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Ni H, Grandi E. Computational Modeling of Cardiac Electrophysiology. Methods Mol Biol 2024; 2735:63-103. [PMID: 38038844 DOI: 10.1007/978-1-0716-3527-8_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Mathematical modeling and simulation are well-established and powerful tools to integrate experimental data of individual components of cardiac electrophysiology, excitation-contraction coupling, and regulatory signaling pathways, to gain quantitative and mechanistic insight into pathophysiological processes and guide therapeutic strategies. Here, we briefly describe the processes governing cardiac myocyte electrophysiology and Ca2+ handling and their regulation, as well as action potential propagation in tissue. We discuss the models and methods used to describe these phenomena, including procedures for model parameterization and validation, in addition to protocols for model interrogation and analysis and techniques that account for phenotypic variability and parameter uncertainty. Our objective is to provide a summary of basic concepts and approaches as a resource for scientists training in this discipline and for all researchers aiming to gain an understanding of cardiac modeling studies.
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Affiliation(s)
- Haibo Ni
- Department of Pharmacology, University of California, Davis, CA, USA.
| | - Eleonora Grandi
- Department of Pharmacology, University of California, Davis, CA, USA.
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2
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Heitmann S, Vandenberg JI, Hill AP. Assessing drug safety by identifying the axis of arrhythmia in cardiomyocyte electrophysiology. eLife 2023; 12:RP90027. [PMID: 38079357 PMCID: PMC10712948 DOI: 10.7554/elife.90027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023] Open
Abstract
Many classes of drugs can induce fatal cardiac arrhythmias by disrupting the electrophysiology of cardiomyocytes. Safety guidelines thus require all new drugs to be assessed for pro-arrhythmic risk prior to conducting human trials. The standard safety protocols primarily focus on drug blockade of the delayed-rectifier potassium current (IKr). Yet the risk is better assessed using four key ion currents (IKr, ICaL, INaL, IKs). We simulated 100,000 phenotypically diverse cardiomyocytes to identify the underlying relationship between the blockade of those currents and the emergence of ectopic beats in the action potential. We call that relationship the axis of arrhythmia. It serves as a yardstick for quantifying the arrhythmogenic risk of any drug from its profile of multi-channel block alone. We tested it on 109 drugs and found that it predicted the clinical risk labels with an accuracy of 88.1-90.8%. Pharmacologists can use our method to assess the safety of novel drugs without resorting to animal testing or unwieldy computer simulations.
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Affiliation(s)
| | - Jamie I Vandenberg
- Victor Chang Cardiac Research InstituteDarlinghurstAustralia
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South WalesSydneyAustralia
| | - Adam P Hill
- Victor Chang Cardiac Research InstituteDarlinghurstAustralia
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South WalesSydneyAustralia
- Victor Chang Cardiac Research Institute Innovation CentreDarlinghurstAustralia
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3
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Hellgren KT, Ni H, Morotti S, Grandi E. Predictive Male-to-Female Translation of Cardiac Electrophysiological Response to Drugs. JACC Clin Electrophysiol 2023; 9:2642-2648. [PMID: 37768254 DOI: 10.1016/j.jacep.2023.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/22/2023] [Accepted: 08/14/2023] [Indexed: 09/29/2023]
Abstract
Despite evidence that women are at higher risk of drug-induced torsade de pointes and sudden cardiac death, female sex is vastly underrepresented in cardiovascular research, thus limiting our fundamental understanding of sex-specific arrhythmia mechanisms and our ability to predict arrhythmia propensity. To address this urgent clinical and preclinical need, we developed a quantitative tool that predicts the electrophysiological response to drug administration in female cardiomyocytes starting from data collected in males. We demonstrate the suitability of our translator for sex-specific cardiac safety assessment and include proof-of-concept application of our translator to in vitro and in vivo data.
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Affiliation(s)
- Kim T Hellgren
- Department of Pharmacology, University of California-Davis, Davis, California, USA
| | - Haibo Ni
- Department of Pharmacology, University of California-Davis, Davis, California, USA
| | - Stefano Morotti
- Department of Pharmacology, University of California-Davis, Davis, California, USA.
| | - Eleonora Grandi
- Department of Pharmacology, University of California-Davis, Davis, California, USA.
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4
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Ni H, Morotti S, Zhang X, Dobrev D, Grandi E. Integrative human atrial modelling unravels interactive protein kinase A and Ca2+/calmodulin-dependent protein kinase II signalling as key determinants of atrial arrhythmogenesis. Cardiovasc Res 2023; 119:2294-2311. [PMID: 37523735 DOI: 10.1093/cvr/cvad118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/18/2023] [Accepted: 06/05/2023] [Indexed: 08/02/2023] Open
Abstract
AIMS Atrial fibrillation (AF), the most prevalent clinical arrhythmia, is associated with atrial remodelling manifesting as acute and chronic alterations in expression, function, and regulation of atrial electrophysiological and Ca2+-handling processes. These AF-induced modifications crosstalk and propagate across spatial scales creating a complex pathophysiological network, which renders AF resistant to existing pharmacotherapies that predominantly target transmembrane ion channels. Developing innovative therapeutic strategies requires a systems approach to disentangle quantitatively the pro-arrhythmic contributions of individual AF-induced alterations. METHODS AND RESULTS Here, we built a novel computational framework for simulating electrophysiology and Ca2+-handling in human atrial cardiomyocytes and tissues, and their regulation by key upstream signalling pathways [i.e. protein kinase A (PKA), and Ca2+/calmodulin-dependent protein kinase II (CaMKII)] involved in AF-pathogenesis. Populations of atrial cardiomyocyte models were constructed to determine the influence of subcellular ionic processes, signalling components, and regulatory networks on atrial arrhythmogenesis. Our results reveal a novel synergistic crosstalk between PKA and CaMKII that promotes atrial cardiomyocyte electrical instability and arrhythmogenic triggered activity. Simulations of heterogeneous tissue demonstrate that this cellular triggered activity is further amplified by CaMKII- and PKA-dependent alterations of tissue properties, further exacerbating atrial arrhythmogenesis. CONCLUSIONS Our analysis reveals potential mechanisms by which the stress-associated adaptive changes turn into maladaptive pro-arrhythmic triggers at the cellular and tissue levels and identifies potential anti-AF targets. Collectively, our integrative approach is powerful and instrumental to assemble and reconcile existing knowledge into a systems network for identifying novel anti-AF targets and innovative approaches moving beyond the traditional ion channel-based strategy.
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Affiliation(s)
- Haibo Ni
- Department of Pharmacology, University of California Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Stefano Morotti
- Department of Pharmacology, University of California Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Xianwei Zhang
- Department of Pharmacology, University of California Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Dobromir Dobrev
- Institute of Pharmacology, Faculty of Medicine, University Duisburg-Essen, Essen, Germany
- Department of Medicine and Research Center, Montreal Heart Institute and Université de Montréal, Montréal, Canada
- Department of Molecular Physiology and Biophysics, Baylor College of Medicine, Houston, TX, USA
| | - Eleonora Grandi
- Department of Pharmacology, University of California Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
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5
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Jin Q, Greenstein JL, Winslow RL. Estimating the probability of early afterdepolarizations and predicting arrhythmic risk associated with long QT syndrome type 1 mutations. Biophys J 2023; 122:4042-4056. [PMID: 37705243 PMCID: PMC10598291 DOI: 10.1016/j.bpj.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/29/2023] [Accepted: 09/08/2023] [Indexed: 09/15/2023] Open
Abstract
Early afterdepolarizations (EADs) are action potential (AP) repolarization abnormalities that can trigger lethal arrhythmias. Simulations using biophysically detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias; however, such analyses can pose a huge computational burden. We have previously developed a highly simplified approach in which logistic regression models (LRMs) map parameters of complex cell models to the probability of ectopic beats. Here, we extend this approach to predict the probability of EADs (P(EAD)) as a mechanistic metric of arrhythmic risk. We use the LRM to investigate how changes in parameters of the slow-activating delayed rectifier current (IKs) affect P(EAD) for 17 different long QT syndrome type 1 (LQTS1) mutations. In this LQTS1 clinical arrhythmic risk prediction task, we compared P(EAD) for these 17 mutations with two other recently published model-based arrhythmia risk metrics (AP morphology metric across populations of myocyte models and transmural repolarization prolongation based on a one-dimensional [1D] tissue-level model). These model-based risk metrics yield similar prediction performance; however, each fails to stratify clinical risk for a significant number of the 17 studied LQTS1 mutations. Nevertheless, an interpretable ensemble model using multivariate linear regression built by combining all of these model-based risk metrics successfully predicts the clinical risk of 17 mutations. These results illustrate the potential of computational approaches in arrhythmia risk prediction.
