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

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 Q(2) test and external validation showed that the models were robust. By inspection of R-pred(2), 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.