Clinical evaluation of QT/QTc prolongation and proarrhythmic potential for nonantiarrhythmic drugs: the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use E14 guideline

A polygenic risk score for the QT interval is an independent predictor of drug-induced QT prolongation

Drug-induced QT prolongation (diLQTS), and subsequent risk of torsade de pointes, is a major concern with use of many medications, including for non-cardiac conditions. The possibility that genetic risk, in the form of polygenic risk scores (PGS), could be integrated into prediction of risk of diLQTS has great potential, although it is unknown how genetic risk is related to clinical risk factors as might be applied in clinical decision-making. In this study, we examined the PGS for QT interval in 2500 subjects exposed to a known QT-prolonging drug on prolongation of the QT interval over 500ms on subsequent ECG using electronic health record data. We found that the normalized QT PGS was higher in cases than controls (0.212±0.954 vs. -0.0270±1.003, P = 0.0002), with an unadjusted odds ratio of 1.34 (95%CI 1.17-1.53, P<0.001) for association with diLQTS. When included with age and clinical predictors of QT prolongation, we found that the PGS for QT interval provided independent risk prediction for diLQTS, in which the interaction for high-risk diagnosis or with certain high-risk medications (amiodarone, sotalol, and dofetilide) was not significant, indicating that genetic risk did not modify the effect of other risk factors on risk of diLQTS. We found that a high-risk cutoff (QT PGS ≥ 2 standard deviations above mean), but not a low-risk cutoff, was associated with risk of diLQTS after adjustment for clinical factors, and provided one method of integration based on the decision-tree framework. In conclusion, we found that PGS for QT interval is an independent predictor of diLQTS, but that in contrast to existing theories about repolarization reserve as a mechanism of increasing risk, the effect is independent of other clinical risk factors. More work is needed for external validation in clinical decision-making, as well as defining the mechanism through which genes that increase QT interval are associated with risk of diLQTS.

Enhancing hERG Risk Assessment with Interpretable Classificatory and Regression Models

The human Ether-à-go-go-Related Gene (hERG) is a transmembrane protein that regulates cardiac action potential, and its inhibition can induce a potentially deadly cardiac syndrome. In vitro tests help identify hERG blockers at early stages; however, the high cost motivates searching for alternative, cost-effective methods. The primary goal of this study was to enhance the Pred-hERG tool for predicting hERG blockage. To achieve this, we developed new QSAR models that incorporated additional data, updated existing classificatory and multiclassificatory models, and introduced new regression models. Notably, we integrated SHAP (SHapley Additive exPlanations) values to offer a visual interpretation of these models. Utilizing the latest data from ChEMBL v30, encompassing over 14,364 compounds with hERG data, our binary and multiclassification models outperformed both the previous iteration of Pred-hERG and all publicly available models. Notably, the new version of our tool introduces a regression model for predicting hERG activity (pIC50). The optimal model demonstrated an R2 of 0.61 and an RMSE of 0.48, surpassing the only available regression model in the literature. Pred-hERG 5.0 now offers users a swift, reliable, and user-friendly platform for the early assessment of chemically induced cardiotoxicity through hERG blockage. The tool provides versatile outcomes, including (i) classificatory predictions of hERG blockage with prediction reliability, (ii) multiclassificatory predictions of hERG blockage with reliability, (iii) regression predictions with estimated pIC50 values, and (iv) probability maps illustrating the contribution of chemical fragments for each prediction. Furthermore, we implemented explainable AI analysis (XAI) to visualize SHAP values, providing insights into the contribution of each feature to binary classification predictions. A consensus prediction calculated based on the predictions of the three developed models is also present to assist the user's decision-making process. Pred-hERG 5.0 has been designed to be user-friendly, making it accessible to users without computational or programming expertise. The tool is freely available at http://predherg.labmol.com.br.

A simple accurate method for concentration-QTc analysis in preclinical animal models

INTRODUCTION

In preclinical cardiovascular safety pharmacology studies, statistical analysis of the rate corrected QT interval (QTc) is the focus for predicting QTc interval changes in the clinic. Modeling of a concentration/QTc relationship, common clinically, is limited due to minimal pharmacokinetic (PK) data in nonclinical testing. It is possible, however, to relate the average drug plasma concentration from sparse PK samples over specific times to the mean corrected QTc. We hypothesize that averaging drug plasma concentration and the QTc-rate relationship over time provides a simple, accurate concentration-QTc relationship bridging statistical and concentration/QTc modeling.

METHODS

Cardiovascular telemetry studies were conducted in non-human primates (NHP; n = 48) and canines (n = 8). Pharmacokinetic samples were collected on separate study days in both species. Average plasma concentrations for specific intervals (CAverage0-X) were calculated for moxifloxacin in canines and NHP using times corresponding to super-intervals for the QTc data statistical analysis. The QTc effect was calculated for each super-interval using a linear regression correction incorporating QT and HR data from the whole super-interval. The concentration QTc effects were then modeled.

RESULTS

In NHP, a 10.9 ± 0.06 ms (mean ± 95% CI) change in QTc was detected at approximately 1.5× the moxifloxacin plasma concentration that causes a 10 ms QTc change in humans, based on a 0-24 h super-interval. When simulating a drug without QT effects, mock, no effect on QTc was detected at up to 3× the clinical concentration. Similarly, in canines, a 16.6 ± 0.1 ms change was detected at 1.7× critical clinical moxifloxacin concentration, and a 0.04 ± 0.1 ms change was seen for mock.

CONCLUSIONS

While simultaneous PK and QTc data points are preferred, practical constraints and the need for QTc averaging did not prevent concentration-QTc analyses. Utilizing a 0-24 h super-interval method illustrates a simple and effective method to address cardiovascular questions when preclinical drug exposures exceed clinical concentrations.

An optimised and validated surrogate analyte A-TEEM-PARAFAC-PLS technique for detecting and quantifying the biological oxygen demand in surface water

A 5-day test duration makes BOD5 measurement unsatisfactory and hinders the development of a quick technique. Protein-like fluorescence peaks show a strong correlation between the BOD characteristics and the fluorescence intensities. For identifying and measuring BOD in surface water, a simultaneous absorbance-transmittance and fluorescence excitation-emission matrices (A-TEEM) method combined with PARAFAC (parallel factor) and PLS (partial least squares) analyses was developed using a tyrosine and tryptophan (tyr-trpt) mix as a surrogate analyte for BOD. The use of a surrogate analyte was decided upon due to lack of fluorescent BOD standards. Tyr-trpt mix standard solutions were added to surface water samples to prepare calibration and validation samples. PARAFAC analysis of excitation-emission matrices detected the tyr-trpt mix in surface water. PLS modelling demonstrated significant linearity (R2 = 0.991) between the predicted and measured tyr-trypt mix concentrations, and accuracy and robustness were all acceptable per the ICH Q2 (R2) and ASTM multivariate calibration/validation procedures guidelines. Based on a suitable and workable surrogate analyte method, these results imply that BOD can be detected and quantified using the A-TEEM-PARAFAC-PLS method. Very positive comparability between tyr-trypt mix concentrations was found, suggesting that tyr-trypt mix might eventually take the place of a BOD-based sampling protocol. Overall, this approach offers a novel tool that can be quickly applied in water treatment plant settings and is a step in supporting the trend toward rapid BOD determination in waters. Further studies should demonstrate the wide application of the method using real wastewater samples from various water treatment facilities.

Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction

In the field of drug discovery, there is a substantial challenge in seeking out chemical structures that possess desirable pharmacological, toxicological, and pharmacokinetic properties. Complications arise when drugs interfere with the functioning of cardiac ion channels, leading to serious cardiovascular consequences. The discontinuation and removal of numerous approved drugs from the market or at late development stages in the pipeline due to such inhibitory effects further highlight the urgency of addressing this issue. Consequently, the early prediction of potential blockers targeting cardiac ion channels during the drug discovery process is of paramount importance. This study introduces a deep learning framework that computationally determines the cardiotoxicity associated with the voltage-gated potassium channel (hERG), the voltage-gated calcium channel (Cav1.2), and the voltage-gated sodium channel (Nav1.5) for drug candidates. The predictive capabilities of three feature representations─molecular fingerprints, descriptors, and graph-based numerical representations─are rigorously benchmarked. Additionally, a novel training and evaluation data set framework is presented, enabling predictive model training of drug off-target cardiotoxicity using a comprehensive and large curated data set covering these three cardiac ion channels. To facilitate these predictions, a robust and comprehensive small molecule cardiotoxicity prediction tool named CToxPred has been developed. It is made available as open source under the permissive MIT license at https://github.com/issararab/CToxPred.