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Affiliation(s)
- Qingchu Jin
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Joseph L Greenstein
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Raimond L Winslow
- Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
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Herrera NT, Zhang X, Ni H, Maleckar MM, Heijman J, Dobrev D, Grandi E, Morotti S. Dual effects of the small-conductance Ca 2+-activated K + current on human atrial electrophysiology and Ca 2+-driven arrhythmogenesis: an in silico study. Am J Physiol Heart Circ Physiol 2023; 325:H896-H908. [PMID: 37624096 PMCID: PMC10659325 DOI: 10.1152/ajpheart.00362.2023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/11/2023] [Accepted: 08/11/2023] [Indexed: 08/26/2023]
Abstract
By sensing changes in intracellular Ca2+, small-conductance Ca2+-activated K+ (SK) channels dynamically regulate the dynamics of the cardiac action potential (AP) on a beat-to-beat basis. Given their predominance in atria versus ventricles, SK channels are considered a promising atrial-selective pharmacological target against atrial fibrillation (AF), the most common cardiac arrhythmia. However, the precise contribution of SK current (ISK) to atrial arrhythmogenesis is poorly understood, and may potentially involve different mechanisms that depend on species, heart rates, and degree of AF-induced atrial remodeling. Both reduced and enhanced ISK have been linked to AF. Similarly, both SK channel up- and downregulation have been reported in chronic AF (cAF) versus normal sinus rhythm (nSR) patient samples. Here, we use our multiscale modeling framework to obtain mechanistic insights into the contribution of ISK in human atrial cardiomyocyte electrophysiology. We simulate several protocols to quantify how ISK modulation affects the regulation of AP duration (APD), Ca2+ transient, refractoriness, and occurrence of alternans and delayed afterdepolarizations (DADs). Our simulations show that ISK activation shortens the APD and atrial effective refractory period, limits Ca2+ cycling, and slightly increases the propensity for alternans in both nSR and cAF conditions. We also show that increasing ISK counteracts DAD development by enhancing the repolarization force that opposes the Ca2+-dependent depolarization. Taken together, our results suggest that increasing ISK in human atrial cardiomyocytes could promote reentry while protecting against triggered activity. Depending on the leading arrhythmogenic mechanism, ISK inhibition may thus be a beneficial or detrimental anti-AF strategy.NEW & NOTEWORTHY Using our established framework for human atrial myocyte simulations, we investigated the role of the small-conductance Ca2+-activated K+ current (ISK) in the regulation of cell function and the development of Ca2+-driven arrhythmias. We found that ISK inhibition, a promising atrial-selective pharmacological strategy against atrial fibrillation, counteracts the reentry-promoting abbreviation of atrial refractoriness, but renders human atrial myocytes more vulnerable to delayed afterdepolarizations, thus potentially increasing the propensity for ectopic (triggered) activity.
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Affiliation(s)
- Nathaniel T Herrera
- Department of Pharmacology, University of California Davis, Davis, California, United States
| | - Xianwei Zhang
- Department of Pharmacology, University of California Davis, Davis, California, United States
| | - Haibo Ni
- Department of Pharmacology, University of California Davis, Davis, California, United States
| | - Mary M Maleckar
- Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Jordi Heijman
- Department of Cardiology, Faculty of Health, Medicine, and Life Sciences, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Dobromir Dobrev
- Faculty of Medicine, West German Heart and Vascular Center, Institute of Pharmacology, University Duisburg-Essen, Essen, Germany
- Department of Medicine, Montreal Heart Institute and Université de Montréal, Montreal, Quebec, Canada
- Department of Integrative Physiology, Baylor College of Medicine, Houston, Texas, United States
| | - Eleonora Grandi
- Department of Pharmacology, University of California Davis, Davis, California, United States
| | - Stefano Morotti
- Department of Pharmacology, University of California Davis, Davis, California, United States
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Kervadec A, Kezos J, Ni H, Yu M, Marchant J, Spiering S, Kannan S, Kwon C, Andersen P, Bodmer R, Grandi E, Ocorr K, Colas AR. Multiplatform modeling of atrial fibrillation identifies phospholamban as a central regulator of cardiac rhythm. Dis Model Mech 2023; 16:dmm049962. [PMID: 37293707 PMCID: PMC10387351 DOI: 10.1242/dmm.049962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 05/26/2023] [Indexed: 06/10/2023] Open
Abstract
Atrial fibrillation (AF) is a common and genetically inheritable form of cardiac arrhythmia; however, it is currently not known how these genetic predispositions contribute to the initiation and/or maintenance of AF-associated phenotypes. One major barrier to progress is the lack of experimental systems to investigate the effects of gene function on rhythm parameters in models with human atrial and whole-organ relevance. Here, we assembled a multi-model platform enabling high-throughput characterization of the effects of gene function on action potential duration and rhythm parameters using human induced pluripotent stem cell-derived atrial-like cardiomyocytes and a Drosophila heart model, and validation of the findings using computational models of human adult atrial myocytes and tissue. As proof of concept, we screened 20 AF-associated genes and identified phospholamban loss of function as a top conserved hit that shortens action potential duration and increases the incidence of arrhythmia phenotypes upon stress. Mechanistically, our study reveals that phospholamban regulates rhythm homeostasis by functionally interacting with L-type Ca2+ channels and NCX. In summary, our study illustrates how a multi-model system approach paves the way for the discovery and molecular delineation of gene regulatory networks controlling atrial rhythm with application to AF.
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Affiliation(s)
- Anaïs Kervadec
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - James Kezos
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Haibo Ni
- Department of Pharmacology, UC Davis, Davis, CA 95616, USA
| | - Michael Yu
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - James Marchant
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Sean Spiering
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Suraj Kannan
- Johns Hopkins University, Baltimore, MD 21205, USA
| | - Chulan Kwon
- Johns Hopkins University, Baltimore, MD 21205, USA
| | | | - Rolf Bodmer
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | | | - Karen Ocorr
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Alexandre R. Colas
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
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Lachaud Q, Aziz MHN, Burton FL, Macquaide N, Myles RC, Simitev RD, Smith GL. Electrophysiological heterogeneity in large populations of rabbit ventricular cardiomyocytes. Cardiovasc Res 2022; 118:3112-3125. [PMID: 35020837 PMCID: PMC9732512 DOI: 10.1093/cvr/cvab375] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 01/07/2022] [Indexed: 01/01/2023] Open
Abstract
AIMS Cardiac electrophysiological heterogeneity includes: (i) regional differences in action potential (AP) waveform, (ii) AP waveform differences in cells isolated from a single region, (iii) variability of the contribution of individual ion currents in cells with similar AP durations (APDs). The aim of this study is to assess intra-regional AP waveform differences, to quantify the contribution of specific ion channels to the APD via drug responses and to generate a population of mathematical models to investigate the mechanisms underlying heterogeneity in rabbit ventricular cells. METHODS AND RESULTS APD in ∼50 isolated cells from subregions of the LV free wall of rabbit hearts were measured using a voltage-sensitive dye. When stimulated at 2 Hz, average APD90 value in cells from the basal epicardial region was 254 ± 25 ms (mean ± standard deviation) in 17 hearts with a mean interquartile range (IQR) of 53 ± 17 ms. Endo-epicardial and apical-basal APD90 differences accounted for ∼10% of the IQR value. Highly variable changes in APD occurred after IK(r) or ICa(L) block that included a sub-population of cells (HR) with an exaggerated (hyper) response to IK(r) inhibition. A set of 4471 AP models matching the experimental APD90 distribution was generated from a larger population of models created by random variation of the maximum conductances (Gmax) of 8 key ion channels/exchangers/pumps. This set reproduced the pattern of cell-specific responses to ICa(L) and IK(r) block, including the HR sub-population. The models exhibited a wide range of Gmax values with constrained relationships linking ICa(L) with IK(r), ICl, INCX, and INaK. CONCLUSION Modelling the measured range of inter-cell APDs required a larger range of key Gmax values indicating that ventricular tissue has considerable inter-cell variation in channel/pump/exchanger activity. AP morphology is retained by relationships linking specific ionic conductances. These interrelationships are necessary for stable repolarization despite large inter-cell variation of individual conductances and this explains the variable sensitivity to ion channel block.
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Affiliation(s)
- Quentin Lachaud
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Muhamad Hifzhudin Noor Aziz
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
- Institute of Mathematical Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Francis L Burton
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Niall Macquaide
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
- School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, UK
| | - Rachel C Myles
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Radostin D Simitev
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Godfrey L Smith
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
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Campana C, Ricci E, Bartolucci C, Severi S, Sobie EA. Coupling and heterogeneity modulate pacemaking capability in healthy and diseased two-dimensional sinoatrial node tissue models. PLoS Comput Biol 2022; 18:e1010098. [PMID: 36409762 DOI: 10.1371/journal.pcbi.1010098] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 12/14/2022] [Accepted: 11/04/2022] [Indexed: 11/22/2022] Open
Abstract
Both experimental and modeling studies have attempted to determine mechanisms by which a small anatomical region, such as the sinoatrial node (SAN), can robustly drive electrical activity in the human heart. However, despite many advances from prior research, important questions remain unanswered. This study aimed to investigate, through mathematical modeling, the roles of intercellular coupling and cellular heterogeneity in synchronization and pacemaking within the healthy and diseased SAN. In a multicellular computational model of a monolayer of either human or rabbit SAN cells, simulations revealed that heterogenous cells synchronize their discharge frequency into a unique beating rhythm across a wide range of heterogeneity and intercellular coupling values. However, an unanticipated behavior appeared under pathological conditions where perturbation of ionic currents led to reduced excitability. Under these conditions, an intermediate range of intercellular coupling (900-4000 MΩ) was beneficial to SAN automaticity, enabling a very small portion of tissue (3.4%) to drive propagation, with propagation failure occurring at both lower and higher resistances. This protective effect of intercellular coupling and heterogeneity, seen in both human and rabbit tissues, highlights the remarkable resilience of the SAN. Overall, the model presented in this work allowed insight into how spontaneous beating of the SAN tissue may be preserved in the face of perturbations that can cause individual cells to lose automaticity. The simulations suggest that certain degrees of gap junctional coupling protect the SAN from ionic perturbations that can be caused by drugs or mutations.
