Development of an Adverse Outcome Pathway From Drug-Mediated Bile Salt Export Pump Inhibition to Cholestatic Liver Injury

From hazard to risk prioritization: a case study to predict drug-induced cholestasis using physiologically based kinetic modeling

Cholestasis is characterized by hepatic accumulation of bile acids. Clinical manifestation of cholestasis only occurs in a small proportion of exposed individuals. The present study aims to develop a new approach methodology (NAM) to predict drug-induced cholestasis as a result of drug-induced hepatic bile acid efflux inhibition and the resulting bile acid accumulation. To this end, hepatic concentrations of a panel of drugs were predicted by a generic physiologically based kinetic (PBK) drug model. Their effects on hepatic bile acid efflux were incorporated in a PBK model for bile acids. The predicted bile acid accumulation was used as a measure for a drug's cholestatic potency. The selected drugs were known to inhibit hepatic bile acid efflux in an assay with primary suspension-cultured hepatocytes and classified as common, rare, or no for cholestasis incidence. Common cholestasis drugs included were atorvastatin, chlorpromazine, cyclosporine, glimepiride, ketoconazole, and ritonavir. The cholestasis incidence of the drugs appeared not to be adequately predicted by their Ki for inhibition of hepatic bile acid efflux, but rather by the AUC of the PBK model predicted internal hepatic drug concentration at therapeutic dose level above this Ki. People with slower drug clearance, a larger bile acid pool, reduced bile salt export pump (BSEP) abundance, or given higher than therapeutic dose levels were predicted to be at higher risk to develop drug-induced cholestasis. The results provide a proof-of-principle of using a PBK-based NAM for cholestasis risk prioritization as a result of transporter inhibition and identification of individual risk factors.

Associations between Ethylene Oxide Exposure and Liver Function in the US Adult Population

BACKGROUND

Ethylene oxide, a reactive epoxy compound, has been widely used in various industries for many years. However, evidence of the combined toxic effects of ethylene oxide exposure on the liver is still lacking.

METHODS

We analyzed the merged data from the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2016. Ultimately, 4141 adults aged 18 and over were selected as the sample. We used linear regression to explore the association between blood ethylene oxide and LFT indicators.

RESULTS

The weighted linear regression model showed that HbEO is positively correlated with ALP (β = 2.61, 95% CI 1.97, 3.24, p < 0.0001), GGT (β = 5.75, 95% CI 4.46, 7/05, p < 0.0001), ALT (β = 0.50, 95% CI 0.09, 0.90, p = 0.0158), and AST (β = 0.71, 95% CI 0.44, 0.98, p < 0.0001) and negatively correlated with TBIL (β = -0.30, 95% CI -0.43, -0.16, p < 0.0001).

CONCLUSIONS

Ethylene oxide exposure is significantly associated with changes in liver function indicators among adults in the United States. Future work should further examine these relationships.

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis

Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

Impact of perfluoroalkyl substances (PFAS) and PFAS mixtures on lipid metabolism in differentiated HepaRG cells as a model for human hepatocytes

Per- and polyfluoroalkyl substances (PFAS) are environmental contaminants with various adverse health effects in humans including disruption of lipid metabolism. Aim of the present study was to elucidate the molecular mechanisms of PFAS-mediated effects on lipid metabolism in human cells. Here, we examined the impact of a number of PFAS (PFOS, PFOA, PFNA, PFDA, PFHxA, PFBA, PFHxS, PFBS, HFPO-DA, and PMPP) and of some exposure-relevant PFAS mixtures being composed of PFOS, PFOA, PFNA and PFHxS on lipid metabolism in human HepaRG cells, an in vitro model for human hepatocytes. At near cytotoxic concentrations, the selected PFAS and PFAS mixtures induced triglyceride accumulation in HepaRG cells and consistently affected the expression of marker genes for steatosis, as well as PPARα target genes and genes related to lipid and cholesterol metabolism, pointing to common molecular mechanisms of PFAS in disrupting cellular lipid and cholesterol homeostasis. PPARα activation was examined by a transactivation assay in HEK293T cells, and synergistic effects were observed for the selected PFAS mixtures at sum concentrations higher than 25 µM, whereas additivity was observed at sum concentrations lower than 25 µM. Of note, any effect observed in the in vitro assays occurred at PFAS concentrations that were at least four to five magnitudes above real-life internal exposure levels of the general population.

Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data

The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains.

Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets

Drug-induced intrahepatic cholestasis (DIC) is a main type of hepatic toxicity that is challenging to predict in early drug development stages. Preclinical animal studies often fail to detect DIC in humans. In vitro toxicogenomics assays using human liver cells have become a practical approach to predict human-relevant DIC. The present study was set up to identify transcriptomic signatures of DIC by applying machine learning algorithms to the Open TG-GATEs database. A total of nine DIC compounds and nine non-DIC compounds were selected, and supervised classification algorithms were applied to develop prediction models using differentially expressed features. Feature selection techniques identified 13 genes that achieved optimal prediction performance using logistic regression combined with a sequential backward selection method. The internal validation of the best-performing model showed accuracy of 0.958, sensitivity of 0.941, specificity of 0.978, and F1-score of 0.956. Applying the model to an external validation set resulted in an average prediction accuracy of 0.71. The identified genes were mechanistically linked to the adverse outcome pathway network of DIC, providing insights into cellular and molecular processes during response to chemical toxicity. Our findings provide valuable insights into toxicological responses and enhance the predictive accuracy of DIC prediction, thereby advancing the application of transcriptome profiling in designing new approach methodologies for hazard identification.

