ArticleCell biology and toxicology2024
Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity.
Article in Cell biology and toxicology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- A Ponatinib-Associated Transcriptomic Signature: Implications for Cardiovascular Toxicity.International journal of molecular sciences · 2026Article
- Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions.Toxics · 2025Review
- Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.Journal of translational medicine · 2025Review
- Machine Learning-Enabled Drug-Induced Toxicity Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.