Computational models in cardiology

Niederer, Steven; Lumens, Joost; Trayanova, Natalia A.

doi:10.1038/s41569-018-0104-y

Cited by 312 publications

(220 citation statements)

References 207 publications

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“…Further, the limited window provided by these criteria onto pro-arrhythmic potential generates false-positives while at the same time preventing many potentially useful drugs from ever reaching the market 7 . Computational modeling and machine learning could significantly accelerate the early stages of drug development, guide the design of safe drugs, and help reduce drug-induced rhythm disorders 8 .…”

Section: Introductionmentioning

confidence: 99%

Classifying drugs by their arrhythmogenic risk using machine learning

Costabal¹,

Seo

Ashley

et al. 2019

Preprint

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All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their pro-arrhythmic potential. We capitalize on recent developments in machine learning and integrate information across ten orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 23 common drugs, exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the pro-arrhythmic potential of new drugs. Our study paves the way towards establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.

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Section: Introductionmentioning

confidence: 99%

Classifying drugs by their arrhythmogenic risk using machine learning

Costabal¹,

Seo

Ashley

et al. 2019

Preprint

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“…Such models provide a useful tool for understanding the mechanics of healthy and diseased heart. Many research groups have developed patient‐specific multiscale heart models . Commonly, those models take into account the geometry of the heart and the distribution of orientation of cardiac muscle fibres.…”

Section: Introductionmentioning

confidence: 99%

“…Many research groups have developed patient-specific multiscale heart models. 1,2 Commonly, those models take into account the geometry of the heart and the distribution of orientation of cardiac muscle fibres. Some of the models use a complex and accurate description of the ion currents and propagation of action potential along the heart chambers.…”

Section: Introductionmentioning

confidence: 99%

Multiscale simulation of the effects of atrioventricular block and valve diseases on heart performance

Syomin

Zberia

Tsaturyan

2019

Numer Methods Biomed Eng

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A new mathematical model of the cardiovascular system is proposed. The left ventricle is described by an axisymmetric multiscale model where myocardium is treated as an incompressible transversely isotropic medium with a realistic distribution of fibre orientation. Active tension and its regulation by Ca2+ ions are described by our recent kinetic model. A lumped parameter model is used for the simulation of blood circulation, in which the left and right atria and the right ventricle are described by a system of ordinary differential equations for active pressure‐volume relationships. The stress and strain of the left ventricle myocardium were calculated by the finite element method implemented by the authors. The changes in the haemodynamics upon changes in preload of a healthy heart, upon physical exercise, and in case of atrioventricular block with different types of arrhythmias were simulated. To simulate the effect of stenosis or regurgitation of the aortic or mitral valves, the hydraulic and inertial flow resistances of the heart valves were set as functions of their orifice areas. The model reproduced a number of phenomena observed in clinical practice, including the classification of the severity of valve disease.

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“…Mathematical modelling and computational simulations have been remarkably successful in providing mechanistic insight into many electrophysiological phenomena. Quantitative models of the action potential have demonstrated their usefulness in basic research and are beginning to be used in safety-critical applications Mirams et al (2012); Niederer et al (2018); Li et al (2019). Mathematical models of ion channels constitute indispensable components of these action potential models.…”

Section: Introductionmentioning

confidence: 99%

Accounting for variability in ion current recordings using a mathematical model of artefacts in voltage-clamp experiments

Lei

Clerx

Whittaker

et al. 2019

Preprint

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Mathematical models of ion channels, which constitute indispensable components of action potential models, are commonly constructed by fitting to whole-cell patch-clamp data. In a previous study we fitted cell-specific models to hERG1a (Kv11.1) recordings simultaneously measured using an automated high-throughput system, and studied cell-cell variability by inspecting the resulting model parameters. However, the origin of the observed variability was not identified. Here we study the source of variability by constructing a model that describes not just ion current dynamics, but the entire voltage-clamp experiment. The experimental artefact components of the model include: series resistance, membrane and pipette capacitance, voltage offsets, imperfect compensations made by the amplifier for these phenomena, and leak current. In this model, variability in the observations can be explained by either cell properties, measurement artefacts, or both. Remarkably, by assuming that variability arises exclusively from measurement artefacts, it is possible to explain a larger amount of the observed variability than when assuming cell-specific ion current kinetics. This assumption also leads to a smaller number of model parameters. This result suggests that most of the observed variability in patch-clamp data measured under the same conditions is caused by experimental artefacts, and hence can be compensated for in post-processing by using our model for the patch-clamp experiment. This study has implications for the question of the extent to which cell-cell variability in ion channel kinetics exists, and opens up routes for better correction of artefacts in patch-clamp data.

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Computational models in cardiology

Cited by 312 publications

References 207 publications

Classifying drugs by their arrhythmogenic risk using machine learning

Classifying drugs by their arrhythmogenic risk using machine learning

Multiscale simulation of the effects of atrioventricular block and valve diseases on heart performance

Accounting for variability in ion current recordings using a mathematical model of artefacts in voltage-clamp experiments

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