Principal component and causal analysis of structural and acutein vitrotoxicity data for nanoparticles

Abstract

Structure toxicity relationship analysis was conducted using principal component analysis (PCA) for a panel of nanoparticles that included dry powders of oxides of titanium, zinc, cerium and silicon, dry powders of silvers, suspensions of polystyrene latex beads, and dry particles of carbon black, nanotubes and fullerene, as well as diesel exhaust particles. Acute in vitro toxicity was assessed by different measures of cell viability, apoptosis and necrosis, haemolytic effects and the impact on cell morphology, while structural properties were characterised by particle size and size distribution, surface area, morphology, metal content, reactivity, free radical generation and zeta potential. Different acute toxicity measures were processed using PCA that classified the particles and identified four materials with an acute toxicity profile: zinc oxide, polystyrene latex amine, nanotubes and nickel oxide. PCA and contribution plot analysis then focused on identifying the structural properties that could determine the acute cytotoxicity of these four materials. It was found that metal content was an explanatory variable for acute toxicity associated with zinc oxide and nickel oxide, whilst high aspect ratio appeared the most important feature in nanotubes.Particle charge was considered as a determinant for high toxicity of polystyrene latex amine.

(Q)SAR modelling of nanomaterial toxicity: A critical review

“…It should be mentioned here that, as the toxicity is highly dose-dependent, the toxicological effects are usually evaluated at multiple concentrations in a series of tests the results of which are represented with a doseresponse curve. Figure 6 shows an example of dose-response curves obtained for 18 NPs (Xue Zhong Wang, et al, 2014). As can be seen from this graph, the cell viability is lower in the cells treated with N3 (nanotubes), N14 (zinc oxide) and N6 (aminated beads).…”

“…There are different methods to analyse and compare dose response curves such as area under the curve, slope of the curve, threshold values, min/max response and benchmark dose approach. In this study, (Xue Zhong Wang, et al, 2014) performed PCA in order to integrate the entire curve and used the resulting principal components as an overall measure of cumulative response. They concluded that, compared to other approaches, PCA-based representation of the dose-response curves provides more reasonable results when ranking the ENMs according to their hazard potential.…”

Current situation on the availability of nanostructure–biological activity data

“…Despite these limitations, there is a growing literature on the use of (Q)SARlike models in nanotoxicology studies. There are a great number of reviews [3,5,10,[12][13][14] and research articles [15][16][17][18][19][20][21][22][23][24][25][26][27][28] devoted to the investigations of in silico modelling of ENM toxicity in peer-reviewed scientific journals.…”

“…The research conducted by Wang et al [23] has been revealed to be one of the most useful datasets for nano-(Q)SAR modelling. The authors selected a panel of 18 ENMs with varying structures and conducted a set of in vitro cytotoxicity assays, including LDH release, apoptosis, necrosis, viability, MTT and haemolytic effects.…”

“…The authors selected a panel of 18 ENMs with varying structures and conducted a set of in vitro cytotoxicity assays, including LDH release, apoptosis, necrosis, viability, MTT and haemolytic effects. The raw data generated by Wang et al [23] have been examined below in the context of their ability to be used for developing nano-(Q)SARs:…”