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Review
. 2013 Mar 19;46(3):607-21.
doi: 10.1021/ar300022h. Epub 2012 Jun 7.

Nanomaterial toxicity testing in the 21st century: use of a predictive toxicological approach and high-throughput screening

Affiliations
Review

Nanomaterial toxicity testing in the 21st century: use of a predictive toxicological approach and high-throughput screening

Andre Nel et al. Acc Chem Res. .

Abstract

The production of engineered nanomaterials (ENMs) is a scientific breakthrough in material design and the development of new consumer products. While the successful implementation of nanotechnology is important for the growth of the global economy, we also need to consider the possible environmental health and safety (EHS) impact as a result of the novel physicochemical properties that could generate hazardous biological outcomes. In order to assess ENM hazard, reliable and reproducible screening approaches are needed to test the basic materials as well as nanoenabled products. A platform is required to investigate the potentially endless number of biophysicochemical interactions at the nano/bio interface, in response to which we have developed a predictive toxicological approach. We define a predictive toxicological approach as the use of mechanisms-based high-throughput screening in vitro to make predictions about the physicochemical properties of ENMs that may lead to the generation of pathology or disease outcomes in vivo. The in vivo results are used to validate and improve the in vitro high-throughput screening (HTS) and to establish structure-activity relationships (SARs) that allow hazard ranking and modeling by an appropriate combination of in vitro and in vivo testing. This notion is in agreement with the landmark 2007 report from the US National Academy of Sciences, "Toxicity Testing in the 21st Century: A Vision and a Strategy" (http://www.nap.edu/catalog.php?record_id=11970), which advocates increased efficiency of toxicity testing by transitioning from qualitative, descriptive animal testing to quantitative, mechanistic, and pathway-based toxicity testing in human cells or cell lines using high-throughput approaches. Accordingly, we have implemented HTS approaches to screen compositional and combinatorial ENM libraries to develop hazard ranking and structure-activity relationships that can be used for predicting in vivo injury outcomes. This predictive approach allows the bulk of the screening analysis and high-volume data generation to be carried out in vitro, following which limited, but critical, validation studies are carried out in animals or whole organisms. Risk reduction in the exposed human or environmental populations can then focus on limiting or avoiding exposures that trigger these toxicological responses as well as implementing safer design of potentially hazardous ENMs. In this Account, we review the tools required for establishing predictive toxicology paradigms to assess inhalation and environmental toxicological scenarios through the use of compositional and combinatorial ENM libraries, mechanism-based HTS assays, hazard ranking, and development of nano-SARs. We will discuss the major injury paradigms that have emerged based on specific ENM properties, as well as describing the safer design of ZnO nanoparticles based on characterization of dissolution chemistry as a major predictor of toxicity.

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Figures

Figure 1
Figure 1. Use of compositional and combinatorial ENM libraries
including metals, metal oxides, carbon nanotubes and silica-based nanomaterials, to perform mechanisms-based toxicological screening that links material composition and systematic variation of specific properties to biological outcome.
Figure 2
Figure 2. Examples of in vitro and in vivo HTS assays and response readouts in cells and zebrafish embryos
Figure 3
Figure 3. Examples of mechanistic injury responses for HTS
A: Oxidative stress; B: Dissolution and release of toxic metal ions; C: Cationic injury to surface membrane and organelles; D: Pro-fibrogenic responses to CNT; E: Inflammasome activation by long aspect ratio materials; F: Photoactivation and influence of bandgap; G: Zebrafish embryo hatching interference; H: Cell membrane lysis by surface reactivity.
Figure 4
Figure 4. Cellular biology of oxidative stress and implementation of a multi-parametric HTS assay premised on Tier 3 oxidative stress parameters
The right side panel provides examples of specific material properties that are capable of engaging a series of inter-related response parameters.
Figure 5
Figure 5. Multi-parametric HTS analysis by automated epifluorescence microscopy
showing that screening at 10 different concentrations and at multiple time points (1-7 and 24 hr) can be used for hazard ranking by heatmaps and similarity analysis.
Figure 6
Figure 6. HTS data analysis framework
The data are pre-processed by normalization and feature analysis tools, and then used to generate nano-QSARs according to property-based descriptors. The diagram shows how a metal and MOx library was used with a pool of 14 nanoparticles descriptors that yielded four descriptors that could accurately classify materials as toxic and non-toxic (Figure 8). In vitro predictions are validated by inhalation toxicology studies in rodents (Figure 11) and can also be used for safer design (Figure 12).
Figure 7
Figure 7. Live animal imaging to demonstrate ZnO-induced oxidative stress in the lung
Transgenic mice expressing the luciferase (luc) gene controlled by the heme oxygenase-1 (HO-1) promoter were used for live imaging of animals receiving oropharyngeal aspiration of undoped and Fe-doped ZnO as well as CdCl2 (positive control). The IVIS image shows undoped ZnO induced strong signals in the lung, liver and intestines while Fe-doping leads to signal reduction. Subsequent sacrifice and ex vivo imaging of the lungs confirmed the in vivo findings.
Figure 8
Figure 8. The correlation of MOx toxicity to bandgap energy structure
The conduction band (CB, blue bar)) and valence band (VB, red bar)) energy of metal oxide nanoparticles are experimentally determined and compared to biological redox potential (zone highlighted in yellow). CB overlap with the biological redox potential could facilitate electron transfer to the redox couples that collectively determine the cellular redox potential. The disruption of the cellular redox equilibrium results in oxidative stress as defined by the multi-parametric HTS assay.
Figure 9
Figure 9. Predictive toxicological paradigm for fibrogenic effects of MWCNTs
Cellular assays were developed based on the synergistic cellular responses in the epithelial-mesenchymal trophic cell unit, which are predictive of the fibrogenic potential of MWCNTs in vivo. Well-dispersed MWCNTs induced more prominent pro-fibrogenic responses in vitro as well as in vivo than non-dispersed tubes.
Figure 10
Figure 10. Multidisciplinary research utilizing a predictive toxicological approach, including ENM libraries, HTS, ecosystem toxicity and computational predictions to facilitate the safe implementation of nanotechnology in the environment in UC CEIN
Figure 11
Figure 11. Wider implementations of predictive toxicological profiling through the use of ENM libraries, high throughput hazard ranking and SARs
The predictive approach assists the logistical planning and execution of costly animal studies that are often required for regulatory decision-making. Once a prediction is established, most analyses can be carried out in vitro.
Figure 12
Figure 12. Use of Fe-doping as a safe-by-design strategy for ZnO nanoparticles
Dissolution and Zn2+ shedding is considered a major mechanism of ZnO toxicity. Iron-doped ZnO nanoparticles were synthesized and shown to exhibit the lower rate of dissolution in biological and environmental media. Cellular HTS and in vivo studies in zebrafish embryos and the rodent lung confirmed a reduction in toxicity by Fe-doping.

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References

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