Sex-Specific Classification of Drug-Induced Torsade de Pointes Susceptibility Using Cardiac Simulations and Machine Learning

Enhanced Ca2+-Driven Arrhythmias in Female Patients with Atrial Fibrillation: Insights from Computational Modeling

BACKGROUND AND AIMS

Substantial sex-based differences have been reported in atrial fibrillation (AF), with female patients experiencing worse symptoms, increased complications from drug side effects or ablation, and elevated risk of AF-related stroke and mortality. Recent studies revealed sex-specific alterations in AF-associated Ca2+ dysregulation, whereby female cardiomyocytes more frequently exhibit potentially proarrhythmic Ca2+-driven instabilities compared to male cardiomyocytes. In this study, we aim to gain a mechanistic understanding of the Ca2+-handling disturbances and Ca2+-driven arrhythmogenic events in males vs females and establish their responses to Ca2+-targeted interventions.

METHODS AND RESULTS

We incorporated known sex differences and AF-associated changes in the expression and phosphorylation of key Ca2+-handling proteins and in ultrastructural properties and dimensions of atrial cardiomyocytes into our recently developed 3D atrial cardiomyocyte model that couples electrophysiology with spatially detailed Ca2+-handling processes. Our simulations of quiescent cardiomyocytes show increased incidence of Ca2+ sparks in female vs male myocytes in AF, in agreement with previous experimental reports. Additionally, our female model exhibited elevated propensity to develop pacing-induced spontaneous Ca2+ releases (SCRs) and augmented beat-to-beat variability in action potential (AP)-elicited Ca2+ transients compared with the male model. Parameter sensitivity analysis uncovered precise arrhythmogenic contributions of each component that was implicated in sex and/or AF alterations. Specifically, increased ryanodine receptor phosphorylation in female AF cardiomyocytes emerged as the major SCR contributor, while reduced L-type Ca2+ current was protective against SCRs for male AF cardiomyocytes. Furthermore, simulations of tentative Ca2+-targeted interventions identified potential strategies to attenuate Ca2+-driven arrhythmogenic events in female atria (e.g., t-tubule restoration, and inhibition of ryanodine receptor and sarcoplasmic/endoplasmic reticulum Ca2+-ATPase), and revealed enhanced efficacy when applied in combination.

CONCLUSIONS

Our sex-specific computational models of human atrial cardiomyocytes uncover increased propensity to Ca2+-driven arrhythmogenic events in female compared to male atrial cardiomyocytes in AF, and point to combined Ca2+-targeted interventions as promising approaches to treat AF in female patients. Our study establishes that AF treatment may benefit from sex-dependent strategies informed by sex-specific mechanisms.

Virtual clinical QT exposure-response studies - A translational computational approach

BACKGROUND AND PURPOSE

A recent paradigm shift in proarrhythmic risk assessment suggests that the integration of clinical, non-clinical, and computational evidence can be used to reach a comprehensive understanding of the proarrhythmic potential of drug candidates. While current computational methodologies focus on predicting the incidence of proarrhythmic events after drug administration, the objective of this study is to predict concentration-response relationships of QTc as a clinical endpoint.

EXPERIMENTAL APPROACH

Full heart computational models reproducing human cardiac populations were created to predict the concentration-response relationship of changes in the QT interval as recommended for clinical trials. The concentration-response relationship of the QT-interval prolongation obtained from the computational cardiac population was compared against the relationship from clinical trial data for a set of well-characterized compounds: moxifloxacin, dofetilide, verapamil, and ondansetron.

