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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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2
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Peirlinck M, Sahli Costabal F, Kuhl E. Sex Differences in Drug-Induced Arrhythmogenesis. Front Physiol 2021; 12:708435. [PMID: 34489728 PMCID: PMC8417068 DOI: 10.3389/fphys.2021.708435] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 07/14/2021] [Indexed: 12/25/2022] Open
Abstract
The electrical activity in the heart varies significantly between men and women and results in a sex-specific response to drugs. Recent evidence suggests that women are more than twice as likely as men to develop drug-induced arrhythmia with potentially fatal consequences. Yet, the sex-specific differences in drug-induced arrhythmogenesis remain poorly understood. Here we integrate multiscale modeling and machine learning to gain mechanistic insight into the sex-specific origin of drug-induced cardiac arrhythmia at differing drug concentrations. To quantify critical drug concentrations in male and female hearts, we identify the most important ion channels that trigger male and female arrhythmogenesis, and create and train a sex-specific multi-fidelity arrhythmogenic risk classifier. Our study reveals that sex differences in ion channel activity, tissue conductivity, and heart dimensions trigger longer QT-intervals in women than in men. We quantify the critical drug concentration for dofetilide, a high risk drug, to be seven times lower for women than for men. Our results emphasize the importance of including sex as an independent biological variable in risk assessment during drug development. Acknowledging and understanding sex differences in drug safety evaluation is critical when developing novel therapeutic treatments on a personalized basis. The general trends of this study have significant implications on the development of safe and efficacious new drugs and the prescription of existing drugs in combination with other drugs.
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Affiliation(s)
- Mathias Peirlinck
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Francisco Sahli Costabal
- Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
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3
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Wu W, Lu C, Liang Y, Zhang H, Deng C, Wang Q, Xu Q, Tan B, Zhou C, Song J. Electrocardiographic effect of artemisinin-piperaquine, dihydroartemisinin-piperaquine, and artemether-lumefantrine treatment in falciparum malaria patients. Rev Soc Bras Med Trop 2021; 54:e05362020. [PMID: 33605379 PMCID: PMC7891559 DOI: 10.1590/0037-8682-0536-2020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 12/15/2020] [Indexed: 01/08/2023] Open
Abstract
INTRODUCTION: Artemisinin-based combination therapy (ACT), such as artemisinin-piperaquine (AP), dihydroartemisinin-piperaquine (DP), and artemether-lumefantrine (AL), is the first-line treatment for malaria in many malaria-endemic areas. However, we lack a detailed evaluation of the cardiotoxicity of these ACTs. This study aimed to analyze the electrocardiographic effects of these three ACTs in malaria patients. METHODS: We analyzed the clinical data of 89 hospitalized patients with falciparum malaria who had received oral doses of three different ACTs. According to the ACTs administered, these patients were divided into three treatment groups: 27 treated with AP (Artequick), 31 with DP (Artekin), and 31 with AL (Coartem). Electrocardiograms and other indicators were recorded before and after the treatment. The QT interval was calculated using Fridericia’s formula (QTcF) and Bazett’s formula (QTcB). RESULTS: Both QTcF and QTcB interval prolongation occurred in all three groups. The incidence of such prolongation between the three groups was not significantly different. The incidence of both moderate and severe prolongation was not significantly different between the three groups. The ΔQTcF and ΔQTcB of the three groups were not significantly different. The intra-group comparison showed significant prolongation of QTcF after AL treatment. CONCLUSIONS: Clinically recommended doses of DP, AL, and AP may cause QT prolongation in some malaria patients but do not cause torsades de pointes ventricular tachycardia or other arrhythmias.
