- Email: [email protected]
Integrative approaches for prediction of cardiotoxic drug effects and mitigation strategies
Integrative approaches for prediction of cardiotoxic drug effects and mitigation strategies Birce Onal, Thomas J. Hund PII: DOI: Reference:
S0022-2828(16)30379-0 doi:10.1016/j.yjmcc.2016.10.003 YJMCC 8464
To appear in:
Journal of Molecular and Cellular Cardiology
Received date: Accepted date:
13 September 2016 10 October 2016
Please cite this article as: Onal Birce, Hund Thomas J., Integrative approaches for prediction of cardiotoxic drug effects and mitigation strategies, Journal of Molecular and Cellular Cardiology (2016), doi:10.1016/j.yjmcc.2016.10.003
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
RI
PT
ACCEPTED MANUSCRIPT
NU
SC
Integrative approaches for prediction of cardiotoxic drug effects and mitigation strategies
Birce Onal, B.S.1,2 and Thomas J. Hund, Ph.D.1,2,3
MA
Dorothy M. Davis Heart and Lung Research Institute1 and Departments of Biomedical Engineering2, and Internal Medicine3; The Ohio State University Wexner Medical Center and
AC CE P
Total word count: 2,039
TE
D
The Ohio State University College of Engineering.
Keywords: late sodium current; acquired long QT; arrhythmia; mathematical model Correspondence to: Thomas J. Hund, Ph.D. Davis Heart and Lung Research Institute 473 W. 12th Avenue Columbus, OH USA [email protected]
1
ACCEPTED MANUSCRIPT Cardiotoxicity poses a tremendous challenge for patient care management and drug development [1, 2]. Underlying cardiotoxic effects associated with a broad spectrum of cardiac
PT
and non-cardiac pharmacological agents is drug-induced prolongation of the QT interval (acquired long QT) and associated risk for lethal ventricular arrhythmias (torsades des pointes, Drug-induced QT prolongation has resulted in removal of over a dozen clinically
RI
TdP) [3].
SC
important drugs from the market since 1989 and has halted development of countless others. Perhaps more alarming is the fact that many drugs with known pro-arrhythmia risk remain on
NU
the market [1, 4]. Methods are already in place for coherent drug risk categorization based on
MA
available experimental/clinical data linking a drug to adverse cardiac events (e.g. CredibleMeds.org) [1]. Computerized clinical decision support systems have been developed to help clinicians minimize drug-induced pro-arrhythmia risk with their patients [2].
However,
TE
D
significant challenges remain for the field and novel approaches are needed to help address the following questions: 1) Can we improve risk stratification to keep dangerous drugs off the
AC CE P
market without blocking development of potentially life-saving therapies?; and 2) Can we mitigate risk associated with drugs already on the market? The study by Yang et al. in this issue of Journal of Molecular and Cellular Cardiology applies advanced mathematical modeling and computer simulation to simultaneously address both of these critical questions while providing a glimpse into the evolving state of drug risk assessment [5]. Acquired long QT occurs primarily due to non-specific block of hERG-encoded delayed rectifier K+ current (IKr), which delays ventricular action potential (AP) repolarization and promotes life-threatening ventricular arrhythmias [3, 6, 7].
Unfortunately, hERG is highly
susceptible to block by a diverse array of pharmacological agents so that, today, therapeutic compounds in development are routinely screened for impact on IKr [7]. Interestingly, not all drugs that affect hERG activity are pro-arrhythmic, due in some cases to effects on multiple channels (e.g. ranolazine, amiodarone) [4, 8]. In fact, a growing body of literature suggests that
2
ACCEPTED MANUSCRIPT adjuvant therapy may be useful in ameliorating pro-arrhythmic effects of some hERG-blocking drugs [4, 9-11]. Taking a cue from this previous work, the Clancy lab and collaborators set out
PT
to determine whether GS-458967, a novel inhibitor of late Na+ current (INa,L), would be effective in reversing AP prolongation and pro-arrhythmia associated with acquired long QT [5]. The
RI
study is timely for its integrative approach and clinical relevance. INa,L is a small (compared to
SC
peak INa) but sustained depolarizing current generated by a minor population of voltage-gated Na+ channels that fail to fully inactivate following rapid activation. Importantly, INa,L block has
NU
been shown to reduce AP duration and prevent arrhythmias in preclinical and clinical studies
MA
leading to a push for development of specific INa,L blockers (e.g. GS-458967) [12-17]. Recently, investigators have begun to ask whether INa,L block might be anti-arrhythmic in the setting of acquired long QT [4, 9]. Yang et al. contribute to this dialogue by using multi-scale modeling to
TE
D
evaluate the efficacy of specific INa,L block in preventing arrhythmia induced by the IKr blocker dofetilide. Specifically, the group first developed a detailed mathematical model of drug-free or
AC CE P
GS-458967-treated voltage-gated Na+ channels (Nav) based in part on original experiments. They then incorporated the Nav models into a physiological ventricular AP model that also included effects of dofetilide on IKr to simulate acquired long QT.
