# In silico predictions of action potential propagation in doxorubicin cardiotoxicity: A parametric study using preclinical 3D magnetic resonance imaging-based fibrotic left ventricle models

Javier Villar-Valero, Jairo Rodríguez Padilla, Nicolas Cedilnik, Buntheng Ly, Juan F. Gómez, Maxime Sermesant, Mihaela Pop, Beatriz Trenor

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Javier Villar-Valero, Jairo Rodríguez Padilla, Nicolas Cedilnik, Buntheng Ly, Juan F. Gómez, et al. In silico predictions of action potential propagation in doxorubicin cardiotoxicity: A parametric study using preclinical 3D magnetic resonance imaging-based fibrotic left ventricle models. The Journal of Physiology, 2025, 10.1113/JP288819. hal-05449683

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# *In silico* predictions of action potential propagation in doxorubicin cardiotoxicity: A parametric study using preclinical 3D magnetic resonance imaging-based fibrotic left ventricle models

Javier Villar-Valero<fnref n="1" />, Jairo Rodríguez Padilla<fnref n="2" />,<fnref n="3" />, Nicolas Cedilnik<fnref n="2" />, Buntheng Ly<fnref n="3" />, Juan F. Gomez<fnref n="4" /> ID,
Maxime Sermesant<fnref n="2" />,<fnref n="3" /> ID, Mihaela Pop<fnref n="2" />,<fnref n="5" /> and Beatriz Trenor<fnref n="1" /> ID

Handling Editors: Bjorn Knollmann & Axel Loewe

The peer review history is available in the Supporting Information section of this article
(https://doi.org/10.1113/JP288819#support-information-section).

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**Abstract figure legend** Schematic overview of the study's methodology and key findings. In a preclinical swine model, doxorubicin administration induced cardiotoxicity, characterized by diffuse fibrotic remodelling and altered electrical function, as assessed by magnetic resonance (MR) imaging and electrophysiological mapping. Patient-specific computational models, built from these data and simulated using high-performance Lattice-Boltzmann methods, demonstrated that this heterogeneous fibrosis substrate provides the arrhythmogenic substrate for malignant ventricular arrhythmias. These *in silico* predictions reveal a direct link between doxorubicin-induced structural changes and an increased risk of life-threatening arrhythmias.

<footnote n="1">¹Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain</footnote>

<footnote n="2">²Centre Inria d'Université Côte d'Azur, Epione team, Sophia Antipolis, France</footnote>

<footnote n="3">³IHU-Liryc, Université de Bordeaux, Pessac, France</footnote>

<footnote n="4">⁴Valencian International University, Valencia, Spain</footnote>

<footnote n="5">⁵Sunnybrook Research Institute, Toronto, Canada</footnote>
**Abstract** Doxorubicin (DOX) is a widely used chemotherapeutic agent, but its cardiotoxic effects, including diffuse myocardial fibrosis, increase the risk of dangerous arrhythmias. There is a critical need for non-invasive tools to predict DOX-related ventricular arrhythmias in early chronic stages following chemotherapy. A computational study was performed using experimental data from three pigs: one control and two at 9 weeks following DOX. Customized 3D left ventricular (LV) models were generated from late gadolinium-enhanced magnetic resonance imaging and electro-anatomical maps, integrating tissue structure, electrical properties (healthy/fibrosis) and fibre directions. Action potential (AP) wave propagation was simulated using a high-performance numerical solver. A virtual programmed stimulation protocol was applied in 96 simulations to assess arrhythmia inducibility, varying the parameters corresponding to excitability and conduction velocity in fibrotic zones. Arrhythmias were inducible only in DOX-treated cases. Reentrant wave genesis depended on: excitability, conduction velocity, fibrosis distribution and AP duration heterogeneity. In one scenario, AP heterogeneities and a ≥70% reduction in diffusion coefficient were required to induce reentry despite unchanged excitability in fibrosis. This study presents the first computational simulation of DOX-induced cardiotoxicity in a realistic 3D LV model using a highly efficient, automated Lattice-Boltzmann approach. Our findings provide insights into arrhythmogenic mechanisms and may aid in developing strategies to prevent and treat DOX-related cardiotoxicity.

(Received 28 February 2025; accepted after revision 7 October 2025; first published online 29 October 2025)

**Corresponding author** B. Trenor: Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain. Email: btrenor@eln.upv.es

**Key points**

* We developed a novel semi-automated computational framework to construct high-resolution 3D magnetic resonance imaging-based left ventricular models designed to study via simulations the electrical activity after chemotherapy using a GPU-optimized Lattice-Boltzmann method solver.

* Our digital heart twins were directly calibrated and validated using measurements of conduction velocity and action potential wave features obtained via catheter-based electro-anatomical mapping after chemotherapy in preclinical swine models.

* This specific virtual parametric study demonstrates that both electrophysiological and structural alterations induced by diffuse fibrosis substantially modulate ventricular arrhythmias in the sub-chronic phase following doxorubicin therapy.

**Introduction**

Cardiotoxicity is a major pathophysiological complication of drug-based therapies, representing a critical clinical

burden (Mamoshina et al., 2021). A considerable number of these cases are due to anti-cancer drugs (e.g. anthracyclines), which are powerful chemicals prescribed to patients with various types of cancer (e.g.

**Javier Villar-Valero** is a PhD candidate in Biomedical Engineering at Universitat Politècnica de València, Spain. His research focuses on computational modelling of ventricular arrhythmias, cardiac fibrosis and electrophysiological remodelling using MRI-based patient-specific simulations. He has collaborated with the Inria Epione team (Université Côte d'Azur, France), contributing to electrical modelling in doxorubicin-induced cardiomyopathy. His expertise spans biomedical signal processing, image-based modelling and high-performance simulation of cardiac electrophysiology. **Jesus Jairo Rodríguez Padilla** received his PhD degree in Applied Mathematics from the University of Sonora, Mexico, in 2017. During his doctoral studies he completed a 1 year internship at the Georgia Institute of Technology, in Atlanta, Georgia, USA (2014–2015). Then, in 2018, he joined the modelling team at IHU-Liryc, The Hearth Rhythm Disease Institute, in Bordeaux, France. Since 2021 he has been a member of the Epione team at the Centre Inria d'Université Côte d'Azur. His research interests focus on applied mathematics to biological systems as well as the numerical solution of partial and ordinary differential equations, with a particular emphasis on modelling of the electromechanical activity in the heart.

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breast, gynaecological, sarcoma, lymphoma) as part of
the standard chemotherapeutic regimen (Kostakou et al.,
2019). Although anti-cancer drugs are highly effective in
killing cancer cells at any point in their life cycle, they
also have devastating repercussions on other organs,
such as the heart. Lowering the chemotherapeutic dose
to avoid irreversible structural and functional changes
in the myocardium would limit the effectiveness of
chemotherapy. Several clinical and preclinical studies
have demonstrated that progressive electro-mechanical
heart dysfunction is a significant adverse consequence of
anthracyclines such as doxorubicin (DOX), leading over
time to increased morbidity and mortality (Christiansen
& Autschbach, 2006). DOX is widely recognized as a major
contributor to long-term cardiovascular complications
in cancer survivors, with cumulative exposure being
strongly associated with the development of cardio-
myopathy and heart failure (Cortes et al., 2023). The
cardiotoxic effects of DOX are multifactorial, involving
mitochondrial dysfunction, oxidative stress and altered
calcium handling, which together compromise cardiac
bioenergetics and contractile function. Notably, the down-
regulation of cardioprotective microRNAs such as miR-30
has been implicated in DOX-induced perturbations of
the β-adrenergic signalling and mitochondrial apoptotic
pathways, thereby accelerating the transition from sub-
clinical cardiac injury to overt heart failure (Roca-Alonso
et al., 2014). Among these complications, oedema and
apoptosis occur in the acute/subacute phases of chemo-
therapy. Subsequently, irreversible structural damage
due to reactive deposition of collagen (i.e. fibrosis) is
presumed to be initiated in early chronic phases (weeks)
and aggravated in late-onset phases (months and beyond)
after DOX therapy. Consequently, this dynamic injury
subtly evolves and worsens over time, leading within
months and years to arrhythmia, cardiomyopathy and
heart failure, respectively.

Clinical diagnostic tools often fail to accurately detect
DOX-induced fibrosis, as myocardial biopsy is highly
invasive and provides only limited tissue samples, which
can lead to false negatives and offer little insight into the
extent of fibrosis. Additionally, echocardiography cannot
directly visualize fibrosis (Safaei et al., 2021), highlighting
the urgent need for better and less invasive methods
to assess the severity of cardiac dysfunction following
DOX therapy. In recent years, magnetic resonance
imaging (MRI) has proven its utility in detecting
DOX-mediated diffuse fibrosis and monitoring cardiac
function (especially the contractile function) in clinical
cohorts of patients (Safaei et al., 2021) and small animal
studies (Podyacheva et al., 2021). However, there is a lack
of tools that could help us mechanistically understand
the abnormal electrical propagation of action potential in
the presence of DOX-mediated diffuse fibrosis and can
accurately predict the risk of arrhythmia, especially

life-threatening ventricular fibrillation after DOX
therapy.

Computational electrophysiology simulations have become essential tools for analysing electrical impulse propagation in the heart (Clayton et al., 2011; Sermesant et al., 2006; Trayanova et al., 2024). In particular, three-dimensional (3D) image-based models enable the study of action potential wave dynamics under various conditions by incorporating high structural detail. This approach has been widely used to investigate the impact of structural alterations associated with different pathologies (Balaban et al., 2019; Dasí et al., 2023; Pop et al., 2012; Roney et al., 2018) and therapeutic interventions, such as ablation (Ashikaga et al., 2013).

