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Pramudito MA, Fuadah YN, Qauli AI, Marcellinus A, Lim KM. Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation. Sci Rep 2024; 14:24045. [PMID: 39402077 DOI: 10.1038/s41598-024-71169-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 08/26/2024] [Indexed: 10/17/2024] Open
Abstract
The Comprehensive In-vitro Proarrhythmia Assay (CiPA) initiative aims to refine the assessment of drug-induced torsades de pointes (TdP) risk, utilizing computational models to predict cardiac drug toxicity. Despite advancements in machine learning applications for this purpose, the specific contribution of in-silico biomarkers to toxicity risk levels has yet to be thoroughly elucidated. This study addresses this gap by implementing explainable artificial intelligence (XAI) to illuminate the impact of individual biomarkers in drug toxicity prediction. We employed the Markov chain Monte Carlo method to generate a detailed dataset for 28 drugs, from which twelve in-silico biomarkers of 12 drugs were computed to train various machine learning models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests (RF), XGBoost, K-Nearest Neighbors (KNN), and Radial Basis Function (RBF) networks. Our study's innovation is leveraging XAI, mainly through the SHAP (SHapley Additive exPlanations) method, to dissect and quantify the contributions of biomarkers across these models. Furthermore, the model performance was evaluated using the test set from 16 drugs. We found that the ANN model coupled with the eleven most influential in-silico biomarkers namelydVm dt repol , dVm dt max , APD 90 , APD 50 , APD tri , CaD 90 , CaD 50 , Ca tri , Ca Diastole , q I n w a r d , a n d q N e t showed the highest classification performance among all classifiers with Area Under the Curve (AUC) scores of 0.92 for predicting high-risk, 0.83 for intermediate-risk, and 0.98 for low-risk drugs. We also found that the optimal in silico biomarkers selected based on SHAP analysis may be different for various classification models. However, we also found that the biomarker selection only sometimes improved the performance; therefore, evaluating various classifiers is still essential to obtain the desired classification performance. Our proposed method could provide a systematic way to assess the best classifier with the optimal in-silico biomarkers for predicting the TdP risk of drugs, thereby advancing the field of cardiac safety evaluations.
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Affiliation(s)
- Muhammad Adnan Pramudito
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Yunendah Nur Fuadah
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
- School of Electrical Engineering, Telkom University, Bandung, 40257, Indonesia
| | - Ali Ikhsanul Qauli
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
- Department of Engineering, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya, 60115, Jawa Timur, Indonesia
| | - Aroli Marcellinus
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea
| | - Ki Moo Lim
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.
- Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.
- Meta Heart Co., Ltd., Gumi, 39253, Republic of Korea.
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2
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Brennan S, Chen S, Makwana S, Esposito S, McGuinness LR, Alnaimi AIM, Sims MW, Patel M, Aziz Q, Ojake L, Roberts JA, Sharma P, Lodwick D, Tinker A, Barrett-Jolley R, Dart C, Rainbow RD. Identification and characterisation of functional K ir6.1-containing ATP-sensitive potassium channels in the cardiac ventricular sarcolemmal membrane. Br J Pharmacol 2024; 181:3380-3400. [PMID: 38763521 DOI: 10.1111/bph.16390] [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: 01/18/2023] [Revised: 02/21/2024] [Accepted: 03/18/2024] [Indexed: 05/21/2024] Open
Abstract
BACKGROUND AND PURPOSE The canonical Kir6.2/SUR2A ventricular KATP channel is highly ATP-sensitive and remains closed under normal physiological conditions. These channels activate only when prolonged metabolic compromise causes significant ATP depletion and then shortens the action potential to reduce contractile activity. Pharmacological activation of KATP channels is cardioprotective, but physiologically, it is difficult to understand how these channels protect the heart if they only open under extreme metabolic stress. The presence of a second KATP channel population could help explain this. Here, we characterise the biophysical and pharmacological behaviours of a constitutively active Kir6.1-containing KATP channel in ventricular cardiomyocytes. EXPERIMENTAL APPROACH Patch-clamp recordings from rat ventricular myocytes in combination with well-defined pharmacological modulators was used to characterise these newly identified K+ channels. Action potential recording, calcium (Fluo-4) fluorescence measurements and video edge detection of contractile function were used to assess functional consequences of channel modulation. KEY RESULTS Our data show a ventricular K+ conductance whose biophysical characteristics and response to pharmacological modulation were consistent with Kir6.1-containing channels. These Kir6.1-containing channels lack the ATP-sensitivity of the canonical channels and are constitutively active. CONCLUSION AND IMPLICATIONS We conclude there are two functionally distinct populations of ventricular KATP channels: constitutively active Kir6.1-containing channels that play an important role in fine-tuning the action potential and Kir6.2/SUR2A channels that activate with prolonged ischaemia to impart late-stage protection against catastrophic ATP depletion. Further research is required to determine whether Kir6.1 is an overlooked target in Comprehensive in vitro Proarrhythmia Assay (CiPA) cardiac safety screens.
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Affiliation(s)
- Sean Brennan
- Department of Cardiovascular and Metabolic Medicine and Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, UK
| | - Shen Chen
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Samir Makwana
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Simona Esposito
- Department of Cardiovascular and Metabolic Medicine and Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, UK
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Lauren R McGuinness
- Department of Cardiovascular and Metabolic Medicine and Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, UK
| | - Abrar I M Alnaimi
- Department of Cardiovascular and Metabolic Medicine and Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, UK
- Department of Cardiac Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Mark W Sims
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Manish Patel
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Qadeer Aziz
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Leona Ojake
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - James A Roberts
- Department of Cardiovascular and Metabolic Medicine and Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, UK
| | - Parveen Sharma
- Department of Cardiovascular and Metabolic Medicine and Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, UK
| | - David Lodwick
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Andrew Tinker
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Richard Barrett-Jolley
- Department of Musculoskeletal and Ageing Science, University of Liverpool, Liverpool, UK
| | - Caroline Dart
- Department of Biochemistry, Cell and Systems Biology, University of Liverpool, Liverpool, UK
| | - Richard D Rainbow
- Department of Cardiovascular and Metabolic Medicine and Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, UK
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3
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Fuadah YN, Qauli AI, Pramudito MA, Marcellinus A, Hanum UL, Lim KM. A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkers. CPT Pharmacometrics Syst Pharmacol 2024. [PMID: 39185761 DOI: 10.1002/psp4.13229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/11/2024] [Accepted: 07/14/2024] [Indexed: 08/27/2024] Open
Abstract
This study addresses the critical issue of drug-induced torsades de pointes (TdP) risk assessment, a vital aspect of new drug development due to its association with arrhythmia and sudden cardiac death. Existing methodologies, particularly those reliant on a single biomarker derived from CiPA O'Hara-Rudy (CiPAORdv1.0) ventricular cell model without the hERG dynamic as input to the individual machine learning model, have limitations in capturing the complexity inherent in the comprehensive range of factors influencing drug-induced TdP risk. This study aims to overcome these limitations by proposing a stacking ensemble machine learning approach by integrating multiple in silico biomarkers derived from the CiPAORdv1.0 with hERG dynamic characteristics. The ensemble machine learning model consisted of three artificial neural network (ANN) models as baseline model and support vector machine (SVM), logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) models as meta-classifier. The highest AUC score of 1.00 (0.90-1.00) for high risk, 0.97 (0.84-1.00) for intermediate risk, and 1.00 (0.87-1.00) for low risk were obtained using seven biomarkers derived from the CiPAORdv1.0 with hERG dynamic characteristics. Furthering our investigation, we explored the model's robustness by incorporating interindividual variability into the generation of in silico biomarkers from a population of human ventricular cell models. This study also enabled an analysis of TdP risk classification under high clinical exposure and therapeutic scenarios for several drugs. Additionally, from a sensitivity analysis, we revealed four important ion channels, namely, CaL, NaL, Na, and Kr channels that affect significantly the important biomarkers for TdP risk prediction.
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Affiliation(s)
- Yunendah Nur Fuadah
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Korea
- School of Electrical Engineering, Telkom University, Bandung, Indonesia
| | - Ali Ikhsanul Qauli
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Korea
- Department of Engineering, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya, Jawa Timur, Indonesia
| | - Muhammad Adnan Pramudito
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Korea
| | - Aroli Marcellinus
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Korea
| | - Ulfa Latifa Hanum
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Korea
| | - Ki Moo Lim
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Korea
- Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Korea
- Meta Heart Co., Ltd., Gumi, Korea
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Trayanova NA, Lyon A, Shade J, Heijman J. Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation. Physiol Rev 2024; 104:1265-1333. [PMID: 38153307 PMCID: PMC11381036 DOI: 10.1152/physrev.00017.2023] [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] [Received: 04/05/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023] Open
Abstract
The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.
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Affiliation(s)
- Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aurore Lyon
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Heart and Lungs, Department of Medical Physiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jordi Heijman
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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5
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Boulay E, Troncy E, Jacquemet V, Huang H, Pugsley MK, Downey AM, Venegas Baca R, Authier S. In Silico Human Cardiomyocyte Action Potential Modeling: Exploring Ion Channel Input Combinations. Int J Toxicol 2024; 43:357-367. [PMID: 38477622 DOI: 10.1177/10915818241237988] [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: 03/14/2024]
Abstract
In silico modeling offers an opportunity to supplement and accelerate cardiac safety testing. With in silico modeling, computational simulation methods are used to predict electrophysiological interactions and pharmacological effects of novel drugs on critical physiological processes. The O'Hara-Rudy's model was developed to predict the response to different ion channel inhibition levels on cardiac action potential duration (APD) which is known to directly correlate with the QT interval. APD data at 30% 60% and 90% inhibition were derived from the model to delineate possible ventricular arrhythmia scenarios and the marginal contribution of each ion channel to the model. Action potential values were calculated for epicardial, myocardial, and endocardial cells, with action potential curve modeling. This study assessed cardiac ion channel inhibition data combinations to consider when undertaking in silico modeling of proarrhythmic effects as stipulated in the Comprehensive in Vitro Proarrhythmia Assay (CiPA). As expected, our data highlight the importance of the delayed rectifier potassium channel (IKr) as the most impactful channel for APD prolongation. The impact of the transient outward potassium channel (Ito) inhibition on APD was minimal while the inward rectifier (IK1) and slow component of the delayed rectifier potassium channel (IKs) also had limited APD effects. In contrast, the contribution of fast sodium channel (INa) and/or L-type calcium channel (ICa) inhibition resulted in substantial APD alterations supporting the pharmacological relevance of in silico modeling using input from a limited number of cardiac ion channels including IKr, INa, and ICa, at least at an early stage of drug development.
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Affiliation(s)
- Emmanuel Boulay
- GREPAQ (Groupe de Recherche en Pharmacologie Animale du Québec), Université de Montréal, Saint-Hyacinthe, QC, Canada
- Charles River Laboratories, Laval, QC, Canada
| | - Eric Troncy
- GREPAQ (Groupe de Recherche en Pharmacologie Animale du Québec), Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Vincent Jacquemet
- Département de Pharmacologie et Physiologie, Université de Montréal, Faculté de Médecine, Montréal, QC, Canada
- Centre de Recherche, Hôpital du Sacré-Cœur, Montréal, QC, Canada
- Institut de Génie Biomédical, Université de Montréal, Montréal, QC, Canada
| | - Hai Huang
- Charles River Laboratories, Laval, QC, Canada
| | - Michael K Pugsley
- Toxicology & Safety Pharmacology, Cytokinetics, San Francisco, CA, USA
| | | | | | - Simon Authier
- GREPAQ (Groupe de Recherche en Pharmacologie Animale du Québec), Université de Montréal, Saint-Hyacinthe, QC, Canada
- Charles River Laboratories, Laval, QC, Canada
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6
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Qauli AI, Danadibrata RZ, Marcellinus A, Lim KM. Development of in-silico drug cardiac toxicity evaluation system with consideration of inter-individual variability. Transl Clin Pharmacol 2024; 32:83-97. [PMID: 38974343 PMCID: PMC11224897 DOI: 10.12793/tcp.2024.32.e7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/30/2024] [Accepted: 05/26/2024] [Indexed: 07/09/2024] Open
Abstract
Safety pharmacology examines the potential for new drugs to have unusual, rare side effects such as torsade de pointes (TdP). Recently, as a part of the Comprehensive in vitro Proarrhythmia Assay (CiPA) project, techniques for predicting the development of drug-induced TdP through computer simulations have been proposed and verified. However, CiPA assessment generally does not consider the effect of cardiac cell inter-individual variability, especially related to metabolic status. The study aimed to explore whether rare proarrhythmic effects may be linked to the inter-individual variability of cardiac cells and whether incorporating this variability into computational models could alter the prediction of drugs' TdP risks. This study evaluated the contribution of two biological characteristics to the proarrhythmic effects. The first was spermine concentration, which varies with metabolic status; the second was L-type calcium permeability that could occur due to mutations. Twenty-eight drugs were examined throughout this study, and qNet was analyzed as an essential feature. Even though there were some discrepancies of TdP risk predictions from the baseline model, we found that considering the inter-individual variability might change the TdP risk of drugs. Several drugs in the high-risk drugs group were predicted to affect as intermediate and low-risk drugs in some individuals and vice versa. Also, most intermediate-risk drugs were expected to act as low-risk drugs. When compared, the effects of inter-individual variability of L-type calcium were more significant than spermine in altering the TdP risk of compounds. These results emphasize the importance of considering inter-individual variability to assess drugs.
