Browse > Article
http://dx.doi.org/10.9718/JBER.2022.43.5.331

Sensitivity Analysis of dVm/dtMax_repol to Ion Channel Conductance for Prediction of Torsades de Pointes Risk  

Jeong, Da Un (Department of IT Convergence Engineering, Kumoh National Institute of Technology)
Yoo, Yedam (Department of IT Convergence Engineering, Kumoh National Institute of Technology)
Marcellinus, Aroli (Department of IT Convergence Engineering, Kumoh National Institute of Technology)
Lim, Ki Moo (Department of IT Convergence Engineering, Kumoh National Institute of Technology)
Publication Information
Journal of Biomedical Engineering Research / v.43, no.5, 2022 , pp. 331-340 More about this Journal
Abstract
Early afterdepolarization (EAD), a significant cause of fatal ventricular arrhythmias including Torsade de Pointes (TdP) in long QT syndromes, is a depolarizing afterpotential at the plateau or repolarization phase in action potential (AP) profile early before completing one pace. AP duration prolongation is related to EAD but is not necessarily accounted for EAD. Several computational studies suggested EAD can form from an abnormality in the late plateau and/or repolarization phase of AP shape. In this sense, we hypothesized the slope during repolarization has the characteristics to predict TdP risk, mainly focusing on the maximum slope during repolarization (dVm/dtmax_repol). This study aimed to predict the sensitivity of dVm/dtmax_repol to ion channel conductances as a TdP risk metric through a population simulation considering multiple effects of simultaneous reduction in six ion channel conductances of gNaL, gKr, gKs, gto, gK1, and gCaL. Additionally, we verified the availability of dVm/dtmax_repol for TdP risk prediction through the correlation analysis with qNet, the representative TdP metric. We performed the population simulations based on the methodology of Gemmel et al. using the human ventricular myocyte model of Dutta et al. Among the sixion channel conductances, dVm/dtmax_repol and qNet responded most sensitively to the change in gKr, followed by gNaL. Furthermore, dVm/dtmax_repol showed a statistically significant high negative correlation with qNet. The dVm/dtmax_repol values were significantly different according to three TdP risk levels of high, intermediate, and low by qNet (p<0.001). In conclusion, we suggested dVm/dtmax_repol as a new biomarker metric for TdP risk assessment.
Keywords
$dVm/dt_{max_-repol}$; Torsades de Pointes (TdP); Population model; Ion channel conductance; Human ventricular cell model;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Cook, D, Brown, D, Alexander, R, March, R, Morgan, P, Satterthwaite, G, Pangalos, MN, Lessons learned from the fate of AstraZeneca's drug pipeline: A five-dimensional framework. Nat. Rev. Drug Discov. Nature Publishing Group. 2014;13(6):419-431.   DOI
2 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. Mosby, Inc. 2014;167(3):292-300.   DOI
3 Crumb, WJ, Vicente, J, Johannesen, L, Strauss, DG, An evaluation of 30 clinical drugs against the comprehensive in vitro proarrhythmia assay (CiPA) proposed ion channel panel. J. Pharmacol. Toxicol. Methods. Elsevier Inc. 2016;81:251-262.   DOI
4 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(19):3819-3833.   DOI
5 Okada, JI, Yoshinaga, T, Kurokawa, J, Washio, T, Furukawa, T, Sawada, K, Sugiura, S, Hisada, T, Screening system for drug-induced arrhythmogenic risk combining a patch clamp and heart simulator. Sci. Adv. 2015;1(4):1-8.
6 Dutta, S, Chang, KC, Beattie, KA, Sheng, J, Tran, PN, Wu, WW, Wu, M, Strauss, DG, Colatsky, T, Li, Z, Optimization of an in silico cardiac cell model for proarrhythmia risk assessment. Front. Physiol. 2017;8(AUG):1-15.
7 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(October):1-18.   DOI
8 ICH Guideline, Guideline S7A Safety pharmacology studies for human pharmaceuticals. Fed. Regist. 2000;(66):36791-36792.
9 Thomas, G, Killeen, MJ, Grace, AA, Huang, CLH, Pharmacological separation of early afterdepolarizations from arrhythmogenic substrate in ΔKPQ Scn5a murine hearts modelling human long QT 3 syndrome. Acta Physiol. 2008;192(4):505-517.   DOI
10 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. 2019;105(2):466-475.   DOI
11 Yim, DS, Five years of the cipa project (2013-2018) - what did we learn?. Transl. Clin. Pharmacol. 2018;26(4):145-149.   DOI
12 Guo, D, Liu, Q, Liu, T, Elliott, G, Gingras, M, Kowey, PR, Yan, GX, Electrophysiological properties of HBI-3000: A new antiarrhythmic agent with multiple-channel blocking properties in human ventricular myocytes. J. Cardiovasc. Pharmacol. 2011;57(1):79-85.   DOI
13 Tomek, J, Bueno-Orovio, A, Passini, E, Zhou, X, Minchole, A, Britton, O, Bartolucci, C, Severi, S, Shrier, A, Virag, L, Varro, A, Rodriguez, B, Development, calibration, and validation of a novel human ventricular myocyte model in health, disease, and drug block. Elife. 2019;8:1-48.
14 Gemmell, P, Burrage, K, Rodriguez, B, Quinn, TA, Population of computational rabbit-specific ventricular action potential models for investigating sources of variability in cellular repolarisation. PLoS One. 2014;9(2):15-19.
