Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) hold promise in personalized medicine, particularly for cardiac diseases and human-data-based pharmacology studies. Assessing hiPSC-CM mechanics and their changes in response to drug action in silico enables more efficient drug testing. For such investigations, hiPSC-CMs also provide a versatile alternative to adult human cardiac tissue which is limited in availability for research. To enable in silico investigations of hiPSC-CM electrophysiology and contraction, we developed and evaluated two versions of hiPSC-CM electromechanical models with different maturation states. The models were based solely on human cardiomyocyte and hiPSC-CM data. The evaluation process involved comparing simulation outcomes with an extensive dataset of experimental data to ensure the reliability of the model within the context of hiPSC-CM pharmacology studies. The models uniquely incorporated the mechanical properties of hiPSC-CMs, providing insights into the mechanisms underlying their contractile behaviour. In our in silico studies, we simulated the effects of 64 different drugs, including those with previously untested inotropic effects. We demonstrated agreement between the simulation and experimental datasets, correctly identifying the inotropic effects of 41 out of 48 drugs. We also compared the effect of pharmacological agents with unknown inotropic effects and conducted novel experiments demonstrating agreement with simulation outcomes. Finally, using the models, we demonstrated the mechanisms of previously unrecognized rate-dependent inotropic effects of paliperidone. Altogether this study presents an in vitro - in silico framework which is evaluated against experimental data and allows for simulating drug-dependent electromechanical effects with high accuracy and prediction of rate-dependent inotropic effects.Key points Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are promising for drug testing and disease modelling, but current computer models that allow us to simulate hiPSC-CM behaviour lack human-specific mechanical properties. We developed and validated hiPSC-CM electromechanical models, allowing accurate simulations of contraction, calcium signalling and electrophysiology for two different maturation stages. Simulations with the new models correctly predicted inotropic effects for 41 out of 48 drugs and identified previously unknown effects of two drugs, later confirmed experimentally. Simulations revealed novel rate-dependent inotropic effects of paliperidone linked to calcium handling differences in paced versus non-paced cells. This in silico framework can enhance drug testing accuracy and understanding through mechanistic studies by integrating experimental data with computational predictions.

BACKGROUND: Cardiovascular disease is the leading global cause of morbidity and mortality. New technologies are needed to improve mechanistic understanding and inform therapeutic strategies. Human-centric cardiac simulations show great promise; however, existing cellular models can reproduce only a few arrhythmia-driving behaviors and show important discrepancies with experimental data. We aimed to develop a new model overcoming this lack of generality, which markedly limits the predictivity and translational utility of virtual cardiomyocytes. METHODS: We developed T-World, a novel virtual human cardiomyocyte, using data-driven differential equations to describe sex-specific excitation-contraction coupling, mechanical contraction, β-adrenergic signaling, and its effects on cellular targets. The model contains several key innovations, including a new approach to coupling L-type calcium channels and ryanodine receptors, with updated calcium-dependent-inactivation of the former and novel calcium-induced refractoriness and complete reparameterization of the latter. We also redeveloped the sodium-potassium pump and made major improvements to the sodium-calcium exchanger formulation. RESULTS: T-World shows broad agreement with experimental data on rate-dependent action potential (AP), calcium handling, and contraction properties. Extensively validated on independent data, T-World demonstrates strong predictive performance, for example, in drug-induced AP changes. The model reproduces the effects of sympathetic stimulation, including AP duration shortening and increased calcium-transient amplitude and contractility. Importantly, it recapitulates for the first time all key cellular mechanisms driving life-threatening arrhythmias (early and delayed afterdepolarizations, alternans, and steep S1-S2 restitution), including experimentally observed responses to interventions such as sympathetic activation, SERCA (sarco/endoplasmic reticulum Ca 2+ ATPase) inhibition, and AP prolongation. Combined with the model’s ability to simulate physiological sex-specific differences in electrophysiology, this revealed increased proclivity of female cardiomyocytes to early afterdepolarizations and steep restitution of AP duration. CONCLUSIONS: T-World is a highly general and predictive open-source computer model of a human ventricular cardiomyocyte, suitable for multiscale research studies investigating determinants of arrhythmogenesis.

