title: 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 (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.
authors.affiliation: Computational Biology, GeneGo, Inc., 500 Renaissance Drive, Suite 106, St. Joseph, MI 49085, USA. sean@genego.com
authors.forename: Sean
authors.initials: S
authors.lastname: Ekins
authors.forename: Sergey
authors.initials: S
authors.lastname: Andreyev
authors.forename: Andy
authors.initials: A
authors.lastname: Ryabov
authors.forename: Eugene
authors.initials: E
authors.lastname: Kirillov
authors.forename: Eugene A
authors.initials: EA
authors.lastname: Rakhmatulin
authors.forename: Svetlana
authors.initials: S
authors.lastname: Sorokina
authors.forename: Andrej
authors.initials: A
authors.lastname: Bugrim
authors.forename: Tatiana
authors.initials: T
authors.lastname: Nikolskaya
mesh_terms: D064420:Drug-Related Side Effects and Adverse Reactions; D046228:Microarray Analysis; D008962:Models, Theoretical; D009025:Morpholines / Q000378:metabolism / Q000493:pharmacokinetics / Q000633:toxicity; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship; D012985:Software Design
chemical_list: C099406:L 742694; D009025:Morpholines; D004364:Pharmaceutical Preparations