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. 2006 Mar;7(2):177-86.
doi: 10.2217/14622416.7.2.177.

Development of a toxicogenomics in vitro assay for the efficient characterization of compounds

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Development of a toxicogenomics in vitro assay for the efficient characterization of compounds

Yi Yang et al. Pharmacogenomics. 2006 Mar.

Abstract

In vitro toxicogenomics represents a useful approach for evaluating the toxic properties of new drug candidates early in the drug discovery process using minimal amounts of compounds. The aim of this study was to develop in vitro-based gene expression assays for two prototypical toxicological classes: aryl hydrocarbon receptor (AhR) agonists and peroxisome proliferator activated receptor alpha (PPAR alpha) agonists. Primary rat hepatocytes were exposed to a number of class-specific compounds, including 3-methylcholanthrene, aroclor, and beta-napthoflavone as AhR agonists, bezafibrate, clofibrate, and Wy-14643 as peroxisome proliferators, and chlorpheniramine, penicillin and spectinomycin as negative controls. Global gene expression profiles were generated with microarrays for each class of compounds. Using linear discriminant analysis coupled with permutation-based t-test, gene signatures were established to classify compounds according to a discriminant score. The final gene signatures consist of eight genes for AhR agonism and 11 genes for PPAR alpha agonism, and were further validated using additional compounds. The assay was initially developed using a microarray platform. The authors then evaluated whether it could be transferred to a more cost-effective platform with higher throughput. The results indicate that a small set of genes can be used to quantitatively assess the degree to which a compound falls into a certain mechanistic toxicological class. While this study only focused on two classes, it could be expanded to encompass other toxicological mechanistic classes as well. Furthermore, by adapting this type of assay to a higher throughput platform, in vitro toxicogenomics can represent an effective approach to generate robust toxicological data early in the drug discovery process.

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