We collected 1173 hERG patch clamp (PC) data (IC(50)) 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.
[Show abstract][Hide abstract] ABSTRACT: Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDA-required procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure-activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83-0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg).
Current topics in medicinal chemistry 05/2014; 14(11). DOI:10.2174/1568026614666140506124442 · 3.40 Impact Factor
"Indeed, there are also reports that channels including hERG have activities in addition to ion conductance [44–46], indicating that independent molecular targets might converge on common signaling pathways or processes also modulated by hERG and leading to the observed correlation. For example, there is a tendency that hERG inhibitors or LQT-causing drugs are also antagonists of the multidrug resistance transporter (MDR) . Alternatively, hERG may be co-expressed with other channels correlated with oncogenesis which possess similar pharmacological profiles, such as hEAG , thus confounding causal inference of the relationship between hERG activity and gene expression response. "
[Show abstract][Hide abstract] ABSTRACT: Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays.
PLoS ONE 07/2013; 8(7):e69513. DOI:10.1371/journal.pone.0069513 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: P-Glycoprotein (Pgp) is involved in the elimination and in the disposition of a significant portion of marketed drugs. So far, publicly available data sets used for modeling Pgp transport included compounds tested in different assays, different cell lines, and different protocols. In this work, we present a collection of 478 Efflux Ratios (ERs) in MDCK-MDR1 cell lines, and from this collection we define a data set of 187 compounds that were tested in the Borst-derived MDCK-MDR1 cell lines. Of the 23 models resulting from the use of different descriptors, classification algorithms, and variable selection techniques, the 4 most accurate in external validation (∼0.86) are based on VolSurf+ (VS+) descriptors. Two of these models are Naïve Bayes (NB) classifiers using 4 descriptors that were selected through a new technique hereby first time extensively described.
Journal of Chemical Information and Modeling 09/2012; 52(9):2462-70. DOI:10.1021/ci3002809 · 3.74 Impact Factor
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