Abstract
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 serum-free 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 multi-electrode array 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. CONCLUSION: 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.