Abstract State-of-the-art cardiac electromechanical modelling and simulation form the basis for recent developments in cardiac Digital Twin technologies. However, a comprehensive evaluation of electromechanical models at cellular, tissue, and organ level has yet to be performed that addresses both ECG and pressure-volume biomarkers. Such an evaluation would build credibility for applications of cardiac Digital Twins in clinical research and therapy development. We aimed to follow ASME V&V40 standards to develop a strategy for calibration, validation, and uncertainty quantification of ventricular electromechanical Digital Twins under healthy conditions. We performed a multi-scaled review of ventricular electromechanics to compile a dataset for calibration and validation incorporating ECG, pressure-volume, displacement, and strain biomarkers. When applied to a biventricular multiscale model, we achieved healthy calibrated values for the QRS duration (89 ms), QT interval (360 ms), left ventricular ejection fraction (LVEF) (51 %), peak systolic pressure (14 kPa), end diastolic (110 mL) and end systolic volumes (50 mL), peak ejection flow rate (180 mL/ms). Model validation was performed by comparison to displacement and strain biomarkers including systolic atrioventricular plane displacement (1.5 cm), systolic fibre strain (−0.18) and longitudinal strain (−0.15). Sensitivity analysis of model parameters at cellular and ventricular scales was also performed. We quantified the effects of variability in ionic conductance, mechanical stiffness, cross-bridge cycling dynamics, and systemic circulation on action potential and active tension dynamics at the cellular scale, and on ECG, pressure-volume, displacement, and strain biomarkers at the ventricular scale. Simulations showed that the relationship between healthy LVEF and T wave biomarkers was primarily underpinned by variability in L-type calcium channel conductance and SERCA activity through multi-scale effects. In this study, we pave the way towards credible cardiac electromechanical Digital Twins by setting the basis for a strategy for calibration and validation based on both ECG and mechanical biomarkers.

Type 2 diabetes is a highly prevalent metabolic disease that significantly impacts the heart and contributes to an increased risk of cardiac complications, notably heart failure with preserved ejection fraction and cardiac arrhythmias, which can cause sudden cardiac death. In type 2 diabetes chronic hyperglycaemia and insulin resistance lead to subcellular changes, including dysregulation of calcium/calmodulin-dependent protein kinase II (CaMKII), intracellular sodium and calcium handling and potassium currents, all of which impair cardiac contractility and repolarisation. Type 2 diabetes induces diffuse myocardial fibrosis and anatomical remodelling, which contribute to diastolic and systolic dysfunction, and the formation of a pro-arrhythmic substrate. Impaired connexin 43-mediated conduction and cardiac autonomic neuropathy further promote cardiac electrophysiological and mechanical dysfunction. Clinical studies using the ECG and cardiac imaging modalities have been successful in detecting some of these changes; however our mechanistic understanding of type 2 diabetes-driven cardiac disorders remains limited. Recent advances in multiscale computational modelling and simulation of human cardiac electrophysiology and mechanics provide new opportunities to study diabetes-induced cardiac remodelling in silico by unravelling disease mechanisms across different scales and assisting in the development of novel therapies. Here we review key pathophysiological mechanisms of electrophysiological, structural and nervous cardiac remodelling in type 2 diabetes; their clinical implications; and the cardiac effects of common glucose-lowering pharmacological agents commonly taken by diabetes patients. We discuss the potential of human-based computational cardiac modelling and simulation in this context to deepen our mechanistic understanding, and guide more precise prevention and treatment of diabetes-driven cardiac arrhythmias and diastolic dysfunction.

