Predicting critical drug concentrations and torsadogenic risk using a multiscale exposure-response simulator.

Torsades de pointes is a serious side effect of many drugs that can trigger sudden cardiac death, even in patients with structurally normal hearts. Torsadogenic risk has traditionally been correlated with the blockage of a specific potassium channel and a prolonged recovery period in the electrocardiogram. However, the precise mechanisms by which single channel block translates into heart rhythm disorders remain incompletely understood. Here we establish a multiscale exposure-response simulator that converts block-concentration characteristics from single cell recordings into three-dimensional excitation profiles and electrocardiograms to rapidly assess torsadogenic risk. For the drug dofetilide, we characterize the QT interval and heart rate at different drug concentrations and identify the critical concentration at the onset of torsades de pointes: For dofetilide concentrations of 2x, 3x, and 4x, as multiples of the free plasma concentration Cmax = 2.1 nM, the QT interval increased by +62.0%, +71.2%, and +82.3% compared to baseline, and the heart rate changed by -21.7%, -23.3%, and +88.3%. The last number indicates that, at the critical concentration of 4x, the heart spontaneously developed an episode of a torsades-like arrhythmia. Strikingly, this critical drug concentration is higher than the concentration estimated from early afterdepolarizations in single cells and lower than in one-dimensional cable models. Our results highlight the importance of whole heart modeling and explain, at least in part, why current regulatory paradigms often fail to accurately quantify the pro-arrhythmic potential of a drug. Our exposure-response simulator could provide a more mechanistic assessment of pro-arrhythmic risk and help establish science-based guidelines to reduce rhythm disorders, design safer drugs, and accelerate drug development.

[1]  J. Wong,et al.  Generating fibre orientation maps in human heart models using Poisson interpolation , 2014, Computer methods in biomechanics and biomedical engineering.

[2]  T. Hisada,et al.  Screening system for drug-induced arrhythmogenic risk combining a patch clamp and heart simulator , 2015, Science Advances.

[3]  David G Strauss,et al.  Evolving regulatory paradigm for proarrhythmic risk assessment for new drugs. , 2016, Journal of electrocardiology.

[4]  Jaimit Parikh,et al.  Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features , 2017, Front. Pharmacol..

[5]  E. Kuhl,et al.  Predicting the cardiac toxicity of drugs using a novel multiscale exposure–response simulator , 2018, Computer methods in biomechanics and biomedical engineering.

[6]  A. Garfinkel,et al.  Early afterdepolarizations in cardiac myocytes: beyond reduced repolarization reserve. , 2013, Cardiovascular research.

[7]  Yoram Rudy,et al.  Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation , 2011, PLoS Comput. Biol..

[8]  D. Durrer,et al.  Total Excitation of the Isolated Human Heart , 1970, Circulation.

[9]  Ellen Kuhl,et al.  The importance of mechano-electrical feedback and inertia in cardiac electromechanics. , 2017, Computer methods in applied mechanics and engineering.

[10]  L E Perotti,et al.  Regional segmentation of ventricular models to achieve repolarization dispersion in cardiac electrophysiology modeling. , 2015, International journal for numerical methods in biomedical engineering.

[11]  B. Lindsay,et al.  Safety of Oral Dofetilide for Rhythm Control of Atrial Fibrillation and Atrial Flutter , 2015, Circulation. Arrhythmia and electrophysiology.

[12]  G. Robertson,et al.  HERG, a human inward rectifier in the voltage-gated potassium channel family. , 1995, Science.

[13]  W. Saliba,et al.  Dofetilide: a new class III antiarrhythmic agent , 2007, Expert review of cardiovascular therapy.

[14]  G. Gintant,et al.  Evolution of strategies to improve preclinical cardiac safety testing , 2016, Nature Reviews Drug Discovery.

[15]  R. Califf,et al.  Evaluation of the dofetilide risk-management program. , 2003, American heart journal.

[16]  E. Sobie,et al.  Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms , 2016, Clinical pharmacology and therapeutics.

[17]  Denis Noble,et al.  Contributions of HERG K+ current to repolarization of the human ventricular action potential. , 2008, Progress in biophysics and molecular biology.

[18]  C Antzelevitch,et al.  Clinical relevance of cardiac arrhythmias generated by afterdepolarizations. Role of M cells in the generation of U waves, triggered activity and torsade de pointes. , 1994, Journal of the American College of Cardiology.

[19]  R Lazzara,et al.  Multiple mechanisms in the long-QT syndrome. Current knowledge, gaps, and future directions. The SADS Foundation Task Force on LQTS. , 1996, Circulation.