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Affiliation(s)
- Chiara Campana
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Eugenio Ricci
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
| | - Chiara Bartolucci
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
| | - Stefano Severi
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
| | - Eric A Sobie
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
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Bai J, Lu Y, Wang H, Zhao J. How synergy between mechanistic and statistical models is impacting research in atrial fibrillation. Front Physiol 2022; 13:957604. [PMID: 36111152 PMCID: PMC9468674 DOI: 10.3389/fphys.2022.957604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Atrial fibrillation (AF) with multiple complications, high morbidity and mortality, and low cure rates, has become a global public health problem. Although significant progress has been made in the treatment methods represented by anti-AF drugs and radiofrequency ablation, the therapeutic effect is not as good as expected. The reason is mainly because of our lack of understanding of AF mechanisms. This field has benefited from mechanistic and (or) statistical methodologies. Recent renewed interest in digital twin techniques by synergizing between mechanistic and statistical models has opened new frontiers in AF analysis. In the review, we briefly present findings that gave rise to the AF pathophysiology and current therapeutic modalities. We then summarize the achievements of digital twin technologies in three aspects: understanding AF mechanisms, screening anti-AF drugs and optimizing ablation strategies. Finally, we discuss the challenges that hinder the clinical application of the digital twin heart. With the rapid progress in data reuse and sharing, we expect their application to realize the transition from AF description to response prediction.
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Affiliation(s)
- Jieyun Bai
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
- *Correspondence: Jieyun Bai, ; Jichao Zhao,
| | - Yaosheng Lu
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Huijin Wang
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- *Correspondence: Jieyun Bai, ; Jichao Zhao,
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11
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Morotti S, Liu C, Hegyi B, Ni H, Fogli Iseppe A, Wang L, Pritoni M, Ripplinger CM, Bers DM, Edwards AG, Grandi E. Quantitative cross-species translators of cardiac myocyte electrophysiology: Model training, experimental validation, and applications. SCIENCE ADVANCES 2021; 7:eabg0927. [PMID: 34788089 PMCID: PMC8598003 DOI: 10.1126/sciadv.abg0927] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 09/28/2021] [Indexed: 05/13/2023]
Abstract
Animal experimentation is key in the evaluation of cardiac efficacy and safety of novel therapeutic compounds. However, interspecies differences in the mechanisms regulating excitation-contraction coupling can limit the translation of experimental findings from animal models to human physiology and undermine the assessment of drugs’ efficacy and safety. Here, we built a suite of translators for quantitatively mapping electrophysiological responses in ventricular myocytes across species. We trained these statistical operators using a broad dataset obtained by simulating populations of our biophysically detailed computational models of action potential and Ca2+ transient in mouse, rabbit, and human. We then tested our translators against experimental data describing the response to stimuli, such as ion channel block, change in beating rate, and β-adrenergic challenge. We demonstrate that this approach is well suited to predicting the effects of perturbations across different species or experimental conditions and suggest its integration into mechanistic studies and drug development pipelines.
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Affiliation(s)
- Stefano Morotti
- Department of Pharmacology, University of California Davis, Davis, CA 95616, USA
| | - Caroline Liu
- Department of Pharmacology, University of California Davis, Davis, CA 95616, USA
| | - Bence Hegyi
- Department of Pharmacology, University of California Davis, Davis, CA 95616, USA
| | - Haibo Ni
- Department of Pharmacology, University of California Davis, Davis, CA 95616, USA
| | - Alex Fogli Iseppe
- Department of Pharmacology, University of California Davis, Davis, CA 95616, USA
| | - Lianguo Wang
- Department of Pharmacology, University of California Davis, Davis, CA 95616, USA
| | - Marco Pritoni
- Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | | | - Donald M. Bers
- Department of Pharmacology, University of California Davis, Davis, CA 95616, USA
| | - Andrew G. Edwards
- Department of Pharmacology, University of California Davis, Davis, CA 95616, USA
| | - Eleonora Grandi
- Department of Pharmacology, University of California Davis, Davis, CA 95616, USA
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12
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Peirlinck M, Sahli Costabal F, Kuhl E. Sex Differences in Drug-Induced Arrhythmogenesis. Front Physiol 2021; 12:708435. [PMID: 34489728 PMCID: PMC8417068 DOI: 10.3389/fphys.2021.708435] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 07/14/2021] [Indexed: 12/25/2022] Open
Abstract
The electrical activity in the heart varies significantly between men and women and results in a sex-specific response to drugs. Recent evidence suggests that women are more than twice as likely as men to develop drug-induced arrhythmia with potentially fatal consequences. Yet, the sex-specific differences in drug-induced arrhythmogenesis remain poorly understood. Here we integrate multiscale modeling and machine learning to gain mechanistic insight into the sex-specific origin of drug-induced cardiac arrhythmia at differing drug concentrations. To quantify critical drug concentrations in male and female hearts, we identify the most important ion channels that trigger male and female arrhythmogenesis, and create and train a sex-specific multi-fidelity arrhythmogenic risk classifier. Our study reveals that sex differences in ion channel activity, tissue conductivity, and heart dimensions trigger longer QT-intervals in women than in men. We quantify the critical drug concentration for dofetilide, a high risk drug, to be seven times lower for women than for men. Our results emphasize the importance of including sex as an independent biological variable in risk assessment during drug development. Acknowledging and understanding sex differences in drug safety evaluation is critical when developing novel therapeutic treatments on a personalized basis. The general trends of this study have significant implications on the development of safe and efficacious new drugs and the prescription of existing drugs in combination with other drugs.
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Affiliation(s)
- Mathias Peirlinck
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Francisco Sahli Costabal
- Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
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13
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Zhang Z, Qu Z. Mechanisms of phase-3 early afterdepolarizations and triggered activities in ventricular myocyte models. Physiol Rep 2021; 9:e14883. [PMID: 34110715 PMCID: PMC8191176 DOI: 10.14814/phy2.14883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 04/29/2021] [Accepted: 05/02/2021] [Indexed: 12/03/2022] Open
Abstract
Early afterdepolarizations (EADs) are abnormal depolarizations during the repolarizing phase of the action potential, which are associated with cardiac arrhythmogenesis. EADs are classified into phase-2 and phase-3 EADs. Phase-2 EADs occur during phase 2 of the action potential, with takeoff potentials typically above -40 mV. Phase-3 EADs occur during phase 3 of the action potential, with takeoff potential between -70 and -50 mV. Since the amplitude of phase-3 EADs can be as large as that of a regular action potential, they are also called triggered activities (TAs). This also makes phase-3 EADs and TAs much more arrhythmogenic than phase-2 EADs since they can propagate easily in tissue. Although phase-2 EADs have been widely observed, phase-3 EADs and TAs have been rarely demonstrated in isolated ventricular myocytes. Here we carry out computer simulations of three widely used ventricular action potential models to investigate the mechanisms of phase-3 EADs and TAs. We show that when the T-type Ca2+ current (ICa,T ) is absent (e.g., in normal ventricular myocytes), besides the requirement of increasing inward currents and reducing outward currents as for phase-2 EADs, the occurrence of phase-3 EADs and TAs requires a substantially large increase of the L-type Ca2+ current and the slow component of the delayed rectifier K+ current. The presence of ICa,T (e.g., in neonatal and failing ventricular myocytes) can greatly reduce the thresholds of these two currents for phase-3 EADs and TAs. This implies that ICa,T may play an important role in arrhythmogenesis in cardiac diseases.
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Affiliation(s)
- Zhaoyang Zhang
- Department of MedicineDavid Geffen School of MedicineUniversity of CaliforniaLos AngelesCAUSA
| | - Zhilin Qu
- Department of MedicineDavid Geffen School of MedicineUniversity of CaliforniaLos AngelesCAUSA
- Department of Computational MedicineDavid Geffen School of MedicineUniversity of CaliforniaLos AngelesCAUSA
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14
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Varshneya M, Irurzun-Arana I, Campana C, Dariolli R, Gutierrez A, Pullinger TK, Sobie EA. Investigational Treatments for COVID-19 may Increase Ventricular Arrhythmia Risk Through Drug Interactions. CPT Pharmacometrics Syst Pharmacol 2021; 10:100-107. [PMID: 33205613 PMCID: PMC7753424 DOI: 10.1002/psp4.12573] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/30/2020] [Indexed: 12/27/2022] Open
Abstract
Many drugs that have been proposed for treatment of coronavirus disease 2019 (COVID-19) are reported to cause cardiac adverse events, including ventricular arrhythmias. In order to properly weigh risks against potential benefits, particularly when decisions must be made quickly, mathematical modeling of both drug disposition and drug action can be useful for predicting patient response and making informed decisions. Here, we explored the potential effects on cardiac electrophysiology of four drugs proposed to treat COVID-19: lopinavir, ritonavir, chloroquine, and azithromycin, as well as combination therapy involving these drugs. Our study combined simulations of pharmacokinetics (PKs) with quantitative systems pharmacology (QSP) modeling of ventricular myocytes to predict potential cardiac adverse events caused by these treatments. Simulation results predicted that drug combinations can lead to greater cellular action potential prolongation, analogous to QT prolongation, compared with drugs given in isolation. The combination effect can result from both PK and pharmacodynamic drug interactions. Importantly, simulations of different patient groups predicted that women with pre-existing heart disease are especially susceptible to drug-induced arrhythmias, compared with diseased men or healthy individuals of either sex. Statistical analysis of population simulations revealed the molecular factors that make certain women with heart failure especially susceptible to arrhythmias. Overall, the results illustrate how PK and QSP modeling may be combined to more precisely predict cardiac risks of COVID-19 therapies.
