A novel computational model of the human ventricular action potential and Ca transient

Grandi, Eleonora; Pasqualini, Francesco S.; Bers, Donald M.

doi:10.1016/j.yjmcc.2009.09.019

Cited by 387 publications

(438 citation statements)

References 49 publications

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“…On top of that, the most relevant ionic currents in the IMW04 model are described using Markovian chains, with the consequent increase in complexity (more than 60 state variables), which imposes serious restrictions on the size of the time step required for stability and increases the overall computational time when using this model in tissue simulations [6]. Recently, a new model of human ventricular AP has been proposed by Grandi et al [7] (GPB). The development of that model departs from the rabbit ECC model proposed by Shannon et al [8], which includes the subsarcolemmal and junctional compartments in the formulation of the currents and provides a detailed description of calcium handling.…”

Section: Introductionmentioning

confidence: 99%

A human ventricular cell model for investigation of cardiac arrhythmias under hyperkalaemic conditions

Carro

Rodrı́guez

Laguna

et al. 2011

Phil. Trans. R. Soc. A.

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In this study, several modifications were introduced to a recently proposed human ventricular action potential (AP) model so as to render it suitable for the study of ventricular arrhythmias. These modifications were driven by new sets of experimental data available from the literature and the analysis of several well-established cellular arrhythmic risk biomarkers, namely AP duration at 90 per cent repolarization (APD 90 ), AP triangulation, calcium dynamics, restitution properties, APD 90 adaptation to abrupt heart rate changes, and rate dependence of intracellular sodium and calcium concentrations. The proposed methodology represents a novel framework for the development of cardiac cell models. Five stimulation protocols were applied to the original model and the ventricular AP model developed here to compute the described arrhythmic risk biomarkers. In addition, those models were tested in a one-dimensional fibre in which hyperkalaemia was simulated by increasing the extracellular potassium concentration, [K + ] o . The effective refractory period (ERP), conduction velocity (CV) and the occurrence of APD alternans were investigated. Results show that modifications improved model behaviour as verified by: (i) AP triangulation well within experimental limits (the difference between APD at 50 and 90 per cent repolarization being 78.1 ms); (ii) APD 90 rate adaptation dynamics characterized by fast and slow time constants within physiological ranges (10.1 and 105.9 s); and (iii) maximum S1S2 restitution slope in accordance with experimental data (S S1S2 = 1.0). In simulated tissues under hyperkalaemic conditions, APD 90 progressively shortened with the degree of hyperkalaemia, whereas ERP increased once a threshold in of AP models on the computation of arrhythmic risk biomarkers, as opposed to joining together independently derived ion channel descriptions to produce a whole-cell AP model, with the new framework providing a better picture of the model performance under a variety of stimulation conditions. On top of replicating experimental data at single-cell level, the model developed here was able to predict the occurrence of APD 90 alternans and areas of conduction block associated with high [K + ] o in tissue, which is of relevance for the investigation of the arrhythmogenic substrate in ischaemic hearts.

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Section: Introductionmentioning

confidence: 99%

A human ventricular cell model for investigation of cardiac arrhythmias under hyperkalaemic conditions

Carro

Rodrı́guez

Laguna

et al. 2011

Phil. Trans. R. Soc. A.

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“…A priority of CiPA is to keep the framework simple and constrain the cost of data generation; therefore, we use only IC50 data for other channels as, based on our current knowledge, they provide enough information to correctly separate drugs into their TdP risk categories. Additionally, calcium transient properties in the ORd model differ from other models, such as the Grandi et al model (Grandi et al, 2010) (Cummins et al, 2014). However, as mentioned earlier in this study Cummins et al define a binary TdP risk stratification that does not follow the same categorization as defined by CiPA.…”

Section: Limitations and Ongoing Workmentioning

confidence: 80%

Optimization of an In Silico Cardiac Cell Model for Proarrhythmia Risk Assessment

