Skip to main content
Log in

How drugs modulate the performance of the human heart

  • Original Paper
  • Published:
Computational Mechanics Aims and scope Submit manuscript

Abstract

Many drugs interact with ion channels in the cells of our heart and trigger heart rhythm disorders with potentially fatal consequences. Computational modeling can provide mechanistic insight into the onset and propagation of drug-induced arrhythmias, but the effect of drugs on the mechanical performance of the heart remains poorly understood. Here we establish a multiphysics framework that integrates the biochemical, electrical, and mechanical effects of drugs, from cellular excitation to cardiac contraction. For the example of the drug dofetilide, we show that drug concentrations of 5x and 8x increase the heart rate to 122 and 114 beats per minute, increase myofiber stretches by 5%, and decrease overall tissue relaxation by 6%. This results in inter-ventricular and atrial-ventricular dyssynchronies and changes in cardiac output by \(-2.5\)% and +7%. Our results emphasize the need for multiphysics modeling to better understand the mechanical implications of drug-induced arrhythmias. Knowing how different drug concentrations affect the performance of the heart has important clinical implications in drug safety evaluation and personalized medicine.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Sager PT (2008) Key clinical considerations for demonstrating the utility of preclinical models to predict clinical drug-induced torsades de pointes. Br J Pharmacol 154:1544–1549

    Article  Google Scholar 

  2. Chabiniok R, Wang V, Hadjicharalambous M, Asner L, Lee J, Sermesant M, Kuhl E, Young A, Moireau P, Nash M, Chapelle D, Nordsletten DA (2016) Multiphysics and multiscale modeling, data-model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics. Interface Focus 6:20150083

    Article  Google Scholar 

  3. Sahli Costabal F, Yao J, Kuhl E (2018) Predicting drug-induced arrhythmias by multiscale modeling. Int J Numer Methods Biomed Eng 34(5):e2964

    Article  Google Scholar 

  4. Sahli Costabal F, Seo K, Ashley E, Kuhl E (2020) Classifying drugs by their arrhythmogenic risk using machine learning. Biophys J 118(5):1165–1176

    Article  Google Scholar 

  5. Sahli Costabal F, Matsuno K, Yao J, Perdikaris P, Kuhl E (2019) Machine learning in drug development: characterizing the effect of 30 drugs on the QT interval using Gaussian Process regression, sensitivity analysis, and uncertainty quantification. Comput Methods Appl Mech Eng 348:313–333

    Article  MathSciNet  MATH  Google Scholar 

  6. Sahli Costabal F, Perdikaris P, Kuhl E, Hurtado DE (2020) Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models. Comput Methods Appl Mech Eng 357:112602

    Article  MathSciNet  MATH  Google Scholar 

  7. Peirlinck M, Sahli Costabal F, Kuhl E (2021) Sex differences in drug-induced arrhythmogenesis. Front Physiol 12(1245):708435

    Article  Google Scholar 

  8. Sahli Costabal F, Yao J, Kuhl E (2018) Predicting the cardiac toxicity of drugs using a novel multiscale exposure-response simulator. Comput Methods Biomech Biomed Eng 21(3):232–246

    Article  Google Scholar 

  9. Krishnamoorthi S, Perotti LE, Borgstrom NP, Ajijola O, Aa Frid, Ponnaluri AV, Weiss JN, Qu Z, Klug WS, Ennis DB, Garfinkel A (2014) Simulation methods and validation criteria for modeling cardiac ventricular electrophysiology. PLoS ONE 9(12):e114494

    Article  Google Scholar 

  10. Göktepe S, Kuhl E (2009) Computational modeling of cardiac electrophysiology: a novel finite element approach. Int J Numer Meth Eng 79(2):156–178

    Article  MathSciNet  MATH  Google Scholar 

  11. Lee LC, Sundnes J, Genet M, Wenk JF, Wall ST (2016) An integrated electromechanical-growth heart model for simulating cardiac therapies. Biomech Model Mechanobiol 15(4):791–803

    Article  Google Scholar 

  12. Wong J, Göktepe S, Kuhl E (2013) Computational modeling of chemo-electro-mechanical coupling: a novel implicit monolithic finite element approach. Int J Numer Methods Biomed Eng 29:1104–1133

