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Much has been written and said about the promise and excitement of microphysiological systems, miniature devices that aim to recreate aspects of human physiology on a chip. The rapid explosion of the offerings and persistent publicity placed high expectations on both product manufacturers and regulatory agencies to adopt the data. Inevitably, discussions of where this technology fits in chemical testing paradigms are ongoing. Some end-users became early adopters, whereas others have taken a more cautious approach because of the high cost and uncertainties of their utility. Here, we detail the experience of a public-private collaboration established for testing of diverse microphysiological systems. Collectively, we present a number of considerations on practical aspects of using microphysiological systems in the context of their applications in decision-making. Specifically, future end-users need to be prepared for extensive on-site optimization and have access to a wide range of imaging and other equipment. We reason that cells, related reagents, and the technical skills of the research staff, not the devices themselves, are the most critical determinants of success. Extrapolation from concentration-response effects in microphysiological systems to human blood or oral exposures, difficulties with replicating the whole organ, and long-term functionality remain as critical challenges. Overall, we conclude that it is unlikely that a rodent- or human-equivalent model is achievable through a finite number of microphysiological systems in the near future; therefore, building consensus and promoting the gradual incorporation of these models into tiered approaches for safety assessment and decision-making is the sensible path to wide adoption.

Per- and polyfluoroalkyl substances (PFAS) are some of the most prominent organic contaminants in human blood. Although the toxicological implications of human exposure to perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) are well established, data on lesser-understood PFAS are limited. New approach methodologies (NAMs) that apply bioinformatic tools to high-throughput data are being increasingly considered to inform risk assessment for data-poor chemicals. The aim of this study was to compare the potencies (ie, benchmark concentrations: BMCs) of PFAS in primary human liver microtissues (3D spheroids) using high-throughput transcriptional profiling. Gene expression changes were measured using TempO-seq, a templated, multiplexed RNA-sequencing platform. Spheroids were exposed for 1 or 10 days to increasing concentrations of 23 PFAS in 3 subgroups: carboxylates (PFCAs), sulfonates (PFSAs), and fluorotelomers and sulfonamides. PFCAs and PFSAs exhibited trends toward increased transcriptional potency with carbon chain-length. Specifically, longer-chain compounds (7–10 carbons) were more likely to induce changes in gene expression and have lower transcriptional BMCs. The combined high-throughput transcriptomic and bioinformatic analyses support the capability of NAMs to efficiently assess the effects of PFAS in liver microtissues. The data enable potency ranking of PFAS for human liver cell spheroid cytotoxicity and transcriptional changes, and assessment of in vitro transcriptomic points of departure. These data improve our understanding of the possible health effects of PFAS and will be used to inform read-across for human health risk assessment.

Nonclinical testing has served as a foundation for evaluating potential risks and effectiveness of investigational new drugs in humans. However, the current two‐dimensional (2D) in vitro cell culture systems cannot accurately depict and simulate the rich environment and complex processes observed in vivo, whereas animal studies present significant drawbacks with inherited species‐specific differences and low throughput for increased demands. To improve the nonclinical prediction of drug safety and efficacy, researchers continue to develop novel models to evaluate and promote the use of improved cell‐ and organ‐based assays for more accurate representation of human susceptibility to drug response. Among others, the three‐dimensional (3D) cell culture models present physiologically relevant cellular microenvironment and offer great promise for assessing drug disposition and pharmacokinetics (PKs) that influence drug safety and efficacy from an early stage of drug development. Currently, there are numerous different types of 3D culture systems, from simple spheroids to more complicated organoids and organs‐on‐chips, and from single‐cell type static 3D models to cell co‐culture 3D models equipped with microfluidic flow control as well as hybrid 3D systems that combine 2D culture with biomedical microelectromechanical systems. This article reviews the current application and challenges of 3D culture systems in drug PKs, safety, and efficacy assessment, and provides a focused discussion and regulatory perspectives on the liver‐, intestine‐, kidney‐, and neuron‐based 3D cellular models.

Per- and poly-fluoroalkyl substances (PFAS) are widely found in the environment because of their extensive use and persistence. Although several PFAS are well studied, most lack toxicity data to inform human health hazard and risk assessment. This study focused on 4 model PFAS: perfluorooctanoic acid (PFOA; 8 carbon), perfluorobutane sulfonate (PFBS; 4 carbon), perfluorooctane sulfonate (PFOS; 8 carbon), and perfluorodecane sulfonate (PFDS; 10 carbon). Human primary liver cell spheroids (pooled from 10 donors) were exposed to 10 concentrations of each PFAS and analyzed at 4 time points. The approach aimed to: (1) identify gene expression changes mediated by the PFAS, (2) identify similarities in biological responses, (3) compare PFAS potency through benchmark concentration analysis, and (4) derive bioactivity exposure ratios (ratio of the concentration at which biological responses occur, relative to daily human exposure). All PFAS induced transcriptional changes in cholesterol biosynthesis and lipid metabolism pathways, and predicted PPARα activation. PFOS exhibited the most transcriptional activity and had a highly similar gene expression profile to PFDS. PFBS induced the least transcriptional changes and the highest benchmark concentration (ie, was the least potent). The data indicate that these PFAS may have common molecular targets and toxicities, but that PFOS and PFDS are the most similar. The transcriptomic bioactivity exposure ratios derived here for PFOA and PFOS were comparable to those derived using rodent apical endpoints in risk assessments. These data provide a baseline level of toxicity for comparison with other known PFAS using this testing strategy.

