Machine learning paired with brightfield optical flow accurately detected drug-induced changes in human iPS-CM contraction, demonstrating sensitivity superior to fluorescence-based methods.
Does machine learning paired with brightfield optical flow improve the detection of cardioactive drug effects in human iPS-CMs compared to fluorescence-based methods?
A novel platform combining machine learning and brightfield optical flow provides a simple, sensitive, and label-free method to detect cardiotoxic and cardioactive drug effects in human iPS-CMs, outperforming traditional fluorescence-based calcium assays.
Current preclinical screening methods do not adequately detect cardiotoxicity. Using human induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs), more physiologically relevant preclinical or patient-specific screening to detect potential cardiotoxic effects of drug candidates may be possible. However, one of the persistent challenges for developing a high-throughput drug screening platform using iPS-CMs is the need to develop a simple and reliable method to measure key electrophysiological and contractile parameters. To address this need, we have developed a platform that combines machine learning paired with brightfield optical flow as a simple and robust tool that can automate the detection of cardiomyocyte drug effects. Using three cardioactive drugs of different mechanisms, including those with primarily electrophysiological effects, we demonstrate the general applicability of this screening method to detect subtle changes in cardiomyocyte contraction. Requiring only brightfield images of cardiomyocyte contractions, we detect changes in cardiomyocyte contraction comparable to - and even superior to - fluorescence readouts. This automated method serves as a widely applicable screening tool to characterize the effects of drugs on cardiomyocyte function.
Lee et al. (Fri,) conducted a other in Cardiotoxicity. Machine learning plus brightfield optical flow vs. GCaMP6 fluorescence-based method was evaluated on Detection of cardioactive drug effects (SVM accuracy). Machine learning paired with brightfield optical flow accurately detected drug-induced changes in human iPS-CM contraction, demonstrating sensitivity superior to fluorescence-based methods.
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