Optimization of an adverse outcome pathway network on chemical-induced cholestasis using an artificial intelligence-assisted data collection and confidence level quantification approach

BACKGROUND

Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships.

METHODS

Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship.

RESULTS

This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis.

CONCLUSIONS

This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.

Using a Graph Convolutional Neural Network Model to Identify Bile Salt Export Pump Inhibitors

The bile salt export pump (BSEP) is a key transporter involved in the efflux of bile salts from hepatocytes to bile canaliculi. Inhibition of BSEP leads to the accumulation of bile salts within the hepatocytes, leading to possible cholestasis and drug-induced liver injury. Screening for and identification of chemicals that inhibit this transporter aid in understanding the safety liabilities of these chemicals. Moreover, computational approaches to identify BSEP inhibitors provide an alternative to the more resource-intensive, gold standard experimental approaches. Here, we used publicly available data to develop predictive machine learning models for the identification of potential BSEP inhibitors. Specifically, we analyzed the utility of a graph convolutional neural network (GCNN)-based approach in combination with multitask learning to identify BSEP inhibitors. Our analyses showed that the developed GCNN model performed better than the variable-nearest neighbor and Bayesian machine learning approaches, with a cross-validation receiver operating characteristic area under the curve of 0.86. In addition, we compared GCNN-based single-task and multitask models and evaluated their utility in addressing data limitation challenges commonly observed in bioactivity modeling. We found that multitask models performed better than single-task models and can be utilized to identify active molecules for targets with limited data availability. Overall, our developed multitask GCNN-based BSEP model provides a useful tool for prioritizing hits during early drug discovery and in risk assessment of chemicals.

Derivation of metabolic point of departure using high-throughput in vitro metabolomics: investigating the importance of sampling time points on benchmark concentration values in the HepaRG cell line

Amongst omics technologies, metabolomics should have particular value in regulatory toxicology as the measurement of the molecular phenotype is the closest to traditional apical endpoints, whilst offering mechanistic insights into the biological perturbations. Despite this, the application of untargeted metabolomics for point-of-departure (POD) derivation via benchmark concentration (BMC) modelling is still a relatively unexplored area. In this study, a high-throughput workflow was applied to derive PODs associated with a chemical exposure by measuring the intracellular metabolome of the HepaRG cell line following treatment with one of four chemicals (aflatoxin B₁, benzo[a]pyrene, cyclosporin A, or rotenone), each at seven concentrations (aflatoxin B₁, benzo[a]pyrene, cyclosporin A: from 0.2048 μM to 50 μM; rotenone: from 0.04096 to 10 μM) and five sampling time points (2, 6, 12, 24 and 48 h). The study explored three approaches to derive PODs using benchmark concentration modelling applied to single features in the metabolomics datasets or annotated metabolites or lipids: (1) the 1st rank-ordered unannotated feature, (2) the 1st rank-ordered putatively annotated feature (using a recently developed HepaRG-specific library of polar metabolites and lipids), and (3) 25th rank-ordered feature, demonstrating that for three out of four chemical datasets all of these approaches led to relatively consistent BMC values, varying less than tenfold across the methods. In addition, using the 1st rank-ordered unannotated feature it was possible to investigate temporal trends in the datasets, which were shown to be chemical specific. Furthermore, a possible integration of metabolomics-driven POD derivation with the liver steatosis adverse outcome pathway (AOP) was demonstrated. The study highlights that advances in technologies enable application of in vitro metabolomics at scale; however, greater confidence in metabolite identification is required to ensure PODs are mechanistically anchored.

Population pharmacokinetic model to generate mechanistic insights in bile acid homeostasis and drug-induced cholestasis

Bile acids (BA) fulfill a wide range of physiological functions, but are also involved in pathologies, such as cholestasis. Cholestasis is characterized by an intrahepatic accumulation of BAs and subsequent spillage to the systemic circulation. The aim of the present study was to develop physiologically based kinetic (PBK) models that would provide a tool to predict dose-dependent BA accumulation in humans upon treatment with a Bile Salt Export Pump (BSEP) inhibitor. We developed a PBK model describing the BA homeostasis using glycochenodeoxycholic acid as an exemplary BA. Population wide distributions of BSEP abundances were incorporated in the PBK model using Markov Chain Monte Carlo simulations, and alternatively the total amount of BAs was scaled empirically to describe interindividual differences in plasma BA levels. Next, the effects of the BSEP inhibitor bosentan on the BA levels were simulated. The PBK model developed adequately predicted the in vivo BA dynamics. Both the Markov Chain Monte Carlo simulations based on a distribution of BSEP abundances and empirical scaling of the total BA pool readily described the variations within and between data in human volunteers. Bosentan treatment disproportionally increased the maximum BA concentration in individuals with a large total BA pool or low BSEP abundance. Especially individuals having a large total BA pool size and a low BSEP abundance were predicted to be at risk for rapid saturation of BSEP and subsequent intrahepatic BA accumulation. This model provides a first estimate of personalized safe therapeutic external dose levels of compounds with BSEP-inhibitory properties.

