Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment

Sara Dutta, Kelly C Chang, Kylie A Beattie, Jiansong Sheng, Phu N Tran, Wendy W Wu, Min Wu, David G Strauss, Thomas Colatsky, Zhihua Li, Sara Dutta, Kelly C Chang, Kylie A Beattie, Jiansong Sheng, Phu N Tran, Wendy W Wu, Min Wu, David G Strauss, Thomas Colatsky, Zhihua Li

Abstract

Drug-induced Torsade-de-Pointes (TdP) has been responsible for the withdrawal of many drugs from the market and is therefore of major concern to global regulatory agencies and the pharmaceutical industry. The Comprehensive in vitro Proarrhythmia Assay (CiPA) was proposed to improve prediction of TdP risk, using in silico models and in vitro multi-channel pharmacology data as integral parts of this initiative. Previously, we reported that combining dynamic interactions between drugs and the rapid delayed rectifier potassium current (IKr) with multi-channel pharmacology is important for TdP risk classification, and we modified the original O'Hara Rudy ventricular cell mathematical model to include a Markov model of IKr to represent dynamic drug-IKr interactions (IKr-dynamic ORd model). We also developed a novel metric that could separate drugs with different TdP liabilities at high concentrations based on total electronic charge carried by the major inward ionic currents during the action potential. In this study, we further optimized the IKr-dynamic ORd model by refining model parameters using published human cardiomyocyte experimental data under control and drug block conditions. Using this optimized model and manual patch clamp data, we developed an updated version of the metric that quantifies the net electronic charge carried by major inward and outward ionic currents during the steady state action potential, which could classify the level of drug-induced TdP risk across a wide range of concentrations and pacing rates. We also established a framework to quantitatively evaluate a system's robustness against the induction of early afterdepolarizations (EADs), and demonstrated that the new metric is correlated with the cell's robustness to the pro-EAD perturbation of IKr conductance reduction. In summary, in this work we present an optimized model that is more consistent with experimental data, an improved metric that can classify drugs at concentrations both near and higher than clinical exposure, and a physiological framework to check the relationship between a metric and EAD. These findings provide a solid foundation for using in silico models for the regulatory assessment of TdP risk under the CiPA paradigm.

Keywords: Comprehensive in vitro Proarrhythmia Assay (CiPA); Torsade-de-Pointes (TdP); drug block; in silico cardiac cell model; model optimization; proarrythmia risk; rapid delayed rectifier potassium current (IKr).

Figures

Figure 1
Figure 1
Steady state action potential duration (APD) rate dependency under the conditions of control (A), 10 μM mexiletine [late sodium current INaL block (B)], 1 μM nisoldipine [L-type calcium current ICaL block (C)], 1 μM HMR-1556 [slow rectifier potassium current IKs block (D)], 1 μM E-4031 [rapid rectifier potassium current IKr block (E)] and 100 μM BACl2 [inwardly rectifying potassium current IK1 block (F)] at varying cycle lengths (CLs) for the original dynamic IKr O'Hara Rudy model (original IKr-dyn ORd; dashed lines) and the optimized dynamic IKr O'Hara Rudy model (optimized IKr-dyn ORd model; solid line). Experimental data mean (symbol) and standard deviation (error bars) are from O'Hara et al. (2011). Control (A) shows APD at 90% (APD90; filled circles), 70% (APD70; filled triangles), 50% (APD50; filled squares) and 30% (APD30; plus sign) repolarization. All other panels show APD90.
Figure 2
Figure 2
Action potential (AP) (A), INaL (B), ICaL (C), IKr (D), IKs (E), IK1 (F) traces under control conditions for the original IKr-dyn ORd (dashed line) and the optimized IKr-dyn ORd (solid line) for CLs of 500 (red), 1,000 (green) and 2,000 (blue) ms.
Figure 3
Figure 3
Transmembrane voltage [Trans. voltage (A)], net current [Inet (B)], charge carried by Inet (C), INaL (D), Ito (E), ICaL (F), IKr (G), IKs (H), IK1 (I) traces for control (black solid line), ranolazine, a low TdP risk drug (green dashed line narrow spacing), cisapride, an intermediate TdP risk drug (blue dashed line normal spacing), and dofetilide, a high TdP risk drug (red dashed line wide spacing), at 25x Cmax for 2,000 ms CL using the optimized IKr-dyn ORd. Charge carried by Inet, INaL and IKr integrated from the beginning to the end of the AP beat (qX) are displayed on the graph.
Figure 4
Figure 4
Classification training error for a range of metrics [resting membrane potential (resting Vm), maximum upstroke velocity (dV/dtmax), peak membrane potential (peak Vm), APD50, APD90, action potential (AP) triangulation (APDtri), diastolic intracellular calcium concentration ([Ca2+]i) (diastolic Ca), peak [Ca2+]i (peak Ca), calcium transient duration at 50 and 90% of the amplitude (CaD50 and CaD90), calcium transient triangulation (Catri), change in amount of charge carried by INaL and ICaL (cqInward) and the charge carried by Inet at the end of the AP beat normalized to control (qNet)] for varying drug doses (0.5–25x Cmax) and varying CLs (1,000, 2,000, and 4,000 ms). Each box represents the mean (across 12 drugs) error (between predicted and known risk levels) for each metric at each concentration (0.5–25X Cmax). A training error of 0 represents perfect separation between the risk categories.
Figure 5
Figure 5
qNet for the 12 CiPA training compounds for a range of doses (0.5–25x Cmax) at a pacing rate of 2,000 ms. (A) Optimized IKr-dyn ORd; (B) A model variation without the incorporation of the IKr dynamic model (note this is the same model as in Dutta et al., 2016) and; (C) A model variation without the optimized channel conductances to accurately quantify block effects of individual currents (note this is the same model as in Li et al., 2017). Different TdP risk levels are color coded (high risk in red, intermediate risk in blue and low/no risk in green). Results are not shown once drug concentrations are high enough to induce early after depolarizations (EADs) (i.e., quinidine).
Figure 6
Figure 6
AP traces for mexiletine (A) at 1x Cmax (black solid line) and 10x Cmax (gray dashed line) without (left panel) and with 95% IKr reduction (right panel); verapamil (B) at 1x Cmax (black solid line) and 3x Cmax (gray dashed line) without (left panel) and with 98% IKr reduction (right panel); and ranolazine (black solid line) and cisapride (dashed gray line) (C) at 25x Cmax without (left panel) and with 75% IKr reduction (right panel) for a CL of 2,000 ms. Corresponding APD90 (ms) and qNet (μC/μF) values are reported in black for mexiletine 1x Cmax, verapamil 1x Cmax and ranolazine 25x Cmax and in gray for mexiletine 10x Cmax, verapamil 3x Cmax and cisapride 25x Cmax. Note the IKr reduction (simulated by scaling the IKr maximum conductance) is applied in addition to the drug block effect and is used to assess the system's robustness against EADs (see Results section).

