Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans.

Physiologically 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 with in vitro or in silico methods. 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 on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined 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 collected in vivo concentration-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 tenfold). 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.

Uncovering the toxicity mechanisms of a series of carboxylic acids in liver cells through computational and experimental approaches.

Hepatotoxicity poses a significant concern in drug design due to the potential liver damage that can be caused by new drugs. Among common manifestations of hepatotoxic damage is lipid accumulation in hepatic tissue, resulting in liver steatosis or phospholipidosis. Carboxylic derivatives are prone to interfere with fatty acid metabolism and cause lipid accumulation in hepatocytes. This study investigates the toxic behaviour of 24 structurally related carboxylic acids in hepatocytes, specifically their ability to cause accumulation of fatty acids and phospholipids. Using high-content screening (HCS) assays, we identified two distinct lipid accumulation patterns. Subsequently, we developed structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) models to determine relevant molecular substructures and descriptors contributing to these adverse effects. Additionally, we calculated physicochemical properties associated with lipid accumulation in hepatocytes and examined their correlation with our chemical structure characteristics. To assess the applicability of our findings to a wide range of chemical compounds, we employed two external datasets to evaluate the distribution of our QSAR descriptors. Our study highlights the significance of subtle molecular structural variations in triggering hepatotoxicity, such as the presence of nitrogen or the specific arrangement of substitutions within the carbon chain. By employing our comprehensive approach, we pinpointed specific molecules and elucidated their mechanisms of toxicity, thus offering valuable insights to guide future toxicology investigations.