    Article  MathSciNet  Google Scholar 

  13. Stewart P, Aslanidi OV, Noble D, Noble PJ, Boyett MR, Zhang H (2009) Mathematical models of the electrical action potential of Purkinje fibre cells. Philos Trans Math Phys Eng Sci 367(1896):2225–2255

    MathSciNet  MATH  Google Scholar 

  14. O Hara T, Virág L, Varró A, Rudy Y (2011) Simulation of the undiseased human cardiac ventricular action potential: model formulation and experimental validation. PLoS Comput Biol 7(5):e1002061

    Article  Google Scholar 

  15. ten Tusscher KH, Noble D, Noble PJ, Panfilov AV (2004) A model for human ventricular tissue. Am J Physiol Heart Circ Physiol 286(4):H1573-89

    Article  Google Scholar 

  16. Priest JR, Gawad C, Kahlig KM, Yu JK, OHara T, Boyle PM, Rajamani S, Clark MJ, Garcia STK, Ceresnak S, Harris J, Boyle S, Dewey FE, Malloy-Walton L, Dunn K, Grove M, Perez MV, Neff NF, Chen R, Maeda K, Dubin A, Belardinelli L, West J, Antolik C, Macaya D, Quertermous T, Trayanova NA, Quake SR (2016) Early somatic mosaicism is a rare cause of long-QT syndrome. Proc Natl Acad Sci 113(41):115550–11560

    Article  Google Scholar 

  17. Crumb W, Vicente J, Johannesen L, Strauss D (2016) An evaluation of 30 clinical drugs against the comprehensive in vitro proarrhythmia assay (CiPA) proposed ion channel panel. J Pharmacol Toxicol Methods 81:251e262

    Article  Google Scholar 

  18. Johannesen L, Vicente J, Mason JW, Sanabria C, Waite-Labott K, Hong M, Guo P, Lin J, Sørensen JS, Galeotti L, Florian J, Ugander M, Stockbridge N, Strauss DG (2014) Differentiating drug-induced multichannel block on the electrocardiogram: randomized study of dofetilide, quinidine, ranolazine, and verapamil. Clin Pharmacol Therapeutics 95(5):549–558

    Article  Google Scholar 

  19. Sahli Costabal F, Yao J, Sher A, Kuhl E (2019) Predicting critical drug concentrations and torsadogenic risk using a multiscale exposure-response simulator. Prog Biophys Mol Biol 144:61–76

    Article  Google Scholar 

  20. Redfern WS, Carlsson L, Davis AS, Lynch WG, MacKenzie II, Palethorpe S, Siegl PKS, Strang I, Sullivan AT (2003) Relationships between preclinical cardiac electrophysiology clinical QT interval prolongation and torsade de pointes for a broad range of drugs: evidence for a provisional safety margin in drug development. Cardiovasc Res 58(1):32–45

    Article  Google Scholar 

  21. Abaqus Analysis User’s Guide (2020) Dassault Systèmes Simulia Corp

  22. Simo JC, Miehe CH (1992) Associative coupled thermoplasticity at finite strains: formulation, numerical analysis and implementation. Comput Methods Appl Mech Eng 98(1):41–104

    Article  MATH  Google Scholar 

  23. Holzapfel GA, Ogden RW (2009) Constitutive modelling of passive myocardium: a structurally based framework for material characterization. Philos Transact A Math Phys Eng Sci 367(1902):3445–75

    MathSciNet  MATH  Google Scholar 

  24. Göktepe S, Abilez OJ, Kuhl E (2010) A generic approach towards finite growth with examples of athletes heart, cardiac dilation, and cardiac wall thickening. J Mech Phys Solids 58(10):1661–1680

    Article  MathSciNet  MATH  Google Scholar 

  25. Gültekin O, Sommer G, Holzapfel GA (2016) An orthotropic viscoelastic model for the passive myocardium: continuum basis and numerical treatment. Comput Methods Biomech Biomed Eng 19(15):1647–1664

    Article  Google Scholar 

  26. Walker JC, Ratcliffe MB, Zhang P, Wallace AW, Fata B, Hsu EW, Saloner D, Guccione JM (2005) MRI-based finite-element analysis of left ventricular aneurysm. Am J Physiol Heart Circ Physiol 289(2):H692–H700

    Article  Google Scholar 

  27. Sack KL, Aliotta E, Ennis DB, Choy JS, Kassab GS, Guccione JM, Franz T (2018) Construction and validation of subject-specific biventricular finite-element models of healthy and failing swine hearts from high-resolution DT-MRI. Front Physiol 9:539