Identification of proteome markers for drug-induced liver injury in zebrafish embryos

The zebrafish embryo (ZFE) is a promising alternative non-rodent model in toxicology, and initial studies suggested its applicability in detecting hepatic responses related to drug-induced liver injury (DILI). Here, we hypothesize that detailed analysis of underlying mechanisms of hepatotoxicity in ZFE contributes to the improved identification of hepatotoxic properties of compounds and to the reduction of rodents used for hepatotoxicity assessment. ZFEs were exposed to nine reference hepatotoxicants, targeted at induction of steatosis, cholestasis, and necrosis, and effects compared with negative controls. Protein profiles of the individual compounds were generated using LC-MS/MS. We identified differentially expressed proteins and pathways, but as these showed considerable overlap, phenotype-specific responses could not be distinguished. This led us to identify a set of common hepatotoxicity marker proteins. At the pathway level, these were mainly associated with cellular adaptive stress-responses, whereas single proteins could be linked to common hepatotoxicity-associated processes. Applying several stringency criteria to our proteomics data as well as information from other data sources resulted in a set of potential robust protein markers, notably Igf2bp1, Cox5ba, Ahnak, Itih3b.2, Psma6b, Srsf3a, Ces2b, Ces2a, Tdo2b, and Anxa1c, for the detection of adverse responses.

Adverse outcome pathway from activation of the AhR to breast cancer-related death

Adverse outcome pathways (AOPs) are formalized and structured linear concepts that connect one molecular initiating event (MIE) to an adverse outcome (AO) via different key events (KE) through key event relationships (KER). They are mainly used in eco-toxicology toxicology, and regulatory health issues. AOPs must respond to specific guidelines from the Organization for Economic Co-operation and Development (OECD) to weight the evidence between each KE. Breast cancer is the deadliest cancer in women with a poor prognosis in case of metastatic breast cancer. The role of the environments in the formation of metastasis has been suggested. We hypothesized that activation of the AhR (MIE), a xenobiotic receptor, could lead to breast cancer related death (AO), through different KEs, constituting a new AOP. An artificial intelligence tool (AOP-helpfinder), which screens the available literature, was used to collect all existing scientific abstracts to build a novel AOP, using a list of key words. Four hundred and seven abstracts were found containing at least a word from our MIE list and either one word from our AO or KE list. A manual curation retained 113 pertinent articles, which were also screened using PubTator. From these analyses, an AOP was created linking the activation of the AhR to breast cancer related death through decreased apoptosis, inflammation, endothelial cell migration, angiogenesis, and invasion. These KEs promote an increased tumor growth, angiogenesis and migration which leads to breast cancer metastasis and breast cancer related death. The evidence of the proposed AOP was weighted using the tailored Bradford Hill criteria and the OECD guidelines. The confidence in our AOP was considered strong. An in vitro validation must be carried out, but our review proposes a strong relationship between AhR activation and breast cancer-related death with an innovative use of an artificial intelligence literature search.

Advances in Animal Models and Cutting-Edge Research in Alternatives: Proceedings of the Second International Conference on 3Rs Research and Progress, Hyderabad, 2021

The fact that animal models fail to replicate human disease faithfully is now being widely accepted by researchers across the globe. As a result, they are exploring the use of alternatives to animal models. The time has come to refine our experimental practices, reduce the numbers and eventually replace the animals used in research with human-derived and human-relevant 3-D disease models. Oncoseek Bio-Acasta Health, which is an innovative biotechnology start-up company based in Hyderabad and Vishakhapatnam, India, organises an annual International Conference on 3Rs Research and Progress. In 2021, this conference was on 'Advances in Research Animal Models and Cutting-Edge Research in Alternatives'. This annual conference is a platform that brings together eminent scientists and researchers from various parts of the world, to share recent advances from their research in the field of alternatives to animals including new approach methodologies, and to promote practices to help refine animal experiments where alternatives are not available. This report presents the proceedings of the conference, which was held in hybrid mode (i.e. virtual and in-person) in November 2021.

Developmental and Hepatic Gene Expression Changes in Chicken Embryos Exposed to p-Tert-Butylphenyl Diphenyl Phosphate and Isopropylphenyl Phosphate via Egg Injection

Organophosphate flame retardants (OPFRs) are used in a variety of products such as clear coats, resins, and plastics; however, research into their toxicological effects is limited. p-Tert-butylphenyl diphenyl phosphate (BPDP) and isopropylphenyl phosphate (IPPP) are two OPFRs that were prioritized for whole-animal toxicological studies based on observed effects in cultured avian hepatocytes in a previous study. The present study investigates the toxicity of BPDP and IPPP in chicken embryos at different developmental stages by evaluating morphological and gene expression endpoints. Chicken eggs were exposed via air cell injection to 0-250 μg/g (nominal) of either compound and then artificially incubated. At day 11 (midincubation), liver samples were collected for mRNA expression analysis; and at day 20 (1 day prehatch), morphological measurements and liver samples for transcriptomic evaluation were collected. At 250 μg/g, gallbladder size was significantly reduced for both compounds, head/bill length and tarsus length were significantly decreased, and liver somatic index was significantly increased following IPPP exposure only. No effects on mortality were observed up to the highest administered concentration for either chemical. Using a ToxChip polymerase chain reaction array, we report significant differences in hepatic gene expression for both compounds and time points; the most pronounced transcriptomic effects occurred at midincubation. Genes related to xenobiotic metabolism, bile acid/cholesterol regulation, and oxidative stress were significantly dysregulated. Given these changes observed throughout avian embryonic development, further research into the long-term effects of BPDP and IPPP are warranted, especially as they pertain to liver cholestasis. Environ Toxicol Chem 2022;41:739-747. © 2021 Her Majesty the Queen in Right of Canada. Environmental Toxicology and Chemistry © 2021 SETAC. Reproduced with the permission of the Minister of Environment and Climate Change Canada.