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Affiliation(s)
- Meera Varshneya
- Department of Pharmacological Sciences and Graduate School of Biomedical SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Itziar Irurzun-Arana
- Department of Pharmacological Sciences and Graduate School of Biomedical SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Chiara Campana
- Department of Pharmacological Sciences and Graduate School of Biomedical SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Rafael Dariolli
- Department of Pharmacological Sciences and Graduate School of Biomedical SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Amy Gutierrez
- Department of Pharmacological Sciences and Graduate School of Biomedical SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Taylor K. Pullinger
- Department of Pharmacological Sciences and Graduate School of Biomedical SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Eric A. Sobie
- Department of Pharmacological Sciences and Graduate School of Biomedical SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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15
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Bai J, Zhu Y, Lo A, Gao M, Lu Y, Zhao J, Zhang H. In Silico Assessment of Class I Antiarrhythmic Drug Effects on Pitx2-Induced Atrial Fibrillation: Insights from Populations of Electrophysiological Models of Human Atrial Cells and Tissues. Int J Mol Sci 2021; 22:1265. [PMID: 33514068 PMCID: PMC7866025 DOI: 10.3390/ijms22031265] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 01/17/2021] [Accepted: 01/18/2021] [Indexed: 02/07/2023] Open
Abstract
Electrical remodelling as a result of homeodomain transcription factor 2 (Pitx2)-dependent gene regulation was linked to atrial fibrillation (AF) and AF patients with single nucleotide polymorphisms at chromosome 4q25 responded favorably to class I antiarrhythmic drugs (AADs). The possible reasons behind this remain elusive. The purpose of this study was to assess the efficacy of the AADs disopyramide, quinidine, and propafenone on human atrial arrhythmias mediated by Pitx2-induced remodelling, from a single cell to the tissue level, using drug binding models with multi-channel pharmacology. Experimentally calibrated populations of human atrial action po-tential (AP) models in both sinus rhythm (SR) and Pitx2-induced AF conditions were constructed by using two distinct models to represent morphological subtypes of AP. Multi-channel pharmaco-logical effects of disopyramide, quinidine, and propafenone on ionic currents were considered. Simulated results showed that Pitx2-induced remodelling increased maximum upstroke velocity (dVdtmax), and decreased AP duration (APD), conduction velocity (CV), and wavelength (WL). At the concentrations tested in this study, these AADs decreased dVdtmax and CV and prolonged APD in the setting of Pitx2-induced AF. Our findings of alterations in WL indicated that disopyramide may be more effective against Pitx2-induced AF than propafenone and quinidine by prolonging WL.
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Affiliation(s)
- Jieyun Bai
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou 510632, China;
| | - Yijie Zhu
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou 510632, China;
| | - Andy Lo
- Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand; (A.L.); (J.Z.)
| | - Meng Gao
- Department of Computer Science and Technology, College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China
| | - Yaosheng Lu
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou 510632, China;
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand; (A.L.); (J.Z.)
| | - Henggui Zhang
- Biological Physics Group, School of Physics and Astronomy, The University of Manchester, Manchester M13 9PL, UK;
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16
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Zhu Y, Bai J, Lo A, Lu Y, Zhao J. Mechanisms underlying pro-arrhythmic abnormalities arising from Pitx2-induced electrical remodelling: an in silico intersubject variability study. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:106. [PMID: 33569408 PMCID: PMC7867875 DOI: 10.21037/atm-20-5660] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Background Electrical remodelling as a result of the homeodomain transcription factor 2 (Pitx2)-dependent gene regulation induces atrial fibrillation (AF) with different mechanisms. The purpose of this study was to identify Pitx2-induced changes in ionic currents that cause action potential (AP) shortening and lead to triggered activity. Methods Populations of computational atrial AP models were developed based on AP recordings from sinus rhythm (SR) and AF patients. Models in the AF population were divided into triggered and untriggered AP groups to evaluate the relationship between each ion current regulated by Pitx2 and triggered APs. Untriggered AP models were then divided into shortened and unshortened AP groups to determine which Pitx2-dependent ion currents contribute to AP shortening. Results According to the physiological range of AP biomarkers measured experimentally, populations of 2,885 SR and 4,781 AF models out of the initial pool of 30,000 models were selected. Models in the AF population predicted AP shortening and triggered activity observed in experiments in Pitx2-induced remodelling conditions. The AF models included 925 triggered AP models, 1,412 shortened AP models and 2,444 unshortened AP models. Intersubject variability in IKs and ICaL primarily modulated variability in AP duration (APD) in all shortened and unshortened AP models, whereas intersubject variability in IK1 and SERCA mainly contributed to the variability in AP morphology in all triggered and untriggered AP models. The incidence of shortened AP was positively correlated with IKs and IK1 and was negatively correlated with INa , ICaL and SERCA, whereas the incidence of triggered AP was negatively correlated with IKs and IK1 and was positively correlated with INa , ICaL and SERCA. Conclusions Electrical remodelling due to Pitx2 upregulation may increase the incidence of shortened AP, whereas electrical remodelling arising from Pitx2 downregulation may favor to the genesis of triggered AP.
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Affiliation(s)
- Yijie Zhu
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jieyun Bai
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Andy Lo
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Yaosheng Lu
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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17
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Landaw J, Yuan X, Chen PS, Qu Z. The transient outward potassium current plays a key role in spiral wave breakup in ventricular tissue. Am J Physiol Heart Circ Physiol 2021; 320:H826-H837. [PMID: 33385322 DOI: 10.1152/ajpheart.00608.2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Spiral wave reentry as a mechanism of lethal ventricular arrhythmias has been widely demonstrated in animal experiments and recordings from human hearts. It has been shown that in structurally normal hearts spiral waves are unstable, breaking up into multiple wavelets via dynamical instabilities. However, many of the second-generation action potential models give rise only to stable spiral waves, raising issues regarding the underlying mechanisms of spiral wave breakup. In this study, we carried out computer simulations of two-dimensional homogeneous tissues using five ventricular action potential models. We show that the transient outward potassium current (Ito), although it is not required, plays a key role in promoting spiral wave breakup in all five models. As the maximum conductance of Ito increases, it first promotes spiral wave breakup and then stabilizes the spiral waves. In the absence of Ito, speeding up the L-type calcium kinetics can prevent spiral wave breakup, however, with the same speedup kinetics, spiral wave breakup can be promoted by increasing Ito. Increasing Ito promotes single-cell dynamical instabilities, including action potential duration alternans and chaos, and increasing Ito further suppresses these action potential dynamics. These cellular properties agree with the observation that increasing Ito first promotes spiral wave breakup and then stabilizes spiral waves in tissue. Implications of our observations to spiral wave dynamics in the real hearts and action potential model improvements are discussed.NEW & NOTEWORTHY Spiral wave breakup manifesting as multiple wavelets is a mechanism of ventricular fibrillation. It has been known that spiral wave breakup in cardiac tissue can be caused by a steeply sloped action potential duration restitution curve, a property mainly determined by the recovery of L-type calcium current. Here, we show that the transient outward potassium current (Ito) is another current that plays a key role in spiral wave breakup, that is, spiral waves can be stable for low and high maximum Ito conductance but breakup occurs for intermediate maximum Ito conductance. Since Ito is present in normal hearts of many species and required for Brugada syndrome, it may play an important role in the spiral wave stability and arrhythmogenesis under both normal condition and Brugada syndrome.
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Affiliation(s)
- Julian Landaw
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California.,Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Xiaoping Yuan
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California.,Information Engineering School, Hangzhou Dianzi University, Hangzhou, People's Republic of China
| | | | - Zhilin Qu
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California.,Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, California
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18
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Zhang Z, Liu MB, Huang X, Song Z, Qu Z. Mechanisms of Premature Ventricular Complexes Caused by QT Prolongation. Biophys J 2020; 120:352-369. [PMID: 33333033 DOI: 10.1016/j.bpj.2020.12.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/18/2020] [Accepted: 12/08/2020] [Indexed: 11/26/2022] Open
Abstract
QT prolongation, due to lengthening of the action potential duration in the ventricles, is a major risk factor of lethal ventricular arrhythmias. A widely known consequence of QT prolongation is the genesis of early afterdepolarizations (EADs), which are associated with arrhythmias through the generation of premature ventricular complexes (PVCs). However, the vast majority of the EADs observed experimentally in isolated ventricular myocytes are phase-2 EADs, and whether phase-2 EADs are mechanistically linked to PVCs in cardiac tissue remains an unanswered question. In this study, we investigate the genesis of PVCs using computer simulations with eight different ventricular action potential models of various species. Based on our results, we classify PVCs as arising from two distinct mechanisms: repolarization gradient (RG)-induced PVCs and phase-2 EAD-induced PVCs. The RG-induced PVCs are promoted by increasing RG and L-type calcium current and are insensitive to gap junction coupling. EADs are not required for this PVC mechanism. In a paced beat, a single or multiple PVCs can occur depending on the properties of the RG. In contrast, phase-2 EAD-induced PVCs occur only when the RG is small and are suppressed by increasing RG and more sensitive to gap junction coupling. Unlike with RG-induced PVCs, in each paced beat, only a single EAD-induced PVC can occur no matter how many EADs in an action potential. In the wide parameter ranges we explore, RG-induced PVCs can be observed in all models, but the EAD-induced PVCs can only be observed in five of the eight models. The links between these two distinct PVC mechanisms and arrhythmogenesis in animal experiments and clinical settings are discussed.
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Affiliation(s)
- Zhaoyang Zhang
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Michael B Liu
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Xiaodong Huang
- Department of Physics, South China University of Technology, Guangzhou, China
| | - Zhen Song
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Zhilin Qu
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, California.
