Non-hepatotoxic Compounds Research Articles

High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model.

To address the challenge of limited throughput with traditional toxicity testing, a newly developed high-throughput transcriptomics (HTT) platform, together with a 5-day in vivo rat model, offers an alternative approach to estimate chemical exposures and provide reasonable estimates of toxicological endpoints. This study contains an HTT analysis of 18 environmental chemicals with known liver toxicity. They were evaluated using male Sprague Dawley rats exposed to various concentrations daily for five consecutive days via oral gavage, with data collected on the sixth day. Here, we further explored the 5-day rat model to identify potential gene signatures that can differentiate between toxic and non-toxic liver responses and provide us with a potential histopathological endpoint of chemical exposure. We identified a distinct gene expression pattern that differentiated non-hepatotoxic compounds from hepatotoxic compounds in a dose-dependent manner, and an analysis of the significantly altered common genes indicated that toxic chemicals predominantly upregulated most of the genes and several pathways in amino acid and lipid metabolism. Finally, our liver injury module analysis revealed that several liver-toxic compounds showed similarities in the key injury phenotypes of cellular inflammation and proliferation, indicating potential molecular initiating processes that may lead to a specific end-stage liver disease.

The assessment of the potential hepatotoxicity of new drugs by in vitro metabolomics.

Drug hepatotoxicity assessment is a relevant issue both in the course of drug development as well as in the post marketing phase. The use of human relevant in vitro models in combination with powerful analytical methods (metabolomic analysis) is a promising approach to anticipate, as well as to understand and investigate the effects and mechanisms of drug hepatotoxicity in man. The metabolic profile analysis of biological liver models treated with hepatotoxins, as compared to that of those treated with non-hepatotoxic compounds, provides useful information for identifying disturbed cellular metabolic reactions, pathways, and networks. This can later be used to anticipate, as well to assess, the potential hepatotoxicity of new compounds. However, the applicability of the metabolomic analysis to assess the hepatotoxicity of drugs is complex and requires careful and systematic work, precise controls, wise data preprocessing and appropriate biological interpretation to make meaningful interpretations and/or predictions of drug hepatotoxicity. This review provides an updated look at recent in vitro studies which used principally mass spectrometry-based metabolomics to evaluate the hepatotoxicity of drugs. It also analyzes the principal drawbacks that still limit its general applicability in safety assessment screenings. We discuss the analytical workflow, essential factors that need to be considered and suggestions to overcome these drawbacks, as well as recent advancements made in this rapidly growing field of research.

Metabolomics-based strategy to assess drug hepatotoxicity and uncover the mechanisms of hepatotoxicity involved

Toxicity studies, among them hepatotoxicity, are key throughout preclinical stages of drug development to minimise undesired toxic effects that might eventually appear in the course of the clinical use of the new drug. Understanding the mechanism of injury of hepatotoxins is essential to efficiently anticipate their potential risk of toxicity in humans. The use of in vitro models and particularly cultured hepatocytes represents an easy and robust alternative to animal drug hepatotoxicity testing for predicting human risk. Here, we envisage an innovative strategy to identify potential hepatotoxic drugs, quantify the magnitude of the alterations caused, and uncover the mechanisms of toxicity. This strategy is based on the comparative analysis of metabolome changes induced by hepatotoxic and non-hepatotoxic compounds on HepG2 cells, assessed by untargeted mass spectrometry. As a training set, we used 25 hepatotoxic and 4 non-hepatotoxic compounds and incubated HepG2 cells for 24 h at a low and a high concentration (IC10 and IC50) to identify mechanism-related and cytotoxicity related metabolomic biomarkers and to elaborate prediction models accounting for global hepatotoxicity and mechanisms-related toxicity. Thereafter, a second set of 69 chemicals with known predominant mechanisms of toxicity and 18 non-hepatotoxic compounds were analysed at 1, 10, 100 and 1000 µM concentrations from which and based on the magnitude of the alterations caused as compared with non-toxic compounds, we defined a “toxicity index” for each compound. In addition, we extracted from the metabolome data the characteristic signatures for each mechanism of hepatotoxicity. The integration of all this information allowed us to identify specific metabolic patterns and, based on the occurrence of that specific metabolome changes, the models predicted the likeliness of a compound to behave as hepatotoxic and to act through a given toxicity mechanism (i.e., oxidative stress, mitochondrial disruption, apoptosis and steatosis) for each compound and concentration.

