Jacqueline G Gerhart1, Fernando O Carreño1, Andrea N Edginton2, Jaydeep Sinha1, Eliana M Perrin3, Karan R Kumar4,5, Aruna Rikhi4, Christoph P Hornik4,5, Vincent Harris1, Samit Ganguly1,6, Michael Cohen-Wolkowiez4,5, Daniel Gonzalez7. 1. Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, 301 Pharmacy Lane, Campus Box #7569, Chapel Hill, NC, 27599-7569, USA. 2. School of Pharmacy, University of Waterloo, Waterloo, ON, Canada. 3. Department of Pediatrics, School of Medicine and School of Nursing, Johns Hopkins University, Baltimore, MD, USA. 4. Duke Clinical Research Institute, Durham, NC, USA. 5. Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA. 6. Regeneron Pharmaceuticals, Inc., Tarrytown, NY, USA. 7. Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, 301 Pharmacy Lane, Campus Box #7569, Chapel Hill, NC, 27599-7569, USA. daniel.gonzalez@unc.edu.
Abstract
BACKGROUND AND OBJECTIVE: While one in five children in the USA are now obese, and more than three-quarters receive at least one drug during childhood, there is limited dosing guidance for this vulnerable patient population. Physiologically based pharmacokinetic modeling can bridge the gap in the understanding of how pharmacokinetics, including drug distribution and clearance, changes with obesity by incorporating known obesity-related physiological changes in children. The objective of this study was to develop a virtual population of children with obesity to enable physiologically based pharmacokinetic modeling, then use the novel virtual population in conjunction with previously developed models of clindamycin and trimethoprim/sulfamethoxazole to better understand dosing of these drugs in children with obesity. METHODS: To enable physiologically based pharmacokinetic modeling, a virtual population of children with obesity was developed using national survey, electronic health record, and clinical trial data, as well as data extracted from the literature. The virtual population accounts for key obesity-related changes in physiology relevant to pharmacokinetics, including increased body size, body composition, organ size and blood flow, plasma protein concentrations, and glomerular filtration rate. The virtual population was then used to predict the pharmacokinetics of clindamycin and trimethoprim/sulfamethoxazole in children with obesity using previously developed physiologically based pharmacokinetic models. RESULTS: Model simulations predicted observed concentrations well, with an overall average fold error of 1.09, 1.24, and 1.53 for clindamycin, trimethoprim, and sulfamethoxazole, respectively. Relative to children without obesity, children with obesity experienced decreased clindamycin and trimethoprim/sulfamethoxazole weight-normalized clearance and volume of distribution, and higher absolute doses under recommended pediatric weight-based dosing regimens. CONCLUSIONS: Model simulations support current recommended weight-based dosing in children with obesity for clindamycin and trimethoprim/sulfamethoxazole, as they met target exposure despite these changes in clearance and volume of distribution.
BACKGROUND AND OBJECTIVE: While one in five children in the USA are now obese, and more than three-quarters receive at least one drug during childhood, there is limited dosing guidance for this vulnerable patient population. Physiologically based pharmacokinetic modeling can bridge the gap in the understanding of how pharmacokinetics, including drug distribution and clearance, changes with obesity by incorporating known obesity-related physiological changes in children. The objective of this study was to develop a virtual population of children with obesity to enable physiologically based pharmacokinetic modeling, then use the novel virtual population in conjunction with previously developed models of clindamycin and trimethoprim/sulfamethoxazole to better understand dosing of these drugs in children with obesity. METHODS: To enable physiologically based pharmacokinetic modeling, a virtual population of children with obesity was developed using national survey, electronic health record, and clinical trial data, as well as data extracted from the literature. The virtual population accounts for key obesity-related changes in physiology relevant to pharmacokinetics, including increased body size, body composition, organ size and blood flow, plasma protein concentrations, and glomerular filtration rate. The virtual population was then used to predict the pharmacokinetics of clindamycin and trimethoprim/sulfamethoxazole in children with obesity using previously developed physiologically based pharmacokinetic models. RESULTS: Model simulations predicted observed concentrations well, with an overall average fold error of 1.09, 1.24, and 1.53 for clindamycin, trimethoprim, and sulfamethoxazole, respectively. Relative to children without obesity, children with obesity experienced decreased clindamycin and trimethoprim/sulfamethoxazole weight-normalized clearance and volume of distribution, and higher absolute doses under recommended pediatric weight-based dosing regimens. CONCLUSIONS: Model simulations support current recommended weight-based dosing in children with obesity for clindamycin and trimethoprim/sulfamethoxazole, as they met target exposure despite these changes in clearance and volume of distribution.
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Authors: Daniel Gonzalez; Paula Delmore; Barry T Bloom; C Michael Cotten; Brenda B Poindexter; Elisabeth McGowan; Karen Shattuck; Kathleen K Bradford; P Brian Smith; Michael Cohen-Wolkowiez; Maurine Morris; Wanrong Yin; Daniel K Benjamin; Matthew M Laughon Journal: Antimicrob Agents Chemother Date: 2016-04-22 Impact factor: 5.938
Authors: D Gonzalez; C Melloni; R Yogev; B B Poindexter; S R Mendley; P Delmore; J E Sullivan; J Autmizguine; A Lewandowski; B Harper; K M Watt; K C Lewis; E V Capparelli; D K Benjamin; M Cohen-Wolkowiez Journal: Clin Pharmacol Ther Date: 2014-06-20 Impact factor: 6.903
Authors: Thomas P Green; Helen J Binns; Huali Wu; Adolfo J Ariza; Eliana M Perrin; Maheen Quadri; Christoph P Hornik; Michael Cohen-Wolkowiez Journal: Clin Transl Sci Date: 2020-11-22 Impact factor: 4.438
Authors: Jacqueline G Gerhart; Fernando O Carreño; Jennifer L Ford; Andrea N Edginton; Eliana M Perrin; Kevin M Watt; William J Muller; Andrew M Atz; Amira Al-Uzri; Paula Delmore; Daniel Gonzalez Journal: CPT Pharmacometrics Syst Pharmacol Date: 2022-05-02
Authors: Jennifer Lynn Ford; Jacqueline G Gerhart; Andrea N Edginton; Jack A Yanovski; Yuen Yi Hon; Daniel Gonzalez Journal: J Clin Pharmacol Date: 2022-03-02 Impact factor: 2.860
Authors: Jacqueline G Gerhart; Stephen Balevic; Jaydeep Sinha; Eliana M Perrin; Jian Wang; Andrea N Edginton; Daniel Gonzalez Journal: Front Pharmacol Date: 2022-03-10 Impact factor: 5.810
Authors: Jacqueline G Gerhart; Fernando O Carreño; Matthew Shane Loop; Craig R Lee; Andrea N Edginton; Jaydeep Sinha; Karan R Kumar; Carl M Kirkpatrick; Christoph P Hornik; Daniel Gonzalez Journal: Clin Pharmacol Ther Date: 2022-05-18 Impact factor: 6.903