Block of HERG potassium channels by the antihistamine astemizole and its metabolites desmethylastemizole and norastemizole

Two-Dimensional Deep Learning Frameworks for Drug-Induced Cardiotoxicity Detection

The identification of drug-induced cardiotoxicity remains a pressing challenge with far-reaching clinical and economic ramifications, often leading to patient harm and resource-intensive drug recalls. Current methodologies, including in vivo and in vitro models, have severe limitations in accurate identification of cardiotoxic substances. Pioneering a paradigm shift from these conventional techniques, our study presents two deep learning-based frameworks, STFT-CNN and SST-CNN, to assess cardiotoxicity with markedly improved accuracy and reliability. Leveraging the power of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) as a more human-relevant cell model, we record mechanical beating signals through impedance measurements. These temporal signals were converted into enriched two-dimensional representations through advanced transformation techniques, specifically short-time Fourier transform (STFT) and synchro-squeezing transform (SST). These transformed data are fed into the proposed frameworks for comprehensive analysis, including drug type classification, concentration classification, cardiotoxicity classification, and new drug identification. Compared to traditional models like recurrent neural network (RNN) and 1-dimensional convolutional neural network (1D-CNN), SST-CNN delivered an impressive test accuracy of 98.55% in drug type classification and 99% in distinguishing cardiotoxic and noncardiotoxic drugs. Its feasibility is further highlighted with a stellar 98.5% average accuracy for classification of various concentrations, and the superiority of our proposed frameworks underscores their promise in revolutionizing drug safety assessments. With a potential for scalability, they represent a major leap in drug safety assessments, offering a pathway to more robust, efficient, and human-relevant cardiotoxicity evaluations.

Cardiotoxicity Assessment through a Polymer-Based Cantilever Platform: An Integrated Electro-Mechanical Screening Approach

Preclinical drug screening for cardiac toxicity has traditionally relied on observing changes in cardiomyocytes' electrical activity, primarily through invasive patch clamp techniques or non-invasive microelectrode arrays (MEA). However, relying solely on field potential duration (FPD) measurements for electrophysiological assessment can miss the full spectrum of drug-induced toxicity, as different drugs affect cardiomyocytes through various mechanisms. A more comprehensive approach, combining field potential and contractility measurements, is essential for accurate toxicity profiling, particularly for drugs targeting contractile proteins without affecting electrophysiology. However, previously proposed platform has significant limitations in terms of simultaneous measurement. The novel platform addresses these issues, offering enhanced, non-invasive evaluation of drug-induced cardiotoxicity. It features eight cantilevers with patterned strain sensors and MEA, enabling real-time monitoring of both cardiomyocyte contraction force and field potential. This system can detect minimum cardiac contraction force of ≈2 µN and field potential signals with 50 µm MEA diameter, using the same cardiomyocytes in measurements of two parameters. Testing with six drugs of varied mechanisms of action, the platform successfully identifies these mechanisms and accurately assesses toxicity profiles, including drugs not inhibiting potassium channels. This innovative approach presents a comprehensive, non-invasive method for cardiac function assessment, poised to revolutionize preclinical cardiotoxicity screening.

Liensinine, a Novel and Food-Derived Compound, Exerts Potent Antihepatoma Efficacy via Inhibiting the Kv10.1 Channel

Plant metabolites from natural product extracts offer unique advantages against carcinogenesis in the development of drugs. The target-based virtual screening from food-derived compounds represents a promising approach for tumor therapy. In this study, we performed virtual screening to target the presumed inhibitor-binding pocket and identified a highly potent Kv10.1 inhibitor, liensinine (Lien), which can inhibit the channel in a dose-dependent way with an IC50 of 0.24 ± 0.07 μM. Combining molecular dynamics simulations with mutagenesis experiments, our data show that Lien interacts with Kv10.1 by binding with Y539, T543, D551, E553, and H601 in the C-linker domain of Kv10.1. In addition, the interaction of sequence alignment and 3D structural modeling revealed differences between the C-linker domain of the Kv10.1 channel and the Kv11.1 channel. Furthermore, antitumor experiments revealed that Lien suppresses the proliferation and migration of HCC both in vitro and in vivo. In summary, the food-derived compound, Lien, may serve as a lead compound for antihepatoma therapeutic drugs targeting Kv10.1.

NaI-Mediated Electrochemical Cyclization-Desulfurization for the Synthesis of N-Substituted 2-Aminobenzimidazoles

An electrochemical method for the synthesis of N-substituted 2-aminobenzimidazoles through a NaI-mediated desulfurization-cyclization process is reported. This electrosynthesis method utilizes cost-effective NaI as both a mediator and an electrolyte in a catalytic amount (0.2 equiv), replacing traditional oxidizing reagents. N-Substituted o-phenylenediamines and isothiocyanates undergo a thiourea formation/cyclization/desulfurization process to provide N-substituted 2-aminobenzimidazoles (55 examples, up to 98% yield) in a single reaction vessel. Importantly, this electrochemical methodology is applicable to gram-scale synthesis, maintaining reaction efficiency.

