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Clancy CE, Santana LF. Advances in induced pluripotent stem cell-derived cardiac myocytes: technological breakthroughs, key discoveries and new applications. J Physiol 2024; 602:3871-3892. [PMID: 39032073 PMCID: PMC11326976 DOI: 10.1113/jp282562] [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: 02/21/2024] [Accepted: 07/02/2024] [Indexed: 07/22/2024] Open
Abstract
A transformation is underway in precision and patient-specific medicine. Rapid progress has been enabled by multiple new technologies including induced pluripotent stem cell-derived cardiac myocytes (iPSC-CMs). Here, we delve into these advancements and their future promise, focusing on the efficiency of reprogramming techniques, the fidelity of differentiation into the cardiac lineage, the functional characterization of the resulting cardiac myocytes, and the many applications of in silico models to understand general and patient-specific mechanisms controlling excitation-contraction coupling in health and disease. Furthermore, we explore the current and potential applications of iPSC-CMs in both research and clinical settings, underscoring the far-reaching implications of this rapidly evolving field.
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Affiliation(s)
- Colleen E Clancy
- Department of Physiology & Membrane Biology, School of Medicine, University of California Davis, Davis, CA, USA
- Center for Precision Medicine and Data Sciences, University of California Davis, School of Medicine, Sacramento, CA, USA
| | - L Fernando Santana
- Department of Physiology & Membrane Biology, School of Medicine, University of California Davis, Davis, CA, USA
- Center for Precision Medicine and Data Sciences, University of California Davis, School of Medicine, Sacramento, CA, USA
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2
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Grandits T, Augustin CM, Haase G, Jost N, Mirams GR, Niederer SA, Plank G, Varró A, Virág L, Jung A. Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies. eLife 2024; 12:RP91911. [PMID: 38598284 PMCID: PMC11006416 DOI: 10.7554/elife.91911] [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] [Indexed: 04/11/2024] Open
Abstract
Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.
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Affiliation(s)
- Thomas Grandits
- Department of Mathematics and Scientific Computing, University of GrazGrazAustria
- NAWI Graz, University of GrazGrazAustria
| | - Christoph M Augustin
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Medical Physics and Biophysics, Medical University of GrazGrazAustria
- BioTechMed-GrazGrazAustria
| | - Gundolf Haase
- Department of Mathematics and Scientific Computing, University of GrazGrazAustria
| | - Norbert Jost
- Department of Pharmacology and Pharmacotherapy, University of SzegedSzegedHungary
- HUN-REN-TKI, Research Group of PharmacologyBudapestHungary
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of NottinghamNottinghamUnited Kingdom
| | - Steven A Niederer
- Division of Imaging Sciences & Biomedical Engineering, King’s College LondonLondonUnited Kingdom
| | - Gernot Plank
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Medical Physics and Biophysics, Medical University of GrazGrazAustria
- BioTechMed-GrazGrazAustria
| | - András Varró
- Department of Pharmacology and Pharmacotherapy, University of SzegedSzegedHungary
- HUN-REN-TKI, Research Group of PharmacologyBudapestHungary
| | - László Virág
- Department of Pharmacology and Pharmacotherapy, University of SzegedSzegedHungary
| | - Alexander Jung
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Medical Physics and Biophysics, Medical University of GrazGrazAustria
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3
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Trovato C, Mohr M, Schmidt F, Passini E, Rodriguez B. Cross clinical-experimental-computational qualification of in silico drug trials on human cardiac purkinje cells for proarrhythmia risk prediction. FRONTIERS IN TOXICOLOGY 2022; 4:992650. [PMID: 36278026 PMCID: PMC9581132 DOI: 10.3389/ftox.2022.992650] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/20/2022] [Indexed: 11/06/2022] Open
Abstract
The preclinical identification of drug-induced cardiotoxicity and its translation into human risk are still major challenges in pharmaceutical drug discovery. The ICH S7B Guideline and Q&A on Clinical and Nonclinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential promotes human in silico drug trials as a novel tool for proarrhythmia risk assessment. To facilitate the use of in silico data in regulatory submissions, explanatory control compounds should be tested and documented to demonstrate consistency between predictions and the historic validation data. This study aims to quantify drug-induced electrophysiological effects on in silico cardiac human Purkinje cells, to compare them with existing in vitro rabbit data, and to assess their accuracy for clinical pro-arrhythmic risk predictions. The effects of 14 reference compounds were quantified in simulations with a population of in silico human cardiac Purkinje models. For each drug dose, five electrophysiological biomarkers were quantified at three pacing frequencies, and results compared with available in vitro experiments and clinical proarrhythmia reports. Three key results were obtained: 1) In silico, repolarization abnormalities in human Purkinje simulations predicted drug-induced arrhythmia for all risky compounds, showing higher predicted accuracy than rabbit experiments; 2) Drug-induced electrophysiological changes observed in human-based simulations showed a high degree of consistency with in vitro rabbit recordings at all pacing frequencies, and depolarization velocity and action potential duration were the most consistent biomarkers; 3) discrepancies observed for dofetilide, sotalol and terfenadine are mainly caused by species differences between humans and rabbit. Taken together, this study demonstrates higher accuracy of in silico methods compared to in vitro animal models for pro-arrhythmic risk prediction, as well as a high degree of consistency with in vitro experiments commonly used in safety pharmacology, supporting the potential for industrial and regulatory adoption of in silico trials for proarrhythmia prediction.
