Human embryonic stem cell derived hepatocyte-like cells as a tool for in vitro hazard assessment of chemical carcinogenicity

Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction

Idiosyncratic drug-induced liver injury (iDILI) encompasses the unexpected harms that prescription and non-prescription drugs, herbal and dietary supplements can cause to the liver. iDILI remains a major public health problem and a major cause of drug attrition. Given the lack of biomarkers for iDILI prediction, diagnosis and prognosis, searching new models to predict and study mechanisms of iDILI is necessary. One of the major limitations of iDILI preclinical assessment has been the lack of correlation between the markers of hepatotoxicity in animal toxicological studies and clinically significant iDILI. Thus, major advances in the understanding of iDILI susceptibility and pathogenesis have come from the study of well-phenotyped iDILI patients. However, there are many gaps for explaining all the complexity of iDILI susceptibility and mechanisms. Therefore, there is a need to optimize preclinical human in vitro models to reduce the risk of iDILI during drug development. Here, the current experimental models and the future directions in iDILI modelling are thoroughly discussed, focusing on the human cellular models available to study the pathophysiological mechanisms of the disease and the most used in vivo animal iDILI models. We also comment about in silico approaches and the increasing relevance of patient-derived cellular models.

Application of transcriptomic and microRNA profiling in the evaluation of potential liver carcinogens

Hepatocarcinogens are agents that increase the incidence of liver cancer in exposed animals or humans. It is now established that carcinogenic exposures have a widespread impact on the transcriptome, inducing both adaptive and adverse changes in the activities of genes and pathways. Chemical hepatocarcinogens have also been shown to affect expression of microRNA (miRNA), the evolutionarily conserved noncoding RNA that regulates gene expression posttranscriptionally. Considerable effort has been invested into examining the involvement of mRNA in chemical hepatocarcinogenesis and their potential usage for the classification and prediction of new chemical entities. For miRNA, there has been an increasing number of studies reported over the past decade, although not to the same degree as for transcriptomic studies. Current data suggest that it is unlikely that any gene or miRNA signature associated with short-term carcinogen exposure can replace the rodent bioassay. In this review, we discuss the application of transcriptomic and miRNA profiles to increase mechanistic understanding of chemical carcinogens and to aid in their classification.

Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering

Background and objectives: Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre-clinical phase of drug development pipeline as well as in toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity and their associated biomarker genes. However, these methods consume more time for computation of the model parameters using the EM (expectation-maximization) based iterative approaches. To overcome this problem, in this paper, an attempt is made to propose an alternative approach based on hierarchical clustering (HC) for the same purpose. Methods and materials: There are several types of HC approaches whose performance depends on different similarity/distance measures. Therefore, we explored suitable combinations of distance measures and HC methods based on Japanese Toxicogenomics Project (TGP) datasets for better clustering/co-clustering between DDs and genes as well as to detect toxic DDs and their associated biomarker genes. Results: We observed that Word’s HC method with each of Euclidean, Manhattan, and Minkowski distance measures produces better clustering/co-clustering results. For an example, in the case of the glutathione metabolism pathway (GMP) dataset LOC100359539/Rrm2, Gpx6, RGD1562107, Gstm4, Gstm3, G6pd, Gsta5, Gclc, Mgst2, Gsr, Gpx2, Gclm, Gstp1, LOC100912604/Srm, Gstm4, Odc1, Gsr, Gss are the biomarker genes and Acetaminophen_Middle, Acetaminophen_High, Methapyrilene_High, Nitrofurazone_High, Nitrofurazone_Middle, Isoniazid_Middle, Isoniazid_High are their regulatory (associated) DDs explored by our proposed co-clustering algorithm based on the distance and HC method combination Euclidean: Word. Similarly, for the peroxisome proliferator-activated receptor signaling pathway (PPAR-SP) dataset Cpt1a, Cyp8b1, Cyp4a3, Ehhadh, Plin5, Plin2, Fabp3, Me1, Fabp5, LOC100910385, Cpt2, Acaa1a, Cyp4a1, LOC100365047, Cpt1a, LOC100365047, Angptl4, Aqp7, Cpt1c, Cpt1b, Me1 are the biomarker genes and Aspirin_Low, Aspirin_Middle, Aspirin_High, Benzbromarone_Middle, Benzbromarone_High, Clofibrate_Middle, Clofibrate_High, WY14643_Low, WY14643_High, WY14643_Middle, Gemfibrozil_Middle, Gemfibrozil_High are their regulatory DDs. Conclusions: Overall, the methods proposed in this article, co-cluster the genes and DDs as well as detect biomarker genes and their regulatory DDs simultaneously consuming less time compared to other mentioned methods. The results produced by the proposed methods have been validated by the available literature and functional annotation.

Stem-cell derived hepatocyte-like cells for the assessment of drug-induced liver injury

Drug-induced liver injury is a major cause of drug discovery failure in clinical trials and a leading cause of liver disease. Current preclinical drug testing does not predict hepatotoxicity which highlights the importance of developing highly predictive cell-based models. The use of stem cell technology and differentiation into hepatocyte-like cells (HLCs) could provide a stable source of hepatocytes for multiple applications, including drug screening. HLCs derived from both embryonic and induced pluripotent stem cells have been used to accurately predict hepatotoxicity as well as to test individual-specific toxicity. Although there are still many limitations, mainly related to the lack of fully maturity of the HLCs derived from pluripotent stem cells, they could provide a relative unlimited and consistent supply of cells with stable phenotype, that could be obtained from different donors, enabling the generation of a library of HLCs representative of the variability of human population.

Robust identification of significant interactions between toxicogenomic biomarkers and their regulatory chemical compounds using logistic moving range chart

Identification of significant interactions between genes and chemical compounds/drugs is an important issue in toxicogenomic studies as well as in drug discovery and development. There are some online and offline computational tools for toxicogenomic data analysis to identify the biomarker genes and their regulatory chemical compounds/drugs. However, none of the researchers has considered yet the identification of significant interactions between genes and compounds. Therefore, in this paper, we have discussed two approaches namely moving range chart (MRC) and logistic moving range chart (LMRC) for the identification of significant up-regulatory (UpR) and down-regulatory (DnR) gene-compound interactions as well as toxicogenomic biomarkers and their regulatory chemical compounds/drugs. We have investigated the performance of both MRC and LMRC approaches using simulated datasets. Simulation results show that both approaches perform almost equally in absence of outliers. However, in presence of outliers, the LMRC shows much better performance than the MRC. In case of real life toxicogenomic data analysis, the proposed LMRC approach detected some important down-regulated biomarker genes those were not detected by other approaches. Therefore, in this paper, our proposal is to use LMRC for robust identification of significant interactions between genes and chemical compounds/drugs.