Ontogeny of renal, hepatic, and placental expression of ATP-binding cassette and solute carrier transporters in the rat and the rabbit

Species differences in developmental toxicity can be due to varying expression of xenobiotic transporters. Hence, knowledge on the ontogeny of these transporters, especially in human, rat and rabbit, is pivotal. Two superfamilies of transporters, the ATP-binding cassette (ABC) and the solute carrier (SLC) transporters, are well known for their role in the absorption, distribution and/or elimination of xenobiotics and endogenous substances. The aim of this study was to compare the expression levels of these xenobiotic transporters in liver, kidney and placenta of man, Wistar rat and New Zealand White rabbit during pre- and postnatal development. For this purpose, qPCR experiments were performed for rat and rabbit tissues and the gene expression profiles were compared with literature data from man, rat and rabbit. Data analysis showed large differences in transporter expression in development and between species. These results can be used to better understand developmental toxicity findings in non-clinical species and their relevance for man.

Adverse Outcome Pathways as Versatile Tools in Liver Toxicity Testing

Adverse outcome pathways (AOPs) are tools to capture and visualize mechanisms driving toxicological effects. They share a common structure consisting of a molecular initiating event, a series of key events connected by key event relationships and an adverse outcome. Development and evaluation of AOPs ideally comply with guidelines issued by the Organization for Economic Cooperation and Development. AOPs have been introduced for major types of hepatotoxicity, which is not a surprise, as the liver is a frequent target for systemic adversity. Various applications for AOPs have been proposed in the areas of toxicology and chemical risk assessment, in particular in relation to the establishment of quantitative structure-activity relationships, the elaboration of prioritization strategies, and the development of novel in vitro toxicity screening tests and testing strategies.

Primary Human Hepatocyte Spheroids as Tools to Study the Hepatotoxic Potential of Non-Pharmaceutical Chemicals

Drug-induced liver injury, including cholestasis, is an important clinical issue and economic burden for pharmaceutical industry and healthcare systems. However, human-relevant in vitro information on the ability of other types of chemicals to induce cholestatic hepatotoxicity is lacking. This work aimed at investigating the cholestatic potential of non-pharmaceutical chemicals using primary human hepatocytes cultured in 3D spheroids. Spheroid cultures were repeatedly (co-) exposed to drugs (cyclosporine-A, bosentan, macitentan) or non-pharmaceutical chemicals (paraquat, tartrazine, triclosan) and a concentrated mixture of bile acids for 4 weeks. Cell viability (adenosine triphosphate content) was checked every week and used to calculate the cholestatic index, an indicator of cholestatic liability. Microarray analysis was performed at specific time-points to verify the deregulation of genes related to cholestasis, steatosis and fibrosis. Despite the evident inter-donor variability, shorter exposures to cyclosporine-A consistently produced cholestatic index values below 0.80 with transcriptomic data partially supporting its cholestatic burden. Bosentan confirmed to be hepatotoxic, while macitentan was not toxic in the tested concentrations. Prolonged exposure to paraquat suggested fibrotic potential, while triclosan markedly deregulated genes involved in different types of hepatotoxicity. These results support the applicability of primary human hepatocyte spheroids to study hepatotoxicity of non-pharmaceutical chemicals in vitro.

Derivation, characterisation and analysis of an adverse outcome pathway network for human hepatotoxicity

Adverse outcome pathways (AOPs) and their networks are important tools for the development of mechanistically based non-animal testing approaches, such as in vitro and/or in silico assays, to assess toxicity induced by chemicals. In the present study, an AOP network connecting 14 linear AOPs related to human hepatotoxicity, currently available in the AOP-Wiki, was derived according to established criteria. The derived AOP network was characterised and analysed with regard to its structure and topological features. In-depth analysis of the AOP network showed that cell injury/death, oxidative stress, mitochondrial dysfunction and accumulation of fatty acids are the most highly connected and central key events. Consequently, these key events may be considered as the rational and mechanistically anchored basis for selecting, developing and/optimising in vitro and/or in silico assays to predict hepatotoxicity induced by chemicals in view of animal-free hazard identification.

Stimulation of de novo glutathione synthesis by nitrofurantoin for enhanced resilience of hepatocytes

Toxicity is not only a function of damage mechanisms, but is also determined by cellular resilience factors. Glutathione has been reported as essential element to counteract negative influences. The present work hence pursued the question how intracellular glutathione can be elevated transiently to render cells more resistant toward harmful conditions. The antibiotic nitrofurantoin (NFT) was identified to stimulate de novo synthesis of glutathione in the human hepatoma cell line, HepG2, and in primary human hepatocytes. In intact cells, activation of NFT yielded a radical anion, which subsequently initiated nuclear-factor-erythroid 2-related-factor-2 (Nrf2)-dependent induction of glutamate cysteine ligase (GCL). Application of siRNA-based intervention approaches confirmed the involvement of the Nrf2-GCL axis in the observed elevation of intracellular glutathione levels. Quantitative activation of Nrf2 by NFT, and the subsequent rise in glutathione, were similar as observed with the potent experimental Nrf2 activator diethyl maleate. The elevation of glutathione levels, observed even 48 h after withdrawal of NFT, rendered cells resistant to different stressors such as the mitochondrial inhibitor rotenone, the redox cycler paraquat, the proteasome inhibitors MG-132 or bortezomib, or high concentrations of NFT. Repurpose of the antibiotic NFT as activator of Nrf2 could thus be a promising strategy for a transient and targeted activation of the endogenous antioxidant machinery. Graphical abstract.