Interpretation of untargeted metabolomics data from both in vivo and physiologically relevant in vitro model systems continues to be a significant challenge for toxicology research. Potency-based modeling of toxicological responses has served as a pillar of interpretive context and translation of testing data. In this study, we leverage the resolving power of concentration-response modeling through benchmark concentration (BMC) analysis to interpret untargeted metabolomics data from differentiated cultures of HepaRG cells exposed to a panel of reference compounds and integrate data in a potency-aligned framework with matched transcriptomic data. For this work, we characterized biological responses to classical human liver injury compounds and comparator compounds, known to not cause liver injury in humans, at 10 exposure concentrations in spent culture media by untargeted liquid chromatography-mass spectrometry analysis. The analyte features observed (with limited metabolites identified) were analyzed using BMC modeling to derive compound-induced points of departure. The results revealed liver injury compounds produced concentration-related increases in metabolomic response compared to those rarely associated with liver injury (ie, sucrose, potassium chloride). Moreover, the distributions of altered metabolomic features were largely comparable with those observed using high throughput transcriptomics, which were further extended to investigate the potential for in vitro observed biological responses to be observed in humans with exposures at therapeutic doses. These results demonstrate the utility of BMC modeling of untargeted metabolomics data as a sensitive and quantitative indicator of human liver injury potential.

A 5-day in vivo rat model was evaluated as an approach to estimate chemical exposures that may pose minimal risk by comparing benchmark dose (BMD) values for transcriptional changes in the liver and kidney to BMD values for toxicological endpoints from traditional toxicity studies. Eighteen chemicals, most having been tested by the National Toxicology Program in 2-year bioassays, were evaluated. Some of these chemicals are potent hepatotoxicants (eg, DE71, PFOA, and furan) in rodents, some exhibit toxicity but have minimal hepatic effects (eg, acrylamide and α,β-thujone), and some exhibit little overt toxicity (eg, ginseng and milk thistle extract) based on traditional toxicological evaluations. Male Sprague Dawley rats were exposed once daily for 5 consecutive days by oral gavage to 8–10 dose levels for each chemical. Liver and kidney were collected 24 h after the final exposure and total RNA was assayed using high-throughput transcriptomics (HTT) with the rat S1500+ platform. HTT data were analyzed using BMD Express 2 to determine transcriptional gene set BMD values. BMDS was used to determine BMD values for histopathological effects from chronic or subchronic toxicity studies. For many of the chemicals, the lowest transcriptional BMDs from the 5-day assays were within a factor of 5 of the lowest histopathological BMDs from the toxicity studies. These data suggest that using HTT in a 5-day in vivo rat model provides reasonable estimates of BMD values for traditional apical endpoints. This approach may be useful to prioritize chemicals for further testing while providing actionable data in a timely and cost-effective manner.

High-throughput chemical screening approaches often employ microscopy to capture photomicrographs from multi-well cell culture plates, generating thousands of images that require time-consuming human analysis. To automate this subjective and time-consuming manual process, we have developed a method that uses deep learning to automatically classify digital assay images. We have trained a convolutional neural network (CNN) to perform binary and multi-class classification. The binary classifier binned assay images into healthy (comparable to untreated controls) and altered (not comparable to untreated-control) classes with >98% accuracy; the multi-class classifier assigned “Healthy,” “Intermediate” and “Altered” labels to assay images with >95% accuracy. Our dataset comprised high-resolution assay images from primary human hepatocytes and undifferentiated (proliferating) and differentiated 2D cultures of HepaRG cells. In this study we have focused on testing and fine-tuning various CNN architectures, including ResNet 34, 50 and 101. To visualize regions in the images that the CNN model used for classification, we employed Class Activation Maps (CAM). This allowed us to better understand the inner workings of the neural network and led to additional optimizations of the algorithm. The results indicate a strong correspondence between dosage and classifier-predicted scores, suggesting that these scores might be useful in further characterizing benchmark dose. Together, these results clearly demonstrate that deep-learning based automated image classification of cell morphology changes upon chemical-induced stress can yield highly accurate and reproducible assessments of cytotoxicity across a variety of cell types.

Botanicals can cause nephrotoxicity via numerous mechanisms, including disrupting renal blood flow, damaging compartments along the nephron, and obstructing urinary flow. While uncommon, there are various reports of botanical-induced nephrotoxicity in the literature, such as from aristolochia (

The liver is a pivotal organ regulating critical developmental stages of fetal metabolism and detoxification. Though numerous studies have evaluated links between prenatal/perinatal exposures and adverse health outcomes in the developing fetus, the central role of liver to health disruptions resulting from these exposures remains understudied, especially concerning early development and later-in-life health outcomes. While numerous in vitro methods for evaluating liver toxicity have been established, the use of iPSC-derived hepatocytes appears to be particularly well suited to contribute to this critical research gap due to their potential to model a diverse range of disease phenotypes and different stages of liver development. The following key aspects are reviewed: (1) an introduction to developmental liver toxicity; (2) an introduction to embryonic and induced pluripotent stem cell models; (3) methods and challenges for deriving liver cells from stem cells; and (4) applications for iPSC-derived hepatocytes to evaluate liver developmental stages and their associated responses to insults. We conclude that iPSC-derived hepatocytes have great potential for informing liver toxicity and underlying disease mechanisms via the generation of patient-specific iPSCs; implementing large-scale drug and chemical screening; evaluating general biological responses as a potential surrogate target cell; and evaluating inter-individual disease susceptibility and response variability.

