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A deep learning platform to assess drug proarrhythmia risk.
Serrano, Ricardo; Feyen, Dries A M; Bruyneel, Arne A N; Hnatiuk, Anna P; Vu, Michelle M; Amatya, Prashila L; Perea-Gil, Isaac; Prado, Maricela; Seeger, Timon; Wu, Joseph C; Karakikes, Ioannis; Mercola, Mark.
Afiliación
  • 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.
Cell Stem Cell ; 30(1): 86-95.e4, 2023 01 05.
Article en En | MEDLINE | ID: mdl-36563695
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Torsades de Pointes / Células Madre Pluripotentes Inducidas / Aprendizaje Profundo Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Cell Stem Cell Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Torsades de Pointes / Células Madre Pluripotentes Inducidas / Aprendizaje Profundo Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Cell Stem Cell Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos