In silico assessment of the effects of various compounds in MEA/hiPSC-CM assays: Modeling and numerical simulations

The simplified Kirchhoff network model (SKNM): a cell-based reaction-diffusion model of excitable tissue

Cell-based models of excitable tissues offer the advantage of cell-level precision, which cannot be achieved using traditional homogenized electrophysiological models. However, this enhanced accuracy comes at the cost of increased computational demands, necessitating the development of efficient cell-based models. The widely-accepted bidomain model serves as the standard in computational cardiac electrophysiology, and under certain anisotropy ratio conditions, it is well known that it can be reduced to the simpler monodomain model. Recently, the Kirchhoff Network Model (KNM) was developed as a cell-based counterpart to the bidomain model. In this paper, we aim to demonstrate that KNM can be simplified using the same steps employed to derive the monodomain model from the bidomain model. We present the cell-based Simplified Kirchhoff Network Model (SKNM), which produces results closely aligned with those of KNM while requiring significantly less computational resources.

Beneficial effects of chronic mexiletine treatment in a human model of SCN5A overlap syndrome

AIMS

SCN5A mutations are associated with various cardiac phenotypes, including long QT syndrome type 3 (LQT3), Brugada syndrome (BrS), and cardiac conduction disease (CCD). Certain mutations, such as SCN5A-1795insD, lead to an overlap syndrome, with patients exhibiting both features of BrS/CCD [decreased sodium current (INa)] and LQT3 (increased late INa). The sodium channel blocker mexiletine may acutely decrease LQT3-associated late INa and chronically increase peak INa associated with SCN5A loss-of-function mutations. However, most studies have so far employed heterologous expression systems and high mexiletine concentrations. We here investigated the effects of a therapeutic dose of mexiletine on the mixed phenotype associated with the SCN5A-1795insD mutation in HEK293A cells and human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs).

METHODS AND RESULTS

To assess only the chronic effects on trafficking, HEK293A cells transfected with wild-type (WT) SCN5A or SCN5A-1795insD were incubated for 48 h with 10 µm mexiletine followed by wash-out, which resulted in an increased peak INa for both SCN5A-WT and SCN5A-1795insD and an increased late INa for SCN5A-1795insD. Acute re-exposure of HEK293A cells to 10 µm mexiletine did not impact on peak INa but significantly decreased SCN5A-1795insD late INa. Chronic incubation of SCN5A-1795insD hiPSC-CMs with mexiletine followed by wash-out increased peak INa, action potential (AP) upstroke velocity, and AP duration. Acute re-exposure did not impact on peak INa or AP upstroke velocity, but significantly decreased AP duration.

CONCLUSION

These findings demonstrate for the first time the therapeutic benefit of mexiletine in a human cardiomyocyte model of SCN5A overlap syndrome.

The three-dimensionality of the hiPSC-CM spheroid contributes to the variability of the field potential

Background: Field potential (FP) signals from human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) spheroid which are used for drug safety tests in the preclinical stage are different from action potential (AP) signals and require working knowledge of the multi-electrode array (MEA) system. In this study, we developed in silico three-dimensional (3-D) models of hiPSC-CM spheroids for the simulation of field potential measurement. We compared our model simulation results against in vitro experimental data under the effect of drugs E-4031 and nifedipine. Methods: In silico 3-D models of hiPSC-CM spheroids were constructed in spherical and discoidal shapes. Tetrahedral meshes were generated inside the models, and the propagation of the action potential in the model was obtained by numerically solving the monodomain reaction-diffusion equation. An electrical model of electrode was constructed and FPs were calculated using the extracellular potentials from the AP propagations. The effects of drugs were simulated by matching the simulation results with in vitro experimental data. Results: The simulated FPs from the 3-D models of hiPSC-CM spheroids exhibited highly variable shapes depending on the stimulation and measurement locations. The values of the IC₅₀ of E-4031 and nifedipine calculated by matching the simulated FP durations with in vitro experimental data were in line with the experimentally measured ones reported in the literature. Conclusion: The 3-D in silico models of hiPSC-CM spheroids generated highly variable FPs similar to those observed in in vitro experiments. The in silico model has the potential to complement the interpretation of the FP signals obtained from in vitro experiments.

Cardiomyocyte electrical-mechanical synchronized model for high-content, dose-quantitative and time-dependent drug assessment

Cardiovascular diseases have emerged as a significant threat to human health. However, drug development is a time-consuming and costly process, and few drugs pass the preclinical assessment of safety and efficacy. The existing patch-clamp, Ca2+ imaging, and microelectrode array technologies in cardiomyocyte models for drug preclinical screening have suffered from issues of low throughput, limited long-term assessment, or inability to synchronously and correlatively analyze electrical and mechanical signals. Here, we develop a high-content, dose-quantitative and time-dependent drug assessment platform based on an electrical-mechanical synchronized (EMS) biosensing system. This microfabricated EMS can record both firing potential (FP) and mechanical beating (MB) signals from cardiomyocytes and extract a variety of characteristic parameters from these two signals (FP-MB) for further analysis. This system was applied to test typical ion channel drugs (lidocaine and isradipine), and the dynamic responses of cardiomyocytes to the tested drugs were recorded and analyzed. The high-throughput characteristics of the system can facilitate simultaneous experiments on a large number of samples. Furthermore, a database of various cardiac drugs can be established by heat map analysis for rapid and effective screening of drugs. The EMS biosensing system is highly promising as a powerful tool for the preclinical development of new medicines.

Identifying Drug Response by Combining Measurements of the Membrane Potential, the Cytosolic Calcium Concentration, and the Extracellular Potential in Microphysiological Systems

Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs) offer a new means to study and understand the human cardiac action potential, and can give key insight into how compounds may interact with important molecular pathways to destabilize the electrical function of the heart. Important features of the action potential can be readily measured using standard experimental techniques, such as the use of voltage sensitive dyes and fluorescent genetic reporters to estimate transmembrane potentials and cytosolic calcium concentrations. Using previously introduced computational procedures, such measurements can be used to estimate the current density of major ion channels present in hiPSC-CMs, and how compounds may alter their behavior. However, due to the limitations of optical recordings, resolving the sodium current remains difficult from these data. Here we show that if these optical measurements are complemented with observations of the extracellular potential using multi electrode arrays (MEAs), we can accurately estimate the current density of the sodium channels. This inversion of the sodium current relies on observation of the conduction velocity which turns out to be straightforwardly computed using measurements of extracellular waves across the electrodes. The combined data including the membrane potential, the cytosolic calcium concentration and the extracellular potential further opens up for the possibility of accurately estimating the effect of novel drugs applied to hiPSC-CMs.

A greedy classifier optimization strategy to assess ion channel blocking activity and pro-arrhythmia in hiPSC-cardiomyocytes

Novel studies conducting cardiac safety assessment using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are promising but might be limited by their specificity and predictivity. It is often challenging to correctly classify ion channel blockers or to sufficiently predict the risk for Torsade de Pointes (TdP). In this study, we developed a method combining in vitro and in silico experiments to improve machine learning approaches in delivering fast and reliable prediction of drug-induced ion-channel blockade and proarrhythmic behaviour. The algorithm is based on the construction of a dictionary and a greedy optimization, leading to the definition of optimal classifiers. Finally, we present a numerical tool that can accurately predict compound-induced pro-arrhythmic risk and involvement of sodium, calcium and potassium channels, based on hiPSC-CM field potential data.

