Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop

What determines the optimal pharmacological treatment of atrial fibrillation? Insights from in silico trials in 800 virtual atria

The best pharmacological treatment for each atrial fibrillation (AF) patient is unclear. We aim to exploit AF simulations in 800 virtual atria to identify key patient characteristics that guide the optimal selection of anti-arrhythmic drugs. The virtual cohort considered variability in electrophysiology and low voltage areas (LVA) and was developed and validated against experimental and clinical data from ionic currents to ECG. AF sustained in 494 (62%) atria, with large inward rectifier K+ current (IK1 ) and Na+ /K+ pump (INaK ) densities (IK1 0.11 ± 0.03 vs. 0.07 ± 0.03 S mF-1 ; INaK 0.68 ± 0.15 vs. 0.38 ± 26 S mF-1 ; sustained vs. un-sustained AF). In severely remodelled left atrium, with LVA extensions of more than 40% in the posterior wall, higher IK1 (median density 0.12 ± 0.02 S mF-1 ) was required for AF maintenance, and rotors localized in healthy right atrium. For lower LVA extensions, rotors could also anchor to LVA, in atria presenting short refractoriness (median L-type Ca2+ current, ICaL , density 0.08 ± 0.03 S mF-1 ). This atrial refractoriness, modulated by ICaL and fast Na+ current (INa ), determined pharmacological treatment success for both small and large LVA. Vernakalant was effective in atria presenting long refractoriness (median ICaL density 0.13 ± 0.05 S mF-1 ). For short refractoriness, atria with high INa (median density 8.92 ± 2.59 S mF-1 ) responded more favourably to amiodarone than flecainide, and the opposite was found in atria with low INa (median density 5.33 ± 1.41 S mF-1 ). In silico drug trials in 800 human atria identify inward currents as critical for optimal stratification of AF patient to pharmacological treatment and, together with the left atrial LVA extension, for accurately phenotyping AF dynamics. KEY POINTS: Atrial fibrillation (AF) maintenance is facilitated by small L-type Ca2+ current (ICaL ) and large inward rectifier K+ current (IK1 ) and Na+ /K+ pump. In severely remodelled left atrium, with low voltage areas (LVA) covering more than 40% of the posterior wall, sustained AF requires higher IK1 and rotors localize in healthy right atrium. For lower LVA extensions, rotors can also anchor to LVA, if the atria present short refractoriness (low ICaL ) Vernakalant is effective in atria presenting long refractoriness (high ICaL ). For short refractoriness, atria with fast Na+ current (INa ) up-regulation respond more favourably to amiodarone than flecainide, and the opposite is found in atria with low INa . The inward currents (ICaL and INa ) are critical for optimal stratification of AF patient to pharmacological treatment and, together with the left atrial LVA extension, for accurately phenotyping AF dynamics.

Pharmacological Management of Hypertrophic Cardiomyopathy: From Bench to Bedside

Hypertrophic cardiomyopathy (HCM), the most common inherited heart disease, is still orphan of a specific drug treatment. The erroneous consideration of HCM as a rare disease has hampered the design and conduct of large, randomized trials in the last 50 years, and most of the indications in the current guidelines are derived from small non-randomized studies, case series, or simply from the consensus of experts. Guideline-directed therapy of HCM includes non-selective drugs such as disopyramide, non-dihydropyridine calcium channel blockers, or β-adrenergic receptor blockers, mainly used in patients with symptomatic obstruction of the outflow tract. Following promising preclinical studies, several drugs acting on potential HCM-specific targets were tested in patients. Despite the huge efforts, none of these studies was able to change clinical practice for HCM patients, because tested drugs were proven to be scarcely effective or hardly tolerated in patients. However, novel compounds have been developed in recent years specifically for HCM, addressing myocardial hypercontractility and altered energetics in a direct manner, through allosteric inhibition of myosin. In this paper, we will critically review the use of different classes of drugs in HCM patients, starting from "old" established agents up to novel selective drugs that have been recently trialed in patients.

Basic Research Approaches to Evaluate Cardiac Arrhythmia in Heart Failure and Beyond

Patients with heart failure often develop cardiac arrhythmias. The mechanisms and interrelations linking heart failure and arrhythmias are not fully understood. Historically, research into arrhythmias has been performed on affected individuals or in vivo (animal) models. The latter however is constrained by interspecies variation, demands to reduce animal experiments and cost. Recent developments in in vitro induced pluripotent stem cell technology and in silico modelling have expanded the number of models available for the evaluation of heart failure and arrhythmia. An agnostic approach, combining the modalities discussed here, has the potential to improve our understanding for appraising the pathology and interactions between heart failure and arrhythmia and can provide robust and validated outcomes in a variety of research settings. This review discusses the state of the art models, methodologies and techniques used in the evaluation of heart failure and arrhythmia and will highlight the benefits of using them in combination. Special consideration is paid to assessing the pivotal role calcium handling has in the development of heart failure and arrhythmia.

CalTrack: High-Throughput Automated Calcium Transient Analysis in Cardiomyocytes

[Figure: see text].