Finally, they performed
multicellular simulations to test the effect of GS-458967 on the electrocardiogram and stability of reentrant arrhythmia in the setting of acquired long QT. Results from their computer simulations show that specific INa,L block by GS-458967 is able to normalize AP duration, reduce cell- and tissue-level indicators of arrhythmogenesis, and prevent sustained reentry. Interestingly, rather than relying on AP duration as the sole cellular indicator of arrhythmogenesis, the team assessed a palette of quantitative measures (so called “TRIaD”): 1) AP Triangulation; 3) Reverse use dependence 2) beat-to-beat Instability; and 4) Dispersion of AP duration. They were able to show that late INa,L block improves all TRIaD measures with respect to cellular proarrhythmia. Another noteworthy aspect of their approach is that rather than simulating drug effects on a single “average” AP profile, they examine the response of a simulated population of 3
ACCEPTED MANUSCRIPT cells, reflecting the growing awareness in the field of the importance of physiological variability in arrhythmia phenotypes and response to therapy [18-20]. Finally, perhaps most compelling
PT
about the study is its demonstration of how modeling and simulation may be used to probe the parameter space of drug-channel interactions at multiple scales in a way that is simply not
RI
feasible experimentally.
SC
We find ourselves in an exciting time as modeling and simulation together with advances
NU
in genetics and high throughput technologies are bringing personalized medicine into the realm of possibility. The potential impact of computational approaches is reflected in the increased
MA
push for integrated in silico approaches by both private industry and federal regulatory agencies [9, 21]. In this vein, the work from the Clancy lab represents a larger effort in the community to
D
explore the ability of biophysical computational modeling to assist with high throughput
TE
screening of drugs [22-25]. As demonstrated by the elegant studies of the Clancy group and others, modeling should be considered an essential component of the drug screening process
AC CE P
due its ability to tackle multi-scale and highly nonlinear problems. For example, biophysical computational studies can easily assess a full range of cellular and tissue factors (e.g. “TRIaD” screen) that would be impossible to assess in a high throughput manner experimentally. Furthermore, modeling serves as an indispensable tool for linking disparate experimental and simulation data across temporal and spatial scales.
Continued development of integrative
approaches combining modeling and experiment, such as the one implemented by Yang et al., has the potential to revolutionize the drug development process. Not to say that modeling is currently, or will ever be, a panacea for the many challenges facing drug risk stratification and management. There remains a real concern that, despite the advancements in modeling, the field is not yet ready for increased reliance on biophysical modeling and simulation in the screening process. Related to this larger concern is the valid question about whether existing biophysical models are accurate enough to improve stratification/outcomes, and whether the
4
ACCEPTED MANUSCRIPT benefit of using detailed physiological models is worth the computational cost compared to statistical approaches that worry less about biophysical mechanism [26]. Of course, any model
PT
requires simplification of the physiological system, which may introduce considerable error into the simulated results. In the case of the difficult task of simulating drug-channel interactions,
RI
potential confounders are numerous, including the off-target effects of drug metabolites,
SC
unresolved mode of drug action on the channel, and cell/organ/patient heterogeneity [27, 28]. In the face of these and other considerable challenges, our best hope is an interdisciplinary
NU
approach that draws from the entire armory of tools at our disposal, including mathematical
MA
modeling and computer simulation, to improve outcomes across a wide range of human
D
disease.
TE
Disclosures: The authors have nothing to disclose.
