Literature DB >> 22198175

An in silico canine cardiac midmyocardial action potential duration model as a tool for early drug safety assessment.

M R Davies1, H B Mistry, L Hussein, C E Pollard, J-P Valentin, J Swinton, N Abi-Gerges.   

Abstract

Cell lines expressing ion channels (IC) and the advent of plate-based electrophysiology device have enabled a molecular understanding of the action potential (AP) as a means of early QT assessment. We sought to develop an in silico AP (isAP) model that provides an assessment of the effect of a compound on the myocyte AP duration (APD) using concentration-effect curve data from a panel of five ICs (hNav1.5, hCav1.2, hKv4.3/hKChIP2.2, hKv7.1/hminK, hKv11.1). A test set of 53 compounds was selected to cover a range of selective and mixed IC modulators that were tested for their effects on optically measured APD. A threshold of >10% change in APD at 90% repolarization (APD(90)) was used to signify an effect at the top test concentration. To capture the variations observed in left ventricular midmyocardial myocyte APD data from 19 different dogs, the isAP model was calibrated to produce an ensemble of 19 model variants that could capture the shape and form of the APs and also quantitatively replicate dofetilide- and diltiazem-induced APD(90) changes. Provided with IC panel data only, the isAP model was then used, blinded, to predict APD(90) changes greater than 10%. At a simulated concentration of 30 μM and based on a criterion that six of the variants had to agree, isAP prediction was scored as showing greater than 80% predictivity of compound activity. Thus, early in drug discovery, the isAP model allows integrating separate IC data and is amenable to the throughput required for use as a virtual screen.

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Year:  2011        PMID: 22198175     DOI: 10.1152/ajpheart.00808.2011

Source DB:  PubMed          Journal:  Am J Physiol Heart Circ Physiol        ISSN: 0363-6135            Impact factor:   4.733


  36 in total

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3.  A new classifier-based strategy for in-silico ion-channel cardiac drug safety assessment.

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Review 4.  Computational approaches to understand cardiac electrophysiology and arrhythmias.

Authors:  Byron N Roberts; Pei-Chi Yang; Steven B Behrens; Jonathan D Moreno; Colleen E Clancy
Journal:  Am J Physiol Heart Circ Physiol       Date:  2012-08-10       Impact factor: 4.733

5.  Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology.

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Review 6.  Calibration of ionic and cellular cardiac electrophysiology models.

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7.  Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator.

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Review 8.  Application of cardiac electrophysiology simulations to pro-arrhythmic safety testing.

Authors:  Gary R Mirams; Mark R Davies; Yi Cui; Peter Kohl; Denis Noble
Journal:  Br J Pharmacol       Date:  2012-11       Impact factor: 8.739

Review 9.  Combining wet and dry research: experience with model development for cardiac mechano-electric structure-function studies.

Authors:  T Alexander Quinn; Peter Kohl
Journal:  Cardiovasc Res       Date:  2013-01-17       Impact factor: 10.787

10.  Computational assessment of drug-induced effects on the electrocardiogram: from ion channel to body surface potentials.

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Journal:  Br J Pharmacol       Date:  2013-02       Impact factor: 8.739

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