Integral approach to organelle profiling in human iPSCderived cardiomyocytes enhances in-vitro cardiac safety classification of known cardiotoxic compounds

Brigitta Rita Szabo^1,2Jeroen Stein¹Anna Savchenko²Thomas Hutschalik^1,3Filip Van Nieuwerburgh⁴Tim Meese⁴Georgios Kosmidis¹Paul G A Volders²Elena Matsa^1,5,6,7*

• ¹Ncardia Services B.V., Leiden, Netherlands
• ²Dept. of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, Netherlands
• ³Dept. of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, Netherlands
• ⁴Laboratory of Pharmaceutical Biotechnology, Ghent University, Ghent, Belgium
• ⁵University College Cork, Cork, Ireland
• ⁶Cellistic, Mont-Saint-Guibert, Belgium
• ⁷National Institute for Bioprocessing Research and Training, Dublin, Ireland

Introduction: Efficient preclinical prediction of cardiovascular side effects poses a pivotal challenge for the pharmaceutical industry. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are becoming increasingly important in this field due to inaccessibility of human native cardiac tissue. Current preclinical hiPSC-CMs models focus on functional changes such as electrophysiological abnormalities, however other parameters, such as structural toxicity, remain less understood.Methods: This study utilized hiPSC-CMs from three independent donors, cultured in serumfree conditions, and treated with a library of 17 small molecules with stratified cardiac side effects. High-content imaging (HCI) targeting ten subcellular organelles, combined with multielectrode array (MEA) data, was employed to profile drug responses. Dimensionality reduction and clustering of the data were performed using principal component analysis (PCA) and sparse partial least squares discriminant analysis (sPLS-DA).Results: Both supervised and unsupervised clustering revealed patterns associated with known clinical side effects. In supervised clustering, morphological features outperformed electrophysiological data alone, and the combined data set achieved a 76% accuracy in recapitulating known clinical cardiotoxicity classifications. RNA-sequencing of all drugs versus vehicle conditions was used to support the mechanistic insights derived from morphological profiling, validating the former as a valuable cardiotoxicity tool.Results demonstrate that a combined approach of analyzing morphology and electrophysiology enhances in-vitro prediction and understanding of drug cardiotoxicity. Our integrative approach introduces a potential framework that is accessible, scalable and better aligned with clinical outcomes.