Importance of modelling hERG binding in predicting drug-induced action potential prolongations for drug safety assessment.

Reduction of the rapid delayed rectifier potassium current (I Kr) via drug binding to the human Ether-à-go-go-Related Gene (hERG) channel is a well recognised mechanism that can contribute to an increased risk of Torsades de Pointes. Mathematical models have been created to replicate the effects of channel blockers, such as reducing the ionic conductance of the channel. Here, we study the impact of including state-dependent drug binding in a mathematical model of hERG when translating hERG inhibition to action potential changes. We show that the difference in action potential predictions when modelling drug binding of hERG using a state-dependent model versus a conductance scaling model depends not only on the properties of the drug and whether the experiment achieves steady state, but also on the experimental protocols. Furthermore, through exploring the model parameter space, we demonstrate that the state-dependent model and the conductance scaling model generally predict different action potential prolongations and are not interchangeable, while at high binding and unbinding rates, the conductance scaling model tends to predict shorter action potential prolongations. Finally, we observe that the difference in simulated action potentials between the models is determined by the binding and unbinding rate, rather than the trapping mechanism. This study demonstrates the importance of modelling drug binding and highlights the need for improved understanding of drug trapping which can have implications for the uses in drug safety assessment.

Models of the cardiac L-type calcium current: A quantitative review.

The L-type calcium current ( I CaL ) plays a critical role in cardiac electrophysiology, and models of I CaL are vital tools to predict arrhythmogenicity of drugs and mutations. Five decades of measuring and modeling I CaL have resulted in several competing theories (encoded in mathematical equations). However, the introduction of new models has not typically been accompanied by a data-driven critical comparison with previous work, so that it is unclear which model is best suited for any particular application. In this review, we describe and compare 73 published mammalian I CaL models and use simulated experiments to show that there is a large variability in their predictions, which is not substantially diminished when grouping by species or other categories. We provide model code for 60 models, list major data sources, and discuss experimental and modeling work that will be required to reduce this huge list of competing theories and ultimately develop a community consensus model of I CaL . This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Molecular and Cellular Physiology.

A model-informed approach to assess the risk of immune checkpoint inhibitor-induced autoimmune myocarditis.

Immune checkpoint inhibitors (ICIs), as a novel immunotherapy, are designed to modulate the immune system to attack malignancies. Despite their promising benefits, immune-related adverse events (IRAEs) may occur, and incidences are bound to increase with surging demand of this class of drugs in treating cancer. Myocarditis, although rare compared to other IRAEs, has a significantly higher fatal frequency. Due to the overwhelming complexity of the immune system, this condition is not well understood, despite the significant research efforts devoted to it. To better understand the development and progression of autoimmune myocarditis and the roles of ICIs therein, we suggest a new approach: mathematical modelling. Mathematical modelling of myocarditis has enormous potential to determine which parts of the immune system are critical to the development and progression of the disease, and therefore warrant further investigation. We provide the immunological background needed to develop a mathematical model of this disease and review relevant existing models of immunology that serve as the mathematical inspiration needed to develop this field.

A Parameter Representing Missing Charge Should Be Considered when Calibrating Action Potential Models.

Computational models of the electrical potential across a cell membrane are longstanding and vital tools in electrophysiology research and applications. These models describe how ionic currents, internal fluxes, and buffering interact to determine membrane voltage and form action potentials (APs). Although this relationship is usually expressed as a differential equation, previous studies have shown it can be rewritten in an algebraic form, allowing direct calculation of membrane voltage. Rewriting in this form requires the introduction of a new parameter, called Γ0 in this manuscript, which represents the net concentration of all charges that influence membrane voltage but are not considered in the model. Although several studies have examined the impact of Γ0 on long-term stability and drift in model predictions, there has been little examination of its effects on model predictions, particularly when a model is refit to new data. In this study, we illustrate how Γ0 affects important physiological properties such as action potential duration restitution, and examine the effects of (in)correctly specifying Γ0 during model calibration. We show that, although physiologically plausible, the range of concentrations used in popular models leads to orders of magnitude differences in Γ0, which can lead to very different model predictions. In model calibration, we find that using an incorrect value of Γ0 can lead to biased estimates of the inferred parameters, but that the predictive power of these models can be restored by fitting Γ0 as a separate parameter. These results show the value of making Γ0 explicit in model formulations, as it forces modellers and experimenters to consider the effects of uncertainty and potential discrepancy in initial concentrations upon model predictions.