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19
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Vagos MR, Arevalo H, Heijman J, Schotten U, Sundnes J. A Novel Computational Model of the Rabbit Atrial Cardiomyocyte With Spatial Calcium Dynamics. Front Physiol 2020; 11:556156. [PMID: 33162894 PMCID: PMC7583320 DOI: 10.3389/fphys.2020.556156] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 08/28/2020] [Indexed: 12/21/2022] Open
Abstract
Models of cardiac electrophysiology are widely used to supplement experimental results and to provide insight into mechanisms of cardiac function and pathology. The rabbit has been a particularly important animal model for studying mechanisms of atrial pathophysiology and atrial fibrillation, which has motivated the development of models for the rabbit atrial cardiomyocyte electrophysiology. Previously developed models include detailed representations of membrane currents and intracellular ionic concentrations, but these so-called “common-pool” models lack a spatially distributed description of the calcium handling system, which reflects the detailed ultrastructure likely found in cells in vivo. Because of the less well-developed T-tubular system in atrial compared to ventricular cardiomyocytes, spatial gradients in intracellular calcium concentrations may play a more significant role in atrial cardiomyocyte pathophysiology, rendering common-pool models less suitable for investigating underlying electrophysiological mechanisms. In this study, we developed a novel computational model of the rabbit atrial cardiomyocyte incorporating detailed compartmentalization of intracellular calcium dynamics, in addition to a description of membrane currents and intracellular processes. The spatial representation of calcium was based on dividing the intracellular space into eighteen different compartments in the transversal direction, each with separate systems for internal calcium storage and release, and tracking ionic fluxes between compartments in addition to the dynamics driven by membrane currents and calcium release. The model was parameterized employing a population-of-models approach using experimental data from different sources. The parameterization of this novel model resulted in a reduced population of models with inherent variability in calcium dynamics and electrophysiological properties, all of which fall within the range of observed experimental values. As such, the population of models may represent natural variability in cardiomyocyte electrophysiology or inherent uncertainty in the underlying experimental data. The ionic model population was also able to reproduce the U-shaped waveform observed in line-scans of triggered calcium waves in atrial cardiomyocytes, characteristic of the absence of T-tubules, resulting in a centripetal calcium wave due to subcellular calcium diffusion. This novel spatial model of the rabbit atrial cardiomyocyte can be used to integrate experimental findings, offering the potential to enhance our understanding of the pathophysiological role of calcium-handling abnormalities under diseased conditions, such as atrial fibrillation.
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Affiliation(s)
- Márcia R Vagos
- Simula Research Laboratory, Computational Physiology Department, Lysaker, Norway.,Department of Informatics, University of Oslo, Oslo, Norway
| | - Hermenegild Arevalo
- Simula Research Laboratory, Computational Physiology Department, Lysaker, Norway.,Center for Cardiological Innovation, Rikshospitalet, Oslo, Norway
| | - Jordi Heijman
- Faculty of Health, Medicine and Life Sciences, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Ulrich Schotten
- Faculty of Health, Medicine and Life Sciences, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Joakim Sundnes
- Simula Research Laboratory, Computational Physiology Department, Lysaker, Norway.,Department of Informatics, University of Oslo, Oslo, Norway.,Center for Cardiological Innovation, Rikshospitalet, Oslo, Norway
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20
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Davies MR, Martinec M, Walls R, Schwarz R, Mirams GR, Wang K, Steiner G, Surinach A, Flores C, Lavé T, Singer T, Polonchuk L. Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk. CELL REPORTS MEDICINE 2020; 1:100076. [PMID: 33205069 PMCID: PMC7659582 DOI: 10.1016/j.xcrm.2020.100076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 06/09/2020] [Accepted: 07/29/2020] [Indexed: 12/30/2022]
Abstract
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. In vitro data and computational models can assist with calculating pro-arrhythmic risk We use patient health records and FDA Adverse Event Reporting System reports Use of such datasets helps assess relative drug risk and cardiac safety models We quantify how patient characteristics can affect arrhythmia incidence
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Affiliation(s)
| | - Michael Martinec
- PHC Data Science, Personalized Healthcare, Product Development, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Robert Walls
- PHC Data Science, Personalized Healthcare, Product Development, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Roman Schwarz
- Safety Analytics and Reporting, Drug Safety, Pharmaceutical Development, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK
| | - Ken Wang
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Guido Steiner
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland
| | | | | | - Thierry Lavé
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Thomas Singer
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Liudmila Polonchuk
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland
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21
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Ni H, Fogli Iseppe A, Giles WR, Narayan SM, Zhang H, Edwards AG, Morotti S, Grandi E. Populations of in silico myocytes and tissues reveal synergy of multiatrial-predominant K + -current block in atrial fibrillation. Br J Pharmacol 2020; 177:4497-4515. [PMID: 32667679 DOI: 10.1111/bph.15198] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 06/22/2020] [Accepted: 07/03/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND AND PURPOSE Pharmacotherapy of atrial fibrillation (AF), the most common cardiac arrhythmia, remains unsatisfactory due to low efficacy and safety concerns. New therapeutic strategies target atrial-predominant ion-channels and involve multichannel block (poly)therapy. As AF is characterized by rapid and irregular atrial activations, compounds displaying potent antiarrhythmic effects at fast and minimal effects at slow rates are desirable. We present a novel systems pharmacology framework to quantitatively evaluate synergistic anti-AF effects of combined block of multiple atrial-predominant K+ currents (ultra-rapid delayed rectifier K+ current, IKur , small conductance Ca2+ -activated K+ current, IKCa , K2P 3.1 2-pore-domain K+ current, IK2P ) in AF. EXPERIMENTAL APPROACH We constructed experimentally calibrated populations of virtual atrial myocyte models in normal sinus rhythm and AF-remodelled conditions using two distinct, well-established atrial models. Sensitivity analyses on our atrial populations was used to investigate the rate dependence of action potential duration (APD) changes due to blocking IKur , IK2P or IKCa and interactions caused by blocking of these currents in modulating APD. Block was simulated in both single myocytes and one-dimensional tissue strands to confirm insights from the sensitivity analyses and examine anti-arrhythmic effects of multi-atrial-predominant K+ current block in single cells and coupled tissue. KEY RESULTS In both virtual atrial myocytes and tissues, multiple atrial-predominant K+ -current block promoted favourable positive rate-dependent APD prolongation and displayed positive rate-dependent synergy, that is, increasing synergistic antiarrhythmic effects at fast pacing versus slow rates. CONCLUSION AND IMPLICATIONS Simultaneous block of multiple atrial-predominant K+ currents may be a valuable antiarrhythmic pharmacotherapeutic strategy for AF.
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Affiliation(s)
- Haibo Ni
- Department of Pharmacology, University of California, Davis, CA, USA
| | - Alex Fogli Iseppe
- Department of Pharmacology, University of California, Davis, CA, USA
| | - Wayne R Giles
- Faculties of Kinesiology and Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Sanjiv M Narayan
- Division of Cardiology, Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Henggui Zhang
- Biological Physics Group, School of Physics and Astronomy, The University of Manchester, Manchester, UK
| | - Andrew G Edwards
- Department of Pharmacology, University of California, Davis, CA, USA
| | - Stefano Morotti
- Department of Pharmacology, University of California, Davis, CA, USA
| | - Eleonora Grandi
- Department of Pharmacology, University of California, Davis, CA, USA
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22
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Sahli-Costabal F, Seo K, Ashley E, Kuhl E. Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning. Biophys J 2020; 118:1165-1176. [PMID: 32023435 DOI: 10.1101/545863] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 11/27/2019] [Accepted: 01/13/2020] [Indexed: 05/25/2023] Open
Abstract
All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here, we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their proarrhythmic potential. We capitalize on recent developments in machine learning and integrate information across 10 orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 22 common drugs exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the proarrhythmic potential of new drugs. Our study paves the way toward establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.
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Affiliation(s)
| | - Kinya Seo
- Department of Medicine, Stanford University, Stanford, California
| | - Euan Ashley
- Department of Medicine, Stanford University, Stanford, California; Department of Pathology, Stanford University, Stanford, California
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, California; Department of Bioengineering, Stanford University, Stanford, California.
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23
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Sahli-Costabal F, Seo K, Ashley E, Kuhl E. Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning. Biophys J 2020; 118:1165-1176. [PMID: 32023435 PMCID: PMC7063479 DOI: 10.1016/j.bpj.2020.01.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 11/27/2019] [Accepted: 01/13/2020] [Indexed: 12/17/2022] Open
Abstract
All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here, we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their proarrhythmic potential. We capitalize on recent developments in machine learning and integrate information across 10 orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 22 common drugs exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the proarrhythmic potential of new drugs. Our study paves the way toward establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.
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Affiliation(s)
| | - Kinya Seo
- Department of Medicine, Stanford University, Stanford, California
| | - Euan Ashley
- Department of Medicine, Stanford University, Stanford, California; Department of Pathology, Stanford University, Stanford, California
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, California; Department of Bioengineering, Stanford University, Stanford, California.
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24
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Li T, Long L. Imaging Examination and Quantitative Detection and Analysis of Gastrointestinal Diseases Based on Data Mining Technology. J Med Syst 2019; 44:31. [PMID: 31838583 DOI: 10.1007/s10916-019-1482-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/11/2019] [Indexed: 01/30/2023]
Abstract
The medical image storage and transmission system completes the collection, storage, management, diagnosis and information processing of digital medical image information generated from digital medical devices, accumulates a large amount of data resources, and uses these valuable data resources to extract corresponding diseases. Diagnostic rules that help improve the accuracy of clinical disease diagnosis have always been the subject of medical research and management. Gastrointestinal diseases are common high-risk digestive diseases. This paper studies the imaging detection and quantitative detection and analysis of gastrointestinal diseases based on data mining, aiming to improve the accuracy of doctors' clinical diagnosis, reduce the misdiagnosis and misdiagnosis of patients' diseases, and reduce the burden on patients. With the high computing speed and computational accuracy of the computer, combined with the flexible analysis and judgment ability of the human body, the doctor can help the semi-structured and unstructured diagnosis problems. Experiments demonstrate the effectiveness and robustness of the proposed method.
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Affiliation(s)
- Tao Li
- Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, 545005, Ghuangxi, China
| | - Liling Long
- First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Ghuangxi, China.