    Article  Google Scholar 

  28. Peirlinck M, Sack KL, De Backer P, Morais P, Segers P, Franz T, De Beule M (2019) Kinematic boundary conditions substantially impact in silico ventricular function. Int J Numer Methods Biomed Eng 35(1):e3151

    Article  Google Scholar 

  29. Baillargeon B, Rebelo N, Fox DD, Taylor RL, Kuhl E (2014) The living heart project: a robust and integrative simulator for human heart function. Eur J Mech A Solids 48:38–47

    Article  MathSciNet  MATH  Google Scholar 

  30. Peirlinck M, Sahli Costabal F, Yao J, Guccione JM, Tripathy S, Wang Y, Ozturk D, Segars P, Morrison TM, Levine S, Kuhl E (2021) Precision medicine in human heart modeling: perspectives, challenges, and opportunities. Biomech Model Mechanobiol 20(3):803–831

    Article  Google Scholar 

  31. Zygote Media Group Inc (2014) Zygote Solid 3D Heart Generations I & II Development Report. Technical Development of 3D Anatomical Systems

  32. Lombaert H, Peyrat JM, Croisille P, Rapacchi S, Fanton L, Cheriet F, Clarysse P, Magnin I, Delingette H, Ayache N (2012) Human atlas of the cardiac fiber architecture: study on a healthy population. IEEE Trans Med Imag 31(7):1436–1447

    Article  Google Scholar 

  33. Bayer JD, Roney CH, Pashaei A, Jais P, Vigmond EJ (2016) Novel radiofrequency ablation strategies for terminating atrial fibrillation in the left atrium: a simulation study. Front Physiol 7:1–14

    Article  Google Scholar 

  34. Sahli Costabal F, Concha FA, Hurtado DE, Kuhl E (2017) The importance of mechano-electrical feedback and inertia in cardiac electromechanics. Comput Methods Appl Mech Eng 320:352–368

    Article  MathSciNet  MATH  Google Scholar 

  35. Pezzuto S, Hake J, Sundness J (2016) Space-discretization error analysis and stabilization schemes for conduction velocity in cardiac electrophysiology. Int J Numer Methods Biomed Eng 32(10):e02762

    Article  MathSciNet  Google Scholar 

  36. Augustin CM, Neic A, Liebmann M, Prassl AJ, Niederer SA, Haase G, Plank G (2016) Anatomically accurate high resolution modeling of human whole heart electromechanics: a strongly scalable algebraic multigrid solver method for nonlinear deformation. J Comput Phys 305:622–646

    Article  MathSciNet  MATH  Google Scholar 

  37. Niederer S, Kerfoot E, Benson AP, Bernabeu MO, Bernus O, Bradley C, Cherry EM, Clayton R, Fenton FH, Garny A, Heidenreich E, Land S, Maleckar M, Pathmanathan P, Plank G, Rodríguez JF, Roy I, Sachse FB, Seemann G, Skavhaug O, Smith NP (2011) Verification of cardiac tissue electrophysiology simulators using an N-version benchmark. Philos Trans A Math Phys Eng Sci 369(1954):4331–51

    MathSciNet  Google Scholar 

  38. Perotti LE, Krishnamoorthi S, Borgstrom NP, Ennis DB, Klug WS (2015) Regional segmentation of ventricular models to achieve repolarization dispersion in cardiac electrophysiology modeling. Int J Numer Methods Biomed Eng 28:e02718

    Article  MathSciNet  Google Scholar 

  39. Okada J, Washio T, Maehara A, Momomura S, Sugiura S, Hisada T (2011) Transmural and apicobasal gradients in repolarization contribute to T-wave genesis in human surface ECG. Am J Physiol Heart Circul Physiol 301(1):H200–H208

    Article  Google Scholar 

  40. Sahli Costabal F, Hurtado DE, Kuhl E (2016) Generating Purkinje networks in the human heart. J Biomech 49:2455–2465

    Article  Google Scholar 

  41. Ponnaluri AVS, Perotti LE, Ennis DB, Klug WS (2016) A viscoactive constitutive modeling framework with variational updates for the myocardium. Comput Methods Appl Mech Eng 314:85–101