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Affiliation(s)
- Cristian Trovato
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Marcel Mohr
- Sanofi-Aventis Deutschland GmbH, R&D Preclinical Safety, Frankfurt, Germany
| | - Friedemann Schmidt
- Sanofi-Aventis Deutschland GmbH, R&D Preclinical Safety, Frankfurt, Germany
| | - Elisa Passini
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, United Kingdom,*Correspondence: Blanca Rodriguez,
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Jæger KH, Edwards AG, Giles WR, Tveito A. Arrhythmogenic influence of mutations in a myocyte-based computational model of the pulmonary vein sleeve. Sci Rep 2022; 12:7040. [PMID: 35487957 PMCID: PMC9054808 DOI: 10.1038/s41598-022-11110-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/12/2022] [Indexed: 11/09/2022] Open
Abstract
In the heart, electrophysiological dysregulation arises from defects at many biological levels (from point mutations in ion channel proteins to gross structural abnormalities). These defects disrupt the normal pattern of electrical activation, producing ectopic activity and reentrant arrhythmia. To interrogate mechanisms that link these primary biological defects to macroscopic electrophysiologic dysregulation most prior computational studies have utilized either (i) detailed models of myocyte ion channel dynamics at limited spatial scales, or (ii) homogenized models of action potential conduction that reproduce arrhythmic activity at tissue and organ levels. Here we apply our recent model (EMI), which integrates electrical activation and propagation across these scales, to study human atrial arrhythmias originating in the pulmonary vein (PV) sleeves. These small structures initiate most supraventricular arrhythmias and include pronounced myocyte-to-myocyte heterogeneities in ion channel expression and intercellular coupling. To test EMI's cell-based architecture in this physiological context we asked whether ion channel mutations known to underlie atrial fibrillation are capable of initiating arrhythmogenic behavior via increased excitability or reentry in a schematic PV sleeve geometry. Our results illustrate that EMI's improved spatial resolution can directly interrogate how electrophysiological changes at the individual myocyte level manifest in tissue and as arrhythmia in the PV sleeve.