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Affiliation(s)
- Ali Ikhsanul Qauli
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
- Department of Engineering, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya 60115, Indonesia
| | | | - Aroli Marcellinus
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
| | - Ki Moo Lim
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
- Meta Heart Inc., Gumi 39177, Korea
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7
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Camps J, Berg LA, Wang ZJ, Sebastian R, Riebel LL, Doste R, Zhou X, Sachetto R, Coleman J, Lawson B, Grau V, Burrage K, Bueno-Orovio A, Weber Dos Santos R, Rodriguez B. Digital twinning of the human ventricular activation sequence to Clinical 12-lead ECGs and magnetic resonance imaging using realistic Purkinje networks for in silico clinical trials. Med Image Anal 2024; 94:103108. [PMID: 38447244 DOI: 10.1016/j.media.2024.103108] [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] [Received: 06/23/2023] [Revised: 02/06/2024] [Accepted: 02/13/2024] [Indexed: 03/08/2024]
Abstract
Cardiac in silico clinical trials can virtually assess the safety and efficacy of therapies using human-based modelling and simulation. These technologies can provide mechanistic explanations for clinically observed pathological behaviour. Designing virtual cohorts for in silico trials requires exploiting clinical data to capture the physiological variability in the human population. The clinical characterisation of ventricular activation and the Purkinje network is challenging, especially non-invasively. Our study aims to present a novel digital twinning pipeline that can efficiently generate and integrate Purkinje networks into human multiscale biventricular models based on subject-specific clinical 12-lead electrocardiogram and magnetic resonance recordings. Essential novel features of the pipeline are the human-based Purkinje network generation method, personalisation considering ECG R wave progression as well as QRS morphology, and translation from reduced-order Eikonal models to equivalent biophysically-detailed monodomain ones. We demonstrate ECG simulations in line with clinical data with clinical image-based multiscale models with Purkinje in four control subjects and two hypertrophic cardiomyopathy patients (simulated and clinical QRS complexes with Pearson's correlation coefficients > 0.7). Our methods also considered possible differences in the density of Purkinje myocardial junctions in the Eikonal-based inference as regional conduction velocities. These differences translated into regional coupling effects between Purkinje and myocardial models in the monodomain formulation. In summary, we demonstrate a digital twin pipeline enabling simulations yielding clinically consistent ECGs with clinical CMR image-based biventricular multiscale models, including personalised Purkinje in healthy and cardiac disease conditions.
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Affiliation(s)
- Julia Camps
- University of Oxford, Oxford, United Kingdom.
| | | | | | | | | | - Ruben Doste
- University of Oxford, Oxford, United Kingdom
| | - Xin Zhou
- University of Oxford, Oxford, United Kingdom
| | - Rafael Sachetto
- Universidade Federal de São João del Rei, São João del Rei, MG, Brazil
| | | | - Brodie Lawson
- Queensland University of Technology, Brisbane, Australia
| | | | - Kevin Burrage
- University of Oxford, Oxford, United Kingdom; Queensland University of Technology, Brisbane, Australia
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8
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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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9
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Mahardika T NQ, Qauli AI, Marcellinus A, Lim KM. Evaluation of cardiac pro-arrhythmic risks using the artificial neural network with ToR-ORd in silico model output. Front Physiol 2024; 15:1374355. [PMID: 38638275 PMCID: PMC11024991 DOI: 10.3389/fphys.2024.1374355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/04/2024] [Indexed: 04/20/2024] Open
Abstract
Torsades de pointes (TdP) is a type of ventricular arrhythmia that can lead to sudden cardiac death. Drug-induced TdP has been an important concern for researchers and international regulatory boards. The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative was proposed that integrates in vitro testing and computational models of cardiac ion channels and human cardiomyocyte cells to evaluate the proarrhythmic risk of drugs. The TdP risk classification performance using only a single TdP metric may require some improvements because of information limitations and the instability of generalizing results. This study evaluates the performance of TdP metrics from the in silico simulations of the Tomek-O'Hara Rudy (ToR-ORd) ventricular cell model for classifying the TdP risk of drugs. We utilized these metrics as an input to an artificial neural network (ANN)-based classifier. The ANN model was optimized through hyperparameter tuning using the grid search (GS) method to find the optimal model. The study outcomes show an area under the curve (AUC) value of 0.979 for the high-risk category, 0.791 for the intermediate-risk category, and 0.937 for the low-risk category. Therefore, this study successfully demonstrates the capability of the ToR-ORd ventricular cell model in classifying the TdP risk into three risk categories, providing new insights into TdP risk prediction methods.
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Affiliation(s)
- Nurul Qashri Mahardika T
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
| | - Ali Ikhsanul Qauli
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
- Department of Engineering, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya, Jawa Timur, Indonesia
| | - Aroli Marcellinus
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
| | - Ki Moo Lim
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
- Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
- Meta Heart Co Ltd., Gumi, Republic of Korea
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10
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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: a tool for more efficient computation in pharmacological studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.16.553497. [PMID: 38234850 PMCID: PMC10793461 DOI: 10.1101/2023.08.16.553497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
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 0.47mV in normal APs and of 14.5mV 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 0.21 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 Graz
- NAWI Graz, University of Graz
| | - Christoph M Augustin
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Medical Physics and Biophysics, Medical University of Graz
- BioTechMed-Graz
| | - Gundolf Haase
- Department of Mathematics and Scientific Computing, University of Graz
| | - Norbert Jost
- Department of Pharmacology and Pharmacotherapy, University of Szeged
- HUN-REN-TKI, Research Group of Pharmacology
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham
| | - Steven A Niederer
- Division of Imaging Sciences & Biomedical Engineering, King's College London
| | - Gernot Plank
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Medical Physics and Biophysics, Medical University of Graz
- BioTechMed-Graz
| | - András Varró
- Department of Pharmacology and Pharmacotherapy, University of Szeged
- HUN-REN-TKI, Research Group of Pharmacology
| | - László Virág
- Department of Pharmacology and Pharmacotherapy, University of Szeged
| | - Alexander Jung
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Medical Physics and Biophysics, Medical University of Graz
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11
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Llopis-Lorente J, Baroudi S, Koloskoff K, Mora MT, Basset M, Romero L, Benito S, Dayan F, Saiz J, Trenor B. Combining pharmacokinetic and electrophysiological models for early prediction of drug-induced arrhythmogenicity. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107860. [PMID: 37844488 DOI: 10.1016/j.cmpb.2023.107860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/28/2023] [Accepted: 10/10/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND AND OBJECTIVE In silico methods are gaining attention for predicting drug-induced Torsade de Pointes (TdP) in different stages of drug development. However, many computational models tended not to account for inter-individual response variability due to demographic covariates, such as sex, or physiologic covariates, such as renal function, which may be crucial when predicting TdP. This study aims to compare the effects of drugs in male and female populations with normal and impaired renal function using in silico methods. METHODS Pharmacokinetic models considering sex and renal function as covariates were implemented from data published in pharmacokinetic studies. Drug effects were simulated using an electrophysiologically calibrated population of cellular models of 300 males and 300 females. The population of models was built by modifying the endocardial action potential model published by O'Hara et al. (2011) according to the experimentally measured gene expression levels of 12 ion channels. RESULTS Fifteen pharmacokinetic models for CiPA drugs were implemented and validated in this study. Eight pharmacokinetic models included the effect of renal function and four the effect of sex. The mean difference in action potential duration (APD) between male and female populations was 24.9 ms (p<0.05). Our simulations indicated that women with impaired renal function were particularly susceptible to drug-induced arrhythmias, whereas healthy men were less prone to TdP. Differences between patient groups were more pronounced for high TdP-risk drugs. The proposed in silico tool also revealed that individuals with impaired renal function, electrophysiologically simulated with hyperkalemia (extracellular potassium concentration [K+]o = 7 mM) exhibited less pronounced APD prolongation than individuals with normal potassium levels. The pharmacokinetic/electrophysiological framework was used to determine the maximum safe dose of dofetilide in different patient groups. As a proof of concept, 3D simulations were also run for dofetilide obtaining QT prolongation in accordance with previously reported clinical values. CONCLUSIONS This study presents a novel methodology that combines pharmacokinetic and electrophysiological models to incorporate the effects of sex and renal function into in silico drug simulations and highlights their impact on TdP-risk assessment. Furthermore, it may also help inform maximum dose regimens that ensure TdP-related safety in a specific sub-population of patients.
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Affiliation(s)
- Jordi Llopis-Lorente
- Centro de Investigación e Innovación en Bioingeniería (Ci(2)B), Universitat Politècnica de València, camino de Vera, s/n, 46022, Valencia, Spain
| | | | | | - Maria Teresa Mora
- Centro de Investigación e Innovación en Bioingeniería (Ci(2)B), Universitat Politècnica de València, camino de Vera, s/n, 46022, Valencia, Spain
| | | | - Lucía Romero
- Centro de Investigación e Innovación en Bioingeniería (Ci(2)B), Universitat Politècnica de València, camino de Vera, s/n, 46022, Valencia, Spain
| | | | | | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (Ci(2)B), Universitat Politècnica de València, camino de Vera, s/n, 46022, Valencia, Spain
| | - Beatriz Trenor
- Centro de Investigación e Innovación en Bioingeniería (Ci(2)B), Universitat Politècnica de València, camino de Vera, s/n, 46022, Valencia, Spain.
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12
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Fuadah YN, Qauli AI, Marcellinus A, Pramudito MA, Lim KM. Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability. Front Physiol 2023; 14:1266084. [PMID: 37860622 PMCID: PMC10584148 DOI: 10.3389/fphys.2023.1266084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/20/2023] [Indexed: 10/21/2023] Open
Abstract
Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing multi-in silico features from a population of human ventricular cell models that could capture a representation of the underlying mechanisms contributing to TdP risk to provide a more reliable assessment of drug-induced cardiotoxicity. Method: We generated a virtual population of human ventricular cell models using a modified O'Hara-Rudy model, allowing inter-individual variation. IC 50 and Hill coefficients from 67 drugs were used as input to simulate drug effects on cardiac cells. Fourteen features (dVm dt repol , dVm dt max , Vm peak , Vm resting , APD tri , APD 90 , APD 50 , Ca peak , Ca diastole , Ca tri , CaD 90 , CaD 50 , qNet, qInward) could be generated from the simulation and used as input to several machine learning models, including k-nearest neighbor (KNN), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). Optimization of the machine learning model was performed using a grid search to select the best parameter of the proposed model. We applied five-fold cross-validation while training the model with 42 drugs and evaluated the model's performance with test data from 25 drugs. Result: The proposed ANN model showed the highest performance in predicting the TdP risk of drugs by providing an accuracy of 0.923 (0.908-0.937), sensitivity of 0.926 (0.909-0.942), specificity of 0.921 (0.906-0.935), and AUC score of 0.964 (0.954-0.975). Discussion and conclusion: According to the performance results, combining the electrophysiological model including inter-individual variation and optimization of machine learning showed good generalization ability when evaluated using the unseen dataset and produced a reliable drug-induced TdP risk prediction system.
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Affiliation(s)
- Yunendah Nur Fuadah
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
- School of Electrical Engineering, Telkom University, Bandung, Indonesia
| | - Ali Ikhsanul Qauli
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
- Department of Engineering, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya, Jawa Timur, Indonesia
| | - Aroli Marcellinus
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
| | - Muhammad Adnan Pramudito
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
| | - Ki Moo Lim
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
- Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
- Meta Heart Co., Ltd., Gumi, Republic of Korea
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13
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Lei CL, Clerx M, Gavaghan DJ, Mirams GR. Model-driven optimal experimental design for calibrating cardiac electrophysiology models. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107690. [PMID: 37478675 DOI: 10.1016/j.cmpb.2023.107690] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 06/09/2023] [Accepted: 06/22/2023] [Indexed: 07/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Models of the cardiomyocyte action potential have contributed immensely to the understanding of heart function, pathophysiology, and the origin of heart rhythm disturbances. However, action potential models are highly nonlinear, making them difficult to parameterise and limiting to describing 'average cell' dynamics, when cell-specific models would be ideal to uncover inter-cell variability but are too experimentally challenging to be achieved. Here, we focus on automatically designing experimental protocols that allow us to better identify cell-specific maximum conductance values for each major current type. METHODS AND RESULTS We developed an approach that applies optimal experimental designs to patch-clamp experiments, including both voltage-clamp and current-clamp experiments. We assessed the models calibrated to these new optimal designs by comparing them to the models calibrated to some of the commonly used designs in the literature. We showed that optimal designs are not only overall shorter in duration but also able to perform better than many of the existing experiment designs in terms of identifying model parameters and hence model predictive power. CONCLUSIONS For cardiac cellular electrophysiology, this approach will allow researchers to define their hypothesis of the dynamics of the system and automatically design experimental protocols that will result in theoretically optimal designs.
