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A Molecularly Detailed NaV1.5 Model Reveals a New Class I Antiarrhythmic Target.
Moreno, Jonathan D; Zhu, Wandi; Mangold, Kathryn; Chung, Woenho; Silva, Jonathan R.
Afiliação
  • Moreno JD; Division of Cardiology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri.
  • Zhu W; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri.
  • Mangold K; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri.
  • Chung W; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri.
  • Silva JR; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri.
JACC Basic Transl Sci ; 4(6): 736-751, 2019 Oct.
Article em En | MEDLINE | ID: mdl-31709321
Antiarrhythmic treatment strategies remain suboptimal due to our inability to predict how drug interactions with ion channels will affect the ability of the tissues to initiate and sustain an arrhythmia. We built a multiscale molecular model of the Na+ channel domain III (domain III voltage-sensing domain) to highlight the molecular underpinnings responsible for mexiletine drug efficacy. This model predicts that a hyperpolarizing shift in the domain III voltage-sensing domain is critical for drug efficacy and may be leveraged to design more potent Class I molecules. The model was therefore used to design, in silico, a theoretical mexiletine booster that can dramatically rescue a mutant resistant to the potent antiarrhythmic effects of mexiletine. Our framework provides a strategy for in silico design of precision-targeted therapeutic agents that simultaneously assesses antiarrhythmic markers of success and failure at multiple spatial and time scales. This approach provides a roadmap for the design of novel molecular-based therapy to treat myriad arrhythmia syndromes, including ventricular tachycardia, heart failure arrhythmias, and inherited arrhythmia syndromes.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article