Article

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

Laboratory for Chemometrics and Cheminformatics, Chemistry Department, University of Perugia, Via Elce di Sotto 10, I-06123 Perugia, Italy.
Molecular Pharmaceutics (Impact Factor: 4.38). 06/2012; 9(8). DOI: 10.1021/mp300156r
Source: PubMed

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.

0 Followers
 · 
12 Reads
  • Source
    • "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). We now report an atomistic hERG model that incorporates all of the previously missing elements described above and which captures realistic modes of binding for known hERG blockers with a ranking of their hERG-blockade activity. "

    Full-text · Dataset · Dec 2015
    • "The development of such models is further hampered by the low quality of training data extracted from bibliographic sources, due to the large disparity of the experimental conditions (e.g., cell line, temperature, pH) used in the original experiments. The ABCB1 dataset (Broccatelli et al. 2012) contains 562 inhibitors and 515 non-inhibitors of ABCB1. ABCB1, also known as P-glycoprotein, is a membrane protein member of the ATP-binding cassette (ABC) transporters superfamily , which transports a variety of compounds through the membrane against a concentration gradient (Choudhuri and Klaassen 2006). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Most computational methods used for the prediction of toxicity endpoints are based on the assumption that similar compounds have similar biological properties. This principle can be exploited using computational methods like read across or quantitative structure-activity relationships. However, there is no general agreement about which method is the most appropriate for quantifying compound similarity neither for exploiting the similarity principle in order to obtain reliable estimations of the compound properties. Moreover, optimal similarity metrics and modeling methods might depend on the characteristics of the endpoints and training series used in each case. This study describes a comparative analysis of the predictive performance of diverse similarity metrics and modeling methods in toxicological applications. A collection of two quantitative (n = 660, n = 1114) and three qualitative (n = 447, n = 905, n = 1220) datasets representing very different endpoints of interest in drug safety evaluation and rigorous methods were used to estimate the external predictive ability in each case. The results confirm that no single approach produces the best results in all instances, and the best predictions were obtained using different tools in different situations. The trends observed in this study were exploited to propose a unifying strategy allowing the use of the most suitable method for every compound. A comparison of the quality of the predictions obtained by the unifying strategy with those obtained by standard prediction methods confirmed the usefulness of the proposed approach.
    No preview · Article · Nov 2015 · Archives of Toxicology
  • Source
    • "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). We now report an atomistic hERG model that incorporates all of the previously missing elements described above and which captures realistic modes of binding for known hERG blockers with a ranking of their hERG-blockade activity. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Acquired cardiac long QT syndrome (LQTS) is a frequent drug-induced toxic event that is often caused through blocking of the human ether-a-go-go-related (hERG) K(+) ion channel. This has led to the removal of several major drugs post-approval and is a frequent cause of termination of clinical trials. We report here a computational atomistic model derived using long molecular dynamics that allows sensitive prediction of hERG blockage. It identified drug-mediated hERG blocking activity of a test panel of 18 compounds with high sensitivity and specificity and was experimentally validated using hERG binding assays and patch clamp electrophysiological assays. The model discriminates between potent, weak, and non-hERG blockers and is superior to previous computational methods. This computational model serves as a powerful new tool to predict hERG blocking thus rendering drug development safer and more efficient. As an example, we show that a drug that was halted recently in clinical development because of severe cardiotoxicity is a potent inhibitor of hERG in two different biological assays which could have been predicted using our new computational model.
    Full-text · Article · Aug 2014 · Toxicology Letters
Show more