Higher-throughput, mode-of-action-based assays provide a valuable approach to expedite chemical evaluation for human health risk assessment. In this study, we combined the high-throughput alkaline DNA damage-sensing CometChip® assay with the TGx-DDI transcriptomic biomarker (DDI = DNA damage-inducing) using high-throughput TempO-Seq®, as an integrated genotoxicity testing approach. We used metabolically competent differentiated human HepaRG™ cell cultures to enable the identification of chemicals that require bioactivation to cause genotoxicity. We studied 12 chemicals (nine DDI, three non-DDI) in increasing concentrations to measure and classify chemicals based on their ability to damage DNA. The CometChip® classified 10/12 test chemicals correctly, missing a positive DDI call for aflatoxin B1 and propyl gallate. The poor detection of aflatoxin B1 adducts is consistent with the insensitivity of the standard alkaline comet assay to bulky lesions (a shortcoming that can be overcome by trapping repair intermediates). The TGx-DDI biomarker accurately classified 10/12 agents. TGx-DDI correctly identified aflatoxin B1 as DDI, demonstrating efficacy for combined used of these complementary methodologies. Zidovudine, a known DDI chemical, was misclassified as it inhibits transcription, which prevents measurable changes in gene expression. Eugenol, a non-DDI chemical known to render misleading positive results at high concentrations, was classified as DDI at the highest concentration tested. When combined, the CometChip® assay and the TGx-DDI biomarker were 100% accurate in identifying chemicals that induce DNA damage. Quantitative benchmark concentration (BMC) modeling was applied to evaluate chemical potencies for both assays. The BMCs for the CometChip® assay and the TGx-DDI biomarker were highly concordant (within 4-fold) and resulted in identical potency rankings. These results demonstrate that these two assays can be integrated for efficient identification and potency ranking of DNA damaging agents in HepaRG™ cell cultures.

Botanical dietary supplements are complex mixtures with numerous potential sources of variation along the supply chain from raw plant material to the market. Approaches for determining sufficient similarity (ie, complex mixture read-across) may be required to extrapolate efficacy or safety data from a tested sample to other products containing the botanical ingredient(s) of interest. In this work, screening-level approaches for generating both chemical and biological-response profiles were used to evaluate the similarity of black cohosh (Actaea racemosa) and Echinacea purpurea samples to well-characterized National Toxicology Program (NTP) test articles. Data from nontargeted chemical analyses and gene expression of toxicologically important hepatic receptor pathways (aryl hydrocarbon receptor [AhR], constitutive androstane receptor [CAR], pregnane X receptor [PXR], farnesoid X receptor [FXR], and peroxisome proliferator-activated receptor alpha [PPARα]) in primary human hepatocyte cultures were used to determine similarity through hierarchical clustering. Although there were differences in chemical profiles across black cohosh samples, these differences were not reflected in the biological-response profiles. These findings highlight the complexity of biological-response dynamics that may not be reflected in chemical composition profiles. Thus, biological-response data could be used as the primary basis for determining similarity among black cohosh samples. Samples of E. purpurea displayed better correlation in similarity across chemical and biological-response measures. The general approaches described herein can be applied to complex mixtures with unidentified active constituents to determine when data from a tested mixture (eg, NTP test article) can be used for hazard identification of sufficiently similar mixtures, with the knowledge of toxicological targets informing assay selection when possible.

Hazard identification regarding adverse effects on the liver is a critical step in safety evaluations of drugs and other chemicals. Current testing paradigms for hepatotoxicity rely heavily on pre-clinical studies in animals and human data (epidemiology and clinical trials). Mechanistic understanding of the molecular and cellular pathways that may cause or exacerbate hepatotoxicity is well advanced, and holds promise for identification of hepatotoxicants. One of the challenges in translating mechanistic evidence into robust decisions about potential hepatotoxicity is the lack of a systematic approach to integrate these data to help identify liver toxicity hazards. Recently, marked improvements were achieved in the practice of hazard identification of carcinogens, female and male reproductive toxicants, and endocrine disrupting chemicals using the key characteristics approach. Here, we describe the methods by which key characteristics of human hepatotoxicants were identified and provide examples for how they could be used to systematically identify, organize and utilize mechanistic data when identifying hepatotoxicants.

The ToxCast in vitro screening program has provided concentration-response bioactivity data across more than a thousand assay endpoints for thousands of chemicals found in our environment and commerce. However, most ToxCast screening assays have evaluated individual biological targets in cancer cell lines lacking integrated physiological functionality (such as receptor signaling, metabolism). We evaluated differentiated HepaRGTM cells, a human liver-derived cell model understood to effectively model physiologically relevant hepatic signaling. Expression of 93 gene transcripts was measured by quantitative polymerase chain reaction using Fluidigm 96.96 dynamic arrays in response to 1060 chemicals tested in eight-point concentration-response. A Bayesian framework quantitatively modeled chemical-induced changes in gene expression via six transcription factors including: aryl hydrocarbon receptor, constitutive androstane receptor, pregnane X receptor, farnesoid X receptor, androgen receptor, and peroxisome proliferator-activated receptor alpha. For these chemicals the network model translates transcriptomic data into Bayesian inferences about molecular targets known to activate toxicological adverse outcome pathways. These data also provide new insights into the molecular signaling network of HepaRGTM cell cultures.

Per- and polyfluoroalkyl substances (PFAS) are some of the most prominent organic contaminants in human blood. Although the toxicological implications from human exposure to perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA) are well established, data on lesser-understood PFAS are limited. New approach methodologies (NAMs) that apply bioinformatic tools to high-throughput data are being increasingly considered to inform risk assessment for data-poor chemicals. The aim of this investigation was to identify biological response potencies (i.e., benchmark concentrations: BMCs) following PFAS exposures to inform read-across for risk assessment of data-poor PFAS. Gene expression changes were measured in primary human liver cell microtissues (i.e., 3D spheroids) after 1-day and 10-day exposures to increasing concentrations of 23 PFAS. The cells were treated with four subgroups of PFAS: carboxylates (PFCAs), sulfonates (PFSAs), fluorotelomers, and sulfonamides. An established pipeline to identify differentially expressed genes and transcriptomic BMCs was applied. We found that both PFCAs and PFSAs exhibited a trend toward increased transcriptional changes with carbon chain-length. Specifically, longer-chain compounds (7 to 10 carbons) were more likely to induce changes in gene expression, and have lower transcriptional BMCs. The combined high-throughput transcriptomic and bioinformatic analyses supports the capability of NAMs to efficiently assess the effects of PFAS in liver microtissues. The data enable potency ranking of PFAS for human liver cell spheroid cytotoxicity and transcriptional changes, and assessment of in vitro transcriptomic points of departure. These data improve our understanding of the health effects of PFAS and will be used to inform read-across for human health risk assessment.

