Machine learning plus optical flow: a simple and sensitive method to detect cardioactive drugs.

Abstract:

: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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Lee EK,Kurokawa YK,Tu R,George SC,Khine M

doi

10.1038/srep11817

subject

Has Abstract

pub_date

2015-07-03 00:00:00

pages

11817

issn

2045-2322

pii

srep11817

journal_volume

5

pub_type

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