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Parikh J, Di Achille P, Kozloski J, Gurev V. Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment. Front Pharmacol 2019; 10:1054. [PMID: 31680938 PMCID: PMC6797832 DOI: 10.3389/fphar.2019.01054] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 08/20/2019] [Indexed: 01/08/2023] Open
Abstract
Multiscale computational models of the heart are being extensively investigated for improved assessment of drug-induced torsades de pointes (TdP) risk, a fatal side effect of many drugs. Model-derived metrics such as action potential duration and net charge carried by ionic currents (qNet) have been proposed as potential candidates for TdP risk stratification after being tested on small datasets. Unlike purely statistical approaches, model-derived metrics are thought to provide mechanism-based classification. In particular, qNet has been recently proposed as a surrogate metric for early afterdepolarizations (EADs), which are known to be cellular triggers of TdP. Analysis of critical model components and of the ion channels that have major impact on model-derived metrics can lead to improvements in the confidence of the prediction. In this paper, we analyze large populations of virtual drugs to systematically examine the influence of different ion channels on model-derived metrics that have been proposed for proarrhythmic risk assessment. We demonstrate via global sensitivity analysis (GSA) that model-derived metrics are most sensitive to different sets of input parameters. Similarly, important differences in sensitivity to specific channel blocks are highlighted when classifying drugs into different risk categories by either qNet or a metric directly based on simulated EADs. In particular, the higher sensitivity of qNet to the block of the late sodium channel might explain why its classification accuracy is better than that of the EAD-based metric, as shown for a small set of known drugs. Our results highlight the need for a better mechanistic interpretation of promising metrics like qNet based on a formal analysis of models. GSA should, therefore, constitute an essential component of the in silico workflow for proarrhythmic risk assessment, as an improved understanding of the structure of model-derived metrics could increase confidence in model-predicted risk.
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Rumbell T, Kozloski J. Dimensions of control for subthreshold oscillations and spontaneous firing in dopamine neurons. PLoS Comput Biol 2019; 15:e1007375. [PMID: 31545787 PMCID: PMC6776370 DOI: 10.1371/journal.pcbi.1007375] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 10/03/2019] [Accepted: 09/04/2019] [Indexed: 11/20/2022] Open
Abstract
Dopaminergic neurons (DAs) of the rodent substantia nigra pars compacta (SNc) display varied electrophysiological properties in vitro. Despite this, projection patterns and functional inputs from DAs to other structures are conserved, so in vivo delivery of consistent, well-timed dopamine modulation to downstream circuits must be coordinated. Here we show robust coordination by linear parameter controllers, discovered through powerful mathematical analyses of data and models, and from which consistent control of DA subthreshold oscillations (STOs) and spontaneous firing emerges. These units of control represent coordinated intracellular variables, sufficient to regulate complex cellular properties with radical simplicity. Using an evolutionary algorithm and dimensionality reduction, we discovered metaparameters, which when regressed against STO features, revealed a 2-dimensional control plane for the neuron’s 22-dimensional parameter space that fully maps the natural range of DA subthreshold electrophysiology. This plane provided a basis for spiking currents to reproduce a large range of the naturally occurring spontaneous firing characteristics of SNc DAs. From it we easily produced a unique population of models, derived using unbiased parameter search, that show good generalization to channel blockade and compensatory intracellular mechanisms. From this population of models, we then discovered low-dimensional controllers for regulating spontaneous firing properties, and gain insight into how currents active in different voltage regimes interact to produce the emergent activity of SNc DAs. Our methods therefore reveal simple regulators of neuronal function lurking in the complexity of combined ion channel dynamics. Electrophysiological activity of the neuronal membrane and concomitant ion channel properties are highly variable within groups of neurons of the same type from the same brain region. Reconciliation of the mechanisms generating neuronal activity is challenging due to the complexity of the interactions between the channel currents involved. Here we present a set of mathematical analyses that uncover the low-dimensional intracellular parameter combinations capable of regulating features of subthreshold oscillations and spontaneous firing in empirically constrained models of nigral dopaminergic neurons. This method generates, from a naive starting point, linear combinations of ion channel properties that are surprisingly capable of reliably controlling a wide variety of emergent electrophysiological activity, thereby predicting drug effects and shedding light on unsuspected compensatory mechanisms that contribute to neuronal function.
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Affiliation(s)
- Timothy Rumbell
- IBM Research, Computational Biology Center, Thomas J. Watson Research Laboratories, Yorktown Heights, New York, United States of America
- * E-mail:
| | - James Kozloski
- IBM Research, Computational Biology Center, Thomas J. Watson Research Laboratories, Yorktown Heights, New York, United States of America
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27
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Kernik DC, Morotti S, Wu H, Garg P, Duff HJ, Kurokawa J, Jalife J, Wu JC, Grandi E, Clancy CE. A computational model of induced pluripotent stem-cell derived cardiomyocytes incorporating experimental variability from multiple data sources. J Physiol 2019; 597:4533-4564. [PMID: 31278749 PMCID: PMC6767694 DOI: 10.1113/jp277724] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 07/05/2019] [Indexed: 12/22/2022] Open
Abstract
Key points Induced pluripotent stem cell‐derived cardiomyocytes (iPSC‐CMs) capture patient‐specific genotype–phenotype relationships, as well as cell‐to‐cell variability of cardiac electrical activity Computational modelling and simulation provide a high throughput approach to reconcile multiple datasets describing physiological variability, and also identify vulnerable parameter regimes We have developed a whole‐cell model of iPSC‐CMs, composed of single exponential voltage‐dependent gating variable rate constants, parameterized to fit experimental iPSC‐CM outputs We have utilized experimental data across multiple laboratories to model experimental variability and investigate subcellular phenotypic mechanisms in iPSC‐CMs This framework links molecular mechanisms to cellular‐level outputs by revealing unique subsets of model parameters linked to known iPSC‐CM phenotypes
Abstract There is a profound need to develop a strategy for predicting patient‐to‐patient vulnerability in the emergence of cardiac arrhythmia. A promising in vitro method to address patient‐specific proclivity to cardiac disease utilizes induced pluripotent stem cell‐derived cardiomyocytes (iPSC‐CMs). A major strength of this approach is that iPSC‐CMs contain donor genetic information and therefore capture patient‐specific genotype–phenotype relationships. A cited detriment of iPSC‐CMs is the cell‐to‐cell variability observed in electrical activity. We postulated, however, that cell‐to‐cell variability may constitute a strength when appropriately utilized in a computational framework to build cell populations that can be employed to identify phenotypic mechanisms and pinpoint key sensitive parameters. Thus, we have exploited variation in experimental data across multiple laboratories to develop a computational framework for investigating subcellular phenotypic mechanisms. We have developed a whole‐cell model of iPSC‐CMs composed of simple model components comprising ion channel models with single exponential voltage‐dependent gating variable rate constants, parameterized to fit experimental iPSC‐CM data for all major ionic currents. By optimizing ionic current model parameters to multiple experimental datasets, we incorporate experimentally‐observed variability in the ionic currents. The resulting population of cellular models predicts robust inter‐subject variability in iPSC‐CMs. This approach links molecular mechanisms to known cellular‐level iPSC‐CM phenotypes, as shown by comparing immature and mature subpopulations of models to analyse the contributing factors underlying each phenotype. In the future, the presented models can be readily expanded to include genetic mutations and pharmacological interventions for studying the mechanisms of rare events, such as arrhythmia triggers. Induced pluripotent stem cell‐derived cardiomyocytes (iPSC‐CMs) capture patient‐specific genotype–phenotype relationships, as well as cell‐to‐cell variability of cardiac electrical activity Computational modelling and simulation provide a high throughput approach to reconcile multiple datasets describing physiological variability, and also identify vulnerable parameter regimes We have developed a whole‐cell model of iPSC‐CMs, composed of single exponential voltage‐dependent gating variable rate constants, parameterized to fit experimental iPSC‐CM outputs We have utilized experimental data across multiple laboratories to model experimental variability and investigate subcellular phenotypic mechanisms in iPSC‐CMs This framework links molecular mechanisms to cellular‐level outputs by revealing unique subsets of model parameters linked to known iPSC‐CM phenotypes
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Affiliation(s)
- Divya C Kernik
- Department of Physiology and Membrane Biology, Department of Pharmacology, School of Medicine, University of California, Davis, Davis, CA, USA
| | - Stefano Morotti
- Department of Pharmacology, School of Medicine, University of California, Davis, CA, USA
| | - HaoDi Wu
- Stanford Cardiovascular Institute, Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Priyanka Garg
- Stanford Cardiovascular Institute, Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Henry J Duff
- Libin Cardiovascular Institute of Alberta, Faculty of Medicine, University of Calgary, Calgary, AB, Canada
| | - Junko Kurokawa
- Department of Bio-Informational Pharmacology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - José Jalife
- Department of Internal Medicine, Center for Arrhythmia Research, Cardiovascular Research Center, University of Michigan, Ann Arbor, MI, USA.,Centro Nacional de Investigaciones Cardiovasculares (CNIC), and CIBERV, Madrid, Spain
| | - Joseph C Wu
- Stanford Cardiovascular Institute, Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Eleonora Grandi
- Department of Pharmacology, School of Medicine, University of California, Davis, CA, USA
| | - Colleen E Clancy
- Department of Physiology and Membrane Biology, Department of Pharmacology, School of Medicine, University of California, Davis, Davis, CA, USA
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Computational modeling: What does it tell us about atrial fibrillation therapy? Int J Cardiol 2019; 287:155-161. [PMID: 30803891 DOI: 10.1016/j.ijcard.2019.01.077] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Revised: 12/09/2018] [Accepted: 01/22/2019] [Indexed: 12/19/2022]
Abstract
Atrial fibrillation (AF) is a complex cardiac arrhythmia with diverse etiology that negatively affects morbidity and mortality of millions of patients. Technological and experimental advances have provided a wealth of information on the pathogenesis of AF, highlighting a multitude of mechanisms involved in arrhythmia initiation and maintenance, and disease progression. However, it remains challenging to identify the predominant mechanisms for specific subgroups of AF patients, which, together with an incomplete understanding of the pleiotropic effects of antiarrhythmic therapies, likely contributes to the suboptimal efficacy of current antiarrhythmic approaches. Computer modeling of cardiac electrophysiology has advanced in parallel to experimental research and provides an integrative framework to attempt to overcome some of these challenges. Multi-scale cardiac modeling and simulation integrate structural and functional data from experimental and clinical work with knowledge of atrial electrophysiological mechanisms and dynamics, thereby improving our understanding of AF mechanisms and therapy. In this review, we describe recent advances in our quantitative understanding of AF through mathematical models. We discuss computational modeling of AF mechanisms and therapy using detailed, mechanistic cell/tissue-level models, including approaches to incorporate variability in patient populations. We also highlight efforts using whole-atria models to improve catheter ablation therapies. Finally, we describe recent efforts and suggest future extensions to model clinical concepts of AF using patient-level models.