Predicting Cardiotoxicity of Molecules Using Attention-Based Graph Neural Networks

In drug discovery, the search for new and effective medications is often hindered by concerns about toxicity. Numerous promising molecules fail to pass the later phases of drug development due to strict toxicity assessments. This challenge significantly increases the cost, time, and human effort needed to discover new therapeutic molecules. Additionally, a considerable number of drugs already on the market have been withdrawn or re-evaluated because of their unwanted side effects. Among the various types of toxicity, drug-induced heart damage is a severe adverse effect commonly associated with several medications, especially those used in cancer treatments. Although a number of computational approaches have been proposed to identify the cardiotoxicity of molecules, the performance and interpretability of the existing approaches are limited. In our study, we proposed a more effective computational framework to predict the cardiotoxicity of molecules using an attention-based graph neural network. Experimental results indicated that the proposed framework outperformed the other methods. The stability of the model was also confirmed by our experiments. To assist researchers in evaluating the cardiotoxicity of molecules, we have developed an easy-to-use online web server that incorporates our model.

Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers

The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure-activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds.

Detection and Quantification of Bisphenol A in Surface Water Using Absorbance-Transmittance and Fluorescence Excitation-Emission Matrices (A-TEEM) Coupled with Multiway Techniques

In the present protocol, we determined the presence and concentrations of bisphenol A (BPA) spiked in surface water samples using EEM fluorescence spectroscopy in conjunction with modelling using partial least squares (PLS) and parallel factor (PARAFAC). PARAFAC modelling of the EEM fluorescence data obtained from surface water samples contaminated with BPA unraveled four fluorophores including BPA. The best outcomes were obtained for BPA concentration (R2 = 0.996; standard deviation to prediction error's root mean square ratio (RPD) = 3.41; and a Pearson's r value of 0.998). With these values of R2 and Pearson's r, the PLS model showed a strong correlation between the predicted and measured BPA concentrations. The detection and quantification limits of the method were 3.512 and 11.708 micro molar (µM), respectively. In conclusion, BPA can be precisely detected and its concentration in surface water predicted using the PARAFAC and PLS models developed in this study and fluorescence EEM data collected from BPA-contaminated water. It is necessary to spatially relate surface water contamination data with other datasets in order to connect drinking water quality issues with health, environmental restoration, and environmental justice concerns.

The Effects of Moxifloxacin and Gemifloxacin on the ECG Morphology in Healthy Volunteers: A Phase 1 Randomized Clinical Trial

Moxifloxacin and gemifloxacin are the two newer broad-spectrum 8-methoxy-quinolone derivatives that are used to treat various bacterial infections in cardiac patients. In this research study, we assessed the impact of moxifloxacin and gemifloxacin on the QT intervals of electrocardiograms in normal adult doses and draw a comparison, in a controlled environment, on healthy volunteers. Additionally, the effect of both test drugs on the QRS complex was checked. Sixty healthy volunteers were randomly assigned to two groups via R-software, and each respectively received moxifloxacin and gemifloxacin for five days. The research ethics committee approved the research, and it was registered for clinical trial under NCT04692623. The participants' electrocardiograms were obtained before the start of the dose (baseline) and on the fifth day. Significant prolongation of QT interval was noted in moxifloxacin (p < 0.0001) as compared to gemifloxacin treated groups. There were no cases of QTc prolongation over the usual limits (450-470 ms) in the gemifloxacin-treated group, however, QTc prolongations at the rate of 30 and 60 ms from the baseline were noted, interpreted as per the EMEA guidelines. These findings indicate that moxifloxacin caused significant (p < 0.0001) QT interval prolongation (QTIP) as compared to gemifloxacin. In contrast to the previously reported literature, the prominent effect of moxifloxacin on the widening of the QRS-complex was noted with no such effect on QRS-widening in the gemifloxacin-treated group. It is concluded that both drugs have the potential for considerable QT interval prolongation (QTIP) effects, which is one of the risk factors for developing torsade de pointes (TdPs) in cardiac patients. Thus, clinicians should exercise caution when prescribing moxifloxacin and gemifloxacin to cardiac patients and should consider alternate treatment options.

Effect of Pevonedistat, an Investigational NEDD8-Activating Enzyme Inhibitor, on the QTc Interval in Patients With Advanced Solid Tumors

The purpose of this study was to assess the effect of pevonedistat, a neural precursor cell expressed, developmentally down-regulated protein 8 (NEDD8)-activating enzyme inhibitor, on the heart rate-corrected QT (QTc) interval in cancer patients. Patients were randomized 1:1 to receive pevonedistat 25 or 50 mg/m² on day 1 and the alternate dose on day 8. Triplicate electrocardiograms were collected at intervals over 0-11 hours and at 24 hours via Holter recorders on days -1 (baseline), 1, and 8. Changes from time-matched baseline values were calculated for QTc by Fridericia (QTcF), PR, and QRS intervals. Serial time-matched blood samples for analysis of pevonedistat plasma pharmacokinetics were collected and a concentration-QTc analysis conducted. Safety was assessed by monitoring vital signs, physical examinations, and clinical laboratory tests. Forty-four patients were included in the QTc analysis. Maximum least square (LS) mean increase from time-matched baseline in QTcF was 3.2 milliseconds at 1 hour postdose for pevonedistat at 25 mg/m² , while the LSs mean change from baseline in QTcF was -1.7 milliseconds 1 hour postdose at 50 mg/m² . The maximum 2-sided 90% upper confidence bound was 6.7 and 2.9 milliseconds for pevonedistat at 25 and 50 mg/m² , respectively. Pevonedistat did not result in clinically relevant effects on heart rate, nor on PR or QRS intervals. Results from pevonedistat concentration-QTc analysis were consistent with these findings. Administration of pevonedistat to cancer patients at a dose of up to 50 mg/m² showed no evidence of QT prolongation, indicative of the lack of clinically meaningful effects on cardiac repolarization. ClinicalTrials.gov identifier: NCT03330106 (first registered on November 6, 2017).

Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism

Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Failure or inhibition of hERG channel activity caused by drug molecules can lead to prolonging QT interval, which will result in serious cardiotoxicity. Thus, evaluating the hERG blocking activity of all these small molecular compounds is technically challenging, and the relevant procedures are expensive and time-consuming. In this study, we develop a novel deep learning predictive model named DMFGAM for predicting hERG blockers. In order to characterize the molecule more comprehensively, we first consider the fusion of multiple molecular fingerprint features to characterize its final molecular fingerprint features. Then, we use the multi-head attention mechanism to extract the molecular graph features. Both molecular fingerprint features and molecular graph features are fused as the final features of the compounds to make the feature expression of compounds more comprehensive. Finally, the molecules are classified into hERG blockers or hERG non-blockers through the fully connected neural network. We conduct 5-fold cross-validation experiment to evaluate the performance of DMFGAM, and verify the robustness of DMFGAM on external validation datasets. We believe DMFGAM can serve as a powerful tool to predict hERG channel blockers in the early stages of drug discovery and development.

A Phase 1 Thorough QT/QTc Study of Therapeutic and Supratherapeutic Doses of Belumosudil in Healthy Subjects

Belumosudil is a selective Rho-associated, coiled-coil-containing protein kinase-2 inhibitor. In this crossover design thorough QT/QTc study, single therapeutic (200 mg) and supratherapeutic (1000 mg) oral doses of belumosudil, moxifloxacin (positive control), and placebo were administered to 34 subjects. Twelve-lead electrocardiograms and serial pharmacokinetic sampling were acquired. The effect of belumosudil on the placebo-corrected, change-from-baseline QTcF was small, and an effect exceeding 10 ms could be excluded across all time points with both doses. Using concentration-QTc analysis, an effect on ΔΔQTcF >10 ms can be excluded up to belumosudil concentrations of ≈12 080 ng/mL, more than 2-fold above mean C_max after the supratherapeutic dose. There was no clinically relevant effect on heart rate or cardiac conduction (ie, the PR and QRS intervals) for belumosudil. No differences in safety were noted between belumosudil and placebo treatment. Assay sensitivity was demonstrated by moxifloxacin's effect on the QTc interval. In conclusion, belumosudil at therapeutic and supratherapeutic doses did not have a clinically meaningful effect on electrocardiogram parameters.