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Affiliation(s)
- Chon Lok Lei
- Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China; Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Macau, China.
| | - Michael Clerx
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - David J Gavaghan
- Department of Computer Science, University of Oxford, Oxford, United Kingdom; Doctoral Training Centre, University of Oxford, Oxford, United Kingdom
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom.
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14
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Kopańska K, Rodríguez-Belenguer P, Llopis-Lorente J, Trenor B, Saiz J, Pastor M. Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models. Arch Toxicol 2023; 97:2721-2740. [PMID: 37528229 PMCID: PMC10474996 DOI: 10.1007/s00204-023-03557-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/12/2023] [Indexed: 08/03/2023]
Abstract
In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC50 values that describe drug-induced ion channel blockade. Using such inputs, an electrophysiological model computes how the ion channel inhibition, exerted by a drug in a certain concentration, translates to an altered shape and duration of the action potential in cardiac cells, which can be represented as arrhythmogenic risk biomarkers such as the APD90. Using this framework, we identify the main sources of aleatory and epistemic uncertainties and propose a method based on probabilistic simulations that replaces single-point estimates predicted using multiple input values, including the IC50s and the electrophysiological parameters, by distributions of values. Two selected variability types associated with these inputs are then propagated through the multi-level model to estimate their impact on the uncertainty levels in the output, expressed by means of intervals. The proposed approach yields single predictions of arrhythmogenic risk biomarkers together with value intervals, providing a more comprehensive and realistic description of drug effects on a human population. The methodology was tested by predicting arrhythmogenic biomarkers on a series of twelve well-characterised marketed drugs, belonging to different arrhythmogenic risk classes.
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Affiliation(s)
- Karolina Kopańska
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Research Institute, Barcelona, Spain
| | - Pablo Rodríguez-Belenguer
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Research Institute, Barcelona, Spain
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, Universitat de València, Valencia, Spain
| | - Jordi Llopis-Lorente
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Beatriz Trenor
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Research Institute, Barcelona, Spain.
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15
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Yoon SH, Lee HL, Jeong DU, Lim KM, Park SJ, Kim KS. Assessment of the proarrhythmic effects of repurposed antimalarials for COVID-19 treatment using a comprehensive in vitro proarrhythmia assay (CiPA). Front Pharmacol 2023; 14:1220796. [PMID: 37649890 PMCID: PMC10464612 DOI: 10.3389/fphar.2023.1220796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/07/2023] [Indexed: 09/01/2023] Open
Abstract
Due to the outbreak of the SARS-CoV-2 virus, drug repurposing and Emergency Use Authorization have been proposed to treat the coronavirus disease 2019 (COVID-19) during the pandemic. While the efficiency of the drugs has been discussed, it was identified that certain compounds, such as chloroquine and hydroxychloroquine, cause QT interval prolongation and potential cardiotoxic effects. Drug-induced cardiotoxicity and QT prolongation may lead to life-threatening arrhythmias such as torsades de pointes (TdP), a potentially fatal arrhythmic symptom. Here, we evaluated the risk of repurposed pyronaridine or artesunate-mediated cardiac arrhythmias alone and in combination for COVID-19 treatment through in vitro and in silico investigations using the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative. The potential effects of each drug or in combinations on cardiac action potential (AP) and ion channels were explored using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and Chinese hamster ovary (CHO) cells transiently expressing cardiac ion channels (Nav1.5, Cav1.2, and hERG). We also performed in silico computer simulation using the optimized O'Hara-Rudy human ventricular myocyte model (ORd model) to classify TdP risk. Artesunate and dihydroartemisinin (DHA), the active metabolite of artesunate, are classified as a low risk of inducing TdP based on the torsade metric score (TMS). Moreover, artesunate does not significantly affect the cardiac APs of hiPSC-CMs even at concentrations up to 100 times the maximum serum concentration (Cmax). DHA modestly prolonged at APD90 (10.16%) at 100 times the Cmax. When considering Cmax, pyronaridine, and the combination of both drugs (pyronaridine and artesunate) are classified as having an intermediate risk of inducing TdP. However, when considering the unbound concentration (the free fraction not bound to carrier proteins or other tissues inducing pharmacological activity), both drugs are classified as having a low risk of inducing TdP. In summary, pyronaridine, artesunate, and a combination of both drugs have been confirmed to pose a low proarrhythmogenic risk at therapeutic and supratherapeutic (up to 4 times) free Cmax. Additionally, the CiPA initiative may be suitable for regulatory use and provide novel insights for evaluating drug-induced cardiotoxicity.
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Affiliation(s)
- Seung-Hyun Yoon
- R&D Center for Advanced Pharmaceuticals and Evaluation, Korea Institute of Toxicology, Daejeon, Republic of Korea
- College of Veterinary Medicine, Research Institute of Veterinary Medicine, Chungnam National University, Daejeon, Republic of Korea
| | - Hyun-Lee Lee
- R&D Center for Advanced Pharmaceuticals and Evaluation, Korea Institute of Toxicology, Daejeon, Republic of Korea
| | - Da Un Jeong
- Intelligent Human Twin Research Center, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea
| | - Ki Moo Lim
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea
| | - Seong-Jun Park
- College of Veterinary Medicine, Research Institute of Veterinary Medicine, Chungnam National University, Daejeon, Republic of Korea
| | - Ki-Suk Kim
- R&D Center for Advanced Pharmaceuticals and Evaluation, Korea Institute of Toxicology, Daejeon, Republic of Korea
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16
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Dokuchaev A, Kursanov A, Balakina-Vikulova NA, Katsnelson LB, Solovyova O. The importance of mechanical conditions in the testing of excitation abnormalities in a population of electro-mechanical models of human ventricular cardiomyocytes. Front Physiol 2023; 14:1187956. [PMID: 37362439 PMCID: PMC10285544 DOI: 10.3389/fphys.2023.1187956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
Background: Populations of in silico electrophysiological models of human cardiomyocytes represent natural variability in cell activity and are thoroughly calibrated and validated using experimental data from the human heart. The models have been shown to predict the effects of drugs and their pro-arrhythmic risks. However, excitation and contraction are known to be tightly coupled in the myocardium, with mechanical loads and stretching affecting both mechanics and excitation through mechanisms of mechano-calcium-electrical feedback. However, these couplings are not currently a focus of populations of cell models. Aim: We investigated the role of cardiomyocyte mechanical activity under different mechanical conditions in the generation, calibration, and validation of a population of electro-mechanical models of human cardiomyocytes. Methods: To generate a population, we assumed 11 input parameters of ionic currents and calcium dynamics in our recently developed TP + M model as varying within a wide range. A History matching algorithm was used to generate a non-implausible parameter space by calibrating the action potential and calcium transient biomarkers against experimental data and rejecting models with excitation abnormalities. The population was further calibrated using experimental data on human myocardial force characteristics and mechanical tests involving variations in preload and afterload. Models that passed the mechanical tests were validated with additional experimental data, including the effects of drugs with high or low pro-arrhythmic risk. Results: More than 10% of the models calibrated on electrophysiological data failed mechanical tests and were rejected from the population due to excitation abnormalities at reduced preload or afterload for cell contraction. The final population of accepted models yielded action potential, calcium transient, and force/shortening outputs consistent with experimental data. In agreement with experimental and clinical data, the models demonstrated a high frequency of excitation abnormalities in simulations of Dofetilide action on the ionic currents, in contrast to Verapamil. However, Verapamil showed a high frequency of failed contractions at high concentrations. Conclusion: Our results highlight the importance of considering mechanoelectric coupling in silico cardiomyocyte models. Mechanical tests allow a more thorough assessment of the effects of interventions on cardiac function, including drug testing.
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Affiliation(s)
- Arsenii Dokuchaev
- Laboratory of Mathematical Physiology, Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia
| | - Alexander Kursanov
- Laboratory of Mathematical Physiology, Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
| | - Nathalie A. Balakina-Vikulova
- Laboratory of Mathematical Physiology, Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
| | - Leonid B. Katsnelson
- Laboratory of Mathematical Physiology, Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
| | - Olga Solovyova
- Laboratory of Mathematical Physiology, Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
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17
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In silico assessment on TdP risks of drug combinations under CiPA paradigm. Sci Rep 2023; 13:2924. [PMID: 36807374 PMCID: PMC9940090 DOI: 10.1038/s41598-023-29208-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 01/31/2023] [Indexed: 02/22/2023] Open
Abstract
Researchers have recently proposed the Comprehensive In-vitro Proarrhythmia Assay (CiPA) to analyze medicines' TdP risks. Using the TdP metric known as qNet, numerous single-drug effects have been studied to classify the medications as low, intermediate, and high-risk. Furthermore, multiple medication therapies are recognized as a potential method for curing patients, mainly when limited drugs are available. This work expands the TdP risk assessment of drugs by introducing a CiPA-based in silico analysis of the TdP risk of combined drugs. The cardiac cell model was simulated using the population of models approach incorporating drug-drug interactions (DDIs) models on several ion channels for various drug pairs. Action potential duration (APD90), qNet, and calcium duration (CaD90) were computed and analyzed as biomarker features. The drug combination maps were also used to illustrate combined medicines' TdP risk. We found that the combined drugs alter cell responses in terms of biomarkers such as APD90, qNet, and CaD90 in a highly nonlinear manner. The results also revealed that combinations of high-risk with low-risk and intermediate-risk with low-risk drugs could result in compounds with varying TdP risks depending on the drug concentrations.
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18
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Jeong DU, Yoo Y, Marcellinus A, Lim KM. Application of Convolutional Neural Networks Using Action Potential Shape for In-Silico Proarrhythmic Risk Assessment. Biomedicines 2023; 11:biomedicines11020406. [PMID: 36830942 PMCID: PMC9953470 DOI: 10.3390/biomedicines11020406] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 01/16/2023] [Accepted: 01/21/2023] [Indexed: 01/31/2023] Open
Abstract
This study proposes a convolutional neural network (CNN) model using action potential (AP) shapes as input for proarrhythmic risk assessment, considering the hypothesis that machine-learning features automatically extracted from AP shapes contain more meaningful information than do manually extracted indicators. We used 28 drugs listed in the comprehensive in vitro proarrhythmia assay (CiPA), consisting of eight high-risk, eleven intermediate-risk, and nine low-risk torsadogenic drugs. We performed drug simulations to generate AP shapes using experimental drug data, obtaining 2000 AP shapes per drug. The proposed CNN model was trained to classify the TdP risk into three levels, high-, intermediate-, and low-risk, based on in silico AP shapes generated using 12 drugs. We then evaluated the performance of the proposed model for 16 drugs. The classification accuracy of the proposed CNN model was excellent for high- and low-risk drugs, with AUCs of 0.914 and 0.951, respectively. The model performance for intermediate-risk drugs was good, at 0.814. Our proposed model can accurately assess the TdP risks of drugs from in silico AP shapes, reflecting the pharmacokinetics of ionic currents. We need to secure more drugs for future studies to improve the TdP-risk-assessment robustness.
