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Prospective in silico trials identify combined SK and K2P channel block as an effective strategy for atrial fibrillation cardioversion

Abstract:
Virtual evaluation of medical therapy through human-based modelling and simulation can accelerate and augment clinical investigations. Treatment of the most common cardiac arrhythmia, atrial fibrillation (AF), requires novel approaches. This study prospectively evaluates and mechanistically explains three novel pharmacological therapies for AF through in silico trials, including single and combined SK and K2P channel block. AF and pharmacological action were assessed in a large cohort of 1000 virtual patients, through 2962 multiscale simulations. Extensive calibration and validation with experimental and clinical data support their credibility. Sustained AF was observed in 654 virtual patients. In this cohort, cardioversion efficacy increased to 82% (535 of 654) through combined SK+K2P channel block, from 33% (213 of 654) and 43% (278 of 654) for single SK and K2P blocks, respectively. Drug-induced prolongation of tissue refractoriness, dependent on the virtual patient's ionic current profile, explained cardioversion efficacy (atrial refractory period increase: 133.0 ± 48.4 ms for combined vs. 45.2 ± 43.0 and 71.0 ± 55.3 ms for single SK and K2P block, respectively). Virtual patients cardioverted by SK channel block presented lower K2P densities, while lower SK densities favoured the success of K2P channel inhibition. Both ionic currents had a crucial role on atrial repolarization, and thus a synergism resulted from the multichannel block. All three strategies, including the multichannel block, preserved atrial electrophysiological function (i.e. conduction velocity and calcium transient dynamics) and thus its contractile properties (safety). In silico trials identify key factors determining treatment success and the combined SK+K2P channel block as a promising strategy for AF management.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1113/jp287124

Authors

More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0002-8649-8203
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0002-8777-0125
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
Kellogg College
Role:
Author
ORCID:
0000-0003-0234-2605
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0002-1634-3601
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0001-6361-3339


More from this funder
Funder identifier:
https://ror.org/00k4n6c32
Grant:
675451
823712
Programme:
Horizon 2020 Framework Programme
More from this funder
Funder identifier:
https://ror.org/029chgv08
Grant:
214290/Z/18/Z
More from this funder
Funder identifier:
https://ror.org/02wdwnk04
Grant:
RE/13/1/30181


Publisher:
Wiley
Journal:
The Journal of Physiology More from this journal
Place of publication:
England
Publication date:
2024-11-18
Acceptance date:
2024-10-23
DOI:
EISSN:
1469-7793
ISSN:
0022-3751
Pmid:
39557619


Language:
English
Keywords:
Pubs id:
2064407
Local pid:
pubs:2064407
Deposit date:
2025-05-19
ARK identifier:

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