Computational translation of drug effects from animal experiments to human ventricular myocytes.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
29 06 2020
29 06 2020
Historique:
received:
27
02
2020
accepted:
26
05
2020
entrez:
1
7
2020
pubmed:
1
7
2020
medline:
16
12
2020
Statut:
epublish
Résumé
Using animal cells and tissues as precise measuring devices for developing new drugs presents a long-standing challenge for the pharmaceutical industry. Despite the very significant resources that continue to be dedicated to animal testing of new compounds, only qualitative results can be obtained. This often results in both false positives and false negatives. Here, we show how the effect of drugs applied to animal ventricular myocytes can be translated, quantitatively, to estimate a number of different effects of the same drug on human cardiomyocytes. We illustrate and validate our methodology by translating, from animal to human, the effect of dofetilide applied to dog cardiomyocytes, the effect of E-4031 applied to zebrafish cardiomyocytes, and, finally, the effect of sotalol applied to rabbit cardiomyocytes. In all cases, the accuracy of our quantitative estimates are demonstrated. Our computations reveal that, in principle, electrophysiological data from testing using animal ventricular myocytes, can give precise, quantitative estimates of the effect of new compounds on human cardiomyocytes.
Identifiants
pubmed: 32601303
doi: 10.1038/s41598-020-66910-0
pii: 10.1038/s41598-020-66910-0
pmc: PMC7324560
doi:
Substances chimiques
Anti-Arrhythmia Agents
0
Phenethylamines
0
Sulfonamides
0
Sotalol
A6D97U294I
dofetilide
R4Z9X1N2ND
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
10537Références
Edwards, A. G. & Louch, W. E. Species-dependent mechanisms of cardiac arrhythmia: a cellular focus. Clinical Medicine Insights: Cardiology, 11, 1179546816686061 (2017).
Yoshida, Y. & Yamanaka, S. Induced pluripotent stem cells 10 years later. Circulation Research 120(12), 1958–1968 (2017).
pubmed: 28596174
Ye, L., Ni, X., Zhao, Z.-A., Lei, W. & Hu, S. The application of induced pluripotent stem cells in cardiac disease modeling and drug testing. Journal of Cardiovascular Translational Research 11(5), 366–374 (2018).
pubmed: 29845439
DiMasi, J. A., Grabowski, H. G. & Hansen, R. W. Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics 47, 20–33 (2016).
pubmed: 26928437
Paul, S. M. et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature Reviews Drug Discovery 9(3), 203–214 (2010).
pubmed: 20168317
Bockorny, M., Chakravarty, S., Schulman, P., Bockorny, B. & Bona, R. Severe heart failure after bortezomib treatment in a patient with multiple myeloma: A case report and review of the literature. Acta Haematologica 128(4), 244–247 (2012).
pubmed: 22964848
Jost, N. et al. Ionic mechanisms limiting cardiac repolarization reserve in humans compared to dogs. The Journal of Physiology 591(17), 4189–4206 (2013).
pubmed: 23878377
pmcid: 3779111
Zicha, S. et al. Molecular basis of species-specific expression of repolarizing K+ currents in the heart. American Journal of Physiology-Heart and Circulatory Physiology 285(4), H1641–H1649 (2003).
pubmed: 12816752
Wang, Z. et al. Potential molecular basis of different physiological properties of the transient outward K+ current in rabbit and human atrial myocytes. Circulation Research 84(5), 551–561 (1999).
pubmed: 10082477
Gemmell, P., Burrage, K., Rodriguez, B. & Quinn, T. A. Population of computational rabbit-specific ventricular action potential models for investigating sources of variability in cellular repolarisation. Plos One 9(2), e90112 (2014).
pubmed: 24587229
pmcid: 3938586
Grandi, E., Pasqualini, F. S. & Bers, D. M. A novel computational model of the human ventricular action potential and Ca transient. Journal of Molecular and Cellular Cardiology 48(1), 112–121 (2010).
pubmed: 19835882
Tveito, A. et al. Inversion and computational maturation of drug response using human stem cell derived cardiomyocytes in microphysiological systems. Scientific Reports 8(1), 17626 (2018).
pubmed: 30514966
pmcid: 6279833
Jaeger, K. H. et al. Improved computational identification of drug response using optical measurements of human stem cell derived cardiomyocytes in microphysiological systems. Frontiers in Pharmacology 10, 1648 (2020).
pubmed: 32116671
pmcid: 7029356
Niederer, S. A., Fink, M., Noble, D. & Smith, N. P. A meta-analysis of cardiacelectrophysiology computational models. Experimental Physiology, 94(5), 486–495, 5 (2009).
