Journal of Environmental Health Sciences (한국환경보건학회지)

Korean Society of Environmental Health (한국환경보건학회)
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QSAR Approach for Toxicity Prediction of Chemicals Used in Electronics Industries

전자산업에서 사용하는 화학물질의 독성예측을 위한 QSAR 접근법

  • Kim, Jiyoung (Samsung Health Research Institute, Samsung Electronics) ;
  • Choi, Kwangmin (Samsung Health Research Institute, Samsung Electronics) ;
  • Kim, Kwansick (Environment Safety Team, Samsung Electronics) ;
  • Kim, Dongil (Department of Occupational Medicine, Sungkyunkwan University College of Medicine)
  • 김지영 (삼성전자 건강연구소) ;
  • 최광민 (삼성전자 건강연구소) ;
  • 김관식 (삼성전자 환경안전팀) ;
  • 김동일 (성균관대학교 의과대학 직업환경의학과)
  • Received : 2014.03.06
  • Accepted : 2014.04.28
  • Published : 2014.04.30
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Abstract

Objectives: It is necessary to apply quantitative structure activity relationship (QSAR) for the various chemicals with insufficient toxicity data that are used in the workplace, based on the precautionary principle. This study aims to find application plan of QSAR software tool for predicting health hazards such as genetic toxicity, and carcinogenicity for some chemicals used in the electronics industries. Methods: Toxicity prediction of 21 chemicals such as 5-aminotetrazole, ethyl lactate, digallium trioxide, etc. used in electronics industries was assessed by Toxicity Prediction by Komputer Assisted Technology (TOPKAT). In order to identify the suitability and reliability of carcinogenicity prediction, 25 chemicals such as 4-aminobiphenyl, ethylene oxide, etc. which are classified as Group 1 carcinogens by the International Agency for Research on Cancer (IARC) were selected. Results: Among 21 chemicals, we obtained prediction results for 5 carcinogens, 8 non-carcinogens and 8 unpredictability chemicals. On the other hand, the carcinogenic potential of 5 carcinogens was found to be low by relevant research testing data and Oncologic TM tool. Seven of the 25 carcinogens (IARC Group 1) were wrongly predicted as non-carcinogens (false negative rate: 36.8%). We confirmed that the prediction error could be improved by combining genetic toxicity information such as mutagenicity. Conclusions: Some compounds, including inorganic chemicals and polymers, were still limited for applying toxicity prediction program. Carcinogenicity prediction may be further improved by conducting cross-validation of various toxicity prediction programs, or application of the theoretical molecular descriptors.

Keywords

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