No abstract

The Stemina devTOX quickPredict platform is a human pluripotent stem cell-based assay that predicts the developmental toxicity potential based on changes in cellular metabolism following chemical exposure [Palmer, J. A., Smith, A. M., Egnash, L. A., Conard, K. R., West, P. R., Burrier, R. E., Donley, E. L. R., and Kirchner, F. R. (2013). Establishment and assessment of a new human embryonic stem cell-based biomarker assay for developmental toxicity screening. Birth Defects Res. B Dev. Reprod. Toxicol. 98, 343–363]. Using this assay, we screened 1065 ToxCast phase I and II chemicals in single-concentration or concentration-response for the targeted biomarker (ratio of ornithine to cystine secreted or consumed from the media). The dataset from the Stemina (STM) assay is annotated in the ToxCast portfolio as STM. Major findings from the analysis of ToxCast_STM dataset include (1) 19% of 1065 chemicals yielded a prediction of developmental toxicity, (2) assay performance reached 79%–82% accuracy with high specificity (> 84%) but modest sensitivity (< 67%) when compared with in vivo animal models of human prenatal developmental toxicity, (3) sensitivity improved as more stringent weights of evidence requirements were applied to the animal studies, and (4) statistical analysis of the most potent chemical hits on specific biochemical targets in ToxCast revealed positive and negative associations with the STM response, providing insights into the mechanistic underpinnings of the targeted endpoint and its biological domain. The results of this study will be useful to improving our ability to predict in vivo developmental toxicants based on in vitro data and in silico models.

The blood-brain barrier (BBB) serves as a gateway for passage of drugs, chemicals, nutrients, metabolites, and hormones between vascular and neural compartments in the brain. Here, we review BBB development with regard to the microphysiology of the neurovascular unit (NVU) and the impact of BBB disruption on brain development. Our focus is on modeling these complex systems. Extant in silico models are available as tools to predict the probability of drug/chemical passage across the BBB; in vitro platforms for high-throughput screening and high-content imaging provide novel data streams for profiling chemical-biological interactions; and engineered human cell-based microphysiological systems provide empirical models with which to investigate the dynamics of NVU function. Computational models are needed that bring together kinetic and dynamic aspects of NVU function across gestation and under various physiological and toxicological scenarios. This integration will inform adverse outcome pathways to reduce uncertainty in translating in vitro data and in silico models for use in risk assessments that aim to protect neurodevelopmental health.

The principal aim of this study was to develop, validate, and demonstrate a physiologically based pharmacokinetic (PBPK) model to predict and characterize the absorption, distribution, metabolism, and excretion of acetaminophen (APAP) in humans. A PBPK model was created that included pharmacologically and toxicologically relevant tissue compartments and incorporated mechanistic descriptions of the absorption and metabolism of APAP, such as gastric emptying time, cofactor kinetics, and transporter-mediated movement of conjugated metabolites in the liver. Through the use of a hierarchical Bayesian framework, unknown model parameters were estimated using a large training set of data from human pharmacokinetic studies, resulting in parameter distributions that account for data uncertainty and inter-study variability. Predictions from the model showed good agreement to a diverse test set of data across several measures, including plasma concentrations over time, renal clearance, APAP absorption, and pharmacokinetic and exposure metrics. The utility of the model was then demonstrated through predictions of cofactor depletion, dose response of several pharmacokinetic endpoints, and the relationship between APAP biomarker levels in the plasma and those in the liver. The model addressed several limitations in previous PBPK models for APAP, and it is anticipated that it will be useful in predicting the pharmacokinetics of APAP in a number of contexts, such as extrapolating across doses, estimating internal concentrations, quantifying population variability, assessing possible impacts of drug coadministration, and, when coupled with a suitable pharmacodynamic model, predicting toxicity.

Aim: Autoimmune disease and CD4+ T-cell alterations are induced in mice exposed to the water pollutant trichloroethylene (TCE). We examined here whether TCE altered gene-specific DNA methylation in CD4 + T cells as a possible mechanism of immunotoxicity. Materials & methods: Naive and effector/memory CD4 + T cells from mice exposed to TCE (0.5 mg/ml in drinking water) for 40 weeks were examined by bisulfite next-generation DNA sequencing. Results: A probabilistic model calculated from multiple genes showed that TCE decreased methylation control in CD4 + T cells. Data from individual genes fitted to a quadratic regression model showed that TCE increased gene-specific methylation variance in both CD4 subsets. Conclusion: TCE increased epigenetic drift of specific CpG sites in CD4 + T cells.

The U.S. EPA Endocrine Disruptor Screening Program utilizes data across the ToxCast/Tox21 high-throughput screening (HTS) programs to evaluate the biological effects of potential endocrine active substances (EAS). A potential limitation to the use of in vitro assay data in regulatory decision-making is the lack of coverage for xenobiotic metabolic processes. Both hepatic- and peripheral-tissue metabolism can yield metabolites that exhibit greater activity than the parent compound (bioactivation) or are inactive (bioinactivation) for a given biological target. Interpretation of biological effect data for both putative EAS, as well as other chemicals, screened in HTS assays may benefit from the addition of xenobiotic metabolic capabilities to decrease the uncertainty in predicting potential hazards to human health. The objective of this study was to develop an approach to retrofit existing HTS assays with hepatic metabolism. The Alginate Immobilization of Metabolic Enzymes (AIME) platform encapsulates hepatic S9 fractions in alginate microspheres attached to 96-well peg lids. Functional characterization across a panel of reference substrates for phase I cytochrome P450 enzymes revealed substrate depletion with expected metabolite accumulation. Performance of the AIME method in the VM7Luc estrogen receptor (ER) transactivation assay was evaluated across 15 reference chemicals and 48 test chemicals that yield metabolites previously identified as ER active or inactive. The results demonstrate the utility of applying the AIME method for identification of false positive and false negative target assay effects, reprioritization of hazard based on metabolism-dependent bioactivity, and enhanced in vivo concordance with the rodent uterotrophic bioassay. Integration of the AIME metabolism method may prove useful for future biochemical and cell-based HTS applications.

No abstract

The U.S. EPA continues to utilize high-throughput screening data to evaluate potential biological effects of endocrine active substances without the use of animal testing. Determining the scope and need for in vitro metabolism in high-throughput assays requires the generation of larger data sets that assess the impact of xenobiotic transformations on toxicity-related endpoints. The objective of the current study was to screen a set of 768 ToxCast chemicals in the VM7Luc estrogen receptor transactivation assay (ERTA) using the Alginate Immobilization of Metabolic Enzymes (AIME) hepatic metabolism method. Chemicals were screened with or without metabolism to identify estrogenic effects and metabolism-dependent changes in bioactivity. Based on estrogenic hit calls, 85 chemicals were active in both assay modes, 16 chemicals were only active without metabolism, and 27 chemicals were only active with metabolism. Using a novel metabolism curve shift method that evaluates the shift in concentration-response curves, 29 of these estrogenic chemicals were identified as bioactivated and 59 were bioinactivated. Human biotransformation routes and associated metabolites were predicted in silico across the chemicals to mechanistically characterize possible transformation-related ERTA effects. Overall, the study profiled novel chemicals associated with metabolism-dependent changes in ERTA bioactivity, and suggested routes of biotransformation and putative metabolites responsible for the observed estrogenic effects. The data demonstrate a range of metabolism-dependent effects across a diverse chemical library and highlight the need to evaluate the role of intrinsic xenobiotic metabolism in endocrine and other toxicity-related health effects.