Prolongation of the rate-corrected QT interval (QTc interval) on the electrocardiogram (ECG) may be related to numerous factors and is associated with an increased risk of malignant ventricular arrhythmias and even sudden cardiac death [1]. Mutations in at least 17 genes have been associated with congenital long-QT syndrome (LQTS), with LQTS types 1–3 accounting for the vast majority of genotype-positive cases and having the strongest evidence as being LQTS-causative [2]. The estimated prevalence of congenital LQTS is 1:2000 to 1:2500, but the true prevalence may be higher because of silent mutation carriers [3]. Notably, LQTS is a leading cause of sudden cardiac death in young, apparently healthy individuals [3], but proper identification of family members at risk is hampered because $\approx$25% of LQTS cases still remain genotype elusive [4].

Long-QT syndrome type 1 (LQTS1 or LQT1) is caused by pathogenic loss-of-function mutations in the KCNQ1 gene, also known as KvLQT1, which encodes the pore-forming Kv7.1 $\alpha$-subunit of the ion channel carrying the slow delayed rectifier potassium current (I_Ks) [5]. If the production of the pathologic protein is not completely zeroed by the mutation of interest, patients heterozygous for such a mutation coassemble wild-type and mutant KCNQ1-encoded $\alpha$-subunits into tetrameric Kv7.1 potassium channels. Similarly, long-QT syndrome type 2 (LQTS2 or LQT2) patients are heterozygous for loss-of-function mutations in the KCNH2 gene, also known as hERG or hERG1 gene, which encodes the pore-forming Kv11.1 $\alpha$-subunit of the ion channel carrying the rapid delayed rectifier potassium current (I_Kr) [6]. In case of LQTS2, the ventricular action potential (AP; Fig. 1A, Ref. [7, 8, 9]) is prolonged as a result of a slowing in its repolarization due to the mutation-induced reduction in I_Kr (Fig. 1B). The prolongation of the ventricular AP results in a prolongation of the QT interval on the ECG (Fig. 1C), which is a risk factor for the development of ventricular arrhythmias known as ‘Torsades de Pointes’ (Fig. 1D) that may lead to dizziness, fainting, cardiac arrest, and eventually even sudden cardiac death in LQTS patients [7].

It has been shown that mutant and wild-type Kv channel subunits randomly co-assemble into functional tetramers [10]. Thus, these tetramers would contain 0 to 4 mutant subunits according to a binomial distribution with parameters n = 4 and p = 0.5, representing all possible combinations of a total of four wild-type or mutant subunits. This explains the common dominant-negative nature of LQTS1 and LQTS2 loss-of-function mutations in the KCNQ1 and KCNH2 genes [11, 12], provided that wild-type (WT) and mutant Kv7.1 and Kv11.1 subunits are similarly translated and processed and then randomly co-assembled into tetramers [6]. Most mutations in KCNH2 result in such a dominant-negative effect, but the severity of the resulting phenotype varies widely [13]. The latter may be envisioned by only 1/16th of the channels (containing only WT subunits) being functional in the case of a severe dominant-negative mutation and 5/16th of the channels (containing at most one mutant subunit) being functional in the case of a mild dominant-negative mutation, not to mention other effects of a mutation, such as changes in channel kinetics or accelerated degradation of the wild-type subunit [14].

