Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative

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

Torsade de Pointes (TdP), a rare but lethal ventricular arrhythmia, is a toxic side effect of many drugs. To assess TdP risk, safety regulatory guidelines require quantification of hERG channel block in vitro and QT interval prolongation in vivo for all new therapeutic compounds. Unfortunately, these have proven to be poor predictors of torsadogenic risk, and are likely to have prevented safe compounds from reaching clinical phases. Although this has stimulated numerous efforts to define new paradigms for cardiac safety, none of the recently developed strategies accounts for patient conditions. In particular, despite being a well-established independent risk factor for TdP, female sex is vastly under-represented in both basic research and clinical studies, and thus current TdP metrics are likely biased toward the male sex. Here, we apply statistical learning to synthetic data, generated by simulating drug effects on cardiac myocyte models capturing male and female electrophysiology, to develop new sex-specific classification frameworks for TdP risk. We show that (i) TdP classifiers require different features in females vs. males; (ii) male-based classifiers perform more poorly when applied to female data; and (iii) female-based classifier performance is largely unaffected by acute effects of hormones (i.e., during various phases of the menstrual cycle). Notably, when predicting TdP risk of intermediate drugs on female simulated data, male-biased predictive models consistently underestimate TdP risk in women. Therefore, we conclude that pipelines for preclinical cardiotoxicity risk assessment should consider sex as a key variable to avoid potentially life-threatening consequences for the female population.

ESC working group on cardiac cellular electrophysiology position paper: relevance, opportunities, and limitations of experimental models for cardiac electrophysiology research

Cardiac arrhythmias are a major cause of death and disability. A large number of experimental cell and animal models have been developed to study arrhythmogenic diseases. These models have provided important insights into the underlying arrhythmia mechanisms and translational options for their therapeutic management. This position paper from the ESC Working Group on Cardiac Cellular Electrophysiology provides an overview of (i) currently available in vitro, ex vivo, and in vivo electrophysiological research methodologies, (ii) the most commonly used experimental (cellular and animal) models for cardiac arrhythmias including relevant species differences, (iii) the use of human cardiac tissue, induced pluripotent stem cell (hiPSC)-derived and in silico models to study cardiac arrhythmias, and (iv) the availability, relevance, limitations, and opportunities of these cellular and animal models to recapitulate specific acquired and inherited arrhythmogenic diseases, including atrial fibrillation, heart failure, cardiomyopathy, myocarditis, sinus node, and conduction disorders and channelopathies. By promoting a better understanding of these models and their limitations, this position paper aims to improve the quality of basic research in cardiac electrophysiology, with the ultimate goal to facilitate the clinical translation and application of basic electrophysiological research findings on arrhythmia mechanisms and therapies.

Dual Mechanisms of Cardiac Action Potential Prolongation by 4-Oxo-Nonenal Increasing the Risk of Arrhythmia; Late Na+ Current Induction and hERG K+ Channel Inhibition

4-Oxo-nonenal (4-ONE) is an endogenous lipid peroxidation product that is more reactive than 4-hydroxy-nonenal (4-HNE). We previously reported the arrhythmic potential of 4-HNE by suppression of cardiac human Ether-a-go-go Related Gene (hERG) K⁺ channels with prolonged action potential duration (APD) in cardiomyocytes. Here, we illustrate the higher arrhythmic risk of 4-ONE by modulating the cardiac hNa_V1.5 channel currents (I_NaV). Although the peak amplitude of I_NaV was not significantly changed by 4-ONE up to 10 μM, the rate of I_NaV inactivation was slowed, and the late Na⁺ current (I_NaL) became larger by 10 μM 4-ONE. The chemical modification of specific residues in hNa_V1.5 by 4-ONE was identified using MS-fingerprinting analysis. In addition to the changes in I_NaV, 4-ONE decreased the delayed rectifier K⁺ channel currents including the hERG current. The L-type Ca²⁺ channel current was decreased, whereas its inactivation was slowed by 4-ONE. The APD prolongation by 10 μM of 4-ONE was more prominent than that by 100 μM of 4-HNE. In the computational in silico cardiomyocyte simulation analysis, the changes of I_NaL by 4-ONE significantly exacerbated the risk of arrhythmia exhibited by the TdP marker, qNet. Our study suggests an arrhythmogenic effect of 4-ONE on cardiac ion channels, especially hNa_V1.5.

A stable cell line inducibly expressing hERG1a/1b heteromeric channels

Heterologously expressed hERG channels represent a mainstay of in vitro drug safety screens intended to mitigate risk of cardiac I_Kr block and sudden cardiac death. This is true even as more channel types are adopted as part of the Comprehensive in vitro Proarrhythmia Assay (CiPA) intended to elevate specificity and thus enhance throughput of promising lead drugs. Until now, hERG1a homomeric channels have been used as a proxy for I_Kr despite a wealth of evidence showing that hERG1a/1b heteromers better represent native channels in terms of protein abundance and channel biophysical and pharmacological properties. Past efforts to create a stable hERG1a/1b cell line were met with unpredictable silencing of hERG1b expression despite stable integration of the gene into the HEK293 cell genome. Here we report a new cell line stably expressing hERG1a, with hERG1b reliably controlled by an inducible promoter sensitive to doxycycline. Co-immunoprecipitation, Western blot analysis and patch-clamp electrophysiology confirm the heteromeric composition of the expressed channels. Association with hERG1b was found to promote hERG1a protein levels and enhance membrane current levels. Optimal conditions for drug screening and experimental investigation were achieved at 24 h exposure to 100 ng/ml doxycycline. Differences in pharmacological sensitivity between homomeric and heteromeric channels were observed for dofetilide and ebastine, but not fluoxetine, as evaluated by their IC₅₀ values. Using these values in the O'Hara-Rudy-CiPA in silico model revealed discrepancies in pro-arrhythmia risk, implying the hERG1a homomeric platform overestimates risk for these two drugs. Dofetilide block was use-dependent and faster for hERG1a/1b than hERG1a channels, whereas ebastine showed considerable block at rest and had a slower progression for hERG1a/1b channels. The hERG1a/1b cell line thus represents an advanced model for contemporary drug safety screening assays such as CiPA that employ IC₅₀ values to estimate risk of proarrhythmia in computational models of ventricular cardiomyocytes. This novel technology fulfills an unmet need to enhance specificity and foster a safe yet expanded drug development pipeline.

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.

