Erin Chung1,2, Jonathan Sen3, Priya Patel4, Winnie Seto4,5. 1. Department of Pharmacy, The Hospital for Sick Children, 555 University Ave, Toronto, ON, M5G 1X8, Canada. erin.chung@utoronto.ca. 2. Graduate Department of Pharmaceutical Sciences, University of Toronto, Toronto, ON, Canada. erin.chung@utoronto.ca. 3. Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia. 4. Department of Pharmacy, The Hospital for Sick Children, 555 University Ave, Toronto, ON, M5G 1X8, Canada. 5. Graduate Department of Pharmaceutical Sciences, University of Toronto, Toronto, ON, Canada.
Abstract
BACKGROUND: Vancomycin is commonly used to treat gram-positive bacterial infections in the paediatric population, but dosing can be challenging. Population pharmacokinetic (popPK) modelling can improve individualization of dosing regimens. The primary objective of this study was to describe popPK models of vancomycin and factors that influence pharmacokinetic (PK) variability in paediatric patients. METHODS: Systematic searches were conducted in the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, International Pharmaceutical Abstracts and the grey literature without language or publication status restrictions from inception to 17 August 2020. Observational studies that described the development of popPK models of vancomycin in paediatric patients (< 18 years of age) were included. Risk of bias was assessed using the National Heart, Lung and Blood Institute Study Quality Assessment Tool for Case Series Studies. RESULTS: Sixty-four observational studies (1 randomized controlled trial, 13 prospective studies and 50 retrospective studies of 9019 patients with at least 25,769 serum vancomycin concentrations) were included. The mean age was 2.5 years (range 1 day-18 years), serum creatinine was 47.1 ± 33.6 µmol/L, and estimated creatinine clearance was 97.4 ± 76 mL/min/1.73m2. Most studies found that vancomycin PK was best described by a one-compartment model (71.9%). There was a wide range of clearance and volume of distribution (Vd) values (range 0.014-0.27 L/kg/h and 0.43-1.46 L/kg, respectively) with interindividual variability as high as 49.7% for clearance and 136% for Vd, proportional residual variability up to 37.5% and additive residual variability up to 17.5 mg/L. The most significant covariates for clearance were weight, age, and serum creatinine or creatinine clearance, and weight for Vd. Variable dosing recommendations were suggested. CONCLUSION: Numerous popPK models of vancomycin were derived, however external validation of suggested dosing regimens and analyses in subgroup paediatric populations such as dialysis patients are still needed before a popPK model with best predictive performance can be applied for dosing recommendations. Significant intraindividual and interindividual PK variability was present, which demonstrated the need for ongoing therapeutic drug monitoring and derivation of PK models for vancomycin for certain subgroup populations, such as dialysis patients.
BACKGROUND:Vancomycin is commonly used to treat gram-positive bacterial infections in the paediatric population, but dosing can be challenging. Population pharmacokinetic (popPK) modelling can improve individualization of dosing regimens. The primary objective of this study was to describe popPK models of vancomycin and factors that influence pharmacokinetic (PK) variability in paediatric patients. METHODS: Systematic searches were conducted in the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, International Pharmaceutical Abstracts and the grey literature without language or publication status restrictions from inception to 17 August 2020. Observational studies that described the development of popPK models of vancomycin in paediatric patients (< 18 years of age) were included. Risk of bias was assessed using the National Heart, Lung and Blood Institute Study Quality Assessment Tool for Case Series Studies. RESULTS: Sixty-four observational studies (1 randomized controlled trial, 13 prospective studies and 50 retrospective studies of 9019 patients with at least 25,769 serum vancomycin concentrations) were included. The mean age was 2.5 years (range 1 day-18 years), serum creatinine was 47.1 ± 33.6 µmol/L, and estimated creatinine clearance was 97.4 ± 76 mL/min/1.73m2. Most studies found that vancomycin PK was best described by a one-compartment model (71.9%). There was a wide range of clearance and volume of distribution (Vd) values (range 0.014-0.27 L/kg/h and 0.43-1.46 L/kg, respectively) with interindividual variability as high as 49.7% for clearance and 136% for Vd, proportional residual variability up to 37.5% and additive residual variability up to 17.5 mg/L. The most significant covariates for clearance were weight, age, and serum creatinine or creatinine clearance, and weight for Vd. Variable dosing recommendations were suggested. CONCLUSION: Numerous popPK models of vancomycin were derived, however external validation of suggested dosing regimens and analyses in subgroup paediatric populations such as dialysis patients are still needed before a popPK model with best predictive performance can be applied for dosing recommendations. Significant intraindividual and interindividual PK variability was present, which demonstrated the need for ongoing therapeutic drug monitoring and derivation of PK models for vancomycin for certain subgroup populations, such as dialysis patients.
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