In vitro/in silico prediction of drug induced steatosis in relation to oral doses and blood concentrations by the Nile Red assay

The accumulation of lipid droplets in hepatocytes is a key feature of drug-induced liver injury (DILI) and can be induced by a subset of hepatotoxic compounds. In the present study, we optimized and evaluated an in vitro technique based on the fluorescent dye Nile Red, further named Nile Red assay to quantify lipid droplets induced by the exposure to chemicals. The Nile Red assay and a cytotoxicity test (CTB assay) were then performed on cells exposed concentration-dependently to 60 different compounds. Of these, 31 were known to induce hepatotoxicity in humans, and 13 were reported to also cause steatosis. In order to compare in vivo relevant blood concentrations, pharmacokinetic models were established for all compounds to simulate the maximal blood concentrations (Cmax) at therapeutic doses. The results showed that several hepatotoxic compounds induced an increase in lipid droplets at sub-cytotoxic concentrations. To compare how well (1) the cytotoxicity test alone, (2) the Nile Red assay alone, and (3) the combination of the cytotoxicity test and the Nile Red assay (based on the lower EC10 of both assays) allow the differentiation between hepatotoxic and non-hepatotoxic compounds, a previously established performance metric, the Toxicity Separation Index (TSI) was calculated. In addition, the Toxicity Estimation Index (TEI) was calculated to determine how well blood concentrations that cause an increased DILI risk can be estimated for hepatotoxic compounds. Our findings indicate that the combination of both assays improved the TSI and TEI compared to each assay alone. In conclusion, the study demonstrates that inclusion of the Nile Red assay into in vitro test batteries may improve the prediction of DILI compounds.

Influence of bile acids on the cytotoxicity of chemicals in cultivated human hepatocytes

Bile acids (BA) are known to influence the susceptibility of hepatocytes to chemicals. We investigated the cytotoxicity of 18 compounds with known hepatotoxicity status and pharmacokinetics in cultivated primary human hepatocytes with and without the addition of a BA mix to the cell culture medium. This BA mix consisted of physiological ratios of the most abundant human BA at a cholestatic sum concentration of 0.5 mM, which corresponds to 50% of the EC10 (cytotoxicity) of the mix. The BA mix decreased the EC10 of 7 compounds by a factor greater than 1.5, but also increased the EC10 of 5 compounds. The compounds with increased susceptibility include the known hepatotoxicants and BSEP/MRP2 inhibitors rifampicin, ketoconazole, atorvastatin, and cyclosporin A. However, the cytotoxicity of some non-hepatotoxic compounds was also enhanced, among them glucose, which is not known to be an inhibitor of canalicular bile acid export. A recently established technique to quantify how well hepatotoxic and non-hepatotoxic compounds are separated by an in vitro test indicated that the addition of the BA mix did not improve separation. In conclusion, the addition of BA to cultivated hepatocytes leads to a complex situation with increased and decreased susceptibilities depending on the specific compound.

Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey

REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) is a global strategy and regulation policy of the EU that aims to improve the protection of human health and the environment through the better and earlier identification of the intrinsic properties of chemical substances. It entered into force on 1st June 2007 (EC 1907/2006). REACH and EU policies plead for the use of robust high-throughput "omic" techniques for the in vitro investigation of the toxicity of chemicals that can provide an estimation of their hazards as well as information regarding the underlying mechanisms of toxicity. In agreement with the 3R’s principles, cultured cells are nowadays widely used for this purpose, where metabolomics can provide a real-time picture of the metabolic effects caused by exposure of cells to xenobiotics, enabling the estimations about their toxicological hazards. High quality and robust metabolomics data sets are essential for precise and accurate hazard predictions. Currently, the acquisition of consistent and representative metabolomic data is hampered by experimental drawbacks that hinder reproducibility and difficult robust hazard interpretation. Using the differentiated human liver HepG2 cells as model system, and incubating with hepatotoxic (acetaminophen and valproic acid) and non-hepatotoxic compounds (citric acid), we evaluated in-depth the impact of several key experimental factors (namely, cell passage, processing day and storage time, and compound treatment) and instrumental factors (batch effect) on the outcome of an UPLC-MS metabolomic analysis data set. Results showed that processing day and storage time had a significant impact on the retrieved cell's metabolome, while the effect of cell passage was minor. Meta-analysis of results from pathway analysis showed that batch effect corrections and quality control (QC) measures are critical to enable consistent and meaningful estimations of the effects caused by compounds on cells. The quantitative analysis of the changes in metabolic pathways upon bioactive compound treatment remained consistent despite the concurrent causes of metabolomic data variation. Thus, upon appropriate data retrieval and correction and by an innovative metabolic pathway analysis, the metabolic alteration predictions remained conclusive despite the acknowledged sources of variability.

The hepatocyte export carrier inhibition assay improves the separation of hepatotoxic from non-hepatotoxic compounds

An in vitro/in silico method that determines the risk of human drug induced liver injury in relation to oral doses and blood concentrations of drugs was recently introduced. This method utilizes information on the maximal blood concentration (Cmax) for a specific dose of a test compound, which can be estimated using physiologically-based pharmacokinetic modelling, and a cytotoxicity test in cultured human hepatocytes. In the present study, we analyzed if the addition of an assay that measures the inhibition of bile acid export carriers, like BSEP and/or MRP2, to the existing method improves the differentiation of hepatotoxic and non-hepatotoxic compounds. Therefore, an export assay for 5-chloromethylfluorescein diacetate (CMFDA) was established. We tested 36 compounds in a concentration-dependent manner for which the risk of hepatotoxicity for specific oral doses and the capacity to inhibit hepatocyte export carriers are known. Compared to the CTB cytotoxicity test, substantially lower EC10 values were obtained using the CMFDA assay for several known BSEP and/or MRP2 inhibitors. To quantify if the addition of the CMFDA assay to our test system improves the overall separation of hepatotoxic from non-hepatotoxic compounds, the toxicity separation index (TSI) was calculated. We obtained a better TSI using the lower alert concentration from either the CMFDA or the CTB test (TSI: 0.886) compared to considering the CTB test alone (TSI: 0.775). In conclusion, the data show that integration of the CMFDA assay with an in vitro test battery improves the differentiation of hepatotoxic and non-hepatotoxic compounds in a set of compounds that includes bile acid export carrier inhibitors.

Exposure of HepaRG Cells to Sodium Saccharin Underpins the Importance of Including Non-Hepatotoxic Compounds When Investigating Toxicological Modes of Action Using Metabolomics.

Metabolites represent the most downstream information of the cellular organisation. Hence, metabolomics experiments are extremely valuable to unravel the endogenous pathways involved in a toxicological mode of action. However, every external stimulus can introduce alterations in the cell homeostasis, thereby obscuring the involved endogenous pathways, biasing the interpretation of the results. Here we report on sodium saccharin, which is considered to be not hepatotoxic and therefore can serve as a reference compound to detect metabolic alterations that are not related to liver toxicity. Exposure of HepaRG cells to high levels of sodium saccharin (>10 mM) induced cell death, probably due to an increase in the osmotic pressure. Yet, a low number (n = 15) of significantly altered metabolites were also observed in the lipidome, including a slight decrease in phospholipids and an increase in triacylglycerols, upon daily exposure to 5 mM sodium saccharin for 72 h. The observation that a non-hepatotoxic compound can affect the metabolome underpins the importance of correct experimental design and data interpretation when investigating toxicological modes of action via metabolomics.

Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations

Drug-induced liver injury (DILI) cannot be accurately predicted by animal models. In addition, currently available in vitro methods do not allow for the estimation of hepatotoxic doses or the determination of an acceptable daily intake (ADI). To overcome this limitation, an in vitro/in silico method was established that predicts the risk of human DILI in relation to oral doses and blood concentrations. This method can be used to estimate DILI risk if the maximal blood concentration (Cmax) of the test compound is known. Moreover, an ADI can be estimated even for compounds without information on blood concentrations. To systematically optimize the in vitro system, two novel test performance metrics were introduced, the toxicity separation index (TSI) which quantifies how well a test differentiates between hepatotoxic and non-hepatotoxic compounds, and the toxicity estimation index (TEI) which measures how well hepatotoxic blood concentrations in vivo can be estimated. In vitro test performance was optimized for a training set of 28 compounds, based on TSI and TEI, demonstrating that (1) concentrations where cytotoxicity first becomes evident in vitro (EC10) yielded better metrics than higher toxicity thresholds (EC50); (2) compound incubation for 48 h was better than 24 h, with no further improvement of TSI after 7 days incubation; (3) metrics were moderately improved by adding gene expression to the test battery; (4) evaluation of pharmacokinetic parameters demonstrated that total blood compound concentrations and the 95%-population-based percentile of Cmax were best suited to estimate human toxicity. With a support vector machine-based classifier, using EC10 and Cmax as variables, the cross-validated sensitivity, specificity and accuracy for hepatotoxicity prediction were 100, 88 and 93%, respectively. Concentrations in the culture medium allowed extrapolation to blood concentrations in vivo that are associated with a specific probability of hepatotoxicity and the corresponding oral doses were obtained by reverse modeling. Application of this in vitro/in silico method to the rat hepatotoxicant pulegone resulted in an ADI that was similar to values previously established based on animal experiments. In conclusion, the proposed method links oral doses and blood concentrations of test compounds to the probability of hepatotoxicity.

The Identification of Pivotal Transcriptional Factors Mediating Cell Responses to Drugs With Drug-Induced Liver Injury Liabilities.

Drug-induced liver injury (DILI) is a leading cause of drug attrition during drug development and a common reason for drug withdrawal from the market. The poor predictability of conventional animal-based approaches necessitates the development of alternative testing approaches. A body of evidence associates DILI with the induction of stress-response genes in liver cells. Here, we set out to identify signal transduction pathways predominantly involved in the regulation of gene transcription by DILI drugs. To this end, we employed ATTAGENE's cell-based multiplexed reporter assay, the FACTORIAL transcription factor (TF), that enables quantitative assessment of the activity of multiple stress-responsive TFs in a single well of cells. Homogeneous reporter system enables quantitative functional assessment of multiple transcription factors. Nat. Methods 5, 253-260). Using this assay, we assessed TF responses of the human hepatoma cell line HepG2 to a panel of 64 drug candidates, including 23 preclinical DILI and 11 clinical DILI compounds and 30 nonhepatotoxic compounds from a diverse physicochemical property space. We have identified 16 TF families that specifically responded to DILI drugs, including nuclear factor (erythroid-derived 2)-like 2 antioxidant response element, octamer, hypoxia inducible factor 1 alpha, farnesoid-X receptor, TCF/beta-catenin, aryl hydrocarbon receptor, activator protein-1, E2F, early growth response-1, metal-response transcription factor 1, sterol regulatory element-binding protein, paired box protein, peroxisome proliferator-activated receptor, liver X receptor, interferone regulating factor, and P53, and 2 promoters that responded to multiple TFs (cytomegalovirus and direct repeat 3/vitamin D receptor). Some of TFs identified here also have previously defined role in pathogenesis of liver diseases. These data demonstrate the utility of cost-effective, animal-free, TF profiling assay for detecting DILI potential of drug candidates at early stages of drug development.

