model of vancomycin for critically ill patients with Gram-positive infections was developed in this study. The population pharmacokinetic parameters and the significant covariates distinguished in the final model can provide helpful information to facilitate individualized vancomycin dosage regimen with similar patient population characteristics.