Recently, Cócera-Ortega et al. [15] demonstrated, both in vitro and in silico, that the effects of LQTS1 can be alleviated by a $\approx$60% inhibition of mutant KCNQ1. They investigated whether this allele-specific inhibition could alleviate the disease by shifting the balance of I_Ks channel subunits towards an increased incorporation of the WT allele. Silencing of the mutant allele reduced the occurrence of arrhythmic events in human-induced pluripotent stem-cell-derived cardiomyocytes (hiPSC-CMs) that were derived from two patients with a pathogenic LQTS1 mutation, whereas silencing of the WT allele increased the occurrence of arrhythmic events. Computer simulations—using the human ventricular cell model by Ten Tusscher et al. [16], as updated by Ten Tusscher and Panfilov [17] (known as the TP06 model)—revealed that 60% suppression of the mutant KCNQ1 allele is particularly effective when only I_Ks channels without a mutant Kv7.1 subunit or with a single mutant Kv7.1 subunit are conductive and I_Ks channels with more mutant subunits are not. In their study of LQTS2-causative mutations, Bains et al. [18] not only achieved a higher level of suppression of $\approx$80% in their hiPSC-CMs, but also reduced the prolonged AP duration (APD) at 90% repolarization (APD₉₀) to near control levels by using a combined suppression and replacement (“SupRep”) KCNH2 gene therapy.

In the present in silico study, we assessed to which extent allele-specific suppression of the mutant KCNH2 allele per se can alleviate the effects of LQTS2, both in the case of severe dominant-negative mutations and in the case of mild dominant-negative mutations. To this end, we determined the effects of such mutations on APD₉₀ at a stimulation rate of 1 Hz and on the APD₉₀ restitution curve obtained with an S1-S2 pacing protocol. These effects were not only determined without silencing of the mutant allele, but also at suppression levels of 70 and 90%, and with SupRep. Computer simulations were carried out with two different recent and comprehensive models of the electrical activity of a single human ventricular cardiomyocyte, i.e., the ‘Bartolucci–Passini–Severi model as published in 2020’ [19] and the ‘Tomek–Rodriguez model following the O’Hara–Rudy dynamic (ORd) model’ [20]. These models are known as the BPS2020 and ToR–ORd models, respectively. A preliminary version of our study—using the O’Hara–Rudy human ventricular cardiomyocyte model [21], as updated with the I_Kr formulation by Li et al. [22] (known as the ORd 2017 model)—was presented at the Computing in Cardiology 2022 conference and published as an extended abstract [23].

The electrophysiology of an isolated human ventricular cardiomyocyte was simulated using the TP06, BPS2020, and ToR–ORd models [17, 19, 20]. For the BPS2020 model [19], we used the CellML code—CellML is a markup language for mathematical models of (sub)cellular function [24, 25, 26]—that the developers of the model made publicly available in the CellML Model Repository [27] (https://models.cellml.org/; accessed on March 4, 2025), with the default extracellular calcium concentration set to 1.7 mmol/L instead of 2.7 mmol/L (model update of November 22, 2024). For the ToR–ORd model [20], we used the CellML code made publicly available by the developers of the model on the GitHub platform (https://github.com/jtmff/torord; accessed on March 4, 2025) in an updated version, termed ToR–ORd–dynCl, with a dynamic representation of the intracellular chloride concentration. This updated model behaves very similarly to the original ToR–ORd model, but with a higher stability over long simulations [28].

For the endocardial, midmyocardial, and epicardial versions of the TP06 model [17], we started from the CellML code that was made publicly available on October 15, 2020, by Penny Noble from the University of Oxford in the CellML Model Repository (accessed on November 3, 2025). The fully-activated conductance of I_Ks (G_Ks) of the default (endocardial) version of the TP06 model [17] was scaled down by a factor of 12.5 from its value of 0.392 nS/pF in the TP06 model to 0.03136 nS/pF. The rationale for doing so is that the TP06 value of G_Ks is based on that in the Ten Tusscher et al. model [16], in which experimental voltage clamp data were scaled up by a factor of 12.5 before fitting the model I_Ks equations to these data in order to obtain specific APD₉₀ values. Conversely, the fully-activated conductance of I_Kr (G_Kr) in the default (endocardial) version of the TP06 model [17] was scaled up by a factor of three, from 0.153 to 0.459 nS/pF, to compensate for the reduced I_Ks and achieve an I_Kr amplitude similar to that in the BPS2020 and ToR–ORd models [19, 20].