AC CE P
Acknowledgements: Authors are supported by NIH HL129766 (to BO) and HL114893 (to TJH), the James S. McDonnell Foundation, the Saving Tiny Hearts Society, the Ross Heart Hospital and Davis Heart and Lung Research Institute.
References [1] Schwartz PJ, Woosley RL. Predicting the Unpredictable: Drug-Induced QT Prolongation and Torsades de Pointes. J Am Coll Cardiol. 2016;67:1639-50. [2] Woosley RL, Whyte J, Mohamadi A, Romero K. Medical decision support systems and therapeutics: The role of autopilots. Clin Pharmacol Ther. 2016;99:161-4. [3] Roden DM. Drug-induced prolongation of the QT interval. N Engl J Med. 2004;350:1013-22. [4] Johannesen L, Vicente J, Mason JW, Erato C, Sanabria C, Waite-Labott K, et al. Late sodium current block for drug-induced long QT syndrome: Results from a prospective clinical trial. Clin Pharmacol Ther. 2016;99:214-23. [5] Yang P-C, El-Bizri N, Romero L, Giles WR, Rajamani S, Belardinelli L, et al. A computational model predicts adjunctive pharmacotherapy for cardiac safety via selective inhibition of the late cardiac Na current. J Mol Cell Cardiol. 2016:In Press.
5
ACCEPTED MANUSCRIPT
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D
MA
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SC
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PT
[6] Sanguinetti MC, Jiang C, Curran ME, Keating MT. A mechanistic link between an inherited and an acquired cardiac arrhythmia: HERG encodes the IKr potassium channel. Cell. 1995;81:299-307. [7] Sanguinetti MC, Tristani-Firouzi M. hERG potassium channels and cardiac arrhythmia. Nature. 2006;440:463-9. [8] Antzelevitch C, Belardinelli L, Zygmunt AC, Burashnikov A, Di Diego JM, Fish JM, et al. Electrophysiological effects of ranolazine, a novel antianginal agent with antiarrhythmic properties. Circulation. 2004;110:904-10. [9] Johannesen L, Vicente J, Mason JW, Sanabria C, Waite-Labott K, Hong M, et al. Differentiating drug-induced multichannel block on the electrocardiogram: randomized study of dofetilide, quinidine, ranolazine, and verapamil. Clin Pharmacol Ther. 2014;96:549-58. [10] Martin RL, McDermott JS, Salmen HJ, Palmatier J, Cox BF, Gintant GA. The utility of hERG and repolarization assays in evaluating delayed cardiac repolarization: influence of multichannel block. J Cardiovasc Pharmacol. 2004;43:369-79. [11] Shimizu W, Antzelevitch C. Sodium channel block with mexiletine is effective in reducing dispersion of repolarization and preventing torsade des pointes in LQT2 and LQT3 models of the long-QT syndrome. Circulation. 1997;96:2038-47. [12] Antzelevitch C, Nesterenko V, Shryock JC, Rajamani S, Song Y, Belardinelli L. The role of late INa in development of cardiac arrhythmias. Handb Exp Pharmacol. 2014;221:137-68. [13] Belardinelli L, Giles WR, Rajamani S, Karagueuzian HS, Shryock JC. Cardiac late Na+ current: proarrhythmic effects, roles in long QT syndromes, and pathological relationship to CaMKII and oxidative stress. Heart Rhythm. 2015;12:440-8. [14] Frommeyer G, Milberg P, Maier LS, Eckardt L. Late sodium current inhibition: the most promising antiarrhythmic principle in the near future? Curr Med Chem. 2014;21:1271-80. [15] Unudurthi SD, Hund TJ. Late sodium current dysregulation as a causal factor in arrhythmia. Expert Rev Cardiovasc Ther. 2016;14:545-7. [16] Glynn P, Musa H, Wu X, Unudurthi SD, Little S, Qian L, et al. Voltage-gated sodium channel phosphorylation at Ser571 regulates late current, arrhythmia, and cardiac function in vivo. Circulation. 2015;132:567-77. [17] Koval OM, Snyder JS, Wolf RM, Pavlovicz RE, Glynn P, Curran J, et al. Ca2+/calmodulindependent protein kinase II-based regulation of voltage-gated Na+ channel in cardiac disease. Circulation. 2012;126:2084-94. [18] Onal B, Hund TJ. Physiological variability and atrial fibrillation therapy: Insights from population-based mathematical modeling. Heart Rhythm. 