Cardiac TdP risk stratification modelling of anti-infective compounds including chloroquine and hydroxychloroquine

Hydroxychloroquine (HCQ), the hydroxyl derivative of chloroquine (CQ), is widely used in the treatment of rheumatological conditions (systemic lupus erythematosus, rheumatoid arthritis) and is being studied for the treatment and prevention of COVID-19. Here, we investigate through mathematical modelling the safety profile of HCQ, CQ and other QT-prolonging anti-infective agents to determine their risk categories for Torsade de Pointes (TdP) arrhythmia. We performed safety modelling with uncertainty quantification using a risk classifier based on the qNet torsade metric score, a measure of the net charge carried by major currents during the action potential under inhibition of multiple ion channels by a compound. Modelling results for HCQ at a maximum free therapeutic plasma concentration (free C max) of approximately 1.2 µM (malaria dosing) indicated it is most likely to be in the high-intermediate-risk category for TdP, whereas CQ at a free C max of approximately 0.7 µM was predicted to most likely lie in the intermediate-risk category. Combining HCQ with the antibacterial moxifloxacin or the anti-malarial halofantrine (HAL) increased the degree of human ventricular action potential duration prolongation at some or all concentrations investigated, and was predicted to increase risk compared to HCQ alone. The combination of HCQ/HAL was predicted to be the riskiest for the free C max values investigated, whereas azithromycin administered individually was predicted to pose the lowest risk. Our simulation approach highlights that the torsadogenic potentials of HCQ, CQ and other QT-prolonging anti-infectives used in COVID-19 prevention and treatment increase with concentration and in combination with other QT-prolonging drugs.

Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation

Many studies have revealed changes in specific protein channels due to physiological causes such as mutation and their effects on action potential duration changes. However, no studies have been conducted to predict the type of protein channel abnormalities that occur through an action potential (AP) shape. Therefore, in this study, we aim to predict the ion channel conductance that is altered from various AP shapes using a machine learning algorithm. We perform electrophysiological simulations using a single-cell model to obtain AP shapes based on variations in the ion channel conductance. In the AP simulation, we increase and decrease the conductance of each ion channel at a constant rate, resulting in 1,980 AP shapes and one standard AP shape without any changes in the ion channel conductance. Subsequently, we calculate the AP difference shapes between them and use them as the input of the machine learning model to predict the changed ion channel conductance. In this study, we demonstrate that the changed ion channel conductance can be predicted with high prediction accuracy, as reflected by an F1 score of 0.985, using only AP shapes and simple machine learning.

In vitro and In silico Models to Study SARS-CoV-2 Infection: Integrating Experimental and Computational Tools to Mimic "COVID-19 Cardiomyocyte"

The rapid dissemination of SARS-CoV-2 has made COVID-19 a tremendous social, economic, and health burden. Despite the efforts to understand the virus and treat the disease, many questions remain unanswered about COVID-19 mechanisms of infection and progression. Severe Acute Respiratory Syndrome (SARS) infection can affect several organs in the body including the heart, which can result in thromboembolism, myocardial injury, acute coronary syndromes, and arrhythmias. Numerous cardiac adverse events, from cardiomyocyte death to secondary effects caused by exaggerated immunological response against the virus, have been clinically reported. In addition to the disease itself, repurposing of treatments by using "off label" drugs can also contribute to cardiotoxicity. Over the past several decades, animal models and more recently, stem cell-derived cardiomyocytes have been proposed for studying diseases and testing treatments in vitro. In addition, mechanistic in silico models have been widely used for disease and drug studies. In these models, several characteristics such as gender, electrolyte imbalance, and comorbidities can be implemented to study pathophysiology of cardiac diseases and to predict cardiotoxicity of drug treatments. In this Mini Review, we (1) present the state of the art of in vitro and in silico cardiomyocyte modeling currently in use to study COVID-19, (2) review in vitro and in silico models that can be adopted to mimic the effects of SARS-CoV-2 infection on cardiac function, and (3) provide a perspective on how to combine some of these models to mimic "COVID-19 cardiomyocytes environment.".

Investigational Treatments for COVID-19 may Increase Ventricular Arrhythmia Risk Through Drug Interactions

Many drugs that have been proposed for treatment of coronavirus disease 2019 (COVID-19) are reported to cause cardiac adverse events, including ventricular arrhythmias. In order to properly weigh risks against potential benefits, particularly when decisions must be made quickly, mathematical modeling of both drug disposition and drug action can be useful for predicting patient response and making informed decisions. Here, we explored the potential effects on cardiac electrophysiology of four drugs proposed to treat COVID-19: lopinavir, ritonavir, chloroquine, and azithromycin, as well as combination therapy involving these drugs. Our study combined simulations of pharmacokinetics (PKs) with quantitative systems pharmacology (QSP) modeling of ventricular myocytes to predict potential cardiac adverse events caused by these treatments. Simulation results predicted that drug combinations can lead to greater cellular action potential prolongation, analogous to QT prolongation, compared with drugs given in isolation. The combination effect can result from both PK and pharmacodynamic drug interactions. Importantly, simulations of different patient groups predicted that women with pre-existing heart disease are especially susceptible to drug-induced arrhythmias, compared with diseased men or healthy individuals of either sex. Statistical analysis of population simulations revealed the molecular factors that make certain women with heart failure especially susceptible to arrhythmias. Overall, the results illustrate how PK and QSP modeling may be combined to more precisely predict cardiac risks of COVID-19 therapies.

Translational Models and Tools to Reduce Clinical Trials and Improve Regulatory Decision Making for QTc and Proarrhythmia Risk (ICH E14/S7B Updates)

After multiple drugs were removed from the market secondary to drug-induced torsade de pointes (TdP) risk, the International Council for Harmonisation (ICH) released guidelines in 2005 that focused on the nonclinical (S7B) and clinical (E14) assessment of surrogate biomarkers for TdP. Recently, Vargas et al. published a pharmaceutical-industry perspective making the case that "double-negative" nonclinical data (negative in vitro hERG and in vivo heart-rate corrected QT (QTc) assays) are associated with such low probability of clinical QTc prolongation and TdP that potentially all double-negative drugs would not need detailed clinical QTc evaluation. Subsequently, the ICH released a new E14/S7B Draft Guideline containing Questions and Answers (Q&As) that defined ways that double-negative nonclinical data could be used to reduce the number of "Thorough QT" (TQT) studies and reach a low-risk determination when a TQT or equivalent could not be performed. We review the Vargas et al. proposal in the context of what was contained in the ICH E14/S7B Draft Guideline and what was proposed by the ICH E14/S7B working group for a "stage 2" of updates (potential expanded roles for nonclinical data and details for assessing TdP risk of QTc-prolonging drugs). Although we do not agree with the exact probability statistics in the Vargas et al. paper because of limitations in the underlying datasets, we show how more modest predictive value of individual assays could still result in low probability for TdP with double-negative findings. Furthermore, we expect that the predictive value of the nonclinical assays will improve with implementation of the new ICH E14/S7B Draft Guideline.

