전자산업에서 사용하는 화학물질의 독성예측을 위한 QSAR 접근법
QSAR Approach for Toxicity Prediction of Chemicals Used in Electronics Industries
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초록
Objectives: It is necessary to apply quantitative structure activity relationship (QSAR) for the various chemicalswith insufficient toxicity data that are used in the workplace, based on the precautionary principle. This studyaims to find application plan of QSAR software tool for predicting health hazards such as genetic toxicity, andcarcinogenicity 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 forResearch on Cancer (IARC) were selected. Results: Among 21 chemicals, we obtained prediction results for 5 carcinogens, 8 non-carcinogens and 8unpredictability chemicals. On the other hand, the carcinogenic potential of 5 carcinogens was found to be lowby relevant research testing data and Oncologic TM tool. Seven of the 25 carcinogens (IARC Group 1) werewrongly predicted as non-carcinogens (false negative rate: 36.8%). We confirmed that the prediction error couldbe improved by combining genetic toxicity information such as mutagenicity. Conclusions: Some compounds, including inorganic chemicals and polymers, were still limited forapplying toxicity prediction program. Carcinogenicity prediction may be further improved by conductingcross-validation of various toxicity prediction programs, or application of the theoretical moleculardescriptors.
Abstract
Objectives: It is necessary to apply quantitative structure activity relationship (QSAR) for the various chemicalswith insufficient toxicity data that are used in the workplace, based on the precautionary principle. This studyaims to find application plan of QSAR software tool for predicting health hazards such as genetic toxicity, andcarcinogenicity 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 forResearch on Cancer (IARC) were selected. Results: Among 21 chemicals, we obtained prediction results for 5 carcinogens, 8 non-carcinogens and 8unpredictability chemicals. On the other hand, the carcinogenic potential of 5 carcinogens was found to be lowby relevant research testing data and Oncologic TM tool. Seven of the 25 carcinogens (IARC Group 1) werewrongly predicted as non-carcinogens (false negative rate: 36.8%). We confirmed that the prediction error couldbe improved by combining genetic toxicity information such as mutagenicity. Conclusions: Some compounds, including inorganic chemicals and polymers, were still limited forapplying toxicity prediction program. Carcinogenicity prediction may be further improved by conductingcross-validation of various toxicity prediction programs, or application of the theoretical moleculardescriptors.
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