Robust Co-clustering to Discover Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds Using Logistic Probabilistic Hidden Variable Model

Detection of biomarker genes and their regulatory doses of chemical compounds (DCCs) is one of the most important tasks in toxicogenomic studies as well as in drug design and development. There is an online computational platform "Toxygates" to identify biomarker genes and their regulatory DCCs by co-clustering approach. Nevertheless, the algorithm of that platform based on hierarchical clustering (HC) does not share gene-DCC two-way information simultaneously during co-clustering between genes and DCCs. Also it is sensitive to outlying observations. Thus, this platform may produce misleading results in some cases. The probabilistic hidden variable model (PHVM) is a more effective co-clustering approach that share two-way information simultaneously, but it is also sensitive to outlying observations. Therefore, in this paper we have proposed logistic probabilistic hidden variable model (LPHVM) for robust co-clustering between genes and DCCs, since gene expression data are often contaminated by outlying observations. We have investigated the performance of the proposed LPHVM co-clustering approach in a comparison with the conventional PHVM and Toxygates co-clustering approaches using simulated and real life TGP gene expression datasets, respectively. Simulation results show that the proposed method improved the performance over the conventional PHVM in presence of outliers; otherwise, it keeps equal performance. In the case of real life TGP data analysis, three DCCs (glibenclamide-low, perhexilline-low, and hexachlorobenzene-medium) for glutathione metabolism pathway dataset as well as two DCCs (acetaminophen-medium and methapyrilene-low) for PPAR signaling pathway dataset were incorrectly co-clustered by the Toxygates online platform, while only one DCC (hexachlorobenzene-low) for glutathione metabolism pathway was incorrectly co-clustered by the proposed LPHVM approach. Our findings from the real data analysis are also supported by the other findings in the literature.

Network and Pathway Analysis of Toxicogenomics Data

Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

Toxic Dose prediction of Chemical Compounds to Biomarkers using an ANOVA based Gene Expression Analysis

The aim of toxicogenomic studies is to optimize the toxic dose levels of chemical compounds (CCs) and their regulated biomarker genes. This is also crucial in drug discovery and development. There are popular online computational tools such as ToxDB and Toxygates to identify toxicogenomic biomarkers using t-test. However, they are not suitable for the identification of biomarker gene regulatory dose of corresponding CCs. Hence, we describe a one-way ANOVA model together with Tukey's HSD test for the identification of toxicogenomic biomarker genes and their influencing CC dose with improved efficiency. Glutathione metabolism pathway data analysis shows high and middle dose for acetaminophen, and nitrofurazone as well as high dose for methapyrilene as significant toxic CC dose. The corresponding regulated top seven toxicogenomic biomarker genes found in this analysis is Gstp1, Gsr, Mgst2, Gclm, G6pd, Gsta5 and Gclc.

Premalignant alteration assessment in liver-like tissue derived from embryonic stem cells by aristolochic acid I exposure

The in vitro predictive evaluation of chemical carcinogenicity based on hepatic premalignance has so far not been established. Here, we report a novel approach to investigate the premalignant events triggered by human carcinogen aristolochic acid I (AAI) in the liver-like tissue derived from mouse embryonic stem cells. By AAI exposure, the liver-like tissue exhibited the paracrine interleukin-6 phenotypic characteristics. Hepatocytes expressed STAT3/p-STAT3, c-Myc and Lin28B in parallel. Some of them displayed the dedifferentiation characteristics, such as full of α-fetoprotein granules, increase in size, and nucleocytoplasmic shuttle of Oct4. When these cells were injected into mice, the xenografts mostly displayed the uniform area of hepatic-like tissue with malignant nuclei. The hepatic malignant markers, α-fetoprotein, cytokeratin 7 and cytokeratin 19, were co-expressed in albumin-positive areas, respectively. In conclusion, we established an approach to predict the hepatic premalignance triggered by carcinogen AAI. This premalignant assay system might aid to evaluate the effects of potential carcinogens in liver, and probably to screen the protecting against hepatocarcinogenic efficacy of pharmaceuticals in vitro.

Computational Network Analysis for Drug Toxicity Prediction

The computational prediction of compound effects from molecular data is an important task in hazard and risk assessment and pivotal for judging the safety of any drug, chemical or cosmetic compound. In particular, the identification of such compound effects at the level of molecular interaction networks can be helpful for the construction of adverse outcome pathways (AOPs). AOPs emerged as a guiding concept for toxicity prediction, because of the inherent mechanistic information of such networks. In fact, integrating molecular interactions in transcriptome analysis and observing expression changes in closely interacting genes might allow identifying the key molecular initiating events of compound toxicity.In this work we describe a computational approach that is suitable for the identification of such network modules from transcriptomics data, which is the major molecular readout of toxicogenomics studies. The approach is composed of different tools (1) for primary data analysis, i.e., the biostatistical quantification of the gene expression changes, (2) for functional annotation and prioritization of genes using literature mining, as well as (3) for the construction of an interaction network that consists of interactions with high confidence and the identification of predictive modules from these networks. We describe the different steps of the approach and demonstrate its performance with public data on drugs that induce hepatic and cardiac toxicity.

