Models of Chemical Communication for Micro/Nanoparticles

Engineering chemical communication between micro/nanosystems (via the exchange of chemical messengers) is receiving increasing attention from the scientific community. Although a number of micro- and nanodevices (e.g., drug carriers, sensors, and artificial cells) have been developed in the last decades, engineering communication at the micro/nanoscale is a recent emergent topic. In fact, most of the studies in this research area have been published within the last 10 years. Inspired by nature─where information is exchanged by means of molecules─the development of chemical communication strategies holds wide implications as it may provide breakthroughs in many areas including nanotechnology, artificial cell research, biomedicine, biotechnology, and ICT. Published examples rely on nanotechnology and synthetic biology for the creation of micro- and nanodevices that can communicate. Communication enables the construction of new complex systems capable of performing advanced coordinated tasks that go beyond those carried out by individual entities. In addition, the possibility to communicate between synthetic and living systems can further advance our understanding of biochemical processes and provide completely new tailored therapeutic and diagnostic strategies, ways to tune cellular behavior, and new biotechnological tools. In this Account, we summarize advances by our laboratories (and others) in the engineering of chemical communication of micro- and nanoparticles. This Account is structured to provide researchers from different fields with general strategies and common ground for the rational design of future communication networks at the micro/nanoscale. First, we cover the basis of and describe enabling technologies to engineer particles with communication capabilities. Next, we rationalize general models of chemical communication. These models vary from simple linear communication (transmission of information between two points) to more complex pathways such as interactive communication and multicomponent communication (involving several entities). Using illustrative experimental designs, we demonstrate the realization of these models which involve communication not only between engineered micro/nanoparticles but also between particles and living systems. Finally, we discuss the current state of the topic and the future challenges to be addressed.</p

Análisis comparativo del efecto farmacológico en células ventriculares sanas y con insuficiencia cardíaca mediante métodos de modelado y simulación

[ES] Uno de los posibles efectos secundarios de un fármaco es la generación de Torsade de Pointes (TdP), una taquicardia ventricular potencialmente letal, responsable de la retirada de muchos fármacos del mercado. Por ello, la predicción de la cardiotoxicidad es un aspecto crucial tanto para empresas farmacéuticas como para agencias reguladoras. Actualmente, las guías regulatorias internacionales utilizadas para la evaluación del riesgo torsadogénico, aunque son seguras, son propensas a dar falsos positivos. El objetivo de este trabajo es estudiar el efecto farmacológico sobre las características electrofisiológicas de cardiomiocitos ventriculares sanos y con insuficiencia cardíaca (IC) y proponer un nuevo método para predecir el riesgo de producir TdP. Se han realizado simulaciones celulares utilizando una versión modificada del modelo endocárdico de O¿Hara et al., un modelo de célula con IC y datos farmacológicos de los 28 medicamentos de la iniciativa CiPA. Así, se ha comprobado mediante un análisis de sensibilidad que la duración del potencial de acción y la dinámica del Ca2+ intracelular son mucho más sensibles a cambios en la corriente IKr y la INaL si el cardiomiocito padece IC. En las simulaciones de los 28 fármacos, se ha visto que ante la misma dosis de fármaco, los cambios electrofisiológicos en el cardiomiocito con IC son mayores. Además, se ha desarrollado un clasificador ternario del riesgo de TdP consiguiendo un 82.1% de precisión, superior a ensayos in-vitro o algunos modelos animales. Al evaluar la cardiotoxicidad sobre cardiomiocitos con IC con este clasificador, se ha visto que muchos de los fármacos de riesgo intermedio (Astemizol, Cisaprida, Claritromicina, Domperidona, Droperidol y Risperidona) pasan a considerarse de alto riesgo, mientras que el resto de fármacos permanece en su categoría. En conclusión, se ha propuesto una herramienta in-silico que puede ser útil para la evaluación farmacológica del riesgo de generar TdP y demuestra que bajo condiciones patológicas algunos medicamentos aumentan su cardiotoxicidad.[EN] One of the possible secondary effects of a drug is generation of Torsade de Pointes (TdP), a potentially lethal ventricular tachycardia. This leads to the withdrawal of many drugs from the market. Therefore, cardiotoxicity assessment is a crucial aspect for both, pharmaceutical companies and regulatory agencies. Although the international regulatory guidelines used nowadays for the torsadogenic risk assessment are reliable, they are prone to produce false positives. The obtjective of this work is to study pharmacological effects on electrophysiological properties of healthy ventricual cardiomyocytes and cardiomyocytes with heart failure (HF), and to propose a new method for the assessment of TdP-risk. Cellular simulations were performed using a modified version of the endocardial O¿Hara et al. model, a model of HF and pharmacological data of the 28 drugs of the CiPA initiative. Thereby, it has been verified through a sensitivity analysis that the action potential duration and the dynamics of intracellular Ca2+ are much more sensitive to changes in IKr current and INaL if the cardiomyocyte suffers from HF. In the simulations of the 28 drugs, electrophysiological changes in HF cardiomyocyte are larger than in normal conditions. In addition, a ternary TdP-risk classifier has been developed achieving an accuracy of 82.1%, higher than in-vitro assays and some animal models. When evaluating cardiotoxicity on HF cardiomyocytes with this classifier, many of the intermediate risk drugs (Astemizole, Cisapride, Clarithromycin, Domperidone, Droperidol and Risperidone) are rated as high risk drugs, while the other drugs remain in its category. In conclusion, an in-silico tool that can be used in the pharmacological assessment of the TdP-risk has been proposed and shows that under pathological conditions certain drugs increase their proarrhythmicity.Llopis Lorente, J. (2018). Análisis comparativo del efecto farmacológico en células ventriculares sanas y con insuficiencia cardíaca mediante métodos de modelado y simulación. http://hdl.handle.net/10251/110094TFG