Utility of Normalized TdP Score System in Drug Proarrhythmic Potential Assessment: A Blinded in vitro Study of CiPA Drugs

Drugs that prolong QT may cause torsade de pointes (TdP). However, translation of nonclinical assessment of QT prolongation or hERG channel, targeted by QT-prolonging drugs, into clinical TdP risk has been insufficient to date. In this blinded study, we confirmed the utility of a Normalized TdP Score System in predicting drug-induced TdP risks among 34 drugs, including 28 with low, intermediate, and high TdP risks under the Comprehensive In Vitro Proarrhythmia Assay (CiPA) initiative plus six compounds with names blinded to the investigators, using the rabbit ventricular wedge assay. Concentration-dependent TdP scores were determined by drug-induced changes in QT, T_p-e , and proarrhythmias. Disclosure of the names and testing concentrations was made after completion of the experiments and report to the sponsors. Drugs' normalized TdP scores were calculated thereafter based on their respective free clinical maximum concentration (C_max ). Drugs' normalized TdP scores were calculated and ranked for 33 drugs, excluding 1 investigational drug, and the TdP risks of the 28 CiPA drugs were correctly distinguished according to their respective categories of low, intermediate, and high TdP risks under the CiPA initiative. Accordingly, we are able to propose the cutoff values of the normalized TdP scores at 1 × C_max : ≤ 0, > 0 to < 0.65 and ≥ 0.65, respectively, for low, intermediate, and high risk. This blinded study supports utility of our Normalized TdP Score System in predicting drug-induced TdP risks in 33 drugs, including 28 used for characterization of other assays under the CiPA initiative. However, these results need to be replicated in other laboratories.

KV11.1, NaV1.5, and CaV1.2 Transporter Proteins as Antitarget for Drug Cardiotoxicity

Safety assessment of pharmaceuticals is a rapidly developing area of pharmacy and medicine. The new advanced guidelines for testing the toxicity of compounds require specialized tools that provide information on the tested drug in a quick and reliable way. Ion channels represent the third-largest target. As mentioned in the literature, ion channels are an indispensable part of the heart's work. In this paper the most important information concerning the guidelines for cardiotoxicity testing and the way the tests are conducted has been collected. Attention has been focused on the role of selected ion channels in this process.

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.

Time for a Fully Integrated Nonclinical-Clinical Risk Assessment to Streamline QT Prolongation Liability Determinations: A Pharma Industry Perspective

Defining an appropriate and efficient assessment of drug‐induced corrected QT interval (QTc) prolongation (a surrogate marker of torsades de pointes arrhythmia) remains a concern of drug developers and regulators worldwide. In use for over 15 years, the nonclinical International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) S7B and clinical ICH E14 guidances describe three core assays (S7B: in vitro hERG current & in vivo QTc studies; E14: thorough QT study) that are used to assess the potential of drugs to cause delayed ventricular repolarization. Incorporating these assays during nonclinical or human testing of novel compounds has led to a low prevalence of QTc‐prolonging drugs in clinical trials and no new drugs having been removed from the marketplace due to unexpected QTc prolongation. Despite this success, nonclinical evaluations of delayed repolarization still minimally influence ICH E14‐based strategies for assessing clinical QTc prolongation and defining proarrhythmic risk. In particular, the value of ICH S7B‐based “double‐negative” nonclinical findings (low risk for hERG block and in vivo QTc prolongation at relevant clinical exposures) is underappreciated. These nonclinical data have additional value in assessing the risk of clinical QTc prolongation when clinical evaluations are limited by heart rate changes, low drug exposures, or high‐dose safety considerations. The time has come to meaningfully merge nonclinical and clinical data to enable a more comprehensive, but flexible, clinical risk assessment strategy for QTc monitoring discussed in updated ICH E14 Questions and Answers. Implementing a fully integrated nonclinical/clinical risk assessment for compounds with double‐negative nonclinical findings in the context of a low prevalence of clinical QTc prolongation would relieve the burden of unnecessary clinical QTc studies and streamline drug development.

Assessment of Cardiotoxicity With Stem Cell-based Strategies

PURPOSE

Adverse cardiovascular drug effects pose a substantial medical risk and represent a common cause of drug withdrawal from the market. Thus, current in vitro assays and in vivo animal models still have shortcomings in assessing cardiotoxicity. A human model for more accurate preclinical cardiotoxicity assessment is highly desirable. Current differentiation protocols allow for the generation of human pluripotent stem cell-derived cardiomyocytes in basically unlimited numbers and offer the opportunity to study drug effects on human cardiomyocytes. The purpose of this review is to provide a brief overview of the current approaches to translate studies with pluripotent stem cell-derived cardiomyocytes from basic science to preclinical risk assessment.

METHODS

A review of the literature was performed to gather data on the pathophysiology of cardiotoxicity, the current cardiotoxicity screening assays, stem cell-derived cardiomyocytes, and their application in cardiotoxicity screening.

FINDINGS

There is increasing evidence that stem cell-derived cardiomyocytes predict arrhythmogenicity with high accuracy. Cardiomyocyte immaturity represents the major limitation so far. However, strategies are being developed to overcome this hurdle, such as tissue engineering. In addition, stem cell-based strategies offer the possibility to assess structural drug toxicity (eg, by anticancer drugs) on complex models that more closely mirror the structure of the heart and contain endothelial cells and fibroblasts.

IMPLICATIONS

Pluripotent stem cell-derived cardiomyocytes have the potential to substantially change how preclinical cardiotoxicity screening is performed. To which extent they will replace or complement current approaches is being evaluated.

Identification of Drug-Induced Multichannel Block and Proarrhythmic Risk in Humans Using Continuous T Vector Velocity Effect Profiles Derived From Surface Electrocardiograms

