Models of Cardiac Electrophysiology:

Transcription

1 Models of Cardiac Electrophysiology: A Useful Tool for Enhancing Experimental Research T. Alexander Quinn, PhD National Heart and Lung Institute, Imperial College London Department of Computer Science, University of Oxford Fulford Junior Research Fellow in Medical Sciences, Somerville College Mayneord-Phillips Summer School - July 4, 2012 Cardiac Electrophysiology Modelling Meeting, 2011

2 Goal of Electrophysiological Modelling To understand integrated cardiac function:

3 Goal of Electrophysiological Modelling To understand integrated cardiac function: Roden et al.. Annu Rev Physiol 64:431, 2002

4 Goal of Electrophysiological Modelling To understand integrated cardiac function: Roden et al.. Annu Rev Physiol 64:431, 2002

5 Hodgkin Huxley Model Before we new all this, back in 1952 Alan Lloyd Hodgkin Andrew Fielding Huxley

6 Hodgkin Huxley Model Before we new all this, back in 1952 Giant Squid Axon Action Potential Hodgkin & Huxley. J Physiol 117:500, 1952

7 Hodgkin Huxley Model Before we new all this, back in 1952 Giant Squid Axon Action Potential [Ohm s Law: I = 1/R V] Hodgkin & Huxley. J Physiol 117:500, 1952

8 Hodgkin Huxley Model Before we new all this, back in 1952 Giant Squid Axon Action Potential Hodgkin & Huxley. J Physiol 117:500, 1952

9 Hodgkin Huxley Model Before we new all this, back in 1952 Experimental Recording Computational Result Hodgkin & Huxley. J Physiol 117:500, 1952

10 H-H Applied to Cardiac Cells then, 10 years later Denis Noble

11 H-H Applied to Cardiac Cells then, 10 years later Hanck et al. Comprehensive Physiology, 2002

12 H-H Applied to Cardiac Cells then, 10 years later Hanck et al. Comprehensive Physiology, 2002 Noble. J Physiol 160:317, 1962 Noble. Nature 188:495, 1960

13 Current State of Cell Modelling now, 50 years on, over 100 cell models in the literature! Noble 1962 McAllister et al DiFrancesco & Noble 1985 Noble et al ten Tusscher et al O Hara et al Nickerson & Hunter. Prog Biophys Mol Biol 90:346, 2006 O Hara et al. PLoS Computational Biology 7:e , 2011

14 Current State of Cell Modelling now, 50 years on, over 100 cell models in the literature! Mahajan et al. Biophys J 94:392, 2008

15 Why Computational Modelling? Modelling for Modelling s Sake? versus Modelling with a Purpose: - Drive Research: Generate hypotheses and inform experimental design - Interpret Results: Understand and explain experimental findings - Integrate Data: Link observations across spatial and temporal scales - Predict Outcomes? Important for clinical applications All models are only partial representations of reality: - Useful tools in a given context - Should be used in combination with experimental input

16 Early Modelling Success Story Noble 1962 Noble et al A main defect of 1962 model: only one voltage-gated inward current (I Na ) (Ca 2+ -currents not yet discovered) Nickerson & Hunter. Prog Biophys Mol Biol 90:346, 2006

17 Early Modelling Success Story Model worked by tweaking : I Na voltage range increased so it accounted for both Na + - and Ca 2+ -channel contributions to plateau Noble. J Physiol 160:317, 1962

18 Early Modelling Success Story Generated clear hypotheses: (1) Na + -channels in heart are quantitatively different from nerve; (2) Other inward current-carrying channels must exist. Both are correct, but (2) helped lead to discovery of inward Ca 2+ -current (with advent of voltage-clamp technique) A lot can be learned from being wrong! Reuter. J Physiol 192:479, 1967

19 Clinical Utility of Cell Models Cell models may now have predictive power Torsade de Pointes: rare, life threatening ventricular arrhythmia induced by various pharmaceutical agents Krikler & Curry. Br Heart J 38:117, 1976 herg Block Mirams et al. Br J Pharmacol In Press, 2012

20 Clinical Utility of Cell Models Cell models may now have predictive power Billions spent in drug development and clinical testing to avoid this Mirams et al. Br J Pharmacol In Press, 2012