KEY RESULTS

Computationally derived concentration-response relationships of QT interval changes for three of the four drugs had slopes within the confidence interval of clinical trials (dofetilide, moxifloxacin and verapamil) when compared to placebo-corrected concentration-ΔQT and concentration-ΔQT regressions. Moxifloxacin showed a higher intercept, outside the confidence interval of the clinical data, demonstrating that in this example, the standard linear regression does not appropriately capture the concentration-response results at very low concentrations. The concentrations corresponding to a mean QTc prolongation of 10 ms were consistently lower in the computational model than in clinical data. The critical concentration varied within an approximate ratio of 0.5 (moxifloxacin and ondansetron) and 1 times (dofetilide, verapamil) the critical concentration observed in human clinical trials. Notably, no other in silico methodology can approximate the human critical concentration values for a QT interval prolongation of 10 ms.

CONCLUSION AND IMPLICATIONS

Computational concentration-response modelling of a virtual population of high-resolution, 3-dimensional cardiac models can provide comparable information to clinical data and could be used to complement pre-clinical and clinical safety packages. It provides access to an unlimited exposure range to support trial design and can improve the understanding of pre-clinical-clinical translation.

Combining pharmacokinetic and electrophysiological models for early prediction of drug-induced arrhythmogenicity

BACKGROUND AND OBJECTIVE

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.

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.

RESULTS

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.

CONCLUSIONS

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.

Predictive Male-to-Female Translation of Cardiac Electrophysiological Response to Drugs

Despite evidence that women are at higher risk of drug-induced torsade de pointes and sudden cardiac death, female sex is vastly underrepresented in cardiovascular research, thus limiting our fundamental understanding of sex-specific arrhythmia mechanisms and our ability to predict arrhythmia propensity. To address this urgent clinical and preclinical need, we developed a quantitative tool that predicts the electrophysiological response to drug administration in female cardiomyocytes starting from data collected in males. We demonstrate the suitability of our translator for sex-specific cardiac safety assessment and include proof-of-concept application of our translator to in vitro and in vivo data.

Sex matters: the frequently overlooked importance of considering sex in computational models

Personalised medicine and the development of a virtual human or a digital twin comprises visions of the future of medicine. To realise these innovations, an understanding of the biology and physiology of all people are required if we wish to apply these technologies at a population level. Sex differences in health and biology is one aspect that has frequently been overlooked, with young white males being seen as the "average" human being. This has not been helped by the lack of inclusion of female cells and animals in biomedical research and preclinical studies or the historic exclusion, and still low in proportion, of women in clinical trials. However, there are many known differences in health between the sexes across all scales of biology which can manifest in differences in susceptibility to diseases, symptoms in a given disease, and outcomes to a given treatment. Neglecting these important differences in the development of any health technologies could lead to adverse outcomes for both males and females. Here we highlight just some of the sex differences in the cardio-respiratory systems with the goal of raising awareness that these differences exist. We discuss modelling studies that have considered sex differences and touch on how and when to create sex-specific models. Scientific studies should ensure sex differences are included right from the study planning phase and results reported using sex as a biological variable. Computational models must have sex-specific versions to ensure a movement towards personalised medicine is realised.

Clinical Decision Support for Precision Dosing: Opportunities for Enhanced Equity and Inclusion in Health Care

Precision dosing aims to tailor doses to individual patients with the goal of improving treatment efficacy and avoiding toxicity. Clinical decision support software (CDSS) plays a crucial role in mediating this process, translating knowledge derived from clinical trials and real-world data (RWD) into actionable insights for clinicians to use at the point of care. However, not all patient populations are proportionally represented in clinical trials and other data sources that inform CDSS tools, limiting the applicability of these tools for underrepresented populations. Here, we review some of the limitations of existing CDSS tools and discuss methods for overcoming these gaps. We discuss considerations for study design and modeling to create more inclusive CDSS, particularly with an eye toward better incorporation of biological indicators in place of race, ethnicity, or sex. We also review inclusive practices for collection of these demographic data, during both study design and in software user interface design. Because of the role CDSS plays in both recording routine clinical care data and disseminating knowledge derived from data, CDSS presents a promising opportunity to continuously improve precision dosing algorithms using RWD to better reflect the diversity of patient populations.