Comparative transcriptomics of hepatic differentiation of human pluripotent stem cells and adult human liver tissue

Hepatocytes derived from human pluripotent stem cells (hPSC-HEP) have the potential to replace presently used hepatocyte sources applied in liver disease treatment and models of drug discovery and development. Established hepatocyte differentiation protocols are effective and generate hepatocytes, which recapitulate some key features of their in vivo counterparts. However, generating mature hPSC-HEP remains a challenge. In this study, we applied transcriptomics to investigate the progress of in vitro hepatic differentiation of hPSCs at the developmental stages, definitive endoderm, hepatoblasts, early hPSC-HEP, and mature hPSC-HEP, to identify functional targets that enhance efficient hepatocyte differentiation. Using functional annotation, pathway and protein interaction network analyses, we observed the grouping of differentially expressed genes in specific clusters representing typical developmental stages of hepatic differentiation. In addition, we identified hub proteins and modules that were involved in the cell cycle process at early differentiation stages. We also identified hub proteins that differed in expression levels between hPSC-HEP and the liver tissue controls. Moreover, we identified a module of genes that were expressed at higher levels in the liver tissue samples than in the hPSC-HEP. Considering that hub proteins and modules generally are essential and have important roles in the protein-protein interactions, further investigation of these genes and their regulators may contribute to a better understanding of the differentiation process. This may suggest novel target pathways and molecules for improvement of hPSC-HEP functionality, having the potential to finally bring this technology to a wider use.

Prediction of drug-induced immune-mediated hepatotoxicity using hepatocyte-like cells derived from human embryonic stem cells

Drug-induced liver injury (DILI) is a leading cause of liver disease and a key safety factor during drug development. In addition to the initiation events of drug-specific hepatotoxicity, dysregulated immune responses have been proposed as major pathological events of DILI. Thus, there is a need for a reliable cell culture model with which to assess drug-induced immune reactions to predict hepatotoxicity for drug development. To this end, stem cell-derived hepatocytes have shown great potentials. Here we report that hepatocyte-like cells derived from human embryonic stem cells (hES-HLCs) can be used to evaluate drug-induced hepatotoxic immunological events. Treatment with acetaminophen significantly elevated the levels of inflammatory cytokines by hES-HLCs. Moreover, three human immune cell lines, Jurkat, THP-1, and NK92MI, were activated when cultured in conditioned medium obtained from acetaminophen-treated hES-HLCs. To further validate, we tested thiazolidinedione (TZD) class, antidiabetic drugs, including troglitazone withdrawn from the market because of severe idiosyncratic drug hepatotoxicity. We found that TZD drug treatment to hES-HLCs resulted in the production of pro-inflammatory cytokines and eventually associated immune cell activation. In summary, our study demonstrates for the first time the potential of hES-HLCs as an in vitro model system for assessment of drug-induced as well as immune-mediated hepatotoxicity.

Validation of Transcriptomics-Based In Vitro Methods

The field of transcriptomics has expanded rapidly during the last decades. This methodology provides an exceptional framework to study not only molecular changes underlying the adverse effects of a given compound, but also to understand its Mode of Action (MoA). However, the implementation of transcriptomics-based tests within the regulatory arena is not a straightforward process. One of the major obstacles in their regulatory implementation is still the interpretation of this new class of data and the judgment of the level of confidence of these tests. A key element in the regulatory acceptance of transcriptomics-based tests is validation, which still represents a major challenge. Although important advances have been made in the development and standardisation of such tests, to date there is limited experience with their validation. Taking into account the experience acquired so far, this chapter describes those aspects that were identified as important in the validation process of transcriptomics-based tests, including the assessment of standardisation, reliability and relevance. It also critically discusses the challenges posed to validation in relation to the specific characteristics of these approaches and their application in the wider context of testing strategies.

Stem cell-derived models to improve mechanistic understanding and prediction of human drug-induced liver injury

Current preclinical drug testing does not predict some forms of adverse drug reactions in humans. Efforts at improving predictability of drug-induced tissue injury in humans include using stem cell technology to generate human cells for screening for adverse effects of drugs in humans. The advent of induced pluripotent stem cells means that it may ultimately be possible to develop personalized toxicology to determine interindividual susceptibility to adverse drug reactions. However, the complexity of idiosyncratic drug-induced liver injury means that no current single-cell model, whether of primary liver tissue origin, from liver cell lines, or derived from stem cells, adequately emulates what is believed to occur during human drug-induced liver injury. Nevertheless, a single-cell model of a human hepatocyte which emulates key features of a hepatocyte is likely to be valuable in assessing potential chemical risk; furthermore, understanding how to generate a relevant hepatocyte will also be critical to efforts to build complex multicellular models of the liver. Currently, hepatocyte-like cells differentiated from stem cells still fall short of recapitulating the full mature hepatocellular phenotype. Therefore, we convened a number of experts from the areas of preclinical and clinical hepatotoxicity and safety assessment, from industry, academia, and regulatory bodies, to specifically explore the application of stem cells in hepatotoxicity safety assessment and to make recommendations for the way forward. In this short review, we particularly discuss the importance of benchmarking stem cell-derived hepatocyte-like cells to their terminally differentiated human counterparts using defined phenotyping, to make sure the cells are relevant and comparable between labs, and outline why this process is essential before the cells are introduced into chemical safety assessment. (Hepatology 2017;65:710-721).

Patient-specific hepatocyte-like cells derived from induced pluripotent stem cells model pazopanib-mediated hepatotoxicity

Idiosyncratic drug-induced hepatotoxicity is a major cause of liver damage and drug pipeline failure, and is difficult to study as patient-specific features are not readily incorporated in traditional hepatotoxicity testing approaches using population pooled cell sources. Here we demonstrate the use of patient-specific hepatocyte-like cells (HLCs) derived from induced pluripotent stem cells for modeling idiosyncratic hepatotoxicity to pazopanib (PZ), a tyrosine kinase inhibitor drug associated with significant hepatotoxicity of unknown mechanistic basis. In vitro cytotoxicity assays confirmed that HLCs from patients with clinically identified hepatotoxicity were more sensitive to PZ-induced toxicity than other individuals, while a prototype hepatotoxin acetaminophen was similarly toxic to all HLCs studied. Transcriptional analyses showed that PZ induces oxidative stress (OS) in HLCs in general, but in HLCs from susceptible individuals, PZ causes relative disruption of iron metabolism and higher burden of OS. Our study establishes the first patient-specific HLC-based platform for idiosyncratic hepatotoxicity testing, incorporating multiple potential causative factors and permitting the correlation of transcriptomic and cellular responses to clinical phenotypes. Establishment of patient-specific HLCs with clinical phenotypes representing population variations will be valuable for pharmaceutical drug testing.

