Investigation of Antileishmanial Activities of Acridines Derivatives against Promastigotes and Amastigotes Form of Parasites Using Quantitative Structure Activity Relationship Analysis

Abstract

In a search of newer and potent antileishmanial (against promastigotes and amastigotes form of parasites) drug, a series of 60 variously substituted acridines derivatives were subjected to a quantitative structure activity relationship (QSAR) analysis for studying, interpreting, and predicting activities and designing new compounds by using multiple linear regression and artificial neural network (ANN) methods. The used descriptors were computed with Gaussian 03, ACD/ChemSketch, Marvin Sketch, and ChemOffice programs. The QSAR models developed were validated according to the principles set up by the Organisation for Economic Co-operation and Development (OECD). The principal component analysis (PCA) has been used to select descriptors that show a high correlation with activities. The univariate partitioning (UP) method was used to divide the dataset into training and test sets. The multiple linear regression (MLR) method showed a correlation coefficient of 0.850 and 0.814 for antileishmanial activities against promastigotes and amastigotes forms of parasites, respectively. Internal and external validations were used to determine the statistical quality of QSAR of the two MLR models. The artificial neural network (ANN) method, considering the relevant descriptors obtained from the MLR, showed a correlation coefficient of 0.933 and 0.918 with 7-3-1 and 6-3-1 ANN models architecture for antileishmanial activities against promastigotes and amastigotes forms of parasites, respectively. The applicability domain of MLR models was investigated using simple and leverage approaches to detect outliers and outsides compounds. The effects of different descriptors in the activities were described and used to study and design new compounds with higher activities compared to the existing ones.

QSRR Study of Linear Retention Indices for Volatile Compounds using Statistical Methods

“…The relationship obtained using this method corresponds to the linear combination of these descriptors (Table 3) It is observed that the coefficient of correlation r is high and the MSE is low, which makes it's possible to indicate that the model is more reliable. A p-value much smaller than 0.05 indicates that the regression equation is statistically significant, we can conclude, with confidence, that the model provides a significant amount of information 11 .…”

“…Multiple linear regression (MLR) is used to study the relationship between a dependent variable and several independent variables; it minimizes the differences between actual and predicted values and has been used to select the descriptors to be used as inputs in multiple non-linear regression (MNLR) and artificial neural network ANN. Linear and non-linear approaches were used to predict the effects on the linear retention indices, the equations were justified by the correlation coefficient (r), the mean square error (MSE), the fisher value (F) and the significance level (p) 11 . MLR, MNLR and ANN are generated using the SPSS 19.0 statistical package 7 .…”

“…1 of the MLR for the new designed compounds are displayed in Table 8. Only three compounds X 2 , X 7 and X 10 are defined as outliers and consequently they are not be considered, because they have higher leverage which is greater than h* (h*=0.155) 11 .…”

Qsar Approach to Estimating the Analgesic Activity of a Series of Tri-Substituted Pyrimidine Derivatives

“…Principal Component Analysis (PCA) [52] and Hierarchical Ascending Classification (HAC) [53,54], implemented in XLSAT software [55], were employed.…”

QSAR Study of (5-Nitroheteroaryl-1,3,4-thiadiazole-2- yl)piperazinyl Derivatives to Predict New Similar Compounds as Antileishmanial Agents

“…Using these new variables, the dimensionality of the system is reduced with a minimum loss of information 15 . The obtained matrix of coordinates allows us to analyze the dispersion of individuals in the new defined space [16][17][18] . After that, the principal component analysis (PCA) was used to determine the non-linearity and nonmulticollinearity among variables and to select descriptors that correlate with the activity.…”