Considering Population Variability of Electrophysiological Models Improves the In Silico Assessment of Drug-Induced Torsadogenic Risk

This repository contains the parameter sets of the population of TorORd models and the the population of ORdmD models, the ORdmD CellML file and MALTAB code used in Llopis-Lorente, J., Trenor, B., Saiz, J. (2022). Considering Population Variability of Electrophysiological Models Improves the In Silico Assessment of Drug-Induced Torsadogenic RiskLlopis Lorente, J.; Trénor Gomis, BA.; Saiz Rodríguez, FJ. (2022). Considering Population Variability of Electrophysiological Models Improves the In Silico Assessment of Drug-Induced Torsadogenic Risk. http://hdl.handle.net/10251/18259

Considering population variability of electrophysiological models improves the assessment of drug-induced torsadogenic risk

[EN] Background and Objective In silico tools are known to aid in drug cardiotoxicity assessment. However, computational models do not usually consider electrophysiological variability, which may be crucial when predicting rare adverse events such as drug-induced Torsade de Pointes (TdP). In addition, classification tools are usually binary and are not validated using an external data set. Here we analyze the role of incorporating electrophysiological variability in the prediction of drug-induced arrhythmogenic-risk, using a ternary classification and two external validation datasets. Methods The effects of the 12 training CiPA drugs were simulated at three different concentrations using a single baseline model and an electrophysiologically calibrated population of models. 9 biomarkers related with action potential (AP), calcium dynamics and net charge were measured for each simulated concentration. These biomarkers were used to build ternary classifiers based on Support Vector Machines (SVM) methodology. Classifiers were validated using two external drug sets: the 16 validation CiPA drugs and 81 drugs from CredibleMeds database. Results Population of models allowed to obtain different AP responses under the same pharmacological intervention and improve the prediction of drug-induced TdP with respect to the baseline model. The classification tools based on population of models achieve an accuracy higher than 0.8 and a mean classification error (MCE) lower than 0.3 for both validation drug sets and for the two electrophysiological action potential models studied (Tomek et al. 2020 and a modified version of O'Hara et al. 2011). In addition, simulations with population of models allowed the identification of individuals with lower conductances of IKr, IKs, and INaK and higher conductances of ICaL, INaL, and INCX, which are more prone to develop TdP. Conclusions The methodology presented here provides new opportunities to assess drug-induced TdP-risk, taking into account electrophysiological variability and may be helpful to improve current cardiac safety screening methods.This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 101016496 (SimCardioTest). This workwas alsopartially supported by the Direccion General de Politica Cientifica de la Generalitat Valenciana (PROMETEO/2020/043). JL is being funded by the Ministerio de Ciencia, Innovacion y Universidades for the Formacion de Profesorado Universitario (grant reference: FPU18/01659). Funding for open access charge: Universitat Politecnica de Valencia.Llopis-Lorente, J.; Trenor Gomis, BA.; Saiz Rodríguez, FJ. (2022). Considering population variability of electrophysiological models improves the assessment of drug-induced torsadogenic risk. Computer Methods and Programs in Biomedicine. 221:1-12. https://doi.org/10.1016/j.cmpb.2022.10693411222

