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- W2335367021 abstract "Because a variety of human-related activities, engineer-ed nanoparticles (ENMs) may be released to various environmental media and may cross environmental boundaries, and thus will be found in most media. Therefore, the potential environmental impacts of ENMs must be assessed from a multimedia perspective and with an integrated risk management approach that considers rapid developments and increasing use of new nanomaterials. Accordingly, this Account presents a rational process for the integration of in silico ENM toxicity and fate and transport analyses for environmental impact assessment. This approach requires knowledge of ENM toxicity and environmental exposure concentrations. Considering the large number of current different types of ENMs and that those numbers are likely to increase, there is an urgent need to accelerate the evaluation of their toxicity and the assessment of their potential distribution in the environment. Developments in high throughput screening (HTS) are now enabling the rapid generation of large data sets for ENM toxicity assessment. However, these analyses require the establishment of reliable toxicity metrics, especially when HTS includes data from multiple assays, cell lines, or organisms. Establishing toxicity metrics with HTS data requires advanced data processing techniques in order to clearly identify significant biological effects associated with exposure to ENMs. HTS data can form the basis for developing and validating in silico toxicity models (e.g., quantitative structure-activity relationships) and for generating data-driven hypotheses to aid in establishing and/or validating possible toxicity mechanisms. To correlate the toxicity of ENMs with their physicochemical properties, researchers will need to develop quantitative structure-activity relationships for nanomaterials (i.e., nano-SARs). However, as nano-SARs are applied in regulatory applications, researchers must consider their applicability and the acceptance level of false positive relative to false negative predictions and the reliability of toxicity data. To establish the environmental impact of ENMs identified as toxic, researchers will need to estimate the potential level of environmental exposure concentration of ENMs in the various media such as air, water, soil, and vegetation. When environmental monitoring data are not available, models of ENMs fate and transport (at various levels of complexity) serve as alternative approaches for estimating exposure concentrations. Risk management decisions regarding the manufacturing, use, and environmental regulations of ENMs would clearly benefit from both the assessment of potential ENMs exposure concentrations and suitable toxicity metrics. The decision process should consider the totality of available information: quantitative and qualitative data and the analysis of nanomaterials toxicity, and fate and transport behavior in the environment. Effective decision-making to address the potential impacts of nanomaterials will require considerations of the relevant environmental, ecological, technological, economic, and sociopolitical factors affecting the complete lifecycle of nanomaterials, while accounting for data and modeling uncertainties. Accordingly, researchers will need to establish standardized data management and analysis tools through nanoinformatics as a basis for the development of rational decision tools." @default.
- W2335367021 created "2016-06-24" @default.
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- W2335367021 date "2012-11-08" @default.
- W2335367021 modified "2023-09-28" @default.
- W2335367021 title "<i>In Silico</i> Analysis of Nanomaterials Hazard and Risk" @default.
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- W2335367021 cites W1911206369 @default.
- W2335367021 cites W1965847248 @default.
- W2335367021 cites W1969896692 @default.
- W2335367021 cites W1978116785 @default.
- W2335367021 cites W1988061479 @default.
- W2335367021 cites W1988973871 @default.
- W2335367021 cites W1990517717 @default.
- W2335367021 cites W1991354265 @default.
- W2335367021 cites W2000662470 @default.
- W2335367021 cites W2010516376 @default.
- W2335367021 cites W2017075650 @default.
- W2335367021 cites W2025001457 @default.
- W2335367021 cites W2031441006 @default.
- W2335367021 cites W2032684570 @default.
- W2335367021 cites W2033192435 @default.
- W2335367021 cites W2036661308 @default.
- W2335367021 cites W2049774671 @default.
- W2335367021 cites W2056660923 @default.
- W2335367021 cites W2058922474 @default.
- W2335367021 cites W2062570963 @default.
- W2335367021 cites W2063252292 @default.
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- W2335367021 cites W2089833889 @default.
- W2335367021 cites W2090801846 @default.
- W2335367021 cites W2094862794 @default.
- W2335367021 cites W2097449851 @default.
- W2335367021 cites W2100049001 @default.
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- W2335367021 doi "https://doi.org/10.1021/ar300049e" @default.
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