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Classifying drugs by their arrhythmogenic risk using machine learning

Francisco Sahli Costabal, Kinya Seo, Euan Ashley, View ORCID ProfileEllen Kuhl
doi: https://doi.org/10.1101/545863
Francisco Sahli Costabal
Stanford University
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Kinya Seo
Stanford University
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Euan Ashley
Stanford University
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Ellen Kuhl
Stanford University
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  • ORCID record for Ellen Kuhl
  • For correspondence: ekuhl@stanford.edu
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Abstract

An undesirable side effect of drugs are cardiac arrhythmias, in particular a condition called torsades de pointes. Current paradigms for drug safety evaluation are costly, lengthy, and conservative, and impede efficient drug development. Here we combine multiscale experiment and simulation, high-performance computing, and machine learning to create an easy-to-use risk assessment diagram to quickly and reliable stratify the pro-arrhythmic potential of new and existing drugs. We capitalize on recent developments in machine learning and integrate information across ten orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay of two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 23 common drugs, exclusively on the basis of their concentrations at 50% current block. Our new risk assessment diagram explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the pro-arrhythmic potential of new drugs. Our study shapes the way towards establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 10, 2019.
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Classifying drugs by their arrhythmogenic risk using machine learning
Francisco Sahli Costabal, Kinya Seo, Euan Ashley, Ellen Kuhl
bioRxiv 545863; doi: https://doi.org/10.1101/545863
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Classifying drugs by their arrhythmogenic risk using machine learning
Francisco Sahli Costabal, Kinya Seo, Euan Ashley, Ellen Kuhl
bioRxiv 545863; doi: https://doi.org/10.1101/545863

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