Combined action potential- and dynamic-clamp for accurate computational modelling of the cardiac IKr current.

In the present work Action-Potential clamp (APC) and Dynamic clamp (DC) were used in combination in order to optimize the Luo-Rudy (LRd) mathematical formulation of the guinea-pig rapid delayed rectifier K(+) current (IKr), and to validate the optimized model. To this end, IKr model parameters were adjusted to fit the experimental E4031-sensitive current (IE4031) recorded under APC in guinea-pig myocytes. Currents generated by LRd model (ILRd) and the optimized one (IOpt) were then compared by testing their suitability to replace IE4031 under DC. Under APC, ILRd was significantly larger than IE4031 (mean current densities 0.51±0.01 vs 0.21±0.05pA/pF; p<0.001), mainly because of different rectification. IOpt mean density (0.17±0.01pA/pF) was similar to the IE4031 one (NS); moreover, IOpt accurately reproduced IE4031 distribution along the different AP phases. Models were then compared under DC by blocking native IKr (5µM E4031) and replacing it with ILRd or IOpt. Whereas injection of ILRd overshortened AP duration (APD90) (by 25% of its pre-block value), IOpt injection restored AP morphology and duration to overlap pre-block values. This study highlights the power of APC and DC for the identification of reliable formulations of ionic current models. An optimized model of IKr has been obtained which fully reversed E4031 effects on the AP. The model strongly diverged from the widely used Luo-Rudy formulation; this can be particularly relevant to the in silico analysis of AP prolongation caused by IKr blocking or alterations.

Lessons from the heart: mirroring electrophysiological characteristics during cardiac development to in vitro differentiation of stem cell derived cardiomyocytes.

The ability of human pluripotent stem cells (hPSCs) to differentiate into any cell type of the three germ layers makes them a very promising cell source for multiple purposes, including regenerative medicine, drug discovery, and as a model to study disease mechanisms and progression. One of the first specialized cell types to be generated from hPSC was cardiomyocytes (CM), and differentiation protocols have evolved over the years and now allow for robust and large-scale production of hPSC-CM. Still, scientists are struggling to achieve the same, mainly ventricular, phenotype of the hPSC-CM in vitro as their adult counterpart in vivo. In vitro generated cardiomyocytes are generally described as fetal-like rather than adult. In this review, we compare the in vivo development of cardiomyocytes to the in vitro differentiation of hPSC into CM with focus on electrophysiology, structure and contractility. Furthermore, known epigenetic changes underlying the differences between adult human CM and CM differentiated from pluripotent stem cells are described. This should provide the reader with an extensive overview of the current status of human stem cell-derived cardiomyocyte phenotype and function. Additionally, the reader will gain insight into the underlying signaling pathways and mechanisms responsible for cardiomyocyte development.

Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment.

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.

Corrigendum: Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment.

[This corrects the article on p. 616 in vol. 8, PMID: 28878692.].

Computational Models for Understanding of Structure, Function and Pharmacology of the Cardiac Potassium Channel Kv11.1 (hERG).

The rapid delayed rectifier current IKr is one of the major K+ currents involved in repolarization of the human cardiac action potential. Various inherited or drug-induced forms of the long QT syndrome (LQTS) in humans are linked to functional and structural modifications in the IKr conducting channels. IKr is carried by the potassium channel Kv11.1 encoded by the gene KCNH2 (commonly referred to as human ether-a-go-go-related gene or hERG) [1, 2]. The first necessary step for predicting emergent drug effects on the heart is determining and modeling the binding thermodynamics and kinetics of primary and major off-target drug interactions with subcellular targets. The bulk of drugs that target hERG channels are known to have complex interactions at the atomic scale. Accordingly, one of the goals for this review is to provide comprehensive guide in the universe of computational models aiming to refine our understanding of structure-function relations in Kv11.1 and its isoforms. The special emphasis is placed on the mapping of drug binding sites and tentative mechanisms of channel inhibition and activation by drugs. An overview over recent structural models and mapping of binding sites for blockers and activators of IKr current along with the discussion on agreements and discrepancies among different models is presented. There is an apparent reciprocity or feedback loop between drug binding and action potential of the cardiac myocytes. Thus one has to connect drug binding to a particular receptor so that its functional consequences impact on the action potential duration. The natural pathway is to develop multi-scale models that connect between receptor and cellular scales. The potential for such multi-scale model development is discussed through the lens of common gating models. Accordingly, the second part of this review covers an ongoing development of the kinetic models of gating transitions and cardiac ion currents carried by hERG channels with and without drug bound.

Evaluation of an in silico cardiac safety assay: using ion channel screening data to predict QT interval changes in the rabbit ventricular wedge.