    Article  MathSciNet  MATH  Google Scholar 

  42. Bordas R, Gillow K, Lou Q, Efimov IR, Gavaghan D, Kohl P, Grau V, Rodriguez B (2011) Rabbit-specific ventricular model of cardiac electrophysiological function including specialized conduction system. Prog Biophys Mol Biol 107(1):90–100

    Article  Google Scholar 

  43. Kotikanyadanam M, Göktepe S, Kuhl E (2010) Computational modeling of electrocardiograms: a finite element approach toward cardiac excitation. Int J Numer Methods Biomed Eng 26(5):524–533

    MathSciNet  MATH  Google Scholar 

  44. Hii JTY, Wyse G, Gillis AM, Duff HJ, Solylo MA, Mitchell LB (1992) Precordial QT interval dispersion as a marker of Torsade de Pointes. Circulation 86:1376–1382

    Article  Google Scholar 

  45. Sadrieh A, Domanski L, Pitt-Francis J, Mann S, Hodkinson EC, Ng CA, Perry MD, Taylor JA, Gavaghan D, Subbiah RN, Vandenberg J, Hill AP (2014) Multiscale cardiac modelling reveals the origins of notched T waves in long QT syndrome type 2. Nat Commun 5:5069

    Article  Google Scholar 

  46. Klabunde R (2011) Cardiovascular physiology concepts. Lippincott Williams & Wilkins, Philadelphia

    Google Scholar 

  47. Gee MW, Förster C, Wall WA (2010) A computational strategy for prestressing patient-specific biomechanical problems under finite deformation. Int J Numer Methods Biomed Eng 26(1):52–72

    Article  MATH  Google Scholar 

  48. Peirlinck M, De Beule M, Segers P, Rebelo N (2018) A modular inverse elastostatics approach to resolve the pressure-induced stress state for in vivo imaging based cardiovascular modeling. J Mech Behav Biomed Mater 85:124–133

    Article  Google Scholar 

  49. Dessertenne F (1966) La tachycardie ventriculaire a deux foyers opposes variables. Arch Mal Coeur Vaiss 2(59):263–272

    Google Scholar 

  50. Johnston J, Pal S, Nagele P (2013) Perioperative torsade de pointes: a systematic review of published case reports. Anesth Analg 117(3):559

    Article  Google Scholar 

  51. Vandael E, Vandenberk B, Vandenberghe J, Pincé H, Willems R, Foulon V (2017) Incidence of torsade de pointes in a tertiary hospital population. Int J Cardiol 243:511–515

    Article  Google Scholar 

  52. Hurtado DE, Rojas G (2018) Non-conforming finite-element formulation for cardiac electrophysiology: an effective approach to reduce the computation time of heart simulations without compromising accuracy. Comput Mech 61(4):485–497

    Article  MathSciNet  MATH  Google Scholar 

  53. Margara F, Wang ZJ, Levrero-Florencio F, Santiago A, Vázquez M, Bueno-Orovio A, Rodriguez B (2021) In-silico human electro-mechanical ventricular modelling and simulation for drug-induced pro-arrhythmia and inotropic risk assessment. Progress in Biophys Mol Biol 159:58–74. https://doi.org/10.1016/j.pbiomolbio.2020.06.007

    Article  Google Scholar 

  54. Kılıcgedik A, Kahveci G, Gurbuz AS, Karabay CY, Guler A, Efe SC, Aung SM, Arslantas U, Demir S, Izgi IA (2017) Papillary muscle free strain in patients with severe degenerative and functional mitral regurgitation. Arq Bras Cardiol 108(4):339–346

    Google Scholar 

  55. Perotti LE, Verzhbinsky IA, Moulin K, Cork TE, Loecher M, Balzani D, Ennis DB (2020) Estimating cardiomyofiber strain in vivo by solving a computational model. Med Image Anal 68:101932

    Article  Google Scholar 

  56. Wang TKM, Kwon DH, Griffin BP, Flamm SD, Popović ZB (2020) Defining the reference range for left ventricular strain in healthy patients by cardiac MRI measurement techniques: systematic review and meta-Analysis. Am J Roentgenol 217(3):569–583

    Article  Google Scholar 

  57. Wang TKM, Grimm RA, Rodriguez LL, Collier P, Griffin BP, Popović ZB (2021) Defining the reference range for right ventricular systolic strain by echocardiography in healthy subjects: a meta-analysis. PLoS ONE 16(8):e0256547

