• Journal of Environmental Health Sciences
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• v.45 no.5
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• pp.457-464
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• 2019

Objectives: Quantitative structure-activity relationship (QSAR) is one of the effective alternatives to animal testing, but its credibility in terms of toxicity prediction has been questionable. Thus, this work aims to evaluate its predictive capacity and find ways of improving its credibility. Methods: Using $TOPKAT^{(R)}$, OECD toolbox, and $Derek^{(R)}$, all of which have been applied world-wide in the research, industrial, and regulatory fields, an analysis of prediction credibility markers including accuracy (A), sensitivity (S), specificity (SP), false negative (FN), and false positive (FP) was conducted. Results: The multi-application of QSARs elevated the precision credibility relative to individual applications of QSARs. Moreover, we found that the type of chemical structure affects the credibility of markers significantly. Conclusions: The credibility of individual QSAR is insufficient for both the prediction of chemical toxicity and regulation of hazardous chemicals. Thus, to increase the credibility, multi-QSAR application, and compensation of the prediction deviation by chemical structure are required.

• The Korean Journal of Pesticide Science
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• v.6 no.1
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• pp.25-30
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• 2002

Quantitative structure-toxicity relationships (QSTRs) between various physicochemical parameters of substituents in new herbicidal N-phenyl-3,4-dimethylphthalimide derivatives and their discriminate score (DS) for chronic and acute toxicities against mouse and rat evaluated using TOPKAT calculation were discussed quantitatively. From the basis on the findings, it was shown that carcinogenicities of female was higher than that of male and mouse had higher tendency than rat. The STR analyses results of Hansch-Fujita type equations suggested that mouse (female & male) and rat male except rat female are dependent on LUMO energy commonly in carcinogenicity. The selective carcinogenicity factor of two species between male mouse and female mouse is dependent on optimal value (ca. $(L)_{opt.}=5.0{\AA}$) for length of $R_2$-substituent mainly. According to Free-Wilson approach, in the case of rat male, alkyl and aryl substituents were superior and in the other case, contribution of fluoro group substituents were superior to chronic toxicity.

• Environmental Mutagens and Carcinogens
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• v.20 no.1
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• pp.21-25
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• 2000

Recently there is increasing interest in the use of structure activity relationships for predicting the biological activity of chemicals. The reasons for the interest include the decrease cost and time per chemical as compared with animal or cell system for identifying toxicological effects of chemicals and the reduction in the use of animals for toxicological testing. This study is to test the validity of the mutagenicity data generated from QSAR (Quantitative Structure Activity Relationship) program. Thirty chemicals, which had been evaluated by Ames test during 1997-1999, were assessed with TOPKAT QSAR mutagenicity prediction module. Among 30chemicals experimented, 28 were negative and 2 were positive for Ames test. On the contrary, 23 chemicals showed the high confidence level indicating high prediction rate in mutagenicity evaluation, and 7 chemicals showed the lsow to moderate confidence level indicating low prediction in mutagenicity evaluation. Overall mutagenicity prediction rate was 77% (23/30). The prediction rates for non-mutagenic chemicals were 79% (22/28) and mutagenic chemicals were 50% (1/2). QSAR could be a useful tool in providing toxicological data for newly introduced chemicals or in furnishing data for MSDS or in determining the dose in toxicity testing for chemicals with no known toxicological data.

• Journal of Environmental Health Sciences
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• v.48 no.3
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• pp.159-166
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• 2022

Background: A quantitative structure-activity relationship (QSAR) model was adopted in the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH, EU) regulations as well as the Act on Registration, Evaluation, etc. of Chemicals (AREC, Republic of Korea). It has been previously used in the registration of chemicals. Objectives: In this study, we investigated the correlation between the predicted data provided by three prediction programs using a QSAR model and actual experimental results (acute fish, daphnia magna toxicity). Through this approach, we aimed to effectively conjecture on the performance and determine the most applicable programs when designating toxic substances through the AREC. Methods: Chemicals that had been registered and evaluated in the Toxic Chemicals Control Act (TCCA, Republic of Korea) were selected for this study. Two prediction programs developed and operated by the U.S. EPA - the Ecological Structure-Activity Relationship (ECOSAR) and Toxicity Estimation Software Tool (T.E.S.T.) models - were utilized along with the TOPKAT (Toxicity Prediction by Komputer Assisted Technology) commercial program. The applicability of these three programs was evaluated according to three parameters: accuracy, sensitivity, and specificity. Results: The prediction analysis on fish and daphnia magna in the three programs showed that the TOPKAT program had better sensitivity than the others. Conclusions: Although the predictive performance of the TOPKAT program when using a single predictive program was found to perform well in toxic substance designation, using a single program involves many restrictions. It is necessary to validate the reliability of predictions by utilizing multiple methods when applying the prediction program to the regulation of chemicals.

• Journal of Environmental Health Sciences
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• v.40 no.2
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• pp.105-113
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• 2014

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.

• Korean Journal of Agricultural Science
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• v.28 no.1
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• pp.33-40
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• 2001

The authors attempted to derive a comprehensive quantitative structure-toxicity relationships (QSTRs) between various physicochemical parameters of phenyl substituents in fungicidal phenylthionocarbamate derivatives and toxicity evaluated using TOPKAT calculation. On the basis of this approach we made preditions for toxicity values for not yet tested substances with respect to these systems. The results suggested that the optimal values, $(B_2)_{opt.}=1.54_{\AA}$(Ames mutagenicity), $(R)_{opt.}=0.16$ (car-cinogenicity of male rat), $(\pi)_{opt.)=0.16$ (carcinogenicity of male mouse), $({\varepsilon}LOMO)_{opt}=-0.52e.v.$ ($LD_{50}$ of rat oral), $(B_3){opt.}=1.54_{\AA}$(chronic LOAEU), $(logP)_{opt.}=4.25$ ($LC_{50}$ of Fathead minnow) and $({\sigma})_{opt}=-0.68$ ($EC_{50}$ of Daphnia magna) of phenyl substituents were strongly correlated with the acute and chronic toxicities.