Toward a broader view of mechanisms of drug cardiotoxicity

Cardiotoxicity, defined as toxicity that affects the heart, is one of the most common adverse drug effects. Numerous drugs have been shown to have the potential to induce lethal arrhythmias by affecting cardiac electrophysiology, which is the focus of current preclinical testing. However, a substantial number of drugs can also affect cardiac function beyond electrophysiology. Within this broader sense of cardiotoxicity, this review discusses the key drug-protein interactions known to be involved in cardiotoxic drug response. We cover adverse effects of anticancer, central nervous system, genitourinary system, gastrointestinal, antihistaminic, anti-inflammatory, and anti-infective agents, illustrating that many share mechanisms of cardiotoxicity, including contractility, mitochondrial function, and cellular signaling.

An Introductory Overview of Image-Based Computational Modeling in Personalized Cardiovascular Medicine

Cardiovascular diseases account for the number one cause of deaths in the world. Part of the reason for such grim statistics is our limited understanding of the underlying mechanisms causing these devastating pathologies, which is made difficult by the invasiveness of the procedures associated with their diagnosis (e.g., inserting catheters into the coronal artery to measure blood flow to the heart). Likewise, it is also difficult to design and test assistive devices without implanting them in vivo. However, with the recent advancements made in biomedical scanning technologies and computer simulations, image-based modeling (IBM) has arisen as the next logical step in the evolution of non-invasive patient-specific cardiovascular medicine. Yet, due to its novelty, it is still relatively unknown outside of the niche field. Therefore, the goal of this manuscript is to review the current state-of-the-art and the limitations of the methods used in this area of research, as well as their applications to personalized cardiovascular investigations and treatments. Specifically, the modeling of three different physics – electrophysiology, biomechanics and hemodynamics – used in the cardiovascular IBM is discussed in the context of the physiology that each one of them describes and the mechanisms of the underlying cardiac diseases that they can provide insight into. Only the “bare-bones” of the modeling approaches are discussed in order to make this introductory material more accessible to an outside observer. Additionally, the imaging methods, the aspects of the unique cardiac anatomy derived from them, and their relation to the modeling algorithms are reviewed. Finally, conclusions are drawn about the future evolution of these methods and their potential toward revolutionizing the non-invasive diagnosis, virtual design of treatments/assistive devices, and increasing our understanding of these lethal cardiovascular diseases.

In-silico human electro-mechanical ventricular modelling and simulation for drug-induced pro-arrhythmia and inotropic risk assessment

Human-based computational modelling and simulation are powerful tools to accelerate the mechanistic understanding of cardiac patho-physiology, and to develop and evaluate therapeutic interventions. The aim of this study is to calibrate and evaluate human ventricular electro-mechanical models for investigations on the effect of the electro-mechanical coupling and pharmacological action on human ventricular electrophysiology, calcium dynamics, and active contraction. The most recent models of human ventricular electrophysiology, excitation-contraction coupling, and active contraction were integrated, and the coupled models were calibrated using human experimental data. Simulations were then conducted using the coupled models to quantify the effects of electro-mechanical coupling and drug exposure on electrophysiology and force generation in virtual human ventricular cardiomyocytes and tissue. The resulting calibrated human electro-mechanical models yielded active tension, action potential, and calcium transient metrics that are in agreement with experiments for endocardial, epicardial, and mid-myocardial human samples. Simulation results correctly predicted the inotropic response of different multichannel action reference compounds and demonstrated that the electro-mechanical coupling improves the robustness of repolarisation under drug exposure compared to electrophysiology-only models. They also generated additional evidence to explain the partial mismatch between in-silico and in-vitro experiments on drug-induced electrophysiology changes. The human calibrated and evaluated modelling and simulation framework constructed in this study opens new avenues for future investigations into the complex interplay between the electrical and mechanical cardiac substrates, its modulation by pharmacological action, and its translation to tissue and organ models of cardiac patho-physiology.

Dual Transcriptomic and Molecular Machine Learning Predicts all Major Clinical Forms of Drug Cardiotoxicity

Computational methods can increase productivity of drug discovery pipelines, through overcoming challenges such as cardiotoxicity identification. We demonstrate prediction and preservation of cardiotoxic relationships for six drug-induced cardiotoxicity types using a machine learning approach on a large collected and curated dataset of transcriptional and molecular profiles (1,131 drugs, 35% with known cardiotoxicities, and 9,933 samples). The algorithm generality is demonstrated through validation in an independent drug dataset, in addition to cross-validation. The best prediction attains an average accuracy of 79% in area under the curve (AUC) for safe versus risky drugs, across all six cardiotoxicity types on validation and 66% on the unseen set of drugs. Individual cardiotoxicities for specific drug types are also predicted with high accuracy, including cardiac disorder signs and symptoms for a previously unseen set of anti-inflammatory agents (AUC = 80%) and heart failures for an unseen set of anti-neoplastic agents (AUC = 76%). Besides, independent testing on transcriptional data from the Drug Toxicity Signature Generation Center (DToxS) produces similar results in terms of accuracy and shows an average AUC of 72% for previously seen drugs and 60% for unseen respectively. Given the ubiquitous manifestation of multiple drug adverse effects in every human organ, the methodology is expected to be applicable to additional tissue-specific side effects beyond cardiotoxicity.

