Deriving waveform parameters from calcium transients in human iPSC-derived cardiomyocytes to predict cardiac activity with machine learning.

Autor:	Yang H; Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK., Stebbeds W; GlaxoSmithKline R&D, Stevenage, UK., Francis J; GlaxoSmithKline R&D, Stevenage, UK., Pointon A; Functional and Mechanistic Safety, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK., Obrezanova O; Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK., Beattie KA; GlaxoSmithKline R&D, Ware, UK., Clements P; GlaxoSmithKline R&D, Ware, UK., Harvey JS; GlaxoSmithKline R&D, Ware, UK., Smith GF; Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK., Bender A; Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK; Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK. Electronic address: ab454@cam.ac.uk.
Jazyk:	angličtina
Zdroj:	Stem cell reports [Stem Cell Reports] 2022 Mar 08; Vol. 17 (3), pp. 556-568. Date of Electronic Publication: 2022 Feb 10.
DOI:	10.1016/j.stemcr.2022.01.009
Abstrakt:	Human induced pluripotent stem cell-derived cardiomyocytes have been established to detect dynamic calcium transients by fast kinetic fluorescence assays that provide insights into specific aspects of clinical cardiac activity. However, the precise derivation and use of waveform parameters to predict cardiac activity merit deeper investigation. In this study, we derived, evaluated, and applied 38 waveform parameters in a novel Python framework, including (among others) peak frequency, peak amplitude, peak widths, and a novel parameter, shoulder-tail ratio. We then trained a random forest model to predict cardiac activity based on the 25 parameters selected by correlation analysis. The area under the curve (AUC) obtained for leave-one-compound-out cross-validation was 0.86, thereby replicating the predictions of conventional methods and outperforming fingerprint-based methods by a large margin. This work demonstrates that machine learning is able to automate the assessment of cardiovascular liability from waveform data, reducing any risk of user-to-user variability and bias. (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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