Journal
CHEMOSPHERE
Volume 217, Issue -, Pages 243-249Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chemosphere.2018.11.014
Keywords
metal oxide nanomaterial; BEAS-2B; HaCaT; Cell viability; Quasi-QSAR; Quasi-SMILES
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Funding
- Industrial Strategic Technology Development Program - Ministry of Trade, Industry and Energy (MOTIE) of Korea [10043929]
- Bio & Medical Technology Development Program of the National Research Foundation (NRF) - Ministry of Science ICT [2017M3A9G8084539]
- Korea Evaluation Institute of Industrial Technology (KEIT) [10043929] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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A quasi-QSAR model was developed to predict the cell viability of human lung (BEAS-2B) and skin (HaCaT) cells exposed to 21 types of metal oxide nanomaterials. A wide range of toxicity datasets obtained from the S2NANO (www.s2nano.org ) database was used. The data of descriptors representing the physicochemical properties and experimental conditions were coded to quasi-SMILES. In particular, hierarchical cluster analysis (HCA) and min-max normalization method were respectively used in assigning alphanumeric codes for numerical descriptors (e.g., core size, hydrodynamic size, surface charge, and dose) and then quasi-QSAR model performances for both methods were compared. The quasi-Q$AR models were developed using CORAL software (www.insilico.euicoral). Quasi-QSAR model built using quasi-SMILES generated by means of HCA showed better performance than the min-max normalization method. The model showed satisfactory statistical results (R-adj(2) for the training dataset: 0.71-0.73; R-adj(2) for the calibration dataset: 0.74-0.82; and R-adj(2) for the validation dataset: 0.70-0.76). (C) 2018 Elsevier Ltd. All rights reserved.
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