One of the most extensively studied aspects is the role of
fibrosis in ventricular arrhythmia (VA) vulnerability. It is
well established that apart from ischaemic/post-infarction
replacement fibrosis, also pharmacologically induced
reactive fibrosis disrupts myocyte connectivity, promoting
unidirectional conduction blocks and increasing
heterogeneity in electrical impulse propagation (Fozzard
& Schoenberg, 1972; Hinderer & Schenke-Layland, 2019).
Previous studies have analysed the burden relationship
between fibrosis and arrhythmia occurrence, showing that
higher fibrosis density and heterogeneity significantly
increase the likelihood of arrhythmic events (Gomez
et al., 2014; Kazbanov et al., 2016; ten Tusscher &
Panfilov, 2003). Aside from structural remodelling,
computational models have also been applied to study the
effects of chemotherapeutic agents on cardiac electrical
function, particularly their impact on VA risk. Recent
advances have integrated computational models with
experimental and clinical data to improve the prediction
of chemotherapy-related cardiac dysfunction. AI-based
approaches have identified correlations between baseline
electrocardiograms and cardiotoxicity risk scores (Yagi
et al., 2024), while longitudinal studies have revealed
subclinical electrical dysfunction following anthracycline
administration, emphasizing the need for continuous
monitoring in affected patients (Tani et al., 2023). In this
context, in silico models could provide a robust framework
to investigate the progression and electrophysiological
consequences of chemotherapy-induced remodelling.
However, further research is needed to fully characterize
its long-term impact.

The main objective of this study is to evaluate the
impact of DOX-induced fibrosis on the occurrence of
VAs. We hypothesize that altered electrophysiological
properties within the fibrotic regions affect the
propagation of electrical impulses. This modification
promotes conduction block and reentrant paths,
increasing the risk of VA. Specifically, we first sought to
develop a methodology for constructing 3D left ventricle
(3D LV) models using high-resolution cardiac MR images
from a preclinical porcine model treated with DOX.
Our novel models integrate a detailed reconstruction of ventricular geometry and fibrosis distribution, providing a computational framework to analyse the effects of structural and electrical remodelling on myocardial electrical activity. Second, using experimental data from catheter-based electro-anatomical mapping we calibrate key model parameters [e.g. action potential duration (APD) and conduction velocity (CV)] in both healthy myocardium and fibrotic zones. We envision that the integration of experimental data into personalized heart models would allow the mathematical model to better reflect realistic pathophysiological conditions, thereby enhancing the simulations’ predictive capacity and its translation into the clinical evaluation of post-DOX effects.

# Methods

## Ethical approval

The animal experiments were approved by the Animal Care Committee at Sunnybrook Research Institute (Toronto), ensuring compliance with ethical regulations in animal research and adhering to the 3Rs principles for humane animal experimentation (NC3Rs, n.d.; Animal protocol #685). The current modelling study used a subset (n = 3) of a previously published dataset (Lin et al., 2022) and included three juvenile Yorkshire pigs (mean initial weight: 25 kg): two treated with DOX and one untreated control. The animals were housed individually in accordance with biosafety standards. They had free access to drinking water and were fed pellets twice daily, supplemented with fresh fruits or vegetables for enrichment. DOX was administered intravenously at 1 mg/kg per week over 4 weeks, mimicking experimental chemotherapy protocols. Prior to procedures, general anaesthesia was induced with atropine (0.05 mg/kg) and ketamine (30 mg/kg) and maintained with isoflurane (1–5%) under mechanical ventilation. The animals were killed via a pentobarbital solution (2 mL per 4.54 kg body weight).

## Semi-automated pipeline

In this work, we developed a computational pipeline that integrates experimental data from cardiac MRI and electro-anatomical mapping to construct 3D LV models for electrophysiological simulations. The pipeline started by segmenting anatomical structures from MR images and calibrating a cellular model to match the experimental action potential morphology and CV. These calibrated models were then incorporated into a Lattice-Boltzmann method (LBM)-based solver, enabling electrical activity simulations without the need for

mesh generation. All simulations, including arrhythmia induction protocols, were executed directly in Python on a local GPU system. The entire workflow was structured within a semi-automated pipeline developed in MATLAB (MathWorks, Natick, MA, USA) for segmentation, Python (Python Software Foundation, Wilmington, DE, USA) for simulation, and ParaView (Kitware Inc., Clifton Park, NJ, USA) for visualization, ensuring a seamless integration of segmentation, model calibration and simulation processes. An overview of the complete pipeline is shown in Fig. 1, followed by complete description of each processing step.

The first step in model construction was the segmentation of anatomical structures from medical images, delineating the endocardial and epicardial surfaces in a reference cardiac phase, specifically end-diastole, where motion artifacts are reduced. This process generated a stack of 2D masks, which served as the foundation for 3D model reconstruction. To characterize tissue, a region of interest (ROI) was first selected from a healthy reference area to establish a signal intensity (SI) threshold, which aided in classifying tissue into two types (e.g. fibrotic zones and healthy myocardium). Specifically, fibrosis was detected by applying a 5SD (standard deviation) threshold to the SI on all late gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) images, as clinically accepted (Karim et al., 2016). Given the potential misalignment errors that would critically affect fibrosis mapping at this resolution, no spatial registration was performed between LGE data and CARTO maps.

Electrical anisotropy is reflected in the computational model through a higher CV along the longitudinal fibre direction compared to the transverse direction by myocardial tissue properties (Caldwell et al., 2009). Thus, we built anisotropic models, in which myocardial fibre directions were computed using a linear interpolation model based on the standard helical rotation of cardiac tissue (Streeter et al., 1969). Helix angles were defined from -70° in the epicardium to +70° in the endocardium, following calibrated parameters for pig ventricles (Pop et al., 2012). This step ensured that physiologically realistic fibre directions were assigned to each voxel in the LV volume, enabling an accurate representation of tissue anisotropy.

Additionally, the pipeline integrated the assignment of computational properties to different tissue regions within the anatomical model, distinguishing between healthy and altered tissue. Healthy and fibrotic regions were assigned electrophysiological properties calibrated from experimental data rather than standard values. This calibration process ensured that conductivity, APD and excitability accurately reflected the characteristics of the specific tissue being modelled, providing a physiologically relevant computational representation.
<figure><img src="image_5.png" /><figcaption>**Figure 1. Schematic diagram of the pipeline for computational model construction**</figcaption></figure>

Blue boxes represent experimental inputs, including medical imaging and electroanatomical maps. Orange boxes correspond to the main processing steps, which include segmentation, parameter extraction, model calibration and simulation. The segmentation process highlights fibre orientation assignment as a sub-process (purple box) to ensure accurate anisotropy representation. Finally, green boxes indicate the pipeline outputs: a fully integrated computational 3D LV mesh containing all relevant anatomical and electrophysiological properties as input to the 3D predictive model, and the resulting simulations.
## Preclinical MRI

CMR imaging was performed on a GE MR750 3T scanner to evaluate DOX-induced myocardial remodelling. All three pigs underwent ECG-gated 2D cine-CMR using a steady-state free precession sequence with breath-holds. To suppress arrhythmias during imaging, amiodarone was administered. The two DOX-treated pigs additionally underwent 3D late gadolinium enhancement (LGE) imaging after a 0.2 mmol/kg bolus of Magnevist. LGE scans were acquired using a free-breathing inversion-recovery fast gradient echo (IR-FGRE) sequence with 1.4 × 1.4 × 1.4 mm³ spatial resolution. The volume of interest fully covered the LV, guided by the cine short-axis views. MR scans were performed 9 weeks after the final DOX administration (post-DOX time point), enabling non-invasive assessment of structural changes and fibrosis in the myocardium.

## Electrophysiology studies: mapping and arrhythmia inducibility

The animals underwent an electrophysiology (EP) study the following day, allowing amiodarone to flush out from their body and avoid interference given the purpose of arrhythmia inducibility. The animals were sedated during the EP mapping and arrhythmia inducibility tests, and chest ECGs monitored the vital function. Each EP study was performed under X-ray guidance using a C-arm (Toshiba INFINIX VF-I/SP-S), with animals being placed in the supine position on the interventional table. Electro-anatomical mapping was performed in sinus rhythm using an invasive PentaRay® multi-electrode catheter (Biosense Webster, Irvine, CA, USA) inserted into the LV cavity. We used a CARTO3 system (Biosense Webster) for data acquisition. Intracardiac electrograms

(iEGMs) were recorded from the LV endocardial surface, yielding 1844 recordings in the control case, 4627 in DOX1 and 1830 in DOX2. The raw maps underwent post-processing in all cases to ensure consistent signal quality and reliability. Specifically, recordings with poor catheter contact, low signal amplitude or acquired near anatomical structures such as the valve annulus were excluded. This clean-up process was particularly relevant in DOX2, where the final number of usable points was considerably reduced. In all three animals, we attempted to induce arrhythmia by rapid pacing from the apex of the right ventricle (RV) via a stimulus pulse S1 of 400 ms followed by several extra-stimuli (S2) with shorter coupling intervals.

From each recorded iEGM, we extracted the activation-recovery interval (ARI), a clinical surrogate of APD (Millar et al., 1985), which we used to calibrate model parameters for prediction of accurate AP waves per individual case. Figure 2 shows an example of an annotated iEGM (left frame) and the resulting calibrated APD in the model (right frame). Note that the APD calibration is performed by adjusting the model parameters, as detailed in the modelling section. The calibration was based on ARI values extracted from the endocardial CARTO maps, following the approach in (Rodríguez-padilla et al., 2024; Silva et al., 2025; Wyatt et al., 1981).

