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

Amir Najafi¹* and 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: Email: am.najafi@yahoo.com, najafi@iauh.ac.ir; Tel/Fax: +98 811 4494143

Received: 23 September 2013                                                   Revised accepted: 10 October 2013

Tropical Journal of Pharmaceutical Research, December 2013; 12(6): 905-912

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.