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Affiliation(s)
- Da Un Jeong
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea
| | - Yedam Yoo
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea
| | - Aroli Marcellinus
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea
| | - Ki Moo Lim
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea
- Meta Heart Inc., Gumi 39253, Republic of Korea
- Correspondence: ; Tel.: +82-054-478-7780
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19
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Chiu K, Racz R, Burkhart K, Florian J, Ford K, Iveth Garcia M, Geiger RM, Howard KE, Hyland PL, Ismaiel OA, Kruhlak NL, Li Z, Matta MK, Prentice KW, Shah A, Stavitskaya L, Volpe DA, Weaver JL, Wu WW, Rouse R, Strauss DG. New science, drug regulation, and emergent public health issues: The work of FDA's division of applied regulatory science. Front Med (Lausanne) 2023; 9:1109541. [PMID: 36743666 PMCID: PMC9893027 DOI: 10.3389/fmed.2022.1109541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 12/13/2022] [Indexed: 01/20/2023] Open
Abstract
The U.S. Food and Drug Administration (FDA) Division of Applied Regulatory Science (DARS) moves new science into the drug review process and addresses emergent regulatory and public health questions for the Agency. By forming interdisciplinary teams, DARS conducts mission-critical research to provide answers to scientific questions and solutions to regulatory challenges. Staffed by experts across the translational research spectrum, DARS forms synergies by pulling together scientists and experts from diverse backgrounds to collaborate in tackling some of the most complex challenges facing FDA. This includes (but is not limited to) assessing the systemic absorption of sunscreens, evaluating whether certain drugs can convert to carcinogens in people, studying drug interactions with opioids, optimizing opioid antagonist dosing in community settings, removing barriers to biosimilar and generic drug development, and advancing therapeutic development for rare diseases. FDA tasks DARS with wide ranging issues that encompass regulatory science; DARS, in turn, helps the Agency solve these challenges. The impact of DARS research is felt by patients, the pharmaceutical industry, and fellow regulators. This article reviews applied research projects and initiatives led by DARS and conducts a deeper dive into select examples illustrating the impactful work of the Division.
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Affiliation(s)
- Kimberly Chiu
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Rebecca Racz
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Keith Burkhart
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Jeffry Florian
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Kevin Ford
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - M. Iveth Garcia
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Robert M. Geiger
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Kristina E. Howard
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Paula L. Hyland
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Omnia A. Ismaiel
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Naomi L. Kruhlak
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Zhihua Li
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Murali K. Matta
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Kristin W. Prentice
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States,Booz Allen Hamilton, McLean, VA, United States
| | - Aanchal Shah
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States,Booz Allen Hamilton, McLean, VA, United States
| | - Lidiya Stavitskaya
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Donna A. Volpe
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - James L. Weaver
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Wendy W. Wu
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - Rodney Rouse
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States
| | - David G. Strauss
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States,*Correspondence: David G. Strauss,
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20
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Nieto Ramos A, Fenton FH, Cherry EM. Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation. Med Biol Eng Comput 2023; 61:75-95. [PMID: 36322242 DOI: 10.1007/s11517-022-02685-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 10/02/2022] [Indexed: 01/07/2023]
Abstract
Customization of cardiac action potential models has become increasingly important with the recognition of patient-specific models and virtual patient cohorts as valuable predictive tools. Nevertheless, developing customized models by fitting parameters to data poses technical and methodological challenges: despite noise and variability associated with real-world datasets, traditional optimization methods produce a single "best-fit" set of parameter values. Bayesian estimation methods seek distributions of parameter values given the data by obtaining samples from the target distribution, but in practice widely known Bayesian algorithms like Markov chain Monte Carlo tend to be computationally inefficient and scale poorly with the dimensionality of parameter space. In this paper, we consider two computationally efficient Bayesian approaches: the Hamiltonian Monte Carlo (HMC) algorithm and the approximate Bayesian computation sequential Monte Carlo (ABC-SMC) algorithm. We find that both methods successfully identify distributions of model parameters for two cardiac action potential models using model-derived synthetic data and an experimental dataset from a zebrafish heart. Although both methods appear to converge to the same distribution family and are computationally efficient, HMC generally finds narrower marginal distributions, while ABC-SMC is less sensitive to the algorithmic settings including the prior distribution.
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Affiliation(s)
- Alejandro Nieto Ramos
- School of Mathematical Sciences, Rochester Institute of Technology, 1 Lomb Memorial Drive, 14623, Rochester, NY, USA.,Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
| | - Flavio H Fenton
- School of Physics, Georgia Institute of Technology, 837 State Street NW, 30332, Atlanta, GA, USA
| | - Elizabeth M Cherry
- School of Computational Science and Engineering, Georgia Institute of Technology, 756 West Peachtree Street, 30308, Atlanta, GA, USA.
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21
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Jeong DU, Qashri Mahardika T N, Marcellinus A, Lim KM. qInward variability-based in-silico proarrhythmic risk assessment of drugs using deep learning model. Front Physiol 2022; 13:1080190. [PMID: 36589462 PMCID: PMC9794579 DOI: 10.3389/fphys.2022.1080190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
Many researchers have suggested evaluation methods and Torsades de Pointes (TdP) metrics to assess the proarrhythmic risk of a drug based on the in silico simulation, as part of the Comprehensive in-vitro Proarrhythmia Assay (CiPA) project. In the previous study, we validated the robustness of 12 in silico features using the ordinal logistic regression (OLR) model by comparing the classification performances of metrics according to the in-vitro experimental datasets used; however, the OLR model using 12 in silico features did not provide desirable results. This study proposed a convolutional neural network (CNN) model using the variability of promising in silico TdP metrics hypothesizing that the variability of in silico features based on beats has more information than the single value of in silico features. We performed the action potential (AP) simulation using a human ventricular myocyte model to calculate seven in silico features representing the electrophysiological cell states of drug effects over 1,000 beats: qNet, qInward, intracellular calcium duration at returning to 50% baseline (CaD50) and 90% baseline (CaD90), AP duration at 50% repolarization (APD50) and 90% repolarization (APD90), and dVm/dtMax_repol. The proposed CNN classifier was trained using 12 train drugs and tested using 16 test drugs among CiPA drugs. The torsadogenic risk of drugs was classified as high, intermediate, and low risks. We determined the CNN classifier by comparing the classification performance according to the variabilities of seven in silico biomarkers computed from the in silico drug simulation using the Chantest dataset. The proposed CNN classifier performed the best when using qInward variability to classify the TdP-risk drugs with 0.94 AUC for high risk and 0.93 AUC for low risk. In addition, the final CNN classifier was validated using the qInward variability obtained after merging three in-vitro datasets, but the model performance decreased to a moderate level of 0.75 and 0.78 AUC. These results suggest the need for the proposed CNN model to be trained and tested using various types of drugs.
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Affiliation(s)
- Da Un Jeong
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Gyeongbuk, South Korea
| | - Nurul Qashri Mahardika T
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Gyeongbuk, South Korea
| | - Aroli Marcellinus
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Gyeongbuk, South Korea
| | - Ki Moo Lim
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Gyeongbuk, South Korea,Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Gyeongbuk, South Korea,Meta Heart Inc., Gumi, Gyeongbuk, South Korea,*Correspondence: Ki Moo Lim,
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22
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Experimental factors that impact CaV1.2 channel pharmacology-Effects of recording temperature, charge carrier, and quantification of drug effects on the step and ramp currents elicited by the "step-step-ramp" voltage protocol. PLoS One 2022; 17:e0276995. [PMID: 36417390 PMCID: PMC9683570 DOI: 10.1371/journal.pone.0276995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 10/18/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND AND PURPOSE CaV1.2 channels contribute to action potential upstroke in pacemaker cells, plateau potential in working myocytes, and initiate excitation-contraction coupling. Understanding drug action on CaV1.2 channels may inform potential impact on cardiac function. However, literature shows large degrees of variability between CaV1.2 pharmacology generated by different laboratories, casting doubt regarding the utility of these data to predict or interpret clinical outcomes. This study examined experimental factors that may impact CaV1.2 pharmacology. EXPERIMENTAL APPROACH Whole cell recordings were made on CaV1.2 overexpression cells. Current was evoked using a "step-step-ramp" waveform that elicited a step and a ramp current. Experimental factors examined were: 1) near physiological vs. room temperature for recording, 2) drug inhibition of the step vs. the ramp current, and 3) Ca2+ vs. Ba2+ as the charge carrier. Eight drugs were studied. KEY RESULTS CaV1.2 current exhibited prominent rundown, exquisite temperature sensitivity, and required a high degree of series resistance compensation to optimize voltage control. Temperature-dependent effects were examined for verapamil and methadone. Verapamil's block potency shifted by up to 4X between room to near physiological temperature. Methadone exhibited facilitatory and inhibitory effects at near physiological temperature, and only inhibitory effect at room temperature. Most drugs inhibited the ramp current more potently than the step current-a preference enhanced when Ba2+ was the charge carrier. The slopes of the concentration-inhibition relationships for many drugs were shallow, temperature-dependent, and differed between the step and the ramp current. CONCLUSIONS AND IMPLICATIONS All experimental factors examined affected CaV1.2 pharmacology. In addition, whole cell CaV1.2 current characteristics-rundown, temperature sensitivity, and impact of series resistance-are also factors that can impact pharmacology. Drug effects on CaV1.2 channels appear more complex than simple pore block mechanism. Normalizing laboratory-specific approaches is key to improve inter-laboratory data reproducibility. Releasing original electrophysiology records is essential to promote transparency and enable the independent evaluation of data quality.
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23
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Jeong DU, Danadibrata RZ, Marcellinus A, Lim KM. Validation of in silico biomarkers for drug screening through ordinal logistic regression. Front Physiol 2022; 13:1009647. [PMID: 36277213 PMCID: PMC9583152 DOI: 10.3389/fphys.2022.1009647] [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: 08/02/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Since the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiation, many studies have suggested various in silico features based on ionic charges, action potentials (AP), or intracellular calcium (Ca) to assess proarrhythmic risk. These in silico features are computed through electrophysiological simulations using in vitro experimental datasets as input, therefore changing with the quality of in vitro experimental data; however, research to validate the robustness of in silico features for proarrhythmic risk assessment of drugs depending on in vitro datasets has not been conducted. This study aims to verify the availability of in silico features commonly used in assessing the cardiac toxicity of drugs through an ordinal logistic regression model and three in vitro datasets measured under different experimental environments and with different purposes. We performed in silico drug simulations using the Tomek-Ohara Rudy (ToR-ORD) ventricular myocyte model and computed 12 in silico features comprising six AP features, four Ca features, and two ion charge features, which reflected the effect and characteristics of each in vitro data for CiPA 28 drugs. We then compared the classific performances of ordinal logistic regressions according to these 12 in silico features and used in vitro datasets to validate which in silico feature is the best for assessing the proarrhythmic risk of drugs at high, intermediate, and low levels. All 12 in silico features helped determine high-risky torsadogenic drugs, regardless of the in vitro datasets used in the in silico simulation as input. In the three types of in silico features, AP features were the most reliable for determining the three Torsade de Pointes (TdP) risk standards. Among AP features, AP duration at 50% repolarization (APD50) was the best when individually using in silico features per in vitro dataset. In contrast, the AP repolarization velocity (dVm/dtMax_repol) was the best when merging all in silico features computed through three in vitro datasets.
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Affiliation(s)
- Da Un Jeong
- Computational Medicine Lab, Kumoh National Institute of Technology, Department of IT Convergence Engineering, Gumi, South Korea
| | - Rakha Zharfarizqi Danadibrata
- Computational Medicine Lab, Kumoh National Institute of Technology, Department of IT Convergence Engineering, Gumi, South Korea
| | - Aroli Marcellinus
- Computational Medicine Lab, Kumoh National Institute of Technology, Department of IT Convergence Engineering, Gumi, South Korea
| | - Ki Moo Lim
- Computational Medicine Lab, Kumoh National Institute of Technology, Department of IT Convergence Engineering, Gumi, South Korea
- Computational Medicine Lab, Kumoh National Institute of Technology, Department of Medical IT Convergence Engineering, Gumi, South Korea
- *Correspondence: Ki Moo Lim,
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24
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Translating the measurement of hERG kinetics and drug block for CiPA to a high throughput platform. J Pharmacol Toxicol Methods 2022; 117:107192. [PMID: 35750310 DOI: 10.1016/j.vascn.2022.107192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/13/2022] [Accepted: 06/16/2022] [Indexed: 11/23/2022]
Abstract
The Comprehensive in vitro Proarrhythmic Assay (CiPA) has promoted use of in silico models of drug effects on cardiac repolarization to improve proarrhythmic risk prediction. These models contain a pharmacodynamic component describing drug binding to hERG channels that required in vitro data for kinetics of block, in addition to potency, to constrain them. To date, development and validation has been undertaken using data from manual patch-clamp. The application of this approach at scale requires the development of a high-throughput, automated patch-clamp (APC) implementation. Here, we present a comprehensive analysis of the implementation of the Milnes, or CiPA dynamic protocol, on an APC platform, including quality control and data analysis. Kinetics and potency of block were assessed for bepridil, cisapride, terfenadine and verapamil with data retention/QC pass rate of 21.8% overall, or as high as 50.4% when only appropriate sweep lengths were considered for drugs with faster kinetics. The variability in IC50 and kinetics between manual and APC was comparable to that seen between sites/platforms in previous APC studies of potency. Whilst the experimental success is less than observed in screens of potency alone, it is still significantly greater than manual patch. With the modifications to protocol design, including sweep length, number of repetitions, and leak correction recommended in this study, this protocol can be applied on APC to acquire data comparable to manual patch clamp.
