Relating Nanoparticle Properties to Biological Outcomes in Exposure Escalation Experiments.
A fundamental goal in nano-toxicology is that of identifying particle physical and chemical properties, which are likely to explain biological hazard. The first line of screening for potentially adverse outcomes often consists of exposure escalation experiments, involving the exposure of micro-organisms or cell lines to a library of nanomaterials. We discuss a modeling strategy, that relates the outcome of an exposure escalation experiment to nanoparticle properties. Our approach makes use of a hierarchical decision process, where we jointly identify particles that initiate adverse biological outcomes and explain the probability of this event in terms of the particle physicochemical descriptors. The proposed inferential framework results in summaries that are easily interpretable as simple probability statements. We present the application of the proposed method to a data set on 24 metal oxides nanoparticles, characterized in relation to their electrical, crystal and dissolution properties.
Department of Biostatistics, UCLA.
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Patel
Department of Biostatistics, UCLA ; California Nanosystems Institute, UCLA.
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Telesca
Department of Biostatistics, UCLA ; California Nanosystems Institute, UCLA.
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Low-Kam
California Nanosystems Institute, UCLA.
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Ji
California Nanosystems Institute, UCLA.
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Zhang
California Nanosystems Institute, UCLA ; Division of Nanomedicine, UCLA.
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Xia
California Nanosystems Institute, UCLA ; Department of Chemistry and Biochemistry, UCLA.
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Zinc
California Nanosystems Institute, UCLA ; Division of Nanomedicine, UCLA.
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Dose-Response Models; Model Selection; Nanoinformatics; Smoothing Splines