Late-Night Overeating and All-Cause and Cardiovascular Disease Mortality in Adults Aged ≥ 50: A Cohort Study

BACKGROUND

Late-night overeating (LNOE) is closely associated with many health risk factors, but whether LNOE can increase the risk of death remains unknown. Thus, the prospective cohort study aimed to investigate the relationship between LNOE and mortality using data from the National Health and Nutrition Examination Survey.

METHODS

11,893 participants aged 50 years and older were included in the study. Dietary information was obtained through 24-h dietary recall interviews. Cox regression, subgroup, sensitivity, and restricted cubic spline analyses were used to assess the association between LNOE and mortality.

RESULTS

During a median follow-up of 8.3 years, 2,498 deaths occurred. After adjusting for major confounders, compared to the non-late-night eating (NLNE) group, the LNOE group was associated with higher risks of all-cause (HR = 1.47, 95% CI = 1.06-2.04) and cardiovascular disease (CVD) mortality (HR = 2.02, 95% CI = 1.13-3.60). No significant association was found between late-night eating (LNE) and mortality. Subgroup analyses showed that the LNOE group had a greater risk of all-cause and CVD mortality in participants older than 70 years, with alcohol consumption and hypertension and demonstrated an increased risk of all-cause mortality in males and higher CVD mortality in females.

CONCLUSION

The habit of LNOE was an independent risk factor for all-cause and CVD mortality in US adults aged 50 years and older, which was also influenced by age, sex, alcohol consumption, and hypertension.

Sex-dependent transcription of cardiac electrophysiology and links to acetylation modifiers based on the GTEx database

Development of safer drugs based on epigenetic modifiers, e.g., histone deacetylase inhibitors (HDACi), requires better understanding of their effects on cardiac electrophysiology. Using RNAseq data from the genotype-tissue-expression database (GTEx), we created models that link the abundance of acetylation enzymes (HDAC/SIRT/HATs), and the gene expression of ion channels (IC) via select cardiac transcription factors (TFs) in male and female adult human hearts (left ventricle, LV). Gene expression data (transcripts per million, TPM) from GTEx donors (21–70 y.o.) were filtered, normalized and transformed to Euclidian space to allow quantitative comparisons in 84 female and 158 male LVs. Sex-specific partial least-square (PLS) regression models, linking gene expression data for HDAC/SIRT/HATs to TFs and to ICs gene expression, revealed tight co-regulation of cardiac ion channels by HDAC/SIRT/HATs, with stronger clustering in the male LV. Co-regulation of genes encoding excitatory and inhibitory processes in cardiac tissue by the acetylation modifiers may help explain their predominantly net-neutral effects on cardiac electrophysiology. ATP1A1, encoding for the Na/K pump, represented an outlier—with orthogonal regulation by the acetylation modifiers to most of the ICs. The HDAC/SIRT/HAT effects were mediated by strong (+) TF regulators of ICs, e.g., MEF2A and TBX5, in both sexes. Furthermore, for male hearts, PLS models revealed a stronger (+/-) mediatory role on ICs for NKX25 and TGF1B/KLF4, respectively, while RUNX1 exhibited larger (-) TF effects on ICs in females. Male-trained PLS models of HDAC/SIRT/HAT effects on ICs underestimated the effects on some ICs in females. Insights from the GTEx dataset about the co-expression and transcriptional co-regulation of acetylation-modifying enzymes, transcription factors and key cardiac ion channels in a sex-specific manner can help inform safer drug design.

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

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 I_Kr, I_Ks, and I_NaK and higher conductances of I_CaL, I_NaL, and I_NCX, 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.