We present continuous T vector velocity (TVV) effect profiles as a new method for identifying drug effects on cardiac ventricular repolarization. TVV measures the temporal change in the myocardial action potential distribution during repolarization. The T vector dynamics were measured as the time required to reach p percent of the total T vector trajectory length, denoted as Tr(p), with p in {1, …, 100%}. The Tr(p) values were individually corrected for heart rate at each trajectory length percentage p. Drug effects were measured by evaluating the placebo corrected changes from baseline of Tr(p)c jointly for all p using functional mixed effects models. The p-dependent model parameters were implemented as cubic splines, providing continuous drug effect profiles along the entire ventricular repolarization process. The effect profile distributions were approximated by bootstrap simulations. We applied this TVV-based analysis approach to ECGs available from three published studies that were conducted in the CiPA context. These studies assessed the effect of 10 drugs and drug combinations with different ion channel blocking properties on myocardial repolarization in a total of 104 healthy volunteers. TVV analysis revealed that blockade of outward potassium currents alone presents an effect profile signature of continuous accumulation of delay throughout the entire repolarization interval. In contrast, block of inward sodium or calcium currents involves acceleration, which accumulates during early repolarization. The balance of blocking inward versus outward currents was reflected in the percentage pzero of the T vector trajectory length where accelerated repolarization transitioned to delayed repolarization. Binary classification using a threshold pzero = 43% separated predominant hERG channel blocking drugs with potentially higher proarrhythmic risk (moxifloxacin, dofetilide, quinidine, chloroquine) from multichannel blocking drugs with low proarrhythmic risk (ranolazine, verapamil, lopinavir/ritonavir) with sensitivity 0.99 and specificity 0.97. The TVV-based effect profile provides a detailed view of drug effects throughout the entire ventricular repolarization interval. It enables the evaluation of drug-induced blocks of multiple cardiac repolarization currents from clinical ECGs. The proposed pzero parameter enhances identification of the proarrhythmic risk of a drug beyond QT prolongation, and therefore constitutes an important tool for cardiac arrhythmia risk assessment.

Twenty years of safety pharmacology model validation and the wider implications of this to drug discovery

This editorial summarizes the content of the current themed issue of J Pharm Tox Methods derived from the 2019 Annual Safety Pharmacology Society (SPS) meeting held in Barcelona, Spain, and reflects on 20 years of innovation in the elaboration of methods for evaluating adversity, particularly during the nonclinical research phase. Given the success of safety pharmacology (SP) in the last 20 years, we propose that the rubric for SP method invention and validation be examined in more detail to explore whether it may have wider relevance to the drug discovery process. Articles arising from the Barcelona meeting are summarized here. They reflect current areas of controversy and innovation in SP. Not for the first time in recent years, the suitability of the No Observable Adverse Effect Level (NOAEL) as a variable in SP was considered in an article derived from a survey of SPS members. It was found from the survey and concluded from the analysis that the NOAEL is not necessary for assessing the safety of a New Chemical Entity (NCE). The meeting included scientific content from more than 190 abstracts (reproduced in the current volume of J Pharm Tox Methods). The impact of the INSPIRE program on the educational endeavor of SP, cardiovascular SP with regard to hERG and advances in CiPA and stem cells assays, the use of the echocardiogram in SP, the applicability of deep learning methods in SP and toxicology studies, the role of biomarkers in renal SP studies, and advances in CNS SP are highlighted in this issue of the Journal. This continued innovation reflects a rubric in SP that identifies problems, seeks solutions and, importantly, validates the solutions. If there is a lesson to be learned from the 20 years of annual SP methods themed issues it is that drug discovery efforts may benefit from a more rigorous validation process for discovery methods, using positive and negative controls for validation, as is done in SP method validation.

In-silico human electro-mechanical ventricular modelling and simulation for drug-induced pro-arrhythmia and inotropic risk assessment

Human-based computational modelling and simulation are powerful tools to accelerate the mechanistic understanding of cardiac patho-physiology, and to develop and evaluate therapeutic interventions. The aim of this study is to calibrate and evaluate human ventricular electro-mechanical models for investigations on the effect of the electro-mechanical coupling and pharmacological action on human ventricular electrophysiology, calcium dynamics, and active contraction. The most recent models of human ventricular electrophysiology, excitation-contraction coupling, and active contraction were integrated, and the coupled models were calibrated using human experimental data. Simulations were then conducted using the coupled models to quantify the effects of electro-mechanical coupling and drug exposure on electrophysiology and force generation in virtual human ventricular cardiomyocytes and tissue. The resulting calibrated human electro-mechanical models yielded active tension, action potential, and calcium transient metrics that are in agreement with experiments for endocardial, epicardial, and mid-myocardial human samples. Simulation results correctly predicted the inotropic response of different multichannel action reference compounds and demonstrated that the electro-mechanical coupling improves the robustness of repolarisation under drug exposure compared to electrophysiology-only models. They also generated additional evidence to explain the partial mismatch between in-silico and in-vitro experiments on drug-induced electrophysiology changes. The human calibrated and evaluated modelling and simulation framework constructed in this study opens new avenues for future investigations into the complex interplay between the electrical and mechanical cardiac substrates, its modulation by pharmacological action, and its translation to tissue and organ models of cardiac patho-physiology.

Calibration of ionic and cellular cardiac electrophysiology models

Cardiac electrophysiology models are among the most mature and well-studied mathematical models of biological systems. This maturity is bringing new challenges as models are being used increasingly to make quantitative rather than qualitative predictions. As such, calibrating the parameters within ion current and action potential (AP) models to experimental data sets is a crucial step in constructing a predictive model. This review highlights some of the fundamental concepts in cardiac model calibration and is intended to be readily understood by computational and mathematical modelers working in other fields of biology. We discuss the classic and latest approaches to calibration in the electrophysiology field, at both the ion channel and cellular AP scales. We end with a discussion of the many challenges that work to date has raised and the need for reproducible descriptions of the calibration process to enable models to be recalibrated to new data sets and built upon for new studies. This article is categorized under: Analytical and Computational Methods > Computational Methods Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Cellular Models.

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.

Considering discrepancy when calibrating a mechanistic electrophysiology model

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions-that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here, we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes and autoregressive-moving-average models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

An audit of uncertainty in multi-scale cardiac electrophysiology models

Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance. We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Accounting for variability in ion current recordings using a mathematical model of artefacts in voltage-clamp experiments

Mathematical models of ion channels, which constitute indispensable components of action potential models, are commonly constructed by fitting to whole-cell patch-clamp data. In a previous study, we fitted cell-specific models to hERG1a (Kv11.1) recordings simultaneously measured using an automated high-throughput system, and studied cell-cell variability by inspecting the resulting model parameters. However, the origin of the observed variability was not identified. Here, we study the source of variability by constructing a model that describes not just ion current dynamics, but the entire voltage-clamp experiment. The experimental artefact components of the model include: series resistance, membrane and pipette capacitance, voltage offsets, imperfect compensations made by the amplifier for these phenomena, and leak current. In this model, variability in the observations can be explained by either cell properties, measurement artefacts, or both. Remarkably, by assuming that variability arises exclusively from measurement artefacts, it is possible to explain a larger amount of the observed variability than when assuming cell-specific ion current kinetics. This assumption also leads to a smaller number of model parameters. This result suggests that most of the observed variability in patch-clamp data measured under the same conditions is caused by experimental artefacts, and hence can be compensated for in post-processing by using our model for the patch-clamp experiment. This study has implications for the question of the extent to which cell-cell variability in ion channel kinetics exists, and opens up routes for better correction of artefacts in patch-clamp data. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Investigational treatments for COVID-19 may increase ventricular arrhythmia risk through drug interactions

