Literature DB >> 29493299

Predicting the cardiac toxicity of drugs using a novel multiscale exposure-response simulator.

Francisco Sahli Costabal1, Jiang Yao2, Ellen Kuhl1.   

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.

Entities:  

Keywords:  Electrophysiology; cardiac toxicity; early afterdepolarizations; finite element analysis; pharmacology; torsades de pointes

Mesh:

Substances:

Year:  2018        PMID: 29493299      PMCID: PMC6361171          DOI: 10.1080/10255842.2018.1439479

Source DB:  PubMed          Journal:  Comput Methods Biomech Biomed Engin        ISSN: 1025-5842            Impact factor:   1.763


  6 in total

1.  Predicting critical drug concentrations and torsadogenic risk using a multiscale exposure-response simulator.

Authors:  Francisco Sahli Costabal; Jiang Yao; Anna Sher; Ellen Kuhl
Journal:  Prog Biophys Mol Biol       Date:  2018-10-26       Impact factor: 3.667

Review 2.  Computational Modeling of Electrophysiology and Pharmacotherapy of Atrial Fibrillation: Recent Advances and Future Challenges.

Authors:  Márcia Vagos; Ilsbeth G M van Herck; Joakim Sundnes; Hermenegild J Arevalo; Andrew G Edwards; Jussi T Koivumäki
Journal:  Front Physiol       Date:  2018-09-04       Impact factor: 4.566

3.  A demonstration of modularity, reuse, reproducibility, portability and scalability for modeling and simulation of cardiac electrophysiology using Kepler Workflows.

Authors:  Pei-Chi Yang; Shweta Purawat; Pek U Ieong; Mao-Tsuen Jeng; Kevin R DeMarco; Igor Vorobyov; Andrew D McCulloch; Ilkay Altintas; Rommie E Amaro; Colleen E Clancy
Journal:  PLoS Comput Biol       Date:  2019-03-08       Impact factor: 4.475

4.  Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment.

Authors:  Jaimit Parikh; Paolo Di Achille; James Kozloski; Viatcheslav Gurev
Journal:  Front Pharmacol       Date:  2019-10-02       Impact factor: 5.810

5.  Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning.

Authors:  Francisco Sahli-Costabal; Kinya Seo; Euan Ashley; Ellen Kuhl
Journal:  Biophys J       Date:  2020-01-22       Impact factor: 4.033

Review 6.  In silico models for evaluating proarrhythmic risk of drugs.

Authors:  Minki Hwang; Chul-Hyun Lim; Chae Hun Leem; Eun Bo Shim
Journal:  APL Bioeng       Date:  2020-06-04
  6 in total

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