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

Victoria P W Au Yeung1,2, Olga Obrezanova3, Jiarui Zhou4

  • 1Safety Sciences, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, UK. victoria.auyeung.publications@gmail.com.

Cell Biology and Toxicology
|June 28, 2024
PubMed

Insights

Structural cardiotoxicity (SCT) is a major drug discovery risk. This study identified key predictive parameters and a gene expression fingerprint to improve mechanistic understanding and mitigate SCT risk in early drug development.

Area of Science:

  • Drug Discovery
  • Cardiology
  • Toxicology

Background:

  • Structural cardiotoxicity (SCT) poses a significant challenge in drug discovery.
  • A deeper mechanistic understanding of SCT is crucial for early risk mitigation.

Purpose of the Study:

  • To enhance the mechanistic understanding of structural cardiotoxicity.
  • To identify predictive parameters and molecular signatures associated with SCT.

Main Methods:

  • Machine learning applied to calcium transient assays in human-induced pluripotent stem cell-derived cardiomyocytes.
  • Transcriptomic profiling of human cardiac microtissues exposed to cardiotoxins.
  • Differential gene expression and network clustering analyses.

Main Results:

  • Nine parameters were identified as predictive of SCT.
  • A 52-gene transcriptomic fingerprint was associated with known SCT mechanisms across cardiomyocytes, endothelial cells, and fibroblasts.
  • This fingerprint can be linked to established SCT pathways.

Conclusions:

  • The identified parameters and transcriptomic fingerprint offer improved mechanistic insights into SCT.
  • This knowledge can guide strategies to mitigate SCT risk during early drug discovery phases.
Keywords:
BioinformaticsCalcium transientsMachine learningStructural cardiotoxicityTranscriptomics

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