دورية أكاديمية
أعرض في EDS

A deep learning platform to assess drug proarrhythmia risk.

التفاصيل البيبلوغرافية
العنوان: A deep learning platform to assess drug proarrhythmia risk.
المؤلفون: Serrano R; Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA., Feyen DAM; Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA., Bruyneel AAN; Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA., Hnatiuk AP; Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA., Vu MM; Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA., Amatya PL; Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA., Perea-Gil I; Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA; Department of Cardiothoracic Surgery, Stanford University, Stanford, CA 94305, USA., Prado M; Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA; Department of Cardiothoracic Surgery, Stanford University, Stanford, CA 94305, USA., Seeger T; Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA., Wu JC; Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA., Karakikes I; Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA; Department of Cardiothoracic Surgery, Stanford University, Stanford, CA 94305, USA., Mercola M; Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305, USA. Electronic address: mmercola@stanford.edu.
المصدر: Cell stem cell [Cell Stem Cell] 2023 Jan 05; Vol. 30 (1), pp. 86-95.e4. Date of Electronic Publication: 2022 Dec 22.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Cell Press Country of Publication: United States NLM ID: 101311472 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1875-9777 (Electronic) Linking ISSN: 18759777 NLM ISO Abbreviation: Cell Stem Cell Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Cambridge, MA : Cell Press
مواضيع طبية MeSH: Deep Learning* , Torsades de Pointes*/chemically induced , Induced Pluripotent Stem Cells*/physiology, Humans ; Arrhythmias, Cardiac/chemically induced ; Action Potentials ; Myocytes, Cardiac/physiology
مستخلص: Drug safety initiatives have endorsed human iPSC-derived cardiomyocytes (hiPSC-CMs) as an in vitro model for predicting drug-induced cardiac arrhythmia. However, the extent to which human-defined features of in vitro arrhythmia predict actual clinical risk has been much debated. Here, we trained a convolutional neural network classifier (CNN) to learn features of in vitro action potential recordings of hiPSC-CMs that are associated with lethal Torsade de Pointes arrhythmia. The CNN classifier accurately predicted the risk of drug-induced arrhythmia in people. The risk profile of the test drugs was similar across hiPSC-CMs derived from different healthy donors. In contrast, pathogenic mutations that cause arrhythmogenic cardiomyopathies in patients significantly increased the proarrhythmic propensity to certain intermediate and high-risk drugs in the hiPSC-CMs. Thus, deep learning can identify in vitro arrhythmic features that correlate with clinical arrhythmia and discern the influence of patient genetics on the risk of drug-induced arrhythmia.
Competing Interests: Declaration of interests R.S. is a paid consultant of Vala Sciences, which manufactures a high content instrument used in these studies. M.M. serves on the scientific advisory board of Vala Sciences. J.C.W. is co-founder and scientific advisory board member of Greenstone Biosciences.
(Copyright © 2022 Elsevier Inc. All rights reserved.)
References: Circulation. 2020 Oct 13;142(15):e214-e233. (PMID: 32929996)
J Mol Cell Cardiol. 2021 Aug;157:77-89. (PMID: 33957110)
Am Heart J. 2014 Mar;167(3):292-300. (PMID: 24576511)
Circ Heart Fail. 2019 Mar;12(3):e005371. (PMID: 30871351)
Cell Rep. 2020 Jul 21;32(3):107925. (PMID: 32697997)
Nature. 2011 Mar 10;471(7337):225-9. (PMID: 21240260)
Eur Heart J. 2022 Sep 21;43(36):3477-3489. (PMID: 35728000)
