Simulating Drug-Induced Effects on the Heart: From Ion Channel to Body Surface Electrocardiogram

  • N. Zemzemi
  • M. O. Bernabeu
  • J. Saiz
  • B. Rodriguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6666)


The electrocardiogram (ECG) is widely used as a clinical tool for the evaluation of cardiac conditions caused by drugs, mutations and diseases. However, the ionic basis underlying changes in the ECG are often unclear. In the present study, we present a computational model of the human ECG capable of representing drug-induced effects from the ionic to the surface potential level. Bidomain simulations are conducted to simulate the electrophysiological activity of the heart and extracellular potentials in the whole body. Membrane kinetics are represented by the most recent version of a human action potential model, modified to include representation of HERG block by dofetilide, a known class III anti-arrhythmic drug with potential pro-arrhythmic effects. Simulation results are presented showing how dofetilide administration results in the prolongation of the action potential duration in the ventricles and the QT interval measured on the surface of the thorax, in agreement with clinical results. The state-of-the-art tools and methodologies presented here could be useful in the investigation and assessment of drug cardiotoxicity and can also be extended to the investigation of the effect of mutations or disease on the ECG.


Action Potential Duration Extracellular Potential Arrhythmic Risk Open Source Software Package Bidomain Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pueyo, E., Husti, Z., Hornyik, T., Baczko, I., Laguna, P., Varro, A., Rodriguez, B.: Mechanisms of ventricular rate adaptation as a predictor of arrhythmic risk. Am. J. Physiol. Heart Circ. Physiol. 298(5), H1577 (2010)CrossRefGoogle Scholar
  2. 2.
    Corrias, A., Jie, X., Romero, L., Bishop, M., Bernabeu, M., Pueyo, E., Rodriguez, B.: Arrhythmic risk biomarkers for the assessment of drug cardiotoxicity: from experiments to computer simulations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368(1921), 3001 (2010)CrossRefGoogle Scholar
  3. 3.
    Potse, M., Dubé, B., Vinet, A.: Cardiac anisotropy in boundary-element models for the electrocardiogram. Medical and Biological Engineering and Computing 47(7), 719–729 (2009)CrossRefGoogle Scholar
  4. 4.
    Chapelle, D., Fernández, M.A., Gerbeau, J.-F., Moireau, P., Sainte-Marie, J., Zemzemi, N.: Numerical Simulation of the Electromechanical Activity of the Heart. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 357–365. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Saiz, J., Tena, G., Monserrat, M., Cardona, K., Chorro, J.: Effects of the antiarrhythmic drug dofetilide on transmural dispersion of repolarization in ventriculum. A computer modeling study. IEEE Trans. Biomed. Eng. 1 (2011)Google Scholar
  6. 6.
    Pitt-Francis, J., Pathmanathan, P., Bernabeu, M., Bordas, R., Cooper, J., Fletcher, A., Mirams, G., Murray, P., Osborne, J., Walter, A., et al.: Chaste: a test-driven approach to software development for biological modelling. Computer Physics Communications 180(12), 2452–2471 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    George, P., Hecht, F., Saltel, E.: Fully automatic mesh generator for 3d domains of any shape. Impact of Comp. in Sci. ans Eng. 2, 187–218 (1990)CrossRefzbMATHGoogle Scholar
  8. 8.
    Streeter, D., Berne, R., Sperelakis, N., Geiger, S.: Gross morphology and fiber geometry of the heart. Handbook of Physiology, Section 2: The Cardiovascular System 1, 61–112 (1979)Google Scholar
  9. 9.
    Sundnes, J., Lines, G., Cai, X., Nielsen, B., Mardal, K.A., Tveito, A.: Computing the electrical activity in the heart. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  10. 10.
    Grandi, E., Pasqualini, F., Bers, D.: A novel computational model of the human ventricular action potential and Ca transient. Journal of molecular and cellular cardiology 48(1), 112–121 (2010)CrossRefGoogle Scholar
  11. 11.
    Southern, J., Wilson, N., Bernabeu, M.O., Pitt-Francis, J.: Chaste: Scalable high-performance simulation of cardiac electrophysiology. In: 1st Virtual Physiological Human Conference - VPH 2010 (2010)Google Scholar
  12. 12.
    Cooper, J., McKeever, S., Garny, A.: On the application of partial evaluation to the optimisation of cardiac electrophysiological simulations, pp. 12–20. ACM, New York (2006)Google Scholar
  13. 13.
    Køber, L., Thomsen, P., Møller, M., Torp-Pedersen, C., Carlsen, J., Sandøe, E., Egstrup, K., Agner, E., Videbæk, J., Marchant, B., et al.: Effect of dofetilide in patients with recent myocardial infarction and left-ventricular dysfunction: a randomised trial. The Lancet 356(9247), 2052–2058 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • N. Zemzemi
    • 1
  • M. O. Bernabeu
    • 1
  • J. Saiz
    • 2
  • B. Rodriguez
    • 1
  1. 1.University of OxfordUnited Kingdom
  2. 2.Universidad Politécnica de ValenciaSpain

Personalised recommendations