Testing in vitro tools for the prediction of cholestatic liver injury induced by non-pharmaceutical chemicals

Bile acid accumulation and subsequent liver damage is a frequent adverse effect induced by drugs. Considerable efforts have therefore been focused on the introduction and characterization of tools that allow reliable prediction of this type of drug-induced liver injury. Among those are the cholestatic index and transcriptomic profiling, which are typically assessed in in vitro settings. The present study was set up to test the applicability of both tools to non-pharmaceutical compounds with cholestatic potential, including the industrial compound bis(2-ethylhexyl)phthalate, the cosmetic ingredients triclosan and octynoic acid, the herbicides paraquat and quizalofop-para-ethyl, and the food additives sunset yellow and tartrazine, in a human hepatoma cell culture model of cholestatic liver injury. The cholestatic index method showed cholestatic liability of sunset yellow, tartrazine and triclosan. Of those, tartrazine induced transcriptional changes reminiscent of the transcriptional profile of cholestatic drugs. Furthermore, a number of genes were found to be uniquely modulated by tartrazine, in accordance with the cholestatic drugs atazanavir, cyclosporin A and nefazodone, which may have potential as novel transcriptomic biomarkers of chemical-induced cholestatic liver injury. In conclusion, unambiguous identification of the non-pharmaceutical compounds tested in this study as inducers of cholestasis could not be achieved.

Rodent models of cholestatic liver disease: A practical guide for translational research

Cholestatic liver disease denotes any situation associated with impaired bile flow concomitant with a noxious bile acid accumulation in the liver and/or systemic circulation. Cholestatic liver disease can be subdivided into different types according to its clinical phenotype, such as biliary atresia, drug-induced cholestasis, gallstone liver disease, intrahepatic cholestasis of pregnancy, primary biliary cholangitis and primary sclerosing cholangitis. Considerable effort has been devoted to elucidating underlying mechanisms of cholestatic liver injuries and explore novel therapeutic and diagnostic strategies using animal models. Animal models employed according to their appropriate applicability domain herein play a crucial role. This review provides an overview of currently available in vivo animal models, fit-for-purpose in modelling different types of cholestatic liver diseases. Moreover, a practical guide and workflow is provided which can be used for translational research purposes, including all advantages and disadvantages of currently available in vivo animal models.

Biomarkers of cholestasis

Cholestasis is a major pathological manifestation, often resulting in detrimental liver conditions, which occurs in a variety of indications collectively termed cholestatic liver diseases. The frequent asymptomatic character and complexity of cholestasis, together with the lack of a straightforward biomarker, hampers early detection and treatment of the condition. The 'omics' era, however, has resulted in a plethora of cholestatic indicators, yet a single clinically applicable biomarker for a given cholestatic disease remains missing. The criteria to fulfil as an ideal biomarker as well as the challenging molecular pathways in cholestatic liver diseases advocate for a scenario in which multiple biomarkers, originating from different domains, will be assessed concomitantly. This review gives an overview of classical clinical and novel molecular biomarkers in cholestasis, focusing on their benefits and drawbacks.

Pyrrolizidine Alkaloids Disturb Bile Acid Homeostasis in the Human Hepatoma Cell Line HepaRG

1,2-unsaturated pyrrolizidine alkaloids (PAs) belong to a group of secondary plant metabolites. Exposure to PA-contaminated feed and food may cause severe hepatotoxicity. A pathway possibly involved in PA toxicity is the disturbance of bile acid homeostasis. Therefore, in this study, the influence of four structurally different PAs on bile acid homeostasis was investigated after single (24 h) and repeated (14 days) exposure using the human hepatoma cell line HepaRG. PAs induce a downregulation of gene expression of various hepatobiliary transporters, enzymes involved in bile acid synthesis, and conjugation, as well as several transcription regulators in HepaRG cells. This repression may lead to a progressive impairment of bile acid homeostasis, having the potential to accumulate toxic bile acids. However, a significant intracellular and extracellular decrease in bile acids was determined, pointing to an overall inhibition of bile acid synthesis and transport. In summary, our data clearly show that PAs structure-dependently impair bile acid homeostasis and secretion by inhibiting the expression of relevant genes involved in bile acid homeostasis. Furthermore, important biliary efflux mechanisms seem to be disturbed due to PA exposure. These mole-cular mechanisms may play an important role in the development of severe liver damage in PA-intoxicated humans.

Perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), and perfluorononanoic acid (PFNA) increase triglyceride levels and decrease cholesterogenic gene expression in human HepaRG liver cells

Per- and polyfluoroalkyl substances (PFASs) are omnipresent in the environment, food chain, and humans. Epidemiological studies have shown a positive association between serum levels of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS), and increased serum cholesterol and, in some cases, also triglyceride levels. However, causality has been questioned, as animal studies, as well as a human trial, showed a decrease in serum cholesterol and no effects or a decrease in plasma triglycerides. To obtain more insight into the effects of PFASs on these processes, the present study investigated the effects of PFOA, PFOS, and perfluorononanoic acid (PFNA) on intracellular triglyceride and cholesterol levels in human HepaRG liver cells. DNA microarray analyses were performed to provide insight into underlying mechanisms. All PFASs induced an increase in cellular triglyceride levels, but had no effect on cholesterol levels. Gene set enrichment analysis (GSEA) of the microarray data indicated that gene sets related to cholesterol biosynthesis were repressed by PFOA, PFOS, and PFNA. Other gene sets commonly affected by all PFAS were related to PERK/ATF4 signaling (induced), tRNA amino-acylation (induced), amino acid transport (induced), and glycolysis/gluconeogenesis (repressed). Moreover, numerous target genes of peroxisome proliferator-activated receptor α (PPARα) were found to be upregulated. Altogether, the present study shows that PFOA, PFOS, and PFNA increase triglyceride levels and inhibit cholesterogenic gene expression in HepaRG cells. In addition, the present study indicates that PFASs induce endoplasmic reticulum stress, which may be an important mechanism underlying some of the toxic effects of these chemicals.

