Background and Objective: More than half of all drugs are still prescribed off-label to children. Pharmacokinetic (PK) data are needed to support off-label dosing, however for many drugs such data are either sparse or not representative. Physiologically-based pharmacokinetic (PBPK) models are increasingly used to study PK and guide dosing decisions. Building compound models to study PK requires expertise and is time-consuming. Therefore, in this paper, we studied the feasibility of predicting pediatric exposure by pragmatically combining existing compound models, developed e.g. for studies in adults, with a pediatric and preterm physiology model. Methods: Seven drugs, with various PK characteristics, were selected (meropenem, ceftazidime, azithromycin, propofol, midazolam, lorazepam, and caffeine) as a proof of concept. Simcyp® v20 was used to predict exposure in adults, children, and (pre)term neonates, by combining an existing compound model with relevant virtual physiology models. Predictive performance was evaluated by calculating the ratios of predicted-to-observed PK parameter values (0.5- to 2-fold acceptance range) and by visual predictive checks with prediction error values. Results: Overall, model predicted PK in infants, children and adolescents capture clinical data. Confidence in PBPK model performance was therefore considered high. Predictive performance tends to decrease when predicting PK in the (pre)term neonatal population. Conclusion: Pragmatic PBPK modeling in pediatrics, based on compound models verified with adult data, is feasible. A thorough understanding of the model assumptions and limitations is required, before model-informed doses can be recommended for clinical use.
Bibliographical noteFunding Information:
This publication is based on research funded by the Bill & Melinda Gates Foundation (INV-001822). The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the Bill & Melinda Gates Foundation.
© 2022, The Author(s).