Validation of in silico biomarkers for drug screening through ordinal logistic regression
Since the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiation, many studies have suggested various in silico features based on ionic charges, action potentials (AP), or intracellular calcium (Ca) to assess proarrhythmic risk. These in silico features are computed through electrophysiologica...
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Published in | Frontiers in physiology Vol. 13; p. 1009647 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Frontiers Media S.A
06.10.2022
|
Subjects | |
Online Access | Get full text |
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Summary: | Since the Comprehensive
in vitro
Proarrhythmia Assay (CiPA) initiation, many studies have suggested various
in silico
features based on ionic charges, action potentials (AP), or intracellular calcium (Ca) to assess proarrhythmic risk. These
in silico
features are computed through electrophysiological simulations using
in vitro
experimental datasets as input, therefore changing with the quality of
in vitro
experimental data; however, research to validate the robustness of
in silico
features for proarrhythmic risk assessment of drugs depending on
in vitro
datasets has not been conducted. This study aims to verify the availability of
in silico
features commonly used in assessing the cardiac toxicity of drugs through an ordinal logistic regression model and three
in vitro
datasets measured under different experimental environments and with different purposes. We performed
in silico
drug simulations using the Tomek-Ohara Rudy (ToR-ORD) ventricular myocyte model and computed 12
in silico
features comprising six AP features, four Ca features, and two ion charge features, which reflected the effect and characteristics of each
in vitro
data for CiPA 28 drugs. We then compared the classific performances of ordinal logistic regressions according to these 12
in silico
features and used
in vitro
datasets to validate which
in silico
feature is the best for assessing the proarrhythmic risk of drugs at high, intermediate, and low levels. All 12
in silico
features helped determine high-risky torsadogenic drugs, regardless of the
in vitro
datasets used in the
in silico
simulation as input. In the three types of
in silico
features, AP features were the most reliable for determining the three Torsade de Pointes (TdP) risk standards. Among AP features, AP duration at 50% repolarization (APD
50
) was the best when individually using
in silico
features per
in vitro
dataset. In contrast, the AP repolarization velocity (dVm/dt
Max_repol
) was the best when merging all
in silico
features computed through three
in vitro
datasets. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Yung E. Earm, Seoul National University, South Korea Reviewed by: Xin Zhou, University of Oxford, United Kingdom Jae Boum Youm, Inje University, South Korea This article was submitted to Computational Physiology and Medicine, a section of the journal Frontiers in Physiology |
ISSN: | 1664-042X 1664-042X |
DOI: | 10.3389/fphys.2022.1009647 |