Per- and poly-fluoroalkyl substances (PFAS) are widely found in the environment because of their extensive use and persistence. Although several PFAS are well studied, most lack toxicity data to inform human health hazard and risk assessment. This study focussed on four model PFAS: perfluorooctanoic acid (PFOA; 8 carbon), perfluorobutane sulfonate (PFBS; 4 carbon), perfluorooctane sulfonate (PFOS; 8 carbon), and perfluorodecane sulfonate (PFDS; 10 carbon). Human primary liver cell spheroids (pooled from 10 donors) were exposed to 10 concentrations of each PFAS and analyzed at four time-points. The approach aimed to: (1) identify gene expression changes mediated by the PFAS; (2) identify similarities in biological responses; (3) compare PFAS potency through benchmark concentration analysis; and (4) derive bioactivity exposure ratios (ratio of the concentration at which biological responses occur, relative to daily human exposure). All PFAS induced transcriptional changes in cholesterol biosynthesis and lipid metabolism pathways, and predicted PPARα activation. PFOS exhibited the most transcriptional activity and had a highly similar gene expression profile to PFDS. PFBS induced the least transcriptional changes and the highest benchmark concentration (i.e., was the least potent). The data indicate that these PFAS may have common molecular targets and toxicities, but that PFOS and PFDS are the most similar. The transcriptomic bioactivity exposure ratios derived here for PFOA and PFOS were comparable to those derived using rodent apical endpoints in risk assessments. These data provide a baseline level of toxicity for comparison with other known PFAS using this testing strategy.

Botanical dietary supplements are complex mixtures containing one or more botanical ingredient(s), each containing numerous constituents potentially responsible for its purported biological activity. Absorption, distribution, metabolism, and excretion (ADME) data are critical to understand the safety of botanical dietary supplements, including their potential for pharmacokinetic botanical-drug or botanical-botanical interactions. However, ADME data for botanical dietary supplements are rarely available and frequently inadequate to characterize their fate in vivo. Based on an assessment of the current status of botanical dietary supplements ADME research, the following key areas are identified that require robust data for human safety assessment: 1) phytochemical characterization including contaminant analysis and botanical authentication; 2) in vitro and/or in vivo data for identifying potential botanical-botanical or botanical-drug interactions and active/marker constituents; 3) robust ADME study design to include systemic exposure data on active/marker constituents using traditional or novel analytical chemistry and statistical approaches such as poly-pharmacokinetics; and 4) investigation of human relevance. A case study with Ginkgo biloba extract is used to highlight the challenges and proposed approaches in using ADME data for human safety assessment of botanical dietary supplements.

In vitro chemical safety testing methods offer the potential for efficient and economical tools to provide relevant assessments of human health risk. To realize this potential, methods are needed to relate in vitro effects to in vivo responses, i.e., in vitro to in vivo extrapolation (IVIVE). Currently available IVIVE approaches need to be refined before they can be utilized for regulatory decision-making. To explore the capabilities and limitations of IVIVE within this context, the U.S. Environmental Protection Agency Office of Research and Development and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods co-organized a workshop and webinar series. Here, we integrate content from the webinars and workshop to discuss activities and resources that would promote inclusion of IVIVE in regulatory decision-making. We discuss properties of models that successfully generate predictions of in vivo doses from effective in vitro concentration, including the experimental systems that provide input parameters for these models, areas of success, and areas for improvement to reduce model uncertainty. Finally, we provide case studies on the uses of IVIVE in safety assessments, which highlight the respective differences, information requirements, and outcomes across various approaches when applied for decision-making.

The Tox21 Program has investigated thousands of chemicals with high-throughput screening assays using cell-based assays to link thousands of chemicals to individual molecular targets/pathways. However, these systems have been widely criticized for their suspected lack of ‘metabolic competence’ to bioactivate or detoxify chemical exposures. In this study, 9 cell line backgrounds used in Tox21 assays (i.e., HepG2, HEK293, Hela, HCT116, ME180, CHO-K1, GH3.TRE-Luc, C3H10T1/2 and MCF7) were evaluated via metabolite formation rates, along with metabolic clearance and metabolite profiling for HepG2, HEK293, and MCF-7aroERE, in comparison to pooled donor (50) suspensions of primary human hepatocytes (PHHs). Using prototype clinical drug substrates for CYP1A2, CYP2B6, and CYP3A4/5, extremely low-to-undetectable CYP450 metabolism was observed (24 h), and consistent with their purported ‘lack’ of metabolic competence. However, for Phase II metabolizing enzymes and metabolic clearance, surprisingly proficient metabolism was observed for bisphenol AF, bisphenol S, and 7-hydroxycoumarin. Here, comparatively low glucuronidation relative to sulfation was observed in contrast to equivalent levels in PHHs. Overall, while a lack of CYP450 metabolism was confirmed in this benchmarking effort, Tox21 cell lines were not ‘incompetent’ for xenobiotic metabolism, and displayed surprisingly high proficiency for sulfation that rivaled PHHs. These findings have implications for the interpretation of Tox21 assay data, and establish a framework for evaluating of ‘metabolic competence’ with in vitro models.

