Blinded In Silico Drug Trial Reveals the Minimum Set of Ion Channels for Torsades de Pointes Risk Assessment

In Silico Human Cardiomyocyte Action Potential Modeling: Exploring Ion Channel Input Combinations

In silico modeling offers an opportunity to supplement and accelerate cardiac safety testing. With in silico modeling, computational simulation methods are used to predict electrophysiological interactions and pharmacological effects of novel drugs on critical physiological processes. The O'Hara-Rudy's model was developed to predict the response to different ion channel inhibition levels on cardiac action potential duration (APD) which is known to directly correlate with the QT interval. APD data at 30% 60% and 90% inhibition were derived from the model to delineate possible ventricular arrhythmia scenarios and the marginal contribution of each ion channel to the model. Action potential values were calculated for epicardial, myocardial, and endocardial cells, with action potential curve modeling. This study assessed cardiac ion channel inhibition data combinations to consider when undertaking in silico modeling of proarrhythmic effects as stipulated in the Comprehensive in Vitro Proarrhythmia Assay (CiPA). As expected, our data highlight the importance of the delayed rectifier potassium channel (IKr) as the most impactful channel for APD prolongation. The impact of the transient outward potassium channel (Ito) inhibition on APD was minimal while the inward rectifier (IK1) and slow component of the delayed rectifier potassium channel (IKs) also had limited APD effects. In contrast, the contribution of fast sodium channel (INa) and/or L-type calcium channel (ICa) inhibition resulted in substantial APD alterations supporting the pharmacological relevance of in silico modeling using input from a limited number of cardiac ion channels including IKr, INa, and ICa, at least at an early stage of drug development.

Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability

Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing multi-in silico features from a population of human ventricular cell models that could capture a representation of the underlying mechanisms contributing to TdP risk to provide a more reliable assessment of drug-induced cardiotoxicity. Method: We generated a virtual population of human ventricular cell models using a modified O'Hara-Rudy model, allowing inter-individual variation. IC 50 and Hill coefficients from 67 drugs were used as input to simulate drug effects on cardiac cells. Fourteen features (dVm dt repol , dVm dt max , Vm peak , Vm resting , APD tri , APD 90 , APD 50 , Ca peak , Ca diastole , Ca tri , CaD 90 , CaD 50 , qNet, qInward) could be generated from the simulation and used as input to several machine learning models, including k-nearest neighbor (KNN), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). Optimization of the machine learning model was performed using a grid search to select the best parameter of the proposed model. We applied five-fold cross-validation while training the model with 42 drugs and evaluated the model's performance with test data from 25 drugs. Result: The proposed ANN model showed the highest performance in predicting the TdP risk of drugs by providing an accuracy of 0.923 (0.908-0.937), sensitivity of 0.926 (0.909-0.942), specificity of 0.921 (0.906-0.935), and AUC score of 0.964 (0.954-0.975). Discussion and conclusion: According to the performance results, combining the electrophysiological model including inter-individual variation and optimization of machine learning showed good generalization ability when evaluated using the unseen dataset and produced a reliable drug-induced TdP risk prediction system.

Linezolid and the risk of QT interval prolongation: A pharmacovigilance study of the Food and Drug Administration Adverse Event Reporting System

AIMS

Few studies have investigated linezolid (LZD)-associated cardiotoxicity. This study explored the potential association between LZD and QT interval prolongation.

METHODS

Adverse event reports of QT interval prolongation associated with LZD from the Food and Drug Administration Adverse Event Reporting System from January 2013 to December 2021 were analysed and the reporting odds ratio (ROR) with 95% confidence intervals were calculated.

RESULTS

A total of 6738 adverse event reports of LZD as the primary and secondary suspected drug were obtained from the database, including 192 reports with electrocardiogram QT prolonged (QTp), and the ROR value was 26.1 (95% CI = 22.6-30.2). There were 8 reports of long QT syndrome, ROR 14.2 (95% CI = 7.1-28.5); 5 reports of torsade de pointes, ROR 3.2 (95% CI = 1.3-7.6); and 5 reports of ventricular tachycardia, ROR 1.9 (95% CI = 0.8-4.5). Subgroup analysis revealed that patients with tuberculosis treated with LZD had a higher reporting rate among all QTp reports, exhibiting an odds ratio of 330.0 (95% CI = 223.1-488.1). The odds ratios of QTp associated with LZD treatments in patients with and without tuberculosis were 4.2 (95% CI = 3.4-5.3) and 1.2 (95% CI = 0.8-1.6), respectively.

CONCLUSION

The study showed an association between LZD and QT interval prolongation. In the report on patients with tuberculosis, the incidence of QTp was higher when treated with LZD.

