Predictive Performance of Next Generation Physiologically Based Kinetic (PBK) Model Predictions in Rats Based on In Vitro and In Silico Input Data

The goal of the present study was to assess the predictive performance of a minimal generic rat physiologically based kinetic (PBK) model based on in vitro and in silico input data to predict peak plasma concentrations (C-max) upon single oral dosing. To this purpose, a dataset was generated of 3960 C-max predictions for 44 compounds, applying different combinations of in vitro and in silico approaches for chemical parameterization, and comparison of the predictions to reported in vivo data. Best performance was obtained when (1) the hepatic clearance was parameterized based on in vitro measured intrinsic clearance values, (2) the method of Rodgers and Rowland for calculating partition coefficients, and (3) in silico calculated fraction unbound plasma and P-app values (the latter especially for very lipophilic compounds). Based on these input data, the median C-max of 32 compounds could be predicted within 10-fold of the observed C-max, with 22 out of these 32 compounds being predicted within 5-fold, and 8 compounds within 2-fold. Overestimations of more than 10-fold were observed for 12 compounds, whereas no underestimations of more than 10-fold occurred. Median C-max predictions were frequently found to be within 10-fold of the observed C-max when the scaled unbound hepatic intrinsic clearance (Cl-int,Cl-u) was either higher than 20 l/h or lower than 1 l/h. Similar findings were obtained with a test set of 5 in-house BASF compounds. Overall, this study provides relevant insights in the predictive performance of a minimal PBK model based on in vitro and in silico input data.

Automated summary

The present study assesses the predictive performance of a minimal generic rat physiologically based kinetic (PBK) model to predict peak plasma concentrations (C-max) upon single oral dosing based on in vitro and in silico input data. The best performance is obtained by parameterizing the hepatic clearance, calculating partition coefficients, and using in silico calculated fraction unbound plasma and P-app values. The study provides relevant insights into the predictive performance of the model.