Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity.
Cell Biol Toxicol
; 40(1): 50, 2024 Jun 28.
Article
em En
| MEDLINE
| ID: mdl-38940987
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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Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Perfilação da Expressão Gênica
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Miócitos Cardíacos
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Células-Tronco Pluripotentes Induzidas
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Transcriptoma
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Cardiotoxicidade
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article