Use of deep learning methods to translate drug-induced gene expression changes from rat to human primary hepatocytes

In clinical trials, animal and cell line models are often used to evaluate the potential toxic effects of a novel compound or candidate drug before progressing to human trials. However, relating the results of animal and in vitro model exposures to relevant clinical outcomes in the human in vivo system still proves challenging, relying on often putative orthologs. In recent years, multiple studies have demonstrated that the repeated dose rodent bioassay, the current gold standard in the field, lacks sufficient sensitivity and specificity in predicting toxic effects of pharmaceuticals in humans. In this study, we evaluate the potential of deep learning techniques to translate the pattern of gene expression measured following an exposure in rodents to humans, circumventing the current reliance on orthologs, and also from in vitro to in vivo experimental designs. Of the applied deep learning architectures applied in this study the convolutional neural network (CNN) and a deep artificial neural network with bottleneck architecture significantly outperform classical machine learning techniques in predicting the time series of gene expression in primary human hepatocytes given a measured time series of gene expression from primary rat hepatocytes following exposure in vitro to a previously unseen compound across multiple toxicologically relevant gene sets. With a reduction in average mean absolute error across 76 genes that have been shown to be predictive for identifying carcinogenicity from 0.0172 for a random regression forest to 0.0166 for the CNN model (p < 0.05). These deep learning architecture also perform well when applied to predict time series of in vivo gene expression given measured time series of in vitro gene expression for rats.

Comprehensive expression analysis of mRNA and microRNA for the investigation of compensatory mechanisms in the rat kidney after unilateral nephrectomy

Compensation is a physiological response that occurs during chemical exposure to maintain homeostasis. Because compensatory responses are not usually considered adverse effects, it is important to understand compensatory mechanisms for chemical risk assessment. Although the kidney is a major target organ for toxicity, there is controversy over whether hyperplasia or hypertrophy contributes to the compensatory mechanism, and there is limited information to apply for chemical risk assessment. In the present study, compensatory mechanisms of the kidney were investigated in a unilateral nephrectomy (UNx) model using adult male and female F344 rats. In residual kidneys of male and female rats after UNx, 5-bromo-2'-deoxyuridine-labeling indices and mRNA expression of cell cycle-related genes were increased, although there were no fluctuations in mRNA expression of transforming growth factor-β1, which contributes to hypertrophy in renal tubules. Pathway analysis using mRNA expression data from a complementary DNA (cDNA) microarray revealed that canonical pathways related to cell proliferation were mainly activated and that forkhead box M1 (FOXM1) was an upstream regulator of compensatory cell proliferation in residual kidneys of male and female rats. cDNA microarray for microRNAs (miRNAs) demonstrated that nine miRNAs were downregulated in residual kidneys, and mRNA/miRNA integrated analysis indicated that miRNAs were associated with the expression of factors downstream of FOXM1. Overall, these results suggested that FOXM1-mediated hyperplasia rather than hypertrophy contributed to compensatory mechanisms in the kidney and that miRNAs regulated downstream FOXM1 signaling. These results will be beneficial for evaluating nephrotoxicity in chemical risk assessment and for developing new biomarkers to predict nephrotoxicity.

Impairment of bile acid metabolism by perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) in human HepaRG hepatoma cells

Perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) are man-made chemicals that are used for the fabrication of many products with water- and dirt-repellent properties. The toxicological potential of both substances is currently under debate. In a recent Scientific Opinion, the European Food Safety Authority (EFSA) has identified increased serum total cholesterol levels in humans as one major critical effect being associated with exposure to PFOA or PFOS. In animal studies, both substances induced a decrease of serum cholesterol levels, and the underlying molecular mechanism(s) for these opposed effects are unclear so far. In the present study, we examined the impact of PFOA and PFOS on cholesterol homoeostasis in the human HepaRG cell line as a model for human hepatocytes. Cholesterol levels in HepaRG cells were not affected by PFOA or PFOS, but both substances strongly decreased synthesis of a number of bile acids. The expression of numerous genes whose products are involved in synthesis, metabolism and transport of cholesterol and bile acids was strongly affected by PFOA and PFOS at concentrations above 10 µM. Notably, both substances led to a strong decrease of CYP7A1, the key enzyme catalyzing the rate-limiting step in the synthesis of bile acids from cholesterol, both at the protein level and at the level of gene expression. Moreover, both substances led to a dilatation of bile canaliculi that are formed by differentiated HepaRG cells in vitro. Similar morphological changes are known to be induced by cholestatic agents in vivo. Thus, the strong impact of PFOA and PFOS on bile acid synthesis and bile canalicular morphology in our in vitro experiments may allow the notion that both substances have a cholestatic potential that is connected to the observed increased serum cholesterol levels in humans in epidemiological studies.

Robustness testing and optimization of an adverse outcome pathway on cholestatic liver injury

Adverse outcome pathways (AOPs) have been recently introduced as tools to map the mechanisms underlying toxic events relevant for chemical risk assessment. AOPs particularly depict the linkage between a molecular initiating event and an adverse outcome through a number of intermediate key events. An AOP has been previously introduced for cholestatic liver injury. The objective of this study was to test the robustness of this AOP for different types of cholestatic insult and the in vitro to in vivo extrapolation. For this purpose, in vitro samples from human hepatoma HepaRG cell cultures were exposed to cholestatic drugs (i.e. intrahepatic cholestasis), while in vivo samples were obtained from livers of cholestatic mice (i.e. extrahepatic cholestasis). The occurrence of cholestasis in vitro was confirmed through analysis of bile transporter functionality and bile acid analysis. Transcriptomic analysis revealed inflammation and oxidative stress as key events in both types of cholestatic liver injury. Major transcriptional differences between intrahepatic and extrahepatic cholestatic liver insults were observed at the level of cell death and metabolism. Novel key events identified by pathway analysis included endoplasmic reticulum stress in intrahepatic cholestasis, and autophagy and necroptosis in both intrahepatic as extrahepatic cholestasis. This study demonstrates that AOPs constitute dynamic tools that should be frequently updated with new input information.

