1 Towards more robust evaluation of the predictive performance of physiologically based pharmacokinetic models

Introduction With the rise in use of physiologically-based pharmacokinetic (PBPK) modeling over the past decade, the use of PBPK modeling to underpin drug dosing in special patient populations (e.g. pediatrics and pregnant women) has become an attractive option. In order to use PBPK models for high-impact decisions, thorough qualification and validation of the model is essential to gain enough confidence in model performance. Currently, there is no consensus on the appropriate validation steps while clinicians demand a clear measure of model performance before considering implementing PBPK model-informed dosing. We aimed to bridge this gap and propose a novel more stringent validation method. Methodology We chose to use a confidence interval for the predicted-to-observed geometric mean ratio. This approach is more in line with currently accepted bioequivalence testing procedures and can therefore aid in improved model credibility and acceptance. Two different constructions for a confidence interval are outlined, depending on whether individual observations in the clinical comparator data sets are available or not. In addition, an easy-to-use implementation tool is provided to make our proposed method more accessible. Results The two testing procedures are demonstrated for both clinical and hypothetical data for an example midazolam PBPK model. The example PBPK model validation demonstrates that creating a confidence interval yields a more robust evaluation of the model than a point estimate, such as the commonly used 2-fold acceptance criterion. Additionally, the use of individual predictions can reduce the amount of required test subjects. Conclusion With this easy-to-implement method, we hope to increase confidence in PBPK model performance to facilitate its use for directly informing drug dosing in clinical care. Especially, in the field of pediatric pharmacology, more thorough validated PBPK models could increase the evidence base of dosing recommendations.