ArticleComputer methods in biomechanics and biomedical engineering2018
Predicting the cardiac toxicity of drugs using a novel multiscale exposure-response simulator.
Article in Computer methods in biomechanics and biomedical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- PersonalizedFrontiers in physiology · 2026Article
- Digital twinning of cardiac electrophysiology for congenital heart disease.Journal of the Royal Society, Interface · 2024Article
- Effects of cardiac growth on electrical dyssynchrony in the single ventricle patient.Computer methods in biomechanics and biomedical engineering · 2024Article
- Digital twinning of cardiac electrophysiology for congenital heart disease.bioRxiv : the preprint server for biology · 2023Article
- Material property alterations for phenotypes of heart failure with preserved ejection fraction: A numerical study of subject-specific porcine models.Frontiers in bioengineering and biotechnology · 2022Article
- Patient-Specific Analysis of Ascending Thoracic Aortic Aneurysm with the Living Heart Human Model.Bioengineering (Basel, Switzerland) · 2021Article
- Sex Differences in Drug-Induced Arrhythmogenesis.Frontiers in physiology · 2021Article
- Review
- Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning.Biophysical journal · 2020Article
- Predicting critical drug concentrations and torsadogenic risk using a multiscale exposure-response simulator.Progress in biophysics and molecular biology · 2019Article
- Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification.Computer methods in applied mechanics and engineering · 2019Article
- Multiscale characterization of heart failure.Acta biomaterialia · 2019Article
- A demonstration of modularity, reuse, reproducibility, portability and scalability for modeling and simulation of cardiac electrophysiology using Kepler Workflows.PLoS computational biology · 2019Article
- Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment.Frontiers in pharmacology · 2019Article
- Computational Modeling of Electrophysiology and Pharmacotherapy of Atrial Fibrillation: Recent Advances and Future Challenges.Frontiers in physiology · 2018Review
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Authors and funding
3 authors.
Funding
Abstract
A common but serious side effect of many drugs is torsades de pointes, a rhythm disorder that can have fatal consequences. Torsadogenic risk has traditionally been associated with blockage of a specific potassium channel and an increased recovery period in the electrocardiogram. However, the mechanisms that trigger torsades de pointes remain incompletely understood. Here we establish a computational model to explore how drug-induced effects propagate from the single channel, via the single cell, to the whole heart level. Our mechanistic exposure-response simulator translates block-concentration characteristics for arbitrary drugs into three-dimensional excitation profiles and electrocardiogram recordings to rapidly assess torsadogenic risk. For the drug of dofetilide, we show that this risk is highly dose-dependent: at a concentration of 1x, QT prolongation is 55% but the heart maintains its regular sinus rhythm; at 5.7x, QT prolongation is 102% and the heart spontaneously transitions into torsades de points; at 30x, QT prolongation is 132% and the heart adapts a quasi-depolarized state with numerous rapidly flickering local excitations. Our simulations suggest that neither potassium channel blockage nor QT interval prolongation alone trigger torsades de pointes. The underlying mechanism predicted by our model is early afterdepolarization, which translates into pronounced U waves in the electrocardiogram, a signature that is correctly predicted by our model. Beyond the risk assessment of existing drugs, our exposure-response simulator can become a powerful tool to optimize the co-administration of drugs and, ultimately, guide the design of new drugs toward reducing life threatening drug-induced rhythm disorders in the heart.
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