At the completion of the mapping procedure, the bipolar voltage maps were analysed as follows: points having bipolar voltages <0.5 mV amplitude were considered *fibrotic* as per accepted clinical thresholds (Cassidy et al., 1986; Kienzle et al., 1984; López-Yunta et al., 2019; Marchlinski et al., 2000), while the rest of the tissue was considered healthy. However, these maps were not used to identify the location of fibrosis in the 3D model. Instead, they were used to stratify iEGMs

<figure><img src="image_6.png" /><figcaption>**Figure 2.** An annotated iEGM (left frame) and the resulting calibrated APD in the model</figcaption></figure>

A, an exemplary iEGM signal with its respective activation (green point) and repolarization (blue point) annotation (top), its derivative (middle) and lead II (bottom) of the electrogram that the algorithm uses to compute the ARI.

B, calibrated APD that matches the value of the mean ARI (243 ms) of the one extracted from the iEGM in the left frame, as well as the parameters needed in the model to produce an APD with this value.
for calibration of local action potential properties in regions previously segmented as fibrotic from LGE-CMR. The unipolar iEGMs were exported into a custom-built analysis software for further analysis.

In all three animals, CV was estimated by dividing the geodesic surface distance by the activation time difference between two selected endocardial points in the lateral free wall. These points were chosen to lie approximately along the myocardial fibre direction and within regions free of fibrosis, as confirmed by LGE-MRI. Local activation times (LATs) were measured using the CARTO system's caliper. This approach provided a baseline CV value representative of non-fibrotic myocardium, which was subsequently used to calibrate tissue conductivity in the simulations. To improve spatial resolution and reduce potential bias associated with single-distance measurements, we implemented an extended CV estimation method based on the algorithm proposed by Wilhelm et al. (2023). This method computes regional CV values using high-density mapping data by evaluating LAT differences over small, 1 cm radius endocardial patches. Geodesic distances across the 3D mesh were calculated and CV was estimated using the surface-based formula:

$$CV = \frac{2 \times \sqrt{\frac{\text{area}}{\pi}}}{\Delta activation}$$

where *area* is the surface area of the patch and $\Delta t$ is the local activation time difference.

## Computational study

To simulate action potential (AP) propagation in myocardial tissue, we used a monodomain formulation based on a modified version of the simplified Mitchell-Schaeffer (MS) model (Mitchell & Schaeffer, 2003), as described in Djabella et al. (2007). This model, known for its simplicity, represents AP given dynamics with only two main currents: an activation current and a repolarization current. Although this minimalist design does not include the most complex biophysical details, it allows us to perform computationally efficient simulations and easily adjust key parameters such as APD and tissue excitability. Given the nature of our experimental data (i.e. catheter-based electro-anatomical maps acquired at the tissue level), it is not feasible to calibrate individual ionic currents. Therefore, we opted for a simplified phenomenological model in which we can directly calibrate the model parameters corresponding to electrical properties such as APD, conduction velocity (through *d*) and excitability (lambda). This choice was well-suited to the main objective of our study, which was not to investigate ionic remodelling *per se* but to evaluate whether the macroscopic alterations in tissue structure

and electrophysiological function induced by DOX create a pro-arrhythmic substrate and vulnerability to VA.

The modified MS formulation incorporates the dimensionless parameter $\lambda$, initially introduced by Djabella et al. (2007) as a scaling factor for the fast current dynamics. This parameter is a fundamental regulator of local activation properties in our implementation. The parameter's physiological significance stems from its simultaneous influence on multiple aspects of cardiac excitation: it directly scales the maximum upstroke velocity of the AP, modulates CV, and affects the stimulation threshold required for successful propagation. This feature is handy for modelling heterogeneities such as fibrosis or areas with altered cellular excitability in 3D models.

The evolution of the system is described by the following equations:

$$\frac{\partial u}{\partial t} = \nabla \cdot (D\nabla u) + \frac{hu(u - \lambda)(u_{\text{max}} - u)}{\tau_{\text{in}}} - \frac{u}{\tau_{\text{out}}} + J_{\text{stim}}(t),$$

$$\frac{\partial h}{\partial t} = \begin{cases} \frac{1-h}{\tau_{\text{open}}} & \text{if } u < u_{\text{gate}} \\ \frac{-h}{\tau_{\text{close}}} & \text{if } u > u_{\text{gate}} \end{cases}$$

where *u* is the normalized AP, *h* is the gating variable that regulates opening and closing during the depolarization and repolarization phases, and $J_{\text{stim}}$ represents the stimulation current at the stimulation site.

The parameters $\tau_{\text{in}}$ and $\tau_{\text{out}}$ define the repolarization phase, whereas the constants $\tau_{\text{open}}$ and $\tau_{\text{close}}$ manage the gate opening or closing depending on the changeover voltage $u_{\text{gate}}$ and $u_{\text{max}}$ controls the maximum amplitude of the AP wave.

To solve the monodomain equation, we implemented an LBM optimized for GPU execution. This approach utilizes a level-set representation of cardiac geometries derived from medical images, bypassing complex mesh generation while maintaining numerical accuracy. The LBM has been previously validated for cardiac electrophysiology simulations, offering significant computational advantages over the traditional finite element methods (FEM) without compromising physiological fidelity for both monodomain (Cedilnik et al., 2023; Rapaka et al., 2012) and bidomain models (Belmiloudi, 2019; Corre & Belmiloudi, 2016) respectively.

The spatial resolution of our simulations was set to 1.4 mm, matching the resolution of the acquired MRI data. This choice is supported by previous convergence studies using the LBM (Campos et al., 2016), which demonstrated that simulations achieve accurate convergence in activation times and conduction velocities at finer resolutions (100–200 µm), with only minor deviations observed at 500 µm. Although our resolution is coarser, the convergence properties of LBM
ensure that the simulations remain numerically robust. Moreover, using the same resolution as the imaging data significantly enhances computational efficiency, avoiding unnecessary interpolation while preserving anatomical fidelity. The temporal resolution was fixed at $\Delta t = 0.1$ ms, in line with established standards for cardiac electrophysiology modelling.

We validated our implementation against the Niederer et al. (2011) benchmark, by comparing results with FEM simulations. Our tests confirmed excellent agreement in wavefront propagation dynamics while demonstrating substantial computational advantages. Benchmark simulations revealed a consistent 140-fold speedup in computational time compared to FEM implementations. For typical VA simulations (3000 ms duration), our LBM implementation completed simulations in under 30 min using an NVIDIA RTX 3090 GPU at the selected 250 µm resolution. Our optimized LBM implementation performed all simulations on NVIDIA GPU hardware (RTX 3090/4090). The solver's architecture efficiently utilizes GPU parallelization to achieve clinically acceptable simulation times while maintaining the required numerical accuracy (see Fig. 2 in the Supplemental Material).

We used a diffusion coefficient of 3.5 m²/s along fibre directions and 1.4 m²/s (d/2.5) transversely for the tissue anisotropy ratio. This ratio resulted in an approximate CV of 0.7 m/s along the fibres in healthy tissue, which agrees with experimental measurements (Caldwell et al., 2009). The MRI-defined fibrotic regions integrated into the 3D LV model were assigned modified electrophysiological properties, including reduced CV and excitability. The diffusion tensor is defined as:

$$D = \Lambda A \Lambda^T$$

where A is a diagonal matrix containing the diffusion coefficients in each direction:

$$A = \begin{pmatrix} d & 0 & 0 \\ 0 & \frac{d}{2.5} & 0 \\ 0 & 0 & \frac{d}{2.5} \end{pmatrix}$$

and Λ includes information about local directions (longitudinal and transverse to the fibres).

## Simulation framework and protocol

Computational simulations were performed to study in silico the impact of DOX-induced fibrosis on LV electrical activation and the conditions of arrhythmia formation. Thus, we designed a parametric study to evaluate the effect of varying several model parameters corresponding to excitability, APD and CV in fibrotic tissue, on the propagation of AP waves. For this, we first investigated the role of tissue excitability by varying the lambda parameter

(λ), which is related to depolarization velocity and cellular excitability. Values of λ = 0.01 (equivalent to healthy tissue) were considered and progressively increased to 0.1, 0.2 and 0.3, reaching a limit where the fibrotic zone behaves as non-excitable tissue, generating a total conduction block.

Additionally, we investigated the AP heterogeneity within the fibrotic region. Initial calibration was performed using the median recorded APD value for fibrotic points recorded by CARTO. Still, extreme conditions were also simulated using the first and third quartiles of the distribution of APD values to assess the influence of intra-subject variability on the electrical impulse propagation.

The pacing and VA induction protocol began with three pacing stimuli (S1) at a basic cycle length (BCL) of 400 ms, delivered to an LV site located near the RV apex. This initial phase aimed to stabilize ionic currents, refractory periods and APD across the myocardium. After stabilization, ectopic extra-stimuli with progressively shorter coupling intervals were delivered near the fibrotic region, but only in the DOX-treated pigs, following a protocol adapted from clinical practice. In the healthy control, stimulation was limited to the apical site, and no ectopic extra stimuli were applied, as there were no fibrotic regions present. This stimulation protocol successfully induced arrhythmias in both DOX-treated animals, while no arrhythmias were elicited in the healthy control, underscoring the pro-arrhythmic substrate created by DOX-induced fibrosis. In DOX1, three extra stimuli were applied following the experimental pacing protocol: S2 at 240 ms, S3 at 200 ms and S4 at 190 ms. In DOX2, due to the prolonged APD measured in healthy tissue (~270 ms), reducing the coupling interval further would have reached the refractory period; thus, four equidistant extra stimuli (S2–S5) were applied with a BCL of 250 ms.