21 % herg % Inhibition Clinical Utility of Cell Models Cell models may now have predictive power Billions spent in drug development and clinical testing to avoid this IC 50 IC 50 value [Drug] log(drug concentration) False positive False negative Mirams et al. Br J Pharmacol In Press, 2012 Mirams et al. Cardiovasc Res 91:53, 2011

22 Clinical Utility of Cell Models Cell models may now have predictive power Drugs often block more than one channel - multiple IC 50 can be integrated using cell models to predict risk Ranolazine 90% herg Block Control 90% herg Block + 50% Late I Na Mirams et al. Cardiovasc Res 91:53, 2011 Noble & Noble. Heart 92:iv1, 2006

23 Clinical Utility of Cell Models Cell models may now have predictive power Drugs often block more than one channel - multiple IC 50 can be integrated using cell models to predict risk Mirams et al. Cardiovasc Res 91:53, 2011

24 Clinical Utility of Cell Models Cell models may now have predictive power Drugs often block more than one channel - multiple IC 50 can be integrated using cell models to predict risk Acceptance & Adoption EMEA, FDA, Canadian Medicines Agency (evaluating for use) 90% herg Block GSK (implementing for drug screening)

25 Projecting to Higher Levels Can be projected to tissue and even to whole organism Simulation of re-entry with flecainide administration Validation by whole heart optical mapping Moreno et al. Sci Tranl Med 3:98ra93, 2011

26 Projecting to Higher Levels Can be projected to tissue and even to whole organism Simulation of ECG changes with dofetilide administration Zemzemi et al. LNCA 6666:259, 2011

27 Voltage (mv) Ventricular Action Potentials Voltage (mv) ECG Linking Cell & Tissue Level Effects Mechanical stimulation of isolated cells Mechanical stimulation of whole heart Stretch QRS T Stretch SAC NS Vulnerable Window for Induction of Re-Entry Time (ms) Stretch Ectopic Action Potential (Trigger) Delayed After Depolarisationlike Activity Ectopic Action Potential (Trigger) Early After Depolarisationlike Activity Changed Repolarisation Increased Heterogeneity (Sustaining Mechanism) Time (ms) Kohl et al. Cardiovasc Res 50:280, 2001

28 ECG Linking Cell & Tissue Level Effects Mechanical stimulation can result in sudden cardiac death Mechanical Stimulation Link et al. J Am Coll Cardiol 37:649, 2001 Due to Cation Non-Selective Stretch-Activated Channel (SAC NS ) SAC NS activity can be included in cell models Craelius et al. Biosci Reports 8:407, 1988 Kohl and Sachs. Phil Trans R Soc Lond 359:1173, 2001

29 Predicting Mechanism of Re-entry 60 mv Repolarisation Refractory Excitable SAC NS (E rev = -10 mv) -100 mv T REP = 40% Garny and Kohl. Ann N Y Acad Sci 1015:133, 2004

30 Predicting Mechanism of Re-entry 60 mv Repolarisation Repolarisation Depolarisation SAC NS (E rev = -10 mv) -100 mv T REP = 40% Garny and Kohl. Ann N Y Acad Sci 1015:133, 2004

31 Predicting Mechanism of Re-entry 60 mv Repolarisation Excitation SAC NS (E rev = -10 mv) Region of Functional Block -100 mv T REP = 40% Garny and Kohl. Ann N Y Acad Sci 1015:133, 2004

32 Predicting Mechanism of Re-entry 60 mv Repolarisation SAC NS (E rev = -10 mv) Region of Functional Block -100 mv T REP = 40% Garny and Kohl. Ann N Y Acad Sci 1015:133, 2004

33 Experimental Testing Quinn et al. Unpublished

34 Optical Mapping of Electrophysiology Preparation

35 Activation Time (ms) Repolarisation Time (ms) APD 90 (ms) Activation Time (ms) Repolarisation Time (ms) APD 90 (ms) Result - Mechanically-Induced Activation Local mechanical stimulation induces focal ventricular activation Sinus Rhythm Activation Repolarization APD 90 Mechanically-Induced Excitation Isolines = 0.6 ms Isolines = 1 ms Isolines = 1 ms Isolines = 1.5 ms Isolines = 1.5 ms Isolines = 2.5 ms Quinn et al. Unpublished