Sensitivity analysis of a strongly-coupled human-based electromechanical cardiac model: Effect of mechanical parameters on physiologically relevant biomarkers

The human heart beats as a result of multiscale nonlinear dynamics coupling subcellular to whole organ processes, achieving electrophysiologically-driven mechanical contraction. Computational cardiac modelling and simulation have achieved a great degree of maturity, both in terms of mathematical models of underlying biophysical processes and the development of simulation software. In this study, we present the detailed description of a human-based physiologically-based, and fully-coupled ventricular electromechanical modelling and simulation framework, and a sensitivity analysis focused on its mechanical properties. The biophysical detail of the model, from ionic to whole-organ, is crucial to enable future simulations of disease and drug action. Key novelties include the coupling of state-of-the-art human-based electrophysiology membrane kinetics, excitation-contraction and active contraction models, and the incorporation of a pre-stress model to allow for pre-stressing and pre-loading the ventricles in a dynamical regime. Through high performance computing simulations, we demonstrate that 50% to 200% - 1000% variations in key parameters result in changes in clinically-relevant mechanical biomarkers ranging from diseased to healthy values in clinical studies. Furthermore mechanical biomarkers are primarily affected by only one or two parameters. Specifically, ejection fraction is dominated by the scaling parameter of the active tension model and its scaling parameter in the normal direction ( k ort 2 ); the end systolic pressure is dominated by the pressure at which the ejection phase is triggered ( P ej ) and the compliance of the Windkessel fluid model ( C ); and the longitudinal fractional shortening is dominated by the fibre angle ( ϕ ) and k ort 2 . The wall thickening does not seem to be clearly dominated by any of the considered input parameters. In summary, this study presents in detail the description and implementation of a human-based coupled electromechanical modelling and simulation framework, and a high performance computing study on the sensitivity of mechanical biomarkers to key model parameters. The tools and knowledge generated enable future investigations into disease and drug action on human ventricles.

The 18th FRAME Annual Lecture, October 2019: Human In Silico Trials in Pharmacology

Safety and efficacy testing is a crucial part of the drug development process, and several different methods are used to obtain the necessary data (e.g. in vitro testing, animal trials and clinical trials). Our group has been investigating the potential of modelling and simulation as an alternative approach to some of the methods used for testing drugs for cardiac effects. To achieve our goal of developing and promoting novel approaches in drug development, we formed multidisciplinary collaborations that included clinicians, computer scientists and biologists. Our in silico models are based on human data (e.g. magnetic resonance images, electrocardiogram) and on current knowledge of human electrophysiology, thus generating predictions that are directly applicable to humans. Such models are a particularly powerful tool because they encompass different sources of population heterogeneity, which is crucial for drug testing and for assessing how interindividual variability might affect clinical endpoints. Our group has shown that computer modelling can be used to predict the effects of a test drug in a virtual population or in combination with machine learning to predict different phenotypes when a drug is given to a diseased population. Furthermore, our user-friendly drug testing software is freely available and is being adopted by industry in their drug development process. We have been engaging with industry and regulators to show that our models can contribute to the replacement of animals in drug development. Our ambition is to generate models for simulation of different diseases and therapies for investigations from subcellular to whole organ.

Improved Computational Identification of Drug Response Using Optical Measurements of Human Stem Cell Derived Cardiomyocytes in Microphysiological Systems

Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs) hold great potential for drug screening applications. However, their usefulness is limited by the relative immaturity of the cells' electrophysiological properties as compared to native cardiomyocytes in the adult human heart. In this work, we extend and improve on methodology to address this limitation, building on previously introduced computational procedures which predict drug effects for adult cells based on changes in optical measurements of action potentials and Ca2+ transients made in stem cell derived cardiac microtissues. This methodology quantifies ion channel changes through the inversion of data into a mathematical model, and maps this response to an adult phenotype through the assumption of functional invariance of fundamental intracellular and membrane channels during maturation. Here, we utilize an updated action potential model to represent both hiPSC-CMs and adult cardiomyocytes, apply an IC50-based model of dose-dependent drug effects, and introduce a continuation-based optimization algorithm for analysis of dose escalation measurements using five drugs with known effects. The improved methodology can identify drug induced changes more efficiently, and quantitate important metrics such as IC50 in line with published values. Consequently, the updated methodology is a step towards employing computational procedures to elucidate drug effects in adult cardiomyocytes for new drugs using stem cell-derived experimental tissues.

In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products

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Regulators now consider also evidences produced in silico.

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We need accepted methods to evaluate the credibility of models.

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In this paper we describe the use of the ASME V&V-40 technical standard.

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We also discuss its application to various types of modelling methods.

