A framework for grouping nanoparticles based on their measurable characteristics

Abstract

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.

Frameworks and tools for risk assessment of manufactured nanomaterials

“…Statistical analysis and machine learning methods (e.g. principal component analysis, neural networks) have also been applied to model the properties and effects of MNs (Puzyn et al, 2009b, Puzyn et al, 2009a, Toropov et al, 2007, Toropov and Leszczynski, 2006, Sayes et al, 2013, Lynch et al, 2014. Such methods were used for example in the EU-funded MODERN project with the aim to establish in silico modelling of the effects of metal and metal oxide MNs (Liu et al, 2013b, Liu et al, 2014, Liu et al, 2013a, Gómez et al, 2013.…”

A critical appraisal of existing concepts for the grouping of nanomaterials

“…The NanoMILE approach is founded on previous work by Sayes et al (2013) demonstrating the feasibility of grouping of NMs based on combinations of related physico-chemical properties. Using principal component analysis (PCA) and linear discriminant analysis, Sayes et al assessed which properties of a set of selected physico-chemical properties (engineered size, concentration, agglomerated size in water, zeta potential, pH and age of the suspension, and oxidant production) were redundant in terms of their detectable linear interdependencies across different silver, copper, nickel, zinc and iron NMs.…”

Particle uptake efficiency is significantly affected by type of capping agent and cell line

“…Studies have shown that NPs can be imported into cells via endocytosis for smaller particles that are less than 120 nm (Conner and Schmid, ). Previous studies have suggested that biological activities, such as cellular uptake and health effects of NPs, are greatly affected by their size, shape ( Chithrani et al ., ; Chithrani and Chan, ) and surface charge (Berg et al ., ; Sayes et al ., ). Researchers explore novel synthesis methods to control these surface properties by using different capping agents (i.e.…”