Male rats were treated with various model compounds or the appropriate vehicle controls. Most substances were either well-known hepatotoxicants or showed hepatotoxicity during preclinical testing. The aim of the present study was to determine if biological samples from rats treated with various compounds can be classified based on gene expression profiles. In addition to gene expression analysis using microarrays, a complete serum chemistry profile and liver and kidney histopathology were performed. We analyzed hepatic gene expression profiles using a supervised learning method (support vector machines; SVMs) to generate classification rules and combined this with recursive feature elimination to improve classification performance and to identify a compact subset of probe sets with potential use as biomarkers. Two different SVM algorithms were tested, and the models obtained were validated with a compound-based external cross-validation approach. Our predictive models were able to discriminate between hepatotoxic and nonhepatotoxic compounds. Furthermore, they predicted the correct class of hepatotoxicant in most cases. We provide an example showing that a predictive model built on transcript profiles from one rat strain can successfully classify profiles from another rat strain. In addition, we demonstrate that the predictive models identify nonresponders and are able to discriminate between gene changes related to pharmacology and toxicity. This work confirms the hypothesis that compound classification based on gene expression data is feasible.

Male rats were treated with various model compounds or the appropriate vehicle controls. Most substances were either well-known hepatotoxicants or showed hepatotoxicity during preclinical testing. The aim of the present study was to determine if biological samples from rats treated with various compounds can be classified based on gene expression profiles. In addition to gene expression analysis using microarrays, a complete serum chemistry profile and liver and kidney histopathology were performed. We analyzed hepatic gene expression profiles using a supervised learning method (support vector machines; SVMs) to generate classification rules and combined this with recursive feature elimination to improve classification performance and to identify a compact subset of probe sets with potential use as biomarkers. Two different SVM algorithms were tested, and the models obtained were validated with a compound-based external cross-validation approach. Our predictive models were able to discriminate between hepatotoxic and nonhepatotoxic compounds. Furthermore, they predicted the correct class of hepatotoxicant in most cases. We provide an example showing that a predictive model built on transcript profiles from one rat strain can successfully classify profiles from another rat strain. In addition, we demonstrate that the predictive models identify nonresponders and are able to discriminate between gene changes related to pharmacology and toxicity. This work confirms the hypothesis that compound classification based on gene expression data is feasible.

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Serotonin is involved in disorders of the central nervous system; thus, specific 5-HT(6) receptor antagonists have therapeutic potential. Nevertheless, preclinical tests showed that Ro 65-7199 caused hepatic steatosis. Here, we investigated the hepatic effects of Ro 65-7199 and Ro 66-0074 using toxicogenomics. The profiles obtained after exposure of rats to both compounds clearly show that two pharmacologically closely related compounds with different toxicological profiles can be distinguished based on gene expression profiles. Moreover, side effects can be detected earlier with toxicogenomics than with conventional end points. A possible link between the sterol metabolic pathway, the induction of CYP2B, and the hepatic fat accumulation was also established. Summarizing, gene expression profiles allow both compounds to be distinguished according to their toxicity and provide mechanistic insights. The results clearly show the power of toxicogenomics as a tool for obtaining characteristic fingerprints at early time-points and for generating mechanistic hypotheses.

Both types of biopsies showed similar high concordance rates with whole blastocyst results. Therefore, regarding the confirmation rates shown in this work, day-3 embryo biopsies can be representative of the whole embryo and both types of biopsy can be used for clinical analysis in PGS following the described array-CGH protocol.

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