Atrial fibrillation (AF) is the most common cardiac arrhythmia, linked to greater risk of heart failure, stroke and death. Inflammation has been connected to AF emergence, however mechanisms of inflammation-caused AF remain thus far elusive, leading to a lack of mechanism-based treatments. An isogenic, 3D tissue model containing hiPSC-derived atrial-like cardiomyocytes (aCM), cardiac fibroblasts (cfb), and cardiac macrophages was engineered using custom injection-molded pillar devices. Electrophysiological changes were examined via sharp electrode recordings, calcium imaging, and multi-electrode assays. Gene function was interrogated using siRNA knock-down, lentiviral overexpression, and pharmacological modulation. In silico tissue and whole-heart models validated findings under simulated stress and heterogeneous conditions. Activation of M1 macrophages led to a 50% reduction in contraction amplitude, action potential spike amplitude (aCM+cfb+M1: 61.3 mV ±13.9 vs control: 71.6 mV ±14.5, p < 0.01) and increased beat irregularity (M1: 150.7% ± 388.9 vs control, p < 0.001). Calcium transient amplitude was reduced (12.3 a.u. ± 14.7, p < 0.05) and upstroke velocity slowed. SCN5A knock-down reduced contraction amplitude (-51.9% ± 37.2, p < 0.01) without inducing arrhythmias, whereas combined GJA5 and ATP1A1 knock-down induced significant irregularity (403% ± 371.3, p < 0.001), increased conduction heterogeneity (+18%), and reduced velocity (-52.4%). In silico modeling confirmed that paired 50% downregulation of sodium-potassium pump and tissue conductivity induced AF under tachycardia even without ectopic activity. This work reveals a novel, inflammation-driven mechanism for AF initiation. Combined downregulation of GJA5 (connexin 40) and ATP1A1 (NaK ATPase) disrupted intercellular connectivity and ion flux, establishing a substrate for arrhythmogenesis. These results were robust across in vitro, genetic/pharmacological, and in silico models, defining new avenues for translational intervention.

AIMS:Titin truncating variants (TTNtv) are a major genetic cause of dilated cardiomyopathy (DCM), accounting for approximately 25% of familial cases. Atrial fibrillation (AF) frequently occurs in DCM patients carrying TTNtv and may precede overt ventricular dysfunction, suggesting an atrial-specific disease mechanism. How TTNtv increase susceptibility to AF, particularly in the absence of established DCM, remains incompletely understood. This study aimed to define the cellular and molecular mechanisms by which a clinically relevant TTNtv predisposes to atrial arrhythmogenesis. METHODS AND RESULTS:We introduced a patient-associated TTNtv (TTN c.59926+1G>A) into human induced pluripotent stem cell-derived atrial cardiomyocytes (hiPSC-CMs). TTNtv hiPSC-CMs exhibited proarrhythmic electrophysiological alterations, including increased spontaneous beating frequency, abnormal sodium channel kinetics, and heightened sensitivity to cholinergic agonists. In silico simulations demonstrated that heightened cholinergic sensitivity was sufficient to trigger AF under conditions of sinus tachycardia. RNA sequencing revealed dysregulation of sarcomere assembly and extracellular matrix pathways, and TTNtv hiPSC-CMs showed structurally shortened sarcomeres. Engineered heart tissues composed of TTNtv hiPSC-CMs co-cultured with cardiac fibroblasts demonstrated reduced contractile force and increased secretion of collagen, fibronectin-1 and TGF-β1, consistent with activation of profibrotic signalling. Together, these findings indicate that a TTNtv can cause intrinsic atrial electrical instability and promote pro-fibrotic signalling. CONCLUSION:Our results identify atrial electrophysiological abnormalities and profibrotic remodelling as key mechanisms by which TTNtv increase AF risk, even in the absence of overt DCM. These findings support a primary atrial contribution to TTNtv-associated arrhythmogenesis and provide mechanistic insight into AF as an early clinical manifestation in carriers.

