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Previous works have shown that differentiated human HepaRG cells can exhibit drug metabolism activities close to those of primary human hepatocytes for several weeks at confluence. The present study was designed to evaluate their long-term functional stability and their response to repeated daily drug treatments over a 14-day period, using a transcriptomic approach. Our data show that less than 1% of the expressed genes were markedly deregulated over this two weeks period and mainly included down-regulation of genes related to the cell cycle and from 3 days, overexpression of genes involved in xenobiotic and lipid metabolism. After daily treatment with the three PPAR agonists, fenofibrate, troglitazone and rosiglitazone qualitative and/or quantitative changes in gene profiling were observed depending on the compound and duration of treatment. The highest increase in the number of deregulated genes as a function of drug treatment was seen with rosiglitazone. The most up-regulated genes common across the three compounds were mainly related to lipid and xenobiotic metabolisms. All the data support the conclusion that human HepaRG cells have an exceptional functional stability at confluence and that they are suitable for investigations on chronic effects of drugs and other chemicals. [ABSTRACT FROM AUTHOR]

Abstract: Concentration–response studies are performed to investigate the potency of the substance under investigation. Data are typically evaluated using non-linear regression. A common model is the log-logistic model which includes parameters for lower and upper boundary of mean response, EC50 and Hill slope. Often, response and/or concentration data are transformed before proceeding with the analysis of their relationship. This is motivated by practical reasons, including comparability of results across different assays. We prove mathematically that a linear transformation of data will not change the EC50 and Hill slope estimates and only results in an identical transformation of the estimated parameters for lower and upper boundary of mean response. However, fixing some of the parameters may lead to erroneous estimates. This is of practical relevance when data are corrected for background signal and normalized by background corrected solvent control and a reduced model is used in which the response range is fixed between 100% and 0%. Computer simulations and a real data example are used to illustrate the impact of data transformations on parameter estimation. We further shed light on some common problems arising in the analysis of concentration–response data. Recommendations for practical implementation in concentration–response analysis are provided. [Copyright &y& Elsevier]