Hsuan Ping Chang1, Valentina Shakhnovich2,3, Adam Frymoyer4, Ryan Sol Funk5, Mara L Becker6,7, K T Park8, Dhaval K Shah1. 1. Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, NY, United States. 2. Children's Mercy Kansas City, Kansas City, MO, United States. 3. University of Missouri-Kansas City School of Medicine, Kansas City, MO, United States. 4. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States. 5. Department of Pharmacy Practice, University of Kansas School of Pharmacy, Kansas City, KS, United States. 6. Department of Pediatrics, Division of Rheumatology, Duke University, Durham, NC, United States. 7. Duke Clinical Research Institute, Durham, NC, United States. 8. Genentech, Inc., South San Francisco, CA, USA.
Abstract
AIMS: In order to better predict the pharmacokinetics (PK) of antibodies in children, and to facilitate dose optimization of antibodies in paediatric patients, there is a need to develop systems PK models that integrate ontogeny-related changes in human physiological parameters. METHODS: A population-based physiological-based PK (PBPK) model to characterize antibody PK in paediatrics has been developed, by incorporating age-related changes in body weight, organ weight, organ blood flow rate and interstitial volumes in a previously published platform model. The model was further used to perform Monte Carlo simulations to investigate clearance vs. age and dose-exposure relationships for infliximab. RESULTS: By estimating only one parameter and associated interindividual variability, the model was able to characterize clinical PK of infliximab from two paediatric cohorts (n = 141, 4-19 years) reasonably well. Model simulations demonstrated that only 50% of children reached desired trough concentrations when receiving FDA-labelled dosing regimen for infliximab, suggesting that higher doses and/or more frequent dosing are needed to achieve target trough concentrations of this antibody. CONCLUSION: The paediatric PBPK model presented here can serve as a framework to characterize the PK of antibodies in paediatric patients. The model can also be applied to other protein therapeutics to advance precision medicine paradigm and optimize antibody dosing regimens in children.
AIMS: In order to better predict the pharmacokinetics (PK) of antibodies in children, and to facilitate dose optimization of antibodies in paediatric patients, there is a need to develop systems PK models that integrate ontogeny-related changes in human physiological parameters. METHODS: A population-based physiological-based PK (PBPK) model to characterize antibody PK in paediatrics has been developed, by incorporating age-related changes in body weight, organ weight, organ blood flow rate and interstitial volumes in a previously published platform model. The model was further used to perform Monte Carlo simulations to investigate clearance vs. age and dose-exposure relationships for infliximab. RESULTS: By estimating only one parameter and associated interindividual variability, the model was able to characterize clinical PK of infliximab from two paediatric cohorts (n = 141, 4-19 years) reasonably well. Model simulations demonstrated that only 50% of children reached desired trough concentrations when receiving FDA-labelled dosing regimen for infliximab, suggesting that higher doses and/or more frequent dosing are needed to achieve target trough concentrations of this antibody. CONCLUSION: The paediatric PBPK model presented here can serve as a framework to characterize the PK of antibodies in paediatric patients. The model can also be applied to other protein therapeutics to advance precision medicine paradigm and optimize antibody dosing regimens in children.
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