This study shows that the level of evidence for off-label pediatric pharmacotherapy is low: for only 14% of off-label records, high quality studies are available. Thirty-seven percent of offlabel records are not supported by any clinical studies at all. HOW MIGHT THIS CHANGE CLINICAL PHARMA COLOGY OR TRANSLATIONAL SCIENCE? Performing high quality randomized controlled trials for the > 2,000 off-label record is a very long pathway to close this information gap. Alternatively, modeling and simulation may be valuable approaches to strengthen the evidence base for offlabel use of drugs, especially in younger age groups.
Physiologically based pharmacokinetic (PBPK) modeling can be an attractive tool to increase the evidence base of pediatric drug dosing recommendations by making optimal use of existing pharmacokinetic (PK) data. A pragmatic approach of combining available compound models with a virtual pediatric physiology model can be a rational solution to predict PK and hence support dosing guidelines for children in real-life clinical care, when it can also be employed by individuals with little experience in PBPK modeling. This comes within reach as user-friendly PBPK modeling platforms exist and, for many drugs and populations, models are ready for use. We have identified a list of drugs that can serve as a starting point for pragmatic PBPK modeling to address current clinical dosing needs.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40272-022-00535-w.
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. Physiologicallybased 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.Rick Greupink and Saskia N. de Wildt: Joint last author.
It is well accepted that off‐label drug dosing recommendations for pediatric patients should be based on the best available evidence. However, the available traditional evidence is often low. To bridge this gap, physiologically‐based pharmacokinetic (PBPK) modeling is a scientifically well‐founded tool that can be used to enable model‐informed dosing (MID) recommendations in children in clinical practice. In this tutorial, we provide a pragmatic, PBPK‐based pediatric modeling workflow. For this approach to be successfully implemented in pediatric clinical practice, a thorough understanding of the model assumptions and limitations is required. More importantly, careful evaluation of a MID approach within the context of overall benefits and the potential risks is crucial. The tutorial is aimed to help modelers, researchers and clinicians, to effectively employ PBPK simulations to support pediatric drug dosing.
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