APPLICATION OF A GENERIC PHYSIOLOGICALLY BASED PHARMACOKINETIC MODEL TO THE ESTIMATION OF XENOBIOTIC LEVELS IN HUMAN PLASMA
- 12 October 2005
- journal article
- research article
- Published by American Society for Pharmacology & Experimental Therapeutics (ASPET) in Drug Metabolism and Disposition
- Vol. 34 (1), 94-101
- https://doi.org/10.1124/dmd.105.004838
Abstract
Estimation of xenobiotic kinetics in humans 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 humans 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, using 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 semilog concentration-time plot, was 0.47 log unit. For the training set, more than 80% of the predicted values of a standardized measure of the area under the concentration-time curve were within 3-fold 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 toward overprediction. We conclude that the generic physiologically based pharmacokinetic 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.Keywords
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