3,3',4,4',5-Pentachlorobiphenyl (PCB126), a dioxin-like polychlorinated biphenyl (PCB) and a potent aryl hydrocarbon receptor (AhR) agonist, is implicated in the disruption of both carbohydrate and lipid metabolism which ultimately leads to wasting disorders, metabolic disease, and nonalcoholic fatty liver disease. However, the mechanisms are unclear. Because liver is the target organ for PCB toxicity and responsible for metabolic homeostasis, we hypothesized that early disruption of glucose and lipid homeostasis contributes to later manifestations such as hepatic steatosis. To test this hypothesis, groups of male Sprague Dawley rats, fed on AIN-93G diet, were injected (intraperitoneal.) with a single bolus of PCB126 (5 µmol/kg) at various time intervals between 9 h and 12 days prior to euthanasia. An early decrease in serum glucose and a gradual decrease in serum triglycerides were observed over time. Liver lipid accumulation was most severe at 6 and 12 days of exposure. Transcript levels of cytosolic phosphoenol-pyruvate carboxykinase (Pepck-c/Pck1) and glucose transporter (Glut2/Slc2a2) involved in gluconeogenesis and hepatic glucose transport were time-dependently downregulated between 9 h and 12 days of PCB126 exposure. Additionally, transcript levels of Pparα, and its targets acyl-CoA oxidase (Acox1) and hydroxy-3-methylglutaryl-CoA synthase 2 (Hmgcs2), were also downregulated, indicating changes in peroxisomal fatty acid oxidation and ketogenesis. In a separate animal study, we found that the measured changes in the transcript levels of Pepck-c, Glut2, Pparα, Acox1, and Hmgcs2 were also dose dependent. Furthermore, PCB126-induced effects on Pepck-c were demonstrated to be AhR dependent in rat H4IIE hepatocytes. These results indicate that PCB126-induced wasting and steatosis are preceded initially by (1) decreased serum glucose caused by decreased hepatic glucose production, followed by (2) decreased peroxisomal fatty acid oxidation.

One of the most challenging areas in regulatory science is assessment of the substances known as UVCB (unknown or variable composition, complex reaction products and biological materials). Because the inherent complexity and variability of UVCBs present considerable challenges for establishing sufficient substance similarity based on chemical characteristics or other data, we hypothesized that new approach methodologies (NAMs), including in vitro test-derived biological activity signatures to characterize substance similarity, could be used to support grouping of UVCBs. We tested 141 petroleum substances as representative UVCBs in a compendium of 15 human cell types representing a variety of tissues. Petroleum substances were assayed in dilution series to derive point of departure estimates for each cell type and phenotype. Extensive quality control measures were taken to ensure that only high-confidence in vitro data were used to determine whether current groupings of these petroleum substances, based largely on the manufacturing process and physico-chemical properties, are justifiable. We found that bioactivity data-based groupings of petroleum substances were generally consistent with the manufacturing class-based categories. We also showed that these data, especially bioactivity from human induced pluripotent stem cell (iPSC)-derived and primary cells, can be used to rank substances in a manner highly concordant with their expected in vivo hazard potential based on their chemical compositional profile. Overall, this study demonstrates that NAMs can be used to inform groupings of UVCBs, to assist in identification of representative substances in each group for testing when needed, and to fill data gaps by read-across.

Endothelial cells (ECs) play a major role in blood vessel formation and function. While there is longstanding evidence for the potential of chemical exposures to adversely affect EC function and vascular development, the hazard potential of chemicals with respect to vascular effects is not routinely evaluated in safety assessments. Induced pluripotent stem cell (iPSC)-derived ECs promise to provide a physiologically relevant, organotypic culture model that is amenable for high-throughput (HT) EC toxicant screening and may represent a viable alternative to traditional in vitro models, including human umbilical vein endothelial cells (HUVECs). To evaluate the utility of iPSC-ECs for multidimensional HT toxicity profiling of chemicals, both iPSC-ECs and HUVECs were exposed to selected positive (angiogenesis inhibitors, cytotoxic agents) and negative compounds in concentration response for either 16 or 24 h in a 384-well plate format. Furthermore, chemical effects on vascularization were quantified using EC angiogenesis on biological (Geltrex™) and synthetic (SP-105 angiogenesis hydrogel) extracellular matrices. Cellular toxicity was assessed using high-content live cell imaging and the CellTiter-Glo assay. Assay performance indicated good to excellent assay sensitivity and reproducibility for both cell types investigated. Both iPSC-derived ECs and HUVECs formed tube-like structures on Geltrex™ and hydrogel, an effect that was inhibited by angiogenesis inhibitors and cytotoxic agents in a concentration-dependent manner. The quality of HT assays in HUVECs was generally higher than that in iPSC-ECs. Altogether, this study demonstrates the capability of ECs for comprehensive assessment of the biological effects of chemicals on vasculature in a HT compatible format.

