RS-Predictor Models Augmented with SMARTCyp Reactivities: Robust Metabolic Regioselectivity Predictions for Nine CYP Isozymes

RS-Predictor is a tool for creating pathway-independent,
isozyme-specific,
site of metabolism (SOM) prediction models using any set of known
cytochrome P450 (CYP) substrates and metabolites. Until now, the RS-Predictor
method was only trained and validated on CYP 3A4 data, but in the
present study, we report on the versatility the RS-Predictor modeling
paradigm by creating and testing regioselectivity models for substrates
of the nine most important CYP isozymes. Through curation of source
literature, we have assembled 680 substrates distributed among CYPs
1A2, 2A6, 2B6, 2C19, 2C8, 2C9, 2D6, 2E1, and 3A4, the largest publicly
accessible collection of P450 ligands and metabolites released to
date. A comprehensive investigation into the importance of different
descriptor classes for identifying the regioselectivity mediated by
each isozyme is made through the generation of multiple independent
RS-Predictor models for each set of isozyme substrates. Two of these
models include a density functional theory (DFT) reactivity descriptor
derived from SMARTCyp. Optimal combinations of RS-Predictor and SMARTCyp
are shown to have stronger performance than either method alone, while
also exceeding the accuracy of the commercial regioselectivity prediction
methods distributed by Optibrium and Schrödinger, correctly
identifying a large proportion of the metabolites in each substrate
set within the top two rank-positions: 1A2 (83.0%), 2A6 (85.7%), 2B6
(82.1%), 2C19 (86.2%), 2C8 (83.8%), 2C9 (84.5%), 2D6 (85.9%), 2E1
(82.8%), 3A4 (82.3%), and merged (86.0%). Comprehensive datamining
of each substrate set and careful statistical analyses of the predictions
made by the different models revealed new insights into molecular
features that control metabolic regioselectivity and enable accurate
prospective prediction of likely SOMs.