Predictive Performance of Next Generation Human Physiologically Based Kinetic (PBK) Models Based on In Vitro and In Silico Input Data

The goal of the present study was to assess the predictive performance of a generic human physiologically based kinetic (PBK) model based on in vitro and in silica input data and the effect of using different input approaches for chemical parameterization on those predictions. For this purpose, a dataset was created of 38,772 C-max predictions for 44 compounds by applying different combinations of in vitro and in silico approaches for chemical parameterization, and these predicted C-max values were compared to reported in vivo data. Best results were achieved when the hepatic clearance was parameterized based on in vitro (i.e., hepatocytes or liver S9) measured intrinsic clearance values, the method of Rodgers and Rowland for calculating tissue:plasma partition coefficients, and the method of Lobell and Sivarajah for calculating the fraction unbound in plasma. With these parameters, the median C-max values of 34 out of the 44 compounds were predicted within 5-fold of the observed C-max, and the C-max values of 19 compounds were predicted within 2-fold. The median C-max values of 10 compounds were more than 5-fold overestimated. Underestimations (> 5-fold) did not occur. A comparison of the current generic PBK model structure with chemical-specific PBK models available in the literature was made to identify possible kinetic processes not included in the generic PBK model that might explain the overestimations. Overall, the results provide crucial insights into the predictive performance of PBK models based on in vitro and in silico input and the influence of different input approaches on the model predictions.