36 Result - Mechanically-Induced Re-entry Stimulation in early ECG T-wave can result in ventricular fibrillation Occurred if mechanically affected tissue overlapped repolarization wave 50% Repolarisation Wave Stimulation Site LV Apex Region of Functional Block Garny and Kohl. Ann NY Acad Sci 1015:133, 2004 Spatio-temporal dependent window for mechanically-induced re-entry Quinn et al. Unpublished

37 Summary - Drive Research: Generate hypotheses and inform experimental design - Interpret Results: Understand and explain experimental findings - Integrate Data: Link observations across spatial and temporal scales - Predict Outcomes! Important for clinical applications Data Interpretation Hypothesis Forming Clinic R E D U C E Organism Organ Tissue Cell Protein Gene Data Input Model Validation I N T E G R A T E

38 Where to Now? Increasing computational power and understanding of subcellular mechanisms offers almost endless possibilities Modelling of variability (temporal, spatial, intra-/inter-subject, experimental) Stocasticity Model Populations (Parameter Space) Pueyo et al. Biophys J 101:2892, 2011 Gemmell et al. Unpublished

39 Where to Now? Increasing computational power and understanding of subcellular mechanisms offers almost endless possibilities Modelling of micro- and nano-scale processes and domains Ion Channel Molecular Dynamics and Electrostatics Silva et al. PNAS 106:11102, 2009

40 Where to Now? Increasing computational power and understanding of subcellular mechanisms offers almost endless possibilities And to other places, as of yet unknown Giant Squid Axon (mm scale) Cardiomyocyte (nm scale) Iribe et al. Circ Res 104:787, 2009

41 Thank You! Colleagues at Imperial College London and the University of Oxford Christian Bollensdorff Gil Bub Rebecca Burton Alan Garny Honghua Jin Skye Kelly-Barret Peter Lee Ula Siedlecka Ken Wang Collaborators Christian Boulin, Avi Epstein - Eur Molecular Biology Laboratory, Heidelberg Ruben Coronel - Academic Medical Center, University of Amsterdam Gentaro Iribe - Graduate School of Medicine, Okayama University Leslie Loew - Ctr for Cell Analysis & Modeling, Univ of Connecticut Health Ctr Fred Sachs - Dept of Physiology & Biophysics, SUNY at Buffalo Funding Bodies Cardiac Biophysics & Systems Biology Group Peter Kohl Computational Biology Group Blanca Rodriguez Martin Bishop Kevin Burrage Ramón Casero Cañas Sara Dutta Katherine Fletcher Philip Gemmell Vicente Grau Gary Mirams Denis Noble Pras Pathmanathan The Magdi Yacoub Institute

42 Reading List 1. Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol. 1952;117: Noble D. A modification of the Hodgkin--Huxley equations applicable to Purkinje fibre action and pace-maker potentials. J Physiol. 1962;160: Roden DM, Balser JR, George AL, Jr., Anderson ME. Cardiac ion channels. Annu Rev Physiol. 2002;64: Kohl P, Bollensdorff C, Garny A. Effects of mechanosensitive ion channels on ventricular electrophysiology: experimental and theoretical models. Exp Physiol. 2006;91: Nickerson DP, Hunter PJ. The Noble cardiac ventricular electrophysiology models in CellML. Prog Biophys Mol Biol. 2006;90: Silva JR, Rudy Y. Multi-scale electrophysiology modeling: from atom to organ. J Gen Physiol. 2010;135: Moreno JD, Zhu ZI, Yang PC, Bankston JR, Jeng MT, Kang C, Wang L, Bayer JD, Christini DJ, Trayanova NA, Ripplinger CM, Kass RS, Clancy CE. A computational model to predict the effects of class I anti-arrhythmic drugs on ventricular rhythms. Sci Transl Med. 2011;3:98ra Mirams GR, Davies MR, Cui Y, Kohl P, Noble D. Application of cardiac electrophysiology simulations to pro-arrhythmic safety testing. Br J Pharmacol Noble D, Garny A, Noble PJ. How the Hodgkin-Huxley equations inspired the cardiac Physiome Project. J Physiol