Toxicogenomics technology defines toxicity gene expression signatures for early predictions and hypotheses generation for mechanistic studies, which are important approaches for evaluating toxicity of drug candidate compounds. A large gene expression database built using cDNA microarrays and liver samples treated with over one hundred paradigm compounds was mined to determine gene expression signatures for nongenotoxic carcinogens (NGTCs). Data were obtained from male rats treated for 24 h. Training/testing sets of 24 NGTCs and 28 noncarcinogens were used to select genes. A semiexhaustive, nonredundant gene selection algorithm yielded six genes (nuclear transport factor 2, NUTF2; progesterone receptor membrane component 1, Pgrmc1; liver uridine diphosphate glucuronyltransferase, phenobarbital-inducible form, UDPGTr2; metallothionein 1A, MT1A; suppressor of lin-12 homolog, Sel1h; and methionine adenosyltransferase 1, alpha, Mat1a), which identified NGTCs with 88.5% prediction accuracy estimated by cross-validation. This six genes signature set also predicted NGTCs with 84% accuracy when samples were hybridized to commercially available CodeLink oligo-based microarrays. To unveil molecular mechanisms of nongenotoxic carcinogenesis, 125 differentially expressed genes (P<0.01) were selected by Student's t-test. These genes appear biologically relevant, of 71 well-annotated genes from these 125 genes, 62 were overrepresented in five biochemical pathway networks (most linked to cancer), and all of these networks were linked by one gene, c-myc. Gene expression profiling at early time points accurately predicts NGTC potential of compounds, and the same data can be mined effectively for other toxicity signatures. Predictive genes confirm prior work and suggest pathways critical for early stages of carcinogenesis.

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Felbamate is an antiepileptic drug that is associated with minimal toxicity in preclinical species such as rat and dog but has an unacceptable incidence of serious idiosyncratic reactions in man. Idiosyncratic reactions account for over half of toxicity-related drug failures in the marketplace, and improving the preclinical detection of idiosyncratic toxicities is thus of paramount importance to the pharmaceutical industry. The formation of reactive metabolites is common among most drugs associated with idiosyncratic drug reactions and may cause deleterious effects through covalent binding and/or oxidative stress. In the present study, felbamate was compared to several other antiepileptic drugs (valproic acid, carbamazepine, phenobarbital, and phenytoin), using covalent binding of radiolabeled drugs and hepatic gene expression responses to evaluate oxidative stress/reactive metabolite potential. Despite causing only very mild effects on covalent binding parameters, felbamate produced robust effects on a previously established oxidative stress/reactive metabolite gene expression signature. The other antiepileptic drugs and acetaminophen are known hepatotoxicants at high doses in the rat, and all increased covalent binding to liver proteins in vivo and/or to liver microsomes from human and rat. With the exception of acetaminophen, valproic acid exhibited the highest covalent binding in vivo, whereas carbamazepine exhibited the highest levels in vitro. Pronounced effects on oxidative stress/reactive metabolite-responsive gene expression were observed after carbamazepine, phenobarbital, and phenytoin administration. Valproic acid had only minor effects on the oxidative stress/reactive metabolite indicator genes. The relative ease of detection of felbamate based on gene expression results in rat liver as having potential oxidative stressor/reactive metabolites indicates that this approach may be useful in screening for potential idiosyncratic toxicity. Together, measurements of gene expression along with covalent binding should improve the safety assessment of candidate drugs.