Many drugs that have been proposed for treatment of COVID-19 are reported to cause cardiac adverse events, including ventricular arrhythmias. In order to properly weigh risks against potential benefits, particularly when decisions must be made quickly, mathematical modeling of both drug disposition and drug action can be useful for predicting patient response and making informed decisions. Here we explored the potential effects on cardiac electrophysiology of 4 drugs proposed to treat COVID-19: lopinavir, ritonavir, chloroquine, and azithromycin, as well as combination therapy involving these drugs. Our study combined simulations of pharmacokinetics (PK) with quantitative systems pharmacology (QSP) modeling of ventricular myocytes to predict potential cardiac adverse events caused by these treatments. Simulation results predicted that drug combinations can lead to greater cellular action potential prolongation, analogous to QT prolongation, compared with drugs given in isolation. The combination effect can result from both pharmacokinetic and pharmacodynamic drug interactions. Importantly, simulations of different patient groups predicted that females with pre-existing heart disease are especially susceptible to drug-induced arrhythmias, compared males with disease or healthy individuals of either sex. Overall, the results illustrate how PK and QSP modeling may be combined to more precisely predict cardiac risks of COVID-19 therapies.

All-Optical Electrophysiology Refines Populations of In Silico Human iPSC-CMs for Drug Evaluation

High-throughput in vitro drug assays have been impacted by recent advances in human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) technology and by contact-free all-optical systems simultaneously measuring action potentials (APs) and Ca2+ transients (CaTrs). Parallel computational advances have shown that in silico simulations can predict drug effects with high accuracy. We combine these in vitro and in silico technologies and demonstrate the utility of high-throughput experimental data to refine in silico hiPSC-CM populations and to predict and explain drug action mechanisms. Optically obtained hiPSC-CM APs and CaTrs were used from spontaneous activity and under optical pacing in control and drug conditions at multiple doses. An updated version of the Paci2018 model was developed to refine the description of hiPSC-CM spontaneous electrical activity; a population of in silico hiPSC-CMs was constructed and calibrated using simultaneously recorded APs and CaTrs. We tested in silico five drugs (astemizole, dofetilide, ibutilide, bepridil, and diltiazem) and compared the outcomes to in vitro optical recordings. Our simulations showed that physiologically accurate population of models can be obtained by integrating AP and CaTr control records. Thus, constructed population of models correctly predicted the drug effects and occurrence of adverse episodes, even though the population was optimized only based on control data and in vitro drug testing data were not deployed during its calibration. Furthermore, the in silico investigation yielded mechanistic insights; e.g., through simulations, bepridil's more proarrhythmic action in adult cardiomyocytes compared to hiPSC-CMs could be traced to the different expression of ion currents in the two. Therefore, our work 1) supports the utility of all-optical electrophysiology in providing high-content data to refine experimentally calibrated populations of in silico hiPSC-CMs, 2) offers insights into certain limitations when translating results obtained in hiPSC-CMs to humans, and 3) shows the strength of combining high-throughput in vitro and population in silico approaches.

Cardiac arrhythmia considerations of hormone cancer therapies

Breast and prostate cancers are among the most prevalent cancers worldwide. Oestradiol and progesterone are major drivers for breast cancer proliferation, and androgens for prostate cancer. Endocrine therapies are drugs that interfere with hormone-activated pathways to slow cancer progression. Multiple new breakthrough drugs improving overall survival have recently been developed within this class. As the use of these latter drugs grows, incidence of cardiac arrhythmias has emerged as an unappreciated complication. These changes are not surprising given that sex hormones alter ventricular repolarization. Testosterone shortens action potential duration and QT interval duration, while oestradiol has an opposite effect. In patients with breast cancer, selective oestrogen receptor modulators are associated with more reports for long QT and torsade de pointes (TdP) than aromatase inhibitors, likely through an oestradiol-like effect on the heart. Cyclin-dependent kinase 4/6 inhibitors, a new class of anticancer drugs used in combination with endocrine therapies in hormone receptor positive breast cancer, are also variably associated with drug-induced long QT, particularly with ribociclib. In prostate cancer, androgen deprivation therapy is associated with long QT and TdP, and possibly atrial fibrillation for abiraterone. In this review, we have summarized the clinical and preclinical data focusing on cardiac arrhythmia considerations of hormone cancer therapies.

Drug-induced QT prolongation: Concordance of preclinical anesthetized canine model in relation to published clinical observations for ten CiPA drugs

INTRODUCTION

The Comprehensive In Vitro Proarrhythmia Assay (CiPA) initiative differentiates torsadogenic risk of 28 drugs affecting ventricular repolarization based on multiple in vitro human derived ionic currents. However, a standardized prospective assessment of the electrophysiologic effects of these drugs in an integrated in vivo preclinical cardiovascular model is lacking. This study questioned whether QTc interval prolongation in a preclinical in vivo model could detect clinically reported QTc prolongation and assign torsadogenic risk for ten CiPA drugs.

METHODS

An acute intravenous administered ascending dose anesthetized dog cardiovascular model was used to assess QTc prolongation along with other electrocardiographic (PR, QRS intervals) and hemodynamic (heart rate, blood pressures, left ventricular contractility) parameters at plasma concentrations spanning and exceeding clinical exposures. hERG current block potency was characterized using IC₅₀ values from automated patch clamp.

RESULTS

All eight drugs eliciting clinical QTc prolongation also delayed repolarization in anesthetized dogs at plasma concentrations within four-fold clinical exposures. In vitro QTc safety margins (defined based on clinical C_max values/plasma concentrations eliciting statistically significant QTc prolongation in dogs) were lower for high vs intermediate torsadogenic risk drugs. In comparison, hERG IC₁₀ values represented as total drug concentrations were better predictors of preclinical QTc prolongation than hERG IC₅₀ values.

CONCLUSION

There was good concordance for QTc prolongation in the anesthetized dog model and clinical torsadogenic risk assignment. QTc assessment in the anesthetized dog remains a valuable part of a more comprehensive preclinical integrated risk assessment for delayed repolarization and torsadogenic risk as part of a global cardiovascular evaluation.

A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm

Introduction

hERG block potency is widely used to calculate a drug's safety margin against its torsadogenic potential. Previous studies are confounded by use of different patch clamp electrophysiology protocols and a lack of statistical quantification of experimental variability. Since the new cardiac safety paradigm being discussed by the International Council for Harmonisation promotes a tighter integration of nonclinical and clinical data for torsadogenic risk assessment, a more systematic approach to estimate the hERG block potency and safety margin is needed.