RNA-protein correlation of liver toxicity markers in HepaRG cells

The liver is a main target organ for the toxicity of many different compounds. While in general, in vivo testing is still routinely used for assessing the hepatotoxic potential of test chemicals, the use of in vitro models offers advantages with regard to throughput, consumption of resources, and animal welfare aspects. Using the human hepatoma cell line HepaRG, we performed a comparative evaluation of a panel of hepatotoxicity marker mRNAs and proteins after exposure of the cells to 30 different pesticidal active compounds comprising herbizides, fungicides, insecticides, and others. The panel of hepatotoxicity markers included nuclear receptor target genes, key players of fatty acid and bile acid metabolism-related pathways, as well as recently identified biomarkers of drug-induced liver injury. Moreover, marker genes and proteins were identified, for example, S100P, ANXA10, CYP1A1, and CYP7A1. These markers respond with high sensitivity to stimulation with chemically diverse test compounds already at non-cytotoxic concentrations. The potency of the test compounds, determined as an overall parameter of their ability to deregulate marker expression in vitro, was very similar between the mRNA and protein levels. Thus, this study does not only characterize the response of human liver cells to 30 different pesticides but also demonstrates that hepatotoxicity testing in human HepaRG cells yields well comparable results at the mRNA and protein levels. Furthermore, robust hepatotoxicity marker genes and proteins were identified in HepaRG cells.

Untargeted liquid chromatography-mass spectrometry metabolomics to assess drug-induced cholestatic features in HepaRG® cells

Cholestasis is a liver disease associated with retention of bile in the liver, which leads to local hepatic inflammation and severe liver damage. In order to investigate the mode of action of drug-induced cholestasis, in vitro models have shown to be able to recapitulate important elements of this disease. In this study, we applied untargeted metabolomics to investigate the metabolic perturbances in HepaRG® cells exposed for 24 h and 72 h to bosentan, a cholestatic reference toxicant. Intracellular profiles were extracted and analysed with liquid chromatography and accurate-mass spectrometry. Metabolites of interest were selected using partial least-squares discriminant analysis and random forest classifier models. The observed metabolic patterns associated with cholestasis in vitro were complex. Acute (24 h) exposure revealed metabolites related to apoptosis, such as ceramide and triglyceride accumulation, in combination with phosphatidylethanolamine, choline and carnitine depletion. Metabolomic alterations during exposure to lower dosages and a prolonged exposure (72 h) included carnitine upregulation and changes in the polyamine metabolism. These metabolites were linked to changes in phospholipid metabolism, mitochondrial pathways and energy homeostasis. The metabolic changes confirmed the mitotoxic effects of bosentan and revealed the potential involvement of phospholipid metabolism as part of the mode of action of drug-induced cholestasis.

Industrial, Biocide, and Cosmetic Chemical Inducers of Cholestasis

A frequent side effect of many drugs includes the occurrence of cholestatic liver toxicity. Over the past couple of decades, drug-induced cholestasis has gained considerable attention, resulting in a plethora of data regarding its prevalence and mechanistic basis. Likewise, several food additives and dietary supplements have been reported to cause cholestatic liver insults in the past few years. The induction of cholestatic hepatotoxicity by other types of chemicals, in particular synthetic compounds, such as industrial chemicals, biocides, and cosmetic ingredients, has been much less documented. Such information can be found in occasional clinical case reports of accidental intake or suicide attempts as well as in basic and translational study reports on mechanisms or testing of new therapeutics in cholestatic animal models. This paper focuses on such nonpharmaceutical and nondietary synthetic chemical inducers of cholestatic liver injury, in particular alpha-naphthylisocyanate, 3,5-diethoxycarbonyl-1,4-dihydrocollidine, methylenedianiline, paraquat, tartrazine, triclosan, 2-octynoic acid, and 2-nonynoic acid. Most of these cholestatic compounds act by similar mechanisms. This could open perspectives for the prediction of cholestatic potential of chemicals.

Activation of TRPA1 by volatile organic chemicals leading to sensory irritation

Many volatile organic chemicals (VOCs) have not been tested for sensory pulmonary irritation. Development of in vitro non-animal sensory irritation assay suitable for a large number of chemicals is needed to replace the mouse assay. An adverse outcome pathway (AOP) is designed to provide a clear description of the biochemical and cellular processes leading to toxicological effects or an adverse outcome. The AOP for chemical sensory pulmonary irritation was developed according to the Organization for Economic Co-operation and Development guidance including the Bradford Hill criteria for a weight of evidence to determine the confidence of the AOP. The proposed AOP is based on an in-depth review of the relevant scientific literature to identify the initial molecular event for respiratory irritation. The activation of TRPA1 receptor (transient receptor potential cation channel, subfamily A, member 1) is the molecular initial event (MIE) leading to sensory irritation. A direct measure of TRPA1 activation in vitro should identify chemical sensory irritants and provide an estimate of potency. Fibroblasts expressing TRPA1 are used to determine TRPA1 activation and irritant potency. We report a linear relationship between the in vivo RD₅₀ and the in vitro pEC₅₀ values (R=0.81) to support this hypothesis. We propose that this in vitro assay after additional analysis and validation could serve as a suitable candidate to replace the mouse sensory irritation assay.

