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

preprint
submitted on 20.08.2017 and posted on 31.08.2017 by Natalia Sizochenko, Alicja Mikolajczyk, Karolina Jagiello, Tomasz Puzyn, Jerzy Leszczynski, Bakhtiyor Rasulev
Application of predictive modeling approaches is able solve the problem of the missing data. There are a lot of studies that investigate the effects of missing values on qualitative or quantitative modeling, but only few publications have been
discussing it in case of applications to nanotechnology related data. Current project aimed at the development of multi-nano-read-across modeling technique that helps in predicting the toxicity of different species: bacteria, algae, protozoa, and mammalian cell lines. In this study, the experimental toxicity for 184 metal- and silica oxides (30 unique chemical types) nanoparticles from 15 experimental datasets was analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis was developed. At the first step, hidden patterns of toxicity among the nanoparticles were identified using a combination of methods. Then the developed model that based on categorization of metal oxide nanoparticles’ toxicity outcomes was evaluated by means of combination of supervised and unsupervised machine learning techniques to find underlying factors responsible for toxicity.

History

Topic

  • Computational chemistry and modeling

Email Address of Submitting Author

bakhtiyor.rasulev@ndsu.edu

Email Address(es) for Other Author(s)

sizochenko@icnanotox.org, mikolajczyk@qsar.eu.org, jagiello@qsar.eu.org, puzyn@qsar.eu.org, jerzy@icnanotox.org

Institution

North Dakota State University

Country

United States

ORCID For Submitting Author

0000-0002-7845-4884

Declaration of Conflict of Interest

Authors state no conflict of interests

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