Bulletin of the Korean Chemical Society

Korean Chemical Society (대한화학회)
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Prediction of the Toxicity of Dimethylformamide, Methyl Ethyl Ketone, and Toluene Mixtures by QSAR Modeling

  • Kim, Ki-Woong (Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency) ;
  • Won, Yong Lim (Occupational Health Research Department, Occupational Safety and Health Research Institute, KOSHA) ;
  • Hong, Mun Ki (Chemical Safety and Health Research Center, Occupational Safety and Health Research Institute, KOSHA) ;
  • Jo, Jihoon (Chemical Safety and Health Research Center, Occupational Safety and Health Research Institute, KOSHA) ;
  • Lee, Sung Kwang (Department of Chemistry, Hannam University)
  • Received : 2014.08.19
  • Accepted : 2014.10.08
  • Published : 2014.12.20
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Abstract

In this study, we analyzed the toxicity of mixtures of dimethylformamide (DMF) and methyl ethyl ketone (MEK) or DMF and toluene (TOL) and predicted their toxicity using quantitative structure-activity relationships (QSAR). A QSAR model for single substances and mixtures was analyzed using multiple linear regression (MLR) by taking into account the statistical parameters between the observed and predicted $EC_{50}$. After preprocessing, the best subsets of descriptors in the learning methods were determined using a 5-fold cross-validation method. Significant differences in physico-chemical properties such as boiling point (BP), specific gravity (SG), Reid vapor pressure (rVP), flash point (FP), low explosion limit (LEL), and octanol/water partition coefficient (Pow) were observed between the single substances and the mixtures. The $EC_{50}$ of the mixture of DMF and TOL was significantly lower than that of DMF. The mixture toxicity was directly related to the mixing ratio of TOL and MEK (MLR $EC_{50}$ equation = $1.76997-1.12249{\times}TOL+1.21045{\times}MEK$), as well as to SG, VP, and LEL (MLR equation $EC_{50}=15.44388-19.84549{\times}SG+0.05091{\times}VP+1.85846{\times}LEL$). These results show that QSAR-based models can be used to quantitatively predict the toxicity of mixtures used in manufacturing industries.

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