A combined approach to drug metabolism and toxicity assessment.

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 (QSARs), as well as empirical data from microarrays. In the current study we have further developed a novel computational approach, MetaDrug, that 1) predicts metabolites for molecules based on their chemical structure, 2) predicts the activity of the original compound and its metabolites with various absorption, distribution, metabolism, excretion, and toxicity 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, 2(S)-((3,5-bis(trifluoromethyl)benzyl)-oxy)-3(S)phenyl-4-((3-oxo-1,2,4-triazol-5-yl)methyl)morpholine (L-742694), trovofloxacin, 4-hydroxytamoxifen, and artemisinin and other artemisinin analogs to show the predicted interactions with cytochromes P450, pregnane X receptor, and P-glycoprotein, and the metabolites and the networks of genes that are affected. As a comparison, we used a second computational approach, MetaCore, to generate statistically significant gene networks with the available expression data. These case studies demonstrate the combination of QSARs and systems biology methods.

Computational prediction of human drug metabolism.

There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery. There is, therefore, a need for computational methodologies for uncovering the relationships between the structure and the biological activity of novel molecules. The convergence of numerous technologies, including high-throughput techniques, databases, ADME/Tox modelling and systems biology modelling, is leading to the foundation of systems-ADME/Tox. Results from experiments can be integrated with predictions to globally simulate and understand the likely complete effects of a molecule in humans. The development and early application of major components of MetaDrug (GeneGo, Inc.) software will be described, which includes rule-based metabolite prediction, quantitative structure-activity relationship models for major drug metabolising enzymes, and an extensive database of human protein-xenobiotic interactions. This represents a combined approach to predicting drug metabolism. MetaDrug can be readily used for visualising Phase I and II metabolic pathways, as well as interpreting high-throughput data derived from microarrays as networks of interacting objects. This will ultimately aid in hypothesis generation and the early triaging of molecules likely to have undesirable predicted properties or measured effects on key proteins and cellular functions.