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dc.contributor.authorChen, Guangchao
dc.contributor.authorPeijnenburg, Willie
dc.contributor.authorXiao, Yinlong
dc.contributor.authorVijver, Martina G
dc.date.accessioned2018-01-03T13:29:39Z
dc.date.available2018-01-03T13:29:39Z
dc.date.issued2017-07-12
dc.identifier.citationCurrent Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials. 2017, 18 (7) Int J Mol Scien
dc.identifier.issn1422-0067
dc.identifier.pmid28704975
dc.identifier.doi10.3390/ijms18071504
dc.identifier.urihttp://hdl.handle.net/10029/621010
dc.description.abstractAs listed by the European Chemicals Agency, the three elements in evaluating the hazards of engineered nanomaterials (ENMs) include the integration and evaluation of toxicity data, categorization and labeling of ENMs, and derivation of hazard threshold levels for human health and the environment. Assessing the hazards of ENMs solely based on laboratory tests is time-consuming, resource intensive, and constrained by ethical considerations. The adoption of computational toxicology into this task has recently become a priority. Alternative approaches such as (quantitative) structure-activity relationships ((Q)SAR) and read-across are of significant help in predicting nanotoxicity and filling data gaps, and in classifying the hazards of ENMs to individual species. Thereupon, the species sensitivity distribution (SSD) approach is able to serve the establishment of ENM hazard thresholds sufficiently protecting the ecosystem. This article critically reviews the current knowledge on the development of in silico models in predicting and classifying the hazard of metallic ENMs, and the development of SSDs for metallic ENMs. Further discussion includes the significance of well-curated experimental datasets and the interpretation of toxicity mechanisms of metallic ENMs based on reported models. An outlook is also given on future directions of research in this frontier.
dc.language.isoenen
dc.rightsArchived with thanks to International journal of molecular sciencesen
dc.titleCurrent Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials.en
dc.typeArticleen
dc.identifier.journalInt J Mol Sci 2017, 18(7):E1504en
html.description.abstractAs listed by the European Chemicals Agency, the three elements in evaluating the hazards of engineered nanomaterials (ENMs) include the integration and evaluation of toxicity data, categorization and labeling of ENMs, and derivation of hazard threshold levels for human health and the environment. Assessing the hazards of ENMs solely based on laboratory tests is time-consuming, resource intensive, and constrained by ethical considerations. The adoption of computational toxicology into this task has recently become a priority. Alternative approaches such as (quantitative) structure-activity relationships ((Q)SAR) and read-across are of significant help in predicting nanotoxicity and filling data gaps, and in classifying the hazards of ENMs to individual species. Thereupon, the species sensitivity distribution (SSD) approach is able to serve the establishment of ENM hazard thresholds sufficiently protecting the ecosystem. This article critically reviews the current knowledge on the development of in silico models in predicting and classifying the hazard of metallic ENMs, and the development of SSDs for metallic ENMs. Further discussion includes the significance of well-curated experimental datasets and the interpretation of toxicity mechanisms of metallic ENMs based on reported models. An outlook is also given on future directions of research in this frontier.


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