Application of machine learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia

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 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.

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.

Testing the nonclinical Comprehensive In Vitro Proarrhythmia Assay (CiPA) paradigm with an established anti-seizure medication: Levetiracetam case study

Levetiracetam (LEV), a well-established anti-seizure medication (ASM), was launched before the original ICH S7B nonclinical guidance assessing QT prolongation potential and the introduction of the Comprehensive In Vitro Proarrhythmia Assay (CiPA) paradigm. No information was available on its effects on cardiac channels. The goal of this work was to "pressure test" the CiPA approach with LEV and check the concordance of nonclinical core and follow-up S7B assays with clinical and post-marketing data. The following experiments were conducted with LEV (0.25-7.5 mM): patch clamp assays on hERG (acute or trafficking effects), NaV 1.5, CaV 1.2, Kir 2.1, KV 7.1/mink, KV 1.5, KV 4.3, and HCN4; in silico electrophysiology modeling (Virtual Assay® software) in control, large-variability, and high-risk human ventricular cell populations; electrophysiology measurements in human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes and dog Purkinje fibers; ECG measurements in conscious telemetered dogs after single oral administration (150, 300, and 600 mg/kg). Except a slight inhibition (<10%) of hERG and KV 7.1/mink at 7.5 mM, that is, 30-fold the free therapeutic plasma concentration (FTPC) at 1500 mg, LEV did not affect any other cardiac channels or hERG trafficking. In both virtual and real human cardiomyocytes, and in dog Purkinje fibers, LEV induced no relevant changes in electrophysiological parameters or arrhythmia. No QTc prolongation was noted up to 2.7 mM unbound plasma levels in conscious dogs, corresponding to 10-fold the FTPC. Nonclinical assessment integrating CiPA assays shows the absence of QT prolongation and proarrhythmic risk of LEV up to at least 10-fold the FTPC and the good concordance with clinical and postmarketing data, although this does not exclude very rare occurrence of QT prolongation cases in patients with underlying risk factors.

Cross clinical-experimental-computational qualification of in silico drug trials on human cardiac purkinje cells for proarrhythmia risk prediction

The preclinical identification of drug-induced cardiotoxicity and its translation into human risk are still major challenges in pharmaceutical drug discovery. The ICH S7B Guideline and Q&A on Clinical and Nonclinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential promotes human in silico drug trials as a novel tool for proarrhythmia risk assessment. To facilitate the use of in silico data in regulatory submissions, explanatory control compounds should be tested and documented to demonstrate consistency between predictions and the historic validation data. This study aims to quantify drug-induced electrophysiological effects on in silico cardiac human Purkinje cells, to compare them with existing in vitro rabbit data, and to assess their accuracy for clinical pro-arrhythmic risk predictions. The effects of 14 reference compounds were quantified in simulations with a population of in silico human cardiac Purkinje models. For each drug dose, five electrophysiological biomarkers were quantified at three pacing frequencies, and results compared with available in vitro experiments and clinical proarrhythmia reports. Three key results were obtained: 1) In silico, repolarization abnormalities in human Purkinje simulations predicted drug-induced arrhythmia for all risky compounds, showing higher predicted accuracy than rabbit experiments; 2) Drug-induced electrophysiological changes observed in human-based simulations showed a high degree of consistency with in vitro rabbit recordings at all pacing frequencies, and depolarization velocity and action potential duration were the most consistent biomarkers; 3) discrepancies observed for dofetilide, sotalol and terfenadine are mainly caused by species differences between humans and rabbit. Taken together, this study demonstrates higher accuracy of in silico methods compared to in vitro animal models for pro-arrhythmic risk prediction, as well as a high degree of consistency with in vitro experiments commonly used in safety pharmacology, supporting the potential for industrial and regulatory adoption of in silico trials for proarrhythmia prediction.

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.

In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities

The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.

Reliable identification of cardiac conduction abnormalities in drug discovery using automated patch clamp II: Best practices for Nav1.5 peak current in a high throughput screening environment

INTRODUCTION

For reliable identification of cardiac safety risk, compounds should be screened for activity on cardiac ion channels in addition to hERG, including Na_V1.5 and Ca_V1.2. We identified different parameters that might affect IC₅₀s of compounds on Na_V1.5 peak and late currents recorded using automated patch clamp (APC) and suggest outlines for best practices.

METHODS

APC instruments SyncroPatch 384 and Patchliner were used to record Na_V1.5 peak and late current. Up to 24 CiPA compounds were used to investigate effects of voltage protocol, holding potential (-80 mV or - 95 mV) and temperature (23 ± 1 °C or 36 ± 1 °C) on IC₅₀ values on hNa_V1.5 overexpressed in HEK or CHO cells either as frozen cells or running cultures.