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25
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Llopis-Lorente J, Trenor B, Saiz J. Considering population variability of electrophysiological models improves the in silico assessment of drug-induced torsadogenic risk. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106934. [PMID: 35687995 DOI: 10.1016/j.cmpb.2022.106934] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 05/31/2022] [Accepted: 06/02/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE In silico tools are known to aid in drug cardiotoxicity assessment. However, computational models do not usually consider electrophysiological variability, which may be crucial when predicting rare adverse events such as drug-induced Torsade de Pointes (TdP). In addition, classification tools are usually binary and are not validated using an external data set. Here we analyze the role of incorporating electrophysiological variability in the prediction of drug-induced arrhythmogenic-risk, using a ternary classification and two external validation datasets. METHODS The effects of the 12 training CiPA drugs were simulated at three different concentrations using a single baseline model and an electrophysiologically calibrated population of models. 9 biomarkers related with action potential (AP), calcium dynamics and net charge were measured for each simulated concentration. These biomarkers were used to build ternary classifiers based on Support Vector Machines (SVM) methodology. Classifiers were validated using two external drug sets: the 16 validation CiPA drugs and 81 drugs from CredibleMeds database. RESULTS Population of models allowed to obtain different AP responses under the same pharmacological intervention and improve the prediction of drug-induced TdP with respect to the baseline model. The classification tools based on population of models achieve an accuracy higher than 0.8 and a mean classification error (MCE) lower than 0.3 for both validation drug sets and for the two electrophysiological action potential models studied (Tomek et al. 2020 and a modified version of O'Hara et al. 2011). In addition, simulations with population of models allowed the identification of individuals with lower conductances of IKr, IKs, and INaK and higher conductances of ICaL, INaL, and INCX, which are more prone to develop TdP. CONCLUSIONS The methodology presented here provides new opportunities to assess drug-induced TdP-risk, taking into account electrophysiological variability and may be helpful to improve current cardiac safety screening methods.
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Affiliation(s)
- Jordi Llopis-Lorente
- Centro de Investigación e Innovación en Bioingeniería (Ci(2)B), Universitat Politècnica de València, camino de Vera, s/n, Valencia 46022, Spain
| | - Beatriz Trenor
- Centro de Investigación e Innovación en Bioingeniería (Ci(2)B), Universitat Politècnica de València, camino de Vera, s/n, Valencia 46022, Spain
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (Ci(2)B), Universitat Politècnica de València, camino de Vera, s/n, Valencia 46022, Spain.
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26
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Jeong DU, Yoo Y, Marcellinus A, Kim K, Lim KM. Proarrhythmic risk assessment of drugs by dV m /dt shapes using the convolutional neural network. CPT Pharmacometrics Syst Pharmacol 2022; 11:653-664. [PMID: 35579100 PMCID: PMC9124356 DOI: 10.1002/psp4.12803] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 03/16/2022] [Accepted: 03/21/2022] [Indexed: 01/08/2023] Open
Abstract
Comprehensive in vitro Proarrhythmia Assay (CiPA) projects for assessing proarrhythmic drugs suggested a logistic regression model using qNet as the Torsades de Pointes (TdP) risk assessment biomarker, obtained from in silico simulation. However, using a single in silico feature, such as qNet, cannot reflect whole characteristics related to TdP in the entire action potential (AP) shape. Thus, this study proposed a deep convolutional neural network (CNN) model using differential action potential shapes to classify three proarrhythmic risk levels: high, intermediate, and low, considering both characteristics related to TdP not only in the depolarization phase but also the repolarization phase of AP shape. We performed an in silico simulation and got AP shapes with drug effects using half-maximal inhibitory concentration and Hill coefficients of 28 drugs released by CiPA groups. Then, we trained the deep CNN model with the differential AP shapes of 12 drugs and tested it with those of 16 drugs. Our model had a better performance for classifying the proarrhythmic risk of drugs than the traditional logistic regression model using qNet. The classification accuracy was 98% for high-risk level drugs, 94% for intermediate-risk level drugs, and 89% for low-risk level drugs.
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Affiliation(s)
- Da Un Jeong
- Department of IT Convergence EngineeringKumoh National Institute of TechnologyGumiKorea
| | - Yedam Yoo
- Department of IT Convergence EngineeringKumoh National Institute of TechnologyGumiKorea
| | - Aroli Marcellinus
- Department of IT Convergence EngineeringKumoh National Institute of TechnologyGumiKorea
| | - Ki‐Suk Kim
- R&D Center for Advanced Pharmaceuticals and EvaluationKorea Institute of ToxicologyDaejeonKorea
| | - Ki Moo Lim
- Department of IT Convergence EngineeringKumoh National Institute of TechnologyGumiKorea
- Department of Medical IT Convergence EngineeringKumoh National Institute of TechnologyGumiKorea
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27
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Occurrence of early afterdepolarization under healthy or hypertrophic cardiomyopathy conditions in the human ventricular endocardial myocyte: In silico study using 109 torsadogenic or non-torsadogenic compounds. Toxicol Appl Pharmacol 2022; 438:115914. [DOI: 10.1016/j.taap.2022.115914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 01/19/2022] [Accepted: 02/05/2022] [Indexed: 11/18/2022]
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28
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Yoo Y, Marcellinus A, Jeong DU, Kim KS, Lim KM. Assessment of Drug Proarrhythmicity Using Artificial Neural Networks With in silico Deterministic Model Outputs. Front Physiol 2021; 12:761691. [PMID: 34955882 PMCID: PMC8703011 DOI: 10.3389/fphys.2021.761691] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 11/23/2021] [Indexed: 11/13/2022] Open
Abstract
As part of the Comprehensive in vitro Proarrhythmia Assay initiative, methodologies for predicting the occurrence of drug-induced torsade de pointes via computer simulations have been developed and verified recently. However, their predictive performance still requires improvement. Herein, we propose an artificial neural networks (ANN) model that uses nine multiple input features, considering the action potential morphology, calcium transient morphology, and charge features to further improve the performance of drug toxicity evaluation. The voltage clamp experimental data for 28 drugs were augmented to 2,000 data entries using an uncertainty quantification technique. By applying these data to the modified O'Hara Rudy in silico model, nine features (dVm/dtmax, APresting, APD90, APD50, Caresting, CaD90, CaD50, qNet, and qInward) were calculated. These nine features were used as inputs to an ANN model to classify drug toxicity into high-risk, intermediate-risk, and low-risk groups. The model was trained with data from 12 drugs and tested using the data of the remaining 16 drugs. The proposed ANN model demonstrated an AUC of 0.92 in the high-risk group, 0.83 in the intermediate-risk group, and 0.98 in the low-risk group. This was higher than the classification performance of the method proposed in previous studies.
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Affiliation(s)
- Yedam Yoo
- Computational Medicine Laboratory, Department of IT convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
| | - Aroli Marcellinus
- Computational Medicine Laboratory, Department of IT convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
| | - Da Un Jeong
- Computational Medicine Laboratory, Department of IT convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
| | - Ki-Suk Kim
- R&D Center for Advanced Pharmaceuticals and Evaluation, Korea Institute of Toxicology, Daejeon, South Korea
| | - Ki Moo Lim
- Computational Medicine Laboratory, Department of IT convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
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29
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Prediction of arrhythmia susceptibility through mathematical modeling and machine learning. Proc Natl Acad Sci U S A 2021; 118:2104019118. [PMID: 34493665 DOI: 10.1073/pnas.2104019118] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 08/04/2021] [Indexed: 01/08/2023] Open
Abstract
At present, the QT interval on the electrocardiographic (ECG) waveform is the most common metric for assessing an individual's susceptibility to ventricular arrhythmias, with a long QT, or, at the cellular level, a long action potential duration (APD) considered high risk. However, the limitations of this simple approach have long been recognized. Here, we sought to improve prediction of arrhythmia susceptibility by combining mechanistic mathematical modeling with machine learning (ML). Simulations with a model of the ventricular myocyte were performed to develop a large heterogenous population of cardiomyocytes (n = 10,586), and we tested each variant's ability to withstand three arrhythmogenic triggers: 1) block of the rapid delayed rectifier potassium current (IKr Block), 2) augmentation of the L-type calcium current (ICaL Increase), and 3) injection of inward current (Current Injection). Eight ML algorithms were trained to predict, based on simulated AP features in preperturbed cells, whether each cell would develop arrhythmic dynamics in response to each trigger. We found that APD can accurately predict how cells respond to the simple Current Injection trigger but cannot effectively predict the response to IKr Block or ICaL Increase. ML predictive performance could be improved by incorporating additional AP features and simulations of additional experimental protocols. Importantly, we discovered that the most relevant features and experimental protocols were trigger specific, which shed light on the mechanisms that promoted arrhythmia formation in response to the triggers. Overall, our quantitative approach provides a means to understand and predict differences between individuals in arrhythmia susceptibility.
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30
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Qauli AI, Marcellinus A, Lim KM. Sensitivity Analysis of Ion Channel Conductance on Myocardial Electromechanical Delay: Computational Study. Front Physiol 2021; 12:697693. [PMID: 34512377 PMCID: PMC8430256 DOI: 10.3389/fphys.2021.697693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 07/29/2021] [Indexed: 02/03/2023] Open
Abstract
It is well known that cardiac electromechanical delay (EMD) can cause dyssynchronous heart failure (DHF), a prominent cardiovascular disease (CVD). This work computationally assesses the conductance variation of every ion channel on the cardiac cell to give rise to EMD prolongation. The electrical and mechanical models of human ventricular tissue were simulated, using a population approach with four conductance reductions for each ion channel. Then, EMD was calculated by determining the difference between the onset of action potential and the start of cell shortening. Finally, EMD data were put into the optimized conductance dimensional stacking to show which ion channel has the most influence in elongating the EMD. We found that major ion channels, such as L-type calcium (CaL), slow-delayed rectifier potassium (Ks), rapid-delayed rectifier potassium (Kr), and inward rectifier potassium (K1), can significantly extend the action potential duration (APD) up to 580 ms. Additionally, the maximum intracellular calcium (Cai) concentration is greatly affected by the reduction in channel CaL, Ks, background calcium, and Kr. However, among the aforementioned major ion channels, only the CaL channel can play a superior role in prolonging the EMD up to 83 ms. Furthermore, ventricular cells with long EMD have been shown to inherit insignificant mechanical response (in terms of how strong the tension can grow and how far length shortening can go) compared with that in normal cells. In conclusion, despite all variations in every ion channel conductance, only the CaL channel can play a significant role in extending EMD. In addition, cardiac cells with long EMD tend to have inferior mechanical responses due to a lack of Cai compared with normal conditions, which are highly likely to result in a compromised pump function of the heart.
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Affiliation(s)
- Ali Ikhsanul Qauli
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
| | - Aroli Marcellinus
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
| | - Ki Moo Lim
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea
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31
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Whittaker DG, Capel RA, Hendrix M, Chan XHS, Herring N, White NJ, Mirams GR, Burton RAB. Cardiac TdP risk stratification modelling of anti-infective compounds including chloroquine and hydroxychloroquine. ROYAL SOCIETY OPEN SCIENCE 2021; 8:210235. [PMID: 33996135 PMCID: PMC8059594 DOI: 10.1098/rsos.210235] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 03/30/2021] [Indexed: 05/06/2023]
Abstract
Hydroxychloroquine (HCQ), the hydroxyl derivative of chloroquine (CQ), is widely used in the treatment of rheumatological conditions (systemic lupus erythematosus, rheumatoid arthritis) and is being studied for the treatment and prevention of COVID-19. Here, we investigate through mathematical modelling the safety profile of HCQ, CQ and other QT-prolonging anti-infective agents to determine their risk categories for Torsade de Pointes (TdP) arrhythmia. We performed safety modelling with uncertainty quantification using a risk classifier based on the qNet torsade metric score, a measure of the net charge carried by major currents during the action potential under inhibition of multiple ion channels by a compound. Modelling results for HCQ at a maximum free therapeutic plasma concentration (free C max) of approximately 1.2 µM (malaria dosing) indicated it is most likely to be in the high-intermediate-risk category for TdP, whereas CQ at a free C max of approximately 0.7 µM was predicted to most likely lie in the intermediate-risk category. Combining HCQ with the antibacterial moxifloxacin or the anti-malarial halofantrine (HAL) increased the degree of human ventricular action potential duration prolongation at some or all concentrations investigated, and was predicted to increase risk compared to HCQ alone. The combination of HCQ/HAL was predicted to be the riskiest for the free C max values investigated, whereas azithromycin administered individually was predicted to pose the lowest risk. Our simulation approach highlights that the torsadogenic potentials of HCQ, CQ and other QT-prolonging anti-infectives used in COVID-19 prevention and treatment increase with concentration and in combination with other QT-prolonging drugs.