QTc interval prolongation due to spinal anesthesia in patients with and without diabetes: an observational study

BACKGROUND

Spinal anesthesia and autonomic neuropathy (caused by diabetes) prolong the QTc interval. Changes in the duration of the QTc interval following subarachnoid blockade in patients with diabetes have not been evaluated. We hypothesized that after subarachnoid blockade, QTc interval prolongation would be greater in patients with diabetes than in those without. Accordingly, we compared the QTc interval, T wave peak-to-end interval (Tp-e interval), blood pressure, heart rate, and heart rate variability before and after spinal anesthesia in patients with and without diabetes.

METHODS

This prospective observational study (Clinical Research Information Service identifier: KCT0004897) was conducted in a tertiary university hospital and included 24 patients with diabetes mellitus (DM group) and 24 patients without it (control group) who were scheduled for spinal anesthesia. The QTc interval, Tp-e interval, heart rate variability, blood pressure, and heart rate were measured before (T1) and 1 (T2), 5 (T3), and 10 min (T4) following subarachnoid blockade.

RESULTS

Ten minutes following subarachnoid blockade, the QTc intervals of patients in the DM group were significantly longer than the baseline values, whereas the change in the QTc interval in the control group was not significant (p < 0.0001 vs. p = 0.06).

CONCLUSION

Spinal anesthesia caused a more significant prolongation of the QTc interval in patients with diabetes than in those without.

Repurposing drugs as COVID-19 therapies: a toxicity evaluation

Drug repurposing is an appealing method to address the Coronavirus 2019 (COVID-19) pandemic because of the low cost and efficiency. We analyzed our in-house database of approved drug screens and compared their activity profiles with results from a severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) cytopathic effect (CPE) assay. The activity profiles of the human ether-à-go-go-related gene (hERG), phospholipidosis (PLD), and many cytotoxicity screens were found significantly correlated with anti-SARS-CoV-2 activity. hERG inhibition is a nonspecific off-target effect that has contributed to promiscuous drug interactions, whereas drug-induced PLD is an undesirable effect linked to hERG blockers. Thus, this study identifies preferred drug candidates as well as chemical structures that should be avoided because of their potential to induce toxicity. Lastly, we highlight the hERG liability of anti-SARS-CoV-2 drugs currently enrolled in clinical trials.

Piperaquine-Induced QTc Prolongation Decreases With Repeated Monthly Dihydroartemisinin-Piperaquine Dosing in Pregnant Ugandan Women

BACKGROUND

Intermittent preventive treatment with monthly dihydroartemisinin-piperaquine (DHA-PQ) is highly effective at preventing both malaria during pregnancy and placental malaria. Piperaquine prolongs the corrected QT interval (QTc), and it is possible that repeated monthly dosing could lead to progressive QTc prolongation. Intensive characterization of the relationship between piperaquine concentration and QTc interval throughout pregnancy can inform effective, safe prevention guidelines.

METHODS

Data were collected from a randomized controlled trial, where pregnant Ugandan women received malaria chemoprevention with monthly DHA-PQ (120/960 mg DHA/PQ; n = 373) or sulfadoxine-pyrimethamine (SP; 1500/75 mg; n = 375) during the second and third trimesters of pregnancy. Monthly trough piperaquine samples were collected throughout pregnancy, and pre- and postdose electrocardiograms were recorded at 20, 28, and 36 weeks' gestation in each woman. The pharmacokinetics-QTc relationship for piperaquine and QTc for SP were assessed using nonlinear mixed-effects modeling.

RESULTS

A positive linear relationship between piperaquine concentration and Fridericia corrected QTc interval was identified. This relationship progressively decreased from a 4.42 to 3.28 to 2.13 millisecond increase per 100 ng/mL increase in piperaquine concentration at 20, 28, and 36 weeks' gestation, respectively. Furthermore, 61% (n = 183) of women had a smaller change in QTc at week 36 than week 20. Nine women given DHA-PQ had grade 3-4 cardiac adverse events. SP was not associated with any change in QTc.

CONCLUSIONS

Repeated DHA-PQ dosing did not result in increased risk of QTc prolongation and the postdose QTc intervals progressively decreased. Monthly dosing of DHA-PQ in pregnant women carries minimal risk of QTc prolongation.

CLINICAL TRIALS REGISTRATION

NCT02793622.

Cardiac Safety Assessment of Lazertinib: Findings From Patients With EGFR Mutation-Positive Advanced NSCLC and Preclinical Studies

Introduction

Lazertinib is a potent, irreversible, brain-penetrant, mutant-selective, and wild-type–sparing third-generation EGFR tyrosine kinase inhibitor (TKI), creating a wide therapeutic index. Cardiovascular adverse events (AEs), including QT prolongation, decreased left ventricular ejection fraction (LVEF), and heart failure, have emerged as potential AEs with certain EGFR TKI therapies.

Methods

Cardiac safety of lazertinib was evaluated in TKI-tolerant adults with EGFR mutation-positive locally advanced or metastatic NSCLC receiving lazertinib (20–320 mg/d). QT intervals corrected with Fridericia’s formula (QTcF) prolongation, time-matched concentration-QTcF relationship, change of LVEF, and cardiac failure-associated AEs were evaluated. The clinical findings were supplemented by the following three preclinical studies: an in vitro hERG inhibition assay, an ex vivo isolated perfused rabbit heart study, and an in vivo telemetry-instrumented beagle dog study.

Results

Preclinical evaluation revealed little to no physiological effect on the basis of electrocardiogram, electrophysiological, proarrhythmic, and hemodynamic parameters. Clinical evaluation of 181 patients revealed no clinically relevant QTcF prolongation by centralized electrocardiogram in any patient and at any dose level. The predicted magnitude of QTcF value increase at maximum steady-state plasma concentration for the therapeutic dose of lazertinib (240 mg/d) was 2.2 msec (upper bound of the two-sided 90% confidence interval: 3.6 msec). No patient had clinically relevant LVEF decrease (i.e., minimum postbaseline LVEF value of <50% and a maximum decrease in LVEF value from baseline of ≥10 percentage points). Cardiac failure-associated AE occurred in one patient (grade 2 decreased LVEF) and resolved without any dose modifications.

Conclusions

Our first-in-human study, together with preclinical data, indicates that lazertinib is not associated with increased cardiac risk.

Improving corrected QT; Why individual correction is not enough

The use of QT-prolongation as a biomarker for arrhythmia risk requires that researchers correct the QT-interval (QT) to control for the influence of heart rate (HR). QT correction methods can vary but most used are the universal correction methods, such as Bazett's or Van de Water's, which use a single correction formula to correct QT-intervals in all the subjects of a study. Such methods fail to account for differences in the QT/HR relationship between subjects or over time, instead relying on the assumption that this relationship is consistent. To address these changes in rate relationships, we test the effectiveness of linear and non-linear individual correction methods. We hypothesize that individual correction methods that account for additional influences on the rate relationship will result in more effective and consistent correction. To increase the scope of this study we use bootstrap sampling on ECG recordings from non-human primates and beagle canines dosed with vehicle control. We then compare linear and non-linear individual correction methods through their ability to reduce HR correlation and standard deviation of corrected QT values. From these results, we conclude that individual correction methods based on post-treatment data are most effective with the linear methods being the best option for most cases in both primates and canines. We also conclude that the non-linear methods are more effective in canines than primates and that accounting for light status can improve correction while examining the data from the light periods separately. Individual correction requires careful consideration of inter-subject and intra-subject variabilities.

User Perceptions and Experiences of a Handheld 12-Lead Electrocardiographic Device in a Clinical Setting: Usability Evaluation

Background

Cardiac arrhythmias are a leading cause of death. The mainstay method for diagnosing arrhythmias (eg, atrial fibrillation) and cardiac conduction disorders (eg, prolonged corrected QT interval [QTc]) is by using 12-lead electrocardiography (ECG). Handheld 12-lead ECG devices are emerging in the market. In tandem with emerging technology options, evaluations of device usability should go beyond validation of the device in a controlled laboratory setting and assess user perceptions and experiences, which are crucial for successful implementation in clinical practice.

