QSAR Investigation on Quinolizidinyl Derivatives in Alzheimer’s Disease

Sets of quinolizidinyl derivatives of bi- and tri-cyclic (hetero) aromatic systems were studied as selective inhibitors. On the pattern, quantitative structure-activity relationship (QSAR) study has been done on quinolizidinyl derivatives as potent inhibitors of acetylcholinesterase in alzheimer’s disease (AD). Multiple linear regression (MLR), partial least squares (PLSs), principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO) were used to create QSAR models. Geometry optimization of compounds was carried out by B3LYP method employing 6–31？G basis set. HyperChem, Gaussian 98？W, and Dragon software programs were used for geometry optimization of the molecules and calculation of the quantum chemical descriptors. Finally, Unscrambler program was used for the analysis of data. In the present study, the root mean square error of the calibration and R2 using MLR method were obtained as 0.1434 and 0.95, respectively. Also, the R and R2 values were obtained as 0.79, 0.62 from stepwise MLR model. The R2 and mean square values using LASSO method were obtained as 0.766 and 3.226, respectively. The root mean square error of the calibration and R2 using PLS method were obtained as 0.3726 and 0.62, respectively. According to the obtained results, it was found that MLR model is the most favorable method in comparison with other statistical methods and is suitable for use in QSAR models. 1. Introduction Alzheimer’s disease (AD) is a debilitating illness with unmet medical needs [1]. The number of people afflicted with the disease worldwide is expected to be triple up to the year 2050 [2]. The multifactorial pathogenesis of AD includes accumulation of aggregates of β-amyloid (Aβ) and tau protein and loss of cholinergic neurons with consequent deficit of the neurotransmitter acetylcholine (ACh) [3, 4]. In advancing AD, AChE levels in the brain are declining [5]. The well-known theory of the quantitative structure-activity relationships (QSARs) [6–8] is based on the hypothesis that the biological activity of a chemical compound is mainly determined by its molecular structure [6]. QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to predict the activity of new compounds from their structures. Today, QSARs are being applied in many disciplines with much emphasis on drug design. Over the years of development, many methods, algorithms, and techniques have been discovered and applied in QSAR studies [9, 10]. To date, QSARs are among the important