Historically, the evidences of safety and efficacy that companies provide to regulatory agencies as support to the request for marketing authorization of a new medical product have been produced experimentally, either in vitro or in vivo. More recently, regulatory agencies started receiving and accepting evidences obtained in silico, i.e. through modelling and simulation. However, before any method (experimental or computational) can be acceptable for regulatory submission, the method itself must be considered “qualified” by the regulatory agency. This involves the assessment of the overall “credibility” that such a method has in providing specific evidence for a given regulatory procedure. In this paper, we describe a methodological framework for the credibility assessment of computational models built using mechanistic knowledge of physical and chemical phenomena, in addition to available biological and physiological knowledge; these are sometimes referred to as “biophysical” models. Using guiding examples, we explore the definition of the context of use, the risk analysis for the definition of the acceptability thresholds, and the various steps of a comprehensive verification, validation and uncertainty quantification process, to conclude with considerations on the credibility of a prediction for a specific context of use. While this paper does not provide a guideline for the formal qualification process, which only the regulatory agencies can provide, we expect it to help researchers to better appreciate the extent of scrutiny required, which should be considered early on in the development/use of any (new) in silico evidence.

Feasibility of Using Adjunctive Optogenetic Technologies in Cardiomyocyte Phenotyping - from the Single Cell to the Whole Heart

In 1791, Galvani established that electricity activated excitable cells. In the two centuries that followed, electrode stimulation of neuronal, skeletal and cardiac muscle became the adjunctive method of choice in experimental, electrophysiological, and clinical arenas. This approach underpins breakthrough technologies like implantable cardiac pacemakers that we currently take for granted. However, the contact dependence, and field stimulation that electrical depolarization delivers brings inherent limitations to the scope and experimental scale that can be achieved. Many of these were not exposed until reliable in vitro stem-cell derived experimental materials, with genotypes of interest, were produced in the numbers needed for multi-well screening platforms (for toxicity or efficacy studies) or the 2D or 3D tissue surrogates required to study propagation of depolarization within multicellular constructs that mimic clinically relevant arrhythmia in the heart or brain. Here the limitations of classical electrode stimulation are discussed. We describe how these are overcome by optogenetic tools which put electrically excitable cells under the control of light. We discuss how this enables studies in cardiac material from the single cell to the whole heart scale. We review the current commercial platforms that incorporate optogenetic stimulation strategies, and summarize the global literature to date on cardiac applications of optogenetics. We show that the advantages of optogenetic stimulation relevant to iPS-CM based screening include independence from contact, elimination of electrical stimulation artefacts in field potential measuring approaches such as the multi-electrode array, and the ability to print re-entrant patterns of depolarization at will on 2D cardiomyocyte monolayers.

Drug-induced shortening of the electromechanical window is an effective biomarker for in silico prediction of clinical risk of arrhythmias

BACKGROUND AND PURPOSE

Early identification of drug-induced cardiac adverse events is key in drug development. Human-based computer models are emerging as an effective approach, complementary to in vitro and animal models. Drug-induced shortening of the electromechanical window has been associated with increased risk of arrhythmias. This study investigates the potential of a cellular surrogate for the electromechanical window (EMw) for prediction of pro-arrhythmic cardiotoxicity, and its underlying ionic mechanisms, using human-based computer models.

EXPERIMENTAL APPROACH

In silico drug trials for 40 reference compounds were performed, testing up to 100-fold the therapeutic concentrations (EFTPCmax ) and using a control population of human ventricular action potential (AP) models, optimised to capture pro-arrhythmic ionic profiles. EMw was calculated for each model in the population as the difference between AP and Ca2+ transient durations at 90%. Drug-induced changes in the EMw and occurrence of repolarisation abnormalities (RA) were quantified.

KEY RESULTS

Drugs with clinical risk of Torsade de Pointes arrhythmias induced a concentration-dependent EMw shortening, while safe drugs lead to increase or small change in EMw. Risk predictions based on EMw shortening achieved 90% accuracy at 10× EFTPCmax , whereas RA-based predictions required 100× EFTPCmax to reach the same accuracy. As it is dependent on Ca2+ transient, the EMw was also more sensitive than AP prolongation in distinguishing between pure hERG blockers and multichannel compounds also blocking the calcium current.

CONCLUSION AND IMPLICATIONS

The EMw is an effective biomarker for in silico predictions of drug-induced clinical pro-arrhythmic risk, particularly for compounds with multichannel blocking action.

The Melding of Drug Screening Platforms for Melanoma

The global incidence of cancer is rising rapidly and continues to be one of the leading causes of death in the world. Melanoma deserves special attention since it represents one of the fastest growing types of cancer, with advanced metastatic forms presenting high mortality rates due to the development of drug resistance. The aim of this review is to evaluate how the screening of drugs and compounds for melanoma has been performed over the last seven decades. Thus, we performed literature searches to identify melanoma drug screening methods commonly used by research groups during this timeframe. In vitro and in vivo tests are essential for the development of new drugs; however, incorporation of in silico analyses increases the possibility of finding more suitable candidates for subsequent tests. In silico techniques, such as molecular docking, represent an important and necessary first step in the screening process. However, these techniques have not been widely used by research groups to date. Our research has shown that the vast majority of research groups still perform in vitro and in vivo tests, with emphasis on the use of in vitro enzymatic tests on melanoma cell lines such as SKMEL and in vivo tests using the B16 mouse model. We believe that the union of these three approaches (in silico, in vitro, and in vivo) is essential for improving the discovery and development of new molecules with potential antimelanoma action. This workflow would provide greater confidence and safety for preclinical trials, which will translate to more successful clinical trials and improve the translatability of new melanoma treatments into clinical practice while minimizing the unnecessary use of laboratory animals under the principles of the 3R's.

