Assessment on the effects on ecosystems and the environment
Title of Research:
12_S04-01-2

Development of in Silico Prediction Model on Environmental Fate ofChemical Substances
Principal Investigator:
Tatsuya Takagi, Graduate School of Pharmaceutical Sciences, Osaka University; Genome
Information Research Center, Research Institute for Microbial Diseases, Osaka Univ.
1-6, Yamadaoka, Suita, Osaka, Japan, 565-0871, (tel) +81-6-6879-8243, (e-mail)ttakagi@phs.osaka-u.ac.jp
Collaborators:
Norihito Kawashita, Graduate School of Pharmaceutical Sciences, Osaka University; Genome
Information Research Center, Research Institute for Microbial Diseases, Osaka Univ.
1-6, Yamadaoka, Suita, Osaka, Japan, 565-0871, (tel) +81-6-6879-8244, (e-mail)kawasita@gen-info.osaka-u.ac.jp
Kousuke Okamoto, Graduate School of Pharmaceutical Sciences, Osaka University;
1-6, Yamadaoka, Suita, Osaka, Japan, 565-0871, (tel) +81-6-6879-8242, (e-mail)okamotok@phs.osaka-u.ac.jp
Summary of Research:
In 2009, since the Act on the Evaluation of Chemical Substances and Regulation of Their
Manufacture, etc., was revised, chemical substances which are readily degradable have become
subject to control under the law. Chemical industries are required to examine various physical,chemical, and biological properties of compounds such as toxicity, accumulation behavior, anddegradation property. If there is a procedure which enables us to predict any properties of new orexisting chemical substances without experiments, we are able to plan the efficient order of theexperiments based on the ranking of the predicted properties. Because most existing prediction
models on abiotic degradation, especially photolysis and hydrolysis are targeted to similar
chemical substances, the development of the prediction models on the photolysis and hydrolysis
of more various chemical substances would be contributed to obtain the effective ranking ofcandidates for experimentation.
In this term, the qualitative models for predicting whether a compound is stable tohydrolysis or not were developed. In our procedures for predicting hydrolysis, a predictedcompound is classified to binary evaluation for hydrolysis by these qualitative models at first. If thecompound is not stable to hydrolysis then its half-life is predicted by the quantitative models. andthus a sequence of our procedures for hydrolysis were completed. Additionally, availability domain
in each prediction model was defined. The ground state and excitation states were calculated by
DFT and TD-DFT in all compounds included in the dataset for predicting direct photolysis, andthen five indices were calculated from excitation energies and oscillator strength of the excitation
states. The plots between the index and the category based on half-life of the compounds
suggested the correlation between the two variables and the area under the curve (AUC) values ofthe binary predictions of direct photolysis half-life based on the index were around 0.75. The website for providing our prediction models of the hydrolysis was limitedly opened and the prototype ofweb site which provides the prediction models of the direct photolysis was constructed.
Timeline: Nov 1, 2013 - Feb 28, 2015
Topics:The 3rd Annual Conference of New LRI, “Development of new tool for in silico prediction ofenvironmental fate of compounds”, Aug 29, 2014
Publications:
5th FIP Pharmaceutical Sciences World Congress, Melbourne, Australia April 2014 (Oral session)
The 42th Symposium of Structure-Activity Relationships, Kumamoto, Japan, Nov 2014 (Oralsession)