Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity

Victoria P.W. Au Yeung; Olga Obrezanova; Jiarui Zhou; Hongbin Yang; Tara J. Bowen; Delyan Ivanov; Izzy Saffadi; Alfie S. Carter; Vigneshwari Subramanian; Inken Dillmann; Andrew Hall; Adam Corrigan; Mark R. Viant; Amy Pointon

doi:10.1007/s10565-024-09880-7

Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity

Victoria P.W. Au Yeung^*, Olga Obrezanova, Jiarui Zhou, Hongbin Yang, Tara J. Bowen, Delyan Ivanov, Izzy Saffadi, Alfie S. Carter, Vigneshwari Subramanian, Inken Dillmann, Andrew Hall, Adam Corrigan, Mark R. Viant, Amy Pointon

^*Corresponding author for this work

Biosciences

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Abstract

Structural cardiotoxicity (SCT) presents a high-impact risk that is poorly tolerated in drug discovery unless significant benefit is anticipated. Therefore, we aimed to improve the mechanistic understanding of SCT. First, we combined machine learning methods with a modified calcium transient assay in human-induced pluripotent stem cell-derived cardiomyocytes to identify nine parameters that could predict SCT. Next, we applied transcriptomic profiling to human cardiac microtissues exposed to structural and non-structural cardiotoxins. Fifty-two genes expressed across the three main cell types in the heart (cardiomyocytes, endothelial cells, and fibroblasts) were prioritised in differential expression and network clustering analyses and could be linked to known mechanisms of SCT. This transcriptomic fingerprint may prove useful for generating strategies to mitigate SCT risk in early drug discovery.

Original language	English
Article number	50
Journal	Cell Biology and Toxicology
Volume	40
Issue number	1
DOIs	https://doi.org/10.1007/s10565-024-09880-7
Publication status	Published - 28 Jun 2024

Bibliographical note

Publisher Copyright:
© Crown 2024.

Keywords

Calcium transients
Structural cardiotoxicity
Transcriptomics
Machine learning
Bioinformatics

ASJC Scopus subject areas

Toxicology
Cell Biology
Health, Toxicology and Mutagenesis

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Cite this

Au Yeung, V. P. W., Obrezanova, O., Zhou, J., Yang, H., Bowen, T. J., Ivanov, D., Saffadi, I., Carter, A. S., Subramanian, V., Dillmann, I., Hall, A., Corrigan, A., Viant, M. R., & Pointon, A. (2024). Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity. Cell Biology and Toxicology, 40(1), Article 50. https://doi.org/10.1007/s10565-024-09880-7

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abstract = "Structural cardiotoxicity (SCT) presents a high-impact risk that is poorly tolerated in drug discovery unless significant benefit is anticipated. Therefore, we aimed to improve the mechanistic understanding of SCT. First, we combined machine learning methods with a modified calcium transient assay in human-induced pluripotent stem cell-derived cardiomyocytes to identify nine parameters that could predict SCT. Next, we applied transcriptomic profiling to human cardiac microtissues exposed to structural and non-structural cardiotoxins. Fifty-two genes expressed across the three main cell types in the heart (cardiomyocytes, endothelial cells, and fibroblasts) were prioritised in differential expression and network clustering analyses and could be linked to known mechanisms of SCT. This transcriptomic fingerprint may prove useful for generating strategies to mitigate SCT risk in early drug discovery.",

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author = "{Au Yeung}, {Victoria P.W.} and Olga Obrezanova and Jiarui Zhou and Hongbin Yang and Bowen, {Tara J.} and Delyan Ivanov and Izzy Saffadi and Carter, {Alfie S.} and Vigneshwari Subramanian and Inken Dillmann and Andrew Hall and Adam Corrigan and Viant, {Mark R.} and Amy Pointon",

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doi = "10.1007/s10565-024-09880-7",

language = "English",

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Au Yeung, VPW, Obrezanova, O, Zhou, J, Yang, H, Bowen, TJ, Ivanov, D, Saffadi, I, Carter, AS, Subramanian, V, Dillmann, I, Hall, A, Corrigan, A, Viant, MR & Pointon, A 2024, 'Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity', Cell Biology and Toxicology, vol. 40, no. 1, 50. https://doi.org/10.1007/s10565-024-09880-7

TY - JOUR

T1 - Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity

AU - Au Yeung, Victoria P.W.

AU - Obrezanova, Olga

AU - Zhou, Jiarui

AU - Yang, Hongbin

AU - Bowen, Tara J.

AU - Ivanov, Delyan

AU - Saffadi, Izzy

AU - Carter, Alfie S.

AU - Subramanian, Vigneshwari

AU - Dillmann, Inken

AU - Hall, Andrew

AU - Corrigan, Adam

AU - Viant, Mark R.

AU - Pointon, Amy

PY - 2024/6/28

Y1 - 2024/6/28

N2 - Structural cardiotoxicity (SCT) presents a high-impact risk that is poorly tolerated in drug discovery unless significant benefit is anticipated. Therefore, we aimed to improve the mechanistic understanding of SCT. First, we combined machine learning methods with a modified calcium transient assay in human-induced pluripotent stem cell-derived cardiomyocytes to identify nine parameters that could predict SCT. Next, we applied transcriptomic profiling to human cardiac microtissues exposed to structural and non-structural cardiotoxins. Fifty-two genes expressed across the three main cell types in the heart (cardiomyocytes, endothelial cells, and fibroblasts) were prioritised in differential expression and network clustering analyses and could be linked to known mechanisms of SCT. This transcriptomic fingerprint may prove useful for generating strategies to mitigate SCT risk in early drug discovery.

AB - Structural cardiotoxicity (SCT) presents a high-impact risk that is poorly tolerated in drug discovery unless significant benefit is anticipated. Therefore, we aimed to improve the mechanistic understanding of SCT. First, we combined machine learning methods with a modified calcium transient assay in human-induced pluripotent stem cell-derived cardiomyocytes to identify nine parameters that could predict SCT. Next, we applied transcriptomic profiling to human cardiac microtissues exposed to structural and non-structural cardiotoxins. Fifty-two genes expressed across the three main cell types in the heart (cardiomyocytes, endothelial cells, and fibroblasts) were prioritised in differential expression and network clustering analyses and could be linked to known mechanisms of SCT. This transcriptomic fingerprint may prove useful for generating strategies to mitigate SCT risk in early drug discovery.

KW - Calcium transients

KW - Structural cardiotoxicity

KW - Transcriptomics

KW - Machine learning

KW - Bioinformatics

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DO - 10.1007/s10565-024-09880-7

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SN - 1573-6822

VL - 40

JO - Cell Biology and Toxicology

JF - Cell Biology and Toxicology

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M1 - 50

ER -

Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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