TOXICITY PROFILING OF ENGINEERED NANOMATERIALS VIA MULTIVARIATE DOSE-RESPONSE SURFACE MODELING

Area of Science:

• Nanotechnology
• Toxicology
• Computational Biology

Background:

• In vitro high-throughput screening (HTS) assays are crucial for assessing engineered nanomaterial (ENM) cellular interactions and toxicity.
• Current HTS assays face challenges like small sample sizes, high measurement error, and high dimensionality due to multiple outcomes and exposure variables.

Purpose of the Study:

• To propose a novel probability model for the toxicity profiling of engineered nanomaterials.
• To address the multivariate nature of HTS data by modeling dependencies between cytotoxicity outcomes.

Main Methods:

• Development of a hierarchical probability model to integrate information across various cytotoxicity pathways.
• Application of a flexible surface-response model for inference and generalization of risk assessment parameters.

Main Results:

• The proposed model effectively handles the multivariate and high-dimensional data typical of HTS assays.
• Demonstrated application of the model to analyze toxicity data for eight different nanoparticles across four cytotoxicity parameters.

Conclusions:

• The developed probability model offers a robust framework for toxicity profiling of engineered nanomaterials.
• This approach enhances the interpretation of HTS data, enabling more reliable risk assessment for ENMs.