Collation, assessment and analysis of literature in vitro data on hERG receptor blocking potency for subsequent modeling of drugs' cardiotoxic properties

Quantifying the efficacy of voltage protocols in characterising ion channel kinetics: A novel information-theoretic approach

Voltage-clamp experiments are commonly utilised to characterise cellular ion channel kinetics. In these experiments, cells are stimulated using a known time-varying voltage, referred to as the voltage protocol, and the resulting cellular response, typically in the form of current, is measured. Parameters of models that describe ion channel kinetics are then estimated by solving an inverse problem which aims to minimise the discrepancy between the predicted response of the model and the actual measured cell response. In this paper, a novel framework to evaluate the information content of voltage-clamp protocols in relation to ion channel model parameters is presented. Additional quantitative information metrics that allow for comparisons among various voltage protocols are proposed. These metrics offer a foundation for future optimal design frameworks to devise novel, information-rich protocols. The efficacy of the proposed framework is evidenced through the analysis of seven voltage protocols from the literature. By comparing known numerical results for inverse problems using these protocols with the information-theoretic metrics, the proposed approach is validated. The essential steps of the framework are: (i) generate random samples of the parameters from chosen prior distributions; (ii) run the model to generate model output (current) for all samples; (iii) construct reduced-dimensional representations of the time-varying current output using proper orthogonal decomposition (POD); (iv) estimate information-theoretic metrics such as mutual information, entropy equivalent variance, and conditional mutual information using non-parametric methods; (v) interpret the metrics; for example, a higher mutual information between a parameter and the current output suggests the protocol yields greater information about that parameter, resulting in improved identifiability; and (vi) integrate the information-theoretic metrics into a single quantitative criterion, encapsulating the protocol's efficacy in estimating model parameters.

Synthesis and Chemoinformatic Analysis of Fluorinated Piperidines as 3D Fragments for Fragment-Based Drug Discovery

The concise synthesis of a small library of fluorinated piperidines from readily available dihydropyridinone derivatives has been described. The effect of the fluorination on different positions has then been evaluated by chemoinformatic tools. In particular, the compounds' pKa's have been calculated, revealing that the fluorine atoms notably lowered their basicity, which is correlated to the affinity for hERG channels resulting in cardiac toxicity. The "lead-likeness" and three-dimensionality have also been evaluated to assess their ability as useful fragments for drug design. A random screening on a panel of representative proteolytic enzymes was then carried out and revealed that one scaffold is recognized by the catalytic pocket of 3CLPro (main protease of SARS-CoV-2 coronavirus).

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.

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank

Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of chemical and biological data to predict cardiotoxicity, using the recently released DICTrank data set from the United States FDA. We found that such data, including protein targets, especially those related to ion channels (e.g., hERG), physicochemical properties (e.g., electrotopological state), and peak concentration in plasma offer strong predictive ability for DICT. Compounds annotated with mechanisms of action such as cyclooxygenase inhibition could distinguish between most-concern and no-concern DICT. Cell Painting features for ER stress discerned most-concern cardiotoxic from nontoxic compounds. Models based on physicochemical properties provided substantial predictive accuracy (AUCPR = 0.93). With the availability of omics data in the future, using biological data promises enhanced predictability and deeper mechanistic insights, paving the way for safer drug development. All models from this study are available at https://broad.io/DICTrank_Predictor.

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

BACKGROUND AND OBJECTIVE

In silico methods are gaining attention for predicting drug-induced Torsade de Pointes (TdP) in different stages of drug development. However, many computational models tended not to account for inter-individual response variability due to demographic covariates, such as sex, or physiologic covariates, such as renal function, which may be crucial when predicting TdP. This study aims to compare the effects of drugs in male and female populations with normal and impaired renal function using in silico methods.

METHODS

Pharmacokinetic models considering sex and renal function as covariates were implemented from data published in pharmacokinetic studies. Drug effects were simulated using an electrophysiologically calibrated population of cellular models of 300 males and 300 females. The population of models was built by modifying the endocardial action potential model published by O'Hara et al. (2011) according to the experimentally measured gene expression levels of 12 ion channels.

RESULTS

Fifteen pharmacokinetic models for CiPA drugs were implemented and validated in this study. Eight pharmacokinetic models included the effect of renal function and four the effect of sex. The mean difference in action potential duration (APD) between male and female populations was 24.9 ms (p<0.05). Our simulations indicated that women with impaired renal function were particularly susceptible to drug-induced arrhythmias, whereas healthy men were less prone to TdP. Differences between patient groups were more pronounced for high TdP-risk drugs. The proposed in silico tool also revealed that individuals with impaired renal function, electrophysiologically simulated with hyperkalemia (extracellular potassium concentration [K+]o = 7 mM) exhibited less pronounced APD prolongation than individuals with normal potassium levels. The pharmacokinetic/electrophysiological framework was used to determine the maximum safe dose of dofetilide in different patient groups. As a proof of concept, 3D simulations were also run for dofetilide obtaining QT prolongation in accordance with previously reported clinical values.

CONCLUSIONS

This study presents a novel methodology that combines pharmacokinetic and electrophysiological models to incorporate the effects of sex and renal function into in silico drug simulations and highlights their impact on TdP-risk assessment. Furthermore, it may also help inform maximum dose regimens that ensure TdP-related safety in a specific sub-population of patients.

Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm

The hERG potassium channel serves as an annexed target for drug discovery because the associated off-target inhibitory activity may cause serious cardiotoxicity. Quantitative structure-activity relationship (QSAR) models were developed to predict inhibitory activities against the hERG potassium channel, utilizing the three-dimensional (3D) distribution of quantum mechanical electrostatic potential (ESP) as the molecular descriptor. To prepare the optimal atomic coordinates of dataset molecules, pairwise 3D structural alignments were carried out in order for the quantum mechanical cross correlation between the template and other molecules to be maximized. This alignment method stands out from the common atom-by-atom matching technique, as it can handle structurally diverse molecules as effectively as chemical derivatives that share an identical scaffold. The alignment problem prevalent in 3D-QSAR methods was ameliorated substantially by dividing the dataset molecules into seven subsets, each of which contained molecules with similar molecular weights. Using an artificial neural network algorithm to find the functional relationship between the quantum mechanical ESP descriptors and the experimental hERG inhibitory activities, highly predictive 3D-QSAR models were derived for all seven molecular subsets to the extent that the squared correlation coefficients exceeded 0.79. Given their simplicity in model development and strong predictability, the 3D-QSAR models developed in this study are expected to function as an effective virtual screening tool for assessing the potential cardiotoxicity of drug candidate molecules.