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Affiliation(s)
| | | | - Wayne R Giles
- Simula Research Laboratory, Oslo, Norway.,Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Canada
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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: 20] [Impact Index Per Article: 6.7] [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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Jæger KH, Edwards AG, Giles WR, Tveito A. A computational method for identifying an optimal combination of existing drugs to repair the action potentials of SQT1 ventricular myocytes. PLoS Comput Biol 2021; 17:e1009233. [PMID: 34383746 PMCID: PMC8360568 DOI: 10.1371/journal.pcbi.1009233] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 07/01/2021] [Indexed: 01/26/2023] Open
Abstract
Mutations are known to cause perturbations in essential functional features of integral membrane proteins, including ion channels. Even restricted or point mutations can result in substantially changed properties of ion currents. The additive effect of these alterations for a specific ion channel can result in significantly changed properties of the action potential (AP). Both AP shortening and AP prolongation can result from known mutations, and the consequences can be life-threatening. Here, we present a computational method for identifying new drugs utilizing combinations of existing drugs. Based on the knowledge of theoretical effects of existing drugs on individual ion currents, our aim is to compute optimal combinations that can ‘repair’ the mutant AP waveforms so that the baseline AP-properties are restored. More specifically, we compute optimal, combined, drug concentrations such that the waveforms of the transmembrane potential and the cytosolic calcium concentration of the mutant cardiomyocytes (CMs) becomes as similar as possible to their wild type counterparts after the drug has been applied. In order to demonstrate the utility of this method, we address the question of computing an optimal drug for the short QT syndrome type 1 (SQT1). For the SQT1 mutation N588K, there are available data sets that describe the effect of various drugs on the mutated K+ channel. These published findings are the basis for our computational analysis which can identify optimal compounds in the sense that the AP of the mutant CMs resembles essential biomarkers of the wild type CMs. Using recently developed insights regarding electrophysiological properties among myocytes from different species, we compute optimal drug combinations for hiPSC-CMs, rabbit ventricular CMs and adult human ventricular CMs with the SQT1 mutation. Since the ‘composition’ of ion channels that form the AP is different for the three types of myocytes under consideration, so is the composition of the optimal drug. Poly-pharmacology (using multiple drugs to treat disease) has been proposed for improving cardiac anti-arrhythmic therapy for at least two decades. However, the specific arrhythmia contexts in which polytherapy is likely to be both safe and effective have remained elusive. Type 1 short QT syndrome (SQT1) is a rare form of cardiac arrhythmia that results from mutations to the human Ether-á-go-go Related Gene (hERG) potassium channel. Functionally, these mutations are remarkably consistent in that they permit the channel to open earlier during each heart beat. While hundreds of compounds are known to inhibit hERG channels, the specific effect of SQT1 mutations that allows for early channel opening also limits the ability of most of those compounds to correct SQT1 dysfunction. Here, we have applied a suite of ventricular cardiomyocyte computational models to ask whether polytherapy may offer a more effective therapeutic strategy in SQT1, and if so, what the likely characteristics of that strategy are. Our analyses suggest that simultaneous induction of late sodium current and partial hERG blockade offers a promising strategy. While no activators of late sodium current have been clinically approved, several experimental compounds are available and may provide a basis for interrogating this strategy. The method presented here can be used to compute optimal drug combinations provided that the effect of each drug on every relevant ion channel is known.
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MESH Headings
- Action Potentials/drug effects
- Amino Acid Substitution
- Animals
- Anti-Arrhythmia Agents/administration & dosage
- Arrhythmias, Cardiac/drug therapy
- Arrhythmias, Cardiac/genetics
- Arrhythmias, Cardiac/physiopathology
- Computational Biology
- Drug Combinations
- Drug Design
- Drug Therapy, Combination/methods
- ERG1 Potassium Channel/drug effects
- ERG1 Potassium Channel/genetics
- ERG1 Potassium Channel/physiology
- Heart Conduction System/abnormalities
- Heart Conduction System/physiopathology
- Heart Defects, Congenital/drug therapy
- Heart Defects, Congenital/genetics
- Heart Defects, Congenital/physiopathology
- Humans
- Induced Pluripotent Stem Cells/drug effects
- Induced Pluripotent Stem Cells/physiology
- Models, Cardiovascular
- Mutation, Missense
- Myocytes, Cardiac/drug effects
- Myocytes, Cardiac/physiology
- Rabbits
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Affiliation(s)
| | - Andrew G. Edwards
- Simula Research Laboratory, Oslo, Norway
- Department of Pharmacology, University of California, Davis, California United States of America
| | - Wayne R. Giles
- Simula Research Laboratory, Oslo, Norway
- Department of Physiology and Pharmacology, Faculty of Medicine, University of Calgary, Calgary, Canada
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Aghasafari P, Yang PC, Kernik DC, Sakamoto K, Kanda Y, Kurokawa J, Vorobyov I, Clancy CE. A deep learning algorithm to translate and classify cardiac electrophysiology. eLife 2021; 10:68335. [PMID: 34212860 PMCID: PMC8282335 DOI: 10.7554/elife.68335] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 06/29/2021] [Indexed: 01/15/2023] Open
Abstract
The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology, and pharmacology. We designed a new deep learning multitask network approach intended to address the low throughput, high variability, and immature phenotype of the iPSC-CM platform. The rationale for combining translation and classification tasks is because the most likely application of the deep learning technology we describe here is to translate iPSC-CMs following application of a perturbation. The deep learning network was trained using simulated action potential (AP) data and applied to classify cells into the drug-free and drugged categories and to predict the impact of electrophysiological perturbation across the continuum of aging from the immature iPSC-CMs to the adult ventricular myocytes. The phase of the AP extremely sensitive to perturbation due to a steep rise of the membrane resistance was found to contain the key information required for successful network multitasking. We also demonstrated successful translation of both experimental and simulated iPSC-CM AP data validating our network by prediction of experimental drug-induced effects on adult cardiomyocyte APs by the latter.