2016:In press. [19] Muszkiewicz A, Britton OJ, Gemmell P, Passini E, Sanchez C, Zhou X, et al. Variability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm. Prog Biophys Mol Biol. 2016;120:11527. [20] Sarkar AX, Christini DJ, Sobie EA. Exploiting mathematical models to illuminate electrophysiological variability between individuals. J Physiol. 2012;590:2555-67. [21] Sager PT, Gintant G, Turner JR, Pettit S, Stockbridge N. Rechanneling the cardiac proarrhythmia safety paradigm: a meeting report from the Cardiac Safety Research Consortium. Am Heart J. 2014;167:292-300. [22] Davies MR, Wang K, Mirams GR, Caruso A, Noble D, Walz A, et al. Recent developments in using mechanistic cardiac modelling for drug safety evaluation. Drug Discov Today. 2016;21:924-38. [23] Di Veroli GY, Davies MR, Zhang H, Abi-Gerges N, Boyett MR. High-throughput screening of drug-binding dynamics to HERG improves early drug safety assessment. Am J Physiol Heart Circ Physiol. 2013;304:H104-17. [24] Yuan Y, Bai X, Luo C, Wang K, Zhang H. The virtual heart as a platform for screening drug cardiotoxicity. Br J Pharmacol. 2015;172:5531-47. 6
ACCEPTED MANUSCRIPT
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D
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[25] Davies MR, Mistry HB, Hussein L, Pollard CE, Valentin JP, Swinton J, et al. An in silico canine cardiac midmyocardial action potential duration model as a tool for early drug safety assessment. Am J Physiol Heart Circ Physiol. 2012;302:H1466-80. [26] Mistry HB, Davies MR, Di Veroli GY. A new classifier-based strategy for in-silico ionchannel cardiac drug safety assessment. Front Pharmacol. 2015;6:59. [27] Wisniowska B, Mendyk A, Fijorek K, Polak S. Computer-based prediction of the drug proarrhythmic effect: problems, issues, known and suspected challenges. Europace. 2014;16:724-35. [28] Potet F, Vanoye CG, George AL, Jr. Use-Dependent Block of Human Cardiac Sodium Channels by GS967. Mol Pharmacol. 2016;90:52-60.
7
S0022-2828(16)30379-0 doi:10.1016/j.yjmcc.2016.10.003 YJMCC 8464
To appear in:
Journal of Molecular and Cellular Cardiology
Received date: Accepted date:
13 September 2016 10 October 2016
Please cite this article as: Onal Birce, Hund Thomas J., Integrative approaches for prediction of cardiotoxic drug effects and mitigation strategies, Journal of Molecular and Cellular Cardiology (2016), doi:10.1016/j.yjmcc.2016.10.003
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
RI
PT
ACCEPTED MANUSCRIPT
NU
SC
Integrative approaches for prediction of cardiotoxic drug effects and mitigation strategies
Birce Onal, B.S.1,2 and Thomas J. Hund, Ph.D.1,2,3
MA
Dorothy M. Davis Heart and Lung Research Institute1 and Departments of Biomedical Engineering2, and Internal Medicine3; The Ohio State University Wexner Medical Center and
AC CE P
Total word count: 2,039
TE
D
The Ohio State University College of Engineering.
Keywords: late sodium current; acquired long QT; arrhythmia; mathematical model Correspondence to: Thomas J. Hund, Ph.D. Davis Heart and Lung Research Institute 473 W. 12th Avenue Columbus, OH USA [email protected]
1
ACCEPTED MANUSCRIPT Cardiotoxicity poses a tremendous challenge for patient care management and drug development [1, 2]. Underlying cardiotoxic effects associated with a broad spectrum of cardiac
PT
and non-cardiac pharmacological agents is drug-induced prolongation of the QT interval (acquired long QT) and associated risk for lethal ventricular arrhythmias (torsades des pointes, Drug-induced QT prolongation has resulted in removal of over a dozen clinically
RI
TdP) [3].
SC
important drugs from the market since 1989 and has halted development of countless others. Perhaps more alarming is the fact that many drugs with known pro-arrhythmia risk remain on
NU
the market [1, 4]. Methods are already in place for coherent drug risk categorization based on
MA
available experimental/clinical data linking a drug to adverse cardiac events (e.g. CredibleMeds.org) [1]. Computerized clinical decision support systems have been developed to help clinicians minimize drug-induced pro-arrhythmia risk with their patients [2].