One Standardized Differentiation Procedure Robustly Generates Homogenous Hepatocyte Cultures Displaying Metabolic Diversity from a Large Panel of Human Pluripotent Stem Cells

Human hepatocytes display substantial functional inter-individual variation regarding drug metabolizing functions. In order to investigate if this diversity is mirrored in hepatocytes derived from different human pluripotent stem cell (hPSC) lines, we evaluated 25 hPSC lines originating from 24 different donors for hepatic differentiation and functionality. Homogenous hepatocyte cultures could be derived from all hPSC lines using one standardized differentiation procedure. To the best of our knowledge this is the first report of a standardized hepatic differentiation procedure that is generally applicable across a large panel of hPSC lines without any adaptations to individual lines. Importantly, with regard to functional aspects, such as Cytochrome P450 activities, we observed that hepatocytes derived from different hPSC lines displayed inter-individual variation characteristic for primary hepatocytes obtained from different donors, while these activities were highly reproducible between repeated experiments using the same line. Taken together, these data demonstrate the emerging possibility to compile panels of hPSC-derived hepatocytes of particular phenotypes/genotypes relevant for drug metabolism and toxicity studies. Moreover, these findings are of significance for applications within the regenerative medicine field, since our stringent differentiation procedure allows the derivation of homogenous hepatocyte cultures from multiple donors which is a prerequisite for the realization of future personalized stem cell based therapies.

Analyzing and interpreting genome data at the network level with ConsensusPathDB

ConsensusPathDB consists of a comprehensive collection of human (as well as mouse and yeast) molecular interaction data integrated from 32 different public repositories and a web interface featuring a set of computational methods and visualization tools to explore these data. This protocol describes the use of ConsensusPathDB (http://consensuspathdb.org) with respect to the functional and network-based characterization of biomolecules (genes, proteins and metabolites) that are submitted to the system either as a priority list or together with associated experimental data such as RNA-seq. The tool reports interaction network modules, biochemical pathways and functional information that are significantly enriched by the user's input, applying computational methods for statistical over-representation, enrichment and graph analysis. The results of this protocol can be observed within a few minutes, even with genome-wide data. The resulting network associations can be used to interpret high-throughput data mechanistically, to characterize and prioritize biomarkers, to integrate different omics levels, to design follow-up functional assay experiments and to generate topology for kinetic models at different scales.

Human hepatocytes derived from pluripotent stem cells: a promising cell model for drug hepatotoxicity screening

Drug-induced liver injury (DILI) is a frequent cause of failure in both clinical and post-approval stages of drug development, and poses a key challenge to the pharmaceutical industry. Current animal models offer poor prediction of human DILI. Although several human cell-based models have been proposed for the detection of human DILI, human primary hepatocytes remain the gold standard for preclinical toxicological screening. However, their use is hindered by their limited availability, variability and phenotypic instability. In contrast, pluripotent stem cells, which include embryonic and induced pluripotent stem cells (iPSCs), proliferate extensively in vitro and can be differentiated into hepatocytes by the addition of soluble factors. This provides a stable source of hepatocytes for multiple applications, including early preclinical hepatotoxicity screening. In addition, iPSCs also have the potential to establish genotype-specific cells from different individuals, which would increase the predictivity of toxicity assays allowing more successful clinical trials. Therefore, the generation of human hepatocyte-like cells derived from pluripotent stem cells seems to be promising for overcoming limitations of hepatocyte preparations, and it is expected to have a substantial repercussion in preclinical hepatotoxicity risk assessment in early drug development stages.

Pluripotent stem cells: An in vitro model for nanotoxicity assessments

The advent of technology has led to an established range of engineered nanoparticles that are used in diverse applications, such as cell-cell interactions, cell-material interactions, medical therapies and the target modulation of cellular processes. The exponential increase in the utilization of nanomaterials and the growing number of associated criticisms has highlighted the potential risks of nanomaterials to human health and the ecosystem. The existing in vivo and in vitro platforms show limitations, with fluctuations being observed in the results of toxicity assessments. Pluripotent stem cells (PSCs) are viable source of cells that are capable of developing into specialized cells of the human body. PSCs can be efficiently used to screen new biomaterials/drugs and are potential candidates for studying impairments of biophysical morphology at both the cellular and tissue levels during interactions with nanomaterials and for diagnosing toxicity. Three-dimensional in vitro models obtained using PSC-derived cells would provide a realistic, patient-specific platform for toxicity assessments and in drug screening applications. The current review focuses on PSCs as an alternative in vitro platform for assessing the hazardous effects of nanomaterials on health systems and highlights the importance of PSC-derived in vitro platforms. Copyright © 2016 John Wiley & Sons, Ltd.

ToxDB: pathway-level interpretation of drug-treatment data

Motivation: Extensive drug treatment gene expression data have been generated in order to identify biomarkers that are predictive for toxicity or to classify compounds. However, such patterns are often highly variable across compounds and lack robustness. We and others have previously shown that supervised expression patterns based on pathway concepts rather than unsupervised patterns are more robust and can be used to assess toxicity for entire classes of drugs more reliably.

Results: We have developed a database, ToxDB, for the analysis of the functional consequences of drug treatment at the pathway level. We have collected 2694 pathway concepts and computed numerical response scores of these pathways for 437 drugs and chemicals and 7464 different experimental conditions. ToxDB provides functionalities for exploring these pathway responses by offering tools for visualization and differential analysis allowing for comparisons of different treatment parameters and for linking this data with toxicity annotation and chemical information.

Database URL:http://toxdb.molgen.mpg.de

Xeno-sensing activity of the aryl hydrocarbon receptor in human pluripotent stem cell-derived hepatocyte-like cells

Although hepatocyte-like cells derived from human pluripotent stem cells (hPSC-HLCs) are considered a promising model for predicting hepatotoxicity, their application has been restricted because of the low activity of drug metabolizing enzymes (DMEs). Here we found that the low expression of xenobiotic receptors (constitutive androstane receptor, CAR; and pregnane X receptor, PXR) contributes to the low activity of DMEs in hPSC-HLCs. Most CAR- and PXR-regulated DMEs and transporters were transcriptionally down-regulated in hPSC-HLC. Transcriptional expression of CAR and PXR was highly repressed in hPSC-HLCs, whereas mRNA levels of aryl hydrocarbon receptor (AHR) were comparable to those of adult liver. Furthermore, ligand-induced transcriptional activation was observed only at AHR in hPSC-HLCs. Bisulfite sequencing analysis demonstrated that promoter hypermethylation of CAR and PXR was associated with diminished transcriptional activity in hPSC-HLCs. Treatment with AHR-selective ligands increased the transcription of AHR-dependent target genes by direct AHR-DNA binding at the xenobiotic response element. In addition, an antagonist of AHR significantly inhibited AHR-dependent target gene expression. Thus, AHR may function intrinsically as a xenosensor as well as a ligand-dependent transcription factor in hPSC-HLCs. Our results indicate that hPSC-HLCs can be used to screen toxic substances related to AHR signaling and to identify potential AHR-targeted therapeutics.

