QSAR Modeling and Data Mining Link Torsades de Pointes Risk to the Interplay of Extent of Metabolism, Active Transport, and hERG Liability

We collected 1173 hERG patch clamp (PC) data (IC50)
from the literature to derive twelve classification models for hERG
inhibition, covering a large variety of chemical descriptors and classification
algorithms. Models were generated using 545 molecules and validated
through 258 external molecules tested in PC experiments. We also evaluated
the suitability of the best models to predict the activity of 26 proprietary
compounds tested in radioligand binding displacement (RBD). Results
proved the necessity to use multiple validation sets for a true estimation
of model accuracy and demonstrated that using various descriptors
and algorithms improves the performance of ligand-based models. Intriguingly,
one of the most accurate models uncovered an unexpected link between
extent of metabolism and hERG liability. This hypothesis was fairly
reinforced by using the Biopharmaceutics Drug Disposition Classification
System (BDDCS) that recognized 94% of the hERG inhibitors as extensively
metabolized in vivo. Data mining suggested that high
Torsades de Pointes (TdP) risk results from an interplay of hERG inhibition,
extent of metabolism, active transport, and possibly solubility. Overall,
these new findings might improve both the decision making skills of
pharmaceutical scientists to mitigate hERG liability during the drug
discovery process and the TdP risk assessment during drug development.