However,
TE
D
significant challenges remain for the field and novel approaches are needed to help address the following questions: 1) Can we improve risk stratification to keep dangerous drugs off the
AC CE P
market without blocking development of potentially life-saving therapies?; and 2) Can we mitigate risk associated with drugs already on the market? The study by Yang et al. in this issue of Journal of Molecular and Cellular Cardiology applies advanced mathematical modeling and computer simulation to simultaneously address both of these critical questions while providing a glimpse into the evolving state of drug risk assessment [5]. Acquired long QT occurs primarily due to non-specific block of hERG-encoded delayed rectifier K+ current (IKr), which delays ventricular action potential (AP) repolarization and promotes life-threatening ventricular arrhythmias [3, 6, 7].
Unfortunately, hERG is highly
susceptible to block by a diverse array of pharmacological agents so that, today, therapeutic compounds in development are routinely screened for impact on IKr [7]. Interestingly, not all drugs that affect hERG activity are pro-arrhythmic, due in some cases to effects on multiple channels (e.g. ranolazine, amiodarone) [4, 8]. In fact, a growing body of literature suggests that
2
ACCEPTED MANUSCRIPT adjuvant therapy may be useful in ameliorating pro-arrhythmic effects of some hERG-blocking drugs [4, 9-11]. Taking a cue from this previous work, the Clancy lab and collaborators set out
PT
to determine whether GS-458967, a novel inhibitor of late Na+ current (INa,L), would be effective in reversing AP prolongation and pro-arrhythmia associated with acquired long QT [5]. The
RI
study is timely for its integrative approach and clinical relevance. INa,L is a small (compared to
SC
peak INa) but sustained depolarizing current generated by a minor population of voltage-gated Na+ channels that fail to fully inactivate following rapid activation. Importantly, INa,L block has
NU
been shown to reduce AP duration and prevent arrhythmias in preclinical and clinical studies
MA
leading to a push for development of specific INa,L blockers (e.g. GS-458967) [12-17]. Recently, investigators have begun to ask whether INa,L block might be anti-arrhythmic in the setting of acquired long QT [4, 9]. Yang et al. contribute to this dialogue by using multi-scale modeling to
TE
D
evaluate the efficacy of specific INa,L block in preventing arrhythmia induced by the IKr blocker dofetilide. Specifically, the group first developed a detailed mathematical model of drug-free or
AC CE P
GS-458967-treated voltage-gated Na+ channels (Nav) based in part on original experiments. They then incorporated the Nav models into a physiological ventricular AP model that also included effects of dofetilide on IKr to simulate acquired long QT.
Finally, they performed
multicellular simulations to test the effect of GS-458967 on the electrocardiogram and stability of reentrant arrhythmia in the setting of acquired long QT. Results from their computer simulations show that specific INa,L block by GS-458967 is able to normalize AP duration, reduce cell- and tissue-level indicators of arrhythmogenesis, and prevent sustained reentry. Interestingly, rather than relying on AP duration as the sole cellular indicator of arrhythmogenesis, the team assessed a palette of quantitative measures (so called “TRIaD”): 1) AP Triangulation; 3) Reverse use dependence 2) beat-to-beat Instability; and 4) Dispersion of AP duration. They were able to show that late INa,L block improves all TRIaD measures with respect to cellular proarrhythmia. Another noteworthy aspect of their approach is that rather than simulating drug effects on a single “average” AP profile, they examine the response of a simulated population of 3
ACCEPTED MANUSCRIPT cells, reflecting the growing awareness in the field of the importance of physiological variability in arrhythmia phenotypes and response to therapy [18-20]. Finally, perhaps most compelling
PT
about the study is its demonstration of how modeling and simulation may be used to probe the parameter space of drug-channel interactions at multiple scales in a way that is simply not
RI
feasible experimentally.
