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

Abstract

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.