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

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.