Non-hepatotoxic Compounds Research Articles

Discriminating Different Classes of Toxicants by Transcript Profiling

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.

Discriminating different classes of toxicants by transcript profiling.

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.

Identification of in vitro protein biomarkers of idiosyncratic liver toxicity

Drug-induced idiosyncratic hepatotoxicity continues to be an important safety issue for the pharmaceutical industry. This toxicity is due, in part, to the limited predictive nature of current pre-clinical study systems. A hypothesis was formed that treatment of existing in vitro hepatocyte cultures with drugs clinically linked to idiosyncratic hepatotoxicity would result in the release of extracellular protein biomarkers indicative of liver toxicity. To test this hypothesis, a combination of proteomic and immunological techniques were used to first identify, and subsequently verify, components of the protein-laden conditioned culture media from immortalized human hepatocytes which overexpressed cytochrome P450 3A4. These cells were treated separately with seven individual compounds made up of a combination of thiazolidinedione and l-tyrosine PPARγ agonists and HIV protease inhibitors, plus a vehicle control (dimethyl sulfoxide). For each drug class, clinically determined hepatotoxic and non-hepatotoxic compounds were compared. Two proteins, BMS-PTX-265 and BMS-PTX-837, were reproducibly and significantly increased in the conditioned media from cells treated with each of the toxic compounds as compared to media from cells treated with the non-toxic compounds (and vehicle). This result supported the hypothesis, and so a series of successive assays (western blots and enzyme linked immunosorbent assays) were used to measure the response of these two proteins as a function of an expanded set of 20 compounds. For all 20 drugs, elevations of BMS-PTX-265 correlated exactly with the known safety profile; whereas changes in BMS-PTX-837 correctly predicted the safety profile in 19 of 20 drugs (one false negative). In summary, the data supports both the pre-clinical in vitro method as a means to identify new biomarkers of liver toxicity, as well as the validity of the biomarkers themselves.

Effect of various non-hepatotoxic compounds on urinary and liver taurine levels in rats.

Administration of compounds which alter protein synthesis or sulphur amino acid metabolism in rats results in changes in the excretion of urinary taurine. Treatment with diethylmaleate (DEM) or phorone, which will deplete glutathione (GSH), reduces taurine excretion, whereas treatment with buthionine sulphoximine (BSO), which will inhibit glutathione synthesis, increases taurine excretion. Treatment with cycloheximide, an inhibitor of protein synthesis, increases taurine excretion, whereas pretreatment with phenobarbital, which will increase protein synthesis, decreases taurine excretion. Administration of agents which damage organs other than the liver such as the kidney, heart and testes, does not increase urinary taurine.