A Respiratory Sensitization Study by a New Quantitative Structure-Activity Relationships (QSAR)

Kazuhiro Sato1, Tomohiro Umemura1, Taro Tamura1, Yukinori Kusaka1, Toshiko Ido2, Kohji Aoyama3, Atsushi Ueda5, Kohichi Harada4, Keiko Minamoto5, Takemi Otsuki5, Kunihiko Yamashita6, Tatsuya Takeshita7, Eiji Shibata8, Kunio Dobashi9, Satomi Kameo10, Muneyuki Miyagawa11, Masaaki Kaniwa12, Takahiko Yoshida13, Tetsuhito Fukushima14 and Kohtaro Yuta15*

1Department of Environmental Health, School of Medicine, University of Fukui, Fukui, Japan, 2Department of Dermatology, School of Medicine, University of Fukui, Fukui, Japan, 3Department of Environmental Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan, 4Department of Environmental Health, Faculty of Medical and Pharmaceutical Sciences, Kumamoto University, Kumamoto, Japan, 5Department of Hygiene, Kawasaki Medical School, Okayama, Japan, 6Daicel Chemical Industries, Ltd., Hyogo, Japan, 7Department of Public Health, School of Medicine, Wakayama Medical University, Wakayama, Japan, 8Department of Hygiene, Aichi Medical University, Aichi, Japan, 9School of Health Sciences, Faculty of Medicine, Gunma University, Gunma, Japan, 10Department of Public Health, Gunma University, Gunma, Japan, 11National Institute of Occupational Safety and Health, Kanagawa, Japan, 12Division of Medical Devices, National Institute of Health Sciences, Tokyo, Japan, 13Department of Health Science, School of Medicine, Asahikawa Medical University, Asahikawa, Japan, 14Department of Hygiene and Preven-tive Medicine, School of Medicine, Fukushima Medical University, Fukushima, Japan, 15Fujitsu Limited, Tokyo, Japan (*present: In Silico Data Ltd., Chiba, Japan)

AATEX 15(3):124-130, 2010

New respiratory sensitization positive/negative prediction models with discriminant functions were generated and parameter analyses were discussed on the basis of QSAR technology. Samples used in this research were selected from the list of "European Chemical Bureau (ECB)": R42, R42/43 for positive samples (respiratory sensitizers) and from the classification results of the Japanese Inter-ministerial Committee for negative respiratory sensitizers (controls). A total of 214 compounds (61 positive sensitizers and 153 negative sensitizers) were used in this study. Parameters were generated from 2-D and 3-D structures of compound. All of the approximately 800 parameters generated were reduced to 12 parameter set by feature selection. Various linear and non-linear discriminant analysis methods were applied using the parameter set. All data analyses were performed using ADMEWORKS/ ModelBuilder software. Perfect classification ratios (100%) were achieved using Iterative Least Squares (ILS) and AdaBoost. The highest prediction ratio of 97.2% by leave-one-out cross-validation was achieved with Support Vector Machine (SVM). This model is applicable to initial prediction of respiratory sensitization.

key words:: respiratory sensitization, quantitative structure-activity relationships (QSAR), animal study


(AATEX: Altern. Animal Test. EXperiment.: Alternatives to Animal Testing and EXperimentation)