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Toxicity profiling of engineered nanomaterials via multivariate dose-response surface modeling
Link to Journal Abstract
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 parameters.
In this paper the authors propose a probability model for the toxicity profiling of engineered nanomaterials. They discuss applications of this model to data on eight nanoparticles evaluated in relation to four cytotoxicity parameters.
Peer Reviewed Journal Article
Exposure Or Hazard Target
Method Of Study
Computational and System Modeling
Risk Exposure Group
Annals of Applied Statistics, 6(4): 1707-1729 (December 2012)
Annals of Applied Statistics
Patel T, Telesca D, George S, Nel AE
Last updated on April 8, 2013
This work is supported in part by the Nanoscale Science and Engineering Initiative of the National Science Foundation
under NSF Award Number EEC-0118007.
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