Uncertainty Assessment of Proarrhythmia Predictions Derived from Multi-Level in Silico Models

[EN] These datasets were generated to train ("PopulationDrugsTrainingKrNaLCaL.xlsx") and test ("PopulationDrugsTestKrCaLNaL.xlsx") the uncertainty assessment models developed in the paper "Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models" by Kopanska, Rodríguez-Belenguer, et al.The authors received funding from the eTRANSAFE project, Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777365, supported from European Union's Horizon 2020 and the EFPIA. We also received funding from the SimCardioTest supported by European Union’s Horizon 2020 research and innovation programme under grant agreement No 101016496. J.L.L. is being funded by the Ministerio de Ciencia, Innovacion y Universidades for the “Formacion de Profesorado Universitario” (Grant Reference: FPU18/01659). The work was also partially support by the Dirección General de Política Científica de la Generalitat Valenciana (PROMETEO/ 2020/043).Kopanska, K.; Rodríguez-Belenguer, P.; Llopis-Lorente, J.; Trénor, B.; Saiz, J.; Pastor, M. (2023). Uncertainty Assessment of Proarrhythmia Predictions Derived from Multi-Level in Silico Models. Universitat Politècnica de València. https://doi.org/10.4995/Dataset/10251/19182

A causal discovery approach for streamline ion channels selection to improve drug-induced TdP risk assessment

The query of causality is of paramount importance in biomedical data analysis: assessing the causal relationships between the observed variables allows to improve our understanding of the tackled medical condition and better support decision-making. Torsade de Pointes (TdP) is an extremely serious drug-induced cardiac side effect, which can provoke ventricular fibrillation and lead to sudden death. TdP is related to abnormal repolarizations in single cells, and the minimum set of ion channels needed to correctly assess TdP risk is still an open question. Discovering causal relations between drug-induced ionic channels' perturbations could shed new light on the underlying mechanisms leading to TdP, and drive variable selection to improve TdP-risk assessment. In this work, we propose to apply the causal discovery method ICA-Linear Non-Gaussian Acyclic Model (ICA-LiNGAM) to uncover the relations across the 7 ion channels identified by the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative as potentially related to the induction of TdP: I Kr , I Na , I NaL , I CaL , I K1 , I Ks and I to. The obtained Bayesian causal network can be explored to infer the downstream impact of the ionic currents in the drug's safety label. We consider 109 drugs of known torsadogenic risk (51 unsafe) listed by CredibleMeds. We identify I Kr , I NaL and I CaL as the ones which directly affect TdP-risk assessment, and suggest that I Na perturbations could potentially have a high impact on proarrhythmic risk induction. Our causalitybased results were further confirmed by independently performing binary drug risk classification, which shows that the combination of the 3 selected ions maximizes the classification accuracy and specificity, outperforming state-ofthe-art approaches based on alternative ion channel combinations