INTRODUCTION: Drugs that prolong the QT interval on the electrocardiogram present a major safety concern for pharmaceutical companies and regulatory agencies. Despite a range of assays performed to assess compound effects on the QT interval, QT prolongation remains a major cause of attrition during compound development. In silico assays could alleviate such problems. In this study we evaluated an in silico method of predicting the results of a rabbit left-ventricular wedge assay. METHODS: Concentration-effect data were acquired from either: the high-throughput IonWorks/FLIPR; the medium-throughput PatchXpress ion channel assays; or QSAR, a statistical IC50 value prediction model, for hERG, fast sodium, L-type calcium and KCNQ1/minK channels. Drug block of channels was incorporated into a mathematical differential equation model of rabbit ventricular myocyte electrophysiology through modification of the maximal conductance of each channel by a factor dependent on the IC50 value, Hill coefficient and concentration of each compound tested. Simulations were performed and agreement with experimental results, based upon input data from the different assays, was evaluated. RESULTS: The assay was found to be 78% accurate, 72% sensitive and 81% specific when predicting QT prolongation (>10%) using PatchXpress assay data (77 compounds). Similar levels of predictivity were demonstrated using IonWorks/FLIPR data (121 compounds) with 78% accuracy, 73% sensitivity and 80% specificity. QT shortening (<-10%) was predicted with 77% accuracy, 33% sensitivity and 90% specificity using PatchXpress data and 71% accuracy, 42% sensitivity and 81% specificity using IonWorks/FLIPR data. Strong quantitative agreement between simulation and experimental results was also evident. DISCUSSION: The in silico action potential assay demonstrates good predictive ability, and is suitable for very high-throughput use in early drug development. Adoption of such an assay into cardiovascular safety assessment, integrating ion channel data from routine screens to infer results of animal-based tests, could provide a cost- and time-effective cardiac safety screen.

Spatial correlation of action potential duration and diastolic dysfunction in transgenic and drug-induced LQT2 rabbits.

BACKGROUND: Enhanced dispersion of action potential duration (APD) is a major contributor to long QT syndrome (LQTS)-related arrhythmias. OBJECTIVE: To investigate spatial correlations of regional heterogeneities in cardiac repolarization and mechanical function in LQTS. METHODS: Female transgenic LQTS type 2 (LQT2; n = 11) and wild-type littermate control (LMC) rabbits (n = 9 without E4031 and n = 10 with E4031) were subjected to phase contrast magnetic resonance imaging to assess regional myocardial velocities. In the same rabbits' hearts, monophasic APDs were assessed in corresponding segments. RESULTS: In LQT2 and E4031-treated rabbits, APD was longer in all left ventricular segments (P < .01) and APD dispersion was greater than that in LMC rabbits (P < .01). In diastole, peak radial velocities (Vr) were reduced in LQT2 and E4031-treated compared to LMC rabbits in LV base and mid (LQT2: -3.36 ± 0.4 cm/s, P < .01; E4031-treated: -3.24 ± 0.6 cm/s, P < .0001; LMC: -4.42 ± 0.5 cm/s), indicating an impaired diastolic function. Regionally heterogeneous diastolic Vr correlated with APD (LQT2: correlation coefficient [CC] 0.38, P = .01; E4031-treated: CC 0.42, P < .05). Time-to-diastolic peak Vr were prolonged in LQT2 rabbits (LQT2: 196.8 ± 2.9 ms, P < .001; E4031-treated: 199.5 ± 2.2 ms, P < .0001, LMC 183.1 ± 1.5), indicating a prolonged contraction duration. Moreover, in transgenic LQT2 rabbits, diastolic time-to-diastolic peak Vr correlated with APD (CC 0.47, P = .001). In systole, peak Vr were reduced in LQT2 and E4031-treated rabbits (P < .01) but longitudinal velocities or ejection fraction did not differ. Finally, random forest machine learning algorithms enabled a differentiation between LQT2, E4031-treated, and LMC rabbits solely based on "mechanical" magnetic resonance imaging data. CONCLUSIONS: The prolongation of APD led to impaired diastolic and systolic function in transgenic and drug-induced LQT2 rabbits. APD correlated with regional diastolic dysfunction, indicating that LQTS is not purely an electrical but an electromechanical disorder.

Variability in high-throughput ion-channel screening data and consequences for cardiac safety assessment.

INTRODUCTION: Unwanted drug interactions with ionic currents in the heart can lead to an increased pro-arrhythmic risk to patients in the clinic. It is therefore a priority for safety pharmacology teams to detect block of cardiac ion channels, and new technologies have enabled the development of automated and high-throughput screening assays using cell lines. As a result of screening multiple ion-channels there is a need to integrate information, particularly for compounds affecting more than one current, and mathematical electrophysiology in-silico action potential models are beginning to be used for this. METHODS: We quantified the variability associated with concentration-effect curves fitted to recordings from high-throughput Molecular Devices IonWorks® Quattro™ screens when detecting block of I(Kr) (hERG), I(Na) (NaV1.5), I(CaL) (CaV1.2), I(Ks) (KCNQ1/minK) and I(to) (Kv4.3/KChIP2.2), and the Molecular Devices FLIPR® Tetra fluorescence screen for I(CaL) (CaV1.2), for control compounds used at AstraZeneca and GlaxoSmithKline. We examined how screening variability propagates through in-silico action potential models for whole cell electrical behaviour, and how confidence intervals on model predictions can be estimated with repeated simulations. RESULTS: There are significant levels of variability associated with high-throughput ion channel electrophysiology screens. This variability is of a similar magnitude for different cardiac ion currents and different compounds. Uncertainty in the Hill coefficients of reported concentration-effect curves is particularly high. Depending on a compound's ion channel blocking profile, the uncertainty introduced into whole-cell predictions can become significant. DISCUSSION: Our technique allows confidence intervals to be placed on computational model predictions that are based on high-throughput ion channel screens. This allows us to suggest when repeated screens should be performed to reduce uncertainty in a compound's action to acceptable levels, to allow a meaningful interpretation of the data.