In the midmyocardial version of the TP06 model, G_Kr was decreased by a factor of 0.8 compared to the default (endocardial) version of the model, as in the BPS2020 and ToR–ORd models [19, 20]. Furthermore, G_Ks was scaled down by a factor of two, in line with electrophysiological observations [29, 30]. This factor of two differs from the factor of four used in the Ten Tusscher et al. [16, 17] and TP06 models. Ten Tusscher et al. [16] introduced the latter factor of four in order to obtain specific APD₉₀ values. In the epicardial version of the TP06 model, G_Kr was increased by a factor of 1.2 compared to the default (endocardial) version of the model, similar to the BPS2020 and ToR–ORd models (G_Kr increased by factors of 1.1 and 1.3 in the epicardial versions of the BPS2020 and ToR–ORd models, respectively), whereas G_Ks was increased by a factor of 1.4 compared to the default (endocardial) version of the model, similar to the BPS2020 and ToR–ORd models [19, 20].

The CellML code of the models was edited and run in version 0.9.31.1409 of the Windows-based Cellular Open Resource (COR) environment developed by Garny et al. [31]. All simulations were run for a simulated period of 100 s, which appeared to be long enough to achieve steady-state behavior under each simulated condition. The shown data are from the final two seconds of this 100 s period. Action potentials were elicited by a 1 ms, $\approx$2$\times$ threshold stimulus. The membrane potential at 90% repolarization was set to a fixed value of –73.54 mV, –77.10 mV, and –73.52 mV in the BPS2020, ToR–ORd, and (updated) TP06 models, respectively, based on the AP shape of the default (endocardial) version of the models when stimulated at a rate of 1 Hz, similar to the approach of Qu et al. [32].

Transmural conduction was studied in a heterogeneous linear strand of 600 transversally coupled human left ventricular myocytes, with the individual myocytes described by the TP06 model [17], updated as set out above. We recently used such a strand of TP06 myocytes in a study investigating SCN10A-short gene therapy for restoring conduction and protecting against malignant cardiac arrhythmias [33]. This simulated strand has a left ventricular wall thickness of 1.2 cm (normal range 0.9–1.4 cm [34]), with the transverse orientation of the left ventricular myocytes based on human and canine data [35, 36]. The strand was stimulated from the endocardial side by injecting an $\approx$20% suprathreshold stimulus of 1 ms duration into the first of 300 myocytes of the endocardial type. The next 240 cells of the strand were myocytes of the midmyocardial type, and the final 60 cells were myocytes of the epicardial type. This distribution of 50% endocardial type cells, 40% midmyocardial type cells, and 10% epicardial type cells was based on data from the human left ventricle [37]. The intercellular coupling conductance was set to 6 µS, which is the geometric mean of the estimated range of 3–12 µS for the gap junctional conductance between human ventricular myocytes [38]. Cytoplasmic resistivity was set to 150 $\mathrm{\Omega }$$\cdot$cm, a value typically used in in silico studies [39, 40, 41] and near the experimentally observed value of 166 $\pm$ 19 $\mathrm{\Omega }$$\cdot$cm (mean $\pm$ SEM, n = 11) at 35 °C [42].

The transmural strand model was coded in Fortran 95 and compiled as a 32-bit Windows application using Intel Visual Fortran Composer XE 2013 (Intel Corporation, Santa Clara, CA, USA) and run on an Intel Core i7 processor-based workstation. We applied a simple and efficient Euler-type integration scheme with a 1 µs time step for numerical integration of differential equations [43]. The spatial discretization step in the strand simulations was 20 µm, i.e., the width of a single myocyte, which was treated as isopotential. Stimulus and end effects, which were restricted to no more than a few cells, were minimized by discarding data obtained from the first three and last three cells of the strand when assessing dispersion in repolarization time (RT). All simulations were run for a sufficiently long period to reach steady-state behavior.

