Drug-induced Torsade De Pointes Research Articles

Evaluation of cardiac pro-arrhythmic risks using the artificial neural network with ToR-ORd in silico model output.

Torsades de pointes (TdP) is a type of ventricular arrhythmia that can lead to sudden cardiac death. Drug-induced TdP has been an important concern for researchers and international regulatory boards. The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative was proposed that integrates in vitro testing and computational models of cardiac ion channels and human cardiomyocyte cells to evaluate the proarrhythmic risk of drugs. The TdP risk classification performance using only a single TdP metric may require some improvements because of information limitations and the instability of generalizing results. This study evaluates the performance of TdP metrics from the in silico simulations of the Tomek-O'Hara Rudy (ToR-ORd) ventricular cell model for classifying the TdP risk of drugs. We utilized these metrics as an input to an artificial neural network (ANN)-based classifier. The ANN model was optimized through hyperparameter tuning using the grid search (GS) method to find the optimal model. The study outcomes show an area under the curve (AUC) value of 0.979 for the high-risk category, 0.791 for the intermediate-risk category, and 0.937 for the low-risk category. Therefore, this study successfully demonstrates the capability of the ToR-ORd ventricular cell model in classifying the TdP risk into three risk categories, providing new insights into TdP risk prediction methods.

Assessment of Quinidine-Induced Torsades de Pointes Risks Using a Whole-Body Physiologically Based Pharmacokinetic Model Linked to Cardiac Ionic Current Inhibition.

The lethality of torsades de pointes (TdP) by drugs is one of main reasons that some drugs were withdrawn from the market. In order to assess drug-induced TdP risks, a model of cardiac ionic current suppression in human ventricular myocytes (ToR-ORd model), combined with the maximum effective free therapeutic plasma concentration or the maximum effective free therapeutic myocyte concentration was often used, with the latter proved to be more relevant and more accurate. We aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model, incorporated with a human cardiomyocyte pharmacodynamic (PD) model, to provide a comprehensive assessment of drug-induced TdP risks in normal and specific scenarios. Quinidine served as an example to validate the PBPK-PD model via predicting plasma quinidine concentrations and quinidine-induced changes in QT interval (ΔQTc). The predicted plasma quinidine concentrations and ΔQTc values following oral administration or intravenous administration of quinidine were comparable to clinic observations. Visual predictive checks showed that most of the observed plasma concentrations and ΔQTc values fell within the 5th and 95th percentiles of simulations. The validated PBPK-PD model was further applied to assess the TdP risks using frequencies of early afterdepolarization and long-QT syndrome occurrence in 4 scenarios, such as therapeutic dose, supra-therapeutic dose, alkalosis, and hyperkalemia in 200 human subjects. In conclusion, the developed PBPK-PD model may be applied to predict the quinidine pharmacokinetics and quinidine-induced TdP risks in healthy subjects, but also simulate quinidine-induced TdP risks under disease conditions, such as hypokalemia and alkalosis.

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

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

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. and Hill coefficients from 67 drugs were used as input to simulate drug effects on cardiac cells. Fourteen features (, , , , , , , , , , , , 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.

Sex-specific repolarization heterogeneity in mouse left ventricle: Optical mapping combined with mathematical modeling predict the contribution of specific ionic currents.

Ventricular repolarization shows notable sex-specificity, with female sex being associated with longer QT-intervals in electrocardiography irrespective of the species studied. From a clinical point of view, women are at a greater risk for drug-induced torsade de pointes and symptomatic long-QT syndrome. Here, we present an optical mapping (OM) approach to reveal sex-specific action potential (AP) heterogeneity in a slice preparation of mouse hearts. Left ventricular epicardial repolarization in female versus male mice shows longer and, interindividually, more variable AP duration (APD), yielding a less prominent transmural APD gradient. By combining OM with mathematical modeling, we suggest a significant role of IKto,f and IKur in AP broadening in females. Other transmembrane currents, including INaL , only marginally affect basal APD. As in many cardiac pathophysiologies, increasing [Ca2+ ]i poses a risk for arrhythmia, the response of AP morphology to enhanced activation of L-type calcium channels (LTCC) was assessed in a sex-selective manner. Both APD and its variation increased significantly more in female versus male mice after pharmacological LTCC activation, which we hypothesize to be due to sex-specific INaL expression based on mathematical modeling. Altogether, we demonstrate a more delayed repolarization of LV epicardium, a leveled LV transmural APD gradient, and a more pronounced epicardial APD response to Ca2+ influx in females versus males. Mathematical modeling quantifies the relative contributions of selected ionic currents to sex-specific AP morphology under normal and pathophysiological conditions.

