Skin Sensitization Study by Quantitative Structure-Activity Relationships (QSAR)

• Material Type: 文書・図像類
• Title: Skin Sensitization Study by Quantitative Structure-Activity Relationships (QSAR)
• Author/Editor: SATO, Kazuhiro; UMEMURA, Tomohiro; TAMURA, Tarou; KUSAKA, Yukinori
• Author Heading: SATO, Kazuhiro; UMEMURA, Tomohiro; TAMURA, Tarou; KUSAKA, Yukinori
• Publication, Distribution, etc.: 日本動物実験代替法学会
• Publication Date: 2009-12
• Publication Date (W3CDTF): 2009-12
• Periodical title: Alternatives to Animal Testing and Experimentation
• No. or year of volume/issue: 14 3
• Volume: 14
• Issue: 3
• Pages: 940-946
• ISSN (Periodical Title): ISSN : 1344-0411
• Text Language Code: eng
• Subject Heading: skin sensitization; QSAR; animal study; occupational exposure limit
• Target Audience: 一般
• Note (General): 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. 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. 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. A11 data analyses were performed using ADMEWORKS/ModelBuilder software. 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.
• Source: https://u-fukui.repo.nii.ac.jp/?action=repository_action_common_download&item_id=19901&item_no=1&attribute_id=22&file_no=1
• Access Restrictions: 限定公開
• Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• Original Data Provider (Database): 福井大学 : 福井大学学術機関リポジトリ