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Affiliation(s)
- Dominic G. Whittaker
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | | | - Maurice Hendrix
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
- Digital Research Service, University of Nottingham, Nottingham, UK
| | - Xin Hui S. Chan
- Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Neil Herring
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Nicholas J. White
- Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Gary R. Mirams
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
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Amuzescu B, Airini R, Epureanu FB, Mann SA, Knott T, Radu BM. Evolution of mathematical models of cardiomyocyte electrophysiology. Math Biosci 2021; 334:108567. [PMID: 33607174 DOI: 10.1016/j.mbs.2021.108567] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/10/2021] [Accepted: 02/04/2021] [Indexed: 12/16/2022]
Abstract
Advanced computational techniques and mathematical modeling have become more and more important to the study of cardiac electrophysiology. In this review, we provide a brief history of the evolution of cardiomyocyte electrophysiology models and highlight some of the most important ones that had a major impact on our understanding of the electrical activity of the myocardium and associated transmembrane ion fluxes in normal and pathological states. We also present the use of these models in the study of various arrhythmogenesis mechanisms, particularly the integration of experimental pharmacology data into advanced humanized models for in silico proarrhythmogenic risk prediction as an essential component of the Comprehensive in vitro Proarrhythmia Assay (CiPA) drug safety paradigm.
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Affiliation(s)
- Bogdan Amuzescu
- Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, Bucharest 050095, Romania; Life, Environmental and Earth Sciences Division, Research Institute of the University of Bucharest (ICUB), 91-95 Splaiul Independentei, Bucharest 050095, Romania.
| | - Razvan Airini
- Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, Bucharest 050095, Romania; Life, Environmental and Earth Sciences Division, Research Institute of the University of Bucharest (ICUB), 91-95 Splaiul Independentei, Bucharest 050095, Romania
| | - Florin Bogdan Epureanu
- Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, Bucharest 050095, Romania; Life, Environmental and Earth Sciences Division, Research Institute of the University of Bucharest (ICUB), 91-95 Splaiul Independentei, Bucharest 050095, Romania
| | - Stefan A Mann
- Cytocentrics Bioscience GmbH, Nattermannallee 1, 50829 Cologne, Germany
| | - Thomas Knott
- CytoBioScience Inc., 3463 Magic Drive, San Antonio, TX 78229, USA
| | - Beatrice Mihaela Radu
- Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, Bucharest 050095, Romania; Life, Environmental and Earth Sciences Division, Research Institute of the University of Bucharest (ICUB), 91-95 Splaiul Independentei, Bucharest 050095, Romania
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Strauss DG, Wu WW, Li Z, Koerner J, Garnett C. Translational Models and Tools to Reduce Clinical Trials and Improve Regulatory Decision Making for QTc and Proarrhythmia Risk (ICH E14/S7B Updates). Clin Pharmacol Ther 2021; 109:319-333. [PMID: 33332579 PMCID: PMC7898549 DOI: 10.1002/cpt.2137] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 12/14/2020] [Indexed: 01/06/2023]
Abstract
After multiple drugs were removed from the market secondary to drug-induced torsade de pointes (TdP) risk, the International Council for Harmonisation (ICH) released guidelines in 2005 that focused on the nonclinical (S7B) and clinical (E14) assessment of surrogate biomarkers for TdP. Recently, Vargas et al. published a pharmaceutical-industry perspective making the case that "double-negative" nonclinical data (negative in vitro hERG and in vivo heart-rate corrected QT (QTc) assays) are associated with such low probability of clinical QTc prolongation and TdP that potentially all double-negative drugs would not need detailed clinical QTc evaluation. Subsequently, the ICH released a new E14/S7B Draft Guideline containing Questions and Answers (Q&As) that defined ways that double-negative nonclinical data could be used to reduce the number of "Thorough QT" (TQT) studies and reach a low-risk determination when a TQT or equivalent could not be performed. We review the Vargas et al. proposal in the context of what was contained in the ICH E14/S7B Draft Guideline and what was proposed by the ICH E14/S7B working group for a "stage 2" of updates (potential expanded roles for nonclinical data and details for assessing TdP risk of QTc-prolonging drugs). Although we do not agree with the exact probability statistics in the Vargas et al. paper because of limitations in the underlying datasets, we show how more modest predictive value of individual assays could still result in low probability for TdP with double-negative findings. Furthermore, we expect that the predictive value of the nonclinical assays will improve with implementation of the new ICH E14/S7B Draft Guideline.
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Affiliation(s)
- David G. Strauss
- Division of Applied Regulatory ScienceOffice of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Wendy W. Wu
- Division of Applied Regulatory ScienceOffice of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Zhihua Li
- Division of Applied Regulatory ScienceOffice of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - John Koerner
- Division of Pharm/Tox for Cardiology, Hematology, Endocrinology and NephrologyOffice of Cardiology, Hematology, Endocrinology and NephrologyOffice of New DrugsCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Christine Garnett
- Division of Cardiology and NephrologyOffice of Cardiology, Hematology, Endocrinology and NephrologyOffice of New DrugsCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
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Corral-Acero J, Margara F, Marciniak M, Rodero C, Loncaric F, Feng Y, Gilbert A, Fernandes JF, Bukhari HA, Wajdan A, Martinez MV, Santos MS, Shamohammdi M, Luo H, Westphal P, Leeson P, DiAchille P, Gurev V, Mayr M, Geris L, Pathmanathan P, Morrison T, Cornelussen R, Prinzen F, Delhaas T, Doltra A, Sitges M, Vigmond EJ, Zacur E, Grau V, Rodriguez B, Remme EW, Niederer S, Mortier P, McLeod K, Potse M, Pueyo E, Bueno-Orovio A, Lamata P. The 'Digital Twin' to enable the vision of precision cardiology. Eur Heart J 2020; 41:4556-4564. [PMID: 32128588 PMCID: PMC7774470 DOI: 10.1093/eurheartj/ehaa159] [Citation(s) in RCA: 206] [Impact Index Per Article: 51.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/29/2019] [Accepted: 02/24/2020] [Indexed: 12/26/2022] Open
Abstract
Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.
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Affiliation(s)
| | - Francesca Margara
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Maciej Marciniak
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Cristobal Rodero
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Filip Loncaric
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Yingjing Feng
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
| | | | - Joao F Fernandes
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Hassaan A Bukhari
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
- Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain
| | - Ali Wajdan
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | | | | | - Mehrdad Shamohammdi
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Hongxing Luo
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Philip Westphal
- Medtronic PLC, Bakken Research Center, Maastricht, the Netherlands
| | - Paul Leeson
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, Oxford Cardiovascular Clinical Research Facility, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Paolo DiAchille
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Viatcheslav Gurev
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Manuel Mayr
- King’s British Heart Foundation Centre, King’s College London, London, UK
| | - Liesbet Geris
- Virtual Physiological Human Institute, Leuven, Belgium
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Tina Morrison
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | | | - Frits Prinzen
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Tammo Delhaas
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Ada Doltra
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Marta Sitges
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- CIBERCV, Instituto de Salud Carlos III, (CB16/11/00354), CERCA Programme/Generalitat de, Catalunya, Spain
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
| | - Ernesto Zacur
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Blanca Rodriguez
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Espen W Remme
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Steven Niederer
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | | | | | - Mark Potse
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
- Inria Bordeaux Sud-Ouest, CARMEN team, Talence F-33400, France
| | - Esther Pueyo
- Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain
- CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER‐BBN), Madrid, Spain
| | - Alfonso Bueno-Orovio
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
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Cano J, Zorio E, Mazzanti A, Arnau MÁ, Trenor B, Priori SG, Saiz J, Romero L. Ranolazine as an Alternative Therapy to Flecainide for SCN5A V411M Long QT Syndrome Type 3 Patients. Front Pharmacol 2020; 11:580481. [PMID: 33519442 PMCID: PMC7845660 DOI: 10.3389/fphar.2020.580481] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 10/16/2020] [Indexed: 12/14/2022] Open
Abstract
The prolongation of the QT interval represents the main feature of the long QT syndrome (LQTS), a life-threatening genetic disease. The heterozygous SCN5A V411M mutation of the human sodium channel leads to a LQTS type 3 with severe proarrhythmic effects due to an increase in the late component of the sodium current (INaL). The two sodium blockers flecainide and ranolazine are equally recommended by the current 2015 ESC guidelines to treat patients with LQTS type 3 and persistently prolonged QT intervals. However, awareness of pro-arrhythmic effects of flecainide in LQTS type 3 patients arose upon the study of the SCN5A E1784K mutation. Regarding SCN5A V411M individuals, flecainide showed good results albeit in a reduced number of patients and no evidence supporting the use of ranolazine has ever been released. Therefore, we ought to compare the effect of ranolazine and flecainide in a SCN5A V411M model using an in-silico modeling and simulation approach. We collected clinical data of four patients. Then, we fitted four Markovian models of the human sodium current (INa) to experimental and clinical data. Two of them correspond to the wild type and the heterozygous SCN5A V411M scenarios, and the other two mimic the effects of flecainide and ranolazine on INa. Next, we inserted them into three isolated cell action potential (AP) models for endocardial, midmyocardial and epicardial cells and in a one-dimensional tissue model. The SCN5A V411M mutation produced a 15.9% APD90 prolongation in the isolated endocardial cell model, which corresponded to a 14.3% of the QT interval prolongation in a one-dimensional strand model, in keeping with clinical observations. Although with different underlying mechanisms, flecainide and ranolazine partially countered this prolongation at the isolated endocardial model by reducing the APD90 by 8.7 and 4.3%, and the QT interval by 7.2 and 3.2%, respectively. While flecainide specifically targeted the mutation-induced increase in peak INaL, ranolazine reduced it during the entire AP. Our simulations also suggest that ranolazine could prevent early afterdepolarizations triggered by the SCN5A V411M mutation during bradycardia, as flecainide. We conclude that ranolazine could be used to treat SCN5A V411M patients, specifically when flecainide is contraindicated.
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Affiliation(s)
- Jordi Cano
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València, Valencia, España
| | - Esther Zorio
- Unidad de Cardiopatías Familiares y Muerte Súbita, Servicio de Cardiología, Hospital Universitario y Politécnico La Fe, Valencia, España.,Center for Biomedical Network Research on Cardiovascular Diseases (CIBERCV), Madrid, Spain
| | - Andrea Mazzanti
- Molecular Cardiology, IRCCS, Istituti Clinici Scientifici Maugeri, Pavia, Italy.,Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Miguel Ángel Arnau
- Unidad de Cardiopatías Familiares y Muerte Súbita, Servicio de Cardiología, Hospital Universitario y Politécnico La Fe, Valencia, España
| | - Beatriz Trenor
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València, Valencia, España
| | - Silvia G Priori
- Molecular Cardiology, IRCCS, Istituti Clinici Scientifici Maugeri, Pavia, Italy.,Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València, Valencia, España
| | - Lucia Romero
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València, Valencia, España
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Pathmanathan P, Galappaththige SK, Cordeiro JM, Kaboudian A, Fenton FH, Gray RA. Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics. Front Physiol 2020; 11:585400. [PMID: 33329034 PMCID: PMC7711195 DOI: 10.3389/fphys.2020.585400] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 10/20/2020] [Indexed: 12/23/2022] Open
Abstract
Computational modeling of cardiac electrophysiology (EP) has recently transitioned from a scientific research tool to clinical applications. To ensure reliability of clinical or regulatory decisions made using cardiac EP models, it is vital to evaluate the uncertainty in model predictions. Model predictions are uncertain because there is typically substantial uncertainty in model input parameters, due to measurement error or natural variability. While there has been much recent uncertainty quantification (UQ) research for cardiac EP models, all previous work has been limited by either: (i) considering uncertainty in only a subset of the full set of parameters; and/or (ii) assigning arbitrary variation to parameters (e.g., ±10 or 50% around mean value) rather than basing the parameter uncertainty on experimental data. In our recent work we overcame the first limitation by performing UQ and sensitivity analysis using a novel canine action potential model, allowing all parameters to be uncertain, but with arbitrary variation. Here, we address the second limitation by extending our previous work to use data-driven estimates of parameter uncertainty. Overall, we estimated uncertainty due to population variability in all parameters in five currents active during repolarization: inward potassium rectifier, transient outward potassium, L-type calcium, rapidly and slowly activating delayed potassium rectifier; 25 parameters in total (all model parameters except fast sodium current parameters). A variety of methods was used to estimate the variability in these parameters. We then propagated the uncertainties through the model to determine their impact on predictions of action potential shape, action potential duration (APD) prolongation due to drug block, and spiral wave dynamics. Parameter uncertainty had a significant effect on model predictions, especially L-type calcium current parameters. Correlation between physiological parameters was determined to play a role in physiological realism of action potentials. Surprisingly, even model outputs that were relative differences, specifically drug-induced APD prolongation, were heavily impacted by the underlying uncertainty. This is the first data-driven end-to-end UQ analysis in cardiac EP accounting for uncertainty in the vast majority of parameters, including first in tissue, and demonstrates how future UQ could be used to ensure model-based decisions are robust to all underlying parameter uncertainties.