Effective prediction of human responses to chemical and drug exposure is of critical importance in environmental toxicology research and drug development. While significant progress has been made to address this challenge using invitro liver models, these approaches often fail due to inadequate tissue model functionality. Herein, we describe the development, optimization, and characterization of a novel three-dimensional (3D) spheroid model using differentiated HepaRG cells that achieve and maintain physiologically relevant levels of xenobiotic metabolism (CYP1A2, CYP2B6, and CYP3A4/5). This invitro model maintains a stable phenotype over multiple weeks in both 96- and 384-well formats, supports highly reproducible tissue-like architectures and models pharmacologically- and environmentally important hepatic receptor pathways (ie AhR, CAR, and PXR) analogous to primary human hepatocyte cultures. HepaRG spheroid cultures use 50-100× fewer cells than conventional two dimensional cultures, and enable the identification of metabolically activated toxicants. Spheroid size, time in culture and culture media composition were important factors affecting basal levels of xenobiotic metabolism and liver enzyme inducibility with activators of hepatic receptors AhR, CAR and PXR. Repeated exposure studies showed higher sensitivity than traditional 2D cultures in identifying compounds that cause liver injury and metabolism-dependent toxicity. This platform combines the well-documented impact of 3D culture configuration for improved tissue functionality and longevity with the requisite throughput and repeatability needed for year-over-year toxicology screening.

Cholestasis remains a major challenge in drug-induced liver injury, and therefore warrants identification of chemical entities that may lead to cholestasis. Recent advances in cell culture methods enable 3D spheroid models to remain viable for much longer periods of time than conventional sandwich cultures of primary human hepatocytes while maintaining native tissue-like functionality, such as drug metabolism activity, receptor signaling functionality, and physiological relevance. These spheroid models enable us to study repeated exposure effects associated with chemicals and their metabolites that may ultimately progress to cholestasis and liver injury. HepaRG cells cultured as spheroids are viable for more than 4 weeks with cytochrome P450 enzymatic activities comparable to ranges observed in freshly isolated/cryopreserved suspensions of primary human hepatocytes. HepaRG spheroids form bile canalicular structures with potential application as a model to study biliary excretion processes and intrahepatic obstruction of bile flow, leading to hepatocellular damage and death. In this chapter, we describe methods to culture 3D spheroids of HepaRG cells with extensive bile canalicular structures/networks, image transport of bile acid (cholyl-lysyl-fluorescein) to the bile canaliculi, and measure cholestatic drug-induced cytotoxicity.

Prediction of human response to chemical exposures is a major challenge in both pharmaceutical and toxicological research. Transcriptomics has been a powerful tool to explore chemical-biological interactions, however, limited throughput, high-costs, and complexity of transcriptomic interpretations have yielded numerous studies lacking sufficient experimental context for predictive application. To address these challenges, we have utilized a novel high-throughput transcriptomics (HTT) platform, TempO-Seq, to apply the interpretive power of concentration-response modeling with exposures to 24 reference compounds in both differentiated and non-differentiated human HepaRG cell cultures. Our goals were to (1) explore transcriptomic characteristics distinguishing liver injury compounds, (2) assess impacts of differentiation state of HepaRG cells on baseline and compound-induced responses (eg, metabolically-activated), and (3) identify and resolve reference biological-response pathways through benchmark concentration (BMC) modeling. Study data revealed the predictive utility of this approach to identify human liver injury compounds by their respective BMCs in relation to human internal exposure plasma concentrations, and effectively distinguished drug analogs with varied associations of human liver injury (eg, withdrawn therapeutics trovafloxacin and troglitazone). Impacts of cellular differentiation state (proliferated vs differentiated) were revealed on baseline drug metabolizing enzyme expression, hepatic receptor signaling, and responsiveness to metabolically-activated toxicants (eg, cyclophosphamide, benzo(a)pyrene, and aflatoxin B1). Finally, concentration-response modeling enabled efficient identification and resolution of plausibly-relevant biological-response pathways through their respective pathway-level BMCs. Taken together, these findings revealed HTT paired with differentiated in vitro liver models as an effective tool to model, explore, and interpret toxicological and pharmacological interactions.

Cholestasis remains a major challenge in drug-induced liver injury, and therefore warrants identification of chemical entities that may lead to cholestasis. Recent advances in cell culture methods enable 3D spheroid models to remain viable for much longer periods of time than conventional sandwich cultures of primary human hepatocytes while maintaining native tissue-like functionality, such as drug metabolism activity, receptor signaling functionality, and physiological relevance. These spheroid models enable us to study repeated exposure effects associated with chemicals and their metabolites that may ultimately progress to cholestasis and liver injury. HepaRG cells cultured as spheroids are viable for more than 4 weeks with cytochrome P450 enzymatic activities comparable to ranges observed in freshly isolated/cryopreserved suspensions of primary human hepatocytes. HepaRG spheroids form bile canalicular structures with potential application as a model to study biliary excretion processes and intrahepatic obstruction of bile flow, leading to hepatocellular damage and death. In this chapter, we describe methods to culture 3D spheroids of HepaRG cells with extensive bile canalicular structures/networks, image transport of bile acid (cholyl-lysyl-fluorescein) to the bile canaliculi, and measure cholestatic drug-induced cytotoxicity.

The pregnane X receptor (PXR; NR1I2) is an important nuclear receptor whose main function is to regulate enzymes within drug metabolism. The main drug metabolizing enzyme regulated by PXR, cytochrome P450 (CYP) 3A4, accounts for the metabolism of nearly 50% of all marketed drugs. Recently, PXR has also been identified as playing a role in energy homeostasis, immune response, and cancer. Due to its interaction with these important roles, alongside its drug-drug interaction function, it is imperative to identify compounds which can modulate PXR. In this study, we screened the Tox21 10,000 compound collection to identify hPXR agonists using a stable hPXR-Luc HepG2 cell line. A pharmacological study in the presence of a PXR antagonist was performed to confirm the activity of the chosen potential hPXR agonists in the same cells. Finally, metabolically competent cell lines – HepaRG and HepaRG-PXR-Knockout (KO) – were used to further confirm the potential PXR activators. We identified a group of structural clusters and singleton compounds which included potentially novel hPXR agonists. Of the 21 selected compounds, 11 potential PXR activators significantly induced CYP3A4 mRNA expression in HepaRG cells. All of these compounds lost their induction when treating HepaRG-PXR-KO cells, confirming their PXR activation. Etomidoline presented as a potentially selective agonist of PXR. In conclusion, the current study has identified 11 compounds as potentially novel or not well-characterized PXR activators. These compounds should further be studied for their potential effects on drug metabolism and drug-drug interactions due to the immense implications of being a PXR agonist.