References

    1. Antzelevitch C., Belardinelli L., Zygmunt A., Burashnikov A., Di Diego J., Fish J., et al. . (2004). Electrophysiological effects of ranolazine, a novel antianginal agent with antiarrhythmic properties. Circulation 110, 904–910. 10.1161/01.CIR.0000139333.83620.5D
    1. Armoundas A., Hobai I., Tomaselli G., Winslow R., O'Rourke B. (2003). Role of sodium-calcium exchanger in modulating the action potential of ventricular myocytes from normal and failing hearts. Circ. Res. 93, 46–53. 10.1161/01.RES.0000080932.98903.D8
    1. Britton O. J., Bueno-Orovio A., Virág L., Varró A., Rodriguez B. (2017). The Electrogenic Na+/K+ pump is a key determinant of repolarization abnormality susceptibility in human ventricular cardiomyocytes: a population-based simulation study. Front. Physiol. 8:278. 10.3389/fphys.2017.00278
    1. Chang M., de Lange E., Calmettes G., Garfinkel A., Qu Z., Weiss J. (2013). Pro- and antiarrhythmic effects of ATP-sensitive potassium current activation on reentry during early afterdepolarization-mediated arrhythmias. Heart Rhythm 10, 575–582. 10.1016/j.hrthm.2012.12.017
    1. Colatsky T., Fermini B., Gintant G., Pierson J., Sager P., Sekino Y., et al. . (2016). The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative—Update on progress. J. Pharmacol. Toxicol. Methods 81, 15–20. 10.1016/j.vascn.2016.06.002
    1. Crumb W., Vicente J., Johannesen L., Strauss D. (2016). An evaluation of 30 clinical drugs against the comprehensive in vitro proarrhythmia assay (CiPA) proposed ion channel panel. J. Pharmacol. Toxicol. Methods. 81, 251–262. 10.1016/j.vascn.2016.03.009
    1. Cummins M., Dalal P., Bugana M., Severi S., Sobie E. (2014). Comprehensive analyses of ventricular myocyte models identify targets exhibiting favorable rate dependence. PLoS Comput. Biol. 10:e1003543. 10.1371/journal.pcbi.1003543
    1. Di Veroli G., Davies M., Zhang H., Abi-Gerges N., Boyett M. (2013). High-throughput screening of drug-binding dynamics to HERG improves early drug safety assessment. Am. J. Physiol. Heart Circ. Physiol. 304, H104–H117. 10.1152/ajpheart.00511.2012
    1. Di Veroli G., Davies M., Zhang H., Abi-Gerges N., Boyett M. (2014). hERG inhibitors with similar potency but different binding kinetics do not pose the same proarrhythmic risk: implications for drug safety assessment. J. Cardiovasc. Electrophysiol. 25, 197–207. 10.1111/jce.12289
    1. Dutta S., Strauss D., Colatsky T., Li Z. (2016). Optimization of an in silico cardiac cell model for proarrhythmia risk assessment, in 2016 Computing in Cardiology Conference (CinC) (Vancouver, BC: ), 869–872.
    1. Fermini B., Hancox J., Abi-Gerges N., Bridgland-Taylor M., Chaudhary K., Colatsky T., et al. . (2016). A new perspective in the field of cardiac safety testing through the comprehensive in vitro proarrhythmia assay paradigm. J. Biomol. Screen. 21, 1–11. 10.1177/1087057115594589
    1. Gintant G. (2008). Preclinical torsades-de-pointes screens: advantages and limitations of surrogate and direct approaches in evaluating proarrhythmic risk. Pharmacol. Ther. 119, 199–209. 10.1016/j.pharmthera.2008.04.010
    1. Grandi E., Pasqualini F., Bers D. (2010). A novel computational model of the human ventricular action potential and Ca transient. J. Mol. Cell. Cardiol. 48, 112–121. 10.1016/j.yjmcc.2009.09.019
    1. Gray R., Huelsing D. (2008). Excito-oscillatory dynamics as a mechanism of ventricular fibrillation. Heart Rhythm 5, 575–584. 10.1016/j.hrthm.2008.01.011