We simulated the electrophysiology of an isolated human ventricular cardiomyocyte using the two most recent comprehensive models of such a cell, which were developed in parallel. These models, i.e., the BPS2020 model [19] and the ToR–ORd [20] model, were published in 2020 and 2019, respectively. Both can be considered as major updates of the well-known and widely used O’Hara–Rudy dynamic (ORd) human ventricular cell model [21], which had become the “gold standard” for in silico human ventricular cellular electrophysiology [19]. To date, these two models are the only two major updates of the ORd model. Both models represent an endocardial myocyte by default, but also have midmyocardial and epicardial versions. In both models, the midmyocardial I_Kr density is 20% lower than in the default model, whereas it is 10% higher than in the default model in the epicardial version of the BPS2020 model, and 30% higher than in the default model in the epicardial version of the ToR–ORd model. The time and voltage dependence of I_Kr are quite similar in the two models (Figs. 3,8). Yet, the effects on their APs are less similar, as for example illustrated in Fig. 8B,E, which underscores the relevance of using more than a single model when assessing the effects of ion channel mutations.

In the present study, we attributed the dominant-negative nature of LQTS2-associated loss-of-function mutations in KCNH2 entirely to a loss in the number of functional channels by assuming that wild-type and mutant KCNH2-encoded $\alpha$-subunits randomly co-assemble into tetrameric Kv11.1 potassium channels and that only 1/16th of these channels (containing only WT $\alpha$-subunits) are functional in the case of a severe dominant-negative mutation and that 5/16th of the channels (containing at most one mutant $\alpha$-subunit) are functional in the case of a mild dominant-negative mutation. One limitation of our study is that we did not take into account any other effects of the mutation that might enhance or attenuate the mutational effects. For example, the majority of functional channels may be composed of three wild-type $\alpha$-subunits and a single mutant $\alpha$-subunit in the case of mild mutations. It cannot be excluded that such channels have mutation-induced changes in their kinetic behavior that may affect I_Kr and its pathophysiological effects on the associated APs. Such effects were not considered in the present study. One may argue that such changes play a relatively minor role, in particular with the severe mutation, given that the amplitude of I_Kr is reduced due to the loss-of-function mutation. In the case of type 1 short-QT mutations, on the other hand, with a gain-of-function mutation in KCNH2 rather than a loss-of-function one, it is of high importance to simulate the increased I_Kr in accordance with the specific mutation-induced changes in the kinetics of I_Kr, as for example performed by Zhang et al. [50]. In their interesting in silico study of the gain-of-function KCNH2-T618I mutation, they created a Markov chain model in order to precisely reproduce the behavior of I_Kr under wild-type and mutant conditions.

In the extreme case where only I_Kr channels composed of four wild-type $\alpha$-subunits are conductive, resembling the effects of severe mutations, the fully-activated conductance of the remaining I_Kr is so low that individual cells show extreme AP prolongation and in some cases EAD development, particularly in the midmyocardial version of the two models (Fig. 3). Together with the mutation-induced steepening of the APD restitution curves (Fig. 5), this may explain the mutation-associated susceptibility to arrhythmogenic re-entrant arrhythmias and the potentially lethal nature of such mutations. If I_Kr channels with exactly one mutant $\alpha$-subunit are also conductive, as in the case of mild mutations, the total percentage of conductive channels (relative to the 100% in the wild-type case) rises to 5 out of 16 instead of 1 out of 16, as illustrated in Fig. 2. Allele-specific silencing of the mutant KCNH2 allele reduces the production of mutant $\alpha$-subunits, making it more likely that an I_Kr channel is composed of WT $\alpha$-subunits only or that it contains exactly one mutant $\alpha$-subunit. This silencing increases the fully-activated conductance of I_Kr with both severe and mild mutations, thus ameliorating the effects of the mutation. However, it should be noted that this silencing also reduces the total number of KCNH2 subunits, and thus the total number of I_Kr channels, so that the silencing may not be sufficient to counteract the effects of the mutation.

