Modelling the effects of chloroquine on KCNJ2-linked short QT syndrome

A gain-of-function KCNJ2 D172N mutation in KCNJ2-encoded Kir2.1 channels underlies one form of short QT syndrome (SQT3), which is associated with increased susceptibility to arrhythmias and sudden death. Anti-malarial drug chloroquine was reported as an effective inhibitor of Kir2.1 channels. Using biophysically-detailed human ventricle computer models, this study assessed the effects of chloroquine on SQT3. The ten Tusscher et al. model of human ventricular cell action potential was modified to recapitulate functional changes in the inward rectifier K+ current (IK1) due to heterozygous and homozygous forms of the D172N mutation. Mutant formulations were incorporated into multi-scale models. The blocking effects of chloroquine on ionic currents were modelled using IC50 and Hill coefficient values from literatures. Effects of chloroquine on action potential duration (APD), effective refractory period (ERP) and pseudo-ECGs were quantified. It was shown that chloroquine caused a dose-dependent reduction in IK1, prolonged APD, and decreased the maximum voltage heterogeneity. Chloroquine prolonged QT interval and declined the T-wave amplitude. Although chloroquine reduced tissue’s temporal vulnerability, it increased the minimum substrate size necessary for sustaining re-entry. The actions of chloroquine decreased arrhythmia risk, due to the reduced tissue vulnerability, prolonged ERP and wavelength of re-entrant excitation waves, which in combination prevented and terminated re-entry in the tissue models. In conclusion, the results of this study provide new evidence that the anti-arrhythmic effects of chloroquine on SQT3 and, by extension, to the possibility that chloroquine may be a potential therapeutic agent for SQT3 treatment.


Measurement of APD
Action potential duration (APD) was computed from single cell models by using an S1-S2 pacing protocol consisting of 10 conditioning stimuli (S1) (during which the model reached a stable solution) and a premature stimulus (S2) [1,2]. S1 was applied at a frequency of 1.25 Hz. S2 was applied with variable diastolic intervals (DI) after the AP evoked by the last S1. S1 and S2 have the same strength and duration (-52 pA/pF and 1 ms). APD at 90% repolarization (APD 90 ) of APs were measured. APD restitution (APD-R) curve was computed as the derivate of APD 90 evoked by the S2 against the DIs.

Measurement of ERP
Effective refractory period (ERP) was computed from single cell models by using a slightly different S1-S2 pacing protocol with 10 conditioning stimuli (S1) at a variable basic cycle length (BCL) followed by a premature stimulus (S2) with variable time delays (Δt). ERP was measured as the minimal Δt for which the S2 produced a peak voltage over 80% of the AP peak value evoked by the last S1 [1,2]. S1 and S2 have the same strength and duration (-52 pA/pF and 1 ms). ERP restitution (ERP-R) curve was generated by the ERP against BCLs [3].

Computing the pseudo-ECG
The pseudo-ECG was computed by using Gima and Rudy method [4]: where Ø e is the computed potential at the electrode, α is the radius of the strand, r is the Euclidean distance from a source point x to the electrode point xʹ, and dx is the spatial resolution. The virtual electrode was placed at a position 2.0 cm away from the ENDO end of the fiber. The QT interval was calculated as the time interval determined by definition of the Q-wave onset (0.0 mV) and the point corresponding to the T-wave end (T end ).
The time, at which the ECG data fell below a threshold (0.01 mV) was defined as T end .

Measurement of temporal vulnerability of ventricle
In cardiac tissue (Supplementary Figure 1A), a propagating wavefront is followed by a refractory tail. A stimulus applied too late after the refractory tail will lead to excitation that propagates in both directions (bi-directional conduction) or if applied too early, excitation propagation that fails in both directions (bi-directional block). During the refractory tail, there is a time window, the vulnerable window (VW), during which stimulation produces a solitary wave that propagates in either the retrograde or anterograde direction, resulting in a uni-directional block that allows reentry [1,2,5]. An S1-S2 protocol was used to investigate this. 10 S1 stimuli (1.25 Hz, strength: -52 pA/pF, duration: 1 ms) was applied to evoke a wave. Following a time delay after the 10th stimulus, an S2 (with the same of stimulus strength and duration as the S1) was applied to the marked region of the stand (marked with an arrow in Figure 8). The width of the time window during which the S2 propagated uni-directionally in the strand was measured as the temporal vulnerability of ventricular tissue.

Measurement of critical size of ventricular tissue to support re-entry -spatial vulnerability
In order to measure the critical substrate size of ventricular tissue (Supplementary Figure 1B) required to sustain re-entry, a standard S1-S2 protocol was used [1]. An S1 was applied to ENDO edge of the 2D idealized tissue to evoke a conditioning planar wave. After a time delay, an S2 was applied to the MIDDLE-EPI junction in the refractory tail of the conditioning wave. If the length of S2 is sufficiently long, the tip has sufficient space to follow its circuit path, and the re-entry survives. However, if the length of S2 is short, there is insufficient space for the tip to follow the path. Then the tip meanders out of the boundary of the tissue and wave terminates. The critical size of the tissue to support re-entry was quantified as the minimal length of S2 stimulus that supports the initiation and maintenance of re-entry, which provides an indication of the susceptibility of tissue to re-entry: the larger the critical size, the more difficult the initiation of re-entry in the tissue [1].

List of model equations and parameters
The inward rectifier K + current (I K1 ) was modified based on the experimentally obtained data [6], which are described previously [7]: WT:  where G K1 is the maximal conductance.

Model independence of the effects of CQ on SQT3
In addition to using the ten Tusscher and Panfilov ventricular cell model [8], we also employed the ORd human ventricular cell model [9] to predict the effects of CQ on SQT3. The data obtained with this model support our original results; APD was prolonged due to the presence of CQ (Supplementary Figure 2). Consequently, the effects of CQ on SQT3 are model independent.