InSilico Classifiers for the Assessment of Drug Proarrhythmicity

[EN] Drug-induced torsade de pointes (TdP) is a life-threatening ventricular arrhythmia responsible for the withdrawal of many drugs from the market. Although currently used TdP risk-assessment methods are effective, they are expensive and prone to produce false positives. In recent years, in silico cardiac simulations have proven to be a valuable tool for the prediction of drug effects. The objective of this work is to evaluate different biomarkers of drug-induced proarrhythmic risk and to develop an in silico risk classifier. Cellular simulations were performed using a modified version of the O'Hara et al. ventricular action potential model and existing pharmacological data (IC50 and effective free therapeutic plasma concentration, EFTPC) for 109 drugs of known torsadogenic risk (51 positive). For each compound, four biomarkers were tested: T-x (drug concentration leading to a 10% prolongation of the action potential over the EFTPC), T-qNet (net charge carried by ionic currents when exposed to 10 times the EFTPC with respect to the net charge in control), T-triang (triangulation for a drug concentration of 10 times the EFTPC over triangulation in control), and T-EAD (drug concentration originating early afterdepolarizations over EFTPC). Receiver operating characteristic (ROC) curves were built for each biomarker to evaluate their individual predictive quality. At the optimal cutoff point, accuracies for T-x, T-qNet, T-triang, and T-EAD were 89.9, 91.7, 90.8, and 78.9% respectively. The resulting accuracy of the hERG IC50 test (current biomarker) was 78.9%. When combining T-x, T-qNet and T-triang into a classifier based on decision trees, the prediction improves, achieving an accuracy of 94.5%. The sensitivity analysis revealed that most of the effects on the action potential are mainly due to changes in I-Kr, I-CaL, I-NaL and I-Ks. In fact, considering that drugs affect only these four currents, TdP risk classification can be as accurate as when considering effects on the seven main currents proposed by the CiPA initiative. Finally, we built a ready-to-use tool (based on more than 450 000 simulations), which can be used to quickly assess the proarrhythmic risk of a compound. In conclusion, our in silico tool can be useful for the preclinical assessment of TdP-risk and to reduce costs related with new drug development. The TdP risk-assessment tool and the software used in this work are available at https://riunet.upv.es/handle/10251/136919.This work was partially supported by the Direccion general de Politica Cientifica de la Generalitat Valenciana (PROMETEO/2020/043); by "Primeros Proyectos de Investigacion" (PAID06-18) from Vicerrectorado de Investigacion, Innovacion y Transferencia de la Universitat Politecnica de Valencia (UPV), Valencia, Spain; as well as from the "Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 20172020" from the Ministerio de Ciencia e Innovacion y Universidades (PID2019-104356RB-C41/AEI/10.13039/501100011033). J.L.L. is being funded by the Ministerio de Ciencia, Innovacion y Universidades for the "Formacion de Profesorado Universitario" (Grant Reference: FPU18/01659).Llopis-Lorente, J.; Gomis-Tena Dolz, J.; Cano, J.; Romero Pérez, L.; Saiz Rodríguez, FJ.; Trenor Gomis, BA. (2020). InSilico Classifiers for the Assessment of Drug Proarrhythmicity. 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Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification. Computer Methods in Applied Mechanics and Engineering, 348, 313-333. doi:10.1016/j.cma.2019.01.033Lacerda, A. E., Kuryshev, Y. A., Chen, Y., Renganathan, M., Eng, H., Danthi, S. J., … Brown, A. M. (2007). Alfuzosin Delays Cardiac Repolarization by a Novel Mechanism. Journal of Pharmacology and Experimental Therapeutics, 324(2), 427-433. doi:10.1124/jpet.107.128405Yang, T., Chun, Y. W., Stroud, D. M., Mosley, J. D., Knollmann, B. C., Hong, C., & Roden, D. M. (2014). Screening for Acute I Kr Block Is Insufficient to Detect Torsades de Pointes Liability. Circulation, 130(3), 224-234. doi:10.1161/circulationaha.113.007765Crumb, W. J., Vicente, J., Johannesen, L., & Strauss, D. G. (2016). 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In-Silico Classifiers for Assessment of Drug Proarrhytmicity

