Stem cells are the most sensitive screening tool to identify toxicity of GATA4-targeted novel small-molecule

S Tuuli Karhu1, Mika J Välimäki1,2, Mikael Jumppanen3

  • 1Drug Research Program and Division of Pharmacology and Pharmacotherapy, Faculty of Pharmacy, University of Helsinki, P.O. Box 56, 00014, Helsinki, Finland.

Insights

Early drug discovery toxicity screening is costly and uses many animals. This study shows human stem cells are sensitive models for identifying compound toxicity and guiding the development of safer GATA4-targeted drugs.

Area of Science:

  • Pharmacology
  • Toxicology
  • Drug Discovery

Background:

  • In vitro and animal models for drug safety assessment are expensive and time-consuming.
  • Early toxicity profiling using human cell models can reduce drug development costs.
  • Novel compounds targeting the cardiac transcription factor GATA4 require thorough safety evaluation.

Purpose of the Study:

  • To compare cardiac and stem cell models for in vitro toxicity testing.
  • To investigate structure-toxicity relationships of GATA4-targeted compounds.
  • To identify optimal cell models for early-stage drug toxicity screening.

Main Methods:

  • Screening eight novel compounds across eight cell types at concentrations from 10 nM to 30 µM.
  • Assessing compound effects on cell viability.
  • Detailed characterization of two compounds using high-content analysis.
  • Structure-toxicity analysis focusing on GATA4-targeted compounds.

Main Results:

  • Identified significant cell type- and structure-dependent toxicity profiles.
  • Stem cells demonstrated the highest sensitivity in toxicity detection.
  • Discovered a specific dihedral angle in GATA4-targeted compounds correlated with stem cell toxicity.
  • Highlighted the critical role of cell type selection in toxicity screening.

Conclusions:

  • Human stem cells are a sensitive and valuable model for early in vitro toxicity testing.
  • Understanding structure-toxicity relationships can guide the development of safer drug candidates.
  • Optimizing cell model selection improves the efficiency and reliability of drug safety assessment.

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