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank

Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of various chemical and biological data to predict cardiotoxicity, using the recently released Drug-Induced Cardiotoxicity Rank (DICTrank) dataset from the United States FDA. We analyzed a diverse set of data sources, including physicochemical properties, annotated mechanisms of action (MOA), Cell Painting, Gene Expression, and more, to identify indications of cardiotoxicity. We found that such data, including protein targets, especially those related to ion channels (such as hERG), physicochemical properties (such as electrotopological state) as well as peak concentration in plasma offer strong predictive ability as well as valuable insights into DICT. We also found compounds annotated with particular mechanisms of action, such as cyclooxygenase inhibition, could distinguish between most-concern and no-concern DICT compounds. Cell Painting features related to ER stress discern the most-concern cardiotoxic compounds from non-toxic compounds. While models based on physicochemical properties currently provide substantial predictive accuracy (AUCPR = 0.93), this study also underscores the potential benefits of incorporating more comprehensive biological data in future DICT predictive models. With the availability of - omics data in the future, using biological data promises enhanced predictability and delivers deeper mechanistic insights, paving the way for safer therapeutic drug development. All models and data used in this study are publicly released at https://broad.io/DICTrank_Predictor.

Association of Hydroxychloroquine Dose With Adverse Cardiac Events in Patients With Systemic Lupus Erythematosus

OBJECTIVE

To determine whether hydroxychloroquine (HCQ) dose is associated with adverse cardiac outcomes in patients with systemic lupus erythematosus (SLE).

METHODS

Patients with SLE taking HCQ and with ≥1 echocardiogram followed at a tertiary care center in the Bronx, New York between 2005 and 2021 were included. The HCQ weight-based dose at the HCQ start date was the main exposure of interest. The outcome was incident all-cause heart failure with reduced ejection fraction (HFrEF), life-threatening arrhythmia, or cardiac death. We used Fine-Gray regression models with death as a competing event to study the association of HCQ dose with the outcome. Due to a significant interaction between smoking and HCQ exposure, models were stratified by smoking status. Propensity score analysis was performed as a secondary analysis.

RESULTS

Of 294 patients, 37 (13%) developed the outcome over a median follow-up time of 7.9 years (interquartile range [IQR] 4.2-12.3 years). In nonsmokers (n = 226), multivariable analysis adjusted for age, body mass index, hypertension, chronic kidney disease, diabetes mellitus, and thromboembolism showed that higher HCQ weight-based doses were not associated with an increased risk of the outcome (subdistribution hazard ratio [HR] 0.62 [IQR 0.41-0.92], P = 0.02). Similarly, higher baseline HCQ doses were not associated with a higher risk of the outcome among smokers (n = 68) (subdistribution HR 0.85 [IQR 0.53-1.34] per mg/kg, P = 0.48). Propensity score analysis showed comparable results.

CONCLUSION

Higher HCQ doses were not associated with an increased risk of HFrEF, life-threatening arrhythmia, or cardiac death among patients with SLE and may decrease the risk among nonsmokers.

Intracellular Binding of Terfenadine Competes with Its Access to Pancreatic ß-cell ATP-Sensitive K+ Channels and Human ether-à-go-go-Related Gene Channels

Most blockers of both hERG (human ether-à-go-go-related gene) channels and pancreatic ß-cell ATP-sensitive K⁺ (K_ATP) channels access their binding sites from the cytoplasmic side of the plasma membrane. It is unknown whether binding to intracellular components competes with binding of these substances to K⁺ channels. The whole-cell configuration of the patch-clamp technique, a laser-scanning confocal microscope, and fluorescence correlation spectroscopy (FCS) were used to study hERG channels expressed in HEK (human embryonic kidney) 293 cells and K_ATP channels from the clonal insulinoma cell line RINm5F. When applied via the pipette solution in the whole-cell configuration, terfenadine blocked both hERG and K_ATP currents with much lower potency than after application via the bath solution, which was not due to P-glycoprotein-mediated efflux of terfenadine. Such a difference was not observed with dofetilide and tolbutamide. 37-68% of hERG/EGFP (enhanced green-fluorescent protein) fusion proteins expressed in HEK 293 cells were slowly diffusible as determined by laser-scanning microscopy in the whole-cell configuration and by FCS in intact cells. Bath application of a green-fluorescent sulphonylurea derivative (Bodipy-glibenclamide) induced a diffuse fluorescence in the cytosol of RINm5F cells under whole-cell patch-clamp conditions. These observations demonstrate the presence of intracellular binding sites for hERG and K_ATP channel blockers not dialyzable by the patch-pipette solution. Intracellular binding of terfenadine was not influenced by a mutated hERG (Y652A) channel. In conclusion, substances with high lipophilicity are not freely diffusible inside the cell but steep concentration gradients might exist within the cell and in the sub-membrane space.

Toward Quantitative Models in Safety Assessment: A Case Study to Show Impact of Dose-Response Inference on hERG Inhibition Models

Due to challenges with historical data and the diversity of assay formats, in silico models for safety-related endpoints are often based on discretized data instead of the data on a natural continuous scale. Models for discretized endpoints have limitations in usage and interpretation that can impact compound design. Here, we present a consistent data inference approach, exemplified on two data sets of Ether-à-go-go-Related Gene (hERG) K+ inhibition data, for dose-response and screening experiments that are generally applicable for in vitro assays. hERG inhibition has been associated with severe cardiac effects and is one of the more prominent safety targets assessed in drug development, using a wide array of in vitro and in silico screening methods. In this study, the IC₅₀ for hERG inhibition is estimated from diverse historical proprietary data. The IC₅₀ derived from a two-point proprietary screening data set demonstrated high correlation (R = 0.98, MAE = 0.08) with IC_50s derived from six-point dose-response curves. Similar IC₅₀ estimation accuracy was obtained on a public thallium flux assay data set (R = 0.90, MAE = 0.2). The IC₅₀ data were used to develop a robust quantitative model. The model's MAE (0.47) and R² (0.46) were on par with literature statistics and approached assay reproducibility. Using a continuous model has high value for pharmaceutical projects, as it enables rank ordering of compounds and evaluation of compounds against project-specific inhibition thresholds. This data inference approach can be widely applicable to assays with quantitative readouts and has the potential to impact experimental design and improve model performance, interpretation, and acceptance across many standard safety endpoints.

