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 Q2 test and external validation showed that the models were robust. By inspection of R2pred, 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.
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