Integrated lung tissue mechanics one piece at a time: Computational modeling across the scales of biology

The lung is a delicately balanced and highly integrated mechanical system. Lung tissue is continuously exposed to the environment via the air we breathe, making it susceptible to damage. As a consequence, respiratory diseases present a huge burden on society and their prevalence continues to rise. Emergent function is produced not only by the sum of the function of its individual components but also by the complex feedback and interactions occurring across the biological scales - from genes to proteins, cells, tissue and whole organ - and back again. Computational modeling provides the necessary framework for pulling apart and putting back together the pieces of the body and organ systems so that we can fully understand how they function in both health and disease. In this review, we discuss models of lung tissue mechanics spanning from the protein level (the extracellular matrix) through to the level of cells, tissue and whole organ, many of which have been developed in isolation. This is a vital step in the process but to understand the emergent behavior of the lung, we must work towards integrating these component parts and accounting for feedback across the scales, such as mechanotransduction. These interactions will be key to unlocking the mechanisms occurring in disease and in seeking new pharmacological targets and improving personalized healthcare.

Large-Scale Simulation of the Phenotypical Variability Induced by Loss-of-Function Long QT Mutations in Human Induced Pluripotent Stem Cell Cardiomyocytes

Loss-of-function long QT (LQT) mutations inducing LQT1 and LQT2 syndromes have been successfully translated to human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) used as disease-specific models. However, their in vitro investigation mainly relies on experiments using small numbers of cells. This is especially critical when working with cells as heterogeneous as hiPSC-CMs. We aim (i) to investigate in silico the ionic mechanisms underlying LQT1 and LQT2 hiPSC-CM phenotypic variability, and (ii) to enable massive in silico drug tests on mutant hiPSC-CMs. We combined (i) data of control and mutant slow and rapid delayed rectifying K⁺ currents, I_Kr and I_Ks respectively, (ii) a recent in silico hiPSC-CM model, and (iii) the population of models paradigm to generate control and mutant populations for LQT1 and LQT2 cardiomyocytes. Our four populations contain from 1008 to 3584 models. In line with the experimental in vitro data, mutant in silico hiPSC-CMs showed prolonged action potential (AP) duration (LQT1: +14%, LQT2: +39%) and large electrophysiological variability. Finally, the mutant populations were split into normal-like hiPSC-CMs (with action potential duration similar to control) and at risk hiPSC-CMs (with clearly prolonged action potential duration). At risk mutant hiPSC-CMs carried higher expression of L-type Ca²⁺, lower expression of I_Kr and increased sensitivity to quinidine as compared to mutant normal-like hiPSC-CMs, resulting in AP abnormalities. In conclusion, we were able to reproduce the two most common LQT syndromes with large-scale simulations, which enable investigating biophysical mechanisms difficult to assess in vitro, e.g., how variations of ion current expressions in a physiological range can impact on AP properties of mutant hiPSC-CMs.

Many Cells Make Life Work-Multicellularity in Stem Cell-Based Cardiac Disease Modelling

Cardiac disease causes 33% of deaths worldwide but our knowledge of disease progression is still very limited. In vitro models utilising and combining multiple, differentiated cell types have been used to recapitulate the range of myocardial microenvironments in an effort to delineate the mechanical, humoral, and electrical interactions that modulate the cardiac contractile function in health and the pathogenesis of human disease. However, due to limitations in isolating these cell types and changes in their structure and function in vitro, the field is now focused on the development and use of stem cell-derived cell types, most notably, human-induced pluripotent stem cell-derived CMs (hiPSC-CMs), in modelling the CM function in health and patient-specific diseases, allowing us to build on the findings from studies using animal and adult human CMs. It is becoming increasingly appreciated that communications between cardiomyocytes (CMs), the contractile cell of the heart, and the non-myocyte components of the heart not only regulate cardiac development and maintenance of health and adult CM functions, including the contractile state, but they also regulate remodelling in diseases, which may cause the chronic impairment of the contractile function of the myocardium, ultimately leading to heart failure. Within the myocardium, each CM is surrounded by an intricate network of cell types including endothelial cells, fibroblasts, vascular smooth muscle cells, sympathetic neurons, and resident macrophages, and the extracellular matrix (ECM), forming complex interactions, and models utilizing hiPSC-derived cell types offer a great opportunity to investigate these interactions further. In this review, we outline the historical and current state of disease modelling, focusing on the major milestones in the development of stem cell-derived cell types, and how this technology has contributed to our knowledge about the interactions between CMs and key non-myocyte components of the heart in health and disease, in particular, heart failure. Understanding where we stand in the field will be critical for stem cell-based applications, including the modelling of diseases that have complex multicellular dysfunctions.