Molecular mechanism of antihistamines recognition and regulation of the histamine H1 receptor

Histamine receptors are a group of G protein-coupled receptors (GPCRs) that play important roles in various physiological and pathophysiological conditions. Antihistamines that target the histamine H1 receptor (H1R) have been widely used to relieve the symptoms of allergy and inflammation. Here, to uncover the details of the regulation of H1R by the known second-generation antihistamines, thereby providing clues for the rational design of newer antihistamines, we determine the cryo-EM structure of H1R in the apo form and bound to different antihistamines. In addition to the deep hydrophobic cavity, we identify a secondary ligand-binding site in H1R, which potentially may support the introduction of new derivative groups to generate newer antihistamines. Furthermore, these structures show that antihistamines exert inverse regulation by utilizing a shared phenyl group that inserts into the deep cavity and block the movement of the toggle switch residue W4286.48. Together, these results enrich our understanding of GPCR modulation and facilitate the structure-based design of novel antihistamines.

A Critical Review on Therapeutic Potential of Benzimidazole Derivatives: A Privileged Scaffold

Benzimidazole nucleus is a predominant heterocycle displaying a wide spectrum of pharmacological activities. The privileged nature of the benzimidazole scaffold has been revealed by its presence in most small molecule drugs and in its ability to bind multiple receptors with high affinity. A literature review of the scaffold reveals several instances where structural modifications of the benzimidazole core have resulted in high-affinity lead compounds against a variety of biological targets. Hence, this structural moiety offers opportunities to discover novel, better, safe and highly potent biological agents. The goal of the present review is to compile the medicinal properties of benzimidazole derivatives with a focus on SAR (Structure-Activity Relationships).

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.

hERG agonists pose challenges to web-based machine learning methods for prediction of drug-hERG channel interaction

Pharmacological blockade of the IKr channel (hERG) by diverse drugs in clinical use is associated with the Long QT Syndrome that can lead to life threatening arrhythmia. Various computational tools including machine learning models (MLM) for the prediction of hERG inhibition have been developed to facilitate the throughput screening of drugs in development and optimise thus the prediction of hERG liabilities. The use of MLM relies on large libraries of training compounds for the quantitative structure-activity relationship (QSAR) modelling of hERG inhibition. The focus on inhibition omits potential effects of hERG channel agonist molecules and their associated QT shortening risk. It is instructive, therefore, to consider how known hERG agonists are handled by MLM. Here, two highly developed online computational tools for the prediction of hERG liability, Pred-hERG and HergSPred were probed for their ability to detect hERG activator drug molecules as hERG interactors. In total, 73 hERG blockers were tested with both computational tools giving overall good predictions for hERG blockers with reported IC50s below Pred-hERG and HergSPred cut-off threshold for hERG inhibition. However, for compounds with reported IC50s above this threshold such as disopyramide or sotalol discrepancies were observed. HergSPred identified all 20 hERG agonists selected as interacting with the hERG channel. Further studies are warranted to improve online MLM prediction of hERG related cardiotoxicity, by explicitly taking into account channel agonism as well as inhibition.

Mitigating hERG Liability of Toll-Like Receptor 9 and 7 Antagonists through Structure-Based Design

hERG is considered to be a primary anti-target in the drug development process, as the K+ channel encoded by hERG plays an important role in cardiac re-polarization. It is desirable to address the hERG safety liability during early-stage development to avoid the expenses of validating leads that will eventually fail at a later stage. We have previously reported the development of highly potent quinazoline-based TLR7 and TLR9 antagonists for possible application against autoimmune disease. Initial experimental hERG assessment showed that most of the lead TLR7 and TLR9 antagonists suffer from hERG liability rendering them ineffective for further development. The present study herein describes a coordinated strategy to integrate the understanding from structure-based protein-ligand interaction to develop non- hERG binders with IC50 >30 μM with retention of TLR7/9 antagonism through a single point change in the scaffold. This structure-guided strategy can serve as a prototype for abolishing hERG liability during lead optimization.