Methods

A cross-industry study was performed to collect hERG data on 28 drugs with known torsadogenic risk using a standardized experimental protocol. A Bayesian hierarchical modeling (BHM) approach was used to assess the hERG block potency of these drugs by quantifying both the inter-site and intra-site variability. A modeling and simulation study was also done to evaluate protocol-dependent changes in hERG potency estimates.

Results

A systematic approach to estimate hERG block potency is established. The impact of choosing a safety margin threshold on torsadogenic risk evaluation is explored based on the posterior distributions of hERG potency estimated by this method. The modeling and simulation results suggest any potency estimate is specific to the protocol used.

Discussion

This methodology can estimate hERG block potency specific to a given voltage protocol. The relationship between safety margin thresholds and torsadogenic risk predictivity suggests the threshold should be tailored to each specific context of use, and safety margin evaluation may need to be integrated with other information to form a more comprehensive risk assessment.

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hERG potency/safety margin is a widely used nonclinical cardiac safety strategy.

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A new regulatory paradigm promotes the integration of nonclinical and clinical data.

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Lack of uncertainty quantification hindered using hERG potency in the new paradigm.

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A systematic method was established to address this limitation.

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Analysis supports using different safety margin thresholds in different context.

A Computational Pipeline to Predict Cardiotoxicity: From the Atom to the Rhythm

RATIONALE

Drug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation is an accepted surrogate marker for arrhythmia. But QT interval is too sensitive a marker and not selective, resulting in many useful drugs eliminated in drug discovery.

OBJECTIVE

To predict the impact of a drug from the drug chemistry on the cardiac rhythm.

METHODS AND RESULTS

In a new linkage, we connected atomistic scale information to protein, cell, and tissue scales by predicting drug-binding affinities and rates from simulation of ion channel and drug structure interactions and then used these values to model drug effects on the hERG channel. Model components were integrated into predictive models at the cell and tissue scales to expose fundamental arrhythmia vulnerability mechanisms and complex interactions underlying emergent behaviors. Human clinical data were used for model framework validation and showed excellent agreement, demonstrating feasibility of a new approach for cardiotoxicity prediction.

CONCLUSIONS

We present a multiscale model framework to predict electrotoxicity in the heart from the atom to the rhythm. Novel mechanistic insights emerged at all scales of the system, from the specific nature of proarrhythmic drug interaction with the hERG channel, to the fundamental cellular and tissue-level arrhythmia mechanisms. Applications of machine learning indicate necessary and sufficient parameters that predict arrhythmia vulnerability. We expect that the model framework may be expanded to make an impact in drug discovery, drug safety screening for a variety of compounds and targets, and in a variety of regulatory processes.

In silico study of the effects of anti-arrhythmic drug treatment on sinoatrial node function for patients with atrial fibrillation

Sinus node dysfunction (SND) is often associated with atrial fibrillation (AF). Amiodarone is the most frequently used agent for maintaining sinus rhythm in patients with AF, but it impairs the sinoatrial node (SAN) function in one-third of AF patients. This study aims to gain mechanistic insights into the effects of the antiarrhythmic agents in the setting of AF-induced SND. We have adapted a human SAN model to characterize the SND conditions by incorporating experimental data on AF-induced electrical remodelling, and then integrated actions of drugs into the modified model to assess their efficacy. Reductions in pacing rate upon the implementation of AF-induced electrical remodelling associated with SND agreed with the clinical observations. And the simulated results showed the reduced funny current (If) in these remodelled targets mainly contributed to the heart rate reduction. Computational drug treatment simulations predicted a further reduction in heart rate during amiodarone administration, indicating that the reduction was the result of actions of amiodarone on INa, IKur, ICaL, ICaT, If and beta-adrenergic receptors. However, the heart rate was increased in the presence of disopyramide. We concluded that disopyramide may be a desirable choice in reversing the AF-induced SND phenotype.

General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy

This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/US Food and Drug Administration-sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the Comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model-based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm.

Human Heart Cardiomyocytes in Drug Discovery and Research: New Opportunities in Translational Sciences

In preclinical drug development, accurate prediction of drug effects on the human heart is critically important, whether in the context of cardiovascular safety or for the purpose of modulating cardiac function to treat heart disease. Current strategies have significant limitations, whereby, cardiotoxic drugs can escape detection or potential life-saving therapies are abandoned due to false positive toxicity signals. Thus, new and more reliable translational approaches are urgently needed to help accelerate the rate of new therapy development. Renewed efforts in the recovery of human donor hearts for research and in cardiomyocyte isolation methods, are providing new opportunities for preclinical studies in adult primary cardiomyocytes. These cells exhibit the native physiological and pharmacological properties, overcoming the limitations presented by artificial cellular models, animal models and have great potential for providing an excellent tool for preclinical drug testing. Adult human primary cardiomyocytes have already shown utility in assessing drug-induced cardiotoxicity risk and helping in the identification of new treatments for cardiac diseases, such as heart failure and atrial fibrillation. Finally, strategies with actionable decision-making trees that rely on data derived from adult human primary cardiomyocytes will provide the holistic insights necessary to accurately predict human heart effects of drugs.

Usefulness of Bnet, a Simple Linear Metric in Discerning Torsades De Pointes Risks in 28 CiPA Drugs

The Comprehensive in vitro Proarrhythmia Assay (CiPA) project suggested the torsade metric score (TMS) which requires substantial computing resources as a useful biomarker to predict proarrhythmic risk from human ether-à-go-go-related gene (hERG) and a few other ion channel block data. The TMS was useful to predict low TdP risks of drugs blocking Na+ (ranolazine) and Ca2+ (verapamil) channels as well as the hERG channel. However, Mistry asserted that the simple linear metric, Bnet reflecting net blockade of a few influential ion channels has similar predictive power. Here we compared the predictability of Bnet and TMS for the 12 training and 16 validation CiPA drugs which were pre-classified into three categories according to the known TdP risks (low, intermediate, and high risk) by CiPA. Bnet at 5×Cmax (Bnet5×Cmax) was calculated using the ion-channel IC50 and Hill coefficients of CiPA drugs collected from previous reports by the CiPA team and others. The receiver operating characteristic curve area under curve (ROC AUC) values for TMS and Bnet5×Cmax as performance metrics in discerning low versus intermediate/high risk categories for the 28 CiPA drugs were similar. However, Bnet5×Cmax was much inferior to TMS at discerning between intermediate- and high-risk drugs. Dynamic Bnet, which used in silico hERG dynamic parameters unlike conventional Bnet, improved the misspecification. Thus, we propose that Bnet5×Cmax is used for quick screening of TdP risks of drug candidates and if the "intermediate/high" risk is predicted by Bnet5×Cmax, in silico approaches, such as dynamic Bnet or TMS, may be further considered.

Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment

Multiscale computational models of the heart are being extensively investigated for improved assessment of drug-induced torsades de pointes (TdP) risk, a fatal side effect of many drugs. Model-derived metrics such as action potential duration and net charge carried by ionic currents (qNet) have been proposed as potential candidates for TdP risk stratification after being tested on small datasets. Unlike purely statistical approaches, model-derived metrics are thought to provide mechanism-based classification. In particular, qNet has been recently proposed as a surrogate metric for early afterdepolarizations (EADs), which are known to be cellular triggers of TdP. Analysis of critical model components and of the ion channels that have major impact on model-derived metrics can lead to improvements in the confidence of the prediction. In this paper, we analyze large populations of virtual drugs to systematically examine the influence of different ion channels on model-derived metrics that have been proposed for proarrhythmic risk assessment. We demonstrate via global sensitivity analysis (GSA) that model-derived metrics are most sensitive to different sets of input parameters. Similarly, important differences in sensitivity to specific channel blocks are highlighted when classifying drugs into different risk categories by either qNet or a metric directly based on simulated EADs. In particular, the higher sensitivity of qNet to the block of the late sodium channel might explain why its classification accuracy is better than that of the EAD-based metric, as shown for a small set of known drugs. Our results highlight the need for a better mechanistic interpretation of promising metrics like qNet based on a formal analysis of models. GSA should, therefore, constitute an essential component of the in silico workflow for proarrhythmic risk assessment, as an improved understanding of the structure of model-derived metrics could increase confidence in model-predicted risk.

Drug-induced shortening of the electromechanical window is an effective biomarker for in silico prediction of clinical risk of arrhythmias

BACKGROUND AND PURPOSE

Early identification of drug-induced cardiac adverse events is key in drug development. Human-based computer models are emerging as an effective approach, complementary to in vitro and animal models. Drug-induced shortening of the electromechanical window has been associated with increased risk of arrhythmias. This study investigates the potential of a cellular surrogate for the electromechanical window (EMw) for prediction of pro-arrhythmic cardiotoxicity, and its underlying ionic mechanisms, using human-based computer models.

EXPERIMENTAL APPROACH

In silico drug trials for 40 reference compounds were performed, testing up to 100-fold the therapeutic concentrations (EFTPCmax ) and using a control population of human ventricular action potential (AP) models, optimised to capture pro-arrhythmic ionic profiles. EMw was calculated for each model in the population as the difference between AP and Ca2+ transient durations at 90%. Drug-induced changes in the EMw and occurrence of repolarisation abnormalities (RA) were quantified.

KEY RESULTS

Drugs with clinical risk of Torsade de Pointes arrhythmias induced a concentration-dependent EMw shortening, while safe drugs lead to increase or small change in EMw. Risk predictions based on EMw shortening achieved 90% accuracy at 10× EFTPCmax , whereas RA-based predictions required 100× EFTPCmax to reach the same accuracy. As it is dependent on Ca2+ transient, the EMw was also more sensitive than AP prolongation in distinguishing between pure hERG blockers and multichannel compounds also blocking the calcium current.

CONCLUSION AND IMPLICATIONS

The EMw is an effective biomarker for in silico predictions of drug-induced clinical pro-arrhythmic risk, particularly for compounds with multichannel blocking action.

Three-Dimensional Heart Model-Based Screening of Proarrhythmic Potential by in silico Simulation of Action Potential and Electrocardiograms

The proarrhythmic risk is a major concern in drug development. The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative has proposed the JTpeak interval on electrocardiograms (ECGs) and qNet, an in silico metric, as new biomarkers that may overcome the limitations of the hERG assay and QT interval. In this study, we simulated body-surface ECGs from patch-clamp data using realistic models of the ventricles and torso to explore their suitability as new in silico biomarkers for cardiac safety. We tested seven drugs in this study: dofetilide (high proarrhythmic risk), ranolazine, verapamil (QT increasing, but safe), bepridil, cisapride, mexiletine, and diltiazem. Human ventricular geometry was reconstructed from computed tomography (CT) images, and a Purkinje fiber network was mapped onto the endocardial surface. The electrical wave propagation in the ventricles was obtained by solving a reaction-diffusion equation using finite-element methods. The body-surface ECG data were calculated using a torso model that included the ventricles. The effects of the drugs were incorporated in the model by partly blocking the appropriate ion channels. The effects of the drugs on single-cell action potential (AP) were examined first, and three-dimensional (3D) body-surface ECG simulations were performed at free Cmax values of 1×, 5×, and 10×. In the single-cell and ECG simulations at 5× Cmax, dofetilide, but not verapamil or ranolazine, caused arrhythmia. However, the non-increasing JTpeak caused by verapamil and ranolazine that has been observed in humans was not reproduced in our simulation. Our results demonstrate the potential of 3D body-surface ECG simulation as a biomarker for evaluation of the proarrhythmic risk of candidate drugs.

Four Ways to Fit an Ion Channel Model

Mathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example, to predict the risks and results of genetic mutations, pharmacological treatments, or surgical procedures. These safety-critical applications depend on accurate characterization of the underlying ionic currents. Four different methods can be found in the literature to fit voltage-sensitive ion channel models to whole-cell current measurements: method 1, fitting model equations directly to time-constant, steady-state, and I-V summary curves; method 2, fitting by comparing simulated versions of these summary curves to their experimental counterparts; method 3, fitting to the current traces themselves from a range of protocols; and method 4, fitting to a single current trace from a short and rapidly fluctuating voltage-clamp protocol. We compare these methods using a set of experiments in which hERG1a current was measured in nine Chinese hamster ovary cells. In each cell, the same sequence of fitting protocols was applied, as well as an independent validation protocol. We show that methods 3 and 4 provide the best predictions on the independent validation set and that short, rapidly fluctuating protocols like that used in method 4 can replace much longer conventional protocols without loss of predictive ability. Although data for method 2 are most readily available from the literature, we find it performs poorly compared to methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications.

Rapid Characterization of hERG Channel Kinetics I: Using an Automated High-Throughput System

Predicting how pharmaceuticals may affect heart rhythm is a crucial step in drug development and requires a deep understanding of a compound’s action on ion channels. In vitro hERG channel current recordings are an important step in evaluating the proarrhythmic potential of small molecules and are now routinely performed using automated high-throughput patch-clamp platforms. These machines can execute traditional voltage-clamp protocols aimed at specific gating processes, but the array of protocols needed to fully characterize a current is typically too long to be applied in a single cell. Shorter high-information protocols have recently been introduced that have this capability, but they are not typically compatible with high-throughput platforms. We present a new 15 second protocol to characterize hERG (Kv11.1) kinetics, suitable for both manual and high-throughput systems. We demonstrate its use on the Nanion SyncroPatch 384PE, a 384-well automated patch-clamp platform, by applying it to Chinese hamster ovary cells stably expressing hERG1a. From these recordings, we construct 124 cell-specific variants/parameterizations of a hERG model at 25°C. A further eight independent protocols are run in each cell and are used to validate the model predictions. We then combine the experimental recordings using a hierarchical Bayesian model, which we use to quantify the uncertainty in the model parameters, and their variability from cell-to-cell; we use this model to suggest reasons for the variability. This study demonstrates a robust method to measure and quantify uncertainty and shows that it is possible and practical to use high-throughput systems to capture full hERG channel kinetics quantitatively and rapidly.