Groenendaal, W. et al. Cell-specific cardiac electrophysiology models. Plos computational biology 11(4), e1004242 (2015).
pubmed: 25928268
pmcid: 4415772
Jaeger, K. H., Wall, S. & Tveito, A. Detecting undetectables: Can conductances of action potential models be changed without appreciable change in the transmembrane potential? Chaos: An Interdisciplinary Journal of Nonlinear Science 29(7), 073102 (2019).
Nemtsas, P., Wettwer, E., Christ, T., Weidinger, G. & Ravens, U. Adult zebrafish heart as a model for human heart? An electrophysiological study. Journal of Molecular and Cellular Cardiology 48(1), 161–171 (2010).
pubmed: 19747484
Bussek, A. et al. Tissue slices from adult mammalian hearts as a model for pharmacological drug testing. Cellular Physiology and Biochemistry 24(5-6), 527–536 (2009).
pubmed: 19910693
Jost, N. et al. Restricting excessive cardiac action potential and QT prolongation: a vital role for IKs in human ventricular muscle. Circulation 112(10), 1392–1399 (2005).
pubmed: 16129791
Baczkó, I., Jost, N., Virág, L., Bösze, Z. & Varró, A. Rabbit models as tools for preclinical cardiac electrophysiological safety testing: importance of repolarization reserve. Progress in Biophysics and Molecular Biology 121(2), 157–168 (2016).
pubmed: 27208697
Orvos, P. et al. Evaluation of possible proarrhythmic potency: Comparison of the effect of dofetilide, cisapride, sotalol, terfenadine, and verapamil on hERG and native IKr currents and on cardiac action potential. Toxicological Sciences 168(2), 365–380 (2019).
pubmed: 30561737
Crumb, W. J., Vicente, J., Johannesen, L. & Strauss, D. G. An evaluation of 30 clinical drugs against the comprehensive in vitro proarrhythmia assay (CiPA) proposed ion channel panel. Journal of Pharmacological and Toxicological Methods, 81:251–262 Focused Issue on Safety Pharmacology (2016).
Kramer, J. et al. MICE models: superior to the HERG model in predicting Torsade de Pointes. Scientific Reports 3, 2100 (2013).
pubmed: 23812503
pmcid: 3696896
Qu, Y. & Vargas, H. M. Proarrhythmia risk assessment in human induced pluripotent stem cell-derived cardiomyocytes using the maestro MEA platform. Toxicological Sciences 147(1), 286–295 (2015).
pubmed: 26117837
Kim, K.-S. & Kim, E.-J. The phenothiazine drugs inhibit hERG potassium channels. Drug and Chemical Toxicology 28(3), 303–313 (2005).
pubmed: 16051556
Gibson, J. K., Yue, Y., Bronson, J., Palmer, C. & Numann, R. Human stem cell-derived cardiomyocytes detect drug-mediated changes in action potentials and ion currents. Journal of Pharmacological and Toxicological Methods 70(3), 255–267 (2014).
pubmed: 25219538
Katayama, Y. et al The inter-cell-line reproducibility of hERG assay using the whole-cell patch-clamping. Journal of Pharmacological Sciences, 97 (2005).
McPate, M. J., Duncan, R. S., Witchel, H. J. & Hancox, J. C. Disopyramide is an effective inhibitor of mutant HERG K
pubmed: 16842817
Piper, D. R. et al. Development of the predictor HERG uorescence polarization assay using a membrane protein enrichment approach. Assay and Drug Development Technologies 6(2), 213–223 (2008).
pubmed: 18471075
Sanguinetti, M. C. & Jurkiewicz, N. K. Two components of cardiac delayed rectifier K
pubmed: 2170562
Shanks, N., Greek, R. & Greek, J. Are animal models predictive for humans? Philosophy, Ethics, and Humanities in Medicine 4(1), 2 (2009).
pubmed: 19146696
pmcid: 2642860
Hill, A. J. & Iaizzo, P. A. Comparative cardiac anatomy. In Handbook of cardiac anatomy, physiology, and devices, pages 89–114. Springer (2015).