Mathematical modelling of autoimmune myocarditis and the effects of immune checkpoint inhibitors.

Autoimmune myocarditis is a rare, but frequently fatal, side effect of immune checkpoint inhibitors (ICIs), a class of cancer therapies. Despite extensive experimental work on the causes, development and progression of this disease, much still remains unknown about the importance of the different immunological pathways involved. We present a mathematical model of autoimmune myocarditis and the effects of ICIs on its development and progression to either resolution or chronic inflammation. From this, we gain a better understanding of the role of immune cells, cytokines and other components of the immune system in driving the cardiotoxicity of ICIs. We parameterise the model using existing data from the literature, and show that qualitative model behaviour is consistent with disease characteristics seen in patients in an ICI-free context. The bifurcation structures of the model show how the presence of ICIs increases the risk of developing autoimmune myocarditis. This predictive modelling approach is a first step towards determining treatment regimens that balance the benefits of treating cancer with the risk of developing autoimmune myocarditis.

Cardiac Safety of Kinase Inhibitors - Improving Understanding and Prediction of Liabilities in Drug Discovery Using Human Stem Cell-Derived Models.

Many small molecule kinase inhibitors (SMKIs) used to fight cancer have been associated with cardiotoxicity in the clinic. Therefore, preventing their failure in clinical development is a priority for preclinical discovery. Our study focused on the integration and concurrent measurement of ATP, apoptosis dynamics and functional cardiac indexes in human stem cell-derived cardiomyocytes (hSC-CMs) to provide further insights into molecular determinants of compromised cardiac function. Ten out of the fourteen tested SMKIs resulted in a biologically relevant decrease in either beating rate or base impedance (cell number index), illustrating cardiotoxicity as one of the major safety liabilities of SMKIs, in particular of those involved in the PI3K-AKT pathway. Pearson's correlation analysis indicated a good correlation between the different read-outs of functional importance. Therefore, measurement of ATP concentrations and apoptosis in vitro could provide important insight into mechanisms of cardiotoxicity. Detailed investigation of the cellular signals facilitated multi-parameter evaluation allowing integrative assessment of cardiomyocyte behavior. The resulting correlation can be used as a tool to highlight changes in cardiac function and potentially to categorize drugs based on their mechanisms of action.

Towards engineering heart tissues from bioprinted cardiac spheroids.

Currentin vivoandin vitromodels fail to accurately recapitulate the human heart microenvironment for biomedical applications. This study explores the use of cardiac spheroids (CSs) to biofabricate advancedin vitromodels of the human heart. CSs were created from human cardiac myocytes, fibroblasts and endothelial cells (ECs), mixed within optimal alginate/gelatin hydrogels and then bioprinted on a microelectrode plate for drug testing. Bioprinted CSs maintained their structure and viability for at least 30 d after printing. Vascular endothelial growth factor (VEGF) promoted EC branching from CSs within hydrogels. Alginate/gelatin-based hydrogels enabled spheroids fusion, which was further facilitated by addition of VEGF. Bioprinted CSs contracted spontaneously and under stimulation, allowing to record contractile and electrical signals on the microelectrode plates for industrial applications. Taken together, our findings indicate that bioprinted CSs can be used to biofabricate human heart tissues for long termin vitrotesting. This has the potential to be used to study biochemical, physiological and pharmacological features of human heart tissue.

Current state and future of 3D bioprinted models for cardiovascular research and drug development.

In the last decade, 3D bioprinting technology has emerged as an innovative tissue engineering approach for regenerative medicine and drug development. This article aims at providing an overview about the most commonly used bioengineered tissues, focusing on 3D bioprinted cardiac cells and how they have been utilized for drug discovery and development. The review describes that, while this field is still developing, cardiovascular research may benefit from laboratory-engineered heart tissues built of specific cell types with precise 3D architecture mimicking the native cardiac microenvironment. It also describes the role played by regulatory agencies and potential commercialization pathways for direct translation from the bench to the bedside of studies using 3D bioprinted cardiac tissues.

Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk.

There is an increasing expectation that computational approaches may supplement existing human decision-making. Frontloading of models for cardiac safety prediction is no exception to this trend, and ongoing regulatory initiatives propose use of high-throughput in vitro data combined with computational models for calculating proarrhythmic risk. Evaluation of these models requires robust assessment of the outcomes. Using FDA Adverse Event Reporting System reports and electronic healthcare claims data from the Truven-MarketScan US claims database, we quantify the incidence rate of arrhythmia in patients and how this changes depending on patient characteristics. First, we propose that such datasets are a complementary resource for determining relative drug risk and assessing the performance of cardiac safety models for regulatory use. Second, the results suggest important determinants for appropriate stratification of patients and evaluation of additional drug risk in prescribing and clinical support algorithms and for precision health.

Publisher Correction: Cross-site and cross-platform variability of automated patch clamp assessments of drug effects on human cardiac currents in recombinant cells.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm.

INTRODUCTION: hERG block potency is widely used to calculate a drug's safety margin against its torsadogenic potential. Previous studies are confounded by use of different patch clamp electrophysiology protocols and a lack of statistical quantification of experimental variability. Since the new cardiac safety paradigm being discussed by the International Council for Harmonisation promotes a tighter integration of nonclinical and clinical data for torsadogenic risk assessment, a more systematic approach to estimate the hERG block potency and safety margin is needed. METHODS: A cross-industry study was performed to collect hERG data on 28 drugs with known torsadogenic risk using a standardized experimental protocol. A Bayesian hierarchical modeling (BHM) approach was used to assess the hERG block potency of these drugs by quantifying both the inter-site and intra-site variability. A modeling and simulation study was also done to evaluate protocol-dependent changes in hERG potency estimates. RESULTS: A systematic approach to estimate hERG block potency is established. The impact of choosing a safety margin threshold on torsadogenic risk evaluation is explored based on the posterior distributions of hERG potency estimated by this method. The modeling and simulation results suggest any potency estimate is specific to the protocol used. DISCUSSION: This methodology can estimate hERG block potency specific to a given voltage protocol. The relationship between safety margin thresholds and torsadogenic risk predictivity suggests the threshold should be tailored to each specific context of use, and safety margin evaluation may need to be integrated with other information to form a more comprehensive risk assessment.

Cross-site and cross-platform variability of automated patch clamp assessments of drug effects on human cardiac currents in recombinant cells.

Automated patch clamp (APC) instruments enable efficient evaluation of electrophysiologic effects of drugs on human cardiac currents in heterologous expression systems. Differences in experimental protocols, instruments, and dissimilar site procedures affect the variability of IC50 values characterizing drug block potency. This impacts the utility of APC platforms for assessing a drug's cardiac safety margin. We determined variability of APC data from multiple sites that measured blocking potency of 12 blinded drugs (with different levels of proarrhythmic risk) against four human cardiac currents (hERG [IKr], hCav1.2 [L-Type ICa], peak hNav1.5, [Peak INa], late hNav1.5 [Late INa]) with recommended protocols (to minimize variance) using five APC platforms across 17 sites. IC50 variability (25/75 percentiles) differed for drugs and currents (e.g., 10.4-fold for dofetilide block of hERG current and 4-fold for mexiletine block of hNav1.5 current). Within-platform variance predominated for 4 of 12 hERG blocking drugs and 4 of 6 hNav1.5 blocking drugs. hERG and hNav1.5 block. Bland-Altman plots depicted varying agreement across APC platforms. A follow-up survey suggested multiple sources of experimental variability that could be further minimized by stricter adherence to standard protocols. Adoption of best practices would ensure less variable APC datasets and improved safety margins and proarrhythmic risk assessments.

General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy.

This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/US Food and Drug Administration-sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the Comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model-based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm.

Rapid Characterization of hERG Channel Kinetics I: Using an Automated High-Throughput System.