Investigating the relevance of CYP2J2 inhibition for drugs known to cause intermediate to high risk torsades de pointes

Cardiac cytochrome P450 2J2 (CYP2J2) metabolizes endogenous polyunsaturated fatty acid, arachidonic acid (AA), to bioactive regioisomeric epoxyeicosatrienoic acid (EET) metabolites. This endogenous metabolic pathway has been postulated to play a homeostatic role in cardiac electrophysiology. However, it is unknown if drugs that cause intermediate to high risk torsades de pointes (TdP) exhibit inhibitory effects against CYP2J2 metabolism of AA to EETs. In this study, we demonstrated that 11 out of 16 drugs screened with intermediate to high risk of TdP as defined by the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative are concurrently reversible inhibitors of CYP2J2 metabolism of AA, with unbound inhibitory constant (Ki,AA,u) values ranging widely from 0.132 to 19.9 µM. To understand the physiological relevancy of Ki,AA,u, the in vivo unbound drug concentration within human heart tissue (Cu,heart) was calculated via experimental determination of in vitro unbound partition coefficient (Kpuu) for 10 CYP2J2 inhibitors using AC16 human ventricular cardiomyocytes as well as literature-derived values of fraction unbound in plasma (fu,p) and plasma drug concentrations in clinical scenarios leading to TdP. Notably, all CYP2J2 inhibitors screened belonging to the high TdP risk category, namely vandetanib and bepridil, exhibited highest Kpuu values of 18.2 ± 1.39 and 7.48 ± 1.16 respectively although no clear relationship between Cu,heart and risk of TdP could eventually be determined. R values based on basic models of reversible inhibition as per FDA guidelines were calculated using unbound plasma drug concentrations (Cu,plasma) and adapted using Cu,heart which suggested that 4 out of 10 CYP2J2 inhibitors with intermediate to high risk of TdP demonstrate greatest potential for clinically relevant in vivo cardiac drug-AA interactions. Our results shed novel insights on the relevance of CYP2J2 inhibition in drugs with risk of TdP. Further studies ascertaining the role of CYP2J2 metabolism of AA in cardiac electrophysiology, characterizing inherent cardiac ion channel activities of drugs with risk of TdP as well as in vivo evidence of drug-AA interactions will be required prior to determining if CYP2J2 inhibition could be an alternative mechanism contributing to drug-induced TdP.

Body surface mapping to detect STT-wave changes during invasive coronary function testing

Drugs belonging to diverse therapeutic classes can prolong myocardial refractoriness or slow conduction. These drugs may be effective and well-tolerated, but the risk of sudden cardiac death from torsades de pointes (TdP) remains a major concern. The corrected QT interval has significant limitations when used for risk stratification. Measurement of global electrical heterogeneity (GEH) could help identify the substrate vulnerable to drug-induced ventricular arrhythmias.The purpose of this study was to improve risk stratification for drug-induced TdP by measuring GEH on the electrocardiogram (ECG).We analyzed ECG data from a case-control study of patients with a history of drug-induced TdP as well as age- and sex-matched controls. Vectorcardiograms were constructed from ECGs. GEH was measured via the spatial ventricular gradient (SVG) vector (magnitude, azimuth, and elevation). Log odds coefficients for TdP were estimated using multivariable logistic regression.Among 17 cases (47% male; age 58.9 ± 12.5 years) and 17 controls (29% male; age 61.0 ± 12.2 years), 34 ECGs were analyzed. SVG azimuth was significantly different between cases and controls (3.4 vs 22.0 degrees, respectively; P = 0.02). After adjusting for sex and QTc interval, odds of TdP increased by a factor of 3.2 for each 1 SD change in SVG azimuth from the control group mean (95% confidence interval 1.07–9.14; P = .04). QTc was not significant in the multivariable analysis (P = .20).SVG azimuth is correlated with a history of drug-induced TdP independent of QTc. GEH measurement may help identify patients at high risk for drug-induced arrhythmias.