To assess the sensitivity of reentrant wave induction to the pacing site, we designed an additional set of simulations in which the location of the S2 stimulus was varied. These simulations were performed on the DOX1 geometry, incorporating the reconstructed fibrotic distribution. In one scenario, the S2 stimulus was applied near the fibrotic region in the septal wall allowing us to evaluate the potential for wavefront fragmentation and reentrant circuit formation in the presence of fibrosis. By pacing close to structurally heterogeneous tissue, we aimed to explore how conduction heterogeneities could promote reentrant arrhythmias.

Conversely, another simulation was conducted where the S2 was applied to a remote location on the LV free wall, far from the fibrotic region. This served as a control condition to assess whether reentrant activity could still be induced in the absence of structural conduction barriers.
Lastly, to assess the influence of anatomical and electro-
physiological differences between cases, we conducted
a cross-validation process to determine whether the
observed reentries were solely attributable to model
parameters or influenced by subject-specific factors, such
as ventricular anatomy and fibrotic region characteristics.
First, we selected a configuration that induced reentry
in both pigs, characterized by $\lambda = 0.3$ and a 90%
reduction in the diffusion coefficient (D). This scenario
effectively rendered the fibrotic region non-conductive
due to near-zero excitability. To investigate the role of
anatomy further, we swapped the calibrated parameters
between subjects, applying the conditions that induced
reentry in DOX1 to the anatomy of DOX2 and vice versa.
This approach allowed us to evaluate whether reentry was
dictated primarily by the model parameters or whether
structural differences between subjects also contributed to
its occurrence.

To enhance the anatomical and electrophysiological
realism of the simulations, we introduced a third tissue
class surrounding the segmented dense fibrotic core.
This transition zone was defined by applying an 8 voxel
morphological dilatation to the binary mask of dense
fibrosis. The resulting shell includes tissue potentially
subjected to electrophysiological remodelling – such as
inflammation or interstitial fibrosis – not detectable on
LGE-MRI but relevant to arrhythmogenesis. A sensitivity
analysis of reentry inducibility for different dilatation sizes
(ranging from 0 to 8 voxels) was performed, and the results
are available in Supplemental Material, Table S2.

The inclusion of this region is supported by both
clinical and preclinical studies showing that fibrotic
lesions in anthracycline-induced cardiomyopathy are
frequently surrounded by areas of oedema, inflammation
or perivascular fibrosis, particularly in subacute phases
(e.g. ~9 weeks after DOX) (Galán-Arriola et al., 2019;
Lin et al., 2022). In preclinical swine models, intra-
cardiomyocyte oedema detected by T2 mapping has been
identified as the earliest and most sensitive CMR marker
of DOX-induced cardiotoxicity, appearing around week
6 before any detectable fibrosis is observed through
T mapping, extracellular volume (ECV) or ventricular
function (Galán-Arriola et al., 2019). This oedema is
reversible upon treatment interruption, highlighting the
dynamic nature of tissue injury during the transition from
acute to sub-chronic phases. Other studies have reported
that T2-derived oedema can persist up to week 9 following
chemotherapy, coexisting with a progressive and diffuse
fibrotic response (Lin et al., 2022). Notably, although
oedema partially resolves by week 5, it remains spatially
heterogeneous and extended, motivating the addition of a
broad transitional tissue zone with altered properties.

This observational pattern is not limited to animal
models. In clinical cohorts of patients undergoing
chemotherapy, significantly elevated myocardial T2

values have been reported compared to controls, with
progressive increases across treatment cycles that are
independently associated with LV mass reduction (Yang
et al., 2022). These findings strongly support the concept
that chemotherapy-induced oedema is spatially and
temporally progressive and may not be confined to
LGE-positive regions.

From an electrophysiological perspective, oedematous
or inflamed tissue – although not overtly fibrotic – can
profoundly alter conduction. Increased interstitial volume
and ionic imbalance, particularly in sodium and calcium
handling, have been shown to reduce conduction velocity
and promote arrhythmogenicity independently of gap
junction expression (George et al., 2016; Veeraraghavan
et al., 2011). This is consistent with evidence identifying
conduction heterogeneity as a key substrate for reentrant
arrhythmias in structurally remodelled but non-scarred
myocardium (Han et al., 2021).

Additional work has emphasized the role of peri-
vascular fibrosis and diffuse interstitial remodelling –
especially within large-vessel territories such as the LAD
– as a common pattern of anthracycline cardiotoxicity
(Packard, 2022). This provides further physiological
support for defining a transitional region surrounding
dense fibrosis.

In these updated simulations, arrhythmia inducibility
was assessed by applying an experimental stimulation
protocol consisting of a baseline train of stimuli at a BCL
of 400 ms, followed by a series of extra stimuli delivered at
progressively shorter coupling intervals, beginning 240 ms
after the last baseline beat and decreasing stepwise down
to 190 ms. Stimulation was applied exclusively at the
apical endocardium to replicate a clinically relevant pacing
site. This protocol was continued until either VA was
induced or the tissue became refractory, allowing a robust
evaluation of the pro-arrhythmic potential associated with
DOX-induced remodelling.

Results

This section presents the results obtained from the
calibration of electrical parameters in the mathematical
model based on the EP recordings, along with the
simulations and arrhythmia induction protocols obtained
in the three cases by personalizing the 3D LV models to
data. We include the outcome of the parametric study
based on combinations of key parameters: cell excitability,
tissue diffusion coefficient and APD. These results helped
us evaluate the influence of each of these factors on
wavefront fragmentation, propagation alterations and the
appearance of heterogeneities in APs, which will lead to
the susceptibility of the tissue to develop arrhythmias.

Figure 3, top frame, shows the distribution of calculated
ARI values in all three cases. Note the higher value
in the control case (blue histogram), compared to the
cases with the DOX drug (yellow and black histograms).
We further used the mean ARI values to calibrate the
APD in the modified MS model. The bottom frame in
Fig. 3 illustrates simulated AP waves for healthy and
fibrotic zones, including the values of the calibration
parameter (τ<sub>close</sub>) used to obtain the corresponding APDs,
in accordance with the mean ARI values derived from the
histograms.

In parallel, we estimated the CV for each case using
two points located in the free wall of the LV, yielding
mean values of 71 cm/s for the control case and 41 and
43.89 cm/s for the DOX-treated animals, respectively. To
validate and generalize these punctual measurements, we
applied an extended method to compute CV across the
entire ventricular volume, producing spatially resolved
CV maps and corresponding distributions for each case.
As shown in Fig. 4, the initially reported values fall
within the central range of these distributions (e.g. control:
0.71 ± 0.37 m/s), supporting the consistency and physio-
logical plausibility of the two-point CV estimates. This

also provides a robust basis for adjusting conduction
parameters in fibrotic and transitional tissue zones.

The resulting CV distributions were also used to inform
the parametrization of a third tissue class – the trans-
ition zone – defined as an 8 voxel morphological dilatation
surrounding the dense fibrotic core. While dense fibrosis
was modelled as non-conductive, this surrounding region
was assigned altered CV and APD values extracted
from the tails of the distributions, reflecting the electro-
physiological variability measured *in vivo*. This refined
parametrization enhances the physiological realism of our
simulations while maintaining computational tractability.

Figure 5 shows the simulation results obtained for each
3D LV case after APD and conduction velocity calibration,
and the subsequent impact on activation dynamics.
The first column presents snapshots of the simulations
performed on the three LV volumes at instant t = 2200 ms
during the stimulation protocol, where three exemplary
points within the healthy tissue were highlighted in
different colours. Further, two plots of exemplary AP
waves are included in the second column. Specifically,
the upper graph shows simulated APs corresponding to
the above-highlighted points for the different cases. As
expected, the variations in conduction velocity led the
simulated AP wave to travel slower in DOX1 and DOX2
than in the control. At the same time, the differences
in simulated APDs were in agreement with the values
calibrated from the ARI.

The lower graph represents a sensitivity analysis of the
model for different lambda values (λ = 0.01, 0.1, 0.2
and 0.3) in the fibrotic tissue of DOX1. It can be seen
that as lambda increases, tissue excitability decreases, also
affecting conduction velocity. For λ = 0.3, tissue is not
activated and behaves as a functional block (purple colour
trace).

Figure 6 depicts a representative example of induced
reentry simulation following the stimulation protocol

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>Figure 3. Simulated AP waves for healthy and fibrotic zones</figcaption></figure>

A, overlaid histograms in all subjects, showing the normal
distribution of ARIs in the tissue zones defined by bipolar voltages:
fibrosis (<0.5 mV) and healthy tissue (>0.5 mV). Note the longer ARI
in the control case (blue histogram) compared to the ARIs in DOX
cases. B, respective calibrated APD from the mean values observed in
the ARI distribution in all cases, along with the modified parameter
needed to generate those APD waves.