Predicting how pharmaceuticals may affect heart rhythm is a crucial step in drug development and requires a deep understanding of a compound's action on ion channels. In vitro hERG channel current recordings are an important step in evaluating the proarrhythmic potential of small molecules and are now routinely performed using automated high-throughput patch-clamp platforms. These machines can execute traditional voltage-clamp protocols aimed at specific gating processes, but the array of protocols needed to fully characterize a current is typically too long to be applied in a single cell. Shorter high-information protocols have recently been introduced that have this capability, but they are not typically compatible with high-throughput platforms. We present a new 15 second protocol to characterize hERG (Kv11.1) kinetics, suitable for both manual and high-throughput systems. We demonstrate its use on the Nanion SyncroPatch 384PE, a 384-well automated patch-clamp platform, by applying it to Chinese hamster ovary cells stably expressing hERG1a. From these recordings, we construct 124 cell-specific variants/parameterizations of a hERG model at 25°C. A further eight independent protocols are run in each cell and are used to validate the model predictions. We then combine the experimental recordings using a hierarchical Bayesian model, which we use to quantify the uncertainty in the model parameters, and their variability from cell-to-cell; we use this model to suggest reasons for the variability. This study demonstrates a robust method to measure and quantify uncertainty and shows that it is possible and practical to use high-throughput systems to capture full hERG channel kinetics quantitatively and rapidly.

Rapid Characterization of hERG Channel Kinetics II: Temperature Dependence.

Ion channel behavior can depend strongly on temperature, with faster kinetics at physiological temperatures leading to considerable changes in currents relative to room temperature. These temperature-dependent changes in voltage-dependent ion channel kinetics (rates of opening, closing, inactivating, and recovery) are commonly represented with Q10 coefficients or an Eyring relationship. In this article, we assess the validity of these representations by characterizing channel kinetics at multiple temperatures. We focus on the human Ether-à-go-go-Related Gene (hERG) channel, which is important in drug safety assessment and commonly screened at room temperature so that results require extrapolation to physiological temperature. In Part I of this study, we established a reliable method for high-throughput characterization of hERG1a (Kv11.1) kinetics, using a 15-second information-rich optimized protocol. In this Part II, we use this protocol to study the temperature dependence of hERG kinetics using Chinese hamster ovary cells overexpressing hERG1a on the Nanion SyncroPatch 384PE, a 384-well automated patch-clamp platform, with temperature control. We characterize the temperature dependence of hERG gating by fitting the parameters of a mathematical model of hERG kinetics to data obtained at five distinct temperatures between 25 and 37°C and validate the models using different protocols. Our models reveal that activation is far more temperature sensitive than inactivation, and we observe that the temperature dependency of the kinetic parameters is not represented well by Q10 coefficients; it broadly follows a generalized, but not the standardly-used, Eyring relationship. We also demonstrate that experimental estimations of Q10 coefficients are protocol dependent. Our results show that a direct fit using our 15-s protocol best represents hERG kinetics at any given temperature and suggests that using the Generalized Eyring theory is preferable if no experimental data are available to derive model parameters at a given temperature.

Stem Cell-Derived Cardiac Spheroids as 3D In Vitro Models of the Human Heart Microenvironment.

Our laboratory has recently developed a novel three-dimensional in vitro model of the human heart, which we call the vascularized cardiac spheroid (VCS). These better recapitulate the human heart's cellular and extracellular microenvironment compared to the existing in vitro models. To achieve this, human-induced pluripotent stem cell (iPSC)-derived cardiomyocytes, cardiac fibroblasts, and human coronary artery endothelial cells are co-cultured in hanging drop culture in ratios similar to those found in the human heart in vivo. The resulting three-dimensional cellular organization, extracellular matrix, and microvascular network formation throughout the VCS has been shown to mimic the one present in the human heart tissue. Therefore, VCSs offer a promising platform to study cardiac physiology, disease, and pharmacology, as well as bioengineering constructs to regenerate heart tissue.

Cross - site comparison of excitation-contraction coupling using impedance and field potential recordings in hiPSC cardiomyocytes.