The U.S. Environmental Protection Agency (EPA) periodically releases in vitro data across a variety of targets, including the estrogen receptor (ER). In 2015, the EPA used this data to construct mathematical models of ER agonist and antagonist pathways to prioritize chemicals for endocrine disruption testing. However, mathematical models require in vitro data prior to predicting estrogenic activity, but machine learning methods are capable of prospective prediction from molecular structure alone. The current study describes the generation and evaluation of Bayesian machine learning models grouped by the EPA's ER agonist pathway model, using multiple data types with proprietary software, Assay Central™. External predictions with three test sets of in vitro and in vivo reference chemicals with agonist activity classifications were compared to previous mathematical model publications. Training datasets were subjected to additional machine learning algorithms and compared with rank normalized scores of internal fivefold cross-validation statistics. External predictions were found to be comparable or superior to previous studies published by the EPA. When assessing six additional algorithms for the training datasets, Assay Central™ performed similarly at a reduced computational cost. This study demonstrates machine learning can prioritize chemicals for future in vitro and in vivo testing of ER agonism.

Computational methods are needed to more efficiently leverage data from in vitro cell-based models to predict what occurs within whole body systems after chemical insults. This study set out to test the hypothesis that in vitro high-throughput screening (HTS) data can more effectively predict in vivo biological responses when chemical disposition and toxicokinetic (TK) modeling are employed. In vitro HTS data from the Tox21 consortium were analyzed in concert with chemical disposition modeling to derive nominal, aqueous, and intracellular estimates of concentrations eliciting 50% maximal activity. In vivo biological responses were captured using rat liver transcriptomic data from the DrugMatrix and TG-Gates databases and evaluated for pathway enrichment. In vivo dosing data were translated to equivalent body concentrations using HTTK modeling. Random forest models were then trained and tested to predict in vivo pathway-level activity across 221 chemicals using in vitro bioactivity data and physicochemical properties as predictor variables, incorporating methods to address imbalanced training data resulting from high instances of inactivity. Model performance was quantified using the area under the receiver operator characteristic curve (AUC-ROC) and compared across pathways for different combinations of predictor variables. All models that included toxicokinetics were found to outperform those that excluded toxicokinetics. Biological interpretation of the model features revealed that rather than a direct mapping of in vitro assays to in vivo pathways, unexpected combinations of multiple in vitro assays predicted in vivo pathway-level activities. To demonstrate the utility of these findings, the highest-performing model was leveraged to make new predictions of in vivo biological responses across all biological pathways for remaining chemicals tested in Tox21 with adequate data coverage (n = 6617). These results demonstrate that, when chemical disposition and toxicokinetics are carefully considered, in vitro HT screening data can be used to effectively predict in vivo biological responses to chemicals.