New in vitro multiple cardiac ion channel screening system for preclinical Torsades de Pointes risk prediction under the Comprehensive in vitro Proarrhythmia Assay concepta

Cardiotoxicity, particularly drug-induced Torsades de Pointes (TdP), is a concern in drug safety assessment. The recent establishment of human induced pluripotent stem cell-derived cardiomyocytes (human iPSC-CMs) has become an attractive human-based platform for predicting cardiotoxicity. Moreover, electrophysiological assessment of multiple cardiac ion channel blocks is emerging as an important parameter to recapitulate proarrhythmic cardiotoxicity. Therefore, we aimed to establish a novel in vitro multiple cardiac ion channel screening-based method using human iPSC-CMs to predict the drug-induced arrhythmogenic risk. To explain the cellular mechanisms underlying the cardiotoxicity of three representative TdP high- (sotalol), intermediate- (chlorpromazine), and low-risk (mexiletine) drugs, and their effects on the cardiac action potential (AP) waveform and voltage-gated ion channels were explored using human iPSC-CMs. In a proof-of-principle experiment, we investigated the effects of cardioactive channel inhibitors on the electrophysiological profile of human iPSC-CMs before evaluating the cardiotoxicity of these drugs. In human iPSC-CMs, sotalol prolonged the AP duration and reduced the total amplitude (TA) via selective inhibition of IKr and INa currents, which are associated with an increased risk of ventricular tachycardia TdP. In contrast, chlorpromazine did not affect the TA; however, it slightly increased AP duration via balanced inhibition of IKr and ICa currents. Moreover, mexiletine did not affect the TA, yet slightly reduced the AP duration via dominant inhibition of ICa currents, which are associated with a decreased risk of ventricular tachycardia TdP. Based on these results, we suggest that human iPSC-CMs can be extended to other preclinical protocols and can supplement drug safety assessments.

Quantitative male-to-female translation of cardiac electrophysiological response to drugs.

Despite evidence that women are at higher risk of drug-induced Torsade de Pointes and sudden cardiac death, female sex is vastly underrepresented in cardiovascular research studies and clinical trials, thus limiting our fundamental understanding of sex-specific arrhythmia mechanisms and our ability to predict arrhythmia propensity. Indeed, we have shown that adoption of male-biased metrics can lead to a systematic underestimation of torsadogenic risk in women. To facilitate evaluation of the impact of sex differences on cardiomyocyte function and arrhythmogenesis, we developed quantitative tools that predict the electrophysiological response to drug administration in female cardiomyocytes starting from data collected in males. Using our mathematical models of excitation-contraction coupling in male and female human ventricular myocytes, we generated populations of models by randomly varying baseline model parameters. We collected action potential (AP) and Ca2+ transient (CaT) properties in each model variants and used multivariable regression to construct a “male-to-female translator”, i.e., a set of regression coefficients mapping male features onto female electrophysiology. We validated our translator against independent simulated data sets describing the effects of a large group of drugs on the baseline male and female models. Our results show high accuracy of prediction of drug-induced effects on female AP and CaT, where the average discrepancy between predicted and actual biomarkers falls within 1-2%. Next, we will use "male-to-female" translated features as inputs into our previously developed female-specific classifier of drug-induced torsadogenic risk to test the suitability of our translator for cardiac safety assessment in women. Classifier performance obtained with simulated data as input features will be used as a benchmark. Completion of this work will facilitate the translatability of male-biased experimental observations to female physiology and advance the characterization of sex differences in cardiac electrophysiology and drug cardiotoxicity.

Characterization of pathological remodeling in the chronic atrioventricular block cynomolgus monkey heart.

We studied time course of pathological remodeling occurring in the cynomolgus monkey hearts against persistent atrioventricular block condition (n = 10). The atrioventricular block induced the ventricular and atrial dilation followed by the ventricular hypertrophy. Interstitial fibrosis in the ventricle was also observed along with gradual increases in the plasma angiotensin II and aldosterone concentrations. These adaptations were associated with the changes in gene expression profiling reflecting fibrosis and hypertrophy. Atrioventricular block reduced the ventricular rate and cardiac output, but the ejection fraction and stroke volume increased, whereas the cardiac output was gradually restored to its basal level. Systolic/diastolic blood pressure after the atrioventricular block was kept equal to or lower than that before the block, according with lack of increase in the plasma catecholamine levels. Chronic atrioventricular block gradually prolonged the QRS width and JT interval, leading to the QT interval prolongation in conscious state. 10mg/kg of dl-sotalol hydrochloride induced torsade de pointes (TdP) in 6 out of 10 animals by 15months. Animals showing longer QTcF under anesthesia after the atrioventricular block developed dl-sotalol-induced TdP earlier. No marked difference was observed in pharmacokinetics of dl-sotalol between 1 and 7months after the atrioventricular block. Each TdP spontaneously terminated, reflecting a monkey's relatively small "effective size of the heart (=∛(left ventricular weight)/wavelength of reentry)". These fundamental knowledge will help better utilize the chronic atrioventricular block monkeys as an in vivo proarrhythmia model for detecting drug-induced TdP.