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Affiliation(s)
- Pras Pathmanathan
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, United States
| | - Suran K. Galappaththige
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, United States
| | - Jonathan M. Cordeiro
- Department of Experimental Cardiology, Masonic Medical Research Institute, Utica, NY, United States
| | - Abouzar Kaboudian
- School of Physics, Georgia Institute of Technology, Atlanta, GA, United States
| | - Flavio H. Fenton
- School of Physics, Georgia Institute of Technology, Atlanta, GA, United States
| | - Richard A. Gray
- U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, United States
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Wan H, Selvaggio G, Pearlstein RA. Toward in vivo-relevant hERG safety assessment and mitigation strategies based on relationships between non-equilibrium blocker binding, three-dimensional channel-blocker interactions, dynamic occupancy, dynamic exposure, and cellular arrhythmia. PLoS One 2020; 15:e0234946. [PMID: 33147278 PMCID: PMC7641409 DOI: 10.1371/journal.pone.0234946] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/16/2020] [Indexed: 12/26/2022] Open
Abstract
The human ether-a-go-go-related voltage-gated cardiac ion channel (commonly known as hERG) conducts the rapid outward repolarizing potassium current in cardiomyocytes (IKr). Inadvertent blockade of this channel by drug-like molecules represents a key challenge in pharmaceutical R&D due to frequent overlap between the structure-activity relationships of hERG and many primary targets. Building on our previous work, together with recent cryo-EM structures of hERG, we set about to better understand the energetic and structural basis of promiscuous blocker-hERG binding in the context of Biodynamics theory. We propose a two-step blocker binding process consisting of: The initial capture step: diffusion of a single fully solvated blocker copy into a large cavity lined by the intra-cellular cyclic nucleotide binding homology domain (CNBHD). Occupation of this cavity is a necessary but insufficient condition for ion current disruption.The IKr disruption step: translocation of the captured blocker along the channel axis, such that: The head group, consisting of a quasi-rod-shaped moiety, projects into the open pore, accompanied by partial de-solvation of the binding interface.One tail moiety packs along a kink between the S6 helix and proximal C-linker helix adjacent to the intra-cellular entrance of the pore, likewise accompanied by mutual de-solvation of the binding interface (noting that the association barrier is comprised largely of the total head + tail group de-solvation cost).Blockers containing a highly planar moiety that projects into a putative constriction zone within the closed channel become trapped upon closing, as do blockers terminating prior to this region.A single captured blocker copy may conceivably associate and dissociate to/from the pore many times before exiting the CNBHD cavity. Lastly, we highlight possible flaws in the current hERG safety index (SI), and propose an alternate in vivo-relevant strategy factoring in: Benefit/risk.The predicted arrhythmogenic fractional hERG occupancy (based on action potential (AP) simulations of the undiseased human ventricular cardiomyocyte).Alteration of the safety threshold due to underlying disease.Risk of exposure escalation toward the predicted arrhythmic limit due to patient-to-patient pharmacokinetic (PK) variability, drug-drug interactions, overdose, and use for off-label indications in which the hERG safety parameters may differ from their on-label counterparts.
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Affiliation(s)
- Hongbin Wan
- Global Discovery Chemistry, Computer-Aided Drug Discovery, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Gianluca Selvaggio
- Global Discovery Chemistry, Computer-Aided Drug Discovery, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
| | - Robert A. Pearlstein
- Global Discovery Chemistry, Computer-Aided Drug Discovery, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America
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Raphel F, De Korte T, Lombardi D, Braam S, Gerbeau JF. A greedy classifier optimization strategy to assess ion channel blocking activity and pro-arrhythmia in hiPSC-cardiomyocytes. PLoS Comput Biol 2020; 16:e1008203. [PMID: 32976482 PMCID: PMC7549820 DOI: 10.1371/journal.pcbi.1008203] [Citation(s) in RCA: 4] [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: 11/21/2019] [Revised: 10/12/2020] [Accepted: 07/28/2020] [Indexed: 02/05/2023] Open
Abstract
Novel studies conducting cardiac safety assessment using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are promising but might be limited by their specificity and predictivity. It is often challenging to correctly classify ion channel blockers or to sufficiently predict the risk for Torsade de Pointes (TdP). In this study, we developed a method combining in vitro and in silico experiments to improve machine learning approaches in delivering fast and reliable prediction of drug-induced ion-channel blockade and proarrhythmic behaviour. The algorithm is based on the construction of a dictionary and a greedy optimization, leading to the definition of optimal classifiers. Finally, we present a numerical tool that can accurately predict compound-induced pro-arrhythmic risk and involvement of sodium, calcium and potassium channels, based on hiPSC-CM field potential data.
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Affiliation(s)
- Fabien Raphel
- Inria, Paris, France
- NOTOCORD part of Instem, Le Pecq, France
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39
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Hwang M, Lim CH, Leem CH, Shim EB. In silico models for evaluating proarrhythmic risk of drugs. APL Bioeng 2020; 4:021502. [PMID: 32548538 PMCID: PMC7274812 DOI: 10.1063/1.5132618] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 04/27/2020] [Indexed: 02/07/2023] Open
Abstract
Safety evaluation of drugs requires examination of the risk of generating Torsade de Pointes (TdP) because it can lead to sudden cardiac death. Until recently, the QT interval in the electrocardiogram (ECG) has been used in the evaluation of TdP risk because the QT interval is known to be associated with the development of TdP. Although TdP risk evaluation based on QT interval has been successful in removing drugs with TdP risk from the market, some safe drugs may have also been affected due to the low specificity of QT interval-based evaluation. For more accurate evaluation of drug safety, the comprehensive in vitro proarrhythmia assay (CiPA) has been proposed by regulatory agencies, industry, and academia. Although the CiPA initiative includes in silico evaluation of cellular action potential as a component, attempts to utilize in silico simulation in drug safety evaluation are expanding, even to simulating human ECG using biophysical three-dimensional models of the heart and torso under the effects of drugs. Here, we review recent developments in the use of in silico models for the evaluation of the proarrhythmic risk of drugs. We review the single cell, one-dimensional, two-dimensional, and three-dimensional models and their applications reported in the literature and discuss the possibility of utilizing ECG simulation in drug safety evaluation.
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Affiliation(s)
- Minki Hwang
- SiliconSapiens Inc., Seoul 06097, South Korea
| | - Chul-Hyun Lim
- Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon 24341, South Korea
| | - Chae Hun Leem
- Department of Physiology, College of Medicine, University of Ulsan, Asan Medical Center, Seoul 05505, South Korea
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Niederer SA, Aboelkassem Y, Cantwell CD, Corrado C, Coveney S, Cherry EM, Delhaas T, Fenton FH, Panfilov AV, Pathmanathan P, Plank G, Riabiz M, Roney CH, dos Santos RW, Wang L. Creation and application of virtual patient cohorts of heart models. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20190558. [PMID: 32448064 PMCID: PMC7287335 DOI: 10.1098/rsta.2019.0558] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/06/2020] [Indexed: 05/21/2023]
Abstract
Patient-specific cardiac models are now being used to guide therapies. The increased use of patient-specific cardiac simulations in clinical care will give rise to the development of virtual cohorts of cardiac models. These cohorts will allow cardiac simulations to capture and quantify inter-patient variability. However, the development of virtual cohorts of cardiac models will require the transformation of cardiac modelling from small numbers of bespoke models to robust and rapid workflows that can create large numbers of models. In this review, we describe the state of the art in virtual cohorts of cardiac models, the process of creating virtual cohorts of cardiac models, and how to generate the individual cohort member models, followed by a discussion of the potential and future applications of virtual cohorts of cardiac models. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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Affiliation(s)
| | | | | | | | | | - E. M. Cherry
- Georgia Institute of Technology, Atlanta, GA, USA
| | - T. Delhaas
- Maastricht University, Maastricht, the Netherlands
| | - F. H. Fenton
- Georgia Institute of Technology, Atlanta, GA, USA
| | - A. V. Panfilov
- Ghent University, Gent, Belgium
- Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg, Russia
| | - P. Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Administration, Rockville, MD, USA
| | - G. Plank
- Medical University of Graz, Graz, Austria
| | | | | | | | - L. Wang
- Rochester Institute of Technology, La JollaRochester, NY, USA
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Kramer J, Himmel HM, Lindqvist A, Stoelzle-Feix S, Chaudhary KW, Li D, Bohme GA, Bridgland-Taylor M, Hebeisen S, Fan J, Renganathan M, Imredy J, Humphries ESA, Brinkwirth N, Strassmaier T, Ohtsuki A, Danker T, Vanoye C, Polonchuk L, Fermini B, Pierson JB, Gintant G. Cross-site and cross-platform variability of automated patch clamp assessments of drug effects on human cardiac currents in recombinant cells. Sci Rep 2020; 10:5627. [PMID: 32221320 PMCID: PMC7101356 DOI: 10.1038/s41598-020-62344-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 03/09/2020] [Indexed: 01/01/2023] Open
Abstract
Automated patch clamp (APC) instruments enable efficient evaluation of electrophysiologic effects of drugs on human cardiac currents in heterologous expression systems. Differences in experimental protocols, instruments, and dissimilar site procedures affect the variability of IC50 values characterizing drug block potency. This impacts the utility of APC platforms for assessing a drug's cardiac safety margin. We determined variability of APC data from multiple sites that measured blocking potency of 12 blinded drugs (with different levels of proarrhythmic risk) against four human cardiac currents (hERG [IKr], hCav1.2 [L-Type ICa], peak hNav1.5, [Peak INa], late hNav1.5 [Late INa]) with recommended protocols (to minimize variance) using five APC platforms across 17 sites. IC50 variability (25/75 percentiles) differed for drugs and currents (e.g., 10.4-fold for dofetilide block of hERG current and 4-fold for mexiletine block of hNav1.5 current). Within-platform variance predominated for 4 of 12 hERG blocking drugs and 4 of 6 hNav1.5 blocking drugs. hERG and hNav1.5 block. Bland-Altman plots depicted varying agreement across APC platforms. A follow-up survey suggested multiple sources of experimental variability that could be further minimized by stricter adherence to standard protocols. Adoption of best practices would ensure less variable APC datasets and improved safety margins and proarrhythmic risk assessments.
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Affiliation(s)
| | | | | | | | | | - Dingzhou Li
- Drug Safety Research & Development, Pfizer, Groton, CT, USA
| | - Georg Andrees Bohme
- Integrated Drug Discovery, High Content Biology Unit, Sanofi R&D, Vitry-Sur-Seine, France
| | | | | | - Jingsong Fan
- Discovery Toxicology, Bristol-Myers Squibb Company, Princeton, NJ, USA
| | | | | | | | | | | | | | - Timm Danker
- Natural and Medical Science Institute at the University of Tübingen, Reutlingen, Germany
| | - Carlos Vanoye
- Northwestern Feinberg School of Medicine, Chicago, IL, USA
| | - Liudmila Polonchuk
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
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42
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Ridder BJ, Leishman DJ, Bridgland-Taylor M, Samieegohar M, Han X, Wu WW, Randolph A, Tran P, Sheng J, Danker T, Lindqvist A, Konrad D, Hebeisen S, Polonchuk L, Gissinger E, Renganathan M, Koci B, Wei H, Fan J, Levesque P, Kwagh J, Imredy J, Zhai J, Rogers M, Humphries E, Kirby R, Stoelzle-Feix S, Brinkwirth N, Rotordam MG, Becker N, Friis S, Rapedius M, Goetze TA, Strassmaier T, Okeyo G, Kramer J, Kuryshev Y, Wu C, Himmel H, Mirams GR, Strauss DG, Bardenet R, Li Z. A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm. Toxicol Appl Pharmacol 2020; 394:114961. [PMID: 32209365 PMCID: PMC7166077 DOI: 10.1016/j.taap.2020.114961] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/14/2020] [Accepted: 03/19/2020] [Indexed: 12/13/2022]
Abstract
Introduction hERG block potency is widely used to calculate a drug's safety margin against its torsadogenic potential. Previous studies are confounded by use of different patch clamp electrophysiology protocols and a lack of statistical quantification of experimental variability. Since the new cardiac safety paradigm being discussed by the International Council for Harmonisation promotes a tighter integration of nonclinical and clinical data for torsadogenic risk assessment, a more systematic approach to estimate the hERG block potency and safety margin is needed. Methods A cross-industry study was performed to collect hERG data on 28 drugs with known torsadogenic risk using a standardized experimental protocol. A Bayesian hierarchical modeling (BHM) approach was used to assess the hERG block potency of these drugs by quantifying both the inter-site and intra-site variability. A modeling and simulation study was also done to evaluate protocol-dependent changes in hERG potency estimates. Results A systematic approach to estimate hERG block potency is established. The impact of choosing a safety margin threshold on torsadogenic risk evaluation is explored based on the posterior distributions of hERG potency estimated by this method. The modeling and simulation results suggest any potency estimate is specific to the protocol used. Discussion This methodology can estimate hERG block potency specific to a given voltage protocol. The relationship between safety margin thresholds and torsadogenic risk predictivity suggests the threshold should be tailored to each specific context of use, and safety margin evaluation may need to be integrated with other information to form a more comprehensive risk assessment. hERG potency/safety margin is a widely used nonclinical cardiac safety strategy. A new regulatory paradigm promotes the integration of nonclinical and clinical data. Lack of uncertainty quantification hindered using hERG potency in the new paradigm. A systematic method was established to address this limitation. Analysis supports using different safety margin thresholds in different context.
