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Convergence of models of human ventricular myocyte electrophysiology after global optimization to recapitulate clinical long QT phenotypes
Umeå University, Faculty of Medicine, Department of Clinical Sciences, Paediatrics.
Umeå University, Faculty of Medicine, Department of Clinical Sciences, Paediatrics.
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2016 (English)In: Journal of Molecular and Cellular Cardiology, ISSN 0022-2828, E-ISSN 1095-8584, Vol. 100, 25-34 p.Article in journal (Refereed) Published
Abstract [en]

In-silico models of human cardiac electrophysiology are now being considered for prediction of cardiotoxicity as part of the preclinical assessment phase of all new drugs. We ask the question whether any of the available models are actually fit for this purpose. We tested three models of the human ventricular action potential, the O'hara-Rudy (ORD11), the Grandi-Bers (GB10) and the Ten Tusscher (TT06) models. We extracted clinical QT data for LQTS1 and LQTS2 patients with nonsense mutations that would be predicted to cause 50% loss of function in I-Ks and I-Kr respectively. We also obtained clinical QT data for LQTS3 patients. We then used a global optimization approach to improve the existing in silico models so that they reproduced all three clinical data sets more closely. We also examined the effects of adrenergic stimulation in the different LQTS subsets. All models, in their original form, produce markedly different and unrealistic predictions of QT prolongation for LQTS1, 2 and 3. After global optimization of the maximum conductances for membrane channels, all models have similar current densities during the action potential, despite differences in kinetic properties of the channels in the different models, and more closely reproduce the prolongation of repolarization seen in all LQTS subtypes. In-silico models of cardiac electrophysiology have the potential to be tremendously useful in complementing traditional preclinical drug testing studies. However, our results demonstrate they should be carefully validated and optimized to clinical data before they can be used for this purpose.

Place, publisher, year, edition, pages
2016. Vol. 100, 25-34 p.
Keyword [en]
Repolarization, Long QT syndrome, In-silico models, Adrenergic regulation, Global optimization, computational cardiology
National Category
Cardiac and Cardiovascular Systems
Identifiers
URN: urn:nbn:se:umu:diva-130249DOI: 10.1016/j.yjmcc.2016.09.011ISI: 000390288500004PubMedID: 27663173OAI: oai:DiVA.org:umu-130249DiVA: diva2:1065719
Available from: 2017-01-16 Created: 2017-01-14 Last updated: 2017-01-16Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
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  • nn-NB
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  • Other locale
More languages
Output format
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