Received for publication April 8, 2005.
Revised October 10, 2005.
Accepted for publication October 11, 2005.

Application of a generic physiologically-based pharmacokinetic model to the estimation of xenobiotic levels in human plasma

Abstract

Estimation of xenobiotic kinetics in man frequently relies upon extrapolation from experimental data generated in animals. In an accompanying paper, we have presented a unique, generic, physiologically-based pharmacokinetic model, and described its application to the prediction of rat plasma pharmacokinetics from in vitro data alone. Here we demonstrate the application of the same model, parameterized for human physiology, to the estimation of plasma pharmacokinetics in man, and report a comparative evaluation against some recently published predictive methods that involve scaling from in vivo animal data. The model was parameterized through an optimization process, employing a training set of in vivo data taken from the literature, and validated using a separate test set of published in vivo data. On average, the vertical divergence of the predicted plasma concentrations from the observed data, on a semi-log concentration-time plot, was 0.47 log units. For the training set, more than 80% of the predicted values of a standardized measure of AUC were within threefold of the observed values; over 70% of the test set predictions were within the same margin. Furthermore, in terms of predicting human clearance for the test set, the model was found to match or exceed the performance of three published interspecies scaling methods, all of which showed a distinct bias towards over-prediction. We conclude that the generic PBPK model, as a means of integrating readily-determined in vitro and/or in silico data, is potentially a powerful, cost- effective tool for predicting human xenobiotic kinetics in drug discovery and risk assessment.

Key words: computational models, computer modeling and simulation, in vitro-in vivo prediction, in vitro-in vivo scaling, mathematical modeling, Monte Carlo simulations, pharmacokinetic modeling, physiologically-based modeling, physiologically-based pharmacokinetics

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