The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk
Open accessAbstract
AIMS: The level of inhibition of the human Ether-à-go-go-related gene (hERG) channel is one of the earliest preclinical markers used to predict the risk of a compound causing Torsade-de-Pointes (TdP) arrhythmias. While avoiding the use of drugs with maximum therapeutic concentrations within 30-fold of their hERG inhibitory concentration 50% (IC(50)) values has been suggested, there are drugs that are exceptions to this rule: hERG inhibitors that do not cause TdP, and drugs that can cause TdP but are not strong hERG inhibitors. In this study, we investigate whether a simulated evaluation of multi-channel effects could be used to improve this early prediction of TdP risk. METHODS AND RESULTS: We collected multiple ion channel data (hERG, Na, L-type Ca) on 31 drugs associated with varied risks of TdP. To integrate the information on multi-channel block, we have performed simulations with a variety of mathematical models of cardiac cells (for rabbit, dog, and human ventricular myocyte models). Drug action is modelled using IC(50) values, and therapeutic drug concentrations to calculate the proportion of blocked channels and the channel conductances are modified accordingly. Various pacing protocols are simulated, and classification analysis is performed to evaluate the predictive power of the models for TdP risk. We find that simulation of action potential duration prolongation, at therapeutic concentrations, provides improved prediction of the TdP risk associated with a compound, above that provided by existing markers. CONCLUSION: The suggested calculations improve the reliability of early cardiac safety assessments, beyond those based solely on a hERG block effect.
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SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers
Hindmarsh, A. C., Brown, P. N., Grant, K. E., Lee, S. L., Serban, R., Shumaker, D. E., & Woodward, C. S. (2005) · ACM Transactions on Mathematical Software · doi:10.1145/1089014.1089020 · Cited by 2,556
A molecular basis for cardiac arrhythmia: HERG mutations cause long QT syndrome
Curran, M. E., Splawski, I., Timothy, K. W., Vincen, G. M., Green, E. D., & Keating, M. T. (1995) · Cell · doi:10.1016/0092-8674(95)90358-5 · Cited by 2,259
REDFERN, W., CARLSSON, L., DAVIS, A., LYNCH, W., MACKENZIE, I., PALETHORPE, S., SIEGL, P., STRANG, I., SULLIVAN, A., & WALLIS, R. (2003) · Cardiovascular Research · doi:10.1016/s0008-6363(02)00846-5 · Cited by 1,532
Alternans and spiral breakup in a human ventricular tissue model
ten Tusscher, K. H. W. J., & Panfilov, A. V. (2006) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00109.2006 · Cited by 1,122
Brugada, R., Brugada, J., Antzelevitch, C., Kirsch, G. E., Potenza, D., Towbin, J. A., & Brugada, P. (2000) · Circulation · doi:10.1161/01.cir.101.5.510 · Cited by 772
A Mathematical Treatment of Integrated Ca Dynamics within the Ventricular Myocyte
Shannon, T. R., Wang, F., Puglisi, J., Weber, C., & Bers, D. M. (2004) · Biophysical Journal · doi:10.1529/biophysj.104.047449 · Cited by 541
A novel computational model of the human ventricular action potential and Ca transient
Grandi, E., Pasqualini, F. S., & Bers, D. M. (2010) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2009.09.019 · Cited by 450
A Rabbit Ventricular Action Potential Model Replicating Cardiac Dynamics at Rapid Heart Rates
Mahajan, A., Shiferaw, Y., Sato, D., Baher, A., Olcese, R., Xie, L.-H., Yang, M.-J., Chen, P.-S., Restrepo, J. G., Karma, A., et al. (2008) · Biophysical Journal · doi:10.1529/biophysj.106.98160 · Cited by 403
Cardiac Action and Pacemaker Potentials based on the Hodgkin-Huxley Equations
NOBLE, D. (1960) · Nature · doi:10.1038/188495b0 · Cited by 335
Self-citationHund, T. J., & Rudy, Y. (2004) · Circulation · doi:10.1161/01.cir.0000147231.69595.d3 · Cited by 327
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hERG K+Channels: Structure, Function, and Clinical Significance
Vandenberg, J. I., Perry, M. D., Perrin, M. J., Mann, S. A., Ke, Y., & Hill, A. P. (2012) · Physiological Reviews · doi:10.1152/physrev.00036.2011 · Cited by 696
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Mirams, G. R., Arthurs, C. J., Bernabeu, M. O., Bordas, R., Cooper, J., Corrias, A., Davit, Y., Dunn, S.-J., Fletcher, A. G., Harvey, D. G., et al. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002970 · Cited by 423
Self-citationEvolution of strategies to improve preclinical cardiac safety testing
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Metadata
- Authors
- Mirams, G. R., Cui, Y., Sher, A., Fink, M., Cooper, J., Heath, B. M., McMahon, N. C., Gavaghan, D. J., & Noble, D.
- Venue
- Cardiovascular Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-02-07
- Volume
- 91
- Issue
- 1
- Pages
- 53-61
- ISSN
- 0008-6363 (ISSN-L)
- 1755-3245
- OpenAlex
- W2137618884
- Cited by
- 326
- References
- 31
- FWCI
- 15.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Mirams, G. R., Cui, Y., Sher, A., Fink, M., Cooper, J., Heath, B. M., McMahon, N. C., Gavaghan, D. J., & Noble, D. (2011). Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk. Cardiovascular Research. https://doi.org/10.1093/cvr/cvr044
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