The present study is aimed to develop a multi-nano-read-across modeling technique that helps in predicting the toxicity of different species such as bacteria, algae, protozoa, and mammalian cell lines simultaneously. Herein, the experimental toxicity of 184 metal and silica oxide (30 unique chemical types) nanoparticles from 15 datasets is analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis is developed. In the first step, hidden patterns of toxicity among nanoparticles are identified using a combination of methods. Subsequently, the developed model based on categorization of the toxicity of the metal oxide nanoparticle outcomes is evaluated via the combination of supervised and unsupervised machine learning techniques to determine the underlying factors responsible for the toxicity.

How the toxicity of nanomaterials towards different species could be simultaneously evaluated: A multi-nano-read-across approach

2019 Spring National Meeting

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