QSAR analysis of the inhibition of recombinant CYP 3A4 activity by structurally diverse compounds using a genetic algorithm-combined partial least squares method.

ABSTRACT

PURPOSE: To develop a quantitative structure/activity relationship (QSAR) model for predicting drug-CYP 3A4 interactions. METHOD: The inhibitory effect of 53 structurally diverse drugs on the metabolism of 7-benzyloxy-4-trifluoromethyl coumarin (BFC) by recombinant CYP 3A4 was evaluated using a rapid microtiter plate assay. For each drug, a total of 220 two-dimensional topological indices were calculated using Molconn-Z software. Using a genetic algorithm-based partial least squares (GA-PLS) method, the desired descriptors were automatically selected to maximize the predictability of the IC50 values. RESULTS: The IC50 values of the drugs tested ranged from 9 nM to 2 mM. Based on the GA-PLS method, five principal components derived from 20 Molconn-Z descriptors were found to be effective for QSAR modeling. Interestingly, these descriptors suggested that the molecular size would be an important factor in determining drug-CYP 3A4 interactions. In the leave-one-out prediction, the rpred and the standard error of prediction (s) were 0.754 and 0.787, respectively. Even in an external validation, the predictions were in good agreement with experimental values (rpred = 0.744, s = 0.769, n = 9). CONCLUSIONS: The proposed model, in which two-dimensional topological descriptors were used as molecular descriptors, was able to predict drug-CYP 3A4 interactions with reasonable accuracy.