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Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning

All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here, we combine multiscale experiment and simulation, high-performance computing, and...

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Detalles Bibliográficos
Autores principales: Sahli-Costabal, Francisco, Seo, Kinya, Ashley, Euan, Kuhl, Ellen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Biophysical Society 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063479/
https://www.ncbi.nlm.nih.gov/pubmed/32023435
http://dx.doi.org/10.1016/j.bpj.2020.01.012
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author Sahli-Costabal, Francisco
Seo, Kinya
Ashley, Euan
Kuhl, Ellen
author_facet Sahli-Costabal, Francisco
Seo, Kinya
Ashley, Euan
Kuhl, Ellen
author_sort Sahli-Costabal, Francisco
collection PubMed
description All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here, we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their proarrhythmic potential. We capitalize on recent developments in machine learning and integrate information across 10 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 between 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 drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 22 common drugs exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool 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 proarrhythmic potential of new drugs. Our study paves the way toward establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.
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spelling pubmed-70634792020-10-10 Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning Sahli-Costabal, Francisco Seo, Kinya Ashley, Euan Kuhl, Ellen Biophys J Articles All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here, we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their proarrhythmic potential. We capitalize on recent developments in machine learning and integrate information across 10 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 between 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 drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 22 common drugs exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool 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 proarrhythmic potential of new drugs. Our study paves the way toward establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders. The Biophysical Society 2020-03-10 2020-01-22 /pmc/articles/PMC7063479/ /pubmed/32023435 http://dx.doi.org/10.1016/j.bpj.2020.01.012 Text en © 2020 Biophysical Society. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Articles
Sahli-Costabal, Francisco
Seo, Kinya
Ashley, Euan
Kuhl, Ellen
Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning
title Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning
title_full Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning
title_fullStr Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning
title_full_unstemmed Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning
title_short Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning
title_sort classifying drugs by their arrhythmogenic risk using machine learning
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063479/
https://www.ncbi.nlm.nih.gov/pubmed/32023435
http://dx.doi.org/10.1016/j.bpj.2020.01.012
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