As set out above, the effects of the silencing may be less beneficial than sometimes anticipated due to the opposing effects of suppressing the mutant allele on the one hand and the associated reduction of the expression level of I_Kr channels on the other hand. These opposing effects are detailed in Fig. 11A. When the mutant allele is suppressed by a factor of sup, the probability p that a subunit is WT becomes 1/(2–sup) (see Fig. 11A, inset). According to the binomial distribution (skewed if sup $>$0), the fraction of functional channels amounts to p ⁴ in the case of a severe mutation (four WT subunits) and p⁴ + 4$\times$p ³$\times$(1–p) in the case of a mild mutation (four or three WT subunits), which both increase with an increase in sup. On the other hand, the total expression of I_Kr channels is reduced by the suppression of the mutant allele to a fraction of (2–sup)/2 of its control. Given these two factors, the expression of functional I_Kr channels, relative to control, can be obtained by multiplying the fraction of functional channels by the total expression of I_Kr channels. Fig. 11A shows how the expression of functional I_Kr channels depends on the degree of mutant allele suppression, for both severe (dark blue line) and mild (light blue line) mutations.

It is important to note that the severe and mild mutations appear to respond differently to silencing. For example, the percentage of functional channels rises considerably from 23% to 38% if the silencing is raised from 70% to 90% with severe mutations (Fig. 11A, dark blue circles), whereas it only rises from 50% to 53% with mild mutations (Fig. 11A, light blue squares). With a severe mutation, increasing the level of suppression substantially and continuously increases the percentage of functional channels, from 6% to 50%, over the entire range of suppression (Fig. 11A, dark blue line). With a mild mutation, this increase is only moderate, from 31% to 50% over the entire range of suppression, and not continuously rising (Fig. 11A, light blue line). The largest percentage of functional channels is achieved at 87% suppression and amounts to 53%. At 100% suppression, only the single wild-type allele is able to provide $\alpha$-subunits and thus the percentage of functional channels is 50% (Fig. 11A, black triangle).

The situation of Fig. 11A changes with the aforementioned SupRep gene therapy, which suppresses and replaces the mutant allele. The probability p that a subunit is WT now becomes (1+sup)/2 (see Fig. 11B, inset), whereas the total expression of I_Kr channels does not change, so that the factor representing the total expression of I_Kr channels relative to control becomes 1. With these factors, the above multiplication yields the curves shown in Fig. 11B. As with suppression per se (Fig. 11A), the severe and mild mutations respond differently to silencing (Fig. 11B). At 70% suppression-and-replacement, the percentage of functional channels amounts to 52% and 89% for severe and mild mutations, respectively (Fig. 11B, circle and square, respectively, at 70% suppression-and-replacement) as opposed to the values of 23% and 50%, respectively, with suppression per se. These numbers increase to 81% and 99%, respectively, with 90% suppression-and-replacement (Fig. 11B, circle and square, respectively, at 90% suppression-and-replacement) as opposed to the values of 38% and 53%, respectively, with suppression per se. With 100% suppression-and-replacement, the percentage of functional channels is 100% (Fig. 11B, black triangle) as opposed to the value of 50% obtained with suppression per se (Fig. 11A, black triangle).

Fig. 11C,D illustrates the effects of 80% suppression-and-replacement as compared to 80% suppression per se in the midmyocardial versions of the BPS2020 model (Fig. 11C) and the ToR–ORd model (Fig. 11D) in the case of a severe mutation in KCNH2. In the BPS2020 model, the repetitive EAD pattern is no longer observed with 80% suppression, as in the case of the 70% and 90% suppression of Fig. 3B. However, the associated APD₉₀ is increased by as much as 251 ms compared to WT/WT (from 283 to 534 ms; +89%). This increase is strongly reduced to 75 ms (+26%) with 80% suppression-and-replacement (Fig. 11C). In the ToR–ORd model, the 80% suppression is not sufficient to stop the development of EADs (Fig. 11D), as in the case of the 70% and 90% suppression of Fig. 3E. EADs are not observed with 80% suppression-and-replacement. A regular AP pattern occurs with an APD₉₀ that is increased by 95 ms compared to WT/WT (from 348 to 444 ms; +27%).