<figure><img src="image_9.png" /><figcaption>Figure 4. Distribution of conduction velocity (CV) values computed for each element in the ventricular volume</figcaption></figure>

The histogram in blue corresponds to the control case, while yellow and black represent the DOX1 and DOX2 cases, respectively.
outlined previously. This example corresponds to the
ventricle of DOX1, calibrated using its electro-anatomical
mapping from the CARTO system, 9 weeks after DOX
treatment, with a lambda value of 0.2 in the fibrotic tissue
(indicating reduced excitability) and a 90% reduction in
the diffusion coefficient in the same area. The figure shows

the wavefront propagation over time during the reentry
induction. The highlighted time points at the top of the
figure represent the absolute simulation times, measured
from the start of the simulation, chosen 150 ms after
each stimulus activation to serve as temporal references

<figure><img src="image_10.png" /><figcaption>Figure 5. Results of 3D simulations in the LV after APD and conduction velocity were calibrated</figcaption></figure>

A, snapshots of the three simulated volumes at instant *t* = 2200 ms, with points highlighted in different colours. *B*, the upper graph presents the action potentials (Vm, transmembrane potential in millivolts) at selected points within healthy tissue for each case. The lower graph shows a sensitivity analysis of lambda in DOX1 fibrotic tissue. This analysis corresponds to the region marked with an asterisk in the DOX1 volume. The stimulation site used in the pacing protocol is indicated with the symbol ⊗.

<figure><img src="image_11.png" /><figcaption>Figure 6. Representative example of induced reentry simulation following the stimulation protocol</figcaption></figure>

A, the geometry of the computational model, where healthy tissue is depicted in pink and fibrotic tissue in black. The cross-section displayed corresponds to the short-axis view shown in *B*. *B*, the time evolution of wavefront propagation during *in silico* reentry induction in the left ventricle (LV) of DOX1 9 weeks after treatment. The first row presents an epicardial view, and the second a short-axis view (the slice position is highlighted on the left in a volume view). *C*, the stimulation protocol, with stimuli represented as square pulses.
for analysing activation dynamics during the pacing sequence.

In the first row, an epicardial view is presented to visualize the propagation of the AP in the entire ventricle. In the second row, a short-axis plane slice of the LV is shown, the position of which is indicated on the left side of the figure. This representation includes the fibrosis mask and the location of the slice plane to facilitate the interpretation of activation patterns in the region affected by fibrosis. Finally, the stimulation protocol used as square pulses is illustrated in the Figure 6C. This protocol consists of three basal stimuli (S1) applied at the apex with a BCL of 400 ms; only the first and last are depicted. Subsequently, four extrastimuli (S2) are applied close to the fibrosis zone with a coupling interval of 250 ms.

The figure's analysis reveals that, after the last S1, activation propagates correctly from the apex throughout the ventricle. However, the first two extra stimuli (S2) fail to induce effective propagation since the tissue in the stimulation zone is still in the refractory period. It is not until the third extra stimulus (S2 at *t* = 1700 ms) that the stimulation propagates completely through the ventricle. The last extra stimulus (S2 at *t* = 1950 ms) also activates the tissue without a conduction block.

After this last stimulus, no further stimulation pulses are applied, as shown in the protocol schematic, where the line remains stable. However, at *t* = 2200 ms, the spontaneous appearance of a reentrant spiral in the fibrotic region is identified, triggering a sustained reentry without the need for additional stimuli. This reentry repeatedly activates the ventricle, confirming that the combination of fibrosis and the applied pacing sequence generates a substrate that enables the perpetuation of reentrant arrhythmias.

For identifying reentrant episodes, a reentry was considered induced if sustained activation persisted for 1000ms. If no reentrant activity was observed or extinguished shortly after initiation, the configuration was classified as non-arrhythmogenic (a sustained reentry lasting 20 complete cycles before termination is shown in Supplemental Material in Movie S1).

Figure 7 shows the evolution of a sustained reentry (top row). In the bottom frame, left, we show the DOX1 LV geometry, the disposition of the fibrotic tissue (in yellow) as well as two selected points (marked in red and magenta). In the bottom right panel we show the respective AP waves of the two selected points. The vertical black lines indicate the end of S1 and S2 pacings.

<figure><img src="image_12.png" /><figcaption>Figure 7. Temporal evolution of wavefront propagation during virtual reentry induction in the LV of DOX1 9 weeks after treatment</figcaption></figure>

A, an epicardial view of the reentry wave evolution. B, the geometric pattern of fibrosis (yellow) and two selected points (red in the septum and magenta in the free wall) for which the simulated AP waves are presented in the respective frames in C.
To demonstrate the sensitivity of reentrant induction to pace location, in Fig. 8 we present a simulation result for which we repeated the same stimulation protocol as in Fig. 7, but the S2 site was selected on the LV free wall, remote from the fibrotic tissue. In this scenario, it was not possible to induce a reentrant wave.

We conducted a parametric study of the fibrotic region, following the methodology described in the Methods. In total, 96 simulations were performed, the results of which are summarized in Table 1. This table indicates for each configuration whether or not reentry was induced, represented as ‘Non-VA’ in the absence of reentrant activity and as ‘VA’ in cases in which the arrhythmia was induced with the established protocol.

Analysis of reentrant activity in the computational LV models of the two DOX-treated pigs reveals distinct behaviours depending on the electrical and structural parameters. Excitability in the fibrotic zone (λ Fib.Z) was a key determinant for generating and maintaining VAs. Reentry was generally not inducible for very low values of λ Fib.Z (0.01), except in specific configurations. However, when λ Fib.Z was set to 0.1, reentry occurred in most cases, particularly in the DOX2 model, where

arrhythmias were consistently induced across all tested fibrosis configurations, even with a healthy zone λ of 0.01. In contrast, for intermediate values (e.g. λ Fib.Z = 0.2), reentrant activity was robustly sustained in both models across multiple structural setups.

The degree of diffusion coefficient (D) in the fibrotic zone also plays a key role in the occurrence of reentry. In the case of DOX1, when λ Fib.Z = 0.2, VA is consistently induced under all conditions, regardless of the level of CV reduction applied. In contrast, for smaller values of λ Fib. Z, the probability of reentry is low and is only elicited when D is drastically reduced (90%).

In the case of DOX2, notable differences are observed. For λ Fib.Z = 0.2, reentry occurs only when the reduction in D is ≥30%, whereas, for λ Fib.Z = 0.3, reentry is induced in all tested configurations.

Figure 9 illustrates the results obtained, showing in four different panels the behaviour of the simulations at the same time frame (3000 ms), corresponding to the final stage of the simulation after completing the stimulation protocol. The panels are arranged as follows: the first (top left) shows DOX1 with its specific configuration; the second (top right) displays DOX1 using

<figure><img src="image_13.png" /><figcaption>**Figure 8.** Temporal evolution of wavefront propagation when selecting the S2 pacing site in the LV free wall of DOX1 9 weeks after treatment</figcaption></figure>

A, an epicardial view of the AP wave propagation. B, the geometry with the fibrotic region (yellow) and two selected points (red and magenta) along with the resulting simulated AP waves illustrated in the respective frames in C. Note that in this case the reentrant VA was not induced.
<table><caption>Table 1. Summary of all VA simulation results obtained for the three cases. The specific variations in the parameter λ for healthy (H.Z, healthy zone) and fibrotic tissue (Fib.Z, fibrotic zone), as well as the reduction of the diffusion coefficient (D, which governs electrical conductivity) within the fibrotic region are included. Following application of the stimulation protocol, the simulation outcomes are marked as 'VA' (ventricular arrhythmia) if the arrhythmia was induced or as 'Non-VA' (no ventricular arrhythmia) if reentry was not induced</caption><thead><tr><th rowspan="2"></th><th rowspan="2">λ (H.Z)</th><th rowspan="2">λ (Fib.Z)</th><th colspan="6">% of D reduction within the fib. zone</th></tr><tr><th>0%</th><th>10%</th><th>30%</th><th>50%</th><th>70%</th><th>90%</th></tr></thead><tbody><tr><td rowspan="2">Control</td><td>0,01</td><td rowspan="2"></td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td></tr><tr><td>0,1</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td></tr><tr><td rowspan="4">DOX1</td><td rowspan="3">0.01</td><td>0,01</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td></tr><tr><td>0,1</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td></tr><tr><td>0,2</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td></tr><tr><td>0,3</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>VA</td></tr><tr><td rowspan="4">DOX2</td><td rowspan="3">0.1</td><td>0,1</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>VA</td><td>VA</td></tr><tr><td>0,2</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td></tr><tr><td>0,3</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td></tr><tr><td>0,01</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>VA</td><td>VA</td></tr><tr><td rowspan="4">DOX1</td><td rowspan="3">0.01</td><td>0,1</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td></tr><tr><td>0,2</td><td>Non-VA</td><td>Non-VA</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td></tr><tr><td>0,3</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td><td>VA</td></tr><tr><td>0,1</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td></tr><tr><td rowspan="4">DOX2</td><td rowspan="3">0.1</td><td>0,2</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td></tr><tr><td>0,3</td><td>Non-VA</td><td>Non-VA</td><td>Non-VA</td><td>VA</td><td>VA</td><td>VA</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

the EP parameters that induce reentry in DOX2; the third (bottom left) presents DOX2 with its particular configuration; and the fourth (bottom right) shows DOX2 using the EP parameters that induce reentry in DOX1. These results illustrate how anatomical and EP configuration differences influence the occurrence of reentry in each case, emphasizing the importance of

model personalization from its own EP characteristics and structural features.

Cross-validation analysis revealed important insights into how the interplay between EP parameters and anatomical substrate governs arrhythmia vulnerability following DOX therapy. When we applied the electro-physiological parameter set from DOX2 to the anatomical

<figure><img src="image_14.png" /><figcaption>Figure 9. Cross-validation of simulation results for DOX1 and DOX2</figcaption></figure>

The first column shows simulations with APs and CV calibrated from CARTO data from each case. The second column presents the same anatomies and scar distributions but with swapped electrophysiological parameters (DOX1 using DOX2 parameters and vice versa). Snapshots correspond to the same time frame (t = 3000 ms), measured from the beginning of the simulation protocol, which starts at t = 0 ms.
substrate of DOX1, sustained reentry could not be
induced in any of the six attempts. This indicates
that the arrhythmogenic potential of a given parameter
configuration depends critically on the specific fibrosis
architecture of the substrate.