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Affiliation(s)
- Wanting Wu
- Guangzhou University of Chinese Medicine, Artemisinin Research Center, Guangzhou, Guangdong, People's Republic of China.,Guangzhou University of Chinese Medicine, Sci-tech Industrial Park, Guanzhou, Guangdong, People's Republic of China
| | - Chenguang Lu
- Guangzhou University of Chinese Medicine, Artemisinin Research Center, Guangzhou, Guangdong, People's Republic of China.,Guangzhou University of Chinese Medicine, Sci-tech Industrial Park, Guanzhou, Guangdong, People's Republic of China
| | - Yuan Liang
- Guangzhou University of Chinese Medicine, Artemisinin Research Center, Guangzhou, Guangdong, People's Republic of China
| | - Hongying Zhang
- Guangzhou University of Chinese Medicine, Artemisinin Research Center, Guangzhou, Guangdong, People's Republic of China.,Guangzhou University of Chinese Medicine, Sci-tech Industrial Park, Guanzhou, Guangdong, People's Republic of China
| | - Changsheng Deng
- Guangzhou University of Chinese Medicine, Artemisinin Research Center, Guangzhou, Guangdong, People's Republic of China.,Guangzhou University of Chinese Medicine, Sci-tech Industrial Park, Guanzhou, Guangdong, People's Republic of China
| | - Qi Wang
- Guangzhou University of Chinese Medicine, Artemisinin Research Center, Guangzhou, Guangdong, People's Republic of China.,Guangzhou University of Chinese Medicine, Sci-tech Industrial Park, Guanzhou, Guangdong, People's Republic of China
| | - Qin Xu
- Guangzhou University of Chinese Medicine, Artemisinin Research Center, Guangzhou, Guangdong, People's Republic of China.,Guangzhou University of Chinese Medicine, Sci-tech Industrial Park, Guanzhou, Guangdong, People's Republic of China
| | - Bo Tan
- Guangzhou University of Chinese Medicine, Institute of Tropical Medicine, Guangzhou, Guangdong, People's Republic of China
| | - Chongjun Zhou
- Guangzhou University of Chinese Medicine, Institute of Tropical Medicine, Guangzhou, Guangdong, People's Republic of China
| | - Jianping Song
- Guangzhou University of Chinese Medicine, Artemisinin Research Center, Guangzhou, Guangdong, People's Republic of China.,Guangzhou University of Chinese Medicine, Sci-tech Industrial Park, Guanzhou, Guangdong, People's Republic of China
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4
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Diekman CO, Wei N. Circadian Rhythms of Early Afterdepolarizations and Ventricular Arrhythmias in a Cardiomyocyte Model. Biophys J 2020; 120:319-333. [PMID: 33285114 DOI: 10.1016/j.bpj.2020.11.2264] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 02/25/2020] [Accepted: 11/10/2020] [Indexed: 11/30/2022] Open
Abstract
Sudden cardiac arrest is a malfunction of the heart's electrical system, typically caused by ventricular arrhythmias, that can lead to sudden cardiac death (SCD) within minutes. Epidemiological studies have shown that SCD and ventricular arrhythmias are more likely to occur in the morning than in the evening, and laboratory studies indicate that these daily rhythms in adverse cardiovascular events are at least partially under the control of the endogenous circadian timekeeping system. However, the biophysical mechanisms linking molecular circadian clocks to cardiac arrhythmogenesis are not fully understood. Recent experiments have shown that L-type calcium channels exhibit circadian rhythms in both expression and function in guinea pig ventricular cardiomyocytes. We developed an electrophysiological model of these cells to simulate the effect of circadian variation in L-type calcium conductance. In our simulations, we found that there is a circadian pattern in the occurrence of early afterdepolarizations (EADs), which are abnormal depolarizations during the repolarization phase of a cardiac action potential that can trigger fatal ventricular arrhythmias. Specifically, the model produces EADs in the morning, but not at other times of day. We show that the model exhibits a codimension-2 Takens-Bogdanov bifurcation that serves as an organizing center for different types of EAD dynamics. We also simulated a two-dimensional spatial version of this model across a circadian cycle. We found that there is a circadian pattern in the breakup of spiral waves, which represents ventricular fibrillation in cardiac tissue. Specifically, the model produces spiral wave breakup in the morning, but not in the evening. Our computational study is the first, to our knowledge, to propose a link between circadian rhythms and EAD formation and suggests that the efficacy of drugs targeting EAD-mediated arrhythmias may depend on the time of day that they are administered.