Validation of risk-stratification method for the chronic atrioventricular block cynomolgus monkey model and its mechanistic interpretation using 6 drugs with pharmacologically-distinct profile

Validation of risk-stratification method for the chronic atrioventricular block cynomolgus monkey model and its mechanistic interpretation were performed using 6 pharmacologically-distinct drugs. The following drugs were orally administered in conscious state, astemizole: 1, 5 and 10 mg/kg (n = 6); haloperidol: 1, 10 and 30 mg/kg (n = 5); amiodarone: 30 mg/kg (n = 4); famotidine: 10 mg/kg (n = 4); levofloxacin: 100 mg/kg (n = 4); and tolterodine: 0.2, 1 and 4.5 mg/kg (n = 4). Astemizole of 5 and 10 mg/kg significantly prolonged ΔΔQTcF, whereas no significant change was observed by the others. Torsade de pointes (TdP) was induced by astemizole of 5 and 10 mg/kg in 3/6 and 6/6, and by haloperidol of 10 and 30 mg/kg in 1/5 and 1/5, respectively, which was not observed in the others. Torsadogenic risk of the drugs was quantified using the criteria for the monkey model specified in our previous study. Namely, high-risk drugs induced TdP at ≤ 3times of their maximum clinical daily dose. Intermediate-risk drugs did not induce TdP at this dose range, but induced it at higher doses. Low/no-risk drugs never induced TdP at any dose tested. The magnitude of risk was intermediate for astemizole and haloperidol, and low/no risk for the others. The pre-specified, risk-stratification method for the monkey model may solve the issue existing between non-clinical models and patients with labile repolarization, which can reinforce the regulatory decision-making and labelling at time of marketing application of non-double-negative drug candidate (hERG assay positive and/or in vivo QT study positive).

Safety pharmacology during the COVID pandemic

This editorial summarizes the content of the current themed issue of J Pharmacol Toxicol Methods derived from the 2020 Annual Safety Pharmacology Society (SPS) meeting that was held virtually September 14–17, 2020 due to the ongoing COVID-19 global pandemic. A selection of articles arising from the virtual meeting is summarized. Like previous years they continue to reflect current areas of innovation in SP including new methodologies to predict human safety, best practices for IKr current measurement, and best practice considerations for the conduct of in vivo nonclinical QT studies. The meeting included scientific content from 94 abstracts (reproduced in the current volume of J Pharmacol Toxicol Methods). This continued innovation reflects a rubric in SP that identifies problems, seeks solutions and, importantly, validates the solutions.

Selection of safe artemisinin derivatives using a machine learning-based cardiotoxicity platform and in vitro and in vivo validation

The majority of drug candidates fails the approval phase due to unwanted toxicities and side effects. Establishment of an effective toxicity prediction platform is of utmost importance, to increase the efficiency of the drug discovery process. For this purpose, we developed a toxicity prediction platform with machine-learning strategies. Cardiotoxicity prediction was performed by establishing a model with five parameters (arrhythmia, cardiac failure, heart block, hypertension, myocardial infarction) and additional toxicity predictions such as hepatotoxicity, reproductive toxicity, mutagenicity, and tumorigenicity are performed by using Data Warrior and Pro-Tox-II software. As a case study, we selected artemisinin derivatives to evaluate the platform and to provide a list of safe artemisinin derivatives. Artemisinin from Artemisia annua was described first as an anti-malarial compound and later its anticancer properties were discovered. Here, random forest feature selection algorithm was used for the establishment of cardiotoxicity models. High AUC scores above 0.830 were achieved for all five cardiotoxicity indications. Using a chemical library of 374 artemisinin derivatives as a case study, 7 compounds (deoxydihydro-artemisinin, 3-hydroxy-deoxy-dihydroartemisinin, 3-desoxy-dihydroartemisinin, dihydroartemisinin-furano acetate-d3, deoxyartemisinin, artemisinin G, artemisinin B) passed the toxicity filtering process for hepatotoxicity, mutagenicity, tumorigenicity, and reproductive toxicity in addition to cardiotoxicity. Experimental validation with the cardiomyocyte cell line AC16 supported the findings from the in silico cardiotoxicity model predictions. Transcriptomic profiling of AC16 cells upon artemisinin B treatment revealed a similar gene expression profile as that of the control compound, dexrazoxane. In vivo experiments with a Zebrafish model further substantiated the in silico and in vitro data, as only slight cardiotoxicity in picomolar range was observed. In conclusion, our machine-learning approach combined with in vitro and in vivo experimentation represents a suitable method to predict cardiotoxicity of drug candidates.

4-Aminoquinoline compounds from the Spanish flu to COVID-19

In 1918, quinine was used as one of the unscientifically based treatments against the H1N1 virus during the Spanish flu pandemic. Originally, quinine was extracted from the bark of Chinchona trees by South American natives of the Amazon forest, and it has been used to treat fever since the seventeenth century. The recent COVID-19 pandemic caused by Sars-Cov-2 infection has forced researchers to search for ways to prevent and treat this disease. Based on the antiviral potential of two 4-aminoquinoline compounds derived from quinine, known as chloroquine (CQ) and hydroxychloroquine (HCQ), clinical investigations for treating COVID-19 are being conducted worldwide. However, there are some discrepancies among the clinical trial outcomes.Thus, even after one hundred years of quinine use during the Spanish flu pandemic, the antiviral properties promoted by 4-aminoquinoline compounds remain unclear. The underlying molecular mechanisms by which CQ and HCQ inhibit viral replication open up the possibility of developing novel analogs of these drugs to combat COVID-19 and other viruses.