    Article  Google Scholar 

  58. Colatsky T, Fermini B, Gintant G, Pierson JB, Sager P, Sekino Y, Strauss DG, Stockbridge N (2016) The comprehensive in vitro proarrhythmia assay (CiPA) initiative-update on progress. J Pharmacol Toxicol Methods 81:15–20

    Article  Google Scholar 

  59. Fermini B, Hancox JC, Abi-Gerges N, Bridgland-Taylor M, Chaudhary KW, Colatsky T, Correll K, Crumb W, Damiano B, Erdemli G, Gintant G (2016) A new perspective in the field of cardiac safety testing through the comprehensive in vitro proarrhythmia assay paradigm. J Biomol Screen 21(1):1–11

    Article  Google Scholar 

  60. Dutta S, Chang KC, Beattie KA, Sheng J, Tran PN, Wu WW, Wu M, Strauss DG, Colatsky T, Li Z (2017) Optimization of an in silico cardiac cell model for Proarrhythmia risk assessment. Front Physiol 8:616

    Article  Google Scholar 

  61. Tomek J, Bueno-Orovio A, Passini E, Zhou X, Minchole A, Britton O, Bartolucci C, Severi S, Shrier A, Virag L, Varro A (2019) Development, calibration, and validation of a novel human ventricular myocyte model in health, disease, and drug block. Elife 8:e48890

    Article  Google Scholar 

  62. Moss R, Wülfers EM, Schuler S, Loewe A, Seemann G (2022) A fully-coupled electro-mechanical whole-heart computational model: influence of cardiac contraction on the ECG. Front Physiol 12:778872

    Article  Google Scholar 

  63. Land S, Park-Holohan SJ, Smith NP, Dos Remedios CG, Kentish JC, Niederer SA (2017) A model of cardiac contraction based on novel measurements of tension development in human cardiomyocytes. J Mol Cell Cardiol 106:68–83

    Article  Google Scholar 

  64. Salvador M, Fedele M, Africa PC, Sung E, Dede L, Prakosa A, Chrispin J, Trayanova N, Quarteroni A (2021) Electromechanical modeling of human ventricles with ischemic cardiomyopathy: numerical simulations in sinus rhythm and under arrhythmia. Comput Biol Med 136:104674

    Article  Google Scholar 

  65. Levrero-Florencio F, Margara F, Zacur E, Bueno-Orovio A, Wang ZJ, Santiago A, Aguado-Sierra J, Houzeaux G, Grau V, Kay D, Vázquez M (2020) Sensitivity analysis of a strongly-coupled human-based electromechanical cardiac model: effect of mechanical parameters on physiologically relevant biomarkers. Comput Methods Appl Mech Eng 361:112762

    Article  MathSciNet  MATH  Google Scholar 

  66. Fresca S, Manzoni A, Dedè L, Quarteroni A (2020) Deep learning-based reduced order models in cardiac electrophysiology. arXiv:2006.03040

  67. Sahli Costabal F, Yang Y, Perdikaris P, Hurtado DE, Kuhl E (2020) Physics-informed neural networks for cardiac activation mapping. Front Phys 8:42

    Article  Google Scholar 

  68. Regazzoni F, Salvador M, Dede L, Quarteroni A (2021) A machine learning method for real-time numerical simulations of cardiac electromechanics. arXiv:2110.13212

  69. Peirlinck M, Sahli Costabal F, Sack KL, Choy JS, Kassab GS, Guccione JM, De Beule M, Segers P, Kuhl E (2019) Using machine learning to characterize heart failure across the scales. Biomech Model Mechanobiol 18:1987–2001

    Article  Google Scholar 

Download references

Acknowledgements

This work used the Extreme Science and Engineering Discovery Environment (XSEDE) project TG-MSS170033 supported by the National Science Foundation Grant Number ACI-1548562, by a Belgian American Education Foundation Postdoctoral Research Fellowship, a Stanford Bio-X IIP Seed Grant, and the National Institutes of Health Grant R01HL131975.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E. Kuhl.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (mp4 20800 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peirlinck, M., Yao, J., Sahli Costabal, F. et al. How drugs modulate the performance of the human heart. Comput Mech 69, 1397–1411 (2022). https://doi.org/10.1007/s00466-022-02146-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00466-022-02146-1

Keywords

Navigation