Risk Prediction Method for Anticholinergic Action Using Auto-quantitative Structure–Activity Relationship and Docking Study with Molecular Operating Environment

Materu Yuyama; Takeshi Ito; Yumiko Arai; Yuki Kadowaki; Natsumi Iiyama; Ayako Keino; Yurina Hiraoka; Takayuki Kanaya; Yasuyuki Momose; Masaaki Kurihara

doi:10.1248/cpb.c20-00249

Regular Articles

Risk Prediction Method for Anticholinergic Action Using Auto-quantitative Structure–Activity Relationship and Docking Study with Molecular Operating Environment

Materu Yuyama, Takeshi Ito, Yumiko Arai, Yuki Kadowaki, Natsumi Iiyama, Ayako Keino, Yurina Hiraoka, Takayuki Kanaya, Yasuyuki Momose, Masaaki Kurihara

Author information

Materu Yuyama
Graduate School of Pharmaceutical Sciences, International University of Health and Welfare Graduate School
Takeshi Ito
Graduate School of Pharmaceutical Sciences, International University of Health and Welfare Graduate School
Department of Pharmaceutical Sciences, International University of Health and Welfare
Yumiko Arai
Graduate School of Pharmaceutical Sciences, International University of Health and Welfare Graduate School
Yuki Kadowaki
Department of Pharmaceutical Sciences, International University of Health and Welfare
Natsumi Iiyama
Department of Pharmaceutical Sciences, International University of Health and Welfare
Ayako Keino
Department of Pharmaceutical Sciences, International University of Health and Welfare
Yurina Hiraoka
Department of Pharmaceutical Sciences, International University of Health and Welfare
Takayuki Kanaya
Department of Pharmaceutical Sciences, International University of Health and Welfare
Yasuyuki Momose
Graduate School of Pharmaceutical Sciences, International University of Health and Welfare Graduate School
Department of Pharmaceutical Sciences, International University of Health and Welfare
Masaaki Kurihara Corresponding author
Graduate School of Pharmaceutical Sciences, International University of Health and Welfare Graduate School
Department of Pharmaceutical Sciences, International University of Health and Welfare

Keywords: lower urinary tract symptom, anticholinergic action, in silico, quantitative structure–activity relationship, docking study

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Supplementary material

2020 Volume 68 Issue 8 Pages 773-778

DOI https://doi.org/10.1248/cpb.c20-00249

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Abstract

Lower urinary tract symptoms (LUTS) induced by anticholinergic drug action impair the QOL of patients and are associated with a poor prognosis. Therefore, it is expedient to develop methods of predicting the anticholinergic side effects of drugs, which we aimed to achieve in this study using a quantitative structure–activity relationship (QSAR) and docking study with molecular operations environment (MOE; Molecular Simulation Informatics Systems [MOLSIS], Inc.) In the QSAR simulation, the QSAR model built using the partial least squares regression (PLS) and genetic algorithm-multiple linear regression (GA-MLR) methods showed remarkable coefficient of determination (R²) and XR² values. In the docking study, a specific relationship was identified between the adjusted docking score (-S) and bioactivity (pKi) values. In conclusion, the methods developed could be useful for in silico risk assessment of LUTS, and plans are potentially applicable to numerous drugs with anticholinergic activity that induce serious side effects, limiting their use.

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