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A framework for grouping nanoparticles based on their measurable characteristics

Authors Sayes CM, Smith PA, Ivanov IV

Received 20 November 2012

Accepted for publication 10 January 2013

Published 18 September 2013 Volume 2013:8(Supplement 1 Nanoinformatics) Pages 45—56

DOI https://doi.org/10.2147/IJN.S40521

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 4


Christie M Sayes,1–3 P Alex Smith,2 Ivan V Ivanov3

1Center for Aerosol and Nanomaterials Engineering, RTI International, Research Triangle Park, NC, USA; 2Department of Biomedical Engineering, 3Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA

Background: There is a need to take a broader look at nanotoxicological studies. Eventually, the field will demand that some generalizations be made. To begin to address this issue, we posed a question: are metal colloids on the nanometer-size scale a homogeneous group? In general, most people can agree that the physicochemical properties of nanomaterials can be linked and related to their induced toxicological responses.
Methods: The focus of this study was to determine how a set of selected physicochemical properties of five specific metal-based colloidal materials on the nanometer-size scale – silver, copper, nickel, iron, and zinc – could be used as nanodescriptors that facilitate the grouping of these metal-based colloids.
Results: The example of the framework pipeline processing provided in this paper shows the utility of specific statistical and pattern recognition techniques in grouping nanoparticles based on experimental data about their physicochemical properties. Interestingly, the results of the analyses suggest that a seemingly homogeneous group of nanoparticles could be separated into sub-groups depending on interdependencies observed in their nanodescriptors.
Conclusion: These particles represent an important category of nanomaterials that are currently mass produced. Each has been reputed to induce toxicological and/or cytotoxicological effects. Here, we propose an experimental methodology coupled with mathematical and statistical modeling that can serve as a prototype for a rigorous framework that aids in the ability to group nanomaterials together and to facilitate the subsequent analysis of trends in data based on quantitative modeling of nanoparticle-specific structure–activity relationships. The computational part of the proposed framework is rather general and can be applied to other groups of nanomaterials as well.

Keywords: structure–activity relationships, principal component analysis, linear discriminant analysis, nanoparticle, modeling framework

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