SC
We find ourselves in an exciting time as modeling and simulation together with advances
NU
in genetics and high throughput technologies are bringing personalized medicine into the realm of possibility. The potential impact of computational approaches is reflected in the increased
MA
push for integrated in silico approaches by both private industry and federal regulatory agencies [9, 21]. In this vein, the work from the Clancy lab represents a larger effort in the community to
D
explore the ability of biophysical computational modeling to assist with high throughput
TE
screening of drugs [22-25]. As demonstrated by the elegant studies of the Clancy group and others, modeling should be considered an essential component of the drug screening process
AC CE P
due its ability to tackle multi-scale and highly nonlinear problems. For example, biophysical computational studies can easily assess a full range of cellular and tissue factors (e.g. “TRIaD” screen) that would be impossible to assess in a high throughput manner experimentally. Furthermore, modeling serves as an indispensable tool for linking disparate experimental and simulation data across temporal and spatial scales.
Continued development of integrative
approaches combining modeling and experiment, such as the one implemented by Yang et al., has the potential to revolutionize the drug development process. Not to say that modeling is currently, or will ever be, a panacea for the many challenges facing drug risk stratification and management. There remains a real concern that, despite the advancements in modeling, the field is not yet ready for increased reliance on biophysical modeling and simulation in the screening process. Related to this larger concern is the valid question about whether existing biophysical models are accurate enough to improve stratification/outcomes, and whether the
4
ACCEPTED MANUSCRIPT benefit of using detailed physiological models is worth the computational cost compared to statistical approaches that worry less about biophysical mechanism [26]. Of course, any model
PT
requires simplification of the physiological system, which may introduce considerable error into the simulated results. In the case of the difficult task of simulating drug-channel interactions,
RI
potential confounders are numerous, including the off-target effects of drug metabolites,
SC
unresolved mode of drug action on the channel, and cell/organ/patient heterogeneity [27, 28]. In the face of these and other considerable challenges, our best hope is an interdisciplinary
NU
approach that draws from the entire armory of tools at our disposal, including mathematical
MA
modeling and computer simulation, to improve outcomes across a wide range of human
D
disease.
TE
Disclosures: The authors have nothing to disclose.
AC CE P
Acknowledgements: Authors are supported by NIH HL129766 (to BO) and HL114893 (to TJH), the James S. McDonnell Foundation, the Saving Tiny Hearts Society, the Ross Heart Hospital and Davis Heart and Lung Research Institute.
References [1] Schwartz PJ, Woosley RL. Predicting the Unpredictable: Drug-Induced QT Prolongation and Torsades de Pointes. J Am Coll Cardiol. 2016;67:1639-50. [2] Woosley RL, Whyte J, Mohamadi A, Romero K. Medical decision support systems and therapeutics: The role of autopilots. Clin Pharmacol Ther. 2016;99:161-4. [3] Roden DM. Drug-induced prolongation of the QT interval. N Engl J Med. 2004;350:1013-22. [4] Johannesen L, Vicente J, Mason JW, Erato C, Sanabria C, Waite-Labott K, et al. Late sodium current block for drug-induced long QT syndrome: Results from a prospective clinical trial. Clin Pharmacol Ther. 2016;99:214-23. [5] Yang P-C, El-Bizri N, Romero L, Giles WR, Rajamani S, Belardinelli L, et al. A computational model predicts adjunctive pharmacotherapy for cardiac safety via selective inhibition of the late cardiac Na current. J Mol Cell Cardiol. 2016:In Press.
