Predicting human tissue exposures to xenobiotics using a bottom-up physiologically-based biokinetic model.

Advances in physiologically-based biokinetic (PBK) modelling, in vitro-to-in vivo extrapolation (IVIVE) methodologies, and development of permeability-limited biokinetic models have allowed predictions of tissue drug concentrations without utilizing in vivo animal or human data. However, there is a lack of in vivo human tissue concentrations to validate these models. Herein, we validated the performance of our previously published bottom-up rosuvastatin (RSV) PBK model with clinical data from a recently published study that made use of positron emission tomography (PET) imaging to quantify the hepatic concentrations of [11C]RSV drug-drug interaction (DDI) with cyclosporine A (CsA). Simulated RSV area under the plasma concentration-time curve (AUC0h-t) and maximum plasma concentration (Cmax) before and after DDI were within 1.5-fold of the observed data. Simulated AUC0-30min and Cmax ratios in the DDI setting matched the observed ratios closely (within 1.1-fold). To predict RSV hepatic concentrations, the model inputs were modified to account for RSV in the bile canaliculi after biliary excretion. The model recapitulated the observed hepatic concentrations before DDI and the decrease in hepatic concentrations after DDI. Simulated area under the liver concentration-time curve (AUC0-30min,liver), maximum liver concentration (Cmax,liver), AUC0-30min,liver ratio and Cmax,liver ratios were predicted within 1.5-fold of the observed data. In summary, we validated the ability of bottom-up PBK modelling to predict RSV hepatic concentrations with and without DDI with CsA. Our findings confirm the importance to account for drug distributed within the bile canaliculi for accurate prediction of hepatic tissue drug levels when compared against in vivo liver PET scan data.