Highly Synchronized Expression of Lineage-Specific Genes during In Vitro Hepatic Differentiation of Human Pluripotent Stem Cell Lines

Human pluripotent stem cells- (hPSCs-) derived hepatocytes have the potential to replace many hepatic models in drug discovery and provide a cell source for regenerative medicine applications. However, the generation of fully functional hPSC-derived hepatocytes is still a challenge. Towards gaining better understanding of the differentiation and maturation process, we employed a standardized protocol to differentiate six hPSC lines into hepatocytes and investigated the synchronicity of the hPSC lines by applying RT-qPCR to assess the expression of lineage-specific genes (OCT4, NANOG, T, SOX17, CXCR4, CER1, HHEX, TBX3, PROX1, HNF6, AFP, HNF4a, KRT18, ALB, AAT, and CYP3A4) which serve as markers for different stages during liver development. The data was evaluated using correlation and clustering analysis, demonstrating that the expression of these markers is highly synchronized and correlated well across all cell lines. The analysis also revealed a distribution of the markers in groups reflecting the developmental stages of hepatocytes. Functional analysis of the differentiated cells further confirmed their hepatic phenotype. Taken together, these results demonstrate, on the molecular level, the highly synchronized differentiation pattern across multiple hPSC lines. Moreover, this study provides additional understanding for future efforts to improve the functionality of hPSC-derived hepatocytes and thereby increase the value of related models.

Inter-laboratory study of human in vitro toxicogenomics-based tests as alternative methods for evaluating chemical carcinogenicity: a bioinformatics perspective

The assessment of the carcinogenic potential of chemicals with alternative, human-based in vitro systems has become a major goal of toxicogenomics. The central read-out of these assays is the transcriptome, and while many studies exist that explored the gene expression responses of such systems, reports on robustness and reproducibility, when testing them independently in different laboratories, are still uncommon. Furthermore, there is limited knowledge about variability induced by the data analysis protocols. We have conducted an inter-laboratory study for testing chemical carcinogenicity evaluating two human in vitro assays: hepatoma-derived cells and hTERT-immortalized renal proximal tubule epithelial cells, representing liver and kidney as major target organs. Cellular systems were initially challenged with thirty compounds, genome-wide gene expression was measured with microarrays, and hazard classifiers were built from this training set. Subsequently, each system was independently established in three different laboratories, and gene expression measurements were conducted using anonymized compounds. Data analysis was performed independently by two separate groups applying different protocols for the assessment of inter-laboratory reproducibility and for the prediction of carcinogenic hazard. As a result, both workflows came to very similar conclusions with respect to (1) identification of experimental outliers, (2) overall assessment of robustness and inter-laboratory reproducibility and (3) re-classification of the unknown compounds to the respective toxicity classes. In summary, the developed bioinformatics workflows deliver accurate measures for inter-laboratory comparison studies, and the study can be used as guidance for validation of future carcinogenicity assays in order to implement testing of human in vitro alternatives to animal testing.

Development of novel tools for the in vitro investigation of drug-induced liver injury

INTRODUCTION

Due to its complex mechanisms and unpredictable occurrence, drug-induced liver injury (DILI) complicates drug identification and classification. Since species-specific differences in metabolism and pharmacokinetics exist, data obtained from animal studies may not be sufficient to predict DILI in humans.

AREAS COVERED

Over the last few decades, numerous in vitro models have been developed to replace animal testing. The advantages and disadvantages of commonly used liver-derived in vitro models (e.g., cell lines, hepatocyte models, liver slices, three-dimensional (3D) hepatospheres, etc.) are discussed. Toxicogenomics-based methodologies (genomics, epigenomics, transcriptomics, proteomics and metabolomics) and next-generation sequencing have also been used to enhance the reliability of DILI prediction. This review presents an overview of the currently used alternative toxicological models and of the most advanced approaches in the field of DILI research.

EXPERT OPINION

It seems unlikely that a single in vitro system will be able to mimic the complex interactions in the human liver. Three-dimensional multicellular systems may bridge the gap between conventional 2D models and in vivo clinical studies in humans and provide a reliable basis for hepatic toxicity assay development. Next-generation sequencing technologies, in comparison to microarray-based technologies, may overcome the current limitations and are promising for the development of predictive models in the near future.

Stem cell-derived liver cells for drug testing and disease modeling

Differences between animals and humans in liver pathways now necessitate the use of in vitro models of the human liver for several applications such as drug screening. However, isolated primary human hepatocytes (PHHs) are a limited resource for building such models given shortages of donor organs. In contrast, human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) can be propagated nearly indefinitely and differentiated into hepatocyte-like cells in vitro using soluble factors inspired from liver development. Additionally, iPSCs can be generated from patients with specific genetic backgrounds to study genotype-phenotype relationships. While current protocols to differentiate hESCs and iPSCs into human hepatocyte-like cells (hESC-HHs and iPSC-HHs) still need improvement to yield cells functionally similar to the adult liver, proof-of-concept studies have already shown utility of these cells in drug development and modeling liver diseases such as α1-antitrypsin deficiency, hepatitis B/C viral infections, and malaria. Here, we present an overview of hESC-HH and iPSC-HH culture platforms that have been utilized for the aforementioned applications. We also discuss the use of semiconductor-driven microfabrication tools to precisely control the microenvironment around these cells to enable higher and longer-term liver functions in vitro. Finally, we discuss areas for improvement in creating next generation stem cell-derived liver models. In the future, stem cell-derived hepatocyte-like cells could provide a sustainable cell source for high-throughput drug screening, enabling better mechanistic understanding of human liver diseases for the development of more efficacious and safer therapeutics, and personalized cell-based therapies in the clinic.