Every sheet in described files contains a table of numbers (doubles), resulting from the transformation of a three-dimensional matrix. To do so, the first three columns of each table contain the Coordinates x, y and z or z' of the original matrix respectively (no units). They range from -3 to 1.5. Coordinates were calculated as the logarithm of the ratio of a drug concentration (C) over the concentration that is required to inhibit a 50% of a given ion channel current (Half inhibitory concentration or IC50). They represent the block effect of such drug on four main currents which control the Action Potential shape and duration, namely, the rapid component of the potassium delayed rectifier current (IKr), the slow component of the potassium delayed rectifier current (IKs), the late sodium current (INaL) and the calcium type L current (ICaL). The following formulae can be used to deduce mentioned Coordinates: x = log_10(C/(IC_50 (I_Kr))) ; y = log_10(C/(IC_50 (I_CaL ) )); z = log_10(C/(IC_50 (I_CaL)); and z' = log_10(C/(IC_50 (I_NaL)). These are indeed part of the Hill equation (Hill coefficient set to 1), where G is the channel’s resulting conductance and G0 is the channel’s control conductance: G_[x || y || z || z'] = G_0·1/(1+C/(IC_(50, [x || y || z || z'] ) )) = G_0·1/(1+10^([x || y || z || z']) ). Conductances of the four main currents are thus translated into cardiac cellular or tissue models to study the effect of a wide range of drug concentrations. The following parameters were obtained and summarized in currently described files. Detailed methods of the performed simulations, as well as biomarker calculations, can be consulted in [1].This repository contains the Matlab functions used in [1] and six Excel files named “Tx matrix KrKsCaL.xlsx”, “Tx matix KrNaLCaL.xlsx”, “TqNet matrix KrKsCaL.xlsx”, “TqNet matrix KrNaLCaL.xlsx”, "Ttirang matrix KrKsCaL.xlsx" and “Ttirang matrix KrNaLCaL.xlsx”. These files contain the value of three arrhythmogenic indices proposed in [1] (Tx, TqNet and Ttriang) resulting from simulations of drug effects on the action potential. The simulations were performed in a modified version of the O’Hara-Rudy model (ORd) [2] of human endocardial ventricular cells (492.536 simulations). References: [1]. Llopis J., Gomis-Tena J., Cano J., Romero L., Saiz J., Trenor B. In-Silico Classifiers for Assessment of Drug Proarrhythmicity. 2020 (Currently under revision) [2]. O’Hara, T.; Virág, L.; Varró, A.; Rudy, Y. Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation. PLoS Comput. Biol. 2011, 7 (5), e1002061.The authors would like to strongly thank Dr. Manuel Pastor from Research Programme on Biomedical Informatics (GRIB), at Universitat Pompeu Fabra (Barcelona, Spain) for its review of the manuscript and expert opinions and suggestions. This work was partially supported by the Dirección general de Política Científica de la Generalitat Valencia (PROMETEU2016/088), Primeros Proyectos de Investigación (PAID-06-18), Vicerrectorado de Investigación, Innovación y Transferencia de la Universitat Politècnica de València (UPV), València, Spain as well as Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 from the Ministerio de Economía, Industria y Competitividad of Spain (DPI2016-75799-R) and AEI/FEDER, UE. JL is being funded by the Ministerio de Ciencia, Innovación y Universidades for the Formación de Profesorado Universitario (grant reference: FPU18/01659).Llopis Lorente, J.; Gomis-Tena Dolz, J.; Cano García, J.; Romero Pérez, L.; Saiz Rodríguez, FJ.; Trénor Gomis, BA. (2020). Tx, TqNet and Ttriang Values. Universitat Politècnica de València. https://doi.org/10.4995/Dataset/10251/13691

