Literature DB >> 27190211

Temperature Effects on Kinetics of KV11.1 Drug Block Have Important Consequences for In Silico Proarrhythmic Risk Prediction.

Monique J Windley1, Stefan A Mann1, Jamie I Vandenberg1, Adam P Hill2.   

Abstract

Drug block of voltage-gated potassium channel subtype 11.1 human ether-a-go-go related gene (Kv11.1) (hERG) channels, encoded by the KCNH2 gene, is associated with reduced repolarization of the cardiac action potential and is the predominant cause of acquired long QT syndrome that can lead to fatal cardiac arrhythmias. Current safety guidelines require that potency of KV11.1 block is assessed in the preclinical phase of drug development. However, not all drugs that block KV11.1 are proarrhythmic, meaning that screening on the basis of equilibrium measures of block can result in high attrition of potentially low-risk drugs. The basis of the next generation of drug-screening approaches is set to be in silico risk prediction, informed by in vitro mechanistic descriptions of drug binding, including measures of the kinetics of block. A critical issue in this regard is characterizing the temperature dependence of drug binding. Specifically, it is important to address whether kinetics relevant to physiologic temperatures can be inferred or extrapolated from in vitro data gathered at room temperature in high-throughout systems. Here we present the first complete study of the temperature-dependent kinetics of block and unblock of a proarrhythmic drug, cisapride, to KV11.1. Our data highlight a complexity to binding that manifests at higher temperatures and can be explained by accumulation of an intermediate, non-blocking encounter-complex. These results suggest that for cisapride, physiologically relevant kinetic parameters cannot be simply extrapolated from those measured at lower temperatures; rather, data gathered at physiologic temperatures should be used to constrain in silico models that may be used for proarrhythmic risk prediction.
Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.

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Year:  2016        PMID: 27190211     DOI: 10.1124/mol.115.103127

Source DB:  PubMed          Journal:  Mol Pharmacol        ISSN: 0026-895X            Impact factor:   4.436


  3 in total

1.  Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel.

Authors:  Soren Wacker; Sergei Yu Noskov
Journal:  Comput Toxicol       Date:  2017-05-13

Review 2.  In Vitro and In Silico Risk Assessment in Acquired Long QT Syndrome: The Devil Is in the Details.

Authors:  William Lee; Monique J Windley; Jamie I Vandenberg; Adam P Hill
Journal:  Front Physiol       Date:  2017-11-16       Impact factor: 4.566

3.  When Does the IC50 Accurately Assess the Blocking Potency of a Drug?

Authors:  Julio Gomis-Tena; Brandon M Brown; Jordi Cano; Beatriz Trenor; Pei-Chi Yang; Javier Saiz; Colleen E Clancy; Lucia Romero
Journal:  J Chem Inf Model       Date:  2020-03-10       Impact factor: 4.956

  3 in total

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