INTRODUCTION: Since 2005 the S7B and E14 guidances from ICH and FDA have been in place to assess a potential drug candidate's ability to cause long QT syndrome. To refine these guidelines, the FDA proposed the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative, where the assessment of drug effects on cardiac repolarization was one subject of investigation. Within the myocyte validation study, effects of pharmaceutical compounds on human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) were assessed and this article will focus on the evaluation of the proarrhythmic potential of 23 blinded drugs in four hiPSC-CM cell lines. METHODS: Experiments were performed on the CardioExcyte 96 at different sites. A combined readout of contractility (via impedance) and electrophysiology endpoints (field potentials) was performed. RESULTS: Our data demonstrates that hERG blockers such as dofetilide and further high risk categorized compounds prolong the field potential duration. Arrhythmia were detected in both impedance as well as field potential recordings. Intermediate risk compounds induced arrhythmia in almost all cases at the highest dose. In the case of low risk compounds, either a decrease in FPDmax was observed, or not a significant change from pre-addition control values. DISCUSSION: With exceptions, hiPSC-CMs are sensitive and exhibit at least 10% delayed or shortened repolarization from pre-addition values and arrhythmia after drug application and thus can provide predictive cardiac electrophysiology data. The baseline electrophysiological parameters vary between iPS cells from different sources, therefore positive and negative control recordings are recommended.

Cardiac spheroids as promising in vitro models to study the human heart microenvironment.

Three-dimensional in vitro cell systems are a promising alternative to animals to study cardiac biology and disease. We have generated three-dimensional in vitro models of the human heart ("cardiac spheroids", CSs) by co-culturing human primary or iPSC-derived cardiomyocytes, endothelial cells and fibroblasts at ratios approximating those present in vivo. The cellular organisation, extracellular matrix and microvascular network mimic human heart tissue. These spheroids have been employed to investigate the dose-limiting cardiotoxicity of the common anti-cancer drug doxorubicin. Viability/cytotoxicity assays indicate dose-dependent cytotoxic effects, which are inhibited by the nitric oxide synthase (NOS) inhibitor L-NIO, and genetic inhibition of endothelial NOS, implicating peroxynitrous acid as a key damaging agent. These data indicate that CSs mimic important features of human heart morphology, biochemistry and pharmacology in vitro, offering a promising alternative to animals and standard cell cultures with regard to mechanistic insights and prediction of toxic effects in human heart tissue.

Electro-mechanical conditioning of human iPSC-derived cardiomyocytes for translational research.

RATIONALE: Impaired maturation of human iPSC-derived cardiomyocytes (hiPSC-CMs) currently limits their use in experimental research and further optimization is required to unlock their full potential. OBJECTIVE: To push hiPSC-CMs towards maturation, we recapitulated the intrinsic cardiac properties by electro-mechanical stimulation and explored how these mimetic biophysical cues interplay and influence the cell behaviour. METHODS AND RESULTS: We introduced a novel device capable of applying synchronized electrical and mechanical stimuli to hiPSC-CM monolayers cultured on a PDMS membrane and evaluated effects of conditioning on cardiomyocyte structure and function. Human iPSC-CMs retained their cardiac phenotype and displayed adaptive structural responses to electrical (E), mechanical (M) and combined electro-mechanical (EM) stimuli, including enhanced membrane N-cadherin signal, stress-fiber formation and sarcomeric length shortening, most prominent under the EM stimulation. On the functional level, EM conditioning significantly reduced the transmembrane calcium current, resulting in a shift towards triangulation of intracellular calcium transients. In contrast, E and M stimulation applied independently increased the proportion of cells with L-Type calcium currents. In addition, calcium transients measured in the M-conditioned samples advanced to a more rectangular shape. CONCLUSION: The new methodology is a simple and elegant technique to systematically investigate and manipulate cardiomyocyte remodelling for translational applications. In the present study, we adjusted critical parameters to optimize a regimen for hiPSC-CM transformation. In the future, this technology will open up new avenues for electro-mechanical stimulation by allowing temporal and spatial control of stimuli which can be easily scaled up in complexity for cardiac development and disease modelling.