GENERATION AND VALIDATION OF RAPID COMPUTATIONAL FILTERS FOR CYP2D6 AND CYP3A4
2003
CYP2D6 and CYP3A4 represent two particularly important members of the
cytochrome P450 enzyme family due to their involvement in the metabolism of
many commercially available drugs. Avoiding potent inhibitory interactions
with both of these enzymes is highly desirable in early drug discovery, long
before entering clinical trials. Computational prediction of this liability as
early as possible is desired. Using a commercially available data set of over
1750 molecules to train computer models that were generated with commercially
available software enabled predictions of inhibition for CYP2D6 and CYP3A4,
which were compared with empirical data. The results suggest that using a
recursive partitioning (tree) technique with augmented atom descriptors
enables a statistically significant rank ordering of test-set molecules
(Spearman9s ρ of 0.61 and 0.48 for CYP2D6 and CYP3A4, respectively), which
represents an increased rate of identifying the best compounds when compared
with the random rate. This approach represents a valuable computational filter
in early drug discovery to identify compounds that may have P450 inhibition
liabilities prior to molecule synthesis. Such computational filters offer a
new approach in which lead optimization in silico can occur with virtual
molecules simultaneously tested against multiple enzymes implicated in
drug-drug interactions, with a resultant cost savings from a decreased level
of molecule synthesis and in vitro screening.
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