Toxicity profiling of engineered nanomaterials via multivariate dose-response surface modeling

TL;DR

This paper introduces a hierarchical probability model for analyzing high-dimensional, multivariate toxicity data from nanomaterials, enabling better risk assessment and understanding of cellular injury pathways.

Contribution

It presents a novel flexible surface-response modeling approach that accounts for outcome dependence and integrates information across multiple cytotoxicity pathways.

Findings

Model successfully applied to data on eight nanoparticles.

Provides inference on classical risk assessment parameters.

Enhances understanding of nanomaterial toxicity mechanisms.

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.