Over the last decade HepaRG cells have emerged as a promising alternative to primary human hepatocytes (PHH) and have been featured in over 300 research publications. Most of these reports employed freshly differentiated HepaRG cells that require time-consuming culture (∼28 days) for full differentiation. Recently, a cryopreserved, predifferentiated format of HepaRG cells (termed here "cryo-HepaRG") has emerged as a new model that improves global availability and experimental flexibility; however, it is largely unknown whether HepaRG cells in this format fully retain their hepatic characteristics. Therefore, we systematically investigated the hepatocyte functionality of cryo-HepaRG cultures in context with the range of interindividual variation observed with PHH in both sandwich-culture and suspension formats. These evaluations uncovered a novel adaptation period for the cryo-HepaRG format and demonstrated the impact of extracellular matrix on cryo-HepaRG functionality. Pharmacologically important drug-metabolizing alleles were genotyped in HepaRG cells and poor metabolizer alleles for CYP2D6, CYP2C9, and CYP3A5 were identified and consistent with higher frequency alleles found in individuals of Caucasian decent. We observed liver enzyme inducibility with aryl hydrocarbon receptor, constitutive androstane receptor (CAR), and pregnane X receptor activators comparable to that of sandwich-cultured PHH. Finally, we show for the first time that cryo-HepaRG supports proper CAR cytosolic sequestration and translocation to hepatocyte nuclei in response to phenobarbital treatment. Taken together, these data reveal important considerations for the use of this cell model and demonstrate that cryo-HepaRG are suitable for metabolism and toxicology screening.

Currently, there are >11,000 synthetic turf athletic fields in the United States and >13,000 in Europe. Concerns have been raised about exposure to carcinogenic chemicals resulting from contact with synthetic turf fields, particularly the infill material (“crumb rubber”), which is commonly fabricated from recycled tires. However, exposure data are scant, and the limited existing exposure studies have focused on a small subset of crumb rubber components. Our objective was to evaluate the carcinogenic potential of a broad range of chemical components of crumb rubber infill using computational toxicology and regulatory agency classifications from the United States Environmental Protection Agency (US EPA) and European Chemicals Agency (ECHA) to inform future exposure studies and risk analyses. Through a literature review, we identified 306 chemical constituents of crumb rubber infill from 20 publications. Utilizing ADMET Predictor™ , a computational program to predict carcinogenicity and genotoxicity, 197 of the identified 306 chemicals met our a priori carcinogenicity criteria. Of these, 52 chemicals were also classified as known, presumed or suspected carcinogens by the US EPA and ECHA. Of the remaining 109 chemicals which were not predicted to be carcinogenic by our computational toxicology analysis, only 6 chemicals were classified as presumed or suspected human carcinogens by US EPA or ECHA. Importantly, the majority of crumb rubber constituents were not listed in the US EPA (n = 207) and ECHA (n = 262) databases, likely due to an absence of evaluation or insufficient information for a reliable carcinogenicity classification. By employing a cancer hazard scoring system to the chemicals which were predicted and classified by the computational analysis and government databases, several high priority carcinogens were identified, including benzene, benzidine, benzo(a)pyrene, trichloroethylene and vinyl chloride. Our findings demonstrate that computational toxicology assessment in conjunction with government classifications can be used to prioritize hazardous chemicals for future exposure monitoring studies for users of synthetic turf fields. This approach could be extended to other compounds or toxicity endpoints.

Background: To effectively incorporate in vitro data into regulatory use, confidence must be established in the quantitative extrapolation of in vitro activity to relevant end points in animals or humans. Objective: Our goal was to evaluate and optimize in vitro to in vivo extrapolation (IVIVE) approaches using in vitro estrogen receptor (ER) activity to predict estrogenic effects measured in rodent uterotrophic studies. Methods: We evaluated three pharmacokinetic (PK) models with varying complexities to extrapolate in vitro to in vivo dosimetry for a group of 29 ER agonists, using data from validated in vitro [U.S. Environmental Protection Agency (U.S. EPA) ToxCast™ ER model] and in vivo (uterotrophic) methods. In vitro activity values were adjusted using mass-balance equations to estimate intracellular exposure via an enrichment factor (EF), and steady-state model calculations were adjusted using fraction of unbound chemical in the plasma (fu) to approximate bioavailability. Accuracy of each model-adjustment combination was assessed by comparing model predictions with lowest effect levels (LELs) from guideline uterotrophic studies. Results: We found little difference in model predictive performance based on complexity or route-specific modifications. Simple adjustments, applied to account for in vitro intracellular exposure (EF) or chemical bioavailability (fu), resulted in significant improvements in the predictive performance of all models. Conclusion: Computational IVIVE approaches accurately estimate chemical exposure levels that elicit positive responses in the rodent uterotrophic bioassay. The simplest model had the best overall performance for predicting both oral (PPK_EF) and injection (PPK_fu) LELs from guideline uterotrophic studies, is freely available, and can be parameterized entirely using freely available in silico tools. https://doi.org/10.1289/EHP1655

Background: To effectively incorporate in vitro data into regulatory use, confidence must be established in the quantitative extrapolation of in vitro activity to relevant end points in animals or ...

