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 (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.
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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 01/2013; 8(7):e69513. · 3.53 Impact Factor
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ABSTRACT: The search for new tuberculosis treatments continues as we need to find molecules that can act more quickly, be accommodated in multi-drug regimens, and overcome ever increasing levels of drug resistance. Multiple large scale phenotypic high-throughput screens against Mycobacterium tuberculosis (Mtb) have generated dose response data, enabling the generation of machine learning models. These models also incorporated cytotoxicity data and were recently validated with a large external dataset. A cheminformatics data-fusion approach followed by Bayesian machine learning, Support Vector Machine or Recursive Partitioning model development (based on publicly available Mtb screening data) was used to compare individual datasets and subsequent combined models. A set of 1924 commercially available molecules with promising antitubercular activity (and lack of relative cytotoxicity to Vero cells) were used to evaluate the predictive nature of the models. We demonstrate that combining three datasets incorporating antitubercular and cytotoxicity data in Vero cells from our previous screens results in external validation receiver operator curve (ROC) of 0.83 (Bayesian or RP Forest). Models that do not have the highest five-fold cross validation ROC scores can outperform other models in a test set dependent manner. We demonstrate with predictions for a recently published set of Mtb leads from GlaxoSmithKline that no single machine learning model may be enough to identify compounds of interest. Dataset fusion represents a further useful strategy for machine learning construction as illustrated with Mtb. Coverage of chemistry and Mtb target spaces may also be limiting factors for the whole-cell screening data generated to date.Journal of Chemical Information and Modeling 10/2013; · 4.30 Impact Factor
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ABSTRACT: Drug discovery is a highly complex and costly process, and in recent years, the pharmaceutical industry has shifted from traditional to genomics‐ and proteomics‐based drug research strategies. The identification of druggable target sites, promising hits, and high quality leads are crucial steps in the early stages of drug discovery projects. Pharmacokinetic (PK) and drug metabolism profiling to optimize bioavailability, clearance, and toxicity are increasingly important areas to prevent costly failures in preclinical and clinical studies. The integration of a wide variety of technologies and expertise in multidisciplinary research teams combining synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds to pass these hurdles is now commonplace, although there remain challenging areas. Molecular interaction fields (MIFs) are widely used in a range of applications to support the discovery teams, characterizing molecules according to their favorable interaction sites and therefore enabling predictions to be made about how molecules might interact. The utility of MIF‐based in silico approaches in drug design is extremely broad, including approaches to support experimental design in hit‐finding, lead‐optimization, physicochemical property prediction and PK modeling, drug metabolism prediction, and toxicity.Wiley interdisciplinary reviews: Computational Molecular Science. 05/2013; · 5.74 Impact Factor