E-005 - PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODELING TO SUPPORT PEDIATRIC CLINICAL DEVELOPMENT: AN IQ WORKING GROUP PERSPECTIVE ON THE CURRENT STATUS AND CHALLENGES
Wednesday, May 28, 2025
5:00 PM - 6:30 PM East Coast USA Time
K. Taskar1, J. Yates2, A. Cheung3; 1GSK, GSK - UK, United Kingdom, 2GSK, GSK- UK, United Kingdom, 3Certara, UK, UK.
Background: Pediatric extrapolation strategies issued by health authorities have streamlined pediatric drug development and reduced the unnecessary burden of conducting pediatric clinical studies. In line with these strategies, physiologically-based pharmacokinetic (PBPK) models have been utilized extensively for initial dosing regimen and sampling timepoint selection for pediatric studies, as well as dose validation throughout pediatric drug development. Methods: Here, the status and challenges of PBPK modeling in pediatric drug development have been summarized by the IQ Pediatric PBPK Working Group. Our work reviews current practices for pediatric PBPK modeling across various therapeutic areas. To enable best practice, we propose an optimized workflow for pediatric PBPK modeling recommendations. Two selected key pediatric PBPK case examples are also described, where modeling impacted the drug label extension to pediatric patients. Moreover, we analyze the current gaps and challenges in our understanding of drug absorption, distribution, metabolism, and elimination in pediatric PBPK model development. Results: Since neonates are the least studied and the most medically fragile, the depth of our understanding of swiftly evolving physiology processes is alarmingly limited and so there exist significant modelling gaps which we summarize here. Conclusion: . An in-depth scrutiny of the strengths and weaknesses of published neonate PBPK model performance for 8 compounds and monoclonal antibodies is undertaken. Finally, we provide recommendations, including building a public data repository, leveraging real-world data, and implementing microdose studies for addressing pediatric PBPK modeling challenges.