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Bayesian Methods in the Quantitative Risk Assessment and Toxicity Profiling of Engineered Nanomaterials


Until recently, very little research has been conducted to assess the potential human health hazards associated with engineered nanomaterials (ENMs). In-vitro high-throughput screening (HTS) assays for the assessment of engineered nanomaterials provide new opportunities to learn how these particles interact at the cellular level, and may aid in reducing the demand for in-vivo testing. The large number of potential factors that could link nanomaterials to adverse human health impacts, create an imperative need to develop a stronger foundation for quantitative risk assessment in nanotoxicology.

In this dissertation we propose a probability model for the analysis of high-throughput cellular assays. In particular, we develop a method that builds a balance between model complexity and interpretability as a tool to be used by subject-matter specialists for assessing cytotoxicity. The resulting multivariate surface-response model allows for joint inference on dose and time kinetics, and associated classical risk assessment parameters of interest. We illustrate the proposed methodology by profiling a multivariate screening study of eight metal-oxide nanomaterials. Next, we present loss-function-based methods for the hazard ranking of engineered nanomaterials. Specifically, we provide a decision-making tool for prioritizing extensive in-vivo testing of emerging nanomaterials. The proposed framework allows for the aggregation of ranks across different sources of evidence while allowing for differential weighting of this evidence based on its reliability and importance in risk ranking. We illustrate the methodology by ranking particles from a multivariate cytotoxicity screening study of eight metal oxides, conducted in two human cell-lines. Finally, we propose methodology for modeling the relationship between physicochemical properties of ENMs and their observed cytotoxicity, as an initial step in the development of a framework for predictive nanotoxicology. In particular, the proposed approach introduces a new measure of toxicity that is seamlessly integrated into a multi-dimensional model that accounts for dose and duration kinetics jointly using a flexible smooth surface fit. Moreover, the designed approach is appropriate for small sample size, and includes data integration and a framework for advanced dimension reduction through variable selection. The proposed method was applied to a library of 24 engineered nanomaterials.

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