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Affiliation(s)
- Bradley J Ridder
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Derek J Leishman
- Department of Toxicology and Pathology, Eli Lilly and Company, Indianapolis, IN, USA
| | | | - Mohammadreza Samieegohar
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Xiaomei Han
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Wendy W Wu
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Aaron Randolph
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Phu Tran
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Jiansong Sheng
- CiPA LAB, 900 Clopper Rd, Suite 130, Gaithersburg, MD 20878, USA
| | - Timm Danker
- NMI-TT GmbH, Markwiesenstr. 55, 72770 Reutlingen, Germany
| | | | - Daniel Konrad
- B'SYS GmbH, The Ion Channel Company, Benkenstrasse 254, CH-4108, Witterswil, Switzerland
| | - Simon Hebeisen
- B'SYS GmbH, The Ion Channel Company, Benkenstrasse 254, CH-4108, Witterswil, Switzerland
| | - Liudmila Polonchuk
- F. Hoffmann-La Roche AG, F. Hoffmann-La Roche Ltd Bldg. 73/R. 103b Grenzacherstrasse, 124, CH-4070 Basel, Switzerland
| | - Evgenia Gissinger
- F. Hoffmann-La Roche AG, F. Hoffmann-La Roche Ltd Bldg. 73/R. 103b Grenzacherstrasse, 124, CH-4070 Basel, Switzerland
| | | | - Bryan Koci
- Eurofins Scientific, Eurofins Discovery, 6 Research Park Drive, St. Charles, MO 63304, USA
| | - Haiyang Wei
- Eurofins Scientific, Eurofins Discovery, 6 Research Park Drive, St. Charles, MO 63304, USA
| | - Jingsong Fan
- Bristol-Myers Squibb Company, Discovery Toxicology, Bristol-Myers Squibb, 3551 Lawrenceville, Princeton Rd, Lawrence Township, NJ 08648, USA
| | - Paul Levesque
- Bristol-Myers Squibb Company, Discovery Toxicology, Bristol-Myers Squibb, 3551 Lawrenceville, Princeton Rd, Lawrence Township, NJ 08648, USA
| | - Jae Kwagh
- Bristol-Myers Squibb Company, Discovery Toxicology, Bristol-Myers Squibb, 3551 Lawrenceville, Princeton Rd, Lawrence Township, NJ 08648, USA
| | | | - Jin Zhai
- Merck & Co., Inc, Kenilworth, NJ, USA
| | - Marc Rogers
- Metrion Biosciences Limited, Riverside 3, Suite 1, Granta Park, Great Abington, Cambridge CB21, 6AD, United Kingdom
| | - Edward Humphries
- Metrion Biosciences Limited, Riverside 3, Suite 1, Granta Park, Great Abington, Cambridge CB21, 6AD, United Kingdom
| | - Robert Kirby
- Metrion Biosciences Limited, Riverside 3, Suite 1, Granta Park, Great Abington, Cambridge CB21, 6AD, United Kingdom
| | | | - Nina Brinkwirth
- Nanion Technologies Munich, Ganghoferstrasse 70A, 80339 Munich, Germany
| | | | - Nadine Becker
- Nanion Technologies Munich, Ganghoferstrasse 70A, 80339 Munich, Germany
| | - Søren Friis
- Nanion Technologies Munich, Ganghoferstrasse 70A, 80339 Munich, Germany
| | - Markus Rapedius
- Nanion Technologies Munich, Ganghoferstrasse 70A, 80339 Munich, Germany
| | - Tom A Goetze
- Nanion Technologies Munich, Ganghoferstrasse 70A, 80339 Munich, Germany
| | - Tim Strassmaier
- Nanion Technologies, USA, 1 Naylon Place, Suite C, Livingston, NJ 07039, USA
| | - George Okeyo
- Nanion Technologies, USA, 1 Naylon Place, Suite C, Livingston, NJ 07039, USA
| | - James Kramer
- Charles River Laboratories, 14656 Neo Parkway, Cleveland, OH 44128, USA
| | - Yuri Kuryshev
- Charles River Laboratories, 14656 Neo Parkway, Cleveland, OH 44128, USA
| | - Caiyun Wu
- Charles River Laboratories, 14656 Neo Parkway, Cleveland, OH 44128, USA
| | - Herbert Himmel
- Bayer AG, RD-TS-TOX-SP-SPL1, Aprather Weg 18a, 42096 Wuppertal, Germany
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - David G Strauss
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Rémi Bardenet
- Université de Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL, Villeneuve d'Ascq, France
| | - Zhihua Li
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA.
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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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44
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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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Li Z, Mirams GR, Yoshinaga T, Ridder BJ, Han X, Chen JE, Stockbridge NL, Wisialowski TA, Damiano B, Severi S, Morissette P, Kowey PR, Holbrook M, Smith G, Rasmusson RL, Liu M, Song Z, Qu Z, Leishman DJ, Steidl‐Nichols J, Rodriguez B, Bueno‐Orovio A, Zhou X, Passini E, Edwards AG, Morotti S, Ni H, Grandi E, Clancy CE, Vandenberg J, Hill A, Nakamura M, Singer T, Polonchuk L, Greiter‐Wilke A, Wang K, Nave S, Fullerton A, Sobie EA, Paci M, Musuamba Tshinanu F, Strauss DG. General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy. Clin Pharmacol Ther 2020; 107:102-111. [PMID: 31709525 PMCID: PMC6977398 DOI: 10.1002/cpt.1647] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 09/06/2019] [Indexed: 12/27/2022]
Abstract
This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/US Food and Drug Administration-sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the Comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model-based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm.
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46
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Comprehensive In Vitro Proarrhythmia Assay (CiPA) Update from a Cardiac Safety Research Consortium / Health and Environmental Sciences Institute / FDA Meeting. Ther Innov Regul Sci 2019; 53:519-525. [DOI: 10.1177/2168479018795117] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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47
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Abi-Gerges N, Miller PE, Ghetti A. Human Heart Cardiomyocytes in Drug Discovery and Research: New Opportunities in Translational Sciences. Curr Pharm Biotechnol 2019; 21:787-806. [PMID: 31820682 DOI: 10.2174/1389201021666191210142023] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 10/14/2019] [Accepted: 10/28/2019] [Indexed: 12/28/2022]
Abstract
In preclinical drug development, accurate prediction of drug effects on the human heart is critically important, whether in the context of cardiovascular safety or for the purpose of modulating cardiac function to treat heart disease. Current strategies have significant limitations, whereby, cardiotoxic drugs can escape detection or potential life-saving therapies are abandoned due to false positive toxicity signals. Thus, new and more reliable translational approaches are urgently needed to help accelerate the rate of new therapy development. Renewed efforts in the recovery of human donor hearts for research and in cardiomyocyte isolation methods, are providing new opportunities for preclinical studies in adult primary cardiomyocytes. These cells exhibit the native physiological and pharmacological properties, overcoming the limitations presented by artificial cellular models, animal models and have great potential for providing an excellent tool for preclinical drug testing. Adult human primary cardiomyocytes have already shown utility in assessing drug-induced cardiotoxicity risk and helping in the identification of new treatments for cardiac diseases, such as heart failure and atrial fibrillation. Finally, strategies with actionable decision-making trees that rely on data derived from adult human primary cardiomyocytes will provide the holistic insights necessary to accurately predict human heart effects of drugs.
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Affiliation(s)
- Najah Abi-Gerges
- AnaBios Corporation, 3030 Bunker Hill St., Suite 312, San Diego, CA 92109, United States
| | - Paul E Miller
- AnaBios Corporation, 3030 Bunker Hill St., Suite 312, San Diego, CA 92109, United States
| | - Andre Ghetti
- AnaBios Corporation, 3030 Bunker Hill St., Suite 312, San Diego, CA 92109, United States
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48
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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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Passini E, Trovato C, Morissette P, Sannajust F, Bueno‐Orovio A, Rodriguez B. Drug-induced shortening of the electromechanical window is an effective biomarker for in silico prediction of clinical risk of arrhythmias. Br J Pharmacol 2019; 176:3819-3833. [PMID: 31271649 PMCID: PMC6780030 DOI: 10.1111/bph.14786] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 05/21/2019] [Accepted: 06/28/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND AND PURPOSE Early identification of drug-induced cardiac adverse events is key in drug development. Human-based computer models are emerging as an effective approach, complementary to in vitro and animal models. Drug-induced shortening of the electromechanical window has been associated with increased risk of arrhythmias. This study investigates the potential of a cellular surrogate for the electromechanical window (EMw) for prediction of pro-arrhythmic cardiotoxicity, and its underlying ionic mechanisms, using human-based computer models. EXPERIMENTAL APPROACH In silico drug trials for 40 reference compounds were performed, testing up to 100-fold the therapeutic concentrations (EFTPCmax ) and using a control population of human ventricular action potential (AP) models, optimised to capture pro-arrhythmic ionic profiles. EMw was calculated for each model in the population as the difference between AP and Ca2+ transient durations at 90%. Drug-induced changes in the EMw and occurrence of repolarisation abnormalities (RA) were quantified. KEY RESULTS Drugs with clinical risk of Torsade de Pointes arrhythmias induced a concentration-dependent EMw shortening, while safe drugs lead to increase or small change in EMw. Risk predictions based on EMw shortening achieved 90% accuracy at 10× EFTPCmax , whereas RA-based predictions required 100× EFTPCmax to reach the same accuracy. As it is dependent on Ca2+ transient, the EMw was also more sensitive than AP prolongation in distinguishing between pure hERG blockers and multichannel compounds also blocking the calcium current. CONCLUSION AND IMPLICATIONS The EMw is an effective biomarker for in silico predictions of drug-induced clinical pro-arrhythmic risk, particularly for compounds with multichannel blocking action.
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Affiliation(s)
- Elisa Passini
- Department of Computer ScienceUniversity of OxfordOxfordUK
| | | | - Pierre Morissette
- SALAR, Safety and Exploratory Pharmacology Department, Merck Research LaboratoriesMerck & Co., Inc.West PointPAUSA
| | - Frederick Sannajust
- SALAR, Safety and Exploratory Pharmacology Department, Merck Research LaboratoriesMerck & Co., Inc.West PointPAUSA
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Clerx M, Beattie KA, Gavaghan DJ, Mirams GR. Four Ways to Fit an Ion Channel Model. Biophys J 2019; 117:2420-2437. [PMID: 31493859 PMCID: PMC6990153 DOI: 10.1016/j.bpj.2019.08.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/20/2019] [Accepted: 08/01/2019] [Indexed: 12/16/2022] Open
Abstract
Mathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example, to predict the risks and results of genetic mutations, pharmacological treatments, or surgical procedures. These safety-critical applications depend on accurate characterization of the underlying ionic currents. Four different methods can be found in the literature to fit voltage-sensitive ion channel models to whole-cell current measurements: method 1, fitting model equations directly to time-constant, steady-state, and I-V summary curves; method 2, fitting by comparing simulated versions of these summary curves to their experimental counterparts; method 3, fitting to the current traces themselves from a range of protocols; and method 4, fitting to a single current trace from a short and rapidly fluctuating voltage-clamp protocol. We compare these methods using a set of experiments in which hERG1a current was measured in nine Chinese hamster ovary cells. In each cell, the same sequence of fitting protocols was applied, as well as an independent validation protocol. We show that methods 3 and 4 provide the best predictions on the independent validation set and that short, rapidly fluctuating protocols like that used in method 4 can replace much longer conventional protocols without loss of predictive ability. Although data for method 2 are most readily available from the literature, we find it performs poorly compared to methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications.
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Affiliation(s)
- Michael Clerx
- Computational Biology & Health Informatics, Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Kylie A Beattie
- Computational Biology & Health Informatics, Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - David J Gavaghan
- Computational Biology & Health Informatics, Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom.
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