Development and assessment of new risk assessment methods

Title of Research:
16_PT01-02-3

Construction of prediction models for toxicities induced by drugs
and chemicals based on chemical structures and AOP
Principal Investigator:
Yoshihiro Uesawa
Summary of Research:
Quantitative structure–activity relationship (QSAR) analysis applies structural and quantum
chemical, and physicochemical features, calculated from molecular geometry as explanatory
variables, to predict physiological activity. In current studies, the applications of QSAR prediction
models in a variety of adverse effects such as Delirium, pulmonary edema, nausea/vomiting,respiratory depression, somnolence, rash, diarrhea, loss of appetite, nephritis, uterine cancer ...
etc., have been constructed by databases such as JADER and FAERS (JAPIC-AERS).
Furthermore, data set that includes FAERS and Tox21-AOP/MIE data was constructed andapplied to analyze the relationship between adverse effects and major MIEs such as nuclear
receptors and stress response pathways.
Timeline:
March 1, 2018 – February 28, 2019
Topics:Uesawa, Y., inventor; Meiji Pharmaceutical University, assignee. Prediction systems, prediction
methods, prediction programs, learning model input data generation systems, and learning model
input data generation programs. Pending Japanese patent application 2018-122565(PTC). 2018.
Matsuzaka, Y., Uesawa, Y.; Excellent Poster, CBI Annual Meeting 2018.
Asato, S, Kagaya, H., Uesawa, Y.; Excellent Presenter, The 4th Regulatory science forum foryoung people. 2018.
Publications:
1. Uesawa Y, "Quantitative structure-activity relationship analysis using deep learning based on anovel molecular image input technique", Bioorg Med Chem Lett, 28(20):3400-3403 (2018)
2. Matsuzaka Y, Uesawa Y, "Optimization of a Deep-Learning Method Based on the Classificationof Images Generated by Parameterized Deep Snap, a Novel Molecular-Image-Input Technique forQuantitative Structure–Activity Relationship (QSAR) Analysis", Frontiers in Bioengineering andBiotechnology, in press (2019)
3. Ayano Shiota, Hajime Kagaya, Yoshihiro Uesawa, “Analysis of drug-induced somnolence basedon JADER with chemical structure information”, 138th annual meeting of the pharmaceuticalsociety of Japan, Sendai Japan, 2018/3
4. Yuina Tatsuhiro, Hajime Kagaya, Yoshihiro Uesawa, “Analysis of structural and physicochemical
features of drugs that cause non-cardiogenic pulmonary edema using Japanese Adverse Drug
Event Report database (JADER)”, 138th annual meeting of the pharmaceutical society of Japan,Sendai Japan, 2018/3
5. Yoshiyasu Ozu, Hajime Kagaya, Yoshihiro Uesawa, “Classification of constipation-relateddiseases using Japanese Adverse Drug Event Report database (JADER)”, 138th annual meetingof the pharmaceutical society of Japan, Sendai Japan, 2018/3.
6. Sumino Asato, Hajime Kagaya, Yoshihiro Uesawa, “Construction of a prediction model fornausea and vomiting based on JADER with chemical structures”, 138th annual meeting of thepharmaceutical society of Japan, Sendai Japan, 2018/3
7. Yuka Seta, Hajime Kagaya, Yoshihiro Uesawa, “The analysis on drug-induced delirium using
Japanese Adverse Drug Event Report database with chemical structure information”, 138th
annual meeting of the pharmaceutical society of Japan, Sendai Japan, 2018/3