Drug-induced cholestasis risk assessment in sandwich-cultured human hepatocytes

Drug-induced cholestasis (DIC) is recognized as one of the prime mechanisms for DILI. Hence, earlier detection of drug candidates with cholestatic signature is crucial. Recently, we introduced an in vitro model for DIC and evaluated its performance with several cholestatic drugs. We presently expand on the validation of this model by 14 training compounds (TCs) of the EU-EFPIA IMI project MIP-DILI.Several batches of human hepatocytes in sandwich-culture were qualified for DIC assessment by verifying the bile acid-dependent increase in sensitivity to the toxic effects of cyclosporin A. The cholestatic potential of the TCs was expressed by determining the drug-induced cholestasis index (DICI). A safety margin (SM) was calculated as the ratio of the lowest TC concentration with a DICI≤0.80 to the Cmax,total. Nefazodone, bosentan, perhexiline and troglitazone were flagged for cholestasis (SM<30). The hepatotoxic (but non-cholestatic) compounds, amiodarone, diclofenac, fialuridine and ximelagatran, and all non-hepatotoxic compounds were cleared as “safe” for DIC. Tolcapone and paracetamol yielded DICI-based SM values equal to or higher than those based on cytotoxicity, thus excluding DIC as a DILI mechanism.This hepatocyte-based in vitro assay provides a unique tool for early and reliable identification of drug candidates with cholestasis risk.

Hepatotoxicants induce cytokine imbalance in response to innate immune system

In recent years, attention has been paid to innate immune systems as mechanisms to initiate or promote drug-induced liver injury (DILI). Kupffer cells are hepatic resident macrophages and might be involved in the pathogenesis of DILI by release of pro- and anti-inflammatory mediators such as cytokines, chemokines, reactive oxygen species, and/or nitric oxides. The purpose of this study was to investigate alterations in mediator levels induced by hepatotoxic compounds in isolated Kupffer cells and discuss the relation between balance of each cytokine or chemokine and potential of innate immune-mediated DILI. Primary cultured rat Kupffer cells were treated with hepatotoxic (acetaminophen, troglitazone, trovafloxacin) or non-hepatotoxic (pioglitazone, levofloxacin) compounds with or without lipopolysaccharide (LPS). After 24 hr treatment, cell supernatants were collected and various levels of mediators released by Kupffer cells were examined. Although hepatotoxicants had no effect on the LPS-induced tumor necrosis factor-alpha (TNF-α) secretion, they enhanced the release of pro-inflammatory cytokine interleukin-1 beta (IL-1β) and suppressed the anti-inflammatory cytokines interleukin-6 (IL-6) and interleukin-10 (IL-10) induced by LPS. These cytokine shifts were not associated with switching the phenotypes of M1 and M2 macrophages in Kupffer cells. In conclusion, the present study suggested that the levels of some specific cytokines are affected by DILI-related drugs with LPS stimulation, and imbalance between pro- and anti-inflammatory cytokines, induced by the up-regulation of IL-1β and the down-regulation of IL-6 or IL-10, plays a key role in innate immune-mediated DILI.

Assessing Hepatoxicants based on High-throughput Quantitative SILAC Proteomics and Causal Biological Networks

Drug-induced liver injury can result in the termination of compounds in preclinical development, as well as withdrawal of marketed drugs. Identification of the signaling pathways and proteins involved in injury is an important step towards establishing assays that could be utilized to identify potential hepatotoxicants early in the drug discovery process. In this study we used high throughput quantitative mass spectrometry proteomics involving Stable Isotope Labeling with Amino acids in Cell culture (SILAC) leveraged by a recently developed systems biology approach (Causal Reasoning Engine, CRE) to investigate the effects of hepatotoxic compounds on the cellular proteome of HepG2 cells. Cells were treated with various concentrations of nefazadone, nimesulide, nomifensine, or glafenine, all of which cause hepatotoxicity in humans. Buspirone and rosiglitazone were used as comparator compounds not associated with hepatotoxicity. In comparison to more traditional proteomic analysis tools CRE results provided detailed molecular hypotheses that condense into biological networks and collectively explain a significant number of the measured protein changes. The CRE hypotheses demonstrate that magnitude of response is not necessarily the differentiating factor between DILI and non-DILI compounds but rather the biological processes implicated. Differentiating CRE molecular hypotheses implicate lipid and glucose metabolism, inflammation, oxidative stress and DNA damage as consistent major in vitro discriminating factors. Overall, this paper provides evidence that SILAC data coupled with the CRE analysis method can provide context and new insight into variation of stress responses in hepatotoxic versus non-hepatotoxic compounds even within the same therapeutic class.