COVID-19, hydroxychloroquine and sudden cardiac death: implications for clinical practice in patients with rheumatic diseases

Sudden cardiac death is commonly seen due to arrhythmias, which is a common cardiac manifestation seen in COVID-19 patients, especially those with underlying cardiovascular disease (CVD). Administration of hydroxychloroquine (HCQ) as a potential treatment option during SARS-CoV-2, initially gained popularity, but later, its safe usage became questionable due to its cardiovascular safety, largely stemming from instances of cardiac arrhythmias in COVID-19. Moreover, in the setting of rheumatic diseases, in which patients are usually on HCQ for their primary disease, there is a need to scale the merits and demerits of HCQ usage for the treatment of COVID-19. In this narrative review, we aim to address the association between usage of HCQ and sudden cardiac death in COVID-19 patients. MEDLINE, EMBASE, ClinicalTrials.gov and SCOPUS databases were used to review articles in English ranging from case reports, case series, letter to editors, systematic reviews, narrative reviews, observational studies and randomized control trials. HCQ is a potential cause of sudden cardiac death in COVID-19 patients. As opposed to the reduction in CVD with HCQ in treatment of systemic lupus erythematous, rheumatoid arthritis, and other rheumatic diseases, safe usage of HCQ in COVID-19 patients is unclear; whereby, it is observed to result in QTc prolongation and Torsades de pointes even in patients with no underlying cardiovascular comorbidity. This is occasionally associated with sudden cardiac death or cardiac arrest; hence, its clinical efficacy needs further investigation by large-scale clinical trials.

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.

Reverse translational analysis of clinically reported, lamotrigine-induced cardiovascular adverse events using the halothane-anesthetized dogs

Lamotrigine has been used for patients with epilepsy and/or bipolar disorder, overdose of which induced the hypotension, elevation of the atrial pacing threshold, cardiac conduction delay, wide complex tachycardia, cardiac arrest and Brugada-like electrocardiographic pattern. To clarify how lamotrigine induces those cardiovascular adverse events, we simultaneously assessed its cardiohemodynamic and electrophysiological effects using the halothane-anesthetized dogs (n = 4). Lamotrigine was intravenously administered in doses of 0.1, 1 and 10 mg/kg/10 min under the monitoring of cardiovascular variables, possibly providing subtherapeutic to supratherapeutic plasma concentrations. The low or middle dose of lamotrigine did not alter any of the variables. The high dose significantly delayed the intra-atrial and intra-ventricular conductions in addition to the prolongation of ventricular effective refractory period, whereas no significant change was detected in the other variables. Lamotrigine by itself has relatively wide safety margin for cardiohemodynamics, indicating that clinically reported hypotension may not be induced through its direct action on the resistance arterioles or capacitance venules. The electrophysiological effects suggested that lamotrigine can inhibit Na⁺ channel in the in situ hearts. This finding may partly explain the onset mechanism of lamotrigine-associated cardiac adverse events in the clinical cases. In addition, elevation of J wave was induced in half of the animals, suggesting that lamotrigine may have some potential to unmask Brugada electrocardiographic genotype in susceptible patients.

In Silico Classifiers for the Assessment of Drug Proarrhythmicity

Drug-induced torsade de pointes (TdP) is a life-threatening ventricular arrhythmia responsible for the withdrawal of many drugs from the market. Although currently used TdP risk-assessment methods are effective, they are expensive and prone to produce false positives. In recent years, in silico cardiac simulations have proven to be a valuable tool for the prediction of drug effects. The objective of this work is to evaluate different biomarkers of drug-induced proarrhythmic risk and to develop an in silico risk classifier. Cellular simulations were performed using a modified version of the O'Hara et al. ventricular action potential model and existing pharmacological data (IC₅₀ and effective free therapeutic plasma concentration, EFTPC) for 109 drugs of known torsadogenic risk (51 positive). For each compound, four biomarkers were tested: T_x (drug concentration leading to a 10% prolongation of the action potential over the EFTPC), T_qNet (net charge carried by ionic currents when exposed to 10 times the EFTPC with respect to the net charge in control), T_triang (triangulation for a drug concentration of 10 times the EFTPC over triangulation in control), and T_EAD (drug concentration originating early afterdepolarizations over EFTPC). Receiver operating characteristic (ROC) curves were built for each biomarker to evaluate their individual predictive quality. At the optimal cutoff point, accuracies for T_x, T_qNet, T_triang, and T_EAD were 89.9, 91.7, 90.8, and 78.9% respectively. The resulting accuracy of the hERG IC₅₀ test (current biomarker) was 78.9%. When combining T_x, T_qNet and T_triang into a classifier based on decision trees, the prediction improves, achieving an accuracy of 94.5%. The sensitivity analysis revealed that most of the effects on the action potential are mainly due to changes in I_Kr, I_CaL, I_NaL and I_Ks. In fact, considering that drugs affect only these four currents, TdP risk classification can be as accurate as when considering effects on the seven main currents proposed by the CiPA initiative. Finally, we built a ready-to-use tool (based on more than 450 000 simulations), which can be used to quickly assess the proarrhythmic risk of a compound. In conclusion, our in silico tool can be useful for the preclinical assessment of TdP-risk and to reduce costs related with new drug development. The TdP risk-assessment tool and the software used in this work are available at https://riunet.upv.es/handle/10251/136919.

Prophylactic (hydroxy)chloroquine in COVID-19: Potential relevance for cardiac arrhythmia risk

(Hydroxy)chloroquine ((H)CQ) is being investigated as a treatment for COVID-19, but studies have so far demonstrated either no or a small benefit. However, these studies have been mostly performed in patients admitted to the hospital and hence likely already (severely) affected. Another suggested approach uses prophylactic (H)CQ treatment aimed at preventing either severe acute respiratory syndrome coronavirus 2 infection or the development of disease. A substantial number of clinical trials are planned or underway aimed at assessing the prophylactic benefit of (H)CQ. However, (H)CQ may lead to QT prolongation and potentially induce life-threatening arrhythmias. This may be of particular relevance to patients with preexisting cardiovascular disease and those taking other QT-prolonging drugs. In addition, it is known that a certain percentage of the population carries genetic variant(s) that reduces their repolarization reserve, predisposing them to (H)CQ-induced QT prolongation, and this may be more relevant to female patients who already have a longer QT interval to start with. This review provides an overview of the current evidence on (H)CQ therapy in patients with COVID-19 and discusses different strategies for prophylactic (H)CQ therapy (ie, preinfection, postexposure, and postinfection). In particular, the potential cardiac effects, including QT prolongation and arrhythmias, will be addressed. Based on these insights, recommendations will be presented as to which preventive measures should be taken when giving (H)CQ prophylactically, including electrocardiographic monitoring.