    1. Guo D., Zhao X., Wu Y., Liu T., Kowey P., Yan G.-X. (2007). L-type calcium current reactivation contributes to arrhythmogenesis associated with action potential triangulation. J. Cardiovasc. Electrophysiol. 18, 196–203. 10.1111/j.1540-8167.2006.00698.x
    1. Johannesen L., Vicente J., Mason J. W., Erato C., Sanabria C., Waite-Labott K., et al. . (2016). Late sodium current block for drug-induced long QT syndrome: results from a prospective clinical trial. Clin. Pharmacol. Ther. 99, 214–223. 10.1002/cpt.205
    1. Kallergis E., Goudis C., Simantirakis E., Kochiadakis G., Vardas P. (2012). Mechanisms, risk factors, and management of acquired long QT syndrome: a comprehensive review. ScientificWorldJournal. 2012:212178. 10.1100/2012/212178
    1. Kramer J., Obejero-Paz C., Myatt G., Kuryshev Y., Bruening-Wright A., Verducci J., et al. . (2013). MICE models: superior to the HERG model in predicting torsade de pointes. Sci. Rep. 3:2100. 10.1038/srep02100
    1. Kurata Y., Matsuda H., Hisatome I., Shibamoto T. (2008). Regional difference in dynamical property of sinoatrial node pacemaking: role of na+ channel current. Biophys. J. 95, 951–977. 10.1529/biophysj.107.112854
    1. Kurata Y., Tsumoto K., Hayashi K., Hisatome I., Tanida M., Kuda Y., et al. . (2017). Dynamical mechanisms of phase-2 early afterdepolarizations in human ventricular myocytes: insights from bifurcation analyses of two mathematical models. Am. J. Physiol. Heart Circ. Physiol. 312, H106–H127. 10.1152/ajpheart.00115.2016
    1. Kurita T., Ohe T., Marui N., Aihara N., Takaki H., Kamakura S., et al. . (1992). Bradycardia-induced abnormal QT prolongation in patients with complete atrioventricular block with torsades de pointes. Am. J. Cardiol. 69, 628–633. 10.1016/0002-9149(92)90154-Q
    1. Lancaster C., Sobie E. A. (2016). Improved prediction of drug-induced torsades de pointes through simulations of dynamics and machine learning algorithms. Clin. Pharmacol. Ther. 100, 371–379. 10.1002/cpt.367
    1. Li Z., Dutta S., Sheng J., Tran P. N., Wu W., Chang K., et al. . (2017). Improving the in silico assessment of proarrhythmia risk by combining herg (human ether-à-go-go-related gene) channel–drug binding kinetics and multichannel pharmacology. Circ. Arrhythm. Electrophysiol. 10:e004628. 10.1161/CIRCEP.116.004628
    1. Li Z., Zhou H., Lu Y., Colatsky T. (2014). A critical role for immune system response in mediating anti-influenza drug synergies assessed by mechanistic modeling. CPT Pharmacometrics Syst. Pharmacol. 3, 1–9. 10.1038/psp.2014.32
    1. Mann S., Imtiaz M., Winbo A., Rydberg A., Perry M., Couderc J.-P., et al. . (2016). Convergence of models of human ventricular myocyte electrophysiology after global optimization to recapitulate clinical long QT phenotypes. J. Mol. Cell. Cardiol. 100, 25–34. 10.1016/j.yjmcc.2016.09.011
    1. Mirams G., Cui Y., Sher A., Fink M., Cooper J., Heath B., et al. . (2011). Simulation of multiple ion channel block provides improved early prediction of compounds' clinical torsadogenic risk. Cardiovasc. Res. 91, 53–61. 10.1093/cvr/cvr044
    1. Nagy Z., Virág L., Tóth A., Biliczki P., Acsai K., Bányász T., et al. . (2004). Selective inhibition of sodium–calcium exchanger by SEA-0400 decreases early and delayed afterdepolarization in canine heart. Br. J. Pharmacol. 143, 827–831. 10.1038/sj.bjp.0706026