J Pharmacol Toxicol Methods. 2016 Sep-Oct;81:263-73. (PMID: 27095424)
Nat Rev Drug Discov. 2016 Jul;15(7):457-71. (PMID: 26893184)
N Engl J Med. 1998 Apr 30;338(18):1248-57. (PMID: 9562578)
Cells. 2021 Oct 18;10(10):. (PMID: 34685769)
Nat Rev Cardiol. 2016 Jan;13(1):36-47. (PMID: 26194552)
Circulation. 2019 Feb 5;139(6):799-811. (PMID: 30586709)
Annu Rev Med. 2022 Jan 27;73:363-375. (PMID: 35084989)
Comput Methods Programs Biomed. 2018 Jul;161:1-13. (PMID: 29852952)
Circulation. 2013 Feb 5;127(5):575-84. (PMID: 23271797)
J Pharmacol Toxicol Methods. 2017 Mar - Apr;84:111-127. (PMID: 27956204)
Nature. 2015 May 28;521(7553):436-44. (PMID: 26017442)
Cell Rep. 2018 Sep 25;24(13):3582-3592. (PMID: 30257217)
Nat Med. 2019 Jan;25(1):65-69. (PMID: 30617320)
Circulation. 2010 Oct 5;122(14):1426-35. (PMID: 20921449)
Clin Pharmacol Ther. 2018 Jun;103(6):1100-1106. (PMID: 28940299)
Cell Chem Biol. 2021 Mar 18;28(3):271-282. (PMID: 33740432)
BMC Med. 2016 Feb 04;14:10. (PMID: 26843061)
Circulation. 2021 Aug 3;144(5):382-392. (PMID: 33928785)
Dis Model Mech. 2018 Feb 26;11(2):. (PMID: 29361520)
Eur Heart J. 2019 Dec 1;40(45):3685-3695. (PMID: 31219556)
Am J Physiol Heart Circ Physiol. 2017 Sep 1;313(3):H620-H630. (PMID: 28646025)
Pharmacol Rev. 2020 Jan;72(1):320-342. (PMID: 31871214)
Br J Pharmacol. 2008 Aug;154(7):1502-7. (PMID: 18552874)
J Pharmacol Toxicol Methods. 2016 Mar-Apr;78:93-102. (PMID: 26657830)
Cardiovasc Res. 2015 Jul 1;107(1):184-96. (PMID: 25990311)
Stem Cell Reports. 2018 Mar 13;10(3):822-833. (PMID: 29429959)
Circulation. 2013 Apr 23;127(16):1677-91. (PMID: 23519760)
Proc Natl Acad Sci U S A. 2006 Jan 31;103(5):1388-93. (PMID: 16432188)
J Pharmacol Toxicol Methods. 2016 Sep-Oct;81:15-20. (PMID: 27282641)
Biomaterials. 2019 Apr;198:3-26. (PMID: 30343824)
Cell Stem Cell. 2020 Nov 5;27(5):813-821.e6. (PMID: 32931730)
PLoS One. 2013 Oct 29;8(10):e78104. (PMID: 24205113)
Mol Genet Metab. 2011 Feb;102(2):200-6. (PMID: 20965760)
Br J Pharmacol. 2011 Jun;163(4):675-93. (PMID: 21306581)
J Electrocardiol. 2016 Nov - Dec;49(6):837-842. (PMID: 27524478)
J Electrocardiol. 2004;37 Suppl:15-24. (PMID: 15534788)
Front Physiol. 2017 Oct 11;8:766. (PMID: 29075196)
Nat Rev Drug Discov. 2015 Jul;14(7):475-86. (PMID: 26091267)
Anesth Analg. 1997 Jun;84(6):1380-1. (PMID: 9174325)
Basic Res Cardiol. 2020 Nov 25;115(6):71. (PMID: 33237428)
Am J Physiol Heart Circ Physiol. 2011 Nov;301(5):H2006-17. (PMID: 21890694)
Nat Protoc. 2013 Nov;8(11):2281-2308. (PMID: 24157548)
Circ Res. 2019 Aug 2;125(4):370-378. (PMID: 31518168)
Eur J Heart Fail. 2012 Nov;14(11):1199-207. (PMID: 22820313)
J Physiol. 2014 Mar 15;592(6):1181-97. (PMID: 24421356)
Proc Natl Acad Sci U S A. 2012 Jul 3;109(27):E1848-57. (PMID: 22645348)
Cell Rep. 2020 Sep 8;32(10):108117. (PMID: 32905764)
Br J Clin Pharmacol. 2014 Apr;77(4):618-25. (PMID: 23834499)
معلومات مُعتمدة: R01 HL138539 United States HL NHLBI NIH HHS; R01 HL130840 United States HL NHLBI NIH HHS; P01 HL141084 United States HL NHLBI NIH HHS; R01 HL139679 United States HL NHLBI NIH HHS; R01 HL152055 United States HL NHLBI NIH HHS; S10 OD030264 United States OD NIH HHS; R01 HL141358 United States HL NHLBI NIH HHS; P01 ES016738 United States ES NIEHS NIH HHS; R42 HL158510 United States HL NHLBI NIH HHS; R01 HL150414 United States HL NHLBI NIH HHS
فهرسة مساهمة: Keywords: AI; CiPA; artificial; cardiomyocytes; deep learning; drug screening; drug-induced arrhythmia; iPSC; induced pluripotent stem cells; intelligence; safety pharmacology
تواريخ الأحداث: Date Created: 20221223 Date Completed: 20230110 Latest Revision: 20240106
رمز التحديث: 20240106
مُعرف محوري في PubMed: PMC9924077
DOI: 10.1016/j.stem.2022.12.002
PMID: 36563695
قاعدة البيانات: MEDLINE
الوصف
تدمد:1875-9777
DOI:10.1016/j.stem.2022.12.002