Dutta¹,

Strauss²,

Colatsky³

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Drug-induced Torsade-de-Pointes (TdP) has been responsible for the withdrawal of many drugs from the market and is therefore of major concern to global regulatory agencies and the pharmaceutical industry. The Comprehensive in vitro Proarrhythmia Assay (CiPA) was proposed to improve prediction of TdP risk, using in silico models and in vitro multi-channel pharmacology data as integral parts of this initiative. Previously, we reported that combining dynamic interactions between drugs and the rapid delayed rectifier potassium current (IKr) with multi-channel pharmacology is important for TdP risk classification, and we modified the original O'Hara Rudy ventricular cell mathematical model to include a Markov model of IKr to represent dynamic drug-IKr interactions (IKr-dynamic ORd model). We also developed a novel metric that could separate drugs with different TdP liabilities at high concentrations based on total electronic charge carried by the major inward ionic currents during the action potential. In this study, we further optimized the IKr-dynamic ORd model by refining model parameters using published human cardiomyocyte experimental data under control and drug block conditions. Using this optimized model and manual patch clamp data, we developed an updated version of the metric that quantifies the net electronic charge carried by major inward and outward ionic currents during the steady state action potential, which could classify the level of drug-induced TdP risk across a wide range of concentrations and pacing rates. We also established a framework to quantitatively evaluate a system's robustness against the induction of early afterdepolarizations (EADs), and demonstrated that the new metric is correlated with the cell's robustness to the pro-EAD perturbation of IKr conductance reduction. In summary, in this work we present an optimized model that is more consistent with experimental data, an improved metric that can classify drugs at concentrations both near and higher than clinical exposure, and a physiological framework to check the relationship between a metric and EAD. These findings provide a solid foundation for using in silico models for the regulatory assessment of TdP risk under the CiPA paradigm.

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“…In the passive model, I ion is given by the linear model where R m represents the resistance of the passive membrane (in k cm 2 ) and v rest denotes the resting potential of the membrane. In the active model, we let I ion be represented by the action potential (AP) model of Grandi et al [48]. In this case, Equation (6) is replaced by a system of the form…”

Section: Membrane Modelmentioning

confidence: 99%

“…A wide variety of models are available through the open CellML library [72]. In our computations, we have used the ventricular cell model by Grandi et al [48]. That model consists of a system of 39 ODEs defined on every computational node of the cell membrane and is believed to provide an accurate representation of the cell's action potential.…”

Section: Membrane Dynamicsmentioning

confidence: 99%

A Cell-Based Framework for Numerical Modeling of Electrical Conduction in Cardiac Tissue

et al. 2017

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In this paper, we study a mathematical model of cardiac tissue based on explicit representation of individual cells. In this EMI model, the extracellular (E) space, the cell membrane (M), and the intracellular (I) space are represented as separate geometrical domains. This representation introduces modeling flexibility needed for detailed representation of the properties of cardiac cells including their membrane. In particular, we will show that the model allows ion channels to be non-uniformly distributed along the membrane of the cell. Such features are difficult to include in classical homogenized models like the monodomain and bidomain models frequently used in computational analyses of cardiac electrophysiology. The EMI model is solved using a finite difference method (FDM) and two variants of the finite element method (FEM). We compare the three schemes numerically, reporting on CPU-efforts and convergence rates. Finally, we illustrate the distinctive capabilities of the EMI model compared to classical models by simulating monolayers of cardiac cells with heterogeneous distributions of ionic channels along the cell membrane. Because of the detailed representation of every cell, the computational problems that result from using the EMI model are much larger than for the classical homogenized models, and thus represent a computational challenge. However, our numerical simulations indicate that the FDM scheme is optimal in the sense that the computational complexity increases proportionally to the number of cardiac cells in the model. Moreover, we present simulations, based on systems of equations involving ∼117 million unknowns, representing up to ∼16,000 cells. We conclude that collections of cardiac cells can be simulated using the EMI model, and that the EMI model enable greater modeling flexibility than the classical monodomain and bidomain models.

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A novel computational model of the human ventricular action potential and Ca transient

Cited by 387 publications

References 49 publications

A human ventricular cell model for investigation of cardiac arrhythmias under hyperkalaemic conditions

A human ventricular cell model for investigation of cardiac arrhythmias under hyperkalaemic conditions

Optimization of an In Silico Cardiac Cell Model for Proarrhythmia Risk Assessment

A Cell-Based Framework for Numerical Modeling of Electrical Conduction in Cardiac Tissue

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