In Silico Classifiers for the Assessment of Drug Proarrhythmicity

[EN] Drug-induced torsade de pointes (TdP) is a life-threatening ventricular arrhythmia responsible for the withdrawal of many drugs from the market. Although currently used TdP risk-assessment methods are effective, they are expensive and prone to produce false positives. In recent years, in silico cardiac simulations have proven to be a valuable tool for the prediction of drug effects. The objective of this work is to evaluate different biomarkers of drug-induced proarrhythmic risk and to develop an in silico risk classifier. Cellular simulations were performed using a modified version of the O'Hara et al. ventricular action potential model and existing pharmacological data (IC50 and effective free therapeutic plasma concentration, EFTPC) for 109 drugs of known torsadogenic risk (51 positive). For each compound, four biomarkers were tested: T-x (drug concentration leading to a 10% prolongation of the action potential over the EFTPC), T-qNet (net charge carried by ionic currents when exposed to 10 times the EFTPC with respect to the net charge in control), T-triang (triangulation for a drug concentration of 10 times the EFTPC over triangulation in control), and T-EAD (drug concentration originating early afterdepolarizations over EFTPC). Receiver operating characteristic (ROC) curves were built for each biomarker to evaluate their individual predictive quality. At the optimal cutoff point, accuracies for T-x, T-qNet, T-triang, and T-EAD were 89.9, 91.7, 90.8, and 78.9% respectively. The resulting accuracy of the hERG IC50 test (current biomarker) was 78.9%. When combining T-x, T-qNet and T-triang into a classifier based on decision trees, the prediction improves, achieving an accuracy of 94.5%. The sensitivity analysis revealed that most of the effects on the action potential are mainly due to changes in I-Kr, I-CaL, I-NaL and I-Ks. In fact, considering that drugs affect only these four currents, TdP risk classification can be as accurate as when considering effects on the seven main currents proposed by the CiPA initiative. Finally, we built a ready-to-use tool (based on more than 450 000 simulations), which can be used to quickly assess the proarrhythmic risk of a compound. In conclusion, our in silico tool can be useful for the preclinical assessment of TdP-risk and to reduce costs related with new drug development. The TdP risk-assessment tool and the software used in this work are available at https://riunet.upv.es/handle/10251/136919.This work was partially supported by the Direccion general de Politica Cientifica de la Generalitat Valenciana (PROMETEO/2020/043); by "Primeros Proyectos de Investigacion" (PAID06-18) from Vicerrectorado de Investigacion, Innovacion y Transferencia de la Universitat Politecnica de Valencia (UPV), Valencia, Spain; as well as from the "Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 20172020" from the Ministerio de Ciencia e Innovacion y Universidades (PID2019-104356RB-C41/AEI/10.13039/501100011033). J.L.L. is being funded by the Ministerio de Ciencia, Innovacion y Universidades for the "Formacion de Profesorado Universitario" (Grant Reference: FPU18/01659).Llopis-Lorente, J.; Gomis-Tena Dolz, J.; Cano, J.; Romero Pérez, L.; Saiz Rodríguez, FJ.; Trenor Gomis, BA. (2020). InSilico Classifiers for the Assessment of Drug Proarrhythmicity. 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Application of Machine Learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia

[EN] Background and Objective In silico prediction of drug-induced ventricular arrhythmia often requires computationally intensive simulations, making its application tedious and non-interactive. This inconvenience can be mitigated using matrices of precomputed simulation results, allowing instantaneous computation of biomarkers such as action potential duration at 90% of the repolarisation (APD90). However, preparing such matrices can be computationally intensive for the method developers, limiting the range of simulated conditions. In this work, we aim to optimise the generation of these matrices so that they can be obtained with less effort and for a broader range of input values. Methods Machine learning methods were applied, building models trained with only a small fraction of the originally simulated results. The predictive performances of the models were assessed by comparing their predicted values with the actual simulation results, using percentual mean absolute error and mean relative error, as well as the percentage of data with a relative error below 5%. Results Our method obtained highly accurate estimations of the original values, leading to a nearly one hundred-fold decrease in computation time. This method also allows precomputing more complex matrices, describing the effect of more ion channels on the APD90. The best results were obtained by applying Support Vector Machine models, which yielded errors below 1% in most cases. This approach was further validated by predicting the APD90 of a set of 12 CiPA compounds and exporting the optimal settings for predicting APD90 using a different set of ion channels, always with satisfactory results. Conclusions The proposed method effectively reduces the computational effort required to generate matrices of precomputed electrophysiological simulation values. The same approach can be applied in other fields where computationally costly simulations are applied repeatedly using slightly different input values.The authors received funding from the eTRANSAFE project, Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777365, supported from European Union's Horizon 2020 and the EFPIA. We also received funding from the SimCardioTest supported by European Union¿s Horizon 2020 research and innovation programme under grant agreement No 101016496. J.L.L. is being funded by the Ministerio de Ciencia, Innovacion y Universidades for the Formacion de Profesorado Universitario (Grant Reference: FPU18/01659). The work was also partially support by the Dirección General de Política Científica de la Generalitat Valenciana (PROMETEO/ 2020/043).https://riunet.upv.es/handle/10251/183067Rodriguez-Belenguer, P.; Kopanska, K.; Llopis-Lorente, J.; Trenor Gomis, BA.; Saiz Rodríguez, FJ.; Pastor, M. (2023). Application of Machine Learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia. Computer Methods and Programs in Biomedicine. 230:1-10. https://doi.org/10.1016/j.cmpb.2023.10734511023