Toxicity classification of oxide nanomaterials : effects of data gap filling and PChem score-based screening approaches

Ha, My Kieu; Trinh, Tung Xuan; Choi, Jang Sik; Maulina, Desy; Byun, Hyung Gi; Yoon, Tae Hyun

doi:10.1038/S41598-018-21431-9

Title

Toxicity classification of oxide nanomaterials : effects of data gap filling and PChem score-based screening approaches

Author

Ha, My Kieu

Trinh, Tung Xuan

Choi, Jang Sik

Maulina, Desy

Byun, Hyung Gi

Yoon, Tae Hyun

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.

Language

English

Source (journal)

Scientific reports. - London, 2011, currens

Publication

London : Nature Publishing Group , 2018

ISSN

2045-2322

DOI

10.1038/S41598-018-21431-9

Volume/pages

8 (2018) , 11 p.