Prediction of hERG potassium channel blockage using ensemble learning methods and molecular fingerprints

The human ether-a-go-go-related gene (hERG) encodes a tetrameric potassium channel called Kv11.1. This channel can be blocked by certain drugs, which leads to long QT syndrome, causing cardiotoxicity. This is a significant problem during drug development. Using computer models to predict compound cardiotoxicity during the early stages of drug design will help to solve this problem. In this study, we used a dataset of 1865 compounds exhibiting known hERG inhibitory activities as a training set. Thirty cardiotoxicity classification models were established using three machine learning algorithms based on molecular fingerprints and molecular descriptors. Through using these models as the base classifier, a new cardiotoxicity classification model with better predictive performance was developed using ensemble learning method. The accuracy of the best base classifier, which was generated using the XGBoost method with molecular descriptors, was 84.8 %, and the area under the receiver-operating characteristic curve (AUC) was 0.876 in the five fold cross-validation. However, all of the ensemble models that we developed had higher predictive performance than the base classifiers in the five fold cross-validation. The best predictive performance was achieved by the Ensemble-Top7 model, with accuracy of 84.9 % and AUC of 0.887. We also tested the ensemble model using external validation data and achieved accuracy of 85.0 % and AUC of 0.786. Furthermore, we identified several hERG-related substructures, which provide valuable information for designing drug candidates.

hERG K+ Channels Promote Survival of Irradiated Leukemia Cells

Many tumor cells express highly elevated activities of voltage-gated K⁺ channels in the plasma membrane which are indispensable for tumor growth. To test for K⁺ channel function during DNA damage response, we subjected human chronic myeloid leukemia (CML) cells to sub-lethal doses of ionizing radiation (0-8 Gy, 6 MV photons) and determined K⁺ channel activity, K⁺ channel-dependent Ca²⁺ signaling, cell cycle progression, DNA repair, and clonogenic survival by whole-cell patch clamp recording, fura-2 Ca²⁺ imaging, Western blotting, flow cytometry, immunofluorescence microscopy, and pre-plating colony formation assay, respectively. As a result, the human erythroid CML cell line K562 and primary human CML cells functionally expressed hERG1. Irradiation stimulated in both cell types an increase in the activity of hERG1 K⁺ channels which became apparent 1-2 h post-irradiation. This increase in K⁺ channel activity was paralleled by an accumulation in S phase of cell cycle followed by a G₂/M cell cycle arrest as analyzed between 8 and 72 h post-irradiation. Attenuating the K⁺ channel function by applying the hERG1 channel inhibitor E4031 modulated Ca²⁺ signaling, impaired inhibition of the mitosis promoting subunit cdc2, overrode cell cycle arrest, and decreased clonogenic survival of the irradiated cells but did not affect repair of DNA double strand breaks suggesting a critical role of the hERG1 K⁺ channels for the Ca²⁺ signaling and the cell cycle control during DNA damage response.

COVID-19, Chloroquine Repurposing, and Cardiac Safety Concern: Chirality Might Help

The desperate need to find drugs for COVID-19 has indicated repurposing strategies as our quickest way to obtain efficacious medicines. One of the options under investigation is the old antimalarial drug, chloroquine, and its analog, hydroxychloroquine. Developed as synthetic succedanea of cinchona alkaloids, these chiral antimalarials are currently in use as the racemate. Besides the ethical concern related to accelerated large-scale clinical trials of drugs with unproven efficacy, the known potential detrimental cardiac effects of these drugs should also be considered. In principle, the safety profile might be ameliorated by using chloroquine/hydroxychloroquine single enantiomers in place of the racemate.

hERG toxicity assessment: Useful guidelines for drug design

All along the drug development process, one of the most frequent adverse side effects, leading to the failure of drugs, is the cardiac arrhythmias. Such failure is mostly related to the capacity of the drug to inhibit the human ether-à-go-go-related gene (hERG) cardiac potassium channel. The early identification of hERG inhibition properties of biological active compounds has focused most of attention over the years. In order to prevent the cardiac side effects, a great number of in silico, in vitro and in vivo assays have been performed. The main goal of these studies is to understand the reasons of these effects, and then to give information or instructions to scientists involved in drug development to avoid the cardiac side effects. To evaluate anticipated cardiovascular effects, early evaluation of hERG toxicity has been strongly recommended for instance by the regulatory agencies such as U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA). Thus, following an initial screening of a collection of compounds to find hits, a great number of pharmacomodulation studies on the novel identified chemical series need to be performed including activity evaluation towards hERG. We provide in this concise review clear guidelines, based on described examples, illustrating successful optimization process to avoid hERG interactions as cases studies and to spur scientists to develop safe drugs.

Computational determination of hERG-related cardiotoxicity of drug candidates

Background

Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates.

Result

In this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when ten-fold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models.

Conclusion

The neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred.

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.

Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities

The hERG (human ether-a-go-go-related gene) encoded potassium ion (K⁺) channel plays a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major cause of potentially lethal ventricular tachycardia termed Torsades de Pointes (TdPs). Therefore, we presented a pharmacoinformatics strategy using combined ligand and structure based models for the prediction of hERG inhibition potential (IC₅₀) of new chemical entities (NCEs) during early stages of drug design and development. Integrated GRid-INdependent Descriptor (GRIND) models, and lipophilic efficiency (LipE), ligand efficiency (LE) guided template selection for the structure based pharmacophore models have been used for virtual screening and subsequent hERG activity (pIC₅₀) prediction of identified hits. Finally selected two hits were experimentally evaluated for hERG inhibition potential (pIC₅₀) using whole cell patch clamp assay. Overall, our results demonstrate a difference of less than ±1.6 log unit between experimentally determined and predicted hERG inhibition potential (IC₅₀) of the selected hits. This revealed predictive ability and robustness of our models and could help in correctly rank the potency order (lower μM to higher nM range) against hERG.

