A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding

摘要

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t(1/2), 100% of drugs), peak plasma concentration (C-max, 100%), area under the plasma concentration-time curve (AUC(0-t), 95.4%), clearance (CLh, 95.4%), mean residence time (MRT, 95.4%) and steady state volume (V-ss, 90.9%). The impact of f(up) errors on CLh and V-ss prediction was evaluated. Errors in f(up) resulted in proportional errors in clearance prediction for low-clearance compounds, and in V-ss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in f(up) did not propagate to errors in V-ss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright (C) 2016 John Wiley & Sons, Ltd.