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
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
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 data- set, and qNet showed the same high performance of 0.7 or more on the training drug dataset, the verified drug data- set, and the entire drug dataset.
Abstract
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 data- set, and qNet showed the same high performance of 0.7 or more on the training drug dataset, the verified drug data- set, and the entire drug dataset.
참고문헌 (17)
- Bernard Fermini, "The impact of drug-induced QT interval prolongation on drug discovery and development", Nature Reviews Drug Discovery, 2(6), pp.439~447 (2003), Springer Science and Business Media LLC[DOI]
- David G. Strauss, "Comprehensive In Vitro Proarrhythmia Assay (CiPA) Update from a Cardiac Safety Research Consortium", Health and Environmental Sciences Institute, Therapeutic Innovation & Regulatory Science, pp.2019, 53(4)[DOI]
- Bernard Fermini, "A New Perspective in the Field of Cardiac Safety Testing through the Comprehensive In Vitro Proarrhythmia Assay Paradigm", SLAS Discovery, 21(1), pp.1~11 (2016), Elsevier BV[DOI]
- Thomas Colatsky, "The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative — Update on progress", Journal of Pharmacological and Toxicological Methods, 81, pp.15~20 (2016), Elsevier BV[DOI]
- Sara Dutta, "Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment", Frontiers in Physiology, 8 (2017), Frontiers Media SA[DOI]
- Beth McMillan, "Early afterdepolarisation tendency as a simulated pro-arrhythmic risk indicator", Toxicology Research, 6(6), pp.912~921 (2017), Oxford University Press (OUP)[DOI]
- Gary R. Mirams, "Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk", Cardiovascular Research, 91(1), pp.53~61 (2011), Oxford University Press (OUP)[DOI]
- Jordi Llopis-Lorente, "InSilico Classifiers for the Assessment of Drug Proarrhythmicity", Journal of Chemical Information and Modeling, 60(10), pp.5172~5187 (2020), American Chemical Society (ACS)[DOI]
- Thomas O'Hara, "Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation", PLoS Computational Biology, 7(5), pp.e1002061~ (2011), Public Library of Science (PLoS)[DOI]
- Xiaomei Han, "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", Journal of Pharmacological and Toxicological Methods, 105, pp.106890~ (2020), Elsevier BV[DOI]
- William J. Crumb, "An evaluation of 30 clinical drugs against the comprehensive in vitro proarrhythmia assay (CiPA) proposed ion channel panel", Journal of Pharmacological and Toxicological Methods, 81, pp.251~262 (2016), Elsevier BV[DOI]
- Kelly C. Chang, "Uncertainty Quantification Reveals the Importance of Data Variability and Experimental Design Considerations for in Silico Proarrhythmia Risk Assessment", Frontiers in Physiology, 8 (2017), Frontiers Media SA[DOI]
- Jaimit Parikh, "Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment", Frontiers in Pharmacology, 10 (2019), Frontiers Media SA[DOI]
- K. Ahmad, "Drug-induced QT prolongation and proarrhythmia: an inevitable link?", Europace, 9(Supplement 4), pp.iv16~iv22 (2007), Oxford University Press (OUP)[DOI]
- M Cummins Lancaster, "Improved Prediction of Drug-Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms", Clinical Pharmacology & Therapeutics, 100(4), pp.371~379 (2016), Wiley[DOI]
- Zhihua Li, "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", Circulation: Arrhythmia and Electrophysiology, 10(2) (2017), Ovid Technologies (Wolters Kluwer Health)[DOI]
- Zhihua Li, "Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative", Clin. Pharmacol. Ther, 105(2), pp.466~475 (2018), Wiley[DOI]