Telemetered common marmosets is useful for the assessment of electrocardiogram parameters changes induced by multiple cardiac ion channel inhibitors

The objective of this study is to assess the response of telemetered common marmosets to multiple cardiac ion channel inhibitors and to clarify the usefulness of this animal model in evaluating the effects of drug candidates on electrocardiogram (ECG). Six multiple cardiac ion channel inhibitors (sotalol, astemizole, flecainide, quinidine, verapamil and terfenadine) were orally administered to telemetered common marmosets and changes in QTc, PR interval and QRS duration were evaluated. Drugs plasma levels were determined to compare the sensitivity in common marmosets to that in humans. QTc prolongation was observed in the marmosets dosed with sotalol, astemizole, flecainide, quinidine, verapamil and terfenadine. PR prolongation was noted after flecainide and verapamil administration, and QRS widening occurred following treatment with flecainide and quinidine. Drugs plasma levels associated with ECG changes in marmosets were similar to those in humans, except for verapamil-induced QTc prolongation. Verapamil-induced change is suggested due to body temperature decrease. These results indicate that telemetered common marmoset is a useful animal for evaluation of the ECG effects of multiple cardiac ion channel inhibitors and the influence of body temperature change should be considered in the assessment.

Comprehensive Uncertainty Quantification and Sensitivity Analysis for Cardiac Action Potential Models

Recent efforts to ensure the reliability of computational model-based predictions in healthcare, such as the ASME V&V40 Standard, emphasize the importance of uncertainty quantification (UQ) and sensitivity analysis (SA) when evaluating computational models. UQ involves empirically determining the uncertainty in model inputs-typically resulting from natural variability or measurement error-and then calculating the resultant uncertainty in model outputs. SA involves calculating how uncertainty in model outputs can be apportioned to input uncertainty. Rigorous comprehensive UQ/SA provides confidence that model-based decisions are robust to underlying uncertainties. However, comprehensive UQ/SA is not currently feasible for whole heart models, due to numerous factors including model complexity and difficulty in measuring variability in the many parameters. Here, we present a significant step to developing a framework to overcome these limitations. We: (i) developed a novel action potential (AP) model of moderate complexity (six currents, seven variables, 36 parameters); (ii) prescribed input variability for all parameters (not empirically derived); (iii) used a single "hyper-parameter" to study increasing levels of parameter uncertainty; (iv) performed UQ and SA for a range of model-derived quantities with physiological relevance; and (v) present quantitative and qualitative ways to analyze different behaviors that occur under parameter uncertainty, including "model failure". This is the first time uncertainty in every parameter (including conductances, steady-state parameters, and time constant parameters) of every ionic current in a cardiac model has been studied. This approach allowed us to demonstrate that, for this model, the simulated AP is fully robust to low levels of parameter uncertainty - to our knowledge the first time this has been shown of any cardiac model. A range of dynamics was observed at larger parameter uncertainty (e.g., oscillatory dynamics); analysis revealed that five parameters were highly influential in these dynamics. Overall, we demonstrate feasibility of performing comprehensive UQ/SA for cardiac cell models and demonstrate how to assess robustness and overcome model failure when performing cardiac UQ analyses. The approach presented here represents an important and significant step toward the development of model-based clinical tools which are demonstrably robust to all underlying uncertainties and therefore more reliable in safety-critical decision-making.

Quantitative Systems Pharmacology Models for a New International Cardiac Safety Regulatory Paradigm: An Overview of the Comprehensive In Vitro Proarrhythmia Assay In Silico Modeling Approach

As a relatively new discipline, quantitative systems pharmacology has seen a significant increase in the application and utility of drug development. One area that could greatly benefit from such an approach is in the proarrhythmia assessment of new drugs. The Comprehensive In Vitro Proarrhythmia Assay (CiPA) Initiative is a global public-private partnership project that has developed an integrated approach using mechanistic in silico models for proarrhythmia risk prediction. Progress to date has led to the formation of the International Council on Harmonisation Implementation Working Group to revise regulatory guidelines via the Questions-and-Answers process to address the best practices for proarrhythmia models and how they can impact clinical drug development. This article reviews the CiPA in silico model-development process, focusing on its unique development and validation strategy, and summarizes the lessons learned as consideration points for the ongoing implementation of CiPA-like in silico models in drug development.

Introduction to in silico model for proarrhythmic risk assessment under the CiPA initiative

In 2005, the International Council for Harmonization (ICH) established cardiotoxicity assessment guidelines to identify the risk of Torsade de Pointes (TdP). It is focused on the blockade of the human ether-à-go-go-related gene (hERG) channel known to cause QT/QTc prolongation and the QT/QTc prolongation shown on the electrocardiogram. However, these biomarkers are not the direct risks of TdP with low specificity as the action potential is influenced by multiple channels along with the hERG channel. Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative emerged to address limitations of the current model. The objective of CiPA is to develop a standardized in silico model of a human ventricular cell to quantitively evaluate the cardiac response for the cardiac toxicity risk and to come up with a metric for the TdP risk assessment. In silico working group under CiPA developed a standardized and reliable in silico model and a metric that can quantitatively evaluate cellular cardiac electrophysiologic activity. The implementation mainly consists of hERG fitting, Hill fitting, and action potential simulation. In this review, we explained how the in silico model of CiPA works, and briefly summarized current overall CiPA studies. We hope this review helps clinical pharmacologists to understand the underlying estimation process of CiPA in silico modeling.

Five years of the CiPA project (2013-2018): what did we learn?

Cases of drug-induced QT prolongation and sudden cardiac deaths resulted in market withdrawal of many drugs and world-wide regulatory changes through accepting the ICH guidelines E14 and S7B. However, because the guidelines were not comprehensive enough to cover the electrophysiological changes by drug-induced cardiac ion channel blocking, CiPA was initiated by experts in governments and academia in the USA, Europe, and Japan in 2013. Five years have passed since the launch of the CiPA initiative that aimed to improve the current ICH guidelines. This report reviews the current achievements of the CiPA initiative and explores unresolved issues.