In silico models for evaluating proarrhythmic risk of drugs-Reference-Cited by-同舟云学术

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In silico models for evaluating proarrhythmic risk of drugs

Published:2020-06-01 Issue:2 Volume:4 Page:021502
ISSN:2473-2877
Container-title:APL Bioengineering
language:en
Short-container-title:APL Bioengineering

Author:

Hwang Minki¹^ORCID,Lim Chul-Hyun²^ORCID,Leem Chae Hun³^ORCID,Shim Eun Bo¹²^ORCID

Affiliation:

1. SiliconSapiens Inc., Seoul 06097, South Korea

2. Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon 24341, South Korea

3. Department of Physiology, College of Medicine, University of Ulsan, Asan Medical Center, Seoul 05505, South Korea

Funder

Ministry of Food and Drug Safety

Publisher

AIP Publishing

Subject

Biomedical Engineering,Biomaterials,Biophysics,Bioengineering

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5132618

Reference74 articles.

1. The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative — Update on progress

2. Mechanistic Model-Informed Proarrhythmic Risk Assessment of Drugs: Review of the “CiPA” Initiative and Design of a Prospective Clinical Validation Study

3. Comprehensive In Vitro Proarrhythmia Assay (CiPA) Update from a Cardiac Safety Research Consortium / Health and Environmental Sciences Institute / FDA Meeting

4. Comprehensive T wave Morphology Assessment in a Randomized Clinical Study of Dofetilide, Quinidine, Ranolazine, and Verapamil

5. A model for human ventricular tissue

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1. In Silico Human Cardiomyocyte Action Potential Modeling: Exploring Ion Channel Input Combinations;International Journal of Toxicology;2024-03-13

2. Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability;Frontiers in Physiology;2023-10-04

3. Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models;Archives of Toxicology;2023-08-01

4. Application of machine learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia;Computer Methods and Programs in Biomedicine;2023-03

5. The contemporary ways of introduction ECG technology: ML, telemetry and bioauthentification;Bratislava Medical Journal;2023

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