Combining in vitro and in vivo pharmacokinetic data for prediction of hepatic drug clearance in humans by artificial neural networks and multivariate statistical techniques.

Abstract

Several statistical regression models and artificial neural networks were used to predict the hepatic drug clearance in humans from in vitro (hepatocyte) and in vivo pharmacokinetic data and to identify the most predictive models for this purpose. Cross-validation was performed to assess prediction accuracy. It turned out that human hepatocyte data was the best predictor, followed by rat hepatocyte data. Dog hepatocyte data and dog and rat in vivo data appear to be uncorrelated with human in vivo clearance and did not significantly contribute to the prediction models. Considering the present evaluation, the most cost-effective and most accurate approach to achieve satisfactory predictions in human is a combination of in vitro clearances on human and rat hepatocytes. Such information is of considerable value to speed up drug discovery. This study also showed some of the limitations of the approach related to the size of the database used in the present evaluation.