Application of human induced pluripotent stem cell-derived cardiomyocytes sheets with microelectrode array system to estimate antiarrhythmic properties of multi-ion channel blockers

We examined electrophysiological indices of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) sheets in order to quantitatively estimate Na⁺, K⁺ and Ca²⁺ channel blocking actions of bepridil and amiodarone using microelectrode array system in comparison with that of E-4031. We analyzed the field potential duration, effective refractory period, current threshold and conduction property using a programmed electrical stimulation protocol to obtain the post repolarization refractoriness and coefficient a of the relationship between the pacing cycle length and field potential duration. Electropharmacological profile of each drug was successfully characterized; namely, 1) the changes in the current threshold and conduction property provided basic information of Na⁺ channel blocking kinetics, 2) the relationship between pacing cycle length and field potential duration reflected drug-induced inhibition of human ether-à-go-go-related gene (hERG) K⁺ channel, 3) the post repolarization refractoriness indicated the relative contribution of these drugs to Na⁺ and K⁺ channel blockade, and 4) L-type Ca²⁺ channel blocking action was more obvious in the field potential waveform of the hiPSC-CMs sheets than that expected in the electrocardiogram in humans. Thus, this information may help to better utilize the hiPSC-CMs sheets for grasping the properties and net effects of drug-induced Na⁺, Ca²⁺ and K⁺ channel blockade.

Early Gestational Hypoxia and Adverse Developmental Outcomes

Hypoxia is a normal and essential part of embryonic development. However, this state may leave the embryo vulnerable to damage when oxygen supply is disturbed. Embryofetal response to hypoxia is dependent on duration and depth of hypoxia, as well as developmental stage. Early postimplantation rat embryos were resilient to hypoxia, with many surviving up to 1.5 hr of uterine clamping, while most mid-gestation embryos were dead after 1 hour of clamping. Survivors were small and many had a range of defects, principally terminal transverse limb reduction defects. Similar patterns of malformations occurred when embryonic hypoxia was induced by maternal hypoxia, interruption of uteroplacental flow, or perfusion and embryonic bradycardia. There is good evidence that high altitude pregnancies are associated with smaller babies and increased risk of some malformations, but these results are complicated by increased risk of pre-eclampsia. Early onset pre-eclampsia itself is associated with small for dates and increased risk of atrio-ventricular septal defects. Limb defects have clearly been associated with chorionic villus sampling, cocaine, and misoprostol use. Similar defects are also observed with increased frequency among fetuses who are homozygous for thalassemia. Drugs that block the potassium current, whether as the prime site of action or as a side effect, are highly teratogenic in experimental animals. They induce embryonic bradycardia, hypoxia, hemorrhage, and blisters, leading to transverse limb defects as well as craniofacial and cardiovascular defects. While evidence linking these drugs to birth defects in humans is not compelling, the reason may methodological rather than biological. Birth Defects Research 109:1358-1376, 2017.© 2017 Wiley Periodicals, Inc.

Analysis of torsadogenic and pharmacokinetic profile of E-4031 in dogs bridging the gap of information between in vitro proarrhythmia assay and clinical observation in human subjects

We analyzed torsadogenic and pharmacokinetic profile of E-4031 using chronic atrioventricular block dogs. E-4031 in intravenous doses of 0.03, 0.1 and 0.3 mg/kg over 10 min prolonged QT/QTc, and increased short-term variability of QT in a dose-related manner (n = 4), resulting in onset of torsade de pointes in 1 animal after the middle dose and 4 animals after the high dose, while it attained peak plasma concentrations of 16.5, 60.5 and 182.5 ng/mL at 10 min after their start of administration, respectively (n = 2). These results bridge the gap of information between in vitro proarrhythmia assay and clinical observation in human subjects.

In Silico Pharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers

Drug-induced cardiovascular complications are the most common adverse drug events and account for the withdrawal or severe restrictions on the use of multitudinous postmarketed drugs. In this study, we developed new in silico models for systematic identification of drug-induced cardiovascular complications in drug discovery and postmarketing surveillance. Specifically, we collected drug-induced cardiovascular complications covering the five most common types of cardiovascular outcomes (hypertension, heart block, arrhythmia, cardiac failure, and myocardial infarction) from four publicly available data resources: Comparative Toxicogenomics Database, SIDER, Offsides, and MetaADEDB. Using these databases, we developed a combined classifier framework through integration of five machine-learning algorithms: logistic regression, random forest, k-nearest neighbors, support vector machine, and neural network. The totality of models included 180 single classifiers with area under receiver operating characteristic curves (AUC) ranging from 0.647 to 0.809 on 5-fold cross-validations. To develop the combined classifiers, we then utilized a neural network algorithm to integrate the best four single classifiers for each cardiovascular outcome. The combined classifiers had higher performance with an AUC range from 0.784 to 0.842 compared to single classifiers. Furthermore, we validated our predicted cardiovascular complications for 63 anticancer agents using experimental data from clinical studies, human pluripotent stem cell-derived cardiomyocyte assays, and literature. The success rate of our combined classifiers reached 87%. In conclusion, this study presents powerful in silico tools for systematic risk assessment of drug-induced cardiovascular complications. This tool is relevant not only in early stages of drug discovery but also throughout the life of a drug including clinical trials and postmarketing surveillance.

Quantitative Comparison of Effects of Dofetilide, Sotalol, Quinidine, and Verapamil between Human Ex vivo Trabeculae and In silico Ventricular Models Incorporating Inter-Individual Action Potential Variability

Background:In silico modeling could soon become a mainstream method of pro-arrhythmic risk assessment in drug development. However, a lack of human-specific data and appropriate modeling techniques has previously prevented quantitative comparison of drug effects between in silico models and recordings from human cardiac preparations. Here, we directly compare changes in repolarization biomarkers caused by dofetilide, dl-sotalol, quinidine, and verapamil, between in silico populations of human ventricular cell models and ex vivo human ventricular trabeculae. Methods and Results:Ex vivo recordings from human ventricular trabeculae in control conditions were used to develop populations of in silico human ventricular cell models that integrated intra- and inter-individual variability in action potential (AP) biomarker values. Models were based on the O'Hara-Rudy ventricular cardiomyocyte model, but integrated experimental AP variability through variation in underlying ionic conductances. Changes to AP duration, triangulation and early after-depolarization occurrence from application of the four drugs at multiple concentrations and pacing frequencies were compared between simulations and experiments. To assess the impact of variability in IC50 measurements, and the effects of including state-dependent drug binding dynamics, each drug simulation was repeated with two different IC50 datasets, and with both the original O'Hara-Rudy hERG model and a recently published state-dependent model of hERG and hERG block. For the selective hERG blockers dofetilide and sotalol, simulation predictions of AP prolongation and repolarization abnormality occurrence showed overall good agreement with experiments. However, for multichannel blockers quinidine and verapamil, simulations were not in agreement with experiments across all IC50 datasets and IKr block models tested. Quinidine simulations resulted in overprolonged APs and high incidence of repolarization abnormalities, which were not observed in experiments. Verapamil simulations showed substantial AP prolongation while experiments showed mild AP shortening. Conclusions: Results for dofetilide and sotalol show good agreement between experiments and simulations for selective compounds, however lack of agreement from simulations of quinidine and verapamil suggest further work is needed to understand the more complex electrophysiological effects of these multichannel blocking drugs.