Computational modeling of drug response with applications to neuroscience

The development of novel high-throughput technologies has opened up the opportunity to deeply characterize patient tissues at various molecular levels and has given rise to a paradigm shift in medicine towards personalized therapies. Computational analysis plays a pivotal role in integrating the various genome data and understanding the cellular response to a drug. Based on that data, molecular models can be constructed that incorporate the known downstream effects of drug-targeted receptor molecules and that predict optimal therapy decisions. In this article, we describe the different steps in the conceptual framework of computational modeling. We review resources that hold information on molecular pathways that build the basis for constructing the model interaction maps, highlight network analysis concepts that have been helpful in identifying predictive disease patterns, and introduce the basic concepts of kinetic modeling. Finally, we illustrate this framework with selected studies related to the modeling of important target pathways affected by drugs.

MicroRNA-122: a novel hepatocyte-enriched in vitro marker of drug-induced cellular toxicity

Emerging hepatic models for the study of drug-induced toxicity include pluripotent stem cell-derived hepatocyte-like cells (HLCs) and complex hepatocyte-non-parenchymal cellular coculture to mimic the complex multicellular interactions that recapitulate the niche environment in the human liver. However, a specific marker of hepatocyte perturbation, required to discriminate hepatocyte damage from non-specific cellular toxicity contributed by non-hepatocyte cell types or immature differentiated cells is currently lacking, as the cytotoxicity assays routinely used in in vitro toxicology research depend on intracellular molecules which are ubiquitously present in all eukaryotic cell types. In this study, we demonstrate that microRNA-122 (miR-122) detection in cell culture media can be used as a hepatocyte-enriched in vitro marker of drug-induced toxicity in homogeneous cultures of hepatic cells, and a cell-specific marker of toxicity of hepatic cells in heterogeneous cultures such as HLCs generated from various differentiation protocols and pluripotent stem cell lines, where conventional cytotoxicity assays using generic cellular markers may not be appropriate. We show that the sensitivity of the miR-122 cytotoxicity assay is similar to conventional assays that measure lactate dehydrogenase activity and intracellular adenosine triphosphate when applied in hepatic models with high levels of intracellular miR-122, and can be multiplexed with other assays. MiR-122 as a biomarker also has the potential to bridge results in in vitro experiments to in vivo animal models and human samples using the same assay, and to link findings from clinical studies in determining the relevance of in vitro models being developed for the study of drug-induced liver injury.

Long-term chronic toxicity testing using human pluripotent stem cell-derived hepatocytes

Human pluripotent stem cells (hPSC) have the potential to become important tools for the establishment of new models for in vitro drug testing of, for example, toxicity and pharmacological effects. Late-stage attrition in the pharmaceutical industry is to a large extent caused by selection of drug candidates using nonpredictive preclinical models that are not clinically relevant. The current hepatic in vivo and in vitro models show clear limitations, especially for studies of chronic hepatotoxicity. For these reasons, we evaluated the potential of using hPSC-derived hepatocytes for long-term exposure to toxic drugs. The differentiated hepatocytes were incubated with hepatotoxic compounds for up to 14 days, using a repeated-dose approach. The hPSC-derived hepatocytes became more sensitive to the toxic compounds after extended exposures and, in addition to conventional cytotoxicity, evidence of phospholipidosis and steatosis was also observed in the cells. This is, to the best of our knowledge, the first report of a long-term toxicity study using hPSC-derived hepatocytes, and the observations support further development and validation of hPSC-based toxicity models for evaluating novel drugs, chemicals, and cosmetics.

Critical differences in toxicity mechanisms in induced pluripotent stem cell-derived hepatocytes, hepatic cell lines and primary hepatocytes

Human-induced pluripotent stem cell-derived hepatocytes (hiPSC-Hep) hold great potential as an unlimited cell source for toxicity testing in drug discovery research. However, little is known about mechanisms of compound toxicity in hiPSC-Hep. In this study, modified mRNA was used to reprogram foreskin fibroblasts into hiPSC that were differentiated into hiPSC-Hep. The hiPSC-Hep expressed characteristic hepatic proteins and exhibited cytochrome P450 (CYP) enzyme activities. Next, the hiPSC-Hep, primary cryopreserved human hepatocytes (cryo-hHep) and the hepatic cell lines HepaRG and Huh7 were treated with staurosporine and acetaminophen, and the toxic responses were compared. In addition, the expression of genes regulating and executing apoptosis was analyzed in the different cell types. Staurosporine, an inducer of apoptosis, decreased ATP levels and activated caspases 3 and 7 in all cell types, but to less extent in Huh7. Furthermore, a hierarchical clustering and a principal component analysis (PCA) of the expression of apoptosis-associated genes separated cryo-hHep from the other cell types, while an enrichment analysis of apoptotic pathways identified hiPSC-Hep as more similar to cryo-hHep than the hepatic cell lines. Finally, acetaminophen induced apoptosis in hiPSC-Hep, HepaRG and Huh7, while the compound initiated a direct necrotic response in cryo-hHep. Our results indicate that for studying compounds initiating apoptosis directly hiPSC-Hep may be a good alternative to cryo-hHep. Furthermore, for compounds with more complex mechanisms of toxicity involving metabolic activation, such as acetaminophen, our data suggest that the cause of cell death depends on a balance between factors controlling death signals and the drug-metabolizing capacity.

Testing chemical carcinogenicity by using a transcriptomics HepaRG-based model?

The EU FP6 project carcinoGENOMICS explored the combination of toxicogenomics and in vitro cell culture models for identifying organotypical genotoxic- and non-genotoxic carcinogen-specific gene signatures. Here the performance of its gene classifier, derived from exposure of metabolically competent human HepaRG cells to prototypical non-carcinogens (10 compounds) and hepatocarcinogens (20 compounds), is reported. Analysis of the data at the gene and the pathway level by using independent biostatistical approaches showed a distinct separation of genotoxic from non-genotoxic hepatocarcinogens and non-carcinogens (up to 88 % correct prediction). The most characteristic pathway responding to genotoxic exposure was DNA damage. Interlaboratory reproducibility was assessed by blindly testing of three compounds, from the set of 30 compounds, by three independent laboratories. Subsequent classification of these compounds resulted in correct prediction of the genotoxicants. As expected, results on the non-genotoxic carcinogens and the non-carcinogens were less predictive. In conclusion, the combination of transcriptomics with the HepaRG in vitro cell model provides a potential weight of evidence approach for the evaluation of the genotoxic potential of chemical substances.

