대한의용생체공학회:의공학회지 (Journal of Biomedical Engineering Research)

  • 제43권1호
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  • Pages.19-26
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  • 2022
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  • 1229-0807(pISSN)
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  • 2288-9396(eISSN)
대한의용생체공학회 (The Korean Society of Medical and Biological Engineering)
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약물의 염전성 부정맥 유발 예측 지표로서 심장의 전기생리학적 특징 값들의 검증

Verification of Cardiac Electrophysiological Features as a Predictive Indicator of Drug-Induced Torsades de pointes

  • 유예담 (금오공과대학교 IT융복합공학과) ;
  • 정다운 (금오공과대학교 IT융복합공학과) ;
  • ;
  • 임기무 (금오공과대학교 IT융복합공학과)
  • Yoo, Yedam (Dept of IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Jeong, Da Un (Dept of IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Marcellinus, Aroli (Dept of IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Lim, Ki Moo (Dept of IT Convergence Engineering, Kumoh National Institute of Technology)
  • 투고 : 2021.11.02
  • 심사 : 2022.02.03
  • 발행 : 2022.02.28
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초록

The Comprehensive in vitro Proarrhythmic Assay(CiPA) project was launched for solving the hERG assay problem of being classified as high-risk groups even though they are low-risk drugs due to their high sensitivity. CiPA presented a protocol to predict drug toxicity using physiological data calculated based on the in-silico model. in this study, features calculated through the in-silico model are analyzed for correlation of changing action potential in the near future, and features are verified through predictive performance according to drug datasets. Using the O'Hara Rudy model modified by Dutta et al., Pearson correlation analysis was performed between 13 features(dVm/dtmax, APpeak, APresting, APD90, APD50, APDtri, Capeak, Caresting, CaD90, CaD50, CaDtri, qNet, qInward) calculated at 100 pacing, and between dVm/dtmax_repol calculated at 1,000 pacing, and linear regression analysis was performed on each of the 12 training drugs, 16 verification drugs, and 28 drugs. Indicators showing high coefficient of determination(R2) in the training drug dataset were qNet 0.93, AP resting 0.83, APDtri 0.78, Ca resting 0.76, dVm/dtmax 0.63, and APD90 0.61. The indicators showing high determinants in the validated drug dataset were APDtri 0.94, APD90 0.92, APD50 0.85, CaD50 0.84, qNet 0.76, and CaD90 0.64. Indicators with high coefficients of determination for all 28 drugs are qNet 0.78, APD90 0.74, and qInward 0.59. The indicators vary in predictive performance depending on the drug dataset, and qNet showed the same high performance of 0.7 or more on the training drug dataset, the verified drug dataset, and the entire drug dataset.

키워드

과제정보

본 연구는 금오공과대학교 학술연구비에 의하여 연구된 논문 임(202001600001).

참고문헌

  1. Fermini B, Fossa AA. "The impact of drug-induced QT interval prolongation on drug discovery and development," Nat. Rev. Drug Discov., 2003;2(6):439-447. doi: 10.1038/nrd1108.
  2. Strauss DG. et al., "Comprehensive In Vitro Proarrhythmia Assay (CiPA) Update from a Cardiac Safety Research Consortium / Health and Environmental Sciences Institute / FDA Meeting," Ther. Innov. Regul. Sci., 2019;53(4):519-525, doi: 10.1177/2168479018795117.
  3. Fermini B. et al., "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: 10.1177/1087057115594589.
  4. Colatsky T. et al., "The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative - Update on progress," J. Pharmacol. Toxicol. Methods, 2016;81:15-20, doi: 10.1016/j.vascn.2016.06.002.
  5. Dutta S. et al., "Optimization of an in silico cardiac cell model for proarrhythmia risk assessment," Front. Physiol., 2017;8:1-15, doi: 10.3389/fphys.2017.00616.
  6. McMillan B, Gavaghan DJ, Mirams GR. "Early afterdepolarisation tendency as a simulated pro-arrhythmic risk indicator," Toxicol. Res. (Camb)., 2017;6(6):912-921, doi: 10.1039/c7tx00141j.
  7. Mirams GR. et al., "Simulation of multiple ion channel block provides improved prediction of compounds' clinical torsadogenic risk," Cardiovasc. Res., 2011;91(1):53-61, doi: 10.1093/cvr/cvr044.
  8. Llopis-Lorente J, Gomis-Tena J, Cano J, Romero L, Saiz J, Trenor B. "InSilico Classifiers for the Assessment of Drug Proarrhythmicity," J. Chem. Inf. Model., 2020;60(10):5172-5187, doi: 10.1021/acs.jcim.0c00201.
  9. 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), doi: 10.1371/journal.pcbi.1002061.
  10. Han X. et al., "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, 2020;105:106890, doi: 10.1016/j.vascn.2020.106890.
  11. 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, 2016;81:251-262, doi: 10.1016/j.vascn.2016.03.009.
  12. Chang KC. et al., "Uncertainty quantification reveals the importance of data variability and experimental design considerations for in silico proarrhythmia risk assessment," Front. Physiol., 2017;8:1-17, doi: 10.3389/fphys.2017.00917.
  13. 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;10:1-18, doi: 10.3389/fphar.2019.01054.
  14. Ahmad K, Dorian P. "Drug-induced QT prolongation and proarrhythmia: an inevitable link?," Europace, 2007;9:4, doi: 10.1093/europace/eum167.
  15. Lancaster MC, Sobie EA. "Improved Prediction of DrugInduced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms," Clin. Pharmacol. Ther., 2016;100(4):371-379, doi: 10.1002/cpt.367.
  16. Li Z. et al., "Improving the in silico assessment of proarrhythmia risk by combining hERG (Human Ether-a-go-go-Related Gene) channel-drug binding kinetics and multichannel pharmacology," Circ. Arrhythmia Electrophysiol., 2017;10(2):1-12, doi: 10.1161/CIRCEP.116.004628.
  17. Li Z. et al., "Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative," Clin. Pharmacol. Ther., 2019;105(2):466-475, doi: 10.1002/cpt.1184.