We should keep in mind that all of the above results were obtained using single-cell models. One could argue that the observed midmyocardial EADs might be absent, or at least diminished, if the midmyocardial cells are electrically coupled to their endocardial and epicardial neighbors, as they are in the whole heart. Fig. 12 illustrates the effects of this electrical coupling. It shows the results of simulations with the computationally efficient human ventricular cell model by Ten Tusscher and Panfilov (known as the TP06 model, updated as described in the Materials and Methods section).

First, we assessed the shape of the APs and associated I_Kr of the endocardial, midmyocardial, and epicardial single-cell versions of the updated TP06 model, not only under control conditions, but also in the case of a severe mutation in KCNH2 and its suppression. The obtained action potentials and I_Kr are shown in Fig. 12A–C. The WT/WT and WT/mutant traces resemble those of the BPS2020 and ToR–ORd models (Fig. 3B,E), including the significant increase in APD₉₀ (by 101% from 308 to 620 ms; Fig. 12A,H) and the emergence of EADs in the midmyocardial version of the model (Fig. 12B, vertical arrow). Similar to the ToR–ORd model (Fig. 3E), the midmyocardial cell exhibits an alternating pattern of incomplete and short APs in the WT/mutant case. Similar to the 70% and 90% suppression in the BPS2020 and ToR–ORd models (Fig. 3A,D, dotted lines), applying the 80% suppression to the default (endocardial) version of the updated TP06 model reduces the increase in APD₉₀ from +101% to +58% (Fig. 12A,H). Silencing by 80% suppression-and-replacement further reduces the increase in APD₉₀ to +21% (Fig. 12A,H).

Next, we ran simulations with a transmural strand composed of 300 endocardial, 240 midmyocardial, and 60 epicardial cells [37] (Fig. 12D) that were coupled by a gap junctional conductance (g_j) of 6 µS [38], as detailed in the Materials and Methods section. The cytoplasmic resistivity was set to 150 $\mathrm{\Omega }$$\cdot$cm [41, 42]. Under control conditions, we obtained an in silico transversal activation time of 55 ms and thus a transversal conduction velocity of 22 cm/s through the 1.2 cm thick left ventricular wall. This value of 22 cm/s aligns well with the in vivo and in vitro observations by Durrer et al. [51], Toyoshima et al. [52], Anderson et al. [53], and Conrath et al. [54].

Fig. 12E–G shows the action potentials of the middle endocardial cell (cell #150 of the strand), the middle midmyocardial cell (cell #420), and the middle epicardial cell (cell #570). Due to the strong electrical coupling, the shapes and durations of the endocardial, midmyocardial, and epicardial APs differ less than in the single-cell simulations (Fig. 12A–C). However, the effects of the severe mutation, as well as those of the 80% suppression or 80% suppression-and-replacement, on the endocardial and epicardial AP shape (Fig. 12E vs. Fig. 12A and Fig. 12G vs. Fig. 12C, respectively) and duration (Fig. 12I vs. Fig. 12H) are only moderately different. With the electrical coupling in the strand, the alternating AP pattern of the single midmyocardial cell of Fig. 12B turns into the more regular pattern of Fig. 12F, but the midmyocardial cells (cell #420 and—not shown in Fig. 12—its neighboring midmyocardial cells #363–#456) still show EADs (with the most prominent EADs occurring in cell #413). The disproportionate AP prolongation and EAD development of the midmyocardial cells upon the mutation-induced strong decrease in I_Kr (Fig. 12B,F) have also been observed in vitro upon application of I_Kr blockers (see Antzelevitch [55] and references cited therein). Now that the transversal activation time is not much affected by the mutation, the disproportionate AP prolongation of the midmyocardial cells in the strand also results in a prominent increase of the transmural dispersion of repolarization (see below).

