Validation of gene expression profiles from cholestatic hepatotoxicants in vitro against human in vivo cholestasis.

Drug-induced liver injury remains the most common cause of acute liver failure and a frequently indicated reason for withdrawal of drugs. For the purpose of evaluating the relevance of liver cell models for assessing hepatotoxic risks in intact humans, we here aimed to benchmark 'omics-derived mechanistic data from three in vitro models for parenchymal liver function, intended for the investigation of drug-induced cholestasis, against 'omics data from cholestatic patients. Transcriptomic changes in HepG2 cells, primary mouse hepatocytes and primary human hepatocytes exposed to known cholestatic compounds were analyzed using microarrays. Some of the differentially expressed genes in HepG2 cells were also differentially expressed into the same direction in human cholestasis. The overlap between drug-induced transcriptomic responses in primary mouse hepatocytes and primary human hepatocytes appeared limited and no genes overlapping with in vivo cholestasis were found. Thereupon, a pathway for drug-induced cholestasis was used to map the drug-induced transcriptomic modifications involved in bile salt homeostasis. Indications of an adaptive response to prevent and reduce intracellular bile salt accumulation were observed in vivo as well as in the in vitro liver models. Furthermore, drug-specific changes were found, which may be indicative for their cholestatic properties. Furthermore, connectivity mapping was applied in order to investigate the predictive value of the in vitro models for in vivo cholestasis. This analysis resulted in a positive connection score for most compounds, which may indicate that for identifying cholestatic compounds the focus should be on gene expression signatures rather than on differentially expressed genes.

Integrating multiple omics to unravel mechanisms of Cyclosporin A induced hepatotoxicity in vitro.

In order to improve attrition rates of candidate-drugs there is a need for a better understanding of the mechanisms underlying drug-induced hepatotoxicity. We aim to further unravel the toxicological response of hepatocytes to a prototypical cholestatic compound by integrating transcriptomic and metabonomic profiling of HepG2 cells exposed to Cyclosporin A. Cyclosporin A exposure induced intracellular cholesterol accumulation and diminished intracellular bile acid levels. Performing pathway analyses of significant mRNAs and metabolites separately and integrated, resulted in more relevant pathways for the latter. Integrated analyses showed pathways involved in cell cycle and cellular metabolism to be significantly changed. Moreover, pathways involved in protein processing of the endoplasmic reticulum, bile acid biosynthesis and cholesterol metabolism were significantly affected. Our findings indicate that an integrated approach combining metabonomics and transcriptomics data derived from representative in vitro models, with bioinformatics can improve our understanding of the mechanisms of action underlying drug-induced hepatotoxicity. Furthermore, we showed that integrating multiple omics and thereby analyzing genes, microRNAs and metabolites of the opposed model for drug-induced cholestasis can give valuable information about mechanisms of drug-induced cholestasis in vitro and therefore could be used in toxicity screening of new drug candidates at an early stage of drug discovery.

Classification of hepatotoxicants using HepG2 cells: A proof of principle study.

With the number of new drug candidates increasing every year, there is a need for high-throughput human toxicity screenings. As the liver is the most important organ in drug metabolism and thus capable of generating relatively high levels of toxic metabolites, it is important to find a reliable strategy to screen for drug-induced hepatotoxicity. Microarray-based transcriptomics is a well-established technique in toxicogenomics research and is an ideal approach to screen for drug-induced injury at an early stage. The aim of this study was to prove the principle of classifying known hepatotoxicants and nonhepatotoxicants using their distinctive gene expression profiles in vitro in HepG2 cells. Furthermore, we undertook to subclassify the hepatotoxic compounds by investigating the subclass of cholestatic compounds. Prediction analysis for microarrays was used for classification of hepatotoxicants and nonhepatotoxicants, which resulted in an accuracy of 92% on the training set and 91% on the validation set, using 36 genes. A second model was set up with the goal of finding classifiers for cholestasis, resulting in 12 genes that appeared capable of correctly classifying 8 of the 9 cholestatic compounds, resulting in an accuracy of 93%. We were able to prove the principle that transcriptomic analyses of HepG2 cells can indeed be used to classify chemical entities for hepatotoxicity. Genes selected for classification of hepatotoxicity and cholestasis indicate that endoplasmic reticulum stress and the unfolded protein response may be important cellular effects of drug-induced liver injury. However, the number of compounds in both the training set and the validation set should be increased to improve the reliability of the prediction.