Xiaoxi Liu1, Anne Smits2, Yuhuan Wang1, Marleen Renard3, Stephanie Wead4, Richard J Kagan5, Daniel P Healy4, Pieter De Cock6,7, Karel Allegaert8,9,10, Catherine M T Sherwin1. 1. Division of Clinical Pharmacology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, Utah. 2. Neonatal Intensive Care Unit, University Hospitals Leuven. 3. Department of Pediatric Oncology, University Hospitals Leuven, Leuven, Belgium. 4. James L. Winkle College of Pharmacy, University of Cincinnati. 5. The Shriners Hospitals for Children, Cincinnati, Ohio. 6. Department of Pharmacy, Ghent University Hospital. 7. Heymans Institute of Pharmacology, Ghent University, Ghent, Belgium. 8. Department of Development and Regeneration, KU Leuven, Leuven, Belgium. 9. Intensive Care, Department of Pediatric Surgery, Erasmus MC Sophia Children's Hospital. 10. Department of Neonatology, Erasmus MC Sophia Children's Hospital, Rotterdam, the Netherlands.
Abstract
BACKGROUND: Amikacin is widely used to treat severe Gram-negative bacterial infections. Its peak concentration in plasma is associated with treatment efficacy. Amikacin pharmacokinetics (PK) is influenced by disease conditions, in addition to other patient characteristics. In this retrospective study, we evaluated the impact of clinical characteristics and disease condition on amikacin PK in children with burn injuries and those with cancer. METHODS: Amikacin PK data from 66 children with burn injuries and 112 children with cancer were analyzed. A population PK model was developed using the nonlinear mixed-effects modeling approach. Models were developed using NONMEM 7.3 (ICON Development Solutions, LLC, Ellicott City, MD). Data processing and visualization was performed using R packages. RESULTS: The amikacin PK data were best described by a 2-compartment model. The parameters were estimated with mean values (95% confidence intervals) as follows: central volume of distribution (V1), 5.70 L (4.64-6.76 L); central clearance, 2.12 L/h (1.79-2.46 L/h); peripheral volume of distribution (V2), 4.79 L (2.36-7.22 L); and distribution clearance (Q), 0.71 L/h (0.25-1.16 L/h). The final model identified the disease condition as a significant covariate and indicated 55% (28%-82%) higher central clearance and 17% (1%-34%) higher V1 in burn patients compared with cancer patients. Volume of distribution was significantly influenced by age and body weight. Clearance was significantly influenced by age, body weight, and creatinine clearance. Using the final PK model, we developed a workflow for selecting optimal dosing strategies for 3 representative pediatric patient profiles. CONCLUSIONS: Disease condition was significant in influencing amikacin PK in children. To reach the same target concentrations (64 mg/L peak concentration) with a daily-dose plan, burn patients need higher doses than cancer patients. Future investigations are needed to explore the impact of other diseases on amikacin disposition in children, and to prospectively validate the proposed dosing strategy.
BACKGROUND:Amikacin is widely used to treat severe Gram-negative bacterial infections. Its peak concentration in plasma is associated with treatment efficacy. Amikacin pharmacokinetics (PK) is influenced by disease conditions, in addition to other patient characteristics. In this retrospective study, we evaluated the impact of clinical characteristics and disease condition on amikacin PK in children with burn injuries and those with cancer. METHODS:Amikacin PK data from 66 children with burn injuries and 112 children with cancer were analyzed. A population PK model was developed using the nonlinear mixed-effects modeling approach. Models were developed using NONMEM 7.3 (ICON Development Solutions, LLC, Ellicott City, MD). Data processing and visualization was performed using R packages. RESULTS: The amikacin PK data were best described by a 2-compartment model. The parameters were estimated with mean values (95% confidence intervals) as follows: central volume of distribution (V1), 5.70 L (4.64-6.76 L); central clearance, 2.12 L/h (1.79-2.46 L/h); peripheral volume of distribution (V2), 4.79 L (2.36-7.22 L); and distribution clearance (Q), 0.71 L/h (0.25-1.16 L/h). The final model identified the disease condition as a significant covariate and indicated 55% (28%-82%) higher central clearance and 17% (1%-34%) higher V1 in burn patients compared with cancerpatients. Volume of distribution was significantly influenced by age and body weight. Clearance was significantly influenced by age, body weight, and creatinine clearance. Using the final PK model, we developed a workflow for selecting optimal dosing strategies for 3 representative pediatric patient profiles. CONCLUSIONS: Disease condition was significant in influencing amikacin PK in children. To reach the same target concentrations (64 mg/L peak concentration) with a daily-dose plan, burn patients need higher doses than cancerpatients. Future investigations are needed to explore the impact of other diseases on amikacin disposition in children, and to prospectively validate the proposed dosing strategy.