[20]  Alan Garfinkel,et al.  So little source, so much sink: requirements for afterdepolarizations to propagate in tissue. , 2010, Biophysical journal.

[21]  Kelly C. Chang,et al.  Improving the In Silico Assessment of Proarrhythmia Risk by Combining hERG (Human Ether-à-go-go-Related Gene) Channel–Drug Binding Kinetics and Multichannel Pharmacology , 2017, Circulation. Arrhythmia and electrophysiology.

[22]  R. W. Hansen,et al.  Journal of Health Economics , 2016 .

[23]  T. Colatsky,et al.  The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative - Update on progress. , 2016, Journal of pharmacological and toxicological methods.

[24]  S. Göktepe,et al.  Computational modeling of electrocardiograms: A finite element approach toward cardiac excitation , 2010 .

[25]  J Vicente,et al.  Differentiating Drug‐Induced Multichannel Block on the Electrocardiogram: Randomized Study of Dofetilide, Quinidine, Ranolazine, and Verapamil , 2014, Clinical pharmacology and therapeutics.

[26]  Trine Krogh-Madsen,et al.  Global Optimization of Ventricular Myocyte Model to Multi-Variable Objective Improves Predictions of Drug-Induced Torsades de Pointes , 2017, Front. Physiol..

[27]  D. Strauss,et al.  An evaluation of 30 clinical drugs against the comprehensive in vitro proarrhythmia assay (CiPA) proposed ion channel panel. , 2016, Journal of pharmacological and toxicological methods.

[28]  Gary Gintant,et al.  Rechanneling the cardiac proarrhythmia safety paradigm: a meeting report from the Cardiac Safety Research Consortium. , 2014, American heart journal.

[29]  P. Sager,et al.  Mechanistic Model‐Informed Proarrhythmic Risk Assessment of Drugs: Review of the “CiPA” Initiative and Design of a Prospective Clinical Validation Study , 2017, Clinical pharmacology and therapeutics.

[30]  Henggui Zhang,et al.  Cardiac cell modelling: observations from the heart of the cardiac physiome project. , 2011, Progress in biophysics and molecular biology.

[31]  Eric Kerfoot,et al.  Verification of cardiac tissue electrophysiology simulators using an N-version benchmark , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[32]  D. Fedida,et al.  Probing the molecular basis of hERG drug block with unnatural amino acids , 2018, Scientific Reports.

[33]  D. Noble,et al.  A model for human ventricular tissue. , 2004, American journal of physiology. Heart and circulatory physiology.

[34]  Edmund J. Crampin,et al.  A global sensitivity tool for cardiac cell modeling: Application to ionic current balance and hypertrophic signaling , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[35]  Thomas L. Lenz,et al.  Dofetilide, a New Class III Antiarrhythmic Agent , 2000, Pharmacotherapy.

[36]  Jack Lee,et al.  Multiphysics and multiscale modelling, data–model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics , 2016, Interface Focus.

[37]  Euan A Ashley,et al.  Early somatic mosaicism is a rare cause of long-QT syndrome , 2016, Proceedings of the National Academy of Sciences.

[38]  Dulciana D. Chan,et al.  Late sodium current block for drug‐induced long QT syndrome: Results from a prospective clinical trial , 2016, Clinical pharmacology and therapeutics.

[39]  P. Taggart,et al.  Early afterdepolarizations promote transmural reentry in ischemic human ventricles with reduced repolarization reserve , 2016, Progress in biophysics and molecular biology.

[40]  Mitra Abbasi,et al.  Early assessment of proarrhythmic risk of drugs using the in vitro data and single-cell-based in silico models: proof of concept , 2017, Toxicology mechanisms and methods.

[41]  Jamie I Vandenberg,et al.  Human ether-a-go-go related gene (hERG) K+ channels: function and dysfunction. , 2008, Progress in biophysics and molecular biology.

[42]  Henggui Zhang,et al.  hERG Inhibitors with Similar Potency But Different Binding Kinetics Do Not Pose the Same Proarrhythmic Risk: Implications for Drug Safety Assessment , 2014, Journal of cardiovascular electrophysiology.

[43]  David F Briceño,et al.  Dofetilide Reloaded: To Admit or Not to Admit, That is the Question. , 2017, Circulation. Arrhythmia and electrophysiology.

[44]  Denis Noble,et al.  Resolving the M-cell debate: why and how. , 2011, Heart rhythm.

[45]  Miss A.O. Penney (b) , 1974, The New Yale Book of Quotations.