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Nain SS, Sihag P, Luthra S. Performance evaluation of fuzzy-logic and BP-ANN methods for WEDM of aeronautics super alloy. MethodsX 2018; 5:890-908. [PMID: 30151349 PMCID: PMC6107888 DOI: 10.1016/j.mex.2018.04.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 04/12/2018] [Indexed: 11/25/2022] Open
Abstract
The main purpose of this research is to check the relative importance of methods fuzzy-logic and back-propagation neural network to evaluate the performance of wire electric discharge machine (WEDM) of aeronautics super alloy. It has been confirmed that BP-ANN method reveals significant result over the fuzzy logic method for the evaluation of surface roughness and waviness of the WEDM of aeronautic super alloy. On the basis of Taguchi analysis, it has been established that the variable pulse-on, interaction amid the pulse-on and pulse-off time, wire tension and spark-gap voltage have a superlative influence on the surface roughness. The waviness is influenced prominently by pulse-on time, pulse-off time and spark-gap voltage. The thickness of recast layer is minimized up to 9.434 μm.
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Affiliation(s)
- Somvir Singh Nain
- Centre of Excellence in Material & Manufacturing, Department of Mechanical Engineering, CMR College of Engineering and Technology, Kandlakoya, Hyderabad-501401,Telangana, India
| | - Parveen Sihag
- Department of Civil Engineering, National Institute of Technology, Kurukshetra, 136119, India
| | - Sunil Luthra
- Department of Mechanical Engineering, State Engineering College, Nilokheri, Haryana, India
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30
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Ni H, Morotti S, Grandi E. A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research. Front Physiol 2018; 9:958. [PMID: 30079031 PMCID: PMC6062641 DOI: 10.3389/fphys.2018.00958] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 06/29/2018] [Indexed: 12/31/2022] Open
Abstract
In cardiac electrophysiology, there exist many sources of inter- and intra-personal variability. These include variability in conditions and environment, and genotypic and molecular diversity, including differences in expression and behavior of ion channels and transporters, which lead to phenotypic diversity (e.g., variable integrated responses at the cell, tissue, and organ levels). These variabilities play an important role in progression of heart disease and arrhythmia syndromes and outcomes of therapeutic interventions. Yet, the traditional in silico framework for investigating cardiac arrhythmias is built upon a parameter/property-averaging approach that typically overlooks the physiological diversity. Inspired by work done in genetics and neuroscience, new modeling frameworks of cardiac electrophysiology have been recently developed that take advantage of modern computational capabilities and approaches, and account for the variance in the biological data they are intended to illuminate. In this review, we outline the recent advances in statistical and computational techniques that take into account physiological variability, and move beyond the traditional cardiac model-building scheme that involves averaging over samples from many individuals in the construction of a highly tuned composite model. We discuss how these advanced methods have harnessed the power of big (simulated) data to study the mechanisms of cardiac arrhythmias, with a special emphasis on atrial fibrillation, and improve the assessment of proarrhythmic risk and drug response. The challenges of using in silico approaches with variability are also addressed and future directions are proposed.
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Affiliation(s)
| | | | - Eleonora Grandi
- Department of Pharmacology, University of California, Davis, Davis, CA, United States
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31
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Morotti S, Grandi E. Quantitative systems models illuminate arrhythmia mechanisms in heart failure: Role of the Na + -Ca 2+ -Ca 2+ /calmodulin-dependent protein kinase II-reactive oxygen species feedback. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2018; 11:e1434. [PMID: 30015404 DOI: 10.1002/wsbm.1434] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 05/29/2018] [Accepted: 06/16/2018] [Indexed: 12/22/2022]
Abstract
Quantitative systems modeling aims to integrate knowledge in different research areas with models describing biological mechanisms and dynamics to gain a better understanding of complex clinical syndromes. Heart failure (HF) is a chronic complex cardiac disease that results from structural or functional disorders impairing the ability of the ventricle to fill with or eject blood. Highly interactive and dynamic changes in mechanical, structural, neurohumoral, metabolic, and electrophysiological properties collectively predispose the failing heart to cardiac arrhythmias, which are responsible for about a half of HF deaths. Multiscale cardiac modeling and simulation integrate structural and functional data from HF experimental models and patients to improve our mechanistic understanding of this complex arrhythmia syndrome. In particular, they allow investigating how disease-induced remodeling alters the coupling of electrophysiology, Ca2+ and Na+ handling, contraction, and energetics that lead to rhythm derangements. The Ca2+ /calmodulin-dependent protein kinase II, which expression and activity are enhanced in HF, emerges as a critical hub that modulates the feedbacks between these various subsystems and promotes arrhythmogenesis. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Cellular Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.
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Affiliation(s)
- Stefano Morotti
- Department of Pharmacology, University of California Davis, Davis, California
| | - Eleonora Grandi
- Department of Pharmacology, University of California Davis, Davis, California
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32
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Grandi E, Morotti S, Pueyo E, Rodriguez B. Editorial: Safety Pharmacology - Risk Assessment QT Interval Prolongation and Beyond. Front Physiol 2018; 9:678. [PMID: 29937733 PMCID: PMC6003136 DOI: 10.3389/fphys.2018.00678] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 05/15/2018] [Indexed: 01/06/2023] Open
Affiliation(s)
- Eleonora Grandi
- Department of Pharmacology, University of California, Davis, Davis, CA, United States
| | - Stefano Morotti
- Department of Pharmacology, University of California, Davis, Davis, CA, United States
| | - Esther Pueyo
- Biomedical Signal Interpretation and Computational Simulation Group, Aragón Institute of Engineering Research, IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
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Population-based mechanistic modeling allows for quantitative predictions of drug responses across cell types. NPJ Syst Biol Appl 2018; 4:11. [PMID: 29507757 PMCID: PMC5825396 DOI: 10.1038/s41540-018-0047-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Revised: 01/18/2018] [Accepted: 01/24/2018] [Indexed: 12/31/2022] Open
Abstract
Quantitative mismatches between human physiology and experimental models can be problematic for the development of effective therapeutics. When the effects of drugs on human adult cardiac electrophysiology are of interest, phenotypic differences with animal cells, and more recently stem cell-derived models, can present serious limitations. We addressed this issue through a combination of mechanistic mathematical modeling and statistical analyses. Physiological metrics were simulated in heterogeneous populations of models describing cardiac myocytes from adult ventricles and those derived from induced pluripotent stem cells (iPSC-CMs). These simulated measures were used to construct a cross-cell type regression model that predicts adult myocyte drug responses from iPSC-CM behaviors. We found that (1) quantitatively accurate predictions of responses to selective or non-selective ion channel blocking drugs could be generated based on iPSC-CM responses under multiple experimental conditions; (2) altering extracellular ion concentrations is an effective experimental perturbation for improving the model’s predictive strength; (3) the method can be extended to predict and contrast drug responses in diseased as well as healthy cells, indicating a broader application of the concept. This cross-cell type model can be of great value in drug development, and the approach, which can be applied to other fields, represents an important strategy for overcoming experimental model limitations. The quantitative limitations of experimental models, which can impair the development of effective therapeutics, can be overcome through a combination of mechanistic simulations and statistical analysis. A team from Icahn School of Medicine at Mount Sinai led by Eric Sobie devised a computational method to quantitatively translate drug responses across cell types. The method involves mechanism-based simulations of heterogeneous populations combined with a multivariable regression model that translates between cell types. Simulation results presented in the study show that the response to a drug in one cell type can be predicted with quantitative accuracy from physiological recordings made in another cell type, as can differential drug responses observed in diseased compared with healthy cells. This methodology can be used in drug development to better predict clinical responses based on experiments performed in preclinical models.
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34
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Silva JR. How to Connect Cardiac Excitation to the Atomic Interactions of Ion Channels. Biophys J 2018; 114:259-266. [PMID: 29401425 PMCID: PMC5984968 DOI: 10.1016/j.bpj.2017.11.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 10/09/2017] [Accepted: 11/16/2017] [Indexed: 12/26/2022] Open
Abstract
Many have worked to create cardiac action potential models that explicitly represent atomic-level details of ion channel structure. Such models have the potential to define new therapeutic directions and to show how nanoscale perturbations to channel function predispose patients to deadly cardiac arrhythmia. However, there have been significant experimental and theoretical barriers that have limited model usefulness. Recently, many of these barriers have come down, suggesting that considerable progress toward creating these long-sought models may be possible in the near term.
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Affiliation(s)
- Jonathan R Silva
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri.