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Affiliation(s)
- Parya Aghasafari
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States
| | - Pei-Chi Yang
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States
| | - Divya C Kernik
- Washington University in St. Louis, St. Louis, United States
| | - Kazuho Sakamoto
- Department of Bio-Informational Pharmacology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Yasunari Kanda
- Division of Pharmacology, National Institute of Health Sciences, Kanagawa, Japan
| | - Junko Kurokawa
- Department of Bio-Informational Pharmacology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Igor Vorobyov
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States.,Department of Pharmacology, University of California, Davis, Davis, United States
| | - Colleen E Clancy
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States
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Simpson KE, Venkateshappa R, Pang ZK, Faizi S, Tibbits GF, Claydon TW. Utility of Zebrafish Models of Acquired and Inherited Long QT Syndrome. Front Physiol 2021; 11:624129. [PMID: 33519527 PMCID: PMC7844309 DOI: 10.3389/fphys.2020.624129] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/21/2020] [Indexed: 01/12/2023] Open
Abstract
Long-QT Syndrome (LQTS) is a cardiac electrical disorder, distinguished by irregular heart rates and sudden death. Accounting for ∼40% of cases, LQTS Type 2 (LQTS2), is caused by defects in the Kv11.1 (hERG) potassium channel that is critical for cardiac repolarization. Drug block of hERG channels or dysfunctional channel variants can result in acquired or inherited LQTS2, respectively, which are typified by delayed repolarization and predisposition to lethal arrhythmia. As such, there is significant interest in clear identification of drugs and channel variants that produce clinically meaningful perturbation of hERG channel function. While toxicological screening of hERG channels, and phenotypic assessment of inherited channel variants in heterologous systems is now commonplace, affordable, efficient, and insightful whole organ models for acquired and inherited LQTS2 are lacking. Recent work has shown that zebrafish provide a viable in vivo or whole organ model of cardiac electrophysiology. Characterization of cardiac ion currents and toxicological screening work in intact embryos, as well as adult whole hearts, has demonstrated the utility of the zebrafish model to contribute to the development of therapeutics that lack hERG-blocking off-target effects. Moreover, forward and reverse genetic approaches show zebrafish as a tractable model in which LQTS2 can be studied. With the development of new tools and technologies, zebrafish lines carrying precise channel variants associated with LQTS2 have recently begun to be generated and explored. In this review, we discuss the present knowledge and questions raised related to the use of zebrafish as models of acquired and inherited LQTS2. We focus discussion, in particular, on developments in precise gene-editing approaches in zebrafish to create whole heart inherited LQTS2 models and evidence that zebrafish hearts can be used to study arrhythmogenicity and to identify potential anti-arrhythmic compounds.
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Affiliation(s)
- Kyle E. Simpson
- Molecular Cardiac Physiology Group, Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
| | - Ravichandra Venkateshappa
- Molecular Cardiac Physiology Group, Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
| | - Zhao Kai Pang
- Molecular Cardiac Physiology Group, Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
| | - Shoaib Faizi
- Molecular Cardiac Physiology Group, Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
| | - Glen F. Tibbits
- Molecular Cardiac Physiology Group, Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
- Department of Cardiovascular Science, British Columbia Children’s Hospital, Vancouver, BC, Canada
| | - Tom W. Claydon
- Molecular Cardiac Physiology Group, Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
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