Interestingly, although DOX2 was inducible under
these EP conditions, DOX1 was not, highlighting that
its spatial distribution and fibrosis organization were
insufficient to sustain reentrant activity. This suggests
that the fibrotic substrate in DOX1 requires more
permissive EP conditions, such as lower conduction
velocity, prolonged AP duration or altered excitability, to
support sustained reentry.

In contrast, DOX2 exhibited a fibrotic substrate whose
structural pattern inherently promotes reentry, even
in less arrhythmogenic EP conditions. These findings
underscore the critical role of fibrotic geometry in reentry
dynamics and how it may act either independently
or synergistically with EP remodelling to facilitate
arrhythmogenesis.

Conversely, when applying DOX1's EP parameters
to DOX2's anatomical substrate, VAs were consistently
induced in all six attempts. However, the characteristics
of these arrhythmias differed significantly from those
observed in the native DOX2 configuration. The
reentrant waves propagated faster, with 280-290 ms
cycle lengths, compared to 390-490 ms in the original
DOX2 simulations. This acceleration reflects the shorter
APDs inherent to the DOX1 parameter set, allowing a
more rapid conduction through the existing anatomical
pathways of DOX2's substrate.

These contrasting predictive outcomes demonstrate
that VA vulnerability emerges from the complex inter-
connection between specific EP remodelling and the
precise geometrical pattern of diffuse fibrosis. While some
parameter configurations can robustly induce VA across
multiple substrates, others exhibit strong dependence
on the fibrotic geometry. This differential behaviour
may help explain why patients with similar levels of
MR-detected fibrosis present variable clinical arrhythmia
risk depending on the microstructural features of the
remodelling.

Therefore, we emphasize the need to jointly consider
both components – the electrophysiological parameter set
and the specific fibrosis pattern – when assessing VA risk
in fibrotic hearts.

Extended simulations with transition zone under
experimental pacing conditions

To replicate the experimental stimulation protocol used
during experimental ablation procedures more closely,
we extended our simulations by incorporating the trans-
ition zone described above. While initial simulations

successfully induced sustained reentrant arrhythmias
using apex-only RV pacing, the stimulation sites were
projected manually onto the LV surface. The inducibility
remained sensitive to the chosen location and timing.

In the extended setup, incorporating the transition
region allowed us to stimulate directly from the RV apex,
following the exact protocol used *in vivo*. This included
a conditioning sequence of four S1 stimuli at a BCL
of 400 ms, followed by premature extrastimuli (S2, S3)
with progressively shortened coupling intervals: 270 and
200 ms in DOX #1, and 220 and 190 ms in DOX #2.

As shown in Fig. 10, the extended simulations
successfully reproduced a sustained VA following RV
apical stimulation. The top panel displays the evolution of
transmembrane voltage during the stimulation protocol,
including the initiation and maintenance of reentry. The
bottom left panel illustrates the spatial distribution of
healthy tissue, dense fibrosis and the added transition
zone. The bottom right panel compares a pseudo-ECG
derived from the simulated arrhythmia with the actual
ECG recorded during the induced VA in the DOX2 case.
The morphology and timing of the simulated signal
closely resemble the experimental trace. A quantitative
comparison of the ventricular arrhythmia cycle length
(VACL) averaged over 15–18 cycles further supports the
model’s physiological plausibility while revealing certain
discrepancies. For the DOX1 case, the simulated VACL
(240 ms) was within 25 ms (9.4%) of the experimental
value (265 ms). A more pronounced difference was
observed for the DOX2 case, where the simulated VACL
was 224 ms compared to the experimental 168 ms,
representing an absolute difference of 56 ms.

Discussion

Advancing the mechanistic understanding of complex
structure–function interaction phenomena associated
with DOX-induced cardiotoxicity is essential for
improving the management of cardiac rhythm
disturbances following chemotherapy. This novel study
represents, to the best of our knowledge, the first *in silico*
investigation using personalized 3D image-based LV
models to evaluate the impact of DOX-induced fibrosis
on the occurrence of VA. Our main results include: (1)
the development of a methodological computational
framework based on a semi-automated pipeline to build
MRI-based models for the analysis of post-chemotherapy
ventricular electrical activity using a GPU-optimized
LBM solver; (2) the first generation of high-resolution
cardiac digital twins calibrated directly from *in vivo*
experimental data, incorporating the cardiotoxic effects
of DOX on the dynamics of myocardial electrophysiology
and subtle structural changes due to the presence
of reactive diffuse fibrosis; and (3) a comprehensive
parametric virtual study to establish how the combination
of electrophysiological alterations and fibrosis distribution
modulates the induction and maintenance of VAs after
DOX administration.

High-efficiency GPU-based simulations with LBM for
personalized high-fidelity computational models

The development of this new methodological framework
represents a significant advance in the simulation of VAs
in hearts affected by pharmacologically or drug-induced
diffuse fibrosis. Unlike traditional FEM approaches
that require manual mesh generation for simulations
(Heikhmakhtiar et al., 2021; Lawson et al., 2023; Soares
et al., 2023), our pipeline operates directly on images
(i.e. segmented masks), enabling fast and efficient
reconstruction of customized models without manual
intervention. This automated process enhances the
reproducibility and accessibility of the model, facilitating
a potentially rapid translation into the clinics for various
simulation-based applications.

A key innovation of our framework is the
implementation of a GPU-optimized LBM solver,
providing substantial computational advantages over
conventional solvers. By utilizing the highly parallel
architecture of GPUs, this approach significantly
accelerates simulations, eliminating the need for

accessing supercomputing clusters. Our computational
times align with other previous studies demonstrating
that GPU-based solvers can be up to 300x faster
than CPU implementations in similar computational
contexts (Radhakrishnan et al., 2024). This efficiency is
particularly beneficial for large-scale cardiac simulations,
where high-resolution models (like our LV models
having approximately 4.5 million voxels) would impose
prohibitive computational costs. Additionally, the
scalability of GPU solvers ensures that performance
remains optimal even as the complexity of the simulations
increases (Radhakrishnan et al., 2024).

Beyond speed, we believe the parallel nature of
GPUs allows efficient handling of large-scale matrix
operations, which is crucial for optimizing electro-
physiological simulations as also shown by other groups
(He et al., 2024). Thus, by applying these principles to
cardiac modelling, our approach lays the foundation for
developing computational tools that could be integrated
into clinical workflows, enabling faster and more precise
assessments of arrhythmia risk in patients undergoing
chemotherapy.

DOX-induced arrhythmogenic effects

Our results provide qualitative and quantitative insights
into how DOX-induced fibrosis alters electrophysiological

<figure><img src="image_15.png" /><figcaption>Figure 10. Simulation of clinically accurate RV apical pacing protocol</figcaption></figure>

A, time evolution of transmembrane potentials during the stimulation protocol, showing the emergence of a sustained ventricular arrhythmia (VA). B, 3D rendering of the simulated volume, highlighting healthy tissue (blue), dense fibrosis (yellow) and the transition zone (orange). C, comparison between simulated pseudo-ECG and real ECG recorded during VA in DOX2, showing morphological concordance.
properties, particularly APD dispersion and conduction stability. We observed that increased fibrosis density in the ventricular tissue, a hallmark of DOX-induced cardio-toxicity, disrupts normal electrical conduction, creating barriers that facilitate conduction block and reentry. This structural remodelling, mediated by reactive diffuse fibrosis, impairs impulse propagation by decreasing conduction velocity and increasing repolarization heterogeneity, contributing to an elevated arrhythmogenic risk.

Specifically, we found that APD in healthy tissue decreased by 12.9% in DOX1 (from 309 to 269 ms) and 32.0% in DOX2 (from 309 to 210 ms). In fibrotic regions, APD increased slightly in DOX1 (by 2.6%, reaching 276 ms), whereas in DOX2, it decreased by 12.4%, reaching 184 ms. Additionally, conduction velocity in healthy tissue decreased by 42.3% in DOX1 (from 71 to 41 cm/s) and 38.2% in DOX2 (from 71 to 43.89 cm/s). These findings align with the study by Lin et al. (2022), which demonstrated that collagen deposition in the myocardium significantly impacts conduction stability, and other mechanisms may also contribute. Specifically, undetected diffuse fibrosis, systemic remodelling of ionic currents (e.g. sodium current), or mechano-electrical remodelling could play a role in the observed reduction in conduction velocity, even in regions that appear structurally preserved. Recent studies have described similar phenomena (Kharin et al., 2013; Packard, 2022; Panpan et al., 2022; Tamargo et al., 2015; Tomsits et al., 2021). This suggests that a combination of moderate excitability and reduced CV promotes the occurrence of reentrant circuits, particularly in fibrotic tissues where spatial heterogeneities in APD and conduction are exacerbated.

The electrophysiological consequences of DOX-induced fibrosis remain an emerging field of study. While most previous research has focused on its impact on ventricular function and heart failure progression, fewer studies have directly examined its pro-arrhythmic effects. For instance, Kinoshita et al. (2023) reported that DOX-treated patients exhibited significant prolongation of the corrected ARI, which was associated with a decline in LV ejection fraction, supporting a link between electro-physiological remodelling and arrhythmic risk. Similarly, Kharin et al. (2013) described increased repolarization heterogeneity and prolonged activation and recovery times following DOX administration, consistent with our *in silico* findings.

