| dc.description.abstract | The lack of adequate toxicity data for the vast majority of chemicals and complex mixtures in the environment has spurred the development of new approach methodologies (NAMs) which span a vast array of in vitro and in silico technologies. This study aims to develop practical, high-throughput in vitro models to rapidly evaluate potential hazards of environmental chemicals and complex mixtures to provide evidence for risk assessment. A panel of human induced pluripotent stem cell (iPSC)-derived cells (hepatocytes, neurons, cardiomyocytes, and endothelial cells) and primary cells (HUVECs) were used to screen environmental chemicals from different classes, “designed” mixtures, and real-life environmental mixtures. First, we found chemical class-specific similarity and cell type-specific patterns among the individual compounds tested, indicating the ability of the proposed in vitro model to recognize effects on different cell types. We also observed that data from the five cell-type model was as good or even better at assigning compounds to chemical classes compared to available NAM datasets such as ToxCast/Tox21 and chemical structure-based descriptors. Second, we observed significant bioactivity of some “designed” mixtures based on individual chemical concentrations considered to be “low” or “safe”. In some cases, the bioactivity of the mixtures appeared to be much greater than that of their components under either concentration addition (CA) or independent action (IA) dose reconstruction model assumptions. CA was much more accurate as a predictor of mixture effects as compared to the IA, suggesting that CA is a preferred first approximation to predict the toxicity of a mixture when data for the constituents are available. Third, the same in vitro models were applied to real-life environmental mixtures. We found significant evidence of spatial correlation of a subset of polycyclic aromatic hydrocarbon (PAH) contaminants and cell-based phenotypes. Furthermore, we show that the cell-based bioactivity data can be used to predict environmental concentrations for several PAH contaminants, as well as for overall PAH summaries and cancer risk. This dissertation thus demonstrates that novel, cell-based in vitro bioassays can be used as rapid hazard screening tools for environmental chemicals and mixtures, providing a practical solution that yields highly informative data for risk assessment. | en |
