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Computer-based assessment of the effects of amiodarone on short qt syndrome variant 1 in human ventricles

  • School of Computer Science and Technology, Harbin Institute of Technology
  • Southwest Medical University
  • School of Computer Science and Technology (School of Software), Harbin Institute of Technology Weihai
  • Space Institute of Southern China
  • University of Manchester

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Short QT syndrome (SQTS) is a channelopathy associated with a short QT interval and an increased risk of arrhythmias. The SQT1 variant results from a gain-of-function mutation (N588K) in the KCNH2-encoded channels. Inherited arrhythmogenic effects of SQTS have been clearly characterized, while little is known about the pharmacological therapy for SQTS. Therefore, this paper assessed the effects of amiodarone, class III anti-arrhythmic agent, on SQT1. The wild-type (WT) and N588K IKr Markov chain formulations were incorporated into human ventricular action potentials (APs). The modified cells were then integrated into one-dimensional (1D) and 2D tissues. The inhibitory effect of amiodarone on channels was modelled using half-maximal inhibitory concentration (IC50) and Hill coefficient (nH) values. Amiodarone prolonged the SQT1 ventricular cell AP duration (APD). Amiodarone caused a QT prolongation and a decrease in T-wave amplitude. Amiodarone decreased vulnerable window, and increased the critical size of ventricular substrates. Amiodarone terminated and prevented re-entrant waves. The actions of amiodarone exhibited in silico, that anti-arrhythmic effects on SQT1. Our data substantiate a causal link between amiodarone and QT interval prolongation, and offer an explanation for decreased vulnerability to re-entry and termination of re-entrant arrhythmias in SQT1.

Original languageEnglish
Title of host publicationComputing in Cardiology 2017, CinC 2017
PublisherIEEE Computer Society
Pages1-4
Number of pages4
ISBN (Print)9781538645550
DOIs
StatePublished - 2017
Externally publishedYes
Event44th Computing in Cardiology Conference, CinC 2017 - Rennes, France
Duration: 24 Sep 201727 Sep 2017

Publication series

NameComputing in Cardiology
Volume44
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X

Conference

Conference44th Computing in Cardiology Conference, CinC 2017
Country/TerritoryFrance
CityRennes
Period24/09/1727/09/17

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