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

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

A biosensing system employing nonlinear dynamic analysis-assisted neural network for drug-induced cardiotoxicity assessment

Preclinical investigation of drug-induced cardiotoxicity is of importance for drug development. To evaluate such cardiotoxicity, in vitro high-throughput interdigitated electrode-based recording of cardiomyocytes mechanical beating is widely used. To automatically analyze the features from the beating signals for drug-induced cardiotoxicity assessment, artificial neural network analysis is conventionally employed and signals are segmented into cycles and feature points are located in the cycles. However, signal segmentation and location of feature points for different signal shapes require design of specific algorithms. Consequently, this may lower the efficiency of research and the applications of such algorithms in signals with different morphologies are limited. Here, we present a biosensing system that employs nonlinear dynamic analysis-assisted neural network (NDANN) to avoid the signal segmentation process and directly extract features from beating signal time series. By processing beating time series with fixed time duration to avoid the signal segmentation process, this NDANN-based biosensing system can identify drug-induced cardiotoxicity with accuracy over 0.99. The individual drugs were classified with high accuracies over 0.94 and drug-induced cardiotoxicity levels were accurately predicted. We also evaluated the generalization performance of the NDANN-based biosensing system in assessing drug-induced cardiotoxicity through an independent dataset. This system achieved accuracy of 0.85-0.95 for different drug concentrations in identification of drug-induced cardiotoxicity. This result demonstrates that our NDANN-based biosensing system has the capacity of screening newly developed drugs, which is crucial in practical applications. This NDANN-based biosensing system can work as a new screening platform for drug-induced cardiotoxicity and improve the efficiency of bio-signal processing.

Ensemble of structure and ligand-based classification models for hERG liability profiling

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.

Novel 3-Trifluoromethyl-1,2,4-oxadiazole Analogues of Astemizole with Multi-stage Antiplasmodium Activity and In Vivo Efficacy in a Plasmodium berghei Mouse Malaria Infection Model

Iterative medicinal chemistry optimization of an ester-containing astemizole (AST) analogue 1 with an associated metabolic instability liability led to the identification of a highly potent 3-trifluoromethyl-1,2,4-oxadiazole analogue 23 (PfNF54 IC₅₀ = 0.012 μM; PfK1 IC₅₀ = 0.040 μM) displaying high microsomal metabolic stability (HLM CL_int < 11.6 μL·min^-1·mg^-1) and > 1000-fold higher selectivity over hERG compared to AST. In addition to asexual blood stage activity, the compound also shows activity against liver and gametocyte life cycle stages and demonstrates in vivo efficacy in Plasmodium berghei-infected mice at 4 × 50 mg·kg^-1 oral dose. Preliminary interrogation of the mode of action using live-cell microscopy and cellular heme speciation revealed that 23 could be affecting multiple processes in the parasitic digestive vacuole, with the possibility of a novel target at play in the organelles associated with it.

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques

Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligand-based classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.

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).

Use of Solvent Mapping for Characterizing the Binding Site and for Predicting the Inhibition of the Human Ether-á-Go-Go-Related K+ Channel

Molecular dynamics was used to optimize the droperidol-hERG complex obtained from docking. To accommodate the inhibitor, residues T623, S624, V625, G648, Y652, and F656 did not move significantly during the simulation, while F627 moved significantly. Binding sites in cryo-EM structures and in structures obtained from molecular dynamics simulations were characterized using solvent mapping and Atlas ligands, which were negative images of the binding site, were generated. Atlas ligands were found to be useful for identifying human ether-á-go-go-related potassium channel (hERG) inhibitors by aligning compounds to them or by guiding the docking of compounds in the binding site. A molecular dynamics optimized structure of hERG led to improved predictions using either compound alignment to the Atlas ligand or docking. The structure was also found to be suitable to define a strategy for lowering inhibition based on the proposed binding mode of compounds in the channel.

BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers

Unintended inhibition of the human ether-à-go-go-related gene (hERG) ion channel by small molecules leads to severe cardiotoxicity. Thus, hERG channel blockage is a significant concern in the development of new drugs. Several computational models have been developed to predict hERG channel blockage, including deep learning models; however, they lack robustness, reliability and interpretability. Here, we developed a graph-based Bayesian deep learning model for hERG channel blocker prediction, named BayeshERG, which has robust predictive power, high reliability and high resolution of interpretability. First, we applied transfer learning with 300 000 large data in initial pre-training to increase the predictive performance. Second, we implemented a Bayesian neural network with Monte Carlo dropout to calibrate the uncertainty of the prediction. Third, we utilized global multihead attentive pooling to augment the high resolution of structural interpretability for the hERG channel blockers and nonblockers. We conducted both internal and external validations for stringent evaluation; in particular, we benchmarked most of the publicly available hERG channel blocker prediction models. We showed that our proposed model outperformed predictive performance and uncertainty calibration performance. Furthermore, we found that our model learned to focus on the essential substructures of hERG channel blockers via an attention mechanism. Finally, we validated the prediction results of our model by conducting in vitro experiments and confirmed its high validity. In summary, BayeshERG could serve as a versatile tool for discovering hERG channel blockers and helping maximize the possibility of successful drug discovery. The data and source code are available at our GitHub repository (https://github.com/GIST-CSBL/BayeshERG).