RESULTS

The IC₅₀s of 18 compounds on the Na_V1.5 peak current recorded on the SyncroPatch 384 using the CiPA step-ramp protocol correlated well with the literature. The use of frozen or cultured cells did not affect IC₅₀s but voltage protocol and holding potential did cause differences in IC₅₀ values. Temperature can affect V_half of inactivation and also compound potency. A compound incubation time of 5-6 min was sufficient for most compounds, however slow acting compounds such as terfenadine required longer to reach maximum effect.

DISCUSSION

We conclude that holding potential, voltage protocol and temperature can affect IC₅₀ values and recommend the use of the CiPA step-ramp protocol at physiological temperature to record Na_V1.5 peak and late currents for cardiac safety. Further recommendations include: a minimum compound incubation time of 5 min, a replicate number of 4 and the use of positive and negative controls for reliable IC₅₀s.

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk‐informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk‐based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick‐start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.

Comprehensive in vitro pro-arrhythmic assays demonstrate that omecamtiv mecarbil has low pro-arrhythmic risk

Omecamtiv mecarbil (OM) is a myosin activator (myotrope), developed as a potential therapeutic agent for heart failure with reduced ejection fraction. To characterize the potential pro-arrhythmic risk of this novel sarcomere activator, we evaluated OM in a series of International Conference on Harmonization S7B core and follow-up assays, including an in silico action potential (AP) model. OM was tested in: (i) hERG, Nav1.5 peak, and Cav1.2 channel assays; (ii) in silico computation in a human ventricular AP (hVAP) population model; (iii) AP recordings in canine cardiac Purkinje fibers (PF); and (iv) electrocardiography analysis in isolated rabbit hearts (IRHs). OM had low potency in the hERG (half-maximal inhibitory concentration [IC50 ] = 125.5 µM) and Nav1.5 and Cav1.2 assays (IC50  > 300 µM). These potency values were used as inputs to investigate the occurrence of repolarization abnormalities (biomarkers of pro-arrhythmia) in an hVAP model over a wide range of OM concentrations. The outcome of hVAP analysis indicated low pro-arrhythmia risk at OM concentration up to 30 µM (100-fold the effective free therapeutic plasma concentration). In the isolated canine PF assay, OM shortened AP duration (APD)60 and APD90 significantly from 3 to 30 µM. In perfused IRH, ventricular repolarization (corrected QT and corrected JT intervals) was decreased significantly at greater than or equal to 1 µM OM. In summary, the comprehensive proarrhythmic assessment in human and non-rodent cardiac models provided data indicative that OM did not delay ventricular repolarization at therapeutic relevant concentrations, consistent with clinical findings.

Applying the CiPA approach to evaluate cardiac proarrhythmia risk of some antimalarials used off-label in the first wave of COVID-19

We applied a set of in silico and in vitro assays, compliant with the Comprehensive In Vitro Proarrhythmia Assay (CiPA) paradigm, to assess the risk of chloroquine (CLQ) or hydroxychloroquine (OH-CLQ)-mediated QT prolongation and Torsades de Pointes (TdP), alone and combined with erythromycin (ERT) and azithromycin (AZI), drugs repurposed during the first wave of coronavirus disease 2019 (COVID-19). Each drug or drug combination was tested in patch clamp assays on seven cardiac ion channels, in in silico models of human ventricular electrophysiology (Virtual Assay) using control (healthy) or high-risk cell populations, and in human-induced pluripotent stem cell (hiPSC)-derived cardiomyocytes. In each assay, concentration-response curves encompassing and exceeding therapeutic free plasma levels were generated. Both CLQ and OH-CLQ showed blocking activity against some potassium, sodium, and calcium currents. CLQ and OH-CLQ inhibited IKr (half-maximal inhibitory concentration [IC50 ]: 1 µM and 3-7 µM, respectively) and IK1 currents (IC50 : 5 and 44 µM, respectively). When combining OH-CLQ with AZI, no synergistic effects were observed. The two macrolides had no or very weak effects on the ion currents (IC50  > 300-1000 µM). Using Virtual Assay, both antimalarials affected several TdP indicators, CLQ being more potent than OH-CLQ. Effects were more pronounced in the high-risk cell population. In hiPSC-derived cardiomyocytes, all drugs showed early after-depolarizations, except AZI. Combining CLQ or OH-CLQ with a macrolide did not aggravate their effects. In conclusion, our integrated nonclinical CiPA dataset confirmed that, at therapeutic plasma concentrations relevant for malaria or off-label use in COVID-19, CLQ and OH-CLQ use is associated with a proarrhythmia risk, which is higher in populations carrying predisposing factors but not worsened with macrolide combination.