Modeling human liver biology using stem cell-derived hepatocytes

Stem cell-derived hepatocytes represent promising models to study human liver biology and disease. This concise review discusses the recent progresses in the field, with a focus on human liver disease, drug metabolism and virus infection.

Stem cell-derived hepatocytes as a predictive model for drug-induced liver injury: are we there yet?

Amongst the different types of adverse drug reactions, drug-induced liver injury is the most prominent cause of patient morbidity and mortality. However, the current available hepatic model systems developed for evaluating safety have limited utility and relevance as they do not fully recapitulate a fully functional hepatocyte, and do not sufficiently represent the genetic polymorphisms present in the population. The rapidly advancing research in stem cells raises the possibility of using human pluripotent stem cells in bridging this gap. The generation of human induced pluripotent stem cells via reprogramming of mature human somatic cells may also allow for disease modelling in vitro for the purposes of assessing drug safety and toxicology. This would also allow for better understanding of disease processes and thus facilitate in the potential identification of novel therapeutic targets. This review will focus on the current state of effort to derive hepatocytes from human pluripotent stem cells for potential use in hepatotoxicity evaluation and aims to provide an insight as to where the future of the field may lie.

Drug metabolizing enzyme and transporter protein profiles of hepatocytes derived from human embryonic and induced pluripotent stem cells

Human embryonic and induced pluripotent stem cell-derived hepatocytes (hESC-Hep and hiPSC-Hep) have the potential to provide relevant human in vitro model systems for toxicity testing and drug discovery studies. In this study, the expression and function of important drug metabolizing cytochrome P450 (CYP) enzymes and transporter proteins in hESC-Hep and hiPSC-Hep were compared to cryopreserved human primary hepatocytes (hphep) and HepG2 cells. Overall, CYP activities in hESC-Hep and hiPSC-Hep were much lower than in hphep cultured for 4 h, but CYP1A and 3A activities were comparable to levels in hphep cultured for 48h (CYP1A: 35% and 26% of 48 h hphep, respectively; CYP3A: 80% and 440% of 48 h hphep, respectively). Importantly, in hESC-Hep and hiPSC-Hep, CYP activities were stable or increasing for at least one week in culture which was in contrast to the rapid loss of CYP activities in cultured hphep between 4 and 48 h after plating. With regard to transporters, in hESC-Hep and hiPSC-Hep, pronounced NTCP activity (17% and 29% of 4 h hphep, respectively) and moderate BSEP activity (6% and 8% of 4 h hphep, respectively) were observed. Analyses of mRNA expression and immunocytochemistry supported the observed CYP and transporter activities and showed expression of additional CYPs and transporters. In conclusion, the stable expression and function of CYPs and transporters in hESC-Hep and hiPSC-Hep for at least one week opens up the possibility to reproducibly perform long term and extensive studies, e.g. chronic toxicity testing, in a stem cell-derived hepatic system.

Transcriptomic responses generated by hepatocarcinogens in a battery of liver-based in vitro models

As the conventional approach to assess the potential of a chemical to cause cancer in humans still includes the 2-year rodent carcinogenicity bioassay, development of alternative methodologies is needed. In the present study, the transcriptomics responses following exposure to genotoxic (GTX) and non-genotoxic (NGTX) hepatocarcinogens and non-carcinogens (NC) in five liver-based in vitro models, namely conventional and epigenetically stabilized cultures of primary rat hepatocytes, the human hepatoma-derived cell lines HepaRG and HepG2 and human embryonic stem cell-derived hepatocyte-like cells, are examined. For full characterization of the systems, several bioinformatics approaches are employed including gene-based, ConsensusPathDB-based and classification analysis. They provide convincingly similar outcomes, namely that upon exposure to carcinogens, the HepaRG generates a gene classifier (a gene classifier is defined as a selected set of characteristic gene signatures capable of distinguishing GTX, NGTX carcinogens and NC) able to discriminate the GTX carcinogens from the NGTX carcinogens and NC. The other in vitro models also yield cancer-relevant characteristic gene groups for the GTX exposure, but some genes are also deregulated by the NGTX carcinogens and NC. Irrespective of the tested in vitro model, the most uniformly expressed pathways following GTX exposure are the p53 and those that are subsequently induced. The NGTX carcinogens triggered no characteristic cancer-relevant gene profiles in all liver-based in vitro systems. In conclusion, liver-based in vitro models coupled with transcriptomics techniques, especially in the case when the HepaRG cell line is used, represent valuable tools for obtaining insight into the mechanism of action and identification of GTX carcinogens.

Labeled stem cells as disease models and in drug discovery

Human pluripotent stem cells provide unique possibilities for in vitro studies of human cells in basic research, disease modeling as well as in industrial applications. By introducing relevant genome engineering technology, and thereby creating, for example, reporter cell lines, one will facilitate and improve safety pharmacology, toxicity testing, and can help the scientists to better understand pathological processes in humans. This review discusses how the merger of these two fields, human pluripotent stem cells and genome engineering, form extremely powerful tools and how they have been implemented already within the scientific community. In sharp contrast to immortalized human cell lines, which are both easy to expand and very simple to transfect, the genetically modified pluripotent stem cell line can be directed to a specific cell lineage and provide the user with highly relevant information. We highlight some of the challenges the field had to solve and how new technology advancements has removed the early bottlenecks.