From our strand simulations, we computed the pseudo-QT interval as the time interval between the earliest activation and the latest repolarization in the strand [54, 56]. For each cell in the strand, we computed the time of its repolarization as the sum of the time of its activation (relative to the time of activation of cell #1) and its APD₉₀ [54, 56]. The results are shown in Fig. 12J. Under control conditions, the pseudo-QT interval is 416 ms (Fig. 12J, leftmost bar). In case of the severe mutation in KCNH2, the pseudo-QT interval is as large as 838 ms, which is reduced to 608 and 486 ms with 80% suppression and 80% suppression-and-replacement, respectively.

Augmentation of the transmural dispersion of repolarization (TDR) has since long been associated with the development of potentially lethal reentrant arrhythmias [57, 58, 59, 60, 61, 62, 63, 64]. Fig. 13 shows that the simulation results of Fig. 12 are accompanied by a substantial increase in TDR with a severe loss-of-function mutation in KCNH2, with an increase in TDR from 73 ms under WT/WT conditions to as much as 175 ms (+139%) under WT/mutant conditions (Fig. 13B). This TDR is reduced to 96 ms (+30%) in response to 80% suppression per se and to 78 ms (+7%) in response to 80% suppression-and-replacement (Fig. 13B).

In an interesting simulation study, Jiang et al. [65] demonstrated how carbon monoxide (CO) increased the AP duration and augmented the transmural dispersion of repolarization in a one-dimensional transmural tissue strand model, based on the O’Hara–Rudy human ventricular cardiomyocyte model [21]. The similarities between the results of the present study and those obtained by Jiang et al. [65] are largely due to the inhibitory effect of CO on I_Kr [65].

As demonstrated in the present study, silencing of the loss-of-function mutant KCNH2 allele may alleviate its effects, but with the imperfection that the effects of the associated long QT syndrome type 2 cannot be completely cancelled. The suppression leads to an increased expression of functional I_Kr channels, but not to a level that corresponds with the wild-type level (Fig. 11A). To achieve perfection, the mutant allele should not only be suppressed but also replaced (Fig. 11B). Such “suppression-and-replacement” (“SupRep”) gene therapy has been proposed and tested in vitro for LQT1 and LQT2 related loss-of-function mutations in KCNQ1 and KCNH2 [18, 66] as well as in vivo for an LQT1 related loss-of-function mutation in KCNQ1 [67] with promising results.

Silencing of the mutant allele can substantially, though not completely, counteract the effects on I_Kr of mild or severe LQTS2 mutations in KCNH2. Allele-specific inhibition of the mutant KCNH2 allele per se is not sufficient to treat the effects of these LQTS2 mutations and should be accompanied by a replacement gene therapy, thus creating a suppression-and-replacement (“SupRep”) gene therapy.

The source code of the BPS2020 model is publicly available in the CellML Model Repository (https://models.cellml.org/workspace/711). The source code of the ToR–ORd model is publicly available on the GitHub platform (https://github.com/jtmff/torord). The source of each of the endocardial, midmyocardial, and epicardial versions of the TP06 model is publicly available in the CellML Model Repository (https://models.cellml.org/workspace/tentusscher_panfilov_2006). The Fortran 95 code for the one-dimensional strand of TP06 myocytes as well as the datasets created and analyzed during this study are available from the corresponding author on reasonable request.

RW confirms sole responsibility for the following: study conception and design and manuscript writing. RW read and approved the final manuscript.

Not applicable.

The author acknowledges the initiative of the developers of the BPS2020 and ToR-ORd models to make their model code publicly available.

This research received no external funding.

The author declares no conflict of interest.