Objective

This study aimed to evaluate clinician and patient perceptions and experiences, regarding the usability of a handheld 12-lead ECG device compared to a conventional 12-lead ECG machine, and generalizability of this user-centered approach.

Methods

International Organization for Standardization Guidelines on Usability and the Technology Acceptance Model were integrated to form the framework for this study, which was conducted in outpatient clinics and cardiology wards at Westmead Hospital, New South Wales, Australia. Each patient underwent 2 ECGs (1 by each device) in 2 postures (supine and standing) acquired in random sequence. The times taken by clinicians to acquire the first ECG (efficiency) using the devices were analyzed using linear regression. Electrocardiographic parameters (QT interval, QTc interval, heart rate, PR interval, QRS interval) and participant satisfaction surveys were collected. Device reliability was assessed by evaluating the mean difference of QTc measurements within ±15 ms, intraclass correlation coefficient, and level of agreement of the devices in detecting atrial fibrillation and prolonged QTc. Clinicians’ perceptions and feedback were assessed with semistructured interviews based on the Technology Acceptance Model.

Results

A total of 100 patients (age: mean 57.9 years, SD 15.2; sex: male: n=64, female n=36) and 11 clinicians (experience acquiring ECGs daily or weekly 10/11, 91%) participated, and 783 ECGs were acquired. Mean differences in QTc measurements of both handheld and conventional devices were within ±15 ms with high intraclass correlation coefficients (range 0.90-0.96), and the devices had a good level of agreement in diagnosing atrial fibrillation and prolonged QTc (κ=0.68-0.93). Regardless of device, QTc measurements when patients were standing were longer duration than QTc measurements when patients were supine. Clinicians’ ECG acquisition times improved with usage (P<.001). Clinicians reported that device characteristics (small size, light weight, portability, and wireless ECG transmission) were highly desired features. Most clinicians agreed that the handheld device could be used for clinician-led mass screening with enhancement in efficiency by increasing user training. Regardless of device, patients reported that they felt comfortable when they were connected to the ECG devices.

Conclusions

Reliability and usability of the handheld 12-lead ECG device were comparable to those of a conventional ECG machine. The user-centered evaluation approach helped us identify remediable action to improve the efficiency in using the device and identified highly desirable device features that could potentially help mass screening and remote assessment of patients. The approach could be applied to evaluate and better understand the acceptability and usability of new medical devices.

CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles

MOTIVATION

Ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Blockade of hERG channels may cause prolonged QT intervals that potentially could lead to cardiotoxicity. Various in-silico techniques including deep learning models are widely used to screen out small molecules with potential hERG related toxicity. Most of the published deep learning methods utilize a single type of features which might restrict their performance. Methods based on more than one type of features such as DeepHIT struggle with the aggregation of extracted information. DeepHIT shows better performance when evaluated against one or two accuracy metrics such as negative predictive value (NPV) and sensitivity (SEN) but struggle when evaluated against others such as Matthew correlation coefficient (MCC), accuracy (ACC), positive predictive value (PPV) and specificity (SPE). Therefore, there is a need for a method that can efficiently aggregate information gathered from models based on different chemical representations and boost hERG toxicity prediction over a range of performance metrics.

RESULTS

In this paper, we propose a deep learning framework based on step-wise training to predict hERG channel blocking activity of small molecules. Our approach utilizes five individual deep learning base models with their respective base features and a separate neural network to combine the outputs of the five base models. By using three external independent test sets with potency activity of IC50 at a threshold of 10 [Formula: see text]m, our method achieves better performance for a combination of classification metrics. We also investigate the effective aggregation of chemical information extracted for robust hERG activity prediction. In summary, CardioTox net can serve as a robust tool for screening small molecules for hERG channel blockade in drug discovery pipelines and performs better than previously reported methods on a range of classification metrics.

Risk of Prolonged Corrected QT Interval With Amisulpride Therapy for Renal Function Management in Patients With Schizophrenia

BACKGROUND

Amisulpride (AMI) is a popular antipsychotic drug prescribed for the management of schizophrenia. However, patients may experience prolonged corrected QT (QTc) interval. We therefore aimed to assess the risk factors for QTc prolongation during AMI therapy in patients with schizophrenia.

METHODS

This study retrospectively enrolled 271 patients with schizophrenia. Continuous variables were analyzed with a t test or analysis of variance, and categorical variables were analyzed with a χ test. Patients with and without QTc prolongation were compared using a backward stepwise logistic regression analysis to identify the important variables.

RESULTS

Comedication of AMI with clozapine (odds ratio, 3.5 [95% confidence interval, 1.3-9.7]) and decreased renal function (mildly decrease, 3.4 [1.2-10.1]; mild to moderately decreased, 4.8 [1.3-17.3]; moderately decreased, 13.6 [2.0-90.6]) were identified as the independent risk factors of QTc prolongation. The dose-normalized plasma concentration of AMI (plasma concentration per dose) was significantly higher in the QTc prolongation group (z = -1.735, P = 0.015) and renal dysfunction group (F = 16.002, P < 0.001).

CONCLUSIONS

Renal function should be monitored in patients prescribed with AMI, particularly in those taking clozapine. Plasma concentration per dose values can be considered as a risk factor of QTc interval prolongation. The founding help clinicians to analyze the risk of QTc prolongation before prescribing AMI and to monitor QTc prolongation during AMI therapy.

Concentration-QT modelling of the novel DHFR inhibitor P218 in healthy male volunteers

Aims

Given the increasing emergence of drug resistance in Plasmodium, new antimalarials are urgently required. P218 is an aminopyridine that inhibits dihydrofolate reductase being developed as a malaria chemoprotective drug. Assessing the effect of new compounds on cardiac intervals is key during early drug development to determine their cardiac safety.

Methods

This double‐blind, randomized, placebo‐controlled, parallel group study evaluated the effect of P218 on electrocardiographic parameters following oral administration of seven single‐ascending doses up to 1000 mg in 56 healthy volunteers. Participants were randomized to treatment or placebo at a 3:1 ratio. P218 was administered in the fasted state with standardized lunch served 4 hours after dosing. 12‐lead ECGs were recorded in triplicate at regular intervals on the test day, and at 48, 72, 120, 168, 192 and 240 hours thereafter. Blood samples for pharmacokinetic evaluations were collected at similar time points. Concentration‐effect modelling was used to assess the effect of P218 and its metabolites on cardiac intervals.

Results

Concentration–effect analysis showed that P218 does not prolong the QTcF, J‐Tpeak or TpTe interval at all doses tested. No significant changes in QRS or PR intervals were observed. Two‐sided 90% confidence intervals of subinterval effects of P218 and its metabolites were consistently below the regulatory concern threshold for all doses. Study sensitivity was confirmed by significant shortening of QTcF after a meal.

Conclusion

Oral administration of P218 up to 1000 mg does not prolong QTcF and does not significantly change QRS or PR intervals, suggesting low risk for drug‐induced proarrhythmia.

Discovery and Optimization of DNA Gyrase and Topoisomerase IV Inhibitors with Potent Activity against Fluoroquinolone-Resistant Gram-Positive Bacteria

Herein, we describe the discovery and optimization of a novel series that inhibits bacterial DNA gyrase and topoisomerase IV via binding to, and stabilization of, DNA cleavage complexes. Optimization of this series led to the identification of compound 25, which has potent activity against Gram-positive bacteria, a favorable in vitro safety profile, and excellent in vivo pharmacokinetic properties. Compound 25 was found to be efficacious against fluoroquinolone-sensitive Staphylococcus aureus infection in a mouse thigh model at lower doses than moxifloxacin. An X-ray crystal structure of the ternary complex formed by topoisomerase IV from Klebsiella pneumoniae, compound 25, and cleaved DNA indicates that this compound does not engage in a water-metal ion bridge interaction and forms no direct contacts with residues in the quinolone resistance determining region (QRDR). This suggests a structural basis for the reduced impact of QRDR mutations on antibacterial activity of 25 compared to fluoroquinolones.

