Open Access Original

2D and 3D-QSAR study on 4-anilinoquinozaline derivatives as potent apoptosis inducer and efficacious anticancer agent

Vivek K Vyas*, Manjunath Ghate and Hitesh Katariya

Author Affiliations

Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University, S.G. Highway, Chharodi, Ahmedabad 382 481, Gujarat, India

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Organic and Medicinal Chemistry Letters 2011, 1:13 doi:10.1186/2191-2858-1-13

Published: 4 October 2011

Abstract

Background

Apoptosis is known as programmed cell death that plays an important role in tumor biology.

Methods

In this study, apoptosis-inducing activity is predicted by using a QSAR modeling approach for a series of 4-anilinoquinozaline derivatives. 2D-QSAR model for the prediction of apoptosis-inducing activity was obtained by applying multiple linear regression giving r2 = 0.8225 and q2 = 0.7626, principal component regression giving r2 = 0.7539 and q2 = 0.6669 and partial least squares giving r2 = 0.8237 and q2 = 0.6224.

Results

QSAR study revealed that alignment-independent descriptors and distance-based topology index are the most important descriptors in predicting apoptosis-inducing activity. 3D-QSAR study was performed using k-nearest neighbor molecular field analysis (kNN-MFA) approach for both electrostatic and steric fields. Three different kNN-MFA 3D-QSAR methods (SW-FB, SA, and GA) were used for the development of models and tested successfully for internal (q2 > 0.62) and external (predictive r2 > 0.52) validation criteria. Thus, 3D-QSAR models showed that electrostatic effects dominantly determine the binding affinities.

Conclusions

The QSAR models developed in this study would be useful for the development of new apoptosis inducer as anticancer agents.

Keywords:
4-anilinoquinozaline; apoptosis inducer; anticancer; QSAR; multiple linear regression (MLR); principle component analysis (PCA); partial least square (PLS); k-nearest neighbor molecular field analysis (kNN-MFA)