Comparison of in vitro and computational experiments on the relation of inter-beat interval and duration of repolarization in a specific type of human induced pluripotent stem cell-derived cardiomyocytes

We compared a published computational model of the action potential of a specific type of human induced pluripotent stem cell -derived cardiomyocytes (hiPSC-CM) with experimental field potential data with regard to their inter-beat interval and the duration of repolarization. In particular, concomitant changes in inter-beat interval and duration of repolarization were calculated after reduction and/or augmentation of specific ion channel conductances as a surrogate for pharmacological manipulation. The observed mismatches between calculations and experimental data indicate that there is information missing about the cellular test system. Based on our results we hypothesize that, among other currents, the actual I_f (“funny current”) may deviate from the prediction. We show that replacement of the I_f formulation by alternative equations causes the model predictions to change qualitatively, however, none of the available formulations is actually achieving a satisfactory match with experimental data. We suggest a strategy to clarify whether the mismatch can be completely resolved at all using single cell models and, if yes, how this goal could be reached.

Representation of Multiple Cellular Phenotypes Within Tissue-Level Simulations of Cardiac Electrophysiology

Distinct electrophysiological phenotypes are exhibited by biological cells that have differentiated into particular cell types. The usual approach when simulating the cardiac electrophysiology of tissue that includes different cell types is to model the different cell types as occupying spatially distinct yet coupled regions. Instead, we model the electrophysiology of well-mixed cells by using homogenisation to derive an extension to the commonly used monodomain or bidomain equations. These new equations permit spatial variations in the distribution of the different subtypes of cells and will reduce the computational demands of solving the governing equations. We validate the homogenisation computationally, and then use the new model to explain some experimental observations from stem cell-derived cardiomyocyte monolayers.

AFM nano-mechanical study of the beating profile of hiPSC-derived cardiomyocytes beating bodies WT and DM1

Myotonic Dystrophy type 1 (DM1) is the most common form of muscular dystrophy in adults, characterized by a variety of multisystemic features and associated with cardiac anomalies. Among cardiac phenomena, conduction defects, ventricular arrhythmias, and dilated cardiomyopathy represent the main cause of sudden death in DM1 patients. Patient-specific induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) represent a powerful in vitro model for molecular, biochemical, and physiological studies of disease in the target cells. Here, we used an Atomic Force Microscope (AFM) to measure the beating profiles of a large number of cells, organized in CM clusters (Beating Bodies, BBs), obtained from wild type (WT) and DM1 patients. We monitored the evolution over time of the frequency and intensity of the beating. We determined the variations between different BBs and over various areas of a single BB, caused by morphological and biomechanical variations. We exploited the AFM tip to apply a controlled force over the BBs, to carefully assess the biomechanical reaction of the different cell clusters over time, both in terms of beating frequency and intensity. Our measurements demonstrated differences between the WT and DM1 clusters highlighting, for the DM1 samples, an instability which was not observed in WT cells. We measured differences in the cellular response to the applied mechanical stimulus in terms of beating synchronicity over time and cell tenacity, which are in good agreement with the cellular behavior in vivo. Overall, the combination of hiPSC-CMs with AFM characterization can become a new tool to study the collective movements of cell clusters in different conditions and can be extended to the characterization of the BB response to chemical and pharmacological stimuli.

Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel Block

The Micro-Electrode Array (MEA) device enables high-throughput electrophysiology measurements that are less labor-intensive than patch-clamp based techniques. Combined with human-induced pluripotent stem cells cardiomyocytes (hiPSC-CM), it represents a new and promising paradigm for automated and accurate in vitro drug safety evaluation. In this article, the following question is addressed: which features of the MEA signals should be measured to better classify the effects of drugs? A framework for the classification of drugs using MEA measurements is proposed. The classification is based on the ion channels blockades induced by the drugs. It relies on an in silico electrophysiology model of the MEA, a feature selection algorithm and automatic classification tools. An in silico model of the MEA is developed and is used to generate synthetic measurements. An algorithm that extracts MEA measurements features designed to perform well in a classification context is described. These features are called composite biomarkers. A state-of-the-art machine learning program is used to carry out the classification of drugs using experimental MEA measurements. The experiments are carried out using five different drugs: mexiletine, flecainide, diltiazem, moxifloxacin, and dofetilide. We show that the composite biomarkers outperform the classical ones in different classification scenarios. We show that using both synthetic and experimental MEA measurements improves the robustness of the composite biomarkers and that the classification scores are increased.