However, the electrophysiological consequences of DOX-induced fibrosis remain an emerging field of study. While most prior work has focused on its impact on ventricular function, fewer studies have addressed its pro-arrhythmic effects. Kinoshita et al. (2023) reported a prolongation of ARI in DOX-treated patients, suggesting increased arrhythmic risk. However,

other studies have shown APD shortening in similar contexts, pointing to a complex and context-dependent relationship (Shenasa et al., 1990). These discrepancies may reflect differences in fibrosis patterns, disease stage or species-specific responses. Our *in silico* results align with the APD shortening trend, but we acknowledge that other remodelling scenarios may lead to opposite effects.

This behaviour is in contrast to that observed in DOX1, suggesting that differences in anatomical structure, such as the heterogeneous fibrosis distribution and particular pattern, along with the prolonged APDs, may influence the tissue's response to the sequence of applied stimuli.

Beyond its direct impact on electrical conduction, DOX cardiotoxicity also affects autonomic regulation, which may further contribute to arrhythmogenesis. Moro et al. (2022) found that DOX reduces sympathetic neuron density and disrupts adrenergic signalling, potentially exacerbating electrophysiological instability. While our model does not explicitly incorporate these autonomic alterations, our results suggest that fibrosis alone can induce substantial tissue activation and repolarization modifications, creating a pro-arrhythmic substrate.

Experimental studies have also suggested that DOX-induced fibrosis may involve the activation of the neurokinin-1 receptor, and its pharmacological blockade has been shown to reduce fibrosis and improve diastolic function (Levick et al., 2019). Additionally, telemetric monitoring in animal models has revealed that dogs treated with DOX develop ventricular tachycardia and fibrillation, probably due to fibrotic remodelling and electrical instability (Vaynblat et al., 2003). While these findings provide insight into potential arrhythmogenic mechanisms in the context of DOX-induced cardio-toxicity, their relevance to human physiology remains speculative and warrants further investigation.

Thus, our study provides computational insights into DOX-induced electrophysiological alterations, complementing existing experimental and clinical findings. Given the limited research on the arrhythmogenic effects of DOX, further investigations are needed to fully understand the interplay between structural remodelling and electrophysiological disturbances in patients undergoing cardiotoxic treatments.

## Mechanistic insights of DOX-induced VA

Our simulations demonstrated that VAs in DOX-induced fibrosis result from a complex interplay between excitability, CV, APD and the spatial organization of fibrotic tissue. Notably, the initiation and maintenance of reentrant activity were highly dependent on the specific fibrosis distribution in DOX1 and DOX2 cases, highlighting the role of structural remodelling
in arrhythmogenesis. These findings are consistent with previous research showing that conduction abnormalities and repolarization heterogeneities in non-ischaemic cardiomyopathies create a pro-arrhythmic substrate. For instance, an analogue observation that conduction remodelling leads to delayed and heterogeneous activation patterns, promoting reentry mechanisms, was noted in human end-stage non-ischaemic LV cardiomyopathy (Glukhov et al., 2012). Similarly, Akar et al. (2005) highlighted that fibrotic conduction barriers in non-ischaemic dilated cardiomyopathy contribute to slowed conduction and fractionated electrograms, which are linked to increased arrhythmic risk. Moreover, Bogun et al. (2009) used delayed-enhanced MRI to identify arrhythmogenic substrates in non-ischaemic cardiomyopathy, reinforcing the idea that fibrosis-driven electrical heterogeneities play a critical role in the development and perpetuation of VAs.

Furthermore, a key observation from our study is that reentrant waves are most likely to occur when excitability is drastically reduced to the point of total conduction block, often due to an excessively high diffusion coefficient. However, this extreme excitability reduction had an anti-arrhythmic effect in certain cases (such as DOX1), with a λ value corresponding to healthy tissue (λ = 0.1). In this scenario, although a conduction block occurs, the necessary heterogeneity to sustain reentry is absent, preventing arrhythmia initiation. In contrast, our analysis of varying levels of diffusion coefficient reduction reveals that a decrease in conduction velocity within the fibrotic region – while maintaining constant excitability and APD – never exerts an anti-arrhythmic effect. When tissue is already unexcitable, such as in a healthy myocardium with very low λ values, a further reduction in CV does not impact arrhythmogenesis. However, decreased CV increases arrhythmia vulnerability in other cases by exacerbating conduction delays and facilitating reentrant circuits. These findings suggest that fibrosis-induced conduction heterogeneities directly contribute to repolarization dispersion, a known risk factor for arrhythmias.

Mechanistic studies have consistently shown that fibrosis-related conduction disturbances promote functional reentrant circuits, increasing the likelihood of arrhythmia (Benjanuwattra et al., 2020). Beyond chemotherapy-induced cardiotoxicity, fibrosis is widely recognized as a critical modulator of arrhythmia initiation and maintenance in various pathological conditions. Computational models have demonstrated that fibrotic conduction barriers enhance wave-break formation and reentry, particularly when combined with CV and APD alterations (Gomez et al., 2014; ten Tusscher & Panfilov, 2003; Vigmond et al., 2009). Experimental evidence further supports these mechanisms, as fibrotic

tissue exhibits impaired gap junctional coupling and altered sodium channel expression, further disrupting conduction and amplifying arrhythmic risk (Rohr, 2009).

Regarding ectopic activity, our results justify the application of extra stimuli (S2–S5) within the fibrotic region, as several studies have shown that areas with higher fibrosis density are more susceptible to early after-depolarizations (EADs) and spontaneous activations. Miragoli et al. (2007) demonstrated that fibroblast–myocyte coupling can induce ectopic discharges due to abnormal electrotonic interactions, providing a mechanistic basis for the spontaneous emergence of ectopic beats near fibrotic zones. Similarly, myofibroblast–myocyte interactions have been implicated in prolonging APD and promoting EADs, increasing arrhythmogenic potential (Nguyen et al., 2016). These findings reinforce the role of fibrosis in generating abnormal electrical activity and justify targeting fibrotic regions in pacing protocols to assess reentry susceptibility.

Interestingly, we observed distinct reentrant cycle lengths in DOX1 and DOX2, suggesting that the fibrosis pattern alters reentry initiation and modulates reentrant dynamics over time. This variability underscores the importance of subject-specific structural remodelling in arrhythmogenesis and suggests that personalized computational approaches could improve arrhythmia risk assessment. Moreover, these findings highlight the potential clinical implications of the model personalization, as individualized predictions could contribute to more effective patient management strategies. Given that some beta-blockers prolong APD and may serve as a first-line treatment for certain patients (Chia et al., 2019), while others might require an implantable cardioverter-defibrillator (ICD) to prevent fast and potentially lethal ventricular fibrillation (VF) (Evertz et al., 2023), the use of computational modelling in clinical decision-making could aid in tailoring therapeutic interventions based on patient-specific electrophysiological characteristics.

Adding a transition zone surrounding dense fibrosis significantly enhanced the physiological realism and predictive accuracy of our simulations. Although not directly visible in LGE-MRI, this tissue class probably represents a pathophysiologically relevant component of post-DOX remodelling (Galán-Arriola et al., 2019; Lin et al., 2022; Yang et al., 2022). Evidence from both clinical and preclinical studies suggests that perilesional or transitional myocardium exhibits oedema, inflammation or diffuse interstitial fibrosis, all of which contribute to slowing conduction and heterogeneity (George et al., 2016; Han et al., 2021; Veeraraghavan et al., 2011). Such properties, even in the absence of dense scarring, facilitate the formation of reentry-prone substrates when combined with fibrotic discontinuities and programmed pacing protocols.
The observed discrepancies between the simulated and experimental VACLs can be attributed to several well-recognized challenges in personalized computational electrophysiology. First, the absence of transmural and epicardial AP recordings limited the spatial comprehensiveness of model calibration, as experimental measurements were restricted to the endocardial surface. Second, the representation of peri-lesion tissue as a continuous layer may not fully capture the micro-scale discontinuities and heterogeneities in conduction properties present in vivo, which influence reentry dynamics.

The electrophysiological calibration of this tissue class in our models, based on *in vivo* CV and ARI data, supports this interpretation and explains the successful reproduction of clinically observed ECG morphologies. Moreover, additional characterization techniques such as T1 mapping, T2 mapping or contrast kinetics could provide non-invasive ways to delineate this transition zone more precisely in future studies (Packard, 2022).

Ultimately, our study combines computational modelling with experimental data and clinical knowledge, providing a deeper understanding of chemotherapy-induced VAs. By systematically exploring the interplay between fibrosis and key electrophysiological properties, our findings reinforce the importance of structural remodelling in arrhythmia susceptibility and provide a foundation for developing computational tools to assess arrhythmic risk in patients undergoing cardiotoxic treatments.

## Limitations

This novel study introduces a computational pipeline to model DOX-induced fibrosis and its electrophysiological consequences in the LV. While the framework represents a methodological advance in the field, several limitations must be acknowledged, many delineating clear directions for future developments.

The modelling approach was designed to balance anatomical fidelity with computational tractability, enabling the simulation of ventricular arrhythmogenesis in a small cohort. However, a generic human-based electrophysiological model may not fully capture the species-specific dynamics relevant to the *in vivo* experimental context. In particular, future developments would benefit from the implementation of a porcine-specific ventricular model, such as the one proposed by Gaur et al. (2021), which more accurately reflects the electrophysiological properties of swine myocardial tissue and would enable direct comparison with experimental data obtained in large-animal models of DOX cardiotoxicity. Incorporating such a model would

enhance the biological relevance of the simulations and improve their translational robustness.

