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

Abstract

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.

A human ether-á-go-go-related (hERG) ion channel atomistic model generated by long supercomputer molecular dynamics simulations and its use in predicting drug cardiotoxicity

“…Most of the previously available hERG computational models are ligand-based but since these rely mainly on the existence of structural similarities between the tested drug candidate and previously reported known hERG blockers, these models have limited value in the frequent case of a novel drug structure (Aronov and Goldman, 2004;Coi et al, 2006;Du-Cuny et al, 2011;Ekins et al, 2002;Keseru, 2003;Song and Clark, 2006;Su et al, 2010;Yoshida and Niwa, 2006). Producing a reliable hERG ion channel structure-based model to directly test drug binding (Broccatelli et al, 2012;Du-Cuny et al, 2011) has therefore been of high priority but in the absence of a hERG crystal structure, this has been limited to creating homology models that have excluded a large portion of the hERG channel and which display very limited conformational flexibility (Boukharta et al, 2011;Di Martino et al, 2013;Farid et al, 2006;Osterberg and Aqvist, 2005).…”

“…In an attempt to improve this challenging situation, computational tools for predicting hERG blockage have been proposed, but so far, these have been limited in their performance due to the use of an incomplete hERG protein structure, the lack of a crystal structure, limitations in computational methods to define it's conformational flexibility, limitations in computational drug-docking methods and the lack of drug-docking algorithms that incorporate both binding energy and binding location. Therefore, an accurate, sensitive and reliable qualitative and quantitative in silico model for hERG blocking remains a high priority (Broccatelli et al, 2012;Du-Cuny et al, 2011).…”

BDDCS Predictions, Self-Correcting Aspects of BDDCS Assignments, BDDCS Assignment Corrections, and Classification for more than 175 Additional Drugs

“…Additionally, BDDCS may predict when certain druginduced toxicities, such as Torsade de Pointes (TdP) (19), drug-induced liver injury (DILI) (20), and anti-epileptic drug cutaneous hypersensitivity (21), may be a clinical concern. BDDCS has linked a major role of intestinal metabolism and intestinal transporters in drug-induced toxicity.…”

QSAR Models for P-Glycoprotein Transport Based on a Highly Consistent Data Set

“…Particularly in this case, multiple external validation sets are necessary to assess the real accuracy of the model. 74 The misclassifications of the 4 most accurate models (NB-MCC-VS+, NB-AUC-VS+, GAkNN-VS+, and RF-VS+) were analyzed by dividing the data set in in four classes: a) "inconsistent" class (including 16 compounds having different Pgp substrate class depending on the reference considered), b) "borderline" class (including 18 compounds having ER in the 1.8−2.5 range), c) "nontransported substrates" class (only the few reported on by Polli et al 17 are considered), and d) "reliable" class (including the compounds not belonging to the other classes).…”