Diagnostic and predictive performance and standardized threshold of traditional biomarkers for drug-induced liver injury in rats.

Traditional biomarkers such as alanine and aspartate aminotransferase (ALT, AST) and total bilirubin (TBIL) have been widely used for detecting drug-induced liver injury (DILI). Although the Food and Drug Administration (FDA) proposed standardized thresholds for human as Hy's law, those for animals have not been determined, and predictability of these biomarkers for future onset of hepatic lesions remains unclear. In this study, we investigated these diagnostic and predictive performance of 10 traditional biomarkers for liver injury by receiver-operating characteristic (ROC) curve, using a free-access database where 142 hepatotoxic or non-hepatotoxic compounds were administrated to male rats (n=5253). Standardization of each biomarker value was achieved by calculating the ratio to control mean value, and the thresholds were determined under the condition of permitting 5% false positive. Of these 10 biomarkers, AST showed the best diagnostic performance. Furthermore, ALT and TBIL also showed high performance under the situation of hepatocellular necrosis and bile duct injury, respectively. Additionally, the availability of the diagnostic thresholds in difference testing facility was confirmed by the application of these thresholds to in-house prepared dataset. Meanwhile, incorrect diagnosis by the thresholds was also observed. Regarding prediction, all 10 biomarkers showed insufficient performance for future onset of hepatic lesions. In conclusion, the standardized diagnostic thresholds enable consistent evaluation of traditional biomarkers among different facilities, whereas it was suggested that novel biomarker is required for more accurate diagnosis and prediction of DILI.

A zebrafish phenotypic assay for assessing drug-induced hepatotoxicity

IntroductionNumerous studies have confirmed that zebrafish and mammalian toxicity profiles are strikingly similar and the transparency of larval zebrafish permits direct in vivo assessment of drug toxicity including hepatotoxicity in zebrafish. MethodsHepatotoxicity of 6 known mammalian hepatotoxic drugs (acetaminophen [APAP], aspirin, tetracycline HCl, sodium valproate, cyclophosphamide and erythromycin) and 2 non-hepatotoxic compounds (sucrose and biotin) were quantitatively assessed in larval zebrafish using three specific phenotypic endpoints of hepatotoxicity: liver degeneration, changes in liver size and yolk sac retention. Zebrafish liver degeneration was originally screened visually, quantified using an image-based morphometric analysis and confirmed by histopathology. ResultsAll the tested mammalian hepatotoxic drugs induced liver degeneration, reduced liver size and delayed yolk sac absorption in larval zebrafish, whereas the non-hepatotoxic compounds did not have observable adverse effect on zebrafish liver. The overall prediction success rate for hepatotoxic drugs and non-hepatotoxic compounds in zebrafish was 100% (8/8) as compared with mammalian results, suggesting that hepatotoxic drugs in mammals also caused similar hepatotoxicity in zebrafish. DiscussionLarval zebrafish phenotypic assay is a highly predictive animal model for rapidly in vivo assessment of compound hepatotoxicity. This convenient, reproducible animal model saves time and money for drug discovery and can serve as an intermediate step between cell-based evaluation and conventional animal testing of hepatotoxicity.

Preclinical Strategy to Reduce Clinical Hepatotoxicity Using in Vitro Bioactivation Data for &gt;200 Compounds