Voriconazole-induced QT prolongation among hemato-oncologic patients: clinical characteristics and risk factors

PURPOSE

The purpose of this study is to determine the rate of QTcP and associated risk factors in patients treated with voriconazole.

METHODS

We conducted a retrospective chart review of all patients treated with voriconazole in a large tertiary center between 2009 and 2015, using paired comparison of QTc intervals on and off voriconazole treatment, adjusted for comorbidities, electrolyte abnormalities, and concurrent medications.

RESULTS

Fifty-four patients were included, of whom 53 were diagnosed with oncologic/hemato-oncologic disease. Mean QTc during voriconazole therapy (448.0 ± 52.9 msec) was significantly longer compared to QTc off voriconazole (421.8 ± 42.2 msec; p = 0.002). QTcP ≥30 msec and ≥60 msec was demonstrated in 43% (23 patients) and 28% (15 patients), respectively. Multivariate analysis showed that QTcP was significantly associated with baseline QTc ≥ 450 msec (upper QTc quartile) (p < 0.01) and low serum potassium levels (p < 0.01). Contrarily, no significant association was found between mean voriconazole daily and cumulative dose and QTcP.

CONCLUSION

Our findings indicate that hemato-oncologic patients treated with voriconazole are at increased risk for QTcP, especially in the presence of baseline QTc ≥ 450 msec and low serum potassium levels.

The Electrogenic Na+/K+ Pump Is a Key Determinant of Repolarization Abnormality Susceptibility in Human Ventricular Cardiomyocytes: A Population-Based Simulation Study

Background: Cellular repolarization abnormalities occur unpredictably due to disease and drug effects, and can occur even in cardiomyocytes that exhibit normal action potentials (AP) under control conditions. Variability in ion channel densities may explain differences in this susceptibility to repolarization abnormalities. Here, we quantify the importance of key ionic mechanisms determining repolarization abnormalities following ionic block in human cardiomyocytes yielding normal APs under control conditions.

Methods and Results: Sixty two AP recordings from non-diseased human heart preparations were used to construct a population of human ventricular models with normal APs and a wide range of ion channel densities. Multichannel ionic block was applied to investigate susceptibility to repolarization abnormalities. I_Kr block was necessary for the development of repolarization abnormalities. Models that developed repolarization abnormalities over the widest range of blocks possessed low Na⁺/K⁺ pump conductance below 50% of baseline, and I_CaL conductance above 70% of baseline. Furthermore, I_NaK made the second largest contribution to repolarizing current in control simulations and the largest contribution under 75% I_Kr block. Reversing intracellular Na⁺ overload caused by reduced I_NaK was not sufficient to prevent abnormalities in models with low Na⁺/K⁺ pump conductance, while returning Na⁺/K⁺ pump conductance to normal substantially reduced abnormality occurrence, indicating I_NaK is an important repolarization current.

Conclusions: I_NaK is an important determinant of repolarization abnormality susceptibility in human ventricular cardiomyocytes, through its contribution to repolarization current rather than homeostasis. While we found I_Kr block to be necessary for repolarization abnormalities to occur, I_NaK decrease, as in disease, may amplify the pro-arrhythmic risk of drug-induced I_Kr block in humans.

Why Can dl-Sotalol Prolong the QT Interval In Vivo Despite Its Weak Inhibitory Effect on hERG K(+) Channels In Vitro? Electrophysiological and Pharmacokinetic Analysis with the Halothane-Anesthetized Guinea Pig Model

In order to bridge the gap of action of dl-sotalol between the human ether-a-go-go-related gene (hERG) K(+) channel inhibition in vitro and QT-interval prolongation in vivo, its electropharmacological effect and pharmacokinetic property were simultaneously studied in comparison with those of 10 drugs having potential to prolong the QT interval (positive drugs: bepridil, haloperidol, dl-sotalol, terfenadine, thioridazine, moxifloxacin, pimozide, sparfloxacin, diphenhydramine, imipramine and ketoconazole) and four drugs lacking such property (negative drugs: enalapril, phenytoin, propranolol or verapamil) with the halothane-anesthetized guinea pig model. A dose of each drug that caused 10 % prolongation of Fridericia-corrected QT interval (QTcF) was calculated, which was compared with respective known hERG K(+) IC50 value and currently obtained heart/plasma concentration ratio. Each positive drug prolonged the QTcF in a dose-related manner, whereas such effect was not observed by the negative drugs. Drugs with more potent hERG K(+) channel inhibition showed higher heart/plasma concentration ratio, resulting in more potent QTcF prolongation in vivo. The potency of dl-sotalol for QTcF prolongation was flat middle, although its hERG K(+) channel inhibitory property as well as heart/plasma concentration ratio was the smallest among the positive drugs, which may be partly explained by its lack of binding to plasma protein.

Pharmacokinetic-pharmacodynamic modelling of drug-induced QTc interval prolongation in man: prediction from in vitro human ether-à-go-go-related gene binding and functional inhibition assays and conscious dog studies

BACKGROUND AND PURPOSE

Functional measures of human ether-à-go-go-related gene (hERG; Kv 11.1) channel inhibition have been prioritized as an in vitro screening tool for candidate molecules. However, it is unclear how these results can be translated to humans. Here, we explore how data on drug binding and functional inhibition in vitro relate to QT prolongation in vivo. Using cisapride, sotalol and moxifloxacin as paradigm compounds, we assessed the relationship between drug concentrations, binding, functional measures and in vivo effects in preclinical species and humans.

EXPERIMENTAL APPROACH

Pharmacokinetic-pharmacodynamic modelling was used to characterize the drug effects in hERG functional patch clamp, hERG radio-labelled dofetilide displacement experiments and QT interval in conscious dogs. Data were analysed in parallel to identify potential correlations between pharmacological activity in vitro and in vivo.