Houser, S. R. et al. Animal models of heart failure: a scientific statement from the American Heart Association. Circulation Research 111(1), 131–150 (2012).
pubmed: 22595296
Gong, J. Q. X. & Sobie, E. A. Population-based mechanistic modeling allows for quantitative predictions of drug responses across cell types. NPJ Systems Biology and Applications 4(1), 11 (2018).
pubmed: 29507757
pmcid: 5825396
Bailey, J., Thew, M. & Balls, M. An analysis of the use of dogs in predicting human toxicology and drug safety. Alternatives to Laboratory Animals 41(5), 335–350 (2013).
pubmed: 24329742
Ando, K. et al. QT PRODACT: In vivo QT assay with a conscious monkey for assessment of the potential for drug-induced QT interval prolongation. Journal of pharmacological sciences 99(5), 487–500 (2005).
pubmed: 16493189
Bergenholm, L., Collins, T., Evans, N. D., Chappell, M. J. & Parkinson, J. PKPD modelling of PR and QRS intervals in conscious dogs using standard safety pharmacology data. Journal of Pharmacological and Toxicological Methods 79, 34–44 (2016).
pubmed: 26780675
Bergenholm, L. et al. Predicting QRS and PR interval prolongations in humans using nonclinical data. British Journal of Pharmacology 174(19), 3268–3283 (2017).
pubmed: 28675424
pmcid: 5595766
Jegla, T. J., Zmasek, C. M., Batalov, S. & Nayak, S. K. Evolution of the human ion channel set. Combinatorial Chemistry & High Throughput Screening (2009).
Jost, N. et al. A kesoi egyeniranyito kaliumaram gyors (IKr) es lassu komponensenek (IKs) osszehasonlito vizsgalata egeszseges emberi, kutya, nyul es tengerimalac kamrai szivizomsejteken. Cardiologia Hungarica 34, 103–113 (2004).
Blechschmidt, S., Haufe, V., Benndorf, K. & Zimmer, T. Voltage-gated Na+ channel transcript patterns in the mammalian heart are speciesdependent. Progress in Biophysics and Molecular Biology (2008).
Zimmer, T., Haufe, V. & Blechschmidt, S. Voltage-gated sodium channels in the mammalian heart. Global Cardiology Science and Practice (2014).
Rudy, Y. From genes and molecules to organs and organisms: Heart. Comprehensive Biophysics, pages 268–327 (2012).
Rudy, Y. & Silva, J. R. Computational biology in the study of cardiac ion channels and cell electrophysiology. Quarterly Reviews of Biophysics 39(01), 57–116 (2006).
pubmed: 16848931
pmcid: 1994938
Plonsey, R. & Barr, R. C. Bioelectricity, A Quantitative Approach. Springer (2007).
Sterratt, D., Graham, B., Gillies, A. & Willshaw, D. Principles of Computational Modelling in Neuroscience. Cambridge University Press (2011).
Tveito, A. & Lines, G. T. Computing Characterizations of Drugs for Ion Channels and Receptors Using Markov Models. Springer-Verlag, Lecture Notes, vol. 111 (2016).
Brennan, T., Fink, M. & Rodriguez, B. Multiscale modelling of drug-induced effects on cardiac electrophysiological activity. EuropeanJournal of Pharmaceutical Sciences 36(1), 62–77 (2009).
Clancy, C. E., Zhu, Z. I. & Rudy, Y. Pharmacogenetics and anti-arrhythmic drug therapy: A theoretical investigation. AJP: Heart and Circulatory Physiology 292(1), H66–H75 (2007).
Tveito, A., Maleckar, M. M. & Lines, G. T. Computing optimal properties of drugs using mathematical models of single channel dynamics. Computational and Mathematical. Biophysics 6(1), 41–64 (2018).
Kernik, D. C. et al. A computational model of inducedpluripotent stem-cell derived cardiomyocytes incorporating experimental variability from multiple data sources. The Journal of Physiology (2019).
O’Hara, T., Virág, L., Varró, A. & Rudy, Y. Simulation of the undiseasedhuman cardiac ventricular action potential: Model formulation and experimental validation. PLoS Computational Biology 7(5), e1002061 (2011).
pubmed: 21637795
pmcid: 3102752
Maltsev, V. A. & Lakatta, E. G. Synergism of coupled subsarcolemmalCa
pubmed: 19136600
pmcid: 2660239
Nelder, J. A. & Mead, R. A simplex method for function minimization. The Computer Journal 7(4), 308–313 (1965).
Carlsson, L. In vitro and in vivo models for testing arrhythmogenesis in drugs. Journal of Internal Medicine 259(1), 70–80 (2006).
pubmed: 16336515