Comparison of the Simulated Response of Three in Silico Human Stem Cell-Derived Cardiomyocytes Models and in Vitro Data Under 15 Drug Actions

Objectives: Improvements in human stem cell-derived cardiomyocyte (hSC-CM) technology have promoted their use for drug testing and disease investigations. Several in silico hSC-CM models have been proposed to augment interpretation of experimental findings through simulations. This work aims to assess the response of three hSC-CM in silico models (Koivumäki2018, Kernik2019, and Paci2020) to simulated drug action, and compare simulation results against in vitro data for 15 drugs.

Methods: First, simulations were conducted considering 15 drugs, using a simple pore-block model and experimental data for seven ion channels. Similarities and differences were analyzed in the in silico responses of the three models to drugs, in terms of Ca²⁺ transient duration (CTD₉₀) and occurrence of arrhythmic events. Then, the sensitivity of each model to different degrees of blockage of Na⁺ (I_Na), L-type Ca²⁺ (I_CaL), and rapid delayed rectifying K⁺ (I_Kr) currents was quantified. Finally, we compared the drug-induced effects on CTD₉₀ against the corresponding in vitro experiments.

Results: The observed CTD₉₀ changes were overall consistent among the in silico models, all three showing changes of smaller magnitudes compared to the ones measured in vitro. For example, sparfloxacin 10 µM induced +42% CTD₉₀ prolongation in vitro, and +17% (Koivumäki2018), +6% (Kernik2019), and +9% (Paci2020) in silico. Different arrhythmic events were observed following drug application, mainly for drugs affecting I_Kr. Paci2020 and Kernik2019 showed only repolarization failure, while Koivumäki2018 also displayed early and delayed afterdepolarizations. The spontaneous activity was suppressed by Na⁺ blockers and by drugs with similar effects on I_CaL and I_Kr in Koivumäki2018 and Paci2020, while only by strong I_CaL blockers, e.g. nisoldipine, in Kernik2019. These results were confirmed by the sensitivity analysis.

Conclusion: To conclude, The CTD₉₀ changes observed in silico are qualitatively consistent with our in vitro data, although our simulations show differences in drug responses across the hSC-CM models, which could stem from variability in the experimental data used in their construction.

In Silico Classifiers for the Assessment of Drug Proarrhythmicity

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.

Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk

There is an increasing expectation that computational approaches may supplement existing human decision-making. Frontloading of models for cardiac safety prediction is no exception to this trend, and ongoing regulatory initiatives propose use of high-throughput in vitro data combined with computational models for calculating proarrhythmic risk. Evaluation of these models requires robust assessment of the outcomes. Using FDA Adverse Event Reporting System reports and electronic healthcare claims data from the Truven-MarketScan US claims database, we quantify the incidence rate of arrhythmia in patients and how this changes depending on patient characteristics. First, we propose that such datasets are a complementary resource for determining relative drug risk and assessing the performance of cardiac safety models for regulatory use. Second, the results suggest important determinants for appropriate stratification of patients and evaluation of additional drug risk in prescribing and clinical support algorithms and for precision health.

In vitro data and computational models can assist with calculating pro-arrhythmic risk

We use patient health records and FDA Adverse Event Reporting System reports

Use of such datasets helps assess relative drug risk and cardiac safety models

We quantify how patient characteristics can affect arrhythmia incidence

In silico models for evaluating proarrhythmic risk of drugs

Safety evaluation of drugs requires examination of the risk of generating Torsade de Pointes (TdP) because it can lead to sudden cardiac death. Until recently, the QT interval in the electrocardiogram (ECG) has been used in the evaluation of TdP risk because the QT interval is known to be associated with the development of TdP. Although TdP risk evaluation based on QT interval has been successful in removing drugs with TdP risk from the market, some safe drugs may have also been affected due to the low specificity of QT interval-based evaluation. For more accurate evaluation of drug safety, the comprehensive in vitro proarrhythmia assay (CiPA) has been proposed by regulatory agencies, industry, and academia. Although the CiPA initiative includes in silico evaluation of cellular action potential as a component, attempts to utilize in silico simulation in drug safety evaluation are expanding, even to simulating human ECG using biophysical three-dimensional models of the heart and torso under the effects of drugs. Here, we review recent developments in the use of in silico models for the evaluation of the proarrhythmic risk of drugs. We review the single cell, one-dimensional, two-dimensional, and three-dimensional models and their applications reported in the literature and discuss the possibility of utilizing ECG simulation in drug safety evaluation.