Systematic Evaluation of High-Throughput PBK Modelling Strategies for the Prediction of Intravenous and Oral Pharmacokinetics in Humans

Abstract

AbstractPhysiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing within vitroorin silicomethods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for Non-Animal Methods. To address this limitation, High-throughput PBK modelling aims to rely exclusively onin vitroandin silicodata for model generation. Here, we evaluate a variety ofin silicotools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000+ publicly available humanin vivoconcentration-time profiles of 200+ compounds (IV and oral administration), as well asin silico,in vitroandin vivodetermined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collectedin vivoconcentration-time profiles. Our results show that even simple, generic High-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within 10-fold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for High-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of High-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.