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Affiliation(s)
- Casey O Diekman
- Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey; EPSRC Centre for Predictive Modelling in Healthcare, Living Systems Institute, University of Exeter, Exeter, United Kingdom.
| | - Ning Wei
- Department of Mathematics, Purdue University, West Lafayette, Indiana
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5
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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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6
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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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7
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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: 52] [Impact Index Per Article: 13.0] [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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8
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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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9
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The effect of anti-emetic drugs on rat embryonic heart activity. Reprod Toxicol 2019; 87:140-145. [PMID: 31199962 DOI: 10.1016/j.reprotox.2019.06.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 06/05/2019] [Accepted: 06/10/2019] [Indexed: 02/07/2023]
Abstract
Nausea and vomiting of pregnancy (NVP) is the most common medical complaint during pregnancy affecting up to 70% of pregnant women worldwide. Some antiemetic medications (AEM) (droperidol, domperidone, granisetron, metoclopramide and trifluoperazine) used to treat NVP have the unwanted side effect of hERG blockade. The hERG potassium channel is essential for normal heart rhythm in both the adult human and the human and rat embryo. Animal studies show hERG blockade in the embryo causes bradycardia and arrhythmia leading to cardiovascular malformations and other birth defects. Whole rat embryo in vitro culture was used to determine the effect of the above listed AEM and meclizine on the heart rate of Gestational day 13 rat embryos. These embryos are similar in size and heart development to 5-6-week human embryo. The results showed that all of the AEMs caused a concentration-dependent bradycardia. Droperidol had the lowest margin of safety.
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10
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Li Z, Garnett C, Strauss DG. Quantitative Systems Pharmacology Models for a New International Cardiac Safety Regulatory Paradigm: An Overview of the Comprehensive In Vitro Proarrhythmia Assay In Silico Modeling Approach. CPT Pharmacometrics Syst Pharmacol 2019; 8:371-379. [PMID: 31044559 PMCID: PMC6617836 DOI: 10.1002/psp4.12423] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 04/15/2019] [Indexed: 12/17/2022] Open
Abstract
As a relatively new discipline, quantitative systems pharmacology has seen a significant increase in the application and utility of drug development. One area that could greatly benefit from such an approach is in the proarrhythmia assessment of new drugs. The Comprehensive In Vitro Proarrhythmia Assay (CiPA) Initiative is a global public-private partnership project that has developed an integrated approach using mechanistic in silico models for proarrhythmia risk prediction. Progress to date has led to the formation of the International Council on Harmonisation Implementation Working Group to revise regulatory guidelines via the Questions-and-Answers process to address the best practices for proarrhythmia models and how they can impact clinical drug development. This article reviews the CiPA in silico model-development process, focusing on its unique development and validation strategy, and summarizes the lessons learned as consideration points for the ongoing implementation of CiPA-like in silico models in drug development.
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Affiliation(s)
- Zhihua Li
- Division of Applied Regulatory ScienceOffice of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Christine Garnett
- Division of Cardiovascular and Renal ProductsOffice of Drug Evaluation IOffice of New DrugsCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - David G. Strauss
- Division of Applied Regulatory ScienceOffice of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
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11
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Kügler P, Erhardt AH, Bulelzai MAK. Early afterdepolarizations in cardiac action potentials as mixed mode oscillations due to a folded node singularity. PLoS One 2018; 13:e0209498. [PMID: 30596698 PMCID: PMC6312222 DOI: 10.1371/journal.pone.0209498] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 12/06/2018] [Indexed: 12/27/2022] Open
Abstract
Early afterdepolarizations (EADs) are pathological voltage oscillations during the repolarization phase of cardiac action potentials. They are considered as potential precursors to cardiac arrhythmias and have recently gained much attention in the context of preclinical drug safety testing under the Comprehensive in vitro Proarrhythmia Assay (CiPA) paradigm. From the viewpoint of multiple time scales theory, the onset of EADs has previously been studied by means of mathematical action potential models with one slow ion channel gating variable. In this article, we for the first time associate EADs with mixed mode oscillations in dynamical systems with two slow gating variables and present a folded node singularity of the slow flow as a novel mechanism for EADs genesis. We derive regions of the pharmacology parameter space in which EADs occur using both the folded node analysis and a full system bifurcation analysis, and we suggest the normal distance to the boundary of the EADs region as a mechanism-based risk metric to computationally estimate a drug’s proarrhythmic liability.