Mechanisms and in vitro models of drug-induced cholestasis

Cholestasis underlies one of the major manifestations of drug-induced liver injury. Drug-induced cholestatic liver toxicity is a complex process, as it can be triggered by a variety of factors that induce 2 types of biological responses, namely a deteriorative response, caused by bile acid accumulation, and an adaptive response, aimed at removing the accumulated bile acids. Several key events in both types of responses have been characterized in the past few years. In parallel, many efforts have focused on the development and further optimization of experimental cell culture models to predict the occurrence of drug-induced cholestatic liver toxicity in vivo. In this paper, a state-of-the-art overview of mechanisms and in vitro models of drug-induced cholestatic liver injury is provided.

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity

In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals. Central to this mission is the advancement of computational toxicology and artificial intelligence approaches to implementing innovative testing methods. In the current big data era, the terms volume (amount of data), velocity (growth of data), and variety (the diversity of sources) have been used to characterize the currently available chemical, in vitro, and in vivo data for toxicity modeling purposes. Furthermore, as suggested by various scientists, the variability (internal consistency or lack thereof) of publicly available data pools, such as PubChem, also presents significant computational challenges. The development of novel artificial intelligence approaches based on public massive toxicity data is urgently needed to generate new predictive models for chemical toxicity evaluations and make the developed models applicable as alternatives for evaluating untested compounds. In this procedure, traditional approaches (e.g., QSAR) purely based on chemical structures have been replaced by newly designed data-driven and mechanism-driven modeling. The resulting models realize the concept of adverse outcome pathway (AOP), which can not only directly evaluate toxicity potentials of new compounds, but also illustrate relevant toxicity mechanisms. The recent advancement of computational toxicology in the big data era has paved the road to future toxicity testing, which will significantly impact on the public health.

Transcriptomic Analysis of Cholestatic Compounds In Vitro

Drug-induced cholestasis is one of the most severe manifestations of drug-induced liver injury. Drug-induced cholestasis is characterized by an accumulation of endogenous metabolites normally excreted in the bile such as bile salts, cholesterol, bilirubin, or drug metabolites. The possibility to determine early in the drug development process whether a compound presents a risk of inducing drug-induced cholestasis is key information. Since preclinical repeated dose toxicity studies have limited predictive value, large efforts in identifying alternative in vitro models with improved prediction are being made. One of the best current models for in vitro human liver is primary human hepatocytes, and we recently reported that primary human hepatocytes can be kept as long-term cultures in 2D-sandwich configuration when regularly renewing the Matrigel overlay, thereby making the model useful for repeat exposure-related toxicities, as well as for the study of adaptive responses. This primary human hepatocyte culture system combined with transcriptomics carries the future promise to identify individual gene expression profiles predictive of increased drug-induced cholestasis risk.This chapter describes the various steps for culturing and exposing primary human hepatocytes to drugs during long-term 2D-sandwich culture, performing RNA extraction, gene chip assay and selecting hepatotoxic signature using the IPA software and highlighting genes involved in bile acid homeostasis.

Mechanisms of Drug-Induced Cholestasis

Cholestasis can be defined as any situation of impaired bile secretion with concomitant accumulation of bile acids in the liver or in the systemic circulation. A variety of factors may evoke cholestasis, including genetic disorders, metabolic pathologies, infectious diseases, immunogenic stimuli, and drugs. Drug-induced cholestasis is a mechanistically complex process. At least three triggering factors of drug-induced cholestasis have been described, including effects on drug transporters, various hepatocellular changes, and altered bile canaliculi dynamics. These stimuli induce two cellular responses, each typified by a number of key events, namely a deteriorative response activated by bile acid accumulation and an adaptive response aimed at decreasing the uptake and increasing the export of bile acids into and from the liver, respectively. The mechanistic scenario of drug-induced cholestasis is described in this chapter.

Primary hepatocytes and their cultures for the testing of drug-induced liver injury

Drug-induced liver injury is a major reason for discontinuation of drug development and withdrawal of drugs from the market. Intensive efforts in the last decades have focused on the establishment and finetuning of liver-based in vitro models for reliable prediction of hepatotoxicity triggered by drug candidates. Of those, primary hepatocytes and their cultures still are considered the gold standard, as they provide an acceptable reflection of the hepatic in vivo situation. Nevertheless, these in vitro systems cope with gradual deterioration of the differentiated morphological and functional phenotype. The present paper gives an overview of traditional and more recently introduced strategies to counteract this dedifferentiation process in an attempt to set up culture models that can be used for long-term testing purposes. The relevance and applicability of such optimized cultures of primary hepatocytes for the testing of drug-induced cholestatic liver injury is demonstrated.

Omics-based responses induced by bosentan in human hepatoma HepaRG cell cultures

Bosentan is well known to induce cholestatic liver toxicity in humans. The present study was set up to characterize the hepatotoxic effects of this drug at the transcriptomic, proteomic, and metabolomic levels. For this purpose, human hepatoma-derived HepaRG cells were exposed to a number of concentrations of bosentan during different periods of time. Bosentan was found to functionally and transcriptionally suppress the bile salt export pump as well as to alter bile acid levels. Pathway analysis of both transcriptomics and proteomics data identified cholestasis as a major toxicological event. Transcriptomics results further showed several gene changes related to the activation of the nuclear farnesoid X receptor. Induction of oxidative stress and inflammation were also observed. Metabolomics analysis indicated changes in the abundance of specific endogenous metabolites related to mitochondrial impairment. The outcome of this study may assist in the further optimization of adverse outcome pathway constructs that mechanistically describe the processes involved in cholestatic liver injury.