    1. O'Hara T., Virág L., Varró A., Rudy Y. (2011). Simulation of the undiseased human cardiac ventricular action potential: model formulation and experimental validation. PLoS Comput. Biol. 7:e1002061. 10.1371/journal.pcbi.1002061
    1. Orth P., Hesketh C., Mak C., Yang Y., Lin S., Beatch G., et al. . (2006). RSD1235 blocks late INa and suppresses early afterdepolarizations and torsades de pointes induced by class III agents. Cardiovasc. Res. 70, 486–496. 10.1016/j.cardiores.2006.01.026
    1. Qu Z., Xie L. H., Olcese R., Karagueuzian H. S., Chen P. S., Garfinkel A., et al. . (2013). Early afterdepolarizations in cardiac myocytes: beyond reduced repolarization reserve. Cardiovasc. Res. 99, 6–15. 10.1093/cvr/cvt,104
    1. R Core Team (2014). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; Available online at: .
    1. Redfern W. S., Carlsson L., Davis A. S., Lynch W. G., MacKenzie I., Palethorpe S., et al. . (2003). Relationships between preclinical cardiac electrophysiology, clinical QT interval prolongation and torsade de pointes for a broad range of drugs: evidence for a provisional safety margin in drug development. Cardiovasc. Res. 58, 32–45. 10.1016/S0008-6363(02)00846-5
    1. Roden D. M. (1998). Taking the “idio” out of “idiosyncratic”: predicting torsades de pointes. Pacing Clin. Electrophysiol. 21, 1029–1034. 10.1111/j.1540-8159.1998.tb00148.x
    1. Saad M., Mahmoud A., Elgendy I., Richard Conti C. (2016). Ranolazine in cardiac arrhythmia. Clin. Cardiol. 39, 170–178. 10.1002/clc.22476
    1. Sager P., Gintant G., Turner R., Pettit S., Stockbridge N. (2014). Rechanneling the cardiac proarrhythmia safety paradigm: a meeting report from the Cardiac Safety Research Consortium. Am. Heart J. 167, 292–300. 10.1016/j.ahj.2013.11.004
    1. Soetaert K., Petzoldt T., Setzer R. W. (2010). Solving differential equations in R: package deSolve. J. Stat. Softw. 33, 1–25. 10.18637/jss.v033.i09
    1. ten Tusscher K. H., Panfilov A. V. (2006). Cell model for efficient simulation of wave propagation in human ventricular tissue under normal and pathological conditions. Phys. Med. Biol. 51, 6141–6156. 10.1088/0031-9155/51/23/014
    1. Tierney L., Rossini A. J., Li N., Sevcikova H. (2015). Snow: Simple Network of Workstations. Available online at: ;
    1. Volders P., Vos M., Szabo B., Sipido K., Marieke de G., Gorgels A., et al. . (2000). Progress in the understanding of cardiac early afterdepolarizations and torsades de pointes: time to revise current concepts. Cardiovasc. Res. 46, 376–392. 10.1016/S0008-6363(00)00022-5
    1. Vos M., Verduyn C., Gorgels A., Lipcsei G., Wellens H. (1995). Reproducible induction of early afterdepolarizations and torsade de pointes arrhythmias by D-sotalol and pacing in dogs with chronic atrioventricular block. Circulation 91, 864–872. 10.1161/01.CIR.91.3.864
    1. Weiss J., Garfinkel A., Karagueuzian H., Chen P. S., Qu Z. (2010). Early afterdepolarizations and cardiac arrhythmias. Heart Rhythm 7, 1891–1899. 10.1016/j.hrthm.2010.09.017
    1. Wu J., Wu J., Zipes D. (2002). Early Afterdepolarizations, U Waves, and Torsades de Pointes. Circulation 105, 675–676. 10.1161/hc0602.104458
    1. Xie Y., Sato D., Garfinkel A., Qu Z., Weiss J. (2010). So little source, so much sink: requirements for afterdepolarizations to propagate in tissue. Biophys. J. 99, 1408–1415. 10.1016/j.bpj.2010.06.042
    1. Yu H. (2002). Rmpi: parallel statistical computing in R. R News 2, 10–14. Available online at:

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