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Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches

Title
Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches
Author
윤태현
Keywords
NANO-QSAR; NANOPARTICLES; CYTOTOXICITY; QUALITY; GENOTOXICITY; METAANALYSIS; SIZE
Issue Date
2018-02
Publisher
NATURE PUBLISHING GROUP
Citation
SCIENTIFIC REPORTS, v. 8, Article no. 3141
Abstract
Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.
URI
https://www.nature.com/articles/s41598-018-21431-9http://repository.hanyang.ac.kr/handle/20.500.11754/117462
ISSN
2045-2322
DOI
10.1038/s41598-018-21431-9
Appears in Collections:
COLLEGE OF NATURAL SCIENCES[S](자연과학대학) > CHEMISTRY(화학과) > Articles
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