Harvesting the promise of AOPs: An assessment and recommendations

The Adverse Outcome Pathway (AOP) concept is a knowledge assembly and communication tool to facilitate the transparent translation of mechanistic information into outcomes meaningful to the regulatory assessment of chemicals. The AOP framework and associated knowledgebases (KBs) have received significant attention and use in the regulatory toxicology community. However, it is increasingly apparent that the potential stakeholder community for the AOP concept and AOP KBs is broader than scientists and regulators directly involved in chemical safety assessment. In this paper we identify and describe those stakeholders who currently-or in the future-could benefit from the application of the AOP framework and knowledge to specific problems. We also summarize the challenges faced in implementing pathway-based approaches such as the AOP framework in biological sciences, and provide a series of recommendations to meet critical needs to ensure further progression of the framework as a useful, sustainable and dependable tool supporting assessments of both human health and the environment. Although the AOP concept has the potential to significantly impact the organization and interpretation of biological information in a variety of disciplines/applications, this promise can only be fully realized through the active engagement of, and input from multiple stakeholders, requiring multi-pronged substantive long-term planning and strategies.

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.

An Introduction to Computational Modeling of Cardiac Electrophysiology and Arrhythmogenicity

Mathematical modeling is a powerful tool to study the complex and orchestrated biological process of cardiac electrical activity. By integrating experimental data from key components of cardiac electrophysiology, systems biology simulations can complement empirical findings, provide quantitative insight into physiological and pathophysiological mechanisms of action, and guide new hypotheses to better understand this complex biological system to develop novel cardiotherapeutic approaches. In this chapter, we briefly introduce in silico methods to describe the dynamics of physiological and pathophysiological single-cell and tissue-level cardiac electrophysiology. Using a "bottom-up" approach, we first describe the basis of ion channel mathematical models. Next, we discuss how the net flux of ions through such channels leads to changes in transmembrane voltage during cardiomyocyte action potentials. By applying these fundamentals, we describe how action potentials propagate in models of cardiac tissue. In addition, we provide case studies simulating single-cell and tissue-level arrhythmogenesis, as well as promising approaches to circumvent or overcome such adverse events. Overall, basic concepts and tools are discussed in this chapter as an accessible introduction to nonmathematicians to foster an understanding of electrophysiological modeling studies and help facilitate communication with dry lab colleagues and collaborators.

From ionic to cellular variability in human atrial myocytes: an integrative computational and experimental study

Variability refers to differences in physiological function between individuals, which may translate into different disease susceptibility and treatment efficacy. Experiments in human cardiomyocytes face wide variability and restricted tissue access; under these conditions, computational models are a useful complementary tool. We conducted a computational and experimental investigation in cardiomyocytes isolated from samples of the right atrial appendage of patients undergoing cardiac surgery to evaluate the impact of variability in action potentials (APs) and subcellular ionic densities on Ca²⁺ transient dynamics. Results showed that 1) variability in APs and ionic densities is large, even within an apparently homogenous patient cohort, and translates into ±100% variation in ionic conductances; 2) experimentally calibrated populations of models with wide variations in ionic densities yield APs overlapping with those obtained experimentally, even if AP characteristics of the original generic model differed significantly from experimental APs; 3) model calibration with AP recordings restricts the variability in ionic densities affecting upstroke and resting potential, but redundancy in repolarization currents admits substantial variability in ionic densities; and 4) model populations constrained with experimental APs and ionic densities exhibit three Ca²⁺ transient phenotypes, differing in intracellular Ca²⁺ handling and Na⁺/Ca²⁺ membrane extrusion. These findings advance our understanding of the impact of variability in human atrial electrophysiology. NEW & NOTEWORTHY Variability in human atrial electrophysiology is investigated by integrating for the first time cellular-level and ion channel recordings in computational electrophysiological models. Ion channel calibration restricts current densities but not cellular phenotypic variability. Reduced Na⁺/Ca²⁺ exchanger is identified as a primary mechanism underlying diastolic Ca²⁺ fluctuations in human atrial myocytes.

Have the Findings from Clinical Risk Prediction and Trials Any Key Messages for Safety Pharmacology?

Anti-arrhythmic drugs are a mainstay in the management of symptoms related to arrhythmias, and are adjuncts in prevention and treatment of life-threatening ventricular arrhythmias. However, they also have the potential for pro-arrhythmia and thus the prediction of arrhythmia predisposition and drug response are critical issues. Clinical trials are the latter stages in the safety testing and efficacy process prior to market release, and as such serve as a critical safeguard. In this review, we look at some of the lessons to be learned from approaches to arrhythmia prediction in patients, clinical trials of drugs used in the treatment of arrhythmias, and the implications for the design of pre-clinical safety pharmacology testing.

Integrating cardiomyocytes from human pluripotent stem cells in safety pharmacology: has the time come?

Cardiotoxicity is a severe side effect of drugs that induce structural or electrophysiological changes in heart muscle cells. As a result, the heart undergoes failure and potentially lethal arrhythmias. It is still a major reason for drug failure in preclinical and clinical phases of drug discovery. Current methods for predicting cardiotoxicity are based on guidelines that combine electrophysiological analysis of cell lines expressing ion channels ectopically in vitro with animal models and clinical trials. Although no new cases of drugs linked to lethal arrhythmias have been reported since the introduction of these guidelines in 2005, their limited predictive power likely means that potentially valuable drugs may not reach clinical practice. Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are now emerging as potentially more predictive alternatives, particularly for the early phases of preclinical research. However, these cells are phenotypically immature and culture and assay methods not standardized, which could be a hurdle to the development of predictive computational models and their implementation into the drug discovery pipeline, in contrast to the ambitions of the comprehensive pro-arrhythmia in vitro assay (CiPA) initiative. Here, we review present and future preclinical cardiotoxicity screening and suggest possible hPSC-CM-based strategies that may help to move the field forward. Coordinated efforts by basic scientists, companies and hPSC banks to standardize experimental conditions for generating reliable and reproducible safety indices will be helpful not only for cardiotoxicity prediction but also for precision medicine.