Development of a drug screening system using three-dimensional cardiac tissues containing multiple cell types

We hypothesized that an appropriate ratio of cardiomyocytes, fibroblasts, endothelial cells, and extracellular matrix (ECM) factors would be required for the development of three-dimensional cardiac tissues (3D-CTs) as drug screening systems. To verify this hypothesis, ECM-coated human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), ECM-coated cardiac fibroblasts (CFs), and uncoated cardiac endothelial cells (CEs) were mixed in the following ratios: 10:0:0 (10CT), 7:2:1 (7CT), 5:4:1 (5CT), and 2:7:1 (2CT). The expression of cardiac-, fibroblasts-, and endothelial-specific markers was assessed by FACS, qPCR, and immunostaining while that of ECM-, cell adhesion-, and ion channel-related genes was examined by qPCR. Finally, the contractile properties of the tissues were evaluated in the absence or presence of E-4031 and isoproterenol. The expression of ECM- and adhesion-related genes significantly increased, while that of ion channel-related genes significantly decreased with the CF proportion. Notably, 7CT showed the greatest contractility of all 3D-CTs. When exposed to E-4031 (hERG K channel blocker), 7CT and 5CT showed significantly decreased contractility and increased QT prolongation. Moreover, 10CT and 7CT exhibited a stronger response to isoproterenol than did the other 3D-CTs. Finally, 7CT showed the highest drug sensitivity among all 3D-CTs. In conclusion, 3D-CTs with an appropriate amount of fibroblasts/endothelial cells (7CT in this study) are suitable drug screening systems, e.g. for the detection of drug-induced arrhythmia.

Assessing Prescriber Behavior with a Clinical Decision Support Tool to Prevent Drug-Induced Long QT Syndrome

OBJECTIVE

Clinical decision support (CDS) alerts built into the electronic health record (EHR) have the potential to reduce the risk of drug-induced long QT syndrome (diLQTS) in susceptible patients. However, the degree to which providers incorporate this information into prescription behavior and the impact on patient outcomes is often unknown.

METHODS

We examined provider response data over a period from October 8, 2016 until November 8, 2018 for a CDS alert deployed within the EHR from a 13-hospital integrated health care system that fires when a patient with a QTc ≥ 500 ms within the past 14 days is prescribed a known QT-prolonging medication. We used multivariate generalized estimating equations to analyze the impact of therapeutic alternatives, relative risk of diLQTS for specific medications, and patient characteristics on provider response to the CDS and overall patient mortality.

RESULTS

The CDS alert fired 15,002 times for 7,510 patients for which the most common response (51.0%) was to override the alert and order the culprit medication. In multivariate models, we found that patient age, relative risk of diLQTS, and presence of alternative agents were significant predictors of adherence to the CDS alerts and that nonadherence itself was a predictor of mortality. Risk of diLQTS and presence of an alternative agent are major factors in provider adherence to a CDS to prevent diLQTS; however, provider nonadherence was associated with a decreased risk of mortality.

CONCLUSION

Surrogate endpoints, such as provider adherence, can be useful measures of CDS value but attention to hard outcomes, such as mortality, is likely needed.

Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the "Big Data" Era

The rise of novel artificial intelligence (AI) methods necessitates their benchmarking against classical machine learning for a typical drug-discovery project. Inhibition of the potassium ion channel, whose alpha subunit is encoded by the human ether-à-go-go-related gene (hERG), leads to a prolonged QT interval of the cardiac action potential and is a significant safety pharmacology target for the development of new medicines. Several computational approaches have been employed to develop prediction models for the assessment of hERG liabilities of small molecules including recent work using deep learning methods. Here, we perform a comprehensive comparison of hERG effect prediction models based on classical approaches (random forests and gradient boosting) and modern AI methods [deep neural networks (DNNs) and recurrent neural networks (RNNs)]. The training set (∼9000 compounds) was compiled by integrating the hERG bioactivity data from the ChEMBL database with experimental data generated from an in-house, high-throughput thallium flux assay. We utilized different molecular descriptors including the latent descriptors, which are real-value continuous vectors derived from chemical autoencoders trained on a large chemical space (>1.5 million compounds). The models were prospectively validated on ∼840 in-house compounds screened in the same thallium flux assay. The best results were obtained with the XGBoost method and RDKit descriptors. The comparison of models based only on latent descriptors revealed that the DNNs performed significantly better than the classical methods. The RNNs that operate on SMILES provided the highest model sensitivity. The best models were merged into a consensus model that offered superior performance compared to reference models from academic and commercial domains. Furthermore, we shed light on the potential of AI methods to exploit the big data in chemistry and generate novel chemical representations useful in predictive modeling and tailoring a new chemical space.

Clinical Pharmacology of Ixazomib: The First Oral Proteasome Inhibitor

Ixazomib, the first oral proteasome inhibitor, is approved in combination with lenalidomide and dexamethasone for the treatment of patients with multiple myeloma (MM) who have received at least one prior therapy. Ixazomib is a selective, potent, and reversible inhibitor of the 20S proteasome, and preferentially binds to and inhibits the β5 chymotrypsin-like proteolytic site. Ixazomib absorption is rapid, with a median time to reach maximum plasma concentration of approximately 1 h post-dose. Ixazomib pharmacokinetics (PK) are adequately described by a three-compartment model (terminal half-life of 9.5 days) with first-order linear absorption (oral bioavailability of 58%). Plasma exposures of ixazomib increase in a dose-proportional manner. A high-fat meal decreases both the rate and extent of ixazomib absorption, supporting administration on an empty stomach. Population PK analyses demonstrated that no dose adjustment is required based on age, body size/weight, race, sex, mild-to-moderate renal impairment, or mild hepatic impairment. Results from dedicated studies indicate that a reduced starting dose (from 4 to 3 mg) is appropriate for patients with severe renal impairment, end-stage renal disease requiring dialysis, or moderate-to-severe hepatic impairment. Non-cytochrome P450 (CYP)-mediated metabolism appears to be the major clearance mechanism for ixazomib. Drug–drug interaction studies have shown no meaningful effects of strong inhibitors of CYP3A on ixazomib PK; however, the strong inducer rifampin caused a clinically relevant reduction in ixazomib exposure, supporting the recommendation to avoid concomitant administration of ixazomib with strong CYP3A inducers. Exposure–response analyses of data from the phase III TOURMALINE-MM1 registrational study demonstrate a favorable benefit–risk profile for the approved dose and regimen of weekly ixazomib 4 mg on days 1, 8, and 15 of each 28-day cycle.

Topoisomerase Inhibitors Addressing Fluoroquinolone Resistance in Gram-Negative Bacteria

Since their discovery over 5 decades ago, quinolone antibiotics have found enormous success as broad spectrum agents that exert their activity through dual inhibition of bacterial DNA gyrase and topoisomerase IV. Increasing rates of resistance, driven largely by target-based mutations in the GyrA/ParC quinolone resistance determining region, have eroded the utility and threaten the future use of this vital class of antibiotics. Herein we describe the discovery and optimization of a series of 4-(aminomethyl)quinolin-2(1H)-ones, exemplified by 34, that inhibit bacterial DNA gyrase and topoisomerase IV and display potent activity against ciprofloxacin-resistant Gram-negative pathogens. X-ray crystallography reveals that 34 occupies the classical quinolone binding site in the topoisomerase IV-DNA cleavage complex but does not form significant contacts with residues in the quinolone resistance determining region.

Are antibiotics substandard in Lebanon? Quantification of active pharmaceutical ingredients between brand and generics of selected antibiotics

Background

In developing countries, brand-generic substitution is not based on validated scientific evidence that confirm the therapeutic equivalence of the generic to the originator. Rather, decisions are made based on the availability of generic medications. Substitution by inappropriate preparations applies to antibiotics, which may increase the risk of resistance in case of underdosing. This analytical study aims to dose and assess for the accuracy of labeling three oral antibiotic preparations, namely ciprofloxacin hydrochloride, amoxicillin trihydrate and amoxicillin trihydrate-clavulanate potassium, the active pharmaceutical ingredients (APIs) found in brand and generic tablets available on the Lebanese market.

Methods

One brand and 4 generics of ciprofloxacin tablets, 3 generic amoxicillin tablets, and 1 brand and 4 generics of amoxicillin-clavulanic acid medications, were quantified, taking 2 batches of each. According to the United States Pharmacopeia (USP) guidelines, ultra-high pressure liquid chromatography was used to measure the APIs content within tablets. The USP required assay limit of the API was taken as the main comparison criteria.