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35
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Ellinwood N, Dobrev D, Morotti S, Grandi E. In Silico Assessment of Efficacy and Safety of I Kur Inhibitors in Chronic Atrial Fibrillation: Role of Kinetics and State-Dependence of Drug Binding. Front Pharmacol 2017; 8:799. [PMID: 29163179 PMCID: PMC5681918 DOI: 10.3389/fphar.2017.00799] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 10/23/2017] [Indexed: 12/14/2022] Open
Abstract
Current pharmacological therapy against atrial fibrillation (AF), the most common cardiac arrhythmia, is limited by moderate efficacy and adverse side effects including ventricular proarrhythmia and organ toxicity. One way to circumvent the former is to target ion channels that are predominantly expressed in atria vs. ventricles, such as KV1.5, carrying the ultra-rapid delayed-rectifier K+ current (IKur). Recently, we used an in silico strategy to define optimal KV1.5-targeting drug characteristics, including kinetics and state-dependent binding, that maximize AF-selectivity in human atrial cardiomyocytes in normal sinus rhythm (nSR). However, because of evidence for IKur being strongly diminished in long-standing persistent (chronic) AF (cAF), the therapeutic potential of drugs targeting IKur may be limited in cAF patients. Here, we sought to simulate the efficacy (and safety) of IKur inhibitors in cAF conditions. To this end, we utilized sensitivity analysis of our human atrial cardiomyocyte model to assess the importance of IKur for atrial cardiomyocyte electrophysiological properties, simulated hundreds of theoretical drugs to reveal those exhibiting anti-AF selectivity, and compared the results obtained in cAF with those in nSR. We found that despite being downregulated, IKur contributes more prominently to action potential (AP) and effective refractory period (ERP) duration in cAF vs. nSR, with ideal drugs improving atrial electrophysiology (e.g., ERP prolongation) more in cAF than in nSR. Notably, the trajectory of the AP during cAF is such that more IKur is available during the more depolarized plateau potential. Furthermore, IKur block in cAF has less cardiotoxic effects (e.g., AP duration not exceeding nSR values) and can increase Ca2+ transient amplitude thereby enhancing atrial contractility. We propose that in silico strategies such as that presented here should be combined with in vitro and in vivo assays to validate model predictions and facilitate the ongoing search for novel agents against AF.
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Affiliation(s)
- Nicholas Ellinwood
- Department of Pharmacology, University of California, Davis, Davis, CA, United States
| | - Dobromir Dobrev
- West German Heart and Vascular Center, Institute of Pharmacology, University Duisburg-Essen, Essen, Germany
| | - Stefano Morotti
- Department of Pharmacology, University of California, Davis, Davis, CA, United States
| | - Eleonora Grandi
- Department of Pharmacology, University of California, Davis, Davis, CA, United States
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McMillan B, Gavaghan DJ, Mirams GR. Early afterdepolarisation tendency as a simulated pro-arrhythmic risk indicator. Toxicol Res (Camb) 2017; 6:912-921. [PMID: 29456831 PMCID: PMC5779076 DOI: 10.1039/c7tx00141j] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Accepted: 09/12/2017] [Indexed: 12/19/2022] Open
Abstract
Drug-induced Torsades de Pointes (TdP) arrhythmia is of major interest in predictive toxicology. Drugs which cause TdP block the hERG cardiac potassium channel. However, not all drugs that block hERG cause TdP. As such, further understanding of the mechanistic route to TdP is needed. Early afterdepolarisations (EADs) are a cell-level phenomenon in which the membrane of a cardiac cell depolarises a second time before repolarisation, and EADs are seen in hearts during TdP. Therefore, we propose a method of predicting TdP using induced EADs combined with multiple ion channel block in simulations using biophysically-based mathematical models of human ventricular cell electrophysiology. EADs were induced in cardiac action potential models using interventions based on diseases that are known to cause EADs, including: increasing the conduction of the L-type calcium channel, decreasing the conduction of the hERG channel, and shifting the inactivation curve of the fast sodium channel. The threshold of intervention that was required to cause an EAD was used to classify drugs into clinical risk categories. The metric that used L-type calcium induced EADs was the most accurate of the EAD metrics at classifying drugs into the correct risk categories, and increased in accuracy when combined with action potential duration measurements. The EAD metrics were all more accurate than hERG block alone, but not as predictive as simpler measures such as simulated action potential duration. This may be because different routes to EADs represent risk well for different patient subgroups, something that is difficult to assess at present.
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Affiliation(s)
- Beth McMillan
- Computational Biology , Dept. of Computer Science , University of Oxford , Oxford , OX1 3QD , UK . ; ; Tel: +44 (0)1865 273838
| | - David J Gavaghan
- Computational Biology , Dept. of Computer Science , University of Oxford , Oxford , OX1 3QD , UK . ; ; Tel: +44 (0)1865 273838
| | - Gary R Mirams
- Centre for Mathematical Biology , School of Mathematical Sciences , University of Nottingham , Nottingham , NG7 2RD , UK
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37
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Wiśniowska B, Tylutki Z, Polak S. Humans Vary, So Cardiac Models Should Account for That Too! Front Physiol 2017; 8:700. [PMID: 28983251 PMCID: PMC5613127 DOI: 10.3389/fphys.2017.00700] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 08/30/2017] [Indexed: 12/25/2022] Open
Abstract
The utilization of mathematical modeling and simulation in drug development encompasses multiple mathematical techniques and the location of a drug candidate in the development pipeline. Historically speaking they have been used to analyze experimental data (i.e., Hill equation) and clarify the involved physical and chemical processes (i.e., Fick laws and drug molecule diffusion). In recent years the advanced utilization of mathematical modeling has been an important part of the regulatory review process. Physiologically based pharmacokinetic (PBPK) models identify the need to conduct specific clinical studies, suggest specific study designs and propose appropriate labeling language. Their application allows the evaluation of the influence of intrinsic (e.g., age, gender, genetics, disease) and extrinsic [e.g., dosing schedule, drug-drug interactions (DDIs)] factors, alone or in combinations, on drug exposure and therefore provides accurate population assessment. A similar pathway has been taken for the assessment of drug safety with cardiac safety being one the most advanced examples. Mechanistic mathematical model-informed safety evaluation, with a focus on drug potential for causing arrhythmias, is now discussed as an element of the Comprehensive in vitro Proarrhythmia Assay. One of the pillars of this paradigm is the use of an in silico model of the adult human ventricular cardiomyocyte to integrate in vitro measured data. Existing examples (in vitro—in vivo extrapolation with the use of PBPK models) suggest that deterministic, epidemiological and clinical data based variability models can be merged with the mechanistic models describing human physiology. There are other methods available, based on the stochastic approach and on population of models generated by randomly assigning specific parameter values (ionic current conductance and kinetic) and further pruning. Both approaches are briefly characterized in this manuscript, in parallel with the drug-specific variability.
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Affiliation(s)
- Barbara Wiśniowska
- Pharmacoepidemiology and Pharmacoeconomics Unit, Faculty of Pharmacy, Jagiellonian University Medical CollegeKrakow, Poland
| | - Zofia Tylutki
- Pharmacoepidemiology and Pharmacoeconomics Unit, Faculty of Pharmacy, Jagiellonian University Medical CollegeKrakow, Poland
| | - Sebastian Polak
- Pharmacoepidemiology and Pharmacoeconomics Unit, Faculty of Pharmacy, Jagiellonian University Medical CollegeKrakow, Poland.,SimcypCertara, Sheffield, United Kingdom
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Ellinwood N, Dobrev D, Morotti S, Grandi E. Revealing kinetics and state-dependent binding properties of I Kur-targeting drugs that maximize atrial fibrillation selectivity. CHAOS (WOODBURY, N.Y.) 2017; 27:093918. [PMID: 28964116 PMCID: PMC5573366 DOI: 10.1063/1.5000226] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 07/06/2017] [Indexed: 06/07/2023]
Abstract
The KV1.5 potassium channel, which underlies the ultra-rapid delayed-rectifier current (IKur) and is predominantly expressed in atria vs. ventricles, has emerged as a promising target to treat atrial fibrillation (AF). However, while numerous KV1.5-selective compounds have been screened, characterized, and tested in various animal models of AF, evidence of antiarrhythmic efficacy in humans is still lacking. Moreover, current guidelines for pre-clinical assessment of candidate drugs heavily rely on steady-state concentration-response curves or IC50 values, which can overlook adverse cardiotoxic effects. We sought to investigate the effects of kinetics and state-dependent binding of IKur-targeting drugs on atrial electrophysiology in silico and reveal the ideal properties of IKur blockers that maximize anti-AF efficacy and minimize pro-arrhythmic risk. To this aim, we developed a new Markov model of IKur that describes KV1.5 gating based on experimental voltage-clamp data in atrial myocytes from patient right-atrial samples in normal sinus rhythm. We extended the IKur formulation to account for state-specificity and kinetics of KV1.5-drug interactions and incorporated it into our human atrial cell model. We simulated 1- and 3-Hz pacing protocols in drug-free conditions and with a [drug] equal to the IC50 value. The effects of binding and unbinding kinetics were determined by examining permutations of the forward (kon) and reverse (koff) binding rates to the closed, open, and inactivated states of the KV1.5 channel. We identified a subset of ideal drugs exhibiting anti-AF electrophysiological parameter changes at fast pacing rates (effective refractory period prolongation), while having little effect on normal sinus rhythm (limited action potential prolongation). Our results highlight that accurately accounting for channel interactions with drugs, including kinetics and state-dependent binding, is critical for developing safer and more effective pharmacological anti-AF options.
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Affiliation(s)
- Nicholas Ellinwood
- Department of Pharmacology, University of California Davis, Davis, California 95616, USA
| | - Dobromir Dobrev
- Institute of Pharmacology, West German Heart and Vascular Center, University Duisburg-Essen, Essen, Germany
| | - Stefano Morotti
- Department of Pharmacology, University of California Davis, Davis, California 95616, USA
| | - Eleonora Grandi
- Department of Pharmacology, University of California Davis, Davis, California 95616, USA
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