LINKED ARTICLES

This article is part of a themed section on New Insights into Cardiotoxicity Caused by Chemotherapeutic Agents. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.21/issuetoc.

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC₅₀/Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca²⁺-transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca²⁺/late Na⁺ currents and Na⁺/Ca²⁺-exchanger, reduced Na⁺/K⁺-pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na⁺ and Ca²⁺ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca²⁺-transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

Quantitative Comparison of Effects of Dofetilide, Sotalol, Quinidine, and Verapamil between Human Ex vivo Trabeculae and In silico Ventricular Models Incorporating Inter-Individual Action Potential Variability

Background:In silico modeling could soon become a mainstream method of pro-arrhythmic risk assessment in drug development. However, a lack of human-specific data and appropriate modeling techniques has previously prevented quantitative comparison of drug effects between in silico models and recordings from human cardiac preparations. Here, we directly compare changes in repolarization biomarkers caused by dofetilide, dl-sotalol, quinidine, and verapamil, between in silico populations of human ventricular cell models and ex vivo human ventricular trabeculae. Methods and Results:Ex vivo recordings from human ventricular trabeculae in control conditions were used to develop populations of in silico human ventricular cell models that integrated intra- and inter-individual variability in action potential (AP) biomarker values. Models were based on the O'Hara-Rudy ventricular cardiomyocyte model, but integrated experimental AP variability through variation in underlying ionic conductances. Changes to AP duration, triangulation and early after-depolarization occurrence from application of the four drugs at multiple concentrations and pacing frequencies were compared between simulations and experiments. To assess the impact of variability in IC50 measurements, and the effects of including state-dependent drug binding dynamics, each drug simulation was repeated with two different IC50 datasets, and with both the original O'Hara-Rudy hERG model and a recently published state-dependent model of hERG and hERG block. For the selective hERG blockers dofetilide and sotalol, simulation predictions of AP prolongation and repolarization abnormality occurrence showed overall good agreement with experiments. However, for multichannel blockers quinidine and verapamil, simulations were not in agreement with experiments across all IC50 datasets and IKr block models tested. Quinidine simulations resulted in overprolonged APs and high incidence of repolarization abnormalities, which were not observed in experiments. Verapamil simulations showed substantial AP prolongation while experiments showed mild AP shortening. Conclusions: Results for dofetilide and sotalol show good agreement between experiments and simulations for selective compounds, however lack of agreement from simulations of quinidine and verapamil suggest further work is needed to understand the more complex electrophysiological effects of these multichannel blocking drugs.

Non-invasive phenotyping and drug testing in single cardiomyocytes or beta-cells by calcium imaging and optogenetics

Identification of drug induced electrical instability of the heart curtails development, and introduction, of potentially proarrhythmic drugs. This problem usually requires complimentary contact based approaches such as patch-clamp electrophysiology combined with field stimulation electrodes to observe and control the cell. This produces data with high signal to noise but requires direct physical contact generally preventing high-throughput, or prolonged, phenotyping of single cells or tissues. Combining genetically encoded optogenetic control and spectrally compatible calcium indicator tools into a single adenoviral vector allows the analogous capability for cell control with simultaneous cellular phenotyping without the need for contact. This combination can be applied to single rodent primary adult cardiomyocytes, and human stem cell derived cardiomyocytes, enabling contactless small molecule evaluation for inhibitors of sodium, potassium and calcium channels suggesting it may be useful for early toxicity work. In pancreatic beta-cells it reveals the effects of glucose and the KATP inhibitor gliclazide.

Proarrhythmia liability assessment and the comprehensive in vitro Proarrhythmia Assay (CiPA): An industry survey on current practice

INTRODUCTION

The Safety Pharmacology Society (SPS) has conducted a survey of its membership to identify industry practices related to testing considered in the Comprehensive In vitro Proarrhythmia Assay (CiPA).

METHODS

Survey topics included nonclinical approaches to address proarrhythmia issues, conduct of in silico studies, in vitro ion channel testing methods used, drugs used as positive controls during the conduct of cardiac ion channel studies, types of arrhythmias observed in non-clinical studies and use of the anticipated CiPA ion channel assay.

RESULTS

In silico studies were used to evaluate effects on ventricular action potentials by only 15% of responders. In vitro assays were used mostly to assess QT prolongation (95%), cardiac Ca²⁺ and Na⁺ channel blockade (82%) and QT shortening or QRS prolongation (53%). For de-risking of candidate drugs for proarrhythmia, those assays most relevant to CiPA including cell lines stably expressing ion channels used to determine potency of drug block were most frequently used (89%) and human stem cell-derived or induced pluripotent stem cell cardiomyocytes (46%). Those in vivo assays related to general proarrhythmia derisking include ECG recording using implanted telemetry technology (88%), jacketed external telemetry (62%) and anesthetized animal models (53%). While the CiPA initiative was supported by 92% of responders, there may be some disconnect between current practice and future expectations, as explained.

