New generation in vitro high-throughput screening (HTS) assays for
the assessment of engineered nanomaterials provide an
opportunity to learn how these particles interact at the
cellular level, particularly in relation to injury pathways.
These types of assays are often characterized by small sample
sizes, high measurement error and high dimensionality, as
multiple cytotoxicity outcomes are measured across an array of
doses and durations of exposure. In this paper we propose a
probability model for the toxicity profiling of engineered
nanomaterials. A hierarchical structure is used to account for
the multivariate nature of the data by modeling dependence
between outcomes and thereby combining information across
cytotoxicity pathways. In this framework we are able to provide
a flexible surface-response model that provides inference and
generalizations of various classical risk assessment parameters.
We discuss applications of this model to data on eight
nanoparticles evaluated in relation to four cytotoxicity
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