The ConsensusPathDB interaction database: 2013 update

Knowledge of the various interactions between molecules in the cell is crucial for understanding cellular processes in health and disease. Currently available interaction databases, being largely complementary to each other, must be integrated to obtain a comprehensive global map of the different types of interactions. We have previously reported the development of an integrative interaction database called ConsensusPathDB (http://ConsensusPathDB.org) that aims to fulfill this task. In this update article, we report its significant progress in terms of interaction content and web interface tools. ConsensusPathDB has grown mainly due to the integration of 12 further databases; it now contains 215 541 unique interactions and 4601 pathways from overall 30 databases. Binary protein interactions are scored with our confidence assessment tool, IntScore. The ConsensusPathDB web interface allows users to take advantage of these integrated interaction and pathway data in different contexts. Recent developments include pathway analysis of metabolite lists, visualization of functional gene/metabolite sets as overlap graphs, gene set analysis based on protein complexes and induced network modules analysis that connects a list of genes through various interaction types. To facilitate the interactive, visual interpretation of interaction and pathway data, we have re-implemented the graph visualization feature of ConsensusPathDB using the Cytoscape.js library.

Hepatic differentiation and maturation of human embryonic stem cells cultured in a perfused three-dimensional bioreactor

Drug-induced liver injury is a serious and frequently occurring adverse drug reaction in the clinics and is hard to predict during preclinical studies. Today, primary hepatocytes are the most frequently used cell model for drug discovery and prediction of toxicity. However, their use is marred by high donor variability regarding drug metabolism and toxicity, and instable expression levels of liver-specific genes such as cytochromes P450. An in vitro model system based on human embryonic stem cells (hESC), with their unique properties of pluripotency and self-renewal, has potential to provide a stable and unlimited supply of human hepatocytes. Much effort has been made to direct hESC toward the hepatic lineage, mostly using 2-dimensional (2D) cultures. Although the results are encouraging, these cells lack important functionality. Here, we investigate if hepatic differentiation of hESC can be improved by using a 3-dimensional (3D) bioreactor system. Human ESCs were differentiated toward the hepatic lineage using the same cells in either the 3D or 2D system. A global transcriptional analysis identified important differences between the 2 differentiation regimes, and we identified 10 pathways, highly related to liver functions, which were significantly upregulated in cells differentiated in the bioreactor compared to 2D control cultures. The enhanced hepatic differentiation observed in the bioreactor system was also supported by immunocytochemistry. Taken together, our results suggest that hepatic differentiation of hESC is improved when using this 3D bioreactor technology as compared to 2D culture systems.

Wind of change challenges toxicological regulators

BACKGROUND

In biomedical research, the past two decades have seen the advent of in vitro model systems based on stem cells, humanized cell lines, and engineered organotypic tissues, as well as numerous cellular assays based on primarily established tumor-derived cell lines and their genetically modified derivatives.

OBJECTIVE

There are high hopes that these systems might replace the need for animal testing in regulatory toxicology. However, despite increasing pressure in recent years to reduce animal testing, regulators are still reluctant to adopt in vitro approaches on a large scale. It thus seems appropriate to consider how we could realistically perform regulatory toxicity testing using in vitro assays only.

DISCUSSION AND CONCLUSION

Here, we suggest an in vitro-only approach for regulatory testing that will benefit consumers, industry, and regulators alike.

Definition of metabolism-dependent xenobiotic toxicity with co-cultures of human hepatocytes and mouse 3T3 fibroblasts in the novel integrated discrete multiple organ co-culture (IdMOC) experimental system: results with model toxicants aflatoxin B1, cyclophosphamide and tamoxifen

The integrated discrete multiple organ co-culture system (IdMOC) allows the co-culturing of multiple cell types as physically separated cells interconnected by a common overlying medium. We report here the application of IdMOC with two cell types: the metabolically competent primary human hepatocytes, and a metabolically incompetent cell line, mouse 3T3 fibroblasts, in the definition of the role of hepatic metabolism on the cytotoxicity of three model toxicants: cyclophosphamide (CPA), aflatoxin B1 (AFB) and tamoxifen (TMX). The presence of hepatic metabolism in IdMOC with human hepatocytes was demonstrated by the metabolism of the P450 isoform 3A4 substrate, luciferin-IPA. The three model toxicants showed three distinct patterns of cytotoxic profile: TMX was cytotoxic to 3T3 cells in the absence of hepatocytes, with slightly lower cytotoxicity towards both 3T3 cells and hepatocytes in the IdMOC. AFB was selective toxic towards the human hepatocytes and relatively noncytotoxic towards 3T3 cells both in the presence and absence of the hepatocytes. CPA cytotoxicity to the 3T3 cells was found to be significantly enhanced by the presence of the hepatocytes, with the cytotoxicity dependent of the number of hepatocytes, and with the cytotoxicity attenuated by the presence of a non-specific P450 inhibitor, 1-aminobenzotriazole. We propose here the following classification of toxicants based on the role of hepatic metabolism as defined by the human hepatocyte-3T3 cell IdMOC assay: type I: direct-acting cytotoxicants represented by TMX as indicated by cytotoxicity in 3T3 cells in the absence of hepatocytes; type II: metabolism-dependent cytotoxicity represented by AFB1 with effects localized within the site of metabolic activation (i. e. hepatocytes); and type III: metabolism-dependent cytotoxicity with metabolites that can diffuse out of the hepatocytes to cause toxicity in cells distal from the site of metabolism, as exemplified by CPA.

Human pluripotent stem cells for modeling toxicity

The development of xenobiotics, driven by the demand for therapeutic, domestic and industrial uses continues to grow. However, along with this increasing demand is the risk of xenobiotic-induced toxicity. Currently, safety screening of xenobiotics uses a plethora of animal and in vitro model systems which have over the decades proven useful during compound development and for application in mechanistic studies of xenobiotic-induced toxicity. However, these assessments have proven to be animal-intensive and costly. More importantly, the prevalence of xenobiotic-induced toxicity is still significantly high, causing patient morbidity and mortality, and a costly impediment during drug development. This suggests that the current models for drug safety screening are not reliable in toxicity prediction, and the results not easily translatable to the clinic due to insensitive assays that do not recapitulate fully the complex phenotype of a functional cell type in vivo. Recent advances in the field of stem cell research have potentially allowed for a readily available source of metabolically competent cells for toxicity studies, derived using human pluripotent stem cells harnessed from embryos or reprogrammed from mature somatic cells. Pluripotent stem cell-derived cell types also allow for potential disease modeling in vitro for the purposes of drug toxicology and safety pharmacology, making this model possibly more predictive of drug toxicity compared with existing models. This article will review the advances and challenges of using human pluripotent stem cells for modeling metabolism and toxicity, and offer some perspectives as to where its future may lie.