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Abstracts: Development of an in silico prediction system for the risk assessment of chemicals—development of a prediction model for skin irritation
Author(s) -
Kouzuki Hirokazu
Publication year - 2010
Publication title -
international journal of cosmetic science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.532
H-Index - 62
eISSN - 1468-2494
pISSN - 0142-5463
DOI - 10.1111/j.1468-2494.2010.00583_2.x
Subject(s) - in silico , quantitative structure–activity relationship , artificial neural network , polarizability , moment (physics) , computer science , biological system , molecular descriptor , machine learning , artificial intelligence , data mining , chemistry , physics , biology , molecule , gene , biochemistry , organic chemistry , classical mechanics
pp.254–259 In order to develop a prediction system for the safety of chemicals, many attempts have been made by examining quantitative structure‐activity relationships (QSAR). The results, however, were not always satisfactory enough in view of predictability when it is assumed that they are used in actual situations. In the present study, therefore, we have attempted to develop an in silico prediction system enabling the risk assessment of cosmetic raw materials by combining a molecular orbital calculation method and an artificial neural network system. Human patch test data on 161 samples were collected from a past publication and experiment results in our laboratory. Molecular weight, polarizabilityα, polarizabilityγ, dipole moment, and ionization potential were obtained from molecular orbital calculations as descriptors to predict the skin irritation. In addition, concentration and exposure time were added as descriptors. A neural network system was employed for the analysis. Consequently, by using leave‐one‐out cross‐validation methods, it was shown that the neural network model can predict the positive rate in a human patch test with reasonable accuracy (root mean square error was 0.352). The above results suggest that their combinational use will enable us not only to predict the toxicological potential of cosmetic raw materials but also to make the risk assessment possible.