Hepatic metabolic clearance is one of the most important factors driving the overall kinetics of chemicals including substances used in various product categories such as pesticides, biocides, pharmaceuticals, and cosmetics. A large number of in vitro systems from purified isozymes and subcellular organelles to hepatocytes in simple cultures and in complex scaffold setups are available for measuring hepatic metabolic clearance for different applications. However, there is currently no approach for systematically characterising and comparing these in vitro methods in terms of their design, applicability and performance. To address this, existing knowledge in the field of in vitro human hepatic metabolic clearance methods was gathered and analysed in order to establish a framework to systematically characterise methods based on a set of relevant components. An analogous framework would be also applicable for non-human in vitro systems. The components are associated with the biological test systems used (e.g. subcellular or cells), the in vitro method (e.g. number of cells, test item solubility), related analytical techniques, data interpretation methods (based on substrate depletion/metabolite formation), and performance assessments (precision and accuracy of clearance measurements). To facilitate the regulatory acceptance of this class of methods, it is intended that the framework provide the basis of harmonisation work within the OECD.

We previously integrated dosimetry and exposure with high-throughput screening (HTS) to enhance the utility of ToxCast HTS data by translating in vitro bioactivity concentrations to oral equivalent doses (OEDs) required to achieve these levels internally. These OEDs were compared against regulatory exposure estimates, providing an activity-to-exposure ratio (AER) useful for a risk-based ranking strategy. As ToxCast efforts expand (ie, Phase II) beyond food-use pesticides toward a wider chemical domain that lacks exposure and toxicity information, prediction tools become increasingly important. In this study, in vitro hepatic clearance and plasma protein binding were measured to estimate OEDs for a subset of Phase II chemicals. OEDs were compared against high-throughput (HT) exposure predictions generated using probabilistic modeling and Bayesian approaches generated by the U.S. Environmental Protection Agency (EPA) ExpoCast program. This approach incorporated chemical-specific use and national production volume data with biomonitoring data to inform the exposure predictions. This HT exposure modeling approach provided predictions for all Phase II chemicals assessed in this study whereas estimates from regulatory sources were available for only 7% of chemicals. Of the 163 chemicals assessed in this study, 3 or 13 chemicals possessed AERs

Effective prediction of human responses to chemical and drug exposure is of critical importance in environmental toxicology research and drug development. While significant progress has been made to address this challenge using invitro liver models, these approaches often fail due to inadequate tissue model functionality. Herein, we describe the development, optimization, and characterization of a novel three-dimensional (3D) spheroid model using differentiated HepaRG cells that achieve and maintain physiologically relevant levels of xenobiotic metabolism (CYP1A2, CYP2B6, and CYP3A4/5). This invitro model maintains a stable phenotype over multiple weeks in both 96- and 384-well formats, supports highly reproducible tissue-like architectures and models pharmacologically- and environmentally important hepatic receptor pathways (ie AhR, CAR, and PXR) analogous to primary human hepatocyte cultures. HepaRG spheroid cultures use 50–100× fewer cells than conventional two dimensional cultures, and enable the identification of metabolically activated toxicants. Spheroid size, time in culture and culture media composition were important factors affecting basal levels of xenobiotic metabolism and liver enzyme inducibility with activators of hepatic receptors AhR, CAR and PXR. Repeated exposure studies showed higher sensitivity than traditional 2D cultures in identifying compounds that cause liver injury and metabolism-dependent toxicity. This platform combines the well-documented impact of 3D culture configuration for improved tissue functionality and longevity with the requisite throughput and repeatability needed for year-over-year toxicology screening.

In vitro-in vivo extrapolation (IVIVE) analyses translating high-throughput screening (HTS) data to human relevance have been limited. This study represents the first report applying IVIVE approaches and exposure comparisons using the entirety of the Tox21 federal collaboration chemical screening data, incorporating assay response efficacy and quality of concentration-response fits, and providing quantitative anchoring to first address the likelihood of human in vivo interactions with Tox21 compounds. This likelihood was assessed using a maximum blood concentration to in vitro response ratio approach (Cmax/AC50), analogous to decision-making methods for clinical drug-drug interactions. Fraction unbound in plasma (fup) and intrinsic hepatic clearance (CLint) parameters were estimated in silico and incorporated in a 3-compartment toxicokinetic (TK) model to first predict Cmax for in vivo corroboration using therapeutic scenarios. Toward lower exposure scenarios, 36 compounds of 3,925 with curated activity in the HTS data using high quality dose-response model fits and ≥40% efficacy gave ‘possible’ human in vivo interaction likelihoods lower than median human exposures predicted in EPA’s ExpoCast program. A publicly available web application has been designed to provide all Tox21/ToxCast dose likelihood predictions. Overall, this approach provides an intuitive framework to relate in vitro toxicology data rapidly and quantitatively to exposures using either in vitro or in silico derived TK parameters, and can be thought of as an important step towards estimating plausible biological interactions in a high throughput risk assessment framework.

1. The liver metabolizes thyroxine (T(4)) through two major pathways: deiodination and conjugation. Following exposure to xenobiotics, T(4) conjugation increases through the induction of hepatic uridine diphosphate glucuronosyltransferase (UGT) in rodents; however, it is uncertain to what degree different species employ deiodination and conjugation in the metabolism of T(4). The objective of this study was to compare the metabolism of T4 in untreated and 2,2',4,4',5,5'-hexachlorobiphenyl (PCB 153)-treated primary sandwich-cultured hepatocytes from rat (SCRH) and human (SCHH). 2. Basal metabolite concentrations were 13 times higher in the medium of SCRH compared to SCHH. Metabolite distribution in the medium of SCRH versus SCHH was as follows: T(4)G, (91.6 versus 5.3%); T4S, (3.6 versus 4.4%) and T(3) + rT(3), (4.9 versus 90.3%). PCB 153 induced T(4)G in the medium of SCRH and SCHH; however, T(4)S and T(3) + rT(3) were changed but to a much lesser degree. 3. The results indicate that baseline T(4) glucuronidation is greater in SCRH compared to SCHH. These data also suggest that glucuronidation may be a more important pathway for T(4) metabolism in rats and deiodination may be a favored pathway in humans; however, with PCB 153 treatment these data support glucuronidation as a primary route of T(4) metabolism in both rat and humans.