15 Han, X, Samieegohar, M, Ridder, BJ, Wu, WW, Randolph, A, Tran, P, Sheng, J, 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, Strauss, DG, Li, Z, A general procedure to select calibration drugs for lab-specific validation and calibration of proarrhythmia risk prediction models: An illustrative example using the CiPA model. J. Pharmacol. Toxicol. Methods. Elsevier. 2020;105(February):106890.   DOI
16 Llopis-Lorente, J, Gomis-Tena, J, Cano, J, Romero, L, Saiz, J, Trenor, B, In silico classifiers for the assessment of drug proarrhythmicity. J. Chem. Inf. Model. 2020;60(10):5172-5187.   DOI
17 Weiss, JN, Garfinkel, A, Karagueuzian, HS, Chen, PS, Qu, Z, Early afterdepolarizations and cardiac arrhythmias. Hear. Rhythm. Elsevier Inc. 2010;7(12):1891-1899.   DOI
18 Peng, W, Clutter-Based Dimension Reordering in Multi-Dimensional Data Visualization., 2005.
19 Peng, W, Ward, MO, Rundensteiner, EA, Clutter reduction in multi-dimensional data visualization using dimension reordering., in Proceedings - IEEE Symposium on Information Visualization, INFO VIS, 2004.
20 Parikh, J, Gurev, V, Rice, JJ, Novel two-step classifier for Torsades de Pointes risk stratification from direct features. Front. Pharmacol. 2017;8(NOV):1-18.
21 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):1-11.
22 Onakpoya, IJ, Heneghan, CJ, Aronson, JK, Post-marketing withdrawal of 462 medicinal products because of adverse drug reactions: A systematic review of the world literature. BMC Med. BMC Medicine. 2016;14(1):1-11.   DOI
23 Luo, CH, Rudy, Y, A dynamic model of the cardiac ventricular action potential: I. Simulations of ionic currents and concentration changes. Circ. Res. 1994;74(6):1071-1096.   DOI
24 J.R., H, A., U, Ionic Basis of the Different Action Potential Configurations of Single Guinea-pig Atrial and Ventricular Myocytes. J. Physiol. 1985;368(1):525-544.   DOI
25 Akanda, N, Molnar, P, Stancescu, M, Hickman, JJ, Analysis of toxin-induced changes in action potential shape for drug development. J. Biomol. Screen. 2009;14(10):1228-1235.   DOI
26 Taylor, AL, Hickey, TJ, Prinz, AA, Marder, E, Structure and visualization of high-dimensional conductance spaces. J. Neurophysiol. 2006;96(2):891-905.   DOI
27 Yoo, Y, Jeong, DU, Marcellinus, A, Lim, KM, 약물의염전성 부정맥유발예측지표로서심장의전기생리학적특징값들의 검증 Verification of Cardiac Electrophysiological Features as a Predictive Indicator of Drug-Induced Torsades de pointes. J. Biomed. Eng. Res. 2022;43(1):19-26.
28 Chang, KC, Dutta, S, Mirams, GR, Beattie, KA, Sheng, J, Tran, PN, Wu, M, Wu, WW, Colatsky, T, Strauss, DG, Li, Z, Uncertainty quantification reveals the importance of data variability and experimental design considerations for in silico proarrhythmia risk assessment. Front. Physiol. 2017;8(NOV):1-17.
29 Sarkar, AX, Sobie, EA, Quantification of repolarization reserve to understand interpatient variability in the response to proarrhythmic drugs: A computational analysis. Hear. Rhythm. Elsevier Inc. 2011;8(11):1749-1755.   DOI
30 Qauli, AI, Marcellinus, A, Lim, KM, Sensitivity Analysis of Ion Channel Conductance on Myocardial Electromechanical Delay: Computational Study. Front. Physiol. 2021;12(August):1-18.
31 O'Hara, T, Virag, L, Varro, A, Rudy, Y, Simulation of the undiseased human cardiac ventricular action potential: Model formulation and experimental validation. PLoS Comput. Biol. 2011;7(5):1-29.
32 Yap, YG, Gamm, AJ, Drug induced QT prolongation and Torsades de Pointes. Heart. 2003;89(11):1363-1372.   DOI
33 Yao, X, Anderson, DL, Ross, SA, Lang, DG, Desai, BZ, Cooper, DC, Wheelan, P, McIntyre, MS, Bergquist, ML, MacKenzie, KI, Becherer, JD, Hashim, MA, Predicting QT prolongation in humans during early drug development using hERG inhibition and an anaesthetized guinea-pig model. Br. J. Pharmacol. 2008;154(7):1446-1456.   DOI
34 Colatsky, T, Fermini, B, Gintant, G, Pierson, JB, Sager, P, Sekino, Y, Strauss, DG, Stockbridge, N, The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative - Update on progress. J. Pharmacol. Toxicol. Methods. The Authors. 2016;81:15-20.   DOI
35 Waring, MJ, Arrowsmith, J, Leach, AR, Leeson, PD, Mandrell, S, Owen, RM, Pairaudeau, G, Pennie, WD, Pickett, SD, Wang, J, Wallace, O, Weir, A, An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Rev. Drug Discov. Nature Publishing Group. 2015;14(7):475-486.   DOI
36 Fermini, B, Hancox, JC, Abi-Gerges, N, Bridgland-Taylor, M, Chaudhary, KW, Colatsky, T, Correll, K, Crumb, W, Damiano, B, Erdemli, G, Gintant, G, Imredy, J, Koerner, J, Kramer, J, Levesque, P, Li, Z, Lindqvist, A, Obejero-Paz, CA, Rampe, D, Sawada, K, Strauss, DG, Vandenberg, JI, A new perspective in the field of cardiac safety testing through the comprehensive in vitro proarrhythmia assay paradigm. J. Biomol. Screen. 2016;21(1):1-11.   DOI