In silico methods are gaining attention for predicting drug-induced Torsade de Pointes (TdP) in different stages of drug development. However, many computational models tended not to account for inter-individual response variability due to demographic covariates, such as sex, or physiologic covariates, such as renal function, which may be crucial when predicting TdP. This study aims to compare the effects of drugs in male and female populations with normal and impaired renal function using in silico methods. Pharmacokinetic models considering sex and renal function as covariates were implemented from data published in pharmacokinetic studies. Drug effects were simulated using an electrophysiologically calibrated population of cellular models of 300 males and 300 females. The population of models was built by modifying the endocardial action potential model published by O'Hara et al. (2011) according to the experimentally measured gene expression levels of 12 ion channels. Fifteen pharmacokinetic models for CiPA drugs were implemented and validated in this study. Eight pharmacokinetic models included the effect of renal function and four the effect of sex. The mean difference in action potential duration (APD) between male and female populations was 24.9 ms (p<0.05). Our simulations indicated that women with impaired renal function were particularly susceptible to drug-induced arrhythmias, whereas healthy men were less prone to TdP. Differences between patient groups were more pronounced for high TdP-risk drugs. The proposed in silico tool also revealed that individuals with impaired renal function, electrophysiologically simulated with hyperkalemia (extracellular potassium concentration [K⁺]_o = 7 mM) exhibited less pronounced APD prolongation than individuals with normal potassium levels. The pharmacokinetic/electrophysiological framework was used to determine the maximum safe dose of dofetilide in different patient groups. As a proof of concept, 3D simulations were also run for dofetilide obtaining QT prolongation in accordance with previously reported clinical values. This study presents a novel methodology that combines pharmacokinetic and electrophysiological models to incorporate the effects of sex and renal function into in silico drug simulations and highlights their impact on TdP-risk assessment. Furthermore, it may also help inform maximum dose regimens that ensure TdP-related safety in a specific sub-population of patients.

We aimed to assess the correlation of cardiovascular magnetic resonance (CMR)-derived epicardial adipose tissue (EAT) with infarct size (IS) and residual systolic function in ST-segment elevation myocardial infarction (STEMI). We enrolled patients discharged for a first anterior reperfused STEMI submitted to undergo CMR. EAT, left ventricular (LV) ejection fraction (LVEF), and IS were quantified at the 1-week (n = 221) and at 6-month CMR (n = 167). At 1-week CMR, mean EAT was 31 ± 13 mL/m². Patients with high EAT volume (n = 72) showed larger 1-week IS. After adjustment, EAT extent was independently related to 1-week IS. In patients with large IS at 1 week (>30% of LV mass, n = 88), those with high EAT showed more preserved 6-month LVEF. This association persisted after adjustment and in a 1:1 propensity score-matched patient subset. Overall, EAT decreased at 6 months. In patients with large IS, a greater reduction of EAT was associated with more preserved 6-month LVEF. In STEMI, a higher presence of EAT was associated with a larger IS. Nevertheless, in patients with large infarctions, high EAT and greater subsequent EAT reduction were linked to more preserved LVEF in the chronic phase. This dual and paradoxical effect of EAT fuels the need for further research in this field.

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. 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%. 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 APD_90. 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. 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.

Heart failure is a cardiac pathology characterized by causing myocardial dysfunction. The need to improve current pharmacotherapy for patients with heart failure has encouraged the development of more promising compounds. Currently, the amount of experimentation required during the early phases of drug development to assess safety and efficacy is costly, but computer simulations can help accelerate the process. In the present study, we performed computer simulations to investigate the electromechanical effects of the sarcomeric drug omecamtiv mecarbil on cardiac tissue. We used a cellular human electromechanical model to develop a concentration‐dependent numerical model for the pharmacological compound based on previous experimental evidence. It was then implemented on a ventricular slab to extrapolate cellular activity to myocardial tissue scale, and heart failure with reduced ejection fraction conditions was reproduced. In silico results reveal that omecamtiv mecarbil can correct the depressed active force developed by failing myocytes and make the slab contract to the same extent as in healthy conditions. However, additional changes involving contraction times and diastolic function, measured as the time to active tension peak and baseline slab stretch, respectively, can compromise the pumping capacity of the heart. After exploring drug effects at different heart rates, results show that elevated stimulation frequencies (2 Hz), together with overdoses (omecamtiv mecarbil > 0.8 µm), are the main factors reducing fractional shortening and leading to loss of function. We demonstrate that electromechanical simulation results can provide a better understanding of the mechanism of action of a drug and facilitate the redirection of future investigations. image Key points: A new pipeline to assess the efficacy of inotropic drugs based on computational electromechanical models is described. Omecamtiv mecarbil increases developed force in failing cardiac tissue. Undesirable effects on diastolic function and contraction duration override the positive effects. Fast heart rates and elevated omecamtiv mecarbil doses aggravate cardiac dysfunction.