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, "organotypic" cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.

The constitutive androstane receptor (CAR, NR1I3) is a xenobiotic sensor governing the transcription of numerous hepatic genes associated with drug metabolism and clearance. Recent evidence suggests that CAR also modulates energy homeostasis and cancer development. Thus, identification of novel human (h) CAR activators is of both clinical importance and scientific interest.Docking and ligand-based structure-activity models were used for virtual screening of a database containing over 2000 FDA-approved drugs. Identified lead compounds were evaluated in cell-based reporter assays to determine hCAR activation. Potential activators were further tested in human primary hepatocytes (HPHs) for the expression of the prototypical hCAR target gene CYP2B6.Nineteen lead compounds with optimal modeling parameters were selected for biological evaluation. Seven of the 19 leads exhibited moderate to potent activation of hCAR. Five out of the seven compounds translocated hCAR from the cytoplasm to the nucleus of HPHs in a concentration-dependent manner. These compounds also induce the expression of CYP2B6 in HPHs with rank-order of efficacies closely resembling that of hCAR activation.These results indicate that our strategically integrated approaches are effective in the identification of novel hCAR modulators, which may function as valuable research tools or potential therapeutic molecules.

Primary human hepatocytes are widely used for metabolic stability evaluations. However, there are limited data directly comparing phase I and phase II drug-metabolizing enzymes in fresh and cryopreserved hepatocytes prepared from the same human donor liver. We evaluated the metabolic competency of human hepatocytes prepared from seven donor tissues before and after cryopreservation. Temporal-dependent enzyme activity in suspension and matched adherent cultures of primary human hepatocytes was also assessed. Cryopreservation of hepatocytes resulted in statistically significant increases in activities of CYP1A2, CYP2B6, CYP2C9, CYP2D6, and CYP3A but not CYP2C8, CYP2C19, FMO, UGT, and SULT, relative to fresh hepatocytes. In suspension cultures of hepatocytes, enzyme stabilities were as follows: UGT

A toxicogenomics approach was used to qualitatively and quantitatively compare the gene expression changes in human and rat primary hepatocytes exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Hepatocytes from five individual rats and five individual humans were exposed for 24 h to 11 concentrations of TCDD ranging from 0.00001 to 100nM and a vehicle control. Gene expression changes were analyzed using whole-genome microarrays containing 13,002 orthologs. Significant changes in expression of individual orthologs at any concentration (fold change [FC] ± 1.5 and false discovery rate < 0.05) were higher in the rat (1547) compared with human hepatocytes (475). Only 158 differentially expressed orthologs were common between rats and humans. Enrichment analysis was performed on the differentially expressed orthologs in each species with 49 and 34 enriched human and rat pathways, respectively. Only 12 enriched pathways were shared between the two species. The results demonstrate significant cross-species differences in expression at both the gene and pathway level. Benchmark dose analysis of gene expression changes showed an average 18-fold cross-species difference in potency among differentially expressed orthologs with the rat more sensitive than the human. Similar cross-species differences in potency were observed for signaling pathways. Using the maximum FC in gene expression as a measure of efficacy, the human hepatocytes showed on average a 20% lower efficacy among the individual orthologs showing differential expression. The results provide evidence for divergent cross-species gene expression changes in response to TCDD and are consistent with epidemiological and clinical evidence showing humans to be less sensitive to TCDD-induced hepatotoxicity.

Hepatocyte nuclear factor 4α (HNF4α) controls the expression of many critical metabolic pathways, and the Mediator complex occupies a central role in recruiting RNA polymerase II (Pol II) to these gene promoters. An impaired transcriptional HNF4α network in human liver is responsible for many pathological conditions, such as altered drug metabolism, fatty liver, and diabetes. Here, we report that Med25, an associated member of the Mediator complex, is required for the association of HNF4α with Mediator, its several cofactors, and RNA Pol II. Further, increases and decreases in endogenous Med25 levels are reflected in the composition of the transcriptional complex, Pol II recruitment, and the expression of HNF4α-bound target genes. A novel feature of Med25 is that it imparts “selectivity.” Med25 affects only a significant subset of HNF4α target genes that selectively regulate drug and lipid metabolism. These results define a role for Med25 and the Mediator complex in the regulation of xenobiotic metabolism and lipid homeostasis.

AZD2624 was pharmacologically characterized as a NK3 receptor antagonist intended for treatment of schizophrenia. The metabolic drug-drug interaction potential of AZD2624 was evaluated in in vitro studies. CYP3A4 and CYP3A5 appeared to be the primary enzymes mediating the formation of pharmacologically active ketone metabolite (M1), whereas CYP3A4, CYP3A5, and CYP2C9 appeared to be the enzymes responsible for the formation of the hydroxylated metabolite (M2). The apparent K(m) values were 1.5 and 6.3 µM for the formation of M1 and M2 in human liver microsomes, respectively. AZD2624 exhibited an inhibitory effect on microsomal CYP3A4/5 activities with apparent IC(50) values of 7.1 and 19.8 µM for midazolam and testosterone assays, respectively. No time-dependent inactivation of CYP3A4/5 activity (midazolam 1'-hydroxylation) by AZD2624 was observed. AZD2624 demonstrated weak to no inhibition of CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, and CYP2D6. AZD2624 was not an inducer of CYP1A2 or CYP2B6. Although AZD2624-induced CYP3A4 activity in hepatocytes, the potential of AZD2624 to cause inductive drug interactions of this enzyme was low at relevant exposure concentration. Together with targeted low efficacious concentration, the results of this study demonstrated AZD2624 has a relatively low metabolic drug-drug interaction potential towards co-administered drugs. However, metabolism of AZD2624 might be inhibited when co-administrated with potent CYP3A4/5 inhibitors.