Prediction of drug pro-arrhythmic cardiotoxicity using a semi-physiologically based pharmacokinetic model linked to cardiac ionic currents inhibition

Drug-induced torsades de pointes (TdP) risks are responsible for the withdrawal of many drugs from the market. Nowadays, assessments of drug-induced TdP risks are mainly based on maximum effective free therapeutic plasma concentration (EFTPCmax) and cardiac ionic current inhibitions using the human ventricular myocytes model (Tor-ORd model). Myocytes are targets of drug-induced TdP. The TdP risks may be directly linked to myocyte drug concentrations. We aimed to develop a semi-physiologically based pharmacokinetic (Semi-PBPK) model linked to cardiac ionic current inhibition (pharmacodynamics, PD) (Semi-PBPK-PD) to simultaneously predict myocyte drug concentrations and their TdP risks in humans. Alterations in action potential duration (ΔAPD90) were simulated using the Tor-ORd model and ionic current inhibition parameters based on myocyte or plasma drug concentrations. The predicted ΔAPD90 values were translated into in vivo alterations in QT interval(ΔQTc) induced by moxifloxacin, dofetilide, or sotalol. Myocyte drug concentrations of moxifloxacin, dofetilide, and sotalol gave better predictions of ΔQTc than plasma. Following validating the developed semi-PBPK-PD model, TdP risks of 37 drugs were assessed using ΔAPD90 and early afterdepolarization occurrence, which were estimated based on 10 × EFTPCmax and 10 × EFTMCmax (maximum effective free therapeutic myocyte concentration). 10 × EFTMCmax gave more sensitive and accurate predictions of pro-arrhythmic cardiotoxicity and the predicted TdP risks were also closer to clinic practice than 10 × EFTPCmax. In conclusion, pharmacokinetics and TdP risks of 37 drugs were successfully predicted using the semi-PBPK-PD model. Myocyte drug concentrations gave better predictions of ΔQTc and TdP risks than plasma.

Prediction of Drug-Induced TdP Risks Using Machine Learning and Rabbit Ventricular Wedge Assay

Torsades de pointes (TdP) is an irregular heart rhythm as a side effect of drugs and may cause sudden cardiac death. A machine learning model that can accurately identify drug TdP risk is necessary. This study uses multinomial logistic regression models to predict three-class drug TdP risks based on datasets generated from rabbit ventricular wedge assay experiments. The training-test split and five-fold cross-validation provide unbiased measurements for prediction accuracy. We utilize bootstrap to construct a 95% confidence interval for prediction accuracy. The model interpretation is further demonstrated by permutation predictor importance. Our study offers an interpretable modeling method suitable for drug TdP risk prediction. Our method can be easily generalized to broader applications of drug side effect assessment.

Proarrhythmia Risk Assessment Using Electro-Mechanical Window in Human iPS Cell-Derived Cardiomyocytes.

Evaluation of drug-induced cardiotoxicity is still challenging to avoid adverse effects, such as torsade de pointes (TdP), in non-clinical and clinical studies. Numerous studies have suggested that human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are a useful platform for detecting drug-induced TdP risks. Comprehensive in vitro Proarrhythmia Assay (CiPA) validation study suggested that hiPSC-CMs can assess clinical TdP risk more accurately than the human ether-a-go-go-related assay and QT interval prolongation. However, there were still some outliers, such as bepridil, mexiletine, and ranolazine, among the CiPA 28 compounds in the CiPA international multi-site study using hiPSC-CMs. In this study, we assessed the effects of the positive compound dofetilide, the negative compound aspirin, and several CiPA compounds (bepridil, mexiletine, and ranolazine) on the electromechanical window (E-M window), which were evaluated using multi-electrode array assay and motion analysis, in hiPSC-CMs. Similar to previous in vivo studies, dofetilide, which has a high TdP risk, decreased the E-M window in hiPSC-CMs, whereas aspirin, which has a low TdP risk, had little effect. Bepridil, classified in the high TdP-risk group in CiPA, decreased the E-M window in hiPSC-CMs, whereas ranolazine and mexiletine, which are classified in the low TdP-risk group in CiPA, slightly decreased or had little effect on the E-M window of hiPSC-CMs. Thus, the E-M window in hiPSC-CMs can be used to classify drugs into high and low TdP risk.