[46]  F Dessertenne,et al.  [Ventricular tachycardia with 2 variable opposing foci]. , 1966, Archives des maladies du coeur et des vaisseaux.

[47]  Jiang Yao,et al.  Predicting drug‐induced arrhythmias by multiscale modeling , 2018, International journal for numerical methods in biomedical engineering.

[48]  Alan Garny,et al.  A numerical guide to the solution of the bi-domain equations of cardiac electrophysiology. , 2010, Progress in biophysics and molecular biology.

[49]  D. Noble,et al.  Mathematical models of the electrical action potential of Purkinje fibre cells , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[50]  Ellen Kuhl,et al.  The Living Heart Project: A robust and integrative simulator for human heart function. , 2014, European journal of mechanics. A, Solids.

[51]  C. Antzelevitch,et al.  Unique Topographical Distribution of M Cells Underlies Reentrant Mechanism of Torsade de Pointes in the Long-QT Syndrome , 2002, Circulation.

[52]  Ellen Kuhl,et al.  Interpreting Activation Mapping of Atrial Fibrillation: A Hybrid Computational/Physiological Study , 2018, Annals of Biomedical Engineering.

[53]  Roderick MacKinnon,et al.  Cryo-EM Structure of the Open Human Ether-à-go-go-Related K+ Channel hERG , 2017, Cell.

[54]  Gary R. Mirams,et al.  Evaluation of an in silico cardiac safety assay: Using ion channel screening data to predict QT interval changes in the rabbit ventricular wedge , 2013, Journal of pharmacological and toxicological methods.

[55]  Min Wu,et al.  Uncertainty Quantification Reveals the Importance of Data Variability and Experimental Design Considerations for in Silico Proarrhythmia Risk Assessment , 2017, Front. Physiol..

[56]  Gary R. Mirams,et al.  Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk , 2011, Cardiovascular research.

[57]  N P Smith,et al.  Coupling multi-physics models to cardiac mechanics. , 2011, Progress in biophysics and molecular biology.

[58]  David Gavaghan,et al.  Multiscale cardiac modelling reveals the origins of notched T waves in long QT syndrome type 2 , 2014, Nature Communications.

[59]  David Gavaghan,et al.  A Bidomain Model of the Ventricular Specialized Conduction System of the Heart , 2012, SIAM J. Appl. Math..

[60]  C Antzelevitch,et al.  A subpopulation of cells with unique electrophysiological properties in the deep subepicardium of the canine ventricle. The M cell. , 1991, Circulation research.

[61]  Ellen Kuhl,et al.  Generating Purkinje networks in the human heart. , 2016, Journal of biomechanics.

[62]  N. Stockbridge,et al.  Dealing with Global Safety Issues , 2013, Drug Safety.

[63]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[64]  Nancy Wilkins-Diehr,et al.  XSEDE: Accelerating Scientific Discovery , 2014, Computing in Science & Engineering.

[65]  K. Sampson,et al.  Molecular Pathophysiology of Congenital Long QT Syndrome. , 2017, Physiological reviews.

[66]  D. Roden Pharmacogenetics of Potassium Channel Blockers. , 2016, Cardiac electrophysiology clinics.

[67]  A. Garfinkel,et al.  Early afterdepolarizations and cardiac arrhythmias. , 2010, Heart rhythm.

[68]  S. Göktepe,et al.  Atrial and ventricular fibrillation: computational simulation of spiral waves in cardiac tissue , 2010 .

[69]  Erik Grandelius The bidomain equations of cardiac electrophysiology , 2017 .

[70]  Donald M Bers,et al.  Screening Drug-Induced Arrhythmia Using Human Induced Pluripotent Stem Cell–Derived Cardiomyocytes and Low-Impedance Microelectrode Arrays , 2013, Circulation.

[71]  A. Camm,et al.  Relationships between preclinical cardiac electrophysiology, clinical QT interval prolongation and torsade de pointes for a broad range of drugs: evidence for a provisional safety margin in drug development. , 2003, Cardiovascular research.

[72]  S. Göktepe,et al.  Computational modeling of cardiac electrophysiology: A novel finite element approach , 2009 .

[73]  T. Hisada,et al.  Transmural and apicobasal gradients in repolarization contribute to T-wave genesis in human surface ECG. , 2011, American journal of physiology. Heart and circulatory physiology.

[74]  Oliver J. Britton,et al.  Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity , 2017, Front. Physiol..

[75]  R. W. Hansen,et al.  The price of innovation: new estimates of drug development costs. , 2003, Journal of health economics.