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Affiliation(s)
- Philipp Kügler
- Institute of Applied Mathematics and Statistics, University of Hohenheim, Stuttgart, Germany
- * E-mail:
| | | | - M. A. K. Bulelzai
- Departmeny of Basic Sciences and Related Studies, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan
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12
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Christophe B, Crumb WJ. Impact of disease state on arrhythmic event detection by action potential modelling in cardiac safety pharmacology. J Pharmacol Toxicol Methods 2018; 96:15-26. [PMID: 30580044 DOI: 10.1016/j.vascn.2018.12.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 12/11/2018] [Accepted: 12/17/2018] [Indexed: 11/15/2022]
Abstract
INTRODUCTION The use of in silico cardiac action potential simulations is one of the pillars of the CiPA initiative (Comprehensive in vitro Proarrhythmia Assay) currently under evaluation designed to detect more accurately proarrhythmic liabilities of new drug candidate. In order to take into account the variability of clinical situations, we propose to improve this method by studying the impact of various disease states on arrhythmic events induced by 30 torsadogenic or non-torsadogenic compounds. METHOD In silico modelling was done on the human myocytes using the Dutta revised O'Hara-Rudy algorithm. Results were analysed using a new metric based on the compound IC50s against the seven cardiac ionic currents considered to be the most important by the CiPA initiative (IKr, IKs, INa, INaL, IK1, Ito, ICaL) and the minimal rate of action potential voltage decrease calculated at the early-afterdepolarization (EAD) take-off membrane voltage (Vmin). RESULTS The specific threshold at which each torsadogenic compounds induced EAD, was exacerbated by the presence of cardiac risk factors ranked as follows: congestive heart failure > hypertrophic cardiomyopathy > cardiac pause > no risk factor. Non-torsadogenic compounds induced no EAD even in the presence of cardiac risk factors. DISCUSSION The present study highlighted the impact of pre-existing cardiovascular disease on arrhythmic event detection suggesting that disease state modelling may need to be incorporated in order to fully realize the goal of the CiPA paradigm in a more accurate predictability of proarrhythmic liabilities of new drug candidate.
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Affiliation(s)
| | - William J Crumb
- Nova Research Laboratories LLC, 1441 Canal Street, New Orleans, LA 70112, USA.
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13
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Li Z, Ridder BJ, Han X, Wu WW, Sheng J, Tran PN, Wu M, Randolph A, Johnstone RH, Mirams GR, Kuryshev Y, Kramer J, Wu C, Crumb WJ, Strauss DG. Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative. Clin Pharmacol Ther 2018; 105:466-475. [PMID: 30151907 PMCID: PMC6492074 DOI: 10.1002/cpt.1184] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 07/06/2018] [Indexed: 12/12/2022]
Abstract
The International Council on Harmonization (ICH) S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are pro‐arrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (CiPA) was proposed that integrates multi‐ion channel pharmacology data in vitro into a human cardiomyocyte model in silico for proarrhythmia risk assessment. Previously, we reported the model optimization and proarrhythmia metric selection based on CiPA training drugs. In this study, we report the application of the prespecified model and metric to independent CiPA validation drugs. Over two validation datasets, the CiPA model performance meets all pre‐specified measures for ranking and classifying validation drugs, and outperforms alternatives, despite some in vitro data differences between the two datasets due to different experimental conditions and quality control procedures. This suggests that the current CiPA model/metric may be fit for regulatory use, and standardization of experimental protocols and quality control criteria could increase the model prediction accuracy even further.
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Affiliation(s)
- Zhihua Li
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Bradley J Ridder
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Xiaomei Han
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Wendy W Wu
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jiansong Sheng
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Phu N Tran
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Min Wu
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Aaron Randolph
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ross H Johnstone
- Department of Computer Science, Healthcare Informatics, University of Oxford, Oxford, UK
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Yuri Kuryshev
- Charles River Laboratories, Wilmington, Massachusetts, USA
| | - James Kramer
- Charles River Laboratories, Wilmington, Massachusetts, USA
| | - Caiyun Wu
- Charles River Laboratories, Wilmington, Massachusetts, USA
| | | | - David G Strauss
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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