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

Background and ObjectiveIn 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. MethodsThe 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. ResultsPopulation of models allowed to obtain different AP responses under the same pharmacological intervention and improve the prediction of drug-induced TdP with respect to the baseline model. The classification tools based on population of models achieve an accuracy higher than 0.8 and a mean classification error (MCE) lower than 0.3 for both validation drug sets and for the two electrophysiological action potential models studied (Tomek et al. 2020 and a modified version of O'Hara et al. 2011). In addition, simulations with population of models allowed the identification of individuals with lower conductances of IKr, IKs, and INaK and higher conductances of ICaL, INaL, and INCX, which are more prone to develop TdP. ConclusionsThe 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.

Statistical learning in preclinical drug proarrhythmic assessment

ABSTRACT Torsades de pointes (TdP) is an irregular heart rhythm characterized by faster beat rates and potentially could lead to sudden cardiac death. Much effort has been invested in understanding the drug-induced TdP in preclinical studies. However, a comprehensive statistical learning framework that can accurately predict the drug-induced TdP risk from preclinical data is still lacking. We proposed ordinal logistic regression and ordinal random forest models to predict low-, intermediate-, and high-risk drugs based on datasets generated from two experimental protocols. Leave-one-drug-out cross-validation, stratified bootstrap, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The potential outlier drugs identified by our models are consistent with their descriptions in the literature. Our method is accurate, interpretable, and thus useable as supplemental evidence in the drug safety assessment.

Torsade de pointes caused by citalopram during the pacemaker battery‐depletion phase: A case report

Drug‐induced QT prolongation, primarily antiarrhythmic drugs, is a common cause of torsade de pointes (TdP). Although there have been previous reports of drug‐induced TdP in patients, it has not been well documented when caused by citalopram during the pacemaker battery‐depletion phase. To improve delirium recognition, we report a case of citalopram‐induced TdP during the pacemaker battery‐depletion phase. An 84‐year‐old Chinese female was brought to the hospital presenting recurrent syncope. She lost consciousness and was admitted after her syncope TdP was documented. Her pacemaker was inspected and found to be operating in an extremely ineffective manner. Although she had prolonged QT interval after the pacemaker was replaced, she did not suffer another syncope attack, and ECG monitoring revealed no cardiac arrhythmia or TdP. During her admission, she was treated with citalopram for depression. Citalopram was discontinued when the QT interval shortened progressively. In this study, we described a case of citalopram‐induced TdP during the depletion phase of a pacemaker battery. This case should serve as a cautionary lesson to clinicians to avoid using citalopram during the pacemaker battery‐depletion phase.

Cost-effectiveness of check of medication appropriateness: methodological approach.

Background Adverse drug events following inappropriate prescribing in the hospital cause a substantial and avoidable medical and economic burden to hospitals, payers and patients alike. A clinical rule-based, pharmacist-led medication-review service, the 'Check of Medication Appropriateness' (CMA) was implemented in the University Hospitals Leuven. The CMA is shown to be effective in reducing potentially inappropriate prescriptions. Aim This study investigated whether this centralised clinical pharmacy service is cost-effective. Method We performed a cost-effectiveness analysis of three clinical rules of the CMA, targeting adverse drug events at three levels of severity: A) persistent opioid-induced constipation, B) ketorolac-induced gastrointestinal bleeding and C) drug-induced Torsade de Pointes. A decision tree was developed for each clinical rule. Both intervention costs as well as total costs associated with the occurrence of an adverse drug event were considered. The outcomes were reported in the form of an incremental cost-effectiveness ratio, expressed as an incremental cost per adverse drug event avoided. Results Applying clinical rules to avoid persistent opioid-induced constipation and ketorolac-induced gastrointestinal bleeding were cost-saving. Implementation of a medication check to avoid drug-induced Torsade de Pointes costed €8,846 per Torsade de Pointes avoided. Conclusion Our study provides strong indications that the CMA is worth its investment for clinical rules targeting (very) common adverse drug events, that can be avoided with limited expenses. Further research is required to assess the full CMA. The proposed model may be useful to perform cost-effectiveness analyses of other centralised clinical pharmacy services targeting inappropriate prescribing, at the level of individual adverse drug events.

Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome.

Congenital long-QT syndromes (cLQTS) or drug-induced long-QT syndromes (diLQTS) can cause torsade de pointes (TdP), a life-threatening ventricular arrhythmia. The current strategy for the identification of drugs at the high risk of TdP relies on measuring the QT interval corrected for heart rate (QTc) on the electrocardiogram (ECG). However, QTc has a low positive predictive value. We used convolutional neural network (CNN) models to quantify ECG alterations induced by sotalol, an IKr blocker associated with TdP, aiming to provide new tools (CNN models) to enhance the prediction of drug-induced TdP (diTdP) and diagnosis of cLQTS. Tested CNN models used single or multiple 10-s recordings/patient using 8 leads or single leads in various cohorts: 1029 healthy subjects before and after sotalol intake (n = 14 135 ECGs); 487 cLQTS patients (n = 1083 ECGs: 560 type 1, 456 type 2, 67 type 3); and 48 patients with diTdP (n = 1105 ECGs, with 147 obtained within 48 h of a diTdP episode). CNN models outperformed models using QTc to identify exposure to sotalol [area under the receiver operating characteristic curve (ROC-AUC) = 0.98 vs. 0.72, P ≤ 0.001]. CNN models had higher ROC-AUC using multiple vs. single 10-s ECG (P ≤ 0.001). Performances were comparable for 8-lead vs. single-lead models. CNN models predicting sotalol exposure also accurately detected the presence and type of cLQTS vs. healthy controls, particularly for cLQT2 (AUC-ROC = 0.9) and were greatest shortly after a diTdP event and declining over time (P ≤ 0.001), after controlling for QTc and intake of culprit drugs. ECG segment analysis identified the J-Tpeak interval as the best discriminator of sotalol intake. CNN models applied to ECGs outperform QTc measurements to identify exposure to drugs altering the QT interval, congenital LQTS, and are greatest shortly after a diTdP episode.

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.

QT Dispersion and Drug-Induced Torsade de Pointes.

BackgroundAmiodarone causes less drug-induced torsade de pointes (TdP) compared to other class III antiarrhythmics. Two theories proposed for this finding include that amiodarone has less repolarization heterogeneity, and/or decreases early after depolarization (EADs). Corrected QT (QTc) dispersion as measured on a surface electrocardiogram (ECG) represents spatial heterogeneity of ventricular repolarization.ObjectiveThe purpose of this study was to analyze the difference in QT dispersion between amiodarone and other class III antiarrhythmics and to determine the etiology of TdP.MethodsThis was a retrospective, observational study at Montefiore Medical Center between January 2005 and January 2015. Inclusion criteria were adults >18 years on amiodarone, dofetilide, or sotalol with prolonged QT interval on 12-lead ECG. ECGs were reviewed by three blinded observers. QTc was calculated using the Bazett and Framingham formulas. QTc dispersion was calculated by subtracting the shortest from the longest QTc. Analysis of variance (ANOVA) was applied for comparison between antiarrhythmic groups with Bonferroni correction for multiple comparisons.ResultsA total of 447 ECGs were reviewed and 77 ECGs met inclusion criteria. The average QT dispersion for amiodarone, dofetilide, and sotalol was 0.050, 0.037, and 0.034, respectively (p=0.006) and the average QTc dispersion by Bazett was 0.053, 0.038, and 0.037 (p=0.008) and by Framingham was 0.049, 0.036, and 0.035 (p=0.009), respectively.ConclusionOur results show that given the increase in QT dispersion seen with amiodarone, heterogeneous ventricular repolarization as measured by QTc dispersion likely does not account for the lower incidence of drug-induced TdP seen with amiodarone. The ability of amiodarone to decrease EADs via sodium-channel blockade is more likely the explanation for its lower incidence of drug-induced TdP.