BACKGROUND AND OBJECTIVE:In recent years, human in silico trials have gained significant traction as a powerful approach to evaluate the effects of drugs, clinical interventions, and medical devices. In silico trials not only minimise patient risks but also reduce reliance on animal testing. However, implementing in silico trials presents several time-consuming challenges. It requires the creation of large cohorts of virtual patients. Each virtual patient is described by their anatomy with a volumetric mesh and electrophysiological and mechanical dynamics through mathematical equations and parameters. Furthermore, simulated conditions need definition including stimulation protocols and therapy evaluation. For large virtual cohorts, this requires automatic and efficient pipelines for the generation of corresponding files. In this work, we present a computational pipeline to automatically create large virtual patient cohort files to conduct large-scale in silico trials through cardiac electromechanical simulations. METHODS:The pipeline automatically generates anatomical labels, volumetric meshes suited for electromechanical simulations, and all necessary fields and files for the simulations, including stimulation information, from unprocessed surface meshes and input parameters, without requiring training data. It also handles patient heterogeneity and supports integration with algorithms for Purkinje network generation and electrocardiogram personalisation. RESULTS:The pipeline was applied across several datasets to generate over 100 virtual patients. Simulations were performed to demonstrate its capacity to conduct in silico trials for virtual patients using verified and validated electrophysiology and electromechanics models for the context of use. The proposed pipeline demonstrated its adaptability to accommodate different types of ventricular geometries and mesh processing tools, ensuring its versatility in handling diverse clinical datasets. CONCLUSIONS:By establishing an automated framework for large scale simulation studies as required for in silico trials and providing open-source code, our work aims to support scalable, personalised cardiac simulations in research and clinical applications.

Abstract The electrocardiogram (ECG) is used for diagnosis and risk stratification following myocardial infarction (MI). Women have a higher incidence of missed MI diagnosis and complications following infarction, and to address this we aim to provide quantitative information on sex-differences in ECG and torso-ventricular anatomy features. A novel computational automated pipeline is presented enabling the three-dimensional reconstruction of torso-ventricular anatomies for 425 post-MI subjects and 1051 healthy controls from UK Biobank clinical images. Regression models were created relating torso-ventricular and ECG parameters. For post-MI women, the heart is positioned more posteriorly and vertically, than in men (with healthy women yet more vertical). Post-MI women exhibit less QRS prolongation, requiring 27% more prolongation than men to exceed 120ms. Only half of the sex difference in QRS is associated with smaller female cavities. Lower STj amplitude in women is striking, associated with smaller ventricles, but also more superior and posterior cardiac position. Post-MI, T wave amplitude and R axis deviations are strongly associated with a more posterior and horizontal cardiac position in women (but not in men). Our study highlights the need to quantify sex differences in anatomical features, their implications in ECG interpretation, and the application of clinical ECG thresholds in post-MI.

Stem cell injection after myocardial infarction aims to improve the contractile capabilities of the injured human heart. However, many uncertainties remain regarding the therapy's safety. While preclinical safety assessments have focused on the risk of spontaneous stem cell depolarisations, the risk of re-entry, particularly as injected cells mature in the ventricles, remains to be thoroughly evaluated.Here, we have employed our established multiscale human electrophysiological modelling and simulation framework of myocardial infarction to investigate the re-entry risk of injected cells as they mature in the ventricles. For this, we used three human-based biventricular models of chronic myocardial infarction with varying scar size and Purkinje. In this in silico framework, we simulate injection of stem cell-derived cardiomyocytes before and after virtual delivery at day 0 and, based on experimentally-informed cell maturation, at day 14. We assess re-entry risk in the absence of spontaneous stem cell depolarisations with an S1-S2 protocol, i.e., through application of premature stimuli at 15 different locations from across the scar border zone at 12 different intervals after the previous sinus beat.Our results show that re-entry risk and sustainability increase from before to after virtual cell delivery in all three scar models – the window during which re-entry was inducible rose from 0 to 640 ms in the small scar, from 60 to 100 ms in the medium scar, and from 60 to 760 ms in the large scar. This was caused by cell injection improving conductivity within the scar, allowing for new re-entrant pathways. Re-entry risk further increased 2.75-fold from day 0 to day 14 after virtual cell injection in the large scar. New re-entries were enabled by a 45% increase in conduction velocity and an 11% increase in action potential duration in the injected cells from day 0 to day 14, caused by maturation of their ionic properties.In conclusion, our in silico results highlight that the risk of re-entry in the infarcted human ventricles may increase after cell injection and is exacerbated by the cells maturing electrophysiology from day 0 to day 14 after delivery.

