Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity.
Victoria P W Au YeungOlga ObrezanovaJiarui ZhouHongbin YangTara J BowenDelyan IvanovIzzy SaffadiAlfie S CarterVigneshwari SubramanianInken DillmannAndrew HallAdam CorriganMark R ViantAmy PointonPublished in: Cell biology and toxicology (2024)
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.
Keyphrases
- drug discovery
- endothelial cells
- single cell
- high glucose
- drug induced
- liver injury
- rna seq
- machine learning
- high throughput
- vascular endothelial growth factor
- pluripotent stem cells
- induced pluripotent stem cells
- gene expression
- stem cells
- cell therapy
- blood brain barrier
- atrial fibrillation
- deep learning
- extracellular matrix
- genome wide identification
- oxidative stress
- subarachnoid hemorrhage