Results

Out of the 5 ciprofloxacin medications tested, all 5 were out of the 2% required range, thus being substandard. For amoxicillin, all 3 medications were within the 20% range. As for amoxicillin-clavulanic acid medications, 4 out of 5 medications met the 30% required range of clavulanic acid and one exceeded the claimed amount of clavulanic acid, while all 5 met the assay limit for amoxicillin.

Conclusion

These findings raise safety and efficacy concerns, providing solid grounds for potential correlations of antibiotic resistance/substandard antibiotics.

DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity

Motivation

Blockade of the human ether-à-go-go-related gene (hERG) channel by small compounds causes a prolonged QT interval that can lead to severe cardiotoxicity and is a major cause of the many failures in drug development. Thus, evaluating the hERG-blocking activity of small compounds is important for successful drug development. To this end, various computational prediction tools have been developed, but their prediction performances in terms of sensitivity and negative predictive value (NPV) need to be improved to reduce false negative predictions.

Results

We propose a computational framework, DeepHIT, which predicts hERG blockers and non-blockers for input compounds. For the development of DeepHIT, we generated a large-scale gold-standard dataset, which includes 6632 hERG blockers and 7808 hERG non-blockers. DeepHIT is designed to contain three deep learning models to improve sensitivity and NPV, which, in turn, produce fewer false negative predictions. DeepHIT outperforms currently available tools in terms of accuracy (0.773), MCC (0.476), sensitivity (0.833) and NPV (0.643) on an external test dataset. We also developed an in silico chemical transformation module that generates virtual compounds from a seed compound, based on the known chemical transformation patterns. As a proof-of-concept study, we identified novel urotensin II receptor (UT) antagonists without hERG-blocking activity derived from a seed compound of a previously reported UT antagonist (KR-36676) with a strong hERG-blocking activity. In summary, DeepHIT will serve as a useful tool to predict hERG-induced cardiotoxicity of small compounds in the early stages of drug discovery and development.

Availability and implementation

https://bitbucket.org/krictai/deephit and https://bitbucket.org/krictai/chemtrans

Supplementary information

Supplementary data are available at Bioinformatics online.

Relationship of clinical adverse event reports to models of arrhythmia risk

A vital aspect of the drug discovery and development process is the identification and filtering of drugs with high risk of dangerous adverse events. Torsade de Pointes (TdP) is one example of an adverse event that requires thorough in vitro and in vivo drug screening. This is because TdP, a tachycardic ventricular arrhythmia, can develop into fatal cardiac events if left unresolved and has been missed during drug development with profound consequences. These factors led to the development of pre-clinical screening guidelines based on the presence of drug induced QT prolongation (QTp), which has been linked to TdP. These guidelines have high sensitivity, but low specificity, as they tend to predict QTp, which precedes TdP events but does not always lead to TdP. Computational models have the potential to improve these prediction methods by bridging the gaps between preclinical and clinical data. This study proposes the use of adverse event reports obtained from the FDA Adverse Event Reporting System as a representation of clinical TdP risk. By incorporating these reports into computational models, a more accurate risk prediction may be developed.

Assessment of the effect of erdafitinib on cardiac safety: analysis of ECGs and exposure-QTc in patients with advanced or refractory solid tumors

PURPOSE

To characterize the effect of erdafitinib on electrocardiogram (ECG) parameters and the relationship between erdafitinib plasma concentrations and QTc interval changes in patients with advanced or refractory solid tumors.

METHODS

Triplicate ECGs and continuous 12-lead Holter data were collected in the dose escalation part (Part 1) of the first-in-human study, with doses ranging from 0.5 to 12 mg. Triplicate ECG monitoring continued in Parts 2-4 where 2 dose regimens selected from Part 1 were expanded in prespecified tumor types. Analyses of ECG data included central tendency analyses, identification of categorical outliers and morphological assessment. A concentration-QTc analysis was conducted using a linear mixed-effect model based on extracted time matching Holter data.

RESULTS

Central tendency, categorical outlier, and ECG morphologic analyses from 187 patients revealed no clinically significant effect of erdafitinib on heart rate, atrioventricular conduction or cardiac depolarization (PR and QRS), and no effect on cardiac repolarization (QTc). Concentration-QTc analysis from 62 patients indicated that the slopes of relationship between total and free erdafitinib plasma concentrations and QTcI (mean exponent of 0.395) were estimated as - 0.00269 ms/(ng/mL) and - 1.138 ms/(ng/mL), respectively. The predicted change in QTcI at the observed geometric mean of total and free concentration at the highest therapeutic erdafitinib dose (9 mg daily) was < 10 ms at the upper bound of the two-sided 90% confidence interval.

CONCLUSIONS

ECG data and the concentration-QTc relationships demonstrate that erdafitinib does not prolong QTc interval and has no effects on cardiac repolarization or other ECG parameters. Clinical trial registration numbers NCT01703481, EudraCT: 2012-000697-34.

Can laypeople identify a drug-induced QT interval prolongation? A psychophysical and eye-tracking experiment examining the ability of nonexperts to interpret an ECG

Objective

The study sought to quantify a layperson’s ability to detect drug-induced QT interval prolongation on an electrocardiogram (ECG) and determine whether the presentation of the trace affects such detection.

Materials and Methods

Thirty layperson participants took part in a psychophysical and eye-tracking experiment. Following training, participants completed 21 experimental trials, in which each trial consisted of 2 ECGs (a baseline and a comparison stimulus, both with a heart rate of 60 beats/min). The experiment used a 1 alternative forced-choice paradigm, in which participants indicated whether or not they perceived a difference in the QT interval length between the 2 ECGs. The ECG trace was presented in 3 ways: a single complex with the signals aligned by the R wave, a single complex without alignment, and a 10-second rhythm strip. Performance was analyzed using the psychometric function to estimate the just noticeable difference threshold, along with eye-tracking metrics.

Results

The just noticeable difference 50% and 75% thresholds were 30 and 88 ms, respectively, showing that the majority of laypeople were able to detect a clinically significant QT-prolongation at a low normal heart rate. Eye movement data indicated that people were more likely to appraise the rhythm strip stimulus systematically and accurately.

Conclusions

People can quickly be trained to self-monitor, which may help with more rapid identification of drug-induced long QT syndrome and prevent the development of life-threatening complications. The rhythm strip is a better form of presentation than a single complex, as it is less likely to be misinterpreted due to artifacts in the signal.

The influence of hERG1a and hERG1b isoforms on drug safety screening in iPSC-CMs

The human Ether-à-go-go Related Gene (hERG) encodes the pore forming subunit of the channel that conducts the rapid delayed rectifier potassium current I_Kr. I_Kr drives repolarization in the heart and when I_Kr is dysfunctional, cardiac repolarization delays, the QT interval on the electrocardiogram (ECG) prolongs and the risk of developing lethal arrhythmias such as Torsade de Pointes (TdP) increases. TdP risk is incorporated in drug safety screening for cardiotoxicity where hERG is the main target since the I_Kr channels appear highly sensitive to blockage. hERG block is also included as an important read-out in the Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative which aims to combine in vitro and in silico experiments on induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to screen for cardiotoxicity. However, the hERG channel has some unique features to consider for drug safety screening, which we will discuss in this study. The hERG channel consists of different isoforms, hERG1a and hERG1b, which individually influence the kinetics of the channel and the drug response in the human heart and in iPSC-CMs. hERG1b is often underappreciated in iPSC-CM studies, drug screening assays and in silico models, and the fact that its contribution might substantially differ between iPSC-CM and healthy but also diseased human heart, adds to this problem. In this study we show that the activation kinetics in iPSC-CMs resemble hERG1b kinetics using Cs⁺ as a charge carrier. Not including hERG1b in drug safety testing might underestimate the actual role of hERG1b in repolarization and drug response, and might lead to inappropriate conclusions. We stress to focus more on including hERG1b in drug safety testing concerning I_Kr.

Number of ECG Replicates and QT Correction Formula Influences the Estimated QT Prolonging Effect of a Drug

INTRODUCTION

The present analysis addressed the effect of the number of ECG replicates extracted from a continuous ECG on estimated QT interval prolongation for different QT correction formulas.