Although the model enables simulation of arrhythmic events in fibrotic ventricles, it does not yet incorporate Purkinje fibres. For this reason, direct comparisons between simulated activation sequences and experimental isochronal maps (e.g. from CARTO) were not pursued. While the omission of the Purkinje system has limited influence during rapid pacing protocols, where stimulation overrides the sinus rhythm, it remains essential for accurate prediction of physiological activation times and for calibrating CVs using electro-anatomical data aligned to the 3D LV geometry. High CV values in our simulations, probably corresponding to fast-conducting pathways, support this interpretation. To address this, our next steps will focus on implementing a synthetic Purkinje network following approaches such as stochastic fractal branching (Miralles et al., 2019; Sebastian et al., 2013) or anatomical distributions corresponding to the experimental observations of Durrer et al. (1970).

Other planned improvements include the incorporation of spatially resolved conduction velocity calibration based on anatomical segmentations (e.g. American Heart Association, AHA 17-segment model from Cerqueira et al., 2002), as well as the implementation of transmural heterogeneity in electrophysiological properties. These refinements will improve the precision of simulations in structurally remodelled substrates and enable more realistic predictions of activation and repolarization patterns.

Moreover, the current model does not account for endo-epicardial gradients in EP properties observed (Myles et al., 2010), due to the limited depth of catheter-based endocardial recordings. Including transmural heterogeneity in the conductivity and repolarization phase of APD could lead to more accurate results, as these features strongly influence reentrant dynamics and may alter the initiation and maintenance of arrhythmia.

The 3D anatomical model is currently limited to the LV, reconstructed from high-resolution LGE MR images. Although this suffices for studying local arrhythmogenic mechanisms, the absence of the RV restricts the physiological realism of the simulations. Expanding the geometry to a complete biventricular model will allow us to investigate global EP interactions and to improve the fidelity of simulated electrocardiographic signals (Mendonca Costa et al., 2022).

In terms of electrical signal modelling, although this study did not focus on simulating surface ECGs, our preliminary work enabled a good qualitative comparison between simulated and recorded VA waveforms. Since multi-lead ECGs were recorded in all cases, we anticipate that upcoming steps will include ECG-based calibration
of global conduction parameters, thereby enhancing the translational potential of the models.

Lastly, another important limitation lies in the small number of available datasets. Only three subjects were included in this study: two with fibrotic remodelling and one healthy control, for which CARTO data were recorded, precluding meaningful statistical analysis or generalization regarding the arrhythmogenic risk associated with DOX-induced fibrosis. Nevertheless, this work is primarily a mechanistic and methodological study aimed at evaluating the feasibility and robustness of the modelling framework, particularly in its integration of parameter estimation from intracardiac electrograms. Increasing the sample size in future studies will enable a more thorough investigation of inter-individual variability in fibrotic expression during the sub-acute and sub-chronic phases following chemotherapy. It may ultimately support patient-specific risk stratification.

Altogether, these developments will strengthen the potential of this modelling approach as a clinically applicable tool for assessing post-chemotherapy cardiac dysfunction – including arrhythmic, contractile and perfusion-related alterations – and for supporting the design of personalized long-term monitoring and therapeutic cardioprotective strategies. Indeed, anthracycline therapy has also been associated with persistent microvascular damage and impaired myocardial perfusion, which may precede overt systolic dysfunction and further contribute to long-term cardiovascular risk of life-threatening arrhythmias (Cadour et al., 2022; Galán-Arriola et al., 2022; Li et al., 2022; Prus et al., 2019). Therefore, future studies should aim to incorporate perfusion and microcirculatory aspects into patient-specific models to provide a more comprehensive framework for risk stratification and clinical decision-making.

## Future perspectives

Building on the current methodological framework, several developments are envisioned to enhance the model's clinical relevance and translational potential. For instance, expanding the anatomical domain to a full biventricular representation and its coupling with electro-mechanical simulations will allow the analysis of arrhythmia mechanisms and the prediction of altered contraction and key functional metrics such as ejection fraction. These capabilities are particularly relevant in the context of long-term chemotherapy-induced cardiotoxicity, where not only is the risk of malignant arrhythmia essential, but also progressive heart failure represents a significant clinical concern.

In parallel, efforts will focus on augmenting the pipeline capabilities based on non-invasive clinical data.

As invasive catheter-based electroanatomical mapping is not routinely performed in oncology patients, future adaptations will explore the integration of conventional 12-lead ECG (or body-surface ECG recordings where available) for model calibration and validation, utilizing recent advances in ECG imaging and patient-specific signal processing (Zettinig et al., 2014). Additionally, translating this work and adapting it to large clinical datasets will be essential to assess interindividual variability and improve the robustness of the model. This step will move beyond the current animal data, focusing on retrospective and prospective patient cohorts with cardiac MRI and serial ECGs available during and after chemotherapy and other chemotherapeutic drugs.

Future technical refinements will also address methodological factors influencing quantitative accuracy, such as optimizing virtual ECG lead placement – a factor known to significantly affect the quantification of simulated arrhythmia cycle lengths. While the current model successfully identified arrhythmogenic substrates by reproducing sustained reentry exclusively in doxorubicin-treated hearts, improving the precision of virtual ECG predictions remains a promising direction for enhancing the quantitative agreement with clinical recordings.

Altogether, these developments will strengthen the potential of this modelling approach as a clinically applicable tool for assessing post-chemotherapy cardiac dysfunction – both arrhythmic and contractile – and for supporting the design of personalized long-term monitoring and therapeutic cardioprotective strategies.

## Conclusion

The present investigation provides detailed insight into the impact of DOX-induced fibrosis on ventricular electrical activity and its relationship to the occurrence of arrhythmias. By developing a methodological framework based on a GPU-optimized LBM solver, we have demonstrated the feasibility of efficiently building high-fidelity custom models without supercomputing. This approach allows us to systematically explore the interaction between fibrosis and electrophysiological properties of the myocardium, providing quantitative information on proarrhythmic mechanisms in this context.

The results underline that arrhythmogenicity in the presence of fibrosis does not depend exclusively on global alterations in electrophysiology but on the combination of excitability, AP duration, conduction velocity and the spatial distribution of fibrosis. This study confirms that DOX-induced fibrosis can generate substrates highly vulnerable to reentrant waves, a finding consistent with previous experimental observations.
Considering these findings, this work enhances our understanding of DOX-induced electrophysiological alterations and establishes a foundation for developing computational tools for arrhythmia risk assessment in clinical settings following chemotherapy

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## Additional information

### Data availability statement

This is a computational modelling study and does not include new experimental data. The original raw experimental data (preclinical MR images and electro-physiological maps) were published previously (Lin et al., 2022) and are not publicly available as they remain under research protection for ongoing projects. To ensure reproducibility of the present work, we provide a dedicated GitHub repository (https://github.com/javilva/doxorubicin_fibrosis_model) that includes segmented 3D ventricular masks integrating anatomical zones (healthy tissue and fibrosis), the modified computational model employed and the calibrated parameter sets used for each simulation.

### Competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

### Author contributions

Conceptualization: M.P., M.S., B.T., J.R.P.; Methodology: N.C., J.V., J.R.P. Software: N.C., J.V., J.R.P., M.S.; Validation: J.V., N.C., M.P., J.R.P.; Formal Analysis: J.V., N.C., J.R.P., M.P., J.F.G., B.T.; Investigation: J.V., J.R.P., B.L., N.C., J.F.G., M.S., M.P., and B.T.; Resources: M.S., B.T.; Data Curation: J.V., N.C., J.R.P., M.P.; Writing – original draft: J.V.; Writing – review & editing: J.V., J.R.P., B.L., N.C., J.F.G., M.S., M.P., and B.T.; Supervision: M.S., J.F.G., M.P., B.T.; Funding Acquisition: M.P., M.S., B.T.
Funding

European Commission Horizon 2020: Beatriz Trenor, 101016496; French Government: Maxime Sermesant, ANR-19-P3IA-0002; Canadian CIHR: Mihaela Pop, (PJT) 153212; Spanish Government: Javier VillarValero, MCIN/AEI/10.13039/501100011033; Spanish Government: Juan F Gomez, PID2022-136273OA-C33; Spanish Government: Beatriz Trenor, PID2022-140553OB-C41; Barcelona Supercomputing Center (BSC): Beatriz Trenor, IM-2021-1-0001 and IM-2021-3-0001.

Acknowledgements

This work was supported by the SimCardioTest project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101016496; by the French government through the 3IA Côte d'Azur Investments in the Future project managed by the National Research Agency (ANR) with reference number ANR-19-P3IA-0002; by a Canadian CIHR project grant (PJT) 153212; by Grant PRE2020-091849 [MCIN/AEI/10.13039/501100011033] and 'ESF Investing in your future'; Grant PID2019-104356RB-C41 [MCIN/AEI/10.13039/501100011033]; PID2022-136273OA-

C33 and PID2022-140553OB-C41 [MICIU/AEI/10.13039/501100011033 and by ERDF/EU]; PEPR Digital Health Chronicardio with reference number 22-PESN-0015; the authors gratefully acknowledge RES resources provided by Barcelona Supercomputing Centre in MareNostrum5 (IM-2024-1-0010 and IM-2024-2-0015), and the OPAL infrastructure from Université Côte d'Azur for providing resources and support.

Keywords

cardiac modelling, cardiotoxicity, computational electro-
physiology, doxorubicin, reentrant arrhythmias

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**Supporting information**

Movie S1