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Quantitative Nanostructure-Activity Relationship Models for the Risk Assessment of NanoMaterials
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Author(s): Eleni Vrontaki (NovaMechanics Ltd., Cyprus & University of Athens, Greece), Thomas Mavromoustakos (University of Athens, Greece), Georgia Melagraki (NovaMechanics Ltd., Cyprus)and Antreas Afantitis (NovaMechanics Ltd., Cyprus)
Copyright: 2017
Pages: 25
Source title:
Materials Science and Engineering: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-1798-6.ch002
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Abstract
In the last few decades, nanotechnology has been deeply established into human's everyday life with a great number of applications in cosmetics, textiles, electronics, optics, medicine, and many more. Although nanotechnology applications are rapidly increasing, the toxicity of some nanomaterials to living organisms and the environment still remains unknown and needs to be explored. The traditional toxicological evaluation of nanoparticles with the wide range of types, shapes, and sizes often involves expensive and time-consuming procedures. An efficient and cheap alternative is the development and application of predictive computational models using Quantitative Nanostructure-Activity Relationship (QNAR) methods. Towards this goal, researchers are mainly focused on the adverse effects of metal oxides and carbon nanotubes, but to date, QNAR studies are rare mainly because of the limited number of available organized datasets. In this chapter, recent studies for predictive QNAR models for the risk assessment of nanomaterials are reported and the perspectives of computational nanotoxicology that deeply relies on the intense collaboration between experimental and computational scientists are discussed.
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