Drug-induced liver injury is the most common cause of market withdrawal of pharmaceuticals, and thus, there is considerable need for better prediction models for DILI early in drug discovery. We present a study involving 223 marketed drugs (51% associated with clinical hepatotoxicity; 49% non-hepatotoxic) to assess the concordance of in vitro bioactivation data with clinical hepatotoxicity and have used these data to develop a decision tree to help reduce late-stage candidate attrition. Data to assess P450 metabolism-dependent inhibition (MDI) for all common drug-metabolizing P450 enzymes were generated for 179 of these compounds, GSH adduct data generated for 190 compounds, covalent binding data obtained for 53 compounds, and clinical dose data obtained for all compounds. Individual data for all 223 compounds are presented here and interrogated to determine what level of an alert to consider termination of a compound. The analysis showed that 76% of drugs with a daily dose of <100 mg were non-hepatotoxic (p < 0.0001). Drugs with a daily dose of ≥100 mg or with GSH adduct formation, marked P450 MDI, or covalent binding ≥200 pmol eq/mg protein tended to be hepatotoxic (∼ 65% in each case). Combining dose with each bioactivation assay increased this association significantly (80-100%, p < 0.0001). These analyses were then used to develop the decision tree and the tree tested using 196 of the compounds with sufficient data (49% hepatotoxic; 51% non-hepatotoxic). The results of these outcome analyses demonstrated the utility of the tree in selectively terminating hepatotoxic compounds early; 45% of the hepatotoxic compounds evaluated using the tree were recommended for termination before candidate selection, whereas only 10% of the non-hepatotoxic compounds were recommended for termination. An independent set of 10 GSK compounds with known clinical hepatotoxicity status were also assessed using the tree, with similar results.

Characterization of primary human hepatocytes, HepG2 cells, and HepaRG cells at the mRNA level and CYP activity in response to inducers and their predictivity for the detection of human hepatotoxins

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.

Identification of classifier genes for hepatotoxicity prediction in non steroidal anti inflammatory drugs

Toxicogenomics has the potential to be used for the regulatory decision making to predict toxicity in developing new drugs. We have identified the classifiers for hepatotoxicity prediction in nonsteroidal anti inflammatory drugs (NSAIDs) through analyzing differential gene expression profiles of hepatotoxic and nonhepatotoxic compounds using HepG2 cell. 100 μM of 8 hepatotoxic and 8 nonhepatotoxic NSAIDs were treated to HepG2 cell and the analysis of gene expression changes after 24 h allowed a set of genes to be identified differentiating hepatotoxicants from nonhepatotoxicants by statistical method. The hepatotoxicity prediction model was built using the selected 77 genes. These genes and pathways, commonly regulated by hepatotoxicants, may be indicative of the early characterization of hepatotoxicity and possibly predictive of later hepatotoxicity onset. 4 test compounds including hepatotoxic and nonhepatotoxic NSAIDs were used for validating the prediction model and the accuracy was 100%. Given that the specificity and sensitivity showed 100%, these are the most precise classifiers identified until now.

Acute Hepatotoxicity: A Predictive Model Based on Focused Illumina Microarrays

Drug-induced hepatotoxicity is a major issue for drug development, and toxicogenomics has the potential to predict toxicity during early toxicity screening. A bead-based Illumina oligonucleotide microarray containing 550 liver specific genes has been developed. We have established a predictive screening system for acute hepatotoxicity by analyzing differential gene expression profiles of well-known hepatotoxic and nonhepatotoxic compounds. Low and high doses of tetracycline, carbon tetrachloride (CCL4), 1-naphthylisothiocyanate (ANIT), erythromycin estolate, acetaminophen (AAP), or chloroform as hepatotoxicants, clofibrate, theophylline, naloxone, estradiol, quinidine, or dexamethasone as nonhepatotoxic compounds, were administered as a single dose to male Sprague-Dawley rats. After 6, 24, and 72 h, livers were taken for histopathological evaluation and for analysis of gene expression alterations. All hepatotoxic compounds tested generated individual gene expression profiles. Based on leave-one-out cross-validation analysis, gene expression profiling allowed the accurate discrimination of all model compounds, 24 h after high dose treatment. Even during the regeneration phase, 72 h after treatment, CCL4, ANIT, and AAP were predicted to be hepatotoxic, and only these three compounds showed histopathological changes at this time. Furthermore, we identified 64 potential marker genes responsible for class prediction, which reflected typical hepatotoxicity responses. These genes and pathways, commonly deregulated by hepatotoxicants, may be indicative of the early characterization of hepatotoxicity and possibly predictive of later hepatotoxicity onset. Two unknown test compounds were used for prevalidating the screening test system, with both being correctly predicted. We conclude that focused gene microarrays are sufficient to classify compounds with respect to toxicity prediction.

Supervised Sorting: Training Computers to Classify Toxicants

Supervised Sorting: Training Computers to Classify Toxicants