Tropical Journal of Pharmaceutical Research

Original Research Article | OPEN ACCESS

Multivariate Modeling of Cytochrome P450 Enzymes for 4-Aminoquinoline Antimalarial Analogues using Genetic-Algorithms Multiple Linear Regression

Amir Najafi¹ , Soheil Sobhanardakani²

¹Young Researchers and Elite Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran; ²Department of the Environment, Hamedan Branch, Islamic Azad University, Hamedan, Iran.

For correspondence:- Amir Najafi Email: am.najafi@yahoo.com Tel:+988114494143

Received: 23 September 2013 Accepted: 10 October 2013 Published: 24 December 2013

Citation: Najafi A, Sobhanardakani S. Multivariate Modeling of Cytochrome P450 Enzymes for 4-Aminoquinoline Antimalarial Analogues using Genetic-Algorithms Multiple Linear Regression. Trop J Pharm Res 2013; 12(6):905-912 doi: 10.4314/tjpr.v12i6.7

© 2013 The authors.
This is an Open Access article that uses a funding model which does not charge readers or their institutions for access and distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0) and the Budapest Open Access Initiative (http://www.budapestopenaccessinitiative.org/read), which permit unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited..

Abstract

Purpose: To develop QSAR modeling of the inhibition of cytochrome P450s (CYPs) by chloroquine and a new series of 4-aminoquinoline derivatives in order to obtain a set of predictive in-silico models using genetic algorithms-multiple linear regression (GA-MLR) methods.

Methods: Austin model 1 (AM1) semi-empirical quantum chemical calculation method was used to find the optimum 3D geometry of the studied molecules. The relevant molecular descriptors were selected by genetic algorithm-based multiple linear regression (GA-MLR) approach. In silico predictive models were generated to predict the inhibition of CYP 2B6, 2C9, 2C19, 2D6, and 3A4 isoforms using a set of descriptors.

Results: The results obtained demonstrate that our model is capable of predicting the potential of new drug candidates to inhibit multiple CYP isoforms. A cross-validated Q² test and external validation showed that the models were robust. By inspection of R²_pred, and RMSE test sets, it can be seen that the predictive ability of the different CYP models varies considerably.

Conclusion: Apart from insights into important molecular properties for CYP inhibition, the findings may also guide further investigations of novel drug candidates that are unlikely to inhibit multiple CYP sub-types.

Keywords: Antimalarial, Chloroquine, Cytochrome P450, Genetic algorithm-based multiple linear regression, QSAR