title: Using molecular features of xenobiotics to predict hepatic gene expression response.
abstract: Despite recent advances in molecular medicine and rational drug design, many drugs still fail because toxic effects arise at the cellular and tissue level. In order to better understand these effects, cellular assays can generate high-throughput measurements of gene expression changes induced by small molecules. However, our understanding of how the chemical features of small molecules influence gene expression is very limited. Therefore, we investigated the extent to which chemical features of small molecules can reliably be associated with significant changes in gene expression. Specifically, we analyzed the gene expression response of rat liver cells to 170 different drugs and searched for genes whose expression could be related to chemical features alone. Surprisingly, we can predict the up-regulation of 87 genes (increased expression of at least 1.5 times compared to controls). We show an average cross-validation predictive area under the receiver operating characteristic curve (AUROC) of 0.7 or greater for each of these 87 genes. We applied our method to an external data set of rat liver gene expression response to a novel drug and achieved an AUROC of 0.7. We also validated our approach by predicting up-regulation of Cytochrome P450 1A2 (CYP1A2) in three drugs known to induce CYP1A2 that were not in our data set. Finally, a detailed analysis of the CYP1A2 predictor allowed us to identify which fragments made significant contributions to the predictive scores.
authors.affiliation: Biomedical Informatics Training Program, Stanford University School of Medicine and ‡Departments of Bioengineering and Genetics, Stanford University , Stanford, California 94305, United States.
authors.forename: Guy Haskin
authors.initials: GH
authors.lastname: Fernald
authors.forename: Russ B
authors.initials: RB
authors.lastname: Altman
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D019388:Cytochrome P-450 CYP1A2; D003580:Cytochromes / Q000235:genetics* / Q000378:metabolism; D062313:Databases, Pharmaceutical; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation / Q000187:drug effects*; D022781:Hepatocytes / Q000166:cytology / Q000187:drug effects* / Q000378:metabolism; D008099:Liver / Q000166:cytology / Q000187:drug effects* / Q000378:metabolism; D011237:Predictive Value of Tests; D011285:Pregnenolone Carbonitrile / Q000494:pharmacology*; D012372:ROC Curve; D051381:Rats; D013329:Structure-Activity Relationship; D015262:Xenobiotics / Q000494:pharmacology*
chemical_list: D003580:Cytochromes; D015262:Xenobiotics; D011285:Pregnenolone Carbonitrile; C017170:Cyp1a2 protein, rat; D019388:Cytochrome P-450 CYP1A2