5
ACCEPTED MANUSCRIPT
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TE
D
MA
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SC
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PT
[6] Sanguinetti MC, Jiang C, Curran ME, Keating MT. A mechanistic link between an inherited and an acquired cardiac arrhythmia: HERG encodes the IKr potassium channel. Cell. 1995;81:299-307. [7] Sanguinetti MC, Tristani-Firouzi M. hERG potassium channels and cardiac arrhythmia. Nature. 2006;440:463-9. [8] Antzelevitch C, Belardinelli L, Zygmunt AC, Burashnikov A, Di Diego JM, Fish JM, et al. Electrophysiological effects of ranolazine, a novel antianginal agent with antiarrhythmic properties. Circulation. 2004;110:904-10. [9] Johannesen L, Vicente J, Mason JW, Sanabria C, Waite-Labott K, Hong M, et al. Differentiating drug-induced multichannel block on the electrocardiogram: randomized study of dofetilide, quinidine, ranolazine, and verapamil. Clin Pharmacol Ther. 2014;96:549-58. [10] Martin RL, McDermott JS, Salmen HJ, Palmatier J, Cox BF, Gintant GA. The utility of hERG and repolarization assays in evaluating delayed cardiac repolarization: influence of multichannel block. J Cardiovasc Pharmacol. 2004;43:369-79. [11] Shimizu W, Antzelevitch C. Sodium channel block with mexiletine is effective in reducing dispersion of repolarization and preventing torsade des pointes in LQT2 and LQT3 models of the long-QT syndrome. Circulation. 1997;96:2038-47. [12] Antzelevitch C, Nesterenko V, Shryock JC, Rajamani S, Song Y, Belardinelli L. The role of late INa in development of cardiac arrhythmias. Handb Exp Pharmacol. 2014;221:137-68. [13] Belardinelli L, Giles WR, Rajamani S, Karagueuzian HS, Shryock JC. Cardiac late Na+ current: proarrhythmic effects, roles in long QT syndromes, and pathological relationship to CaMKII and oxidative stress. Heart Rhythm. 2015;12:440-8. [14] Frommeyer G, Milberg P, Maier LS, Eckardt L. Late sodium current inhibition: the most promising antiarrhythmic principle in the near future? Curr Med Chem. 2014;21:1271-80. [15] Unudurthi SD, Hund TJ. Late sodium current dysregulation as a causal factor in arrhythmia. Expert Rev Cardiovasc Ther. 2016;14:545-7. [16] Glynn P, Musa H, Wu X, Unudurthi SD, Little S, Qian L, et al. Voltage-gated sodium channel phosphorylation at Ser571 regulates late current, arrhythmia, and cardiac function in vivo. Circulation. 2015;132:567-77. [17] Koval OM, Snyder JS, Wolf RM, Pavlovicz RE, Glynn P, Curran J, et al. Ca2+/calmodulindependent protein kinase II-based regulation of voltage-gated Na+ channel in cardiac disease. Circulation. 2012;126:2084-94. [18] Onal B, Hund TJ. Physiological variability and atrial fibrillation therapy: Insights from population-based mathematical modeling. Heart Rhythm. 2016:In press. [19] Muszkiewicz A, Britton OJ, Gemmell P, Passini E, Sanchez C, Zhou X, et al. Variability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm. Prog Biophys Mol Biol. 2016;120:11527. [20] Sarkar AX, Christini DJ, Sobie EA. Exploiting mathematical models to illuminate electrophysiological variability between individuals. J Physiol. 2012;590:2555-67. [21] Sager PT, Gintant G, Turner JR, Pettit S, Stockbridge N. Rechanneling the cardiac proarrhythmia safety paradigm: a meeting report from the Cardiac Safety Research Consortium. Am Heart J. 2014;167:292-300. [22] Davies MR, Wang K, Mirams GR, Caruso A, Noble D, Walz A, et al. Recent developments in using mechanistic cardiac modelling for drug safety evaluation. Drug Discov Today. 2016;21:924-38. [23] Di Veroli GY, Davies MR, Zhang H, Abi-Gerges N, Boyett MR. High-throughput screening of drug-binding dynamics to HERG improves early drug safety assessment. Am J Physiol Heart Circ Physiol. 2013;304:H104-17. [24] Yuan Y, Bai X, Luo C, Wang K, Zhang H. The virtual heart as a platform for screening drug cardiotoxicity. Br J Pharmacol. 2015;172:5531-47. 6
ACCEPTED MANUSCRIPT
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TE
D
MA
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[25] Davies MR, Mistry HB, Hussein L, Pollard CE, Valentin JP, Swinton J, et al. An in silico canine cardiac midmyocardial action potential duration model as a tool for early drug safety assessment. Am J Physiol Heart Circ Physiol. 2012;302:H1466-80. [26] Mistry HB, Davies MR, Di Veroli GY. A new classifier-based strategy for in-silico ionchannel cardiac drug safety assessment. Front Pharmacol. 2015;6:59. [27] Wisniowska B, Mendyk A, Fijorek K, Polak S. Computer-based prediction of the drug proarrhythmic effect: problems, issues, known and suspected challenges. Europace. 2014;16:724-35. [28] Potet F, Vanoye CG, George AL, Jr. Use-Dependent Block of Human Cardiac Sodium Channels by GS967. Mol Pharmacol. 2016;90:52-60.
7