Toxicity Profiling of Engineered Nanomaterials via Multivariate Dose Response Surface Modeling

• Activity Number: 613
• Type: Topic Contributed
• Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Trina R. Patel*+ and Donatello Telesca and Saji George and Andre E. Nel
• Companies: University of California at Los Angeles and University of California at Los Angeles and University of California at Los Angeles and University of California at Los Angeles
• Keywords: Dose Response Models ; Hierarchical Models ; Nanotoxicology ; Multivariate

Abstract

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 article we propose a probability model for toxicity profiling of engineered nanomaterials. A hierarchical framework 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 on various classical risk assessment parameters. We discuss applications of this model to data on eight nanoparticles evaluated in relation to four cytotoxicity parameters.