Contents

📒 Roberts 2012

Computational approaches to understand cardiac electrophysiology and arrhythmias1

Sciwheel.

Introduction

  • customizable modeling platforms: Continuity(404 NOT FOUND), CHASTE and OpenCMISS

Models to Address Ion Channel-Based Mechanisms of Cardiac Arrhythmia

  • For diseases like long-QT (LQT) syndrome (LQTS), Brugada syndrome (BrS), and isolated cardiac conduction disorder (ICCD)
    • Increase in late Na => prolongation of the AP duration (APD) and consequent QT interval prolongation
  • mutations at multiple loci can produce the same phenotype (56, 186), or a single mutation at a particular locus can surprisingly result in different phenotypes => need for Computational modeling
  • potassium current defects: hERG (IKr) defect => longer QT interval
  • estrogen also reduces Ikr => longer QT interval => arrythmia https://www.physiology.org/na101/home/literatum/publisher/physio/journals/content/ajpheart/2012/ajpheart.2012.303.issue-7/ajpheart.01081.2011/production/images/large/zh40201205410001.jpeg
  • progesterone appears to shorten the QT interval and reduce arrhythmia incidence associated with LQTS

Models of Normal and Pathological Cardiac Regulation by Subcellular Signaling

  • CaMKII signaling => multiple ion channels and pumps (HRd model) https://www.physiology.org/na101/home/literatum/publisher/physio/journals/content/ajpheart/2012/ajpheart.2012.303.issue-7/ajpheart.01081.2011/production/images/large/zh40201205410002.jpeg
  • β-adrenergic signaling cascade => INa, IKs, RyRs, SERCA, the Na-K pump, troponin I, glycolysis, cross-bridge formation
  • PKA and CaMKII-dependent modulation : β-adrenergic enhancement of calcium transients was assisted by a synergistic relationship between PKA and CaMKII-dependent phosphorylation of multiple targets (Soltis-Saucerman model) https://www.physiology.org/na101/home/literatum/publisher/physio/journals/content/ajpheart/2012/ajpheart.2012.303.issue-7/ajpheart.01081.2011/production/images/large/zh40201205410003.jpeg

Models of EC Coupling

  • Arrhythmogenic phenomena including delayed afterdepolarizations (DADs) and EADs, premature ectopic beats, alternans, and initiation of ventricular tachycardias and fibrillation are all linked to aberrant calcium dynamics
  • accurate representation of calcium dynamics and EC coupling is among the most complicated aspects of cardiac electrophysiology modeling
    • Negative feedback: VDI, CDI
    • Positive feedback: RyR activation, EC coupling gain
    • SR leak
  • cardiac diad where L-type calcium channels and RyRs are found in close proximity and even within the SR
  • Trans' SERCA model2 includes dependence on myoplasmic and SR calcium concentrations and allows for variable calcium-proton transport ratios
  • NCX: voltage, sodium and calcium concentration dependence, as well as slippage
  • mechanisms of SR calcium release and the propagation of calcium waves?
  • defects in cell metabolism and mitochondrial function have also been implicated in the genesis of arrhythmias

ROS-induced ROS release (RIRR)

  • The mitochondria also exhibit complex behavior such as traversing waves of membrane depolarization
    • ROS-induced ROS release (RIRR)
    • altered intracellular calcium dynamics (via MCU)
    • collapse of mitochondrial membrane potential (ΔΨ)
    • mitochondrial permeability transition pore (mPTP)
    • mitochondrial ATP-sensitive potassium channels (mKATP)
      https://www.physiology.org/na101/home/literatum/publisher/physio/journals/content/ajpheart/2012/ajpheart.2012.303.issue-7/ajpheart.01081.2011/production/images/large/zh40201205410004.jpeg
      The ECME-RIRR model

Metabolic stress that results from myocardial ischemia and reperfusion (I/R)

https://www.physiology.org/na101/home/literatum/publisher/physio/journals/content/ajpheart/2012/ajpheart.2012.303.issue-7/ajpheart.01081.2011/production/images/large/zh40201205410005.jpeg

  • Ion inbalance (Na, Ca, K, H)
  • Increased mitochondrial-derived ROS from RET due to succinate accumulation during ischemia

Perturbations in Channel Function Alter Cardiac Dynamics

  • Cell level alteration of ion dynamics related to arrythmia prediction

Source-Sink Relationships and Propagation of Arrhythmia Triggers

  • In a tissue level => PVCs, which serve as arrhythmia triggers

Tissue Structure and the Role of Geometry

https://www.physiology.org/na101/home/literatum/publisher/physio/journals/content/ajpheart/2012/ajpheart.2012.303.issue-7/ajpheart.01081.2011/production/images/large/zh40201205410006.jpeg

Models to Predict Drug Therapy Effects

  • computer-based drug screening
  • flecainide => reduced excitability => Proarrhythmic conduction block. (lidocaine is safer in this case)
  • atrial-specific sodium channel modeling (6, 153) and focusing on specific drugs such as lidocaine (147), flecainide (147), ranolazine (148, 153), and bupivacaine (212) in detailed models
  • Models can be used to quickly survey a large range of drug compounds and concentrations under different conditions of pacing protocol, heart rate, and additional conditions
  • An additional advantage of computational models is that many parameters can be monitored throughout a simulation
  • many drugs are promiscuous and interact with proteins other than the intended targets and that biological systems exhibit many nonlinear dependencies across multiple scales of time and space
  • Furthermore, computational approaches potentially allow certain problems associated with animal studies to be circumvented

Computational Models to Guide Ablation Procedures and Biological Pacemakers

Patient-Specific Modeling

Validation of Model Predictions Is Necessary

  • In silicon models not only reproduce previously observed physiological behavior but also yield predictions that are correct and can guide further experimental work

Utility of Computational Modeling Studies in the Clinical Setting

  • direct use of patient data and guiding procedures
  • Making use of computational tools in a different way, a technique called noninvasive electrocardiographic imaging (ECGI), reconstructs an electrical activation map by merging ECGI data with a reconstruction of a patient’s heart from CT scan images

Reference


  1. Roberts BN, Yang P-C, Behrens SB, Moreno JD, Clancy CE. Computational approaches to understand cardiac electrophysiology and arrhythmias. Am. J. Physiol. Heart Circ. Physiol. 2012;303(7):H766-83. doi:10.1152/ajpheart.01081.2011. PMC3774200↩︎

  2. Tran K, Smith NP, Loiselle DS, Crampin EJ. A thermodynamic model of the cardiac sarcoplasmic/endoplasmic Ca(2+) (SERCA) pump. Biophys. J. 2009;96(5):2029-2042. doi:10.1016/j.bpj.2008.11.045. ↩︎