BACKGROUND:Mechanistic cardiac simulations are increasingly used in research, pharmaceutical development, and regulatory science, yet most existing human cardiomyocyte models lack the generality required for predictive translation across scales. Our recently developed T-World model overcomes this barrier by reproducing all major cellular arrhythmia mechanisms and showing comprehensive agreement with experimental and clinical data. Here, we aimed to demonstrate the utility of T-World for organ-level and translational research, from ionic mechanisms of arrhythmogenesis to emergent whole-heart physiology.METHODS:T-World was embedded into anatomically realistic models of biventricular electrophysiology and electromechanics derived from clinical imaging for organ-scale simulations. Drug safety was assessed using populations-of-single-cell models exposed to 60 compounds with updated CredibleMeds annotations. Mechanistic drug-efficacy studies on mexiletine were conducted using long QT syndrome type 2 model variants. Disease applications included arrhythmia mechanisms in human type 2 diabetes and the proarrhythmic potential of NaV1.8, a neuronal sodium channel ectopically expressed in cardiac disease.RESULTS:T-World reproduced human-like ECG morphology and ventricular mechanics (ejection fraction of 61%) and generated ventricular fibrillation under physiologically relevant ischemic conditions without parameter tuning. In the drug safety assessment of torsadogenic risk, T-World achieved 87% accuracy and 100% specificity, and exposed incomplete pharmacological descriptions based on in vitro measurements for lidocaine and cilostazol. Mexiletine simulations revealed that both INaL and ICaL inhibition underlie its antiarrhythmic benefit in long QT syndrome type 2. Cellular simulations of type 2 diabetes remodeling explained heightened vulnerability to early afterdepolarizations and increased risk of alternans associated with diastolic dysfunction, mechanistically linked to SERCA (sarco/endoplasmic reticulum Ca2+ ATPase) reduction. Finally, even minor expression of NaV1.8 can directly trigger early afterdepolarizations through uniquely right-shifted activation and inactivation properties.CONCLUSIONS:T-World provides a unified, human-specific open-source platform bridging cellular mechanisms with organ-level dynamics and translational outcomes. Its predictive performance across arrhythmia, contraction, drug safety/mechanisms, and disease physiology makes it a powerful tool for multiscale cardiac research, therapeutic discovery, and next-generation cardiac digital twins.

Assessing drug-induced changes in contractility and pro-arrhythmic risk is essential in pre-clinical safety pharmacology. While current methods for concurrent assessment of pro-arrhythmic and inotropic cardiotoxicity, like the Langendorff isolated rabbit heart model deliver key insights, they are resource-intensive, relatively low throughput and carry translatability challenges to human physiology. Human-based modelling and simulation may complement traditional methods by providing a cost-effective alternative through accurate, high-throughput drug screening to supplement or replace animal-based models. The objective is to quantitatively evaluate simulations using in silico human-based models to predict and explain contractility and pro-arrhythmic drug effects, benchmarked against ex vivo Langendorff rabbit data.A virtual cohort of (N = 166) of human ventricular myocytes was calibrated to perform a fast, blind assessment of 37 compounds using in vitro IC50 data on the four key channels (hERG, Cav1.2, NaV1.5, Kv4.3). Human physiology was represented in silico by the human electromechanical model ToR-ORd-Land. Simulated active tension, calcium transient and action potential biomarkers were benchmarked against ex vivo rabbit experimental ventricular pressure and QT measurements, commonly used in preclinical assessment. Moreover, a comprehensive sensitivity analysis was performed to identify ionic mechanisms, in addition to the 4 channels, potentially driving drug-induced changes in contractility, and additional experiments and simulations were performed to evaluate their impact.86% of compounds show agreement between in silico and ex vivo using the four-channel data. Simulations identified hNCX1 and late Nav1.5 current inhibition, as additional key mechanisms for contractility. Introducing their measurements increases qualitative agreement to 95% improving both inotropic and electrophysiology matches for several compounds. Close quantitative agreement across contractility and electrophysiology biomarkers (within 25% of ex vivo value) is observed for 76% of compounds. Disagreements correlated with uncertainty in IC50 measurements of hERG and Cav1.2, or in the case for Dobutamine, the need for gain-of-function CaV1.2 data.In silico human-based simulations simultaneously predict and explain drug-induced contractile and electrophysiology effects with high accuracy. This approach offers a robust, cost-effective alternative to Langendorff rabbit studies, supporting reduction, refinement and replacement of animal models in pre-clinical safety pharmacology.

