1. Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity.
- Author
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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, and Pointon A
- Subjects
- Humans, Computational Biology methods, Machine Learning, Cardiotoxins toxicity, Fibroblasts drug effects, Fibroblasts metabolism, Endothelial Cells drug effects, Endothelial Cells metabolism, Cardiotoxicity genetics, Transcriptome drug effects, Transcriptome genetics, Myocytes, Cardiac drug effects, Myocytes, Cardiac metabolism, Induced Pluripotent Stem Cells drug effects, Induced Pluripotent Stem Cells metabolism, Gene Expression Profiling methods
- 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., (© 2024. Crown.)
- Published
- 2024
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