In the pharmaceutical industry, improving the early detection of drug-induced hepatotoxicity is essential as it is one of the most important reasons for attrition of candidate drugs during the later stages of drug development. The first objective of this study was to better characterize different cellular models (i.e., HepG2, HepaRG cells, and fresh primary human hepatocytes) at the gene expression level and analyze their metabolic cytochrome P450 capabilities. The cellular models were exposed to three different CYP450 inducers; beta-naphthoflavone (BNF), phenobarbital (PB), and rifampicin (RIF). HepG2 cells responded very weakly to the different inducers at the gene expression level, and this translated generally into low CYP450 activities in the induced cells compared with the control cells. On the contrary, HepaRG cells and the three human donors were inducible after exposure to BNF, PB, and RIF according to gene expression responses and CYP450 activities. Consequently, HepaRG cells could be used in screening as a substitute and/or in complement to primary hepatocytes for CYP induction studies. The second objective was to investigate the predictivity of the different cellular models to detect hepatotoxins (16 hepatotoxic and 5 nonhepatotoxic compounds). Specificity was 100% with the different cellular models tested. Cryopreserved human hepatocytes gave the highest sensitivity, ranging from 31% to 44% (depending on the donor), followed by lower sensitivity (13%) for HepaRG and HepG2 cells (6.3%). Overall, none of the models under study gave desirable sensitivities (80–100%). Consequently, a high metabolic capacity and CYP inducibility in cell lines does not necessarily correlate with a high sensitivity for the detection of hepatotoxic drugs. Further investigations are necessary to compare different cellular models and determine those that are best suited for the detection of hepatotoxic compounds.

Drug-induced liver injury (DILI) is a patient-specific, temporal, multifactorial pathophysiological process that cannot yet be recapitulated in a single in vitro model. Current pre-clinical testing regimes for the detection of human DILI thus remain inadequate. A systematic and concerted research effort is required to address the deficiencies in current models and to present a defined approach towards the development of new or adapted model systems for DILI prediction. This Perspective defines the current status of available models and mechanistic understanding of DILI, and proposes our vision of a roadmap for the development of predictive preclinical models of human DILI.

The use of impedance-based label-free technology applied to drug discovery is nowadays receiving more and more attention. Indeed, such a simple and noninvasive assay that interferes minimally with cell morphology and function allows one to perform kinetic measurements and to obtain information on proliferation, migration, cytotoxicity, and receptor-mediated signaling. The objective of the study was to further assess the usefulness of a real-time cell analyzer (RTCA) platform based on impedance in the context of quality control and data reproducibility. The data indicate that this technology is useful to determine the best coating and cellular density conditions for different adherent cellular models including hepatocytes, cardiomyocytes, fibroblasts, and hybrid neuroblastoma/neuronal cells. Based on 31 independent experiments, the reproducibility of cell index data generated from HepG2 cells exposed to DMSO and to Triton X-100 was satisfactory, with a coefficient of variation close to 10%. Cell index data were also well reproduced when cardiomyocytes and fibroblasts were exposed to 21 compounds three times (correlation >0.91, p < 0.0001). The data also show that a cell index decrease is not always associated with cytotoxicity effects and that there are some confounding factors that can affect the analysis. Finally, another drawback is that the correlation analysis between cellular impedance measurements and classical toxicity endpoints has been performed on a limited number of compounds. Overall, despite some limitations, the RTCA technology appears to be a powerful and reliable tool in drug discovery because of the reasonable throughput, rapid and efficient performance, technical optimization, and cell quality control.

Assessing the potential of a new drug to cause drug-induced liver injury (DILI) is a challenge for the pharmaceutical industry. We therefore determined whether cell models currently used in safety assessment (HepG2, HepaRG, Upcyte and primary human hepatocytes in conjunction with basic but commonly used endpoints) are actually able to distinguish between novel chemical entities (NCEs) with respect to their potential to cause DILI. A panel of thirteen compounds (nine DILI implicated and four non-DILI implicated in man) were selected for our study, which was conducted, for the first time, across multiple laboratories. None of the cell models could distinguish faithfully between DILI and non-DILI compounds. Only when nominal in vitro concentrations were adjusted for in vivo exposure levels were primary human hepatocytes (PHH) found to be the most accurate cell model, closely followed by HepG2. From a practical perspective, this study revealed significant inter-laboratory variation in the response of PHH, HepG2 and Upcyte cells, but not HepaRG cells. This variation was also observed to be compound dependent. Interestingly, differences between donors (hepatocytes), clones (HepG2) and the effect of cryopreservation (HepaRG and hepatocytes) were less important than differences between the cell models per se. In summary, these results demonstrate that basic cell health endpoints will not predict hepatotoxic risk in simple hepatic cells in the absence of pharmacokinetic data and that a multicenter assessment of more sophisticated signals of molecular initiating events is required to determine whether these cells can be incorporated in early safety assessment.Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-016-1745-4) contains supplementary material, which is available to authorized users.

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The use of label-free technologies based on electrical impedance is becoming more and more popular in drug discovery. Indeed, such a methodology allows the continuous monitoring of diverse cellular processes, including proliferation, migration, cytotoxicity and receptor-mediated signaling. The objective of the present study was to further assess the usefulness of the real-time cell analyzer (RTCA) and, in particular, the xCELLigence platform, in the context of early drug development for pharmacology and toxicology investigations. In the present manuscript, four cellular models were exposed to 50 compounds to compare the cell index generated by RTCA and cell viability measured with a traditional viability assay. The data revealed an acceptable correlation (ca. 80%) for both cell lines (i.e., HepG2 and HepaRG), but a lack of correlation (ca. 55%) for the primary human and rat hepatocytes. In addition, specific RTCA profiles (signatures) were generated when HepG2 and HepaRG cells were exposed to calcium modulators, antimitotics, DNA damaging and nuclear receptor agents, with a percentage of prediction close to 80% for both cellular models. In a subsequent experiment, HepG2 cells were exposed to 81 proprietary UCB compounds known to be genotoxic or not. Based on the DNA damaging signatures, the RTCA technology allowed the detection of ca. 50% of the genotoxic compounds (n = 29) and nearly 100% of the non-genotoxic compounds (n = 52). Overall, despite some limitations, the xCELLigence platform is a powerful and reliable tool that can be used in drug discovery for toxicity and pharmacology studies.

The current test systems employed by pharmaceutical industry are poorly predictive for drug-induced liver injury (DILI). The ‘MIP-DILI’ project addresses this situation by the development of innovative preclinical test systems which are both mechanism-based and of physiological, pharmacological and pathological relevance to DILI in humans. An iterative, tiered approach with respect to test compounds, test systems, bioanalysis and systems analysis is adopted to evaluate existing models and develop new models that can provide validated test systems with respect to the prediction of specific forms of DILI and further elucidation of mechanisms. An essential component of this effort is the choice of compound training set that will be used to inform refinement and/or development of new model systems that allow prediction based on knowledge of mechanisms, in a tiered fashion. In this review, we focus on the selection of MIP-DILI training compounds for mechanism-based evaluation of non-clinical prediction of DILI. The selected compounds address both hepatocellular and cholestatic DILI patterns in man, covering a broad range of pharmacologies and chemistries, and taking into account available data on potential DILI mechanisms (e.g. mitochondrial injury, reactive metabolites, biliary transport inhibition, and immune responses). Known mechanisms by which these compounds are believed to cause liver injury have been described, where many if not all drugs in this review appear to exhibit multiple toxicological mechanisms. Thus, the training compounds selection offered a valuable tool to profile DILI mechanisms and to interrogate existing and novel in vitro systems for the prediction of human DILI.