METHODS

For 100 healthy volunteers, who received a compound prolonging the QT interval, 18 ECG replicates within a 3-minute window were extracted from 12-lead Holter ECGs. Ten QT correction formulas were deployed, and the QTc interval was controlled for baseline and placebo and averaged per dose level.

RESULTS

The mean prolongation difference was >4 ms for single and >2 ms for triplicate ECG measurements compared with the 18 ECG replicate mean values. The difference was <0.5 ms after 14 replicates. By contrast, concentration-effect analysis was independent of replicate count and also of the QT correction formula.

CONCLUSION

The number of ECG replicates impacted the estimated QT interval prolongation for all deployed QT correction formulas. However, concentration-effect analysis was independent of both the replicate number and correction formula.

Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity

Blockade of the human ether-à-go-go-related gene (hERG) channel by small molecules induces the prolongation of the QT interval which leads to fatal cardiotoxicity and accounts for the withdrawal or severe restrictions on the use of many approved drugs. In this study, we develop a deep learning approach, termed deephERG, for prediction of hERG blockers of small molecules in drug discovery and postmarketing surveillance. In total, we assemble 7,889 compounds with well-defined experimental data on the hERG and with diverse chemical structures. We find that deephERG models built by a multitask deep neural network (DNN) algorithm outperform those built by single-task DNN, naı̈ve Bayes (NB), support vector machine (SVM), random forest (RF), and graph convolutional neural network (GCNN). Specifically, the area under the receiver operating characteristic curve (AUC) value for the best model of deephERG is 0.967 on the validation set. Furthermore, based on 1,824 U.S. Food and Drug Administration (FDA) approved drugs, 29.6% drugs are computationally identified to have potential hERG inhibitory activities by deephERG, highlighting the importance of hERG risk assessment in early drug discovery. Finally, we showcase several novel predicted hERG blockers on approved antineoplastic agents, which are validated by clinical case reports, experimental evidence, and the literature. In summary, this study presents a powerful deep learning-based tool for risk assessment of hERG-mediated cardiotoxicities in drug discovery and postmarketing surveillance.

Evaluating cardiac risk: exposure response analysis in early clinical drug development

The assessment of a drug's cardiac liability has undergone considerable metamorphosis by regulators since International Council for Harmonization of Technical Requirement for Pharmaceuticals for Human Use E14 guideline was introduced in 2005. Drug developers now have a choice in how proarrhythmia risk can be evaluated; the options include a dedicated thorough QT (TQT) study or exposure response (ER) modeling of intensive electrocardiogram (ECG) captured in early clinical development. The alternative approach of ER modeling was incorporated into a guidance document in 2015 as a primary analysis tool which could be utilized in early phase dose escalation studies as an option to perform a dedicated TQT trial. This review will describe the current state of ER modeling of intensive ECG data collected during early clinical drug development; the requirements with regard to the use of a positive control; and address the challenges and opportunities of this alternative approach to assessing QT liability.

Corrected QT interval prolongation during anesthetic induction for laryngeal mask airway insertion with or without cisatracurium

Objective

This study was performed to observe the occurrence of corrected QT (QTc) interval prolongation during anesthetic induction for laryngeal mask airway insertion and the effects of cisatracurium administration on the QTc interval.

Methods

Eighty-eight patients were assigned to two groups: the cisatracurium administration group (n = 45) and non-cisatracurium administration group (n = 43). The QTc interval was continuously recorded by a 12-lead Holter electrocardiogram beginning in the hospital ward and continuing until after anesthetic induction.

Results

In the cisatracurium administration group, the QTc interval significantly increased from 417.9 ± 27.9 to 451.6 ± 32.5 ms after arrival in the operating room and significantly decreased to 432.4 ± 32.5 ms after a 15-minute rest; it significantly increased to 459.7 ± 23.8 ms again after propofol and fentanyl injection. However, the QTc interval decreased after cisatracurium injection. In the non-cisatracurium administration group, the QTc interval initially showed changes similar to those in the cisatracurium group until fentanyl and propofol were injected.

Conclusions

The QTc interval was significantly prolonged on arrival in the operating room and after propofol and fentanyl injection. The QTc interval did not significantly change by laryngeal mask airway insertion regardless of the administration of cisatracurium.

Model-Informed Drug Development for Ixazomib, an Oral Proteasome Inhibitor

Model‐informed drug development (MIDD) was central to the development of the oral proteasome inhibitor ixazomib, facilitating internal decisions (switch from body surface area (BSA)‐based to fixed dosing, inclusive phase III trials, portfolio prioritization of ixazomib‐based combinations, phase III dose for maintenance treatment), regulatory review (model‐informed QT analysis, benefit–risk of 4 mg dose), and product labeling (absolute bioavailability and intrinsic/extrinsic factors). This review discusses the impact of MIDD in enabling patient‐centric therapeutic optimization during the development of ixazomib.

Relation of Chronic Obstructive Pulmonary Disease to Cardiovascular Disease in the General Population

Chronic obstructive pulmonary disease (COPD) is a major health problem that contributes to substantial morbidity and mortality globally. This study investigated the relation between COPD and the risk of cardiovascular disease in the general population. We evaluated the cardiovascular effect of COPD using Korean National Health Insurance Service data from 2002 to 2013. We compared selected cardiovascular disease risk factors depending on pulmonary function using the Korean Health and Nutritional Examination Survey (KNHANES, n = 24,429) data. COPD was diagnosed in 11,771 patients (2.4%) in the National Health Insurance Service cohort. During the follow-up period (45.5 ± 14.9 months), subjects with COPD had lower cumulative survival rate for all-cause mortality, cardiovascular mortality, and sudden cardiac death (SCD, all p values <0.001). COPD was associated with an increased risk of all-cause mortality even after adjustment for potential confounding variables (hazard ratio [HR] 1.43, 95% confidence interval [CI] 1.33 to 1.55, p <0.001). However, COPD did not significantly increase the risk of cardiovascular mortality (HR 1.02, 95% CI 0.84 to 1.22, p = 0.876) and SCD (HR 1.07, 95% CI 0.79 to 1.44, p = 0.664) when adjusted for potential confounding variables. Analysis of the KNHANES cohort showed that systolic blood pressure, current smoking status, and Framingham risk score increased progressively with a decrease in pulmonary function (all p <0.001). In conclusion, COPD was associated with all-cause mortality, but not with cardiovascular mortality and SCD, whereas poor pulmonary function was associated with a heightened cardiovascular risk.

Influence of Meals and Glycemic Changes on QT Interval Dynamics

Thorough QT/QTc studies have become an integral part of early drug development programs, with major clinical and regulatory implications. This analysis expands on existing pharmacodynamic models of QT interval analysis by incorporating the influence of glycemic changes on the QT interval in a semimechanistic manner. A total of 21 healthy subjects enrolled in an open-label phase 1 pilot study and provided continuous electrocardiogram monitoring and plasma glucose and insulin concentrations associated with a 24-hour baseline assessment. The data revealed a transient decrease in QTc, with peak suppression occurring approximately 3 hours after the meal. A semimechanistic modeling approach was applied to evaluate temporal delays between meals and subsequent changes that might influence QT measurements. The food effect was incorporated into a model of heart rate dynamics, and additional delayed effects of the meal on QT were incorporated using a glucose-dependent hypothetical transit compartment. The final model helps to provide a foundation for the future design and analysis of QT studies that may be confounded by meals. This study has significant implications for QT study assessment following a meal or when a cohort is receiving a medication that influences postprandial glucose concentrations.

Safety, tolerability, and efficacy of repeated doses of dihydroartemisinin-piperaquine for prevention and treatment of malaria: a systematic review and meta-analysis

BACKGROUND

Intermittent preventive treatment (IPT) for malaria is used in infants, children, adults, and pregnant women. Dihydroartemisinin-piperaquine (DP) is an effective, well tolerated artemisinin-based combination therapy. The long half-life of piperaquine makes it attractive for IPT. We conducted a systematic review and meta-analysis to establish the efficacy and safety of repeated treatment with DP.

METHODS

Following PRISMA guidelines, we searched multiple databases on Sept 1, 2016, with the terms: "human" AND "dihydroartemisinin-piperaquine" OR "DHA-PPQ". Studies were eligible if they were randomised controlled trials (RCTs) or prospective cohort studies involving repeat exposures to standard 3-day courses of DP for either seasonal malaria chemoprevention, mass drug administration, or treatment of clinical malaria, conducted at any time and in any geographic location. Random-effects meta-analysis was used to generate pooled incidence rate ratios and relative risks, or risk differences.