Sex-Related Differences in Genetic Cardiomyopathies

Cardiomyopathies are a heterogeneous collection of diseases that have in common primary functional and structural abnormalities of the heart muscle, often genetically determined. The most effective categorization of cardiomyopathies is based on the presenting phenotype, with hypertrophic, dilated, arrhythmogenic, and restrictive cardiomyopathy as the prototypes. Sex modulates the prevalence, morpho-functional manifestations and clinical course of cardiomyopathies. Aspects as diverse as ion channel expression and left ventricular remodeling differ in male and female patients with myocardial disease, although the reasons for this are poorly understood. Moreover, clinical differences may also result from complex societal/environmental discrepancies between sexes that may disadvantage women. This review provides a state-of-the-art appraisal of the influence of sex on cardiomyopathies, highlighting the many gaps in knowledge and open research questions.

Sex Matters: A Comprehensive Comparison of Female and Male Hearts

Cardiovascular disease in women remains under-diagnosed and under-treated. Recent studies suggest that this is caused, at least in part, by the lack of sex-specific diagnostic criteria. While it is widely recognized that the female heart is smaller than the male heart, it has long been ignored that it also has a different microstructural architecture. This has severe implications on a multitude of cardiac parameters. Here, we systematically review and compare geometric, functional, and structural parameters of female and male hearts, both in the healthy population and in athletes. Our study finds that, compared to the male heart, the female heart has a larger ejection fraction and beats at a faster rate but generates a smaller cardiac output. It has a lower blood pressure but produces universally larger contractile strains. Critically, allometric scaling, e.g., by lean body mass, reduces but does not completely eliminate the sex differences between female and male hearts. Our results suggest that the sex differences in cardiac form and function are too complex to be ignored: the female heart is not just a small version of the male heart. When using similar diagnostic criteria for female and male hearts, cardiac disease in women is frequently overlooked by routine exams, and it is diagnosed later and with more severe symptoms than in men. Clearly, there is an urgent need to better understand the female heart and design sex-specific diagnostic criteria that will allow us to diagnose cardiac disease in women equally as early, robustly, and reliably as in men.

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Quantitative cross-species translators of cardiac myocyte electrophysiology: Model training, experimental validation, and applications

Animal experimentation is key in the evaluation of cardiac efficacy and safety of novel therapeutic compounds. However, interspecies differences in the mechanisms regulating excitation-contraction coupling can limit the translation of experimental findings from animal models to human physiology and undermine the assessment of drugs’ efficacy and safety. Here, we built a suite of translators for quantitatively mapping electrophysiological responses in ventricular myocytes across species. We trained these statistical operators using a broad dataset obtained by simulating populations of our biophysically detailed computational models of action potential and Ca2+ transient in mouse, rabbit, and human. We then tested our translators against experimental data describing the response to stimuli, such as ion channel block, change in beating rate, and β-adrenergic challenge. We demonstrate that this approach is well suited to predicting the effects of perturbations across different species or experimental conditions and suggest its integration into mechanistic studies and drug development pipelines.

Sex Differences in Drug-Induced Arrhythmogenesis

The electrical activity in the heart varies significantly between men and women and results in a sex-specific response to drugs. Recent evidence suggests that women are more than twice as likely as men to develop drug-induced arrhythmia with potentially fatal consequences. Yet, the sex-specific differences in drug-induced arrhythmogenesis remain poorly understood. Here we integrate multiscale modeling and machine learning to gain mechanistic insight into the sex-specific origin of drug-induced cardiac arrhythmia at differing drug concentrations. To quantify critical drug concentrations in male and female hearts, we identify the most important ion channels that trigger male and female arrhythmogenesis, and create and train a sex-specific multi-fidelity arrhythmogenic risk classifier. Our study reveals that sex differences in ion channel activity, tissue conductivity, and heart dimensions trigger longer QT-intervals in women than in men. We quantify the critical drug concentration for dofetilide, a high risk drug, to be seven times lower for women than for men. Our results emphasize the importance of including sex as an independent biological variable in risk assessment during drug development. Acknowledging and understanding sex differences in drug safety evaluation is critical when developing novel therapeutic treatments on a personalized basis. The general trends of this study have significant implications on the development of safe and efficacious new drugs and the prescription of existing drugs in combination with other drugs.