Copper is essential for the function of the mitochondrial electron transport chain and several antioxidant proteins. However, in its free form copper can participate in Fenton-like reactions that produce reactive hydroxyl radicals. Aryl-hydrocarbon receptor (AhR) agonists, including the most potent polychlorinated biphenyl (PCB) congener, 3,3',4,4',5-pentachlorobiphenyl (PCB126), increase copper levels in rodent livers. This is accompanied by biochemical and toxic changes. To assess the involvement of copper in PCB toxicity, male Sprague Dawley rats were fed an AIN-93G diet with differing dietary copper levels: low (2 ppm), adequate (6 ppm), and high (10 ppm). After three weeks, rats from each group were given a single ip injection of corn oil (control), 1, or 5 μmol/kg body weight PCB126. Two weeks following injections, biochemical and morphological markers of hepatic toxicity, trace metal status, and hepatic gene expression of metalloproteins were evaluated. Increasing dietary copper was associated with elevated tissue levels of copper and ceruloplasmin. In the livers of PCB126-treated rats the hallmark signs of AhR activation were present, including increased cytochrome P-450 and lipid levels, and decreased glutathione. In addition a doubling of hepatic copper levels was seen and overall metals homeostasis was disturbed, resulting in decreased hepatic selenium, manganese, zinc and iron. Expression of key metalloproteins was either decreased (cytochrome c oxidase), unchanged (ceruloplasmin and CuZnSOD) or increased (tyrosinase, metallothionein 1 and 2) with exposure to PCB126. Increases in metallothionein may contribute/reflect the increased copper seen. Alterations in dietary copper did not amplify or abrogate the hepatic toxicity of PCB126. PCB126 toxicity, i.e. oxidative stress and steatosis, is clearly associated with disturbed metals homeostasis. Understanding the mechanisms of this disturbance may provide tools to prevent liver toxicity by other AhR agonists.

Polychlorinated Biphenyls (PCBs) are industrial chemicals that have become a persistent threat to human health due to ongoing exposure. A subset of PCBs, known as dioxin-like PCBs, pose a special threat given their potent hepatic effects. Micronutrients, especially Cu, Zn and Se, homeostatic dysfunction is commonly seen after exposure to dioxin-like PCBs. This study investigates whether micronutrient alteration is the byproduct of the ongoing hepatotoxicity, marked by lipid accumulation, or a concurrent, yet independent event of hepatic damage. A time course study was carried out using male Sprague-Dawley rats with treatments of PCB126, the prototypical dioxin-like PCB, resulting in 6 different time points. Animals were fed a purified diet, based on AIN-93G, for three weeks to ensure micronutrient equilibration. A single IP injection of either tocopherol-stripped soy oil vehicle (5 mL/kg) or 5 umol/kg PCB126 dose in vehicle was given at various time points resulting in exposures of 9 hr, 18 hr, 36 hr, 3 days, 6 days, and 12 days. Mild hepatic vacuolar change was seen as early as 36 hours with drastic changes at the later time points, 6 and 12 days. Micronutrient alterations, specifically Cu, Zn, and Se, were not seen until after day 3 and only observed in the liver. No alterations were seen in the duodenum, suggesting that absorption and excretion may not be involved. Micronutrient alterations occur with ROS formation, lipid accumulation, and hepatomegaly. To probe the mechanistic underpinnings, alteration of gene expression of several copper chaperones was investigated; only metallothionein appeared elevated. These data suggest that the disruption in micronutrient status is a result of the hepatic injury elicited by PCB126 and is mediated in part by metallothionein.

Recent efforts aimed at integrating in vitro high-throughput screening (HTS) data into chemical toxicity assessments are necessitating increased understanding of concordance between chemical-induced responses observed in vitro versus in vivo. This investigation set out to (i) measure concordance between in vitro HTS data and transcriptomic responses observed in vivo, focusing on the liver, and (ii) identify attributes that can influence concordance. Signal response profiles from 130 substances were compared between in vitro data produced through Tox21 and liver transcriptomic data through DrugMatrix, collected from rats exposed to a chemical for ≤5 days. A global in vitro-to-in vivo comparative analysis based on pathway-level responses resulted in an overall average percent agreement of 79%, ranging on a per-chemical basis between 41 and 100%. While concordance amongst inactive chemicals was high (89%), concordance amongst chemicals showing in vitro activity was only 13%, suggesting that follow-up in vivo and/or orthogonal in vitro assays would improve interpretations of in vitro activity. Attributes identified to influence concordance included experimental design attributes (e.g., cell type), target pathways, and physicochemical properties (e.g., logP). The attribute that most consistently increased concordance was dose applicability, evaluated by filtering for experimental doses administered to rats that were within 10-fold of those related to likely bioactivity, derived using Tox21 data and high-throughput toxicokinetic modeling. Together, findings suggest that in vitro screening approaches to predict in vivo toxicity are viable particularly when certain attributes are considered, including whether activity versus inactivity is observed, experimental design, chemical properties, and dose applicability.