QSAR Analysis of the Inhibition of Recombinant CYP 3A4 Activity by Structurally Diverse Compounds Using a Genetic Algorithm-Combined Partial Least Squares Method

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 IC 50 values. Results . The IC 50 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 r pred 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 ( r pred = 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.