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- Sato Kazuhiro
- Department of Environmental Health, School of Medicine, University of Fukui
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- Umemura Tomohiro
- Department of Environmental Health, School of Medicine, University of Fukui
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- Tamura Tarou
- Department of Environmental Health, School of Medicine, University of Fukui
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- Kusaka Yukinori
- Department of Environmental Health, School of Medicine, University of Fukui
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- Aoyama Kohji
- Department of Environmental Medicine, Kagoshima University, Graduate School of Medical and Dental Sciences
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- Ueda Atsushi
- Department of Environmental Health, Faculty of Medical and Pharmaceutical Sciences, Kumamoto University
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- Harada Kohichi
- Department of Environmental Health, Faculty of Medical and Pharmaceutical Sciences, Kumamoto University
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- Minamoto Keiko
- Department of Environmental Health, Faculty of Medical and Pharmaceutical Sciences, Kumamoto University
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- Otsuki Takemi
- Department of Hygiene, Kawasaki Medical School
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- Yamashita Kunihiko
- Daicel Chemical Industries, Ltd.
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- Takeshita Tatsuya
- Department of Public Health, School of Medicine, Wakayama Medical University
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- Shibata Eiji
- Department of Hygiene, Aichi Medical University
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- Dobashi Kunio
- School of Health Sciences, Faculty of Medicine, Gunma University
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- Kameo Satomi
- Department of Public Health, Gunma University
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- Miyagawa Muneyuki
- National Institute of Occupational Safety and Health
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- Kaniwa Masaaki
- Division of Medical Devices, National Institute of Health Sciences
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- Endo Yoko
- Research Center for Occupational Poisoning, Tokyo Rosai Hospital
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- Yuta Kohtaro
- Fujitsu Limited, Tokyo, Japan (present: Research Center for Environmental Risk, National Institute for Environmental Studies)
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抄録
In silico assessment of skin sensitization is increasingly needed owing to the problems concerning animal welfare, as well as excessive time consumed and cost involved in the development and testing of new chemicals. Skin sensitization positive/negative prediction models with discriminant function were generated and parameter analysis was discussed on the basis of QSAR technology.<br>Samples used in this research were selected from the list of "Maximale Arbeitsplatz-Konzentration" (MAK) and "Biologischer Arbeitsstoff-Toleranz-Wert" (BAT) values 2008, Deutschen Forschungsgemeinschaft (DFG) for positive samples (skin sensitizers) and from the classification results of the Japanese Globally Harmonized System of Classification and Labeling of Chemicals (GHS) Inter-ministerial Committee of the National Institute for Technology and Evaluation for negative skin sensitizers (controls). A total of 291 compounds (122 positive sensitizers and 169 negative sensitizers) were used in this study.<br>Parameters were generated from 2-D and 3-D structures of compounds. All of the approximately 800 parameters generated were reduced to 47 parameter sets and 32 parameter sets by feature selection. Various linear and non-linear discriminant analysis methods were applied using 2 parameter sets. All data analyses were performed using ADMEWORKS/ModelBuilder software.<br>Perfect classification ratios (100%) were achieved using Support Vector Machine and AdaBoost for 32 parameters. The highest prediction ratio of 81.44% by Leave-Ten-Out Cross-Validation was achieved with Neutral Network for 47 parameter sets. Log P was not found to be important. This is the first QSAR model for skin sensitization from Japan. Future studies of this QSAR model are needed to improve its efficacy.
収録刊行物
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- Alternatives to Animal Testing and Experimentation
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Alternatives to Animal Testing and Experimentation 14 (3), 940-946, 2009
日本動物実験代替法学会
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詳細情報 詳細情報について
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- CRID
- 1390001205234950656
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- NII論文ID
- 10026777817
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- NII書誌ID
- AA11161787
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- ISSN
- 21854726
- 13440411
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可