DISCUSSION

Proarrhythmia liability assessment in drug development presently includes study types consistent with CiPA. It is anticipated that CiPA will develop into a workable solution to the concern that proarrhythmia liability testing remains suboptimal.

Electrophysiological properties of computational human ventricular cell action potential models under acute ischemic conditions

Acute myocardial ischemia is one of the main causes of sudden cardiac death. The mechanisms have been investigated primarily in experimental and computational studies using different animal species, but human studies remain scarce. In this study, we assess the ability of four human ventricular action potential models (ten Tusscher and Panfilov, 2006; Grandi et al., 2010; Carro et al., 2011; O'Hara et al., 2011) to simulate key electrophysiological consequences of acute myocardial ischemia in single cell and tissue simulations. We specifically focus on evaluating the effect of extracellular potassium concentration and activation of the ATP-sensitive inward-rectifying potassium current on action potential duration, post-repolarization refractoriness, and conduction velocity, as the most critical factors in determining reentry vulnerability during ischemia. Our results show that the Grandi and O'Hara models required modifications to reproduce expected ischemic changes, specifically modifying the intracellular potassium concentration in the Grandi model and the sodium current in the O'Hara model. With these modifications, the four human ventricular cell AP models analyzed in this study reproduce the electrophysiological alterations in repolarization, refractoriness, and conduction velocity caused by acute myocardial ischemia. However, quantitative differences are observed between the models and overall, the ten Tusscher and modified O'Hara models show closest agreement to experimental data.

The control of cardiac ventricular excitability by autonomic pathways

Central to the genesis of ventricular cardiac arrhythmia are variations in determinants of excitability. These involve individual ionic channels and transporters in cardiac myocytes but also tissue factors such as variable conduction of the excitation wave, fibrosis and source-sink mismatch. It is also known that in certain diseases and particularly the channelopathies critical events occur with specific stressors. For example, in hereditary long QT syndrome due to mutations in KCNQ1 arrhythmic episodes are provoked by exercise and in particular swimming. Thus not only is the static substrate important but also how this is modified by dynamic signalling events associated with common physiological responses. In this review, we examine the regulation of ventricular excitability by signalling pathways from a cellular and tissue perspective in an effort to identify key processes, effectors and potential therapeutic approaches. We specifically focus on the autonomic nervous system and related signalling pathways.

Opportunities to Apply the 3Rs in Safety Assessment Programs

Before a potential new medicine can be administered to humans it is essential that its safety is adequately assessed. Safety assessment in animals forms an integral part of this process, from early drug discovery and initial candidate selection to the program of recommended regulatory tests in animals. The 3Rs (replacement, reduction, and refinement of animals in research) are integrated in the current regulatory requirements and expectations and, in the EU, provide a legal and ethical framework for in vivo research to ensure the scientific objectives are met whilst minimizing animal use and maintaining high animal welfare standards. Though the regulations are designed to uncover potential risks, they are intended to be flexible, so that the most appropriate approach can be taken for an individual product. This article outlines current and future opportunities to apply the 3Rs in safety assessment programs for pharmaceuticals, and the potential (scientific, financial, and ethical) benefits to the industry, across the drug discovery and development process. For example, improvements to, or the development of, novel, early screens (e.g., in vitro, in silico, or nonmammalian screens) designed to identify compounds with undesirable characteristics earlier in development have the potential to reduce late-stage attrition by improving the selection of compounds that require regulatory testing in animals. Opportunities also exist within the current regulatory framework to simultaneously reduce and/or refine animal use and improve scientific outcomes through improvements to technical procedures and/or adjustments to study designs. It is important that approaches to safety assessment are continuously reviewed and challenged to ensure they are science-driven and predictive of relevant effects in humans.

Recent developments in using mechanistic cardiac modelling for drug safety evaluation

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Modelling and simulation can streamline decision making in drug safety testing.

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Computational cardiac electrophysiology is a mature technology with a long heritage.

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There are many challenges and opportunities in using in silico techniques in future.

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We discuss how models can be used at different stages of drug discovery.

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CiPA will combine screening platforms, human cell assays and in silico predictions.

On the tenth anniversary of two key International Conference on Harmonisation (ICH) guidelines relating to cardiac proarrhythmic safety, an initiative aims to consider the implementation of a new paradigm that combines in vitro and in silico technologies to improve risk assessment. The Comprehensive In Vitro Proarrhythmia Assay (CiPA) initiative (co-sponsored by the Cardiac Safety Research Consortium, Health and Environmental Sciences Institute, Safety Pharmacology Society and FDA) is a bold and welcome step in using computational tools for regulatory decision making. This review compares and contrasts the state-of-the-art tools from empirical to mechanistic models of cardiac electrophysiology, and how they can and should be used in combination with experimental tests for compound decision making.