Myocardial infarction remains a frequent cause of heart failure and mortality. Cell therapy has shown promise in regenerating the damaged tissue, but delivered cells may beat spontaneously and produce ventricular arrhythmias, hindering clinical application. Here, we conducted multiscale computer simulations of the electrical activity of the infarcted human ventricles including the cardiac conduction system to identify and mitigate pro-arrhythmic mechanisms following cell delivery. Firstly, our simulations show how arrhythmic risk increases from before to after cell injection and further during the first two weeks post-delivery. Secondly, we suggest that concurrently targeting the funny current, the inward rectifier potassium current, the sodium-potassium pump current, and the rapid delayed outward rectifier potassium current may reduce automaticity and re-entry while maximizing calcium transient amplitude and thus contractility. Our study demonstrates how modeling and simulation enables the design of anti-arrhythmic strategies to improve therapy safety while preserving efficacy.

Modelling and simulation are essential in biomedicine, and specifically in computational cardiology. Reliable, efficient and accurate solvers are critical. This study presents an open-source, GPU-based cardiac electrophysiology solver for scalable multiscale simulations (MONOALG3D), incorporating conduction system calibration and performance optimization. The solver employs the monodomain equation coupled with the Purkinje network, solved via the finite volume method, featuring a GPU-based linear solver and concurrent simulation dispatch with MPI. We demonstrate a [Formula: see text] speedup over a CPU-based solution and scalability by running 512 simulations on 128 compute nodes. Coarse and fine biventricular mesh simulations with 855, 670 and 6, 845, 360 control volumes are completed in less than 24 min and 303 min, respectively, considering a single beat and a human-based ventricular cellular model with 43 state variables. The proposed open-source solver enhances computational efficiency and physiological fidelity through Purkinje-muscle-junction calibration, enabling large-scale, high-speed cardiac simulations including the conduction system. This work marks a significant step toward fast and scalable cardiac simulations on GPU architectures by providing execution of concurrent simulations with the novel MPI batch feature and calibration of Purkinje coupling parameters, paving the way for integration into a Digital Twin personalisation pipeline, including the conduction system.

Cardiac anatomy and physiology vary considerably across the human population. Understanding and taking into account this variability is crucial for both accurate clinical decision-making and realistic in silico modeling of cardiac function. In this work, we propose multi-class variational point cloud autoencoders (Point VAE) as a novel geometric deep learning approach for 3D cardiac shape and function analysis. Its encoder-decoder architecture enables efficient multi-scale feature learning directly on high resolution point cloud representations of the multi-class 3D cardiac anatomy and can capture complex non-linear 3D shape variability in a low-dimensional and interpretable latent space. We first evaluate the Point VAE's reconstruction ability on a dataset of over 10,000 subjects and find mean Chamfer distances between input and reconstructed point clouds below the pixel resolution of the underlying image acquisitions. Furthermore, we analyze the Point VAE's latent space and observe a realistic and disentangled representation of morphological and functional variability. We test the latent space for pathology prediction and find it to outperform clinical benchmarks by 13% and 16% in area under the receiver operating characteristic (AUROC) curves for the tasks of prevalent myocardial infarction (MI) detection and incident MI prediction, respectively, and by 10% in terms of Harrell's concordance index for MI survival analysis. Finally, we use the generated populations for in silico simulations of cardiac electrophysiology, demonstrating its ability to introduce realistic natural variability.

