Received for publication November 22, 2005.
Revised December 21, 2005.
Accepted for publication December 21, 2005.

A COMBINED APPROACH TO DRUG METABOLISM AND TOXICITY ASSESSMENT

Abstract

The challenge of predicting the metabolism or toxicity of a drug in humans has been approached using in vivo animal models, in vitro systems, high throughput genomics and proteomics methods and more recently computational approaches. Understanding the complexity of biological systems requires a broader perspective rather than focusing on just one method in isolation for prediction. Multiple methods may therefore be necessary and combined for a more accurate prediction. In the field of drug metabolism and toxicology we have seen the growth in recent years of computational quantitative structure activity relationships (QSAR) as well as empirical data from microarrays. In the current study we have further developed a novel computational approach MetaDrug^TM that: 1) predicts metabolites for molecules based on their chemical structure, 2) predicts the activity of the original compound and its metabolites with various ADME/Tox models, 3) incorporates the predictions with human cell signaling and metabolic pathways and networks and 4) integrates networks and metabolites, with relevant toxicogenomic or other high throughput data. We have demonstrated the utility of such an approach using recently published data from in vitro metabolism and microarray studies for Aprepitant, L-742694, Trovofloxacin, 4-hydroxytamoxifen and artemisinin and other artemisinin analogs to show the predicted interactions with CYPs, PXR and P-gp, the metabolites and the networks of genes that are affected. As a comparison we used a second computational approach MetaCore^TM, to generate statistically significant gene networks with the available expression data. These case studies demonstrate the combination of QSAR and systems biology methods.

Key words: computational models, computer modeling and simulation, genomics, nuclear receptors, toxicology

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