KEY RESULTS

An Emax model could not be used due to large variability in the functional patch clamp assay. Dofetilide displacement revealed that binding curves are unrelated to the in vivo potency estimates for QTc prolongation in dogs and humans. Mean in vitro potency estimates ranged from 99.9 nM for cisapride to 1030 μM for moxifloxacin.

CONCLUSIONS AND IMPLICATIONS

The lack of standardized protocols for in vitro assays leads to significant differences in experimental conditions, making the assessment of in vitro-in vivo correlations unreliable. Identification of an accurate safety window during the screening of candidate molecules requires a quantitative framework that disentangles system- from drug-specific properties under physiological conditions, enabling translation of the results to humans. Similar considerations will be relevant for the comprehensive in vitro pro-arrhythmia assay initiative.

A k-nearest neighbor classification of hERG K(+) channel blockers

A series of 172 molecular structures that block the hERG K⁺ channel were used to develop a classification model where, initially, eight types of PaDEL fingerprints were used for k-nearest neighbor model development. A consensus model constructed using Extended-CDK, PubChem and Substructure count fingerprint-based models was found to be a robust predictor of hERG activity. This consensus model demonstrated sensitivity and specificity values of 0.78 and 0.61 for the internal dataset compounds and 0.63 and 0.54 for the external (PubChem) dataset compounds, respectively. This model has identified the highest number of true positives (i.e. 140) from the PubChem dataset so far, as compared to other published models, and can potentially serve as a basis for the prediction of hERG active compounds. Validating this model against FDA-withdrawn substances indicated that it may even be useful for differentiating between mechanisms underlying QT prolongation.

In silico prediction of hERG potassium channel blockage by chemical category approaches

The human ether-a-go-go related gene (hERG) plays an important role in cardiac action potential. It encodes an ion channel protein named Kv11.1, which is related to long QT syndrome and may cause avoidable sudden cardiac death. Therefore, it is important to assess the hERG channel blockage of lead compounds in an early drug discovery process. In this study, we collected a large data set containing 1163 diverse compounds with IC₅₀ values determined by the patch clamp method on mammalian cell lines. The whole data set was divided into 80% as the training set and 20% as the test set. Then, five machine learning methods were applied to build a series of binary classification models based on 13 molecular descriptors, five fingerprints and molecular descriptors combining fingerprints at four IC₅₀ thresholds to discriminate hERG blockers from nonblockers, respectively. Models built by molecular descriptors combining fingerprints were validated by using an external validation set containing 407 compounds collected from the hERGCentral database. The performance indicated that the model built by molecular descriptors combining fingerprints yielded the best results and each threshold had its best suitable method, which means that hERG blockage assessment might depend on threshold values. Meanwhile, kNN and SVM methods were better than the others for model building. Furthermore, six privileged substructures were identified using information gain and frequency analysis methods, which could be regarded as structural alerts of cardiac toxicity mediated by hERG channel blockage.

hERG Channel Inhibitory Daphnane Diterpenoid Orthoesters and Polycephalones A and B with Unprecedented Skeletons from Gnidia polycephala

The hERG channel is an important antitarget in safety pharmacology. Several drugs have been withdrawn from the market or received severe usage restrictions because of hERG-related cardiotoxicity. In a screening of medicinal plants for hERG channel inhibition using a two-microelectrode voltage clamp assay with Xenopus laevis oocytes, a dichloromethane extract of the roots of Gnidia polycephala reduced the peak tail hERG current by 58.8 ± 13.4% (n = 3) at a concentration of 100 μg/mL. By means of HPLC-based activity profiling daphnane-type diterpenoid orthoesters (DDOs) 1, 4, and 5 were identified as the active compounds [55.4 ± 7.0% (n = 4), 42.5 ± 16.0% (n = 3), and 51.3 ± 9.4% (n = 4), respectively, at 100 μM]. In a detailed phytochemical profiling of the active extract, 16 compounds were isolated and characterized, including two 2-phenylpyranones (15 and 16) with an unprecedented tetrahydro-4H-5,8-epoxypyrano[2,3-d]oxepin-4-one skeleton, two new DDOs (3 and 4), two new guaiane sesquiterpenoids (11 and 12), and 10 known compounds (1, 2, 5-10, 13, and 14). Structure elucidation was achieved by extensive spectroscopic analysis (1D and 2D NMR, HRMS, and electronic circular dichroism), computational methods, and X-ray crystallography.

Computational investigations of hERG channel blockers: New insights and current predictive models

Identification of potential human Ether-a-go-go Related-Gene (hERG) potassium channel blockers is an essential part of the drug development and drug safety process in pharmaceutical industries or academic drug discovery centers, as they may lead to drug-induced QT prolongation, arrhythmia and Torsade de Pointes. Recent reports also suggest starting to address such issues at the hit selection stage. In order to prioritize molecules during the early drug discovery phase and to reduce the risk of drug attrition due to cardiotoxicity during pre-clinical and clinical stages, computational approaches have been developed to predict the potential hERG blockage of new drug candidates. In this review, we will describe the current in silico methods developed and applied to predict and to understand the mechanism of actions of hERG blockers, including ligand-based and structure-based approaches. We then discuss ongoing research on other ion channels and hERG polymorphism susceptible to be involved in LQTS and how systemic approaches can help in the drug safety decision.

Molecular docking and molecular dynamics studies on the structure-activity relationship of fluoroquinolone for the HERG channel

Fluoroquinolones play an important role in the treatment of serious bacterial infections, but at the same time they could lead to cardiac toxicity due to the blockage of the HERG potassium channel, which even leads to the withdrawal of some fluoroquinolones. Blockage of the HERG potassium channel by drugs or drug-like compounds has become a critical problem in drug discovery. Though there were large amounts of bioactivity data of fluoroquinolones on the blockage of HERG, little structural basis of binding of blockers to the HERG channel was known. Here, we combined molecular docking, molecular dynamics simulations, free energy calculations and binding energy decomposition analysis to explore the binding modes of fluoroquinolones in the HERG potassium channel. The calculated binding free energies were consistent with the experimental binding affinities. Our results showed that the CH3 group in MX was favorable for the binding to the HERG channel, while Tyr652 and Phe656 were critical for the hydrophobic interaction between fluoroquinolones and the HERG channel. We expected that our results of calculation could provide important insights for the rational design and discovery of drugs.