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Nanomaterial Toxicity Testing in the 21st Century: Use of a Predictive Toxicological Approach and High-Throughput Screening
Open accessAbstract
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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Citations
Cites
Toxic potential of materials at the nanolevel
Nel, A., Xia, T., Mädler, L., & Li, N. (2006) · Science · doi:10.1126/science.1114397 · Cited by 9,324
Self-citationUnderstanding biophysicochemical interactions at the nano–bio interface
Nel, A. E., Mädler, L., Velegol, D., Xia, T., Hoek, E. M. V., Somasundaran, P., Klaessig, F., Castranova, V., & Thompson, M. (2009) · Nature Materials · doi:10.1038/nmat2442 · Cited by 7,083
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Self-citationPotential Health Impact of Nanoparticles
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Self-citationA Predictive Toxicological Paradigm for the Safety Assessment of Nanomaterials
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Self-citationHigh Content Screening in Zebrafish Speeds up Hazard Ranking of Transition Metal Oxide Nanoparticles
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Self-citationClassification NanoSAR Development for Cytotoxicity of Metal Oxide Nanoparticles
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Self-citationRalló, R., France, B., Liu, R., Nair, S. S., George, S., Damoiseaux, R. D., Giralt, F., Nel, A. E., Bradley, K. A., & Cohen, Y. (2011) · Environmental Science & Technology · doi:10.1021/es103606x · Cited by 86
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Self-citationThe University of California Center for the Environmental Implications of Nanotechnology
Godwin, H. A., Chopra, K., Bradley, K. A., Cohen, Y., Harthorn, B. H., Hoek, E. M. V., Holden, P. A., Keller, A. A., Lenihan, H. S., Nisbet, R. M., et al. (2009) · Environmental Science & Technology · doi:10.1021/es8034544 · Cited by 77
Self-citationLiu, R., Lin, S., Ralló, R., Zhao, Y., Damoiseaux, R. D., Xia, T., Lin, S., Nel, A. E., & Cohen, Y. (2012) · PLoS ONE · doi:10.1371/journal.pone.0035014 · Cited by 68
Self-citation
Cited by
Hua, S., de Matos, M. B. C., Metselaar, J. M., & Storm, G. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00790 · Cited by 1,048
GDNF, NGF and BDNF as Therapeutic Options for Neurodegeneration
Allen, S. J., Watson, J. J., Shoemark, D. K., Barua, N. U., & Patel, N. K. (2013) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2013.01.004 · Cited by 820
Domingues, C., Santos, A., Alvarez-Lorenzo, C., Concheiro, A., Jarak, I., Veiga, F., Barbosa, I., Dourado, M., & Figueiras, A. (2022) · ACS Nano · doi:10.1021/acsnano.2c00128 · Cited by 235
Wiemann, M., Vennemann, A., Sauer, U. G., Wiench, K., Ma-Hock, L., & Landsiedel, R. (2016) · Journal of Nanobiotechnology · doi:10.1186/s12951-016-0164-2 · Cited by 152
Rasmussen, K., González, M., Kearns, P., Sintes, J. R., Rossi, F., & Sayre, P. (2016) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2015.11.004 · Cited by 150
Metadata
- Authors
- Nel, A., Xia, T., Meng, H., Wang, X., Lin, S., Ji, Z., & Zhang, H.
- Venue
- Accounts of Chemical Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-06-07
- Volume
- 46
- Issue
- 3
- Pages
- 607-621
- ISSN
- 0001-4842 (ISSN-L)
- 1520-4898
- OpenAlex
- W2049426807
- Cited by
- 579
- References
- 19
- FWCI
- 20.71
- Type
- journal article
- Language
- en
- Open access
- green
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