다형 심실빈맥의 예측을 위한 dVm/dtMax_repol의 이온채널 전도도에 대한 민감도 분석

키워드

참고문헌 (36)

1. Yap, "Drug induced QT prolongation and Torsades de Pointes", Heart, 89(11), pp.1363~1372 (2003)
2. Onakpoya, "Post-marketing withdrawal of 462 medicinal products because of adverse drug reactions: A systematic review of the world literature", BMC Medicine, 14(1), pp.1~11 (2016)
3. ICH Guideline, "Guideline S7A Safety pharmacology studies for human pharmaceuticals", Fed. Regist, 66, pp.36791~36792
4. Yao, X, "Predicting QT prolongation in humans during early drug development using hERG inhi-bition and an anaesthetized guinea-pig model", Br. J. Pharma-col, 154(7), pp.1446~1456 (2008)
5. Thomas, G, "Pharma-cological separation of early afterdepolarizations from arrhyth-mogenic substrate in ΔKPQ Scn5a murine hearts modelling human long QT 3 syndrome", Acta Physiol, 192(4), pp.505~517 (2008)
6. Li, Z, "Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Pre-diction Under the CiPA Initiative", Clin. Pharmacol. Ther, 105(2), pp.466~475 (2019)
7. Colatsky, T, "The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative − Update on progress", J. Pharmacol. Toxicol. Methods. The Authors, 81, pp.15~20 (2016)
8. 임동석, "Five years of the CiPA project (2013–2018) - what did we learn?", Translational and Clinical Pharmacology, 26(4), pp.145~149 (2018), 대한임상약리학회[DOI]
9. Cook, D, "Lessons learned from the fate of AstraZeneca’s drug pipeline: A five-dimensional frame-work", Nature Publishing Group, 13(6), pp.419~431 (2014)
10. Waring, MJ, "An analysis of the attrition of drug can-didates from four major pharmaceutical companies", Nat. Rev. Drug Discov, 14(7), pp.475~486 (2015), Nature Publishing Group
11. Sager, PT, "Rechanneling the cardiac proarrhythmia safety paradigm: A meeting report from the Cardiac Safety Research Consortium", Am. Heart J. Mosby, Inc, 167(3), pp.292~300 (2014)
12. Crumb, WJ, "An eval-uation of 30 clinical drugs against the comprehensive in vitro proarrhythmia assay (CiPA) proposed ion channel panel", J. Pharmacol. Toxicol. Methods. Elsevier Inc, 81, pp.251~262 (2016)
13. Fermini, B, "A new perspec-tive in the field of cardiac safety testing through the compre-hensive in vitro proarrhythmia assay paradigm", J. Biomol. Screen, 21(1), pp.1~11 (2016)
14. Passini, E, "Drug-induced shortening of the electrome-chanical window is an effective biomarker for in silico prediction of clinical risk of arrhythmias", Br. J. Pharmacol, 176(19), pp.3819~3833 (2019)
15. Okada, JI, "Screening system for drug-induced arrhythmogenic risk combining a patch clamp and heart simulator", Sci. Adv, 1(4), pp.1~8 (2015)
16. O’Hara, T, "Simulation of the undiseased human cardiac ventricular action potential: Model formulation and experimental validation", PLoS Comput. Biol, 7(5), pp.1~29 (2011)
17. Dutta, S, "Optimization of an in silico cardiac cell model for proarrhythmia risk assessment", Front. Physiol, 8, pp.1~15 (2017)
18. Parikh, J, "Global sensitiv-ity analysis of ventricular myocyte model-derived metrics for proarrhythmic risk assessment", Front. Pharmacol, 10, pp.1~18 (2019)
19. Llopis-Lorente, J, "In silico classifiers for the assessment of drug proarrhythmicity", Chem. Inf. Model, 60(10), pp.5172~5187 (2020)
20. Guo, D, "Electrophysiological properties of HBI-3000: A new antiarrhythmic agent with multiple-channel blocking proper-ties in human ventricular myocytes", J. Cardiovasc. Pharma-col, 57(1), pp.79~85 (2011)
21. Tomek, J, "Development, calibration, and validation of a novel human ventricular myocyte model in health, disease, and drug block", Elife, 8, pp.1~48 (2019)
22. Weiss, JN, "Early afterdepolarizations and cardiac arrhythmias", Hear. Rhythm. Elsevier Inc, 7(12), pp.1891~1899 (2010)
23. Gemmell, P, "Population of computational rabbit-specific ventricular action potential models for investigating sources of variability in cellular repolarisa-tion", PLoS One, 9(2), pp.15~19 (2014)
24. Peng, W, "Clutter-Based Dimension Reordering in Multi-Dimen-sional Data Visualization", ~ (2005)
25. Taylor, AL, "Structure and visualization of high-dimensional conductance spaces", J. Neurophysiol, 96(2), pp.891~905 (2006)
26. Peng, W, "Clutter reduction in multi-dimensional data visualization using dimension reor-dering", Proceedings - IEEE Symposium on Information Visualization, INFO VIS (2004)
27. Parikh, J, "Novel two-step classifier for Torsades de Pointes risk stratification from direct features", Front. Pharmacol, 8, pp.1~18 (2017)
28. 유예담, "약물의 염전성 부정맥 유발 예측 지표로서 심장의 전기생리학적 특징 값들의 검증", 의공학회지, 43(1), pp.19~26 (2022), 대한의용생체공학회
29. Han, X, "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, 105, pp.106890~ (2020)
30. Jeong, DU, "Val-idation of in silico biomarkers for drug screening through ordinal logistic regression", Front. Physiol, 13(1009647), pp.1~11 (2022)
31. Chang, KC, "Uncertainty quantification reveals the importance of data variability and experimental design considerations for in silico proarrhythmia risk assessment", Front. Physiol, 8, pp.1~17 (2017)
32. Luo, CH, "A dynamic model of the cardiac ventric-ular action potential: I. Simulations of ionic currents and con-centration changes", Circ. Res, 74(6), pp.1071~1096 (1994)
33. J.R., H, "Ionic Basis of the Different Action Potential Configurations of Single Guinea-pig Atrial and Ventricular Myocytes", J. Physiol, 368(1), pp.525~544 (1985)
34. Akanda, N, "Analysis of toxin-induced changes in action potential shape for drug development", J. Biomol. Screen, 14(10), pp.1228~1235 (2009)
35. Qauli, AI, "Sensitivity Analysis of Ion Channel Conductance on Myocardial Electromechanical Delay: Computational Study", Front. Physiol, 12, pp.1~18 (2021)
36. Sarkar, AX, "Quantification of repolarization reserve to understand interpatient variability in the response to proar-rhythmic drugs: A computational analysis", Hear. Rhythm. Else-vier Inc, 8(11), pp.1749~1755 (2011)

관련 논문AI추천