FINDINGS

11 studies were included: two repeat treatment studies (one in children younger than 5 years and one in pregnant women), and nine IPT trials (five in children younger than 5 years, one in schoolchildren, one in adults, two in pregnant women). Comparator interventions included placebo, artemether-lumefantrine, sulfadoxine-pyrimethamine (SP), SP+amodiaquine, SP+piperaquine, SP+chloroquine, and co-trimoxazole. Of 14 628 participants, 3935 received multiple DP courses (2-18). Monthly IPT-DP was associated with an 84% reduction in the incidence of malaria parasitaemia measured by microscopy compared with placebo. Monthly IPT-DP was associated with fewer serious adverse events than placebo, daily co-trimoxazole, or monthly SP. Among 56 IPT-DP recipients (26 children, 30 pregnant women) with cardiac parameters, all QTc intervals were within normal limits, with no significant increase in QTc prolongation with increasing courses of DP.

INTERPRETATION

Monthly DP appears well tolerated and effective for IPT. Additional data are needed in pregnancy and to further explore the cardiac safety with monthly dosing.

FUNDING

Bill & Melinda Gates Foundation and NIH.

Cardiovascular toxicities of systemic treatments of prostate cancer

Prostate cancer is the most common cancer in men, with an incidence that is expected to increase in the coming years. Prostate cancer is usually diagnosed in men >65 years of age, thus the concurrent presence of cardiovascular diseases might influence the treatment, owing to the increased risk of cardiovascular mortality. The introduction of new drugs, such as abiraterone and enzalutamide for the management of metastatic disease has created further interest in treatment-related cardiovascular toxicities, although limited data from trials specifically designed to identify cardiovascular toxicities of these agents are currently available. The only available data are derived from published phase II-III study reports, expanded access or compassionate use programmes and meta-analyses of the effects of systemic therapies that are already approved for use in clinical practice or are in the early phases of development. These data are conflicting, although they seem to suggest that certain drugs are associated with an increased risk of cardiovascular adverse events. Clinical trial methodology could be improved by the enrolment of greater numbers of patients >65 years of age, and the use of comprehensive cardiological evaluations. Moreover, closer collaboration between oncologists and cardiologists is essential for the identification and/or management of cardiovascular adverse events in patients with prostate cancer.

Effect of Cabotegravir on Cardiac Repolarization in Healthy Subjects

A randomized, partial‐blind, repeat‐dose, 3‐period crossover study (NCT02027454) assessed the effect of cabotegravir on QT interval in healthy subjects. To achieve a supratherapeutic dose, each subject received cabotegravir 150 mg (30 mg × 5 tablets) every 12 hours for a total of 3 doses over 2 days, matching placebo (every 12 hours) over 2 days, or a single open‐label 400‐mg dose of the positive control moxifloxacin, with a 21‐day washout between treatments. Blood samples for pharmacokinetic analyses were collected up to 24 hours after the third dose on day 2. QT interval data were obtained by continuous Holter monitoring for approximately 24 hours at baseline (day ‐1) and from 2 hours before to 24 hours after the third dose on day 2. Plasma cabotegravir exposure was approximately 3‐fold above clinically relevant doses. After 3 doses of 150 mg of cabotegravir administered every 12 hours, all upper limits of 2‐sided 90% confidence intervals for ΔΔQTcF (difference in time‐matched change from baseline for QTcF between cabotegravir and placebo) were <10 milliseconds. There was no relationship between cabotegravir plasma concentrations and ΔΔQTcF. No subject receiving cabotegravir had a QTcF value > 450 milliseconds. There were no serious or grade 3 or 4 adverse events or clinically significant changes in laboratory values, vital signs, or electrocardiogram results. These data demonstrate that cabotegravir at a supratherapeutic dose had no effect on cardiac repolarization.

Which QT Correction Formulae to Use for QT Monitoring?

Background

Drug safety precautions recommend monitoring of the corrected QT interval. To determine which QT correction formula to use in an automated QT‐monitoring algorithm in our electronic medical record, we studied rate correction performance of different QT correction formulae and their impact on risk assessment for mortality.

Methods and Results

All electrocardiograms (ECGs) in patients >18 years with sinus rhythm, normal QRS duration and rate <90 beats per minute (bpm) in the University Hospitals of Leuven (Leuven, Belgium) during a 2‐month period were included. QT correction was performed with Bazett, Fridericia, Framingham, Hodges, and Rautaharju formulae. In total, 6609 patients were included (age, 59.8±16.2 years; 53.6% male and heart rate 68.8±10.6 bpm). Optimal rate correction was observed using Fridericia and Framingham; Bazett performed worst. A healthy subset showed 99% upper limits of normal for Bazett above current clinical standards: men 472 ms (95% CI, 464–478 ms) and women 482 ms (95% CI 474–490 ms). Multivariate Cox regression, including age, heart rate, and prolonged QTc, identified Framingham (hazard ratio [HR], 7.31; 95% CI, 4.10–13.05) and Fridericia (HR, 5.95; 95% CI, 3.34–10.60) as significantly better predictors of 30‐day all‐cause mortality than Bazett (HR, 4.49; 95% CI, 2.31–8.74). In a point‐prevalence study with haloperidol, the number of patients classified to be at risk for possibly harmful QT prolongation could be reduced by 50% using optimal QT rate correction.

Conclusions

Fridericia and Framingham correction formulae showed the best rate correction and significantly improved prediction of 30‐day and 1‐year mortality. With current clinical standards, Bazett overestimated the number of patients with potential dangerous QTc prolongation, which could lead to unnecessary safety measurements as withholding the patient of first‐choice medication.

Update on Cardiovascular Safety of Tyrosine Kinase Inhibitors: With a Special Focus on QT Interval, Left Ventricular Dysfunction and Overall Risk/Benefit

We previously reviewed the cardiovascular safety of 16 tyrosine kinase inhibitors (TKIs), approved for use in oncology as of 30 September 2012. Since then, the indications for some of them have been widened and an additional nine TKIs have also been approved as of 30 April 2015. Eight of these nine are indicated for use in oncology and one (nintedanib) for idiopathic pulmonary fibrosis. This report is an update on the cardiovascular safety of those 16 TKIs, including the post-marketing data concerning their pro-arrhythmic effects, and reviews the cardiovascular safety of the nine new TKIs approved since (afatinib, cabozantinib, ceritinib, dabrafenib, ibrutinib, lenvatinib, nintedanib, ponatinib, and trametinib). As before, we focus on specific aspects of cardiovascular safety, namely their potential to induce QT interval prolongation, left ventricular (LV) dysfunction and hypertension but now also summarise the risks of arterial thromboembolic events (ATEs) associated with these agents. Of the newer TKIs, cabozantinib and ceritinib have been shown to induce a mild to moderate degree of QTc interval prolongation while cardiac dysfunction has been reported with the use of afatinib, dabrafenib, lenvatinib, ponatinib and trametinib. The label for axitinib was revised to include a new association with cardiac dysfunction. Hypertension is associated with cabozantinib, lenvatinib, nintedanib, ponatinib and trametinib. Ponatinib, within 10 months of its approval in December 2012, required voluntary (temporary) suspension of its marketing until significant safety revisions (restricted indication, additional warnings and precautions about the risk of arterial occlusion and thromboembolic events and amended dose) were made to its label. Compared with the previous 16 TKIs, more of the recently introduced TKIs are associated with the risk of LV dysfunction, and fewer with QT prolongation. Available data on morbidity and mortality associated with TKIs, together with post-marketing experience with lapatinib and ponatinib, emphasise the need for effective pharmacovigilance and ongoing re-assessment of their risk/benefit after approval of these novel agents. If not adequately managed, these cardiovascular effects significantly decrease the quality of life and increase the morbidity and mortality in a population already at high risk. Evidence accumulated over the last decade suggests that their clinical benefit, although worthwhile, is modest and extends only to progression-free survival and complete response without any effect on overall survival. During uncontrolled use in routine clinical practice, their risk/benefit is likely to be inferior to that perceived from highly controlled clinical trials.