Cardiac digital twins (CDTs) offer personalized in-silico cardiac representations for the inference of multi-scale properties tied to cardiac mechanisms. The creation of CDTs requires precise information about the electrode position on the torso, especially for the personalized electrocardiogram (ECG) calibration. However, current studies commonly rely on additional acquisition of torso imaging and manual/semi-automatic methods for ECG electrode localization. In this study, we propose a novel and efficient topology-informed model to fully automatically extract personalized ECG standard electrode locations from 2D clinically standard cardiac MRIs. Specifically, we obtain the sparse torso contours from the cardiac MRIs and then localize the standard electrodes of 12-lead ECG from the contours. Cardiac MRIs aim at imaging of the heart instead of the torso, leading to incomplete torso geometry within the imaging. To tackle the missing topology, we incorporate the electrodes as a subset of the keypoints, which can be explicitly aligned with the 3D torso topology. The experimental results demonstrate that the proposed model outperforms the time-consuming conventional model projection-based method in terms of accuracy (Euclidean distance: 1.24 ± 0.293 cm vs. 1.48 ± 0.362 cm) and efficiency (2 s vs. 30-35 min). We further demonstrate the effectiveness of using the detected electrodes for in-silico ECG simulation, highlighting their potential for creating accurate and efficient CDT models. The code is available at https://github.com/lileitech/12lead_ECG_electrode_localizer.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) hold promise in predictive personalized medicine, particularly for cardiac diseases and human data-based pharmacology studies. However, hiPSC-CM mechanics and their changes in response to drug action are rarely assessed in-silico, despite being one of the principal functional readouts. To enable hiPSC-CM based electro-mechanical in-silico investigations, we have developed and validated a new human data-based hiPSC-CM model by coupling an ODE-based hiPSC-CM electrophysiology model with a human cardiomyocyte mechanical model and calibrating the parameters using a genetic algorithm. For calibration, experimental hiPSC-CM force biomarkers were used. We then evaluated the model by comparing simulated and experimental biomarkers of action potentials (AP) and calcium transients (CaT), which were in agreement. The model features different ion channels and a crossbridge model that allows active tension generation, enabling mechanistically interpretable simulations of hiPSC-CM behavior. In this way, the model permits comprehensive in-silico drug response investigations and simulations of hiPSC-CM electromechanical behavior under various conditions. In our in-silico studies, we simulated the responses to more than 50 different drugs, including various channel agonists and blockers, with multiple concentrations investigated for each compound. We first aimed to replicate inotropic changes observed in pharmacological studies, and evaluated the contraction force amplitude, time to peak force, and relaxation time. Our findings indicate that the new hiPSC-CM model can replicate the electro-mechanical effects of the pharmacological compounds tested, demonstrating agreement with experimental observations from the hiPSC-CM studies. This further reinforces the validity of the model, and provides a basis for drug screening, and can aid in promoting safer, more effective therapeutic strategies. After validation, we use the model to simulate the activity of pharmacological agents of unknown inotropic effects, providing novel predictions for safety pharmacology. Furthermore, we explore the differences between in-silico hiPSC-CM simulation results and experimental data from rat ventricular cardiomyocytes to evaluate the advantages and disadvantages of research models for drug safety assessment related to inotropy. Taken together, this study showcases the new hiPSC-CM electromechanical model as a valuable platform for future research into cardiac drug responses and the prediction of inotropic cardiomyocyte responses based on human data.

This clinical consensus document proposes standardized atrial segments for 3D imaging, electroanatomical mapping and computational modelling, based on anatomical, electrophysiological and clinical considerations, with precise definitions of regional borders allowing for reproducible and automated regionalization. 3D imaging and high-resolution electroanatomical mapping have become an integral part of cardiac electrophysiology and the management of patients with arrhythmias. However, to perform regional quantitative analyses and intra- and inter-individual, as well as cross-modality comparisons, a universal definition of atrial regions and their boundaries is required. While for the left ventricle there is already an established standardized regionalization (AHA 17-segment model), there is no such consensus for the atria. In a multi-disciplinary writing group consisting of cardiologists, cardiac electrophysiologists, cardiovascular imaging specialists, and anatomists as well as specialists in computational cardiac modelling from European Heart Rhythm Association and European Association of Cardiovascular Imaging, a standardized regionalization based on a 15-segment bi-atrial model was elaborated. This clinical consensus document will enable consistent regional analyses and homogeneous data acquisition across different centres and modalities, and may thus have a significant impact on atrial arrhythmia research and personalized treatment approaches based on individual arrhythmia patterns and phenotypes.