Impact of In Vitro Experimental Variation in Kinetic Parameters on Physiologically Based Kinetic (PBK) Model Simulations

In vitro toxicokinetic data are critical in meeting an increased regulatory need to improve chemical safety evaluations towards a better understanding of internal human chemical exposure and toxicity. In vitro intrinsic hepatic clearance (CLint), the fraction unbound in plasma (fup), and the intestinal apparent permeability (Papp) are important parameters as input in a phys-iologically based kinetic (PBK) model to make first estimates of internal exposure after oral dosing. In the present study we explored the experimental variation in the values for these parameters as reported in the literature. Furthermore, the impact that this experimental variation has on PBK model predictions of maximum plasma concentration (Cmax) and the area under the concentration time curve (AUC0-24h) was determined. As a result of the experimental variation in CLint, Papp, and fup, the predicted variation in Cmax for individual compounds ranged between 1.4-to 28-fold, and the predicted variation in AUC0-24h ranged between 1.4-and 23-fold. These results indicate that there are still some important steps to take to achieve robust data that can be used in regulatory applications. To gain regulatory acceptance of in vitro kinetic data and PBK models based on in vitro input data, the boundaries in experimental conditions as well as the applicability domain and the use of different in vitro kinetic models need to be described in guidance documents.

Automated summary

In vitro toxicokinetic data are crucial for improving chemical safety evaluations and understanding internal human chemical exposure and toxicity. This study examined the experimental variation in important parameters, such as CLint, fup, and Papp, reported in the literature. The results showed that the predicted variation in Cmax ranged from 1.4 to 28-fold and in AUC0-24h ranged from 1.4 to 23-fold due to the experimental variation in these parameters. These findings highlight the need for robust data in regulatory applications.