The ERG1 K+ Channel and Its Role in Neuronal Health and Disease

The ERG1 potassium channel, encoded by KCNH2, has long been associated with cardiac electrical excitability. Yet, a growing body of work suggests that ERG1 mediates physiology throughout the human body, including the brain. ERG1 is a regulator of neuronal excitability, ERG1 variants are associated with neuronal diseases (e.g., epilepsy and schizophrenia), and ERG1 serves as a potential therapeutic target for neuronal pathophysiology. This review summarizes the current state-of-the-field regarding the ERG1 channel structure and function, ERG1’s relationship to the mammalian brain and highlights key questions that have yet to be answered.

Predicting hERG channel blockers with directed message passing neural networks

Compounds with human ether-à-go-go related gene (hERG) blockade activity may cause severe cardiotoxicity. Assessing the hERG liability in the early stages of the drug discovery process is important, and the in silico methods for predicting hERG channel blockers are actively pursued. In the present study, the directed message passing neural network (D-MPNN) was applied to construct classification models for identifying hERG blockers based on diverse datasets. Several descriptors and fingerprints were tested along with the D-MPNN model. Among all these combinations, D-MPNN with the moe206 descriptors generated from MOE (D-MPNN + moe206) showed significantly improved performances. The AUC-ROC values of the D-MPNN + moe206 model reached 0.956 ± 0.005 under random split and 0.922 ± 0.015 under scaffold split on Cai's hERG dataset, respectively. Moreover, the comparisons between our models and several recently reported machine learning models were made based on various datasets. Our results indicated that the D-MPNN + moe206 model is among the best classification models. Overall, the excellent performance of the DMPNN + moe206 model achieved in this study highlights its potential application in the discovery of novel and effective hERG blockers.

Compounds with human ether-à-go-go related gene (hERG) blockade activity may cause severe cardiotoxicity.

Linked biaromatic compounds as cardioprotective agents

Cardiovascular diseases (CVDs) are widespread in the modern world, and their number is constantly growing. For a long time, CVDs have been the leading cause of morbidity and mortality worldwide. Drugs for the treatment of CVD have been developed almost since the beginning of the 20th century, and a large number of effective cardioprotective agents of various classes have been created. Nevertheless, the need for the design and development of new safe drugs for the treatment of CVD remains. Literature data indicate that a huge number of cardioprotective agents of various generations and mechanisms correspond to a single generalized pharmacophore model containing two aromatic nuclei linked by a linear linker. In this regard, we put forward a concept for the design of a new generation of cardioprotective agents with a multitarget mechanism of action within the indicated pharmacophore model. This review is devoted to a generalization of the currently known compounds with cardioprotective properties and corresponding to the pharmacophore model of biaromatic compounds linked by a linear linker. Particular attention is paid to the history of the creation of these drugs, approaches to their design, and analysis of the structure-action relationship within each class.

Levocetirizine and montelukast in the COVID-19 treatment paradigm

Levocetirizine, a third-generation antihistamine, and montelukast, a leukotriene receptor antagonist, exhibit remarkable synergistic anti-inflammatory activity across a spectrum of signaling proteins, cell adhesion molecules, and leukocytes. By targeting cellular protein activity, they are uniquely positioned to treat the symptoms of COVID-19. Clinical data to date with an associated six-month follow-up, suggests the combination therapy may prevent the progression of the disease from mild to moderate to severe, as well as prevent/treat many of the aspects of ‘Long COVID,’ thereby cost effectively reducing both morbidity and mortality. To investigate patient outcomes, 53 consecutive COVID-19 test (+) cases (ages 3–90) from a well-established, single-center practice in Boston, Massachusetts, between March – November 2020, were treated with levocetirizine and montelukast in addition to then existing protocols [2]. The data set was retrospectively reviewed. Thirty-four cases were considered mild (64%), 17 moderate (32%), and 2 (4%) severe. Several patients presented with significant comorbidities (obesity: n = 22, 41%; diabetes: n = 10, 19%; hypertension: n = 24, 45%). Among the cohort there were no exclusions, no intubations, and no deaths. The pilot study in Massachusetts encompassed the first COVID-19 wave which peaked on April 23, 2020 as well as the ascending portion of the second wave in the fall. During this period the average weekly COVID-19 case mortality rate (confirmed deaths/confirmed cases) varied considerably between 1 and 7.5% [37]. FDA has approved a multicenter, randomized, placebo-controlled, Phase 2 clinical trial design, replete with electronic diaries and laboratory metrics to explore scientific questions not addressed herein.

longfin causes cis-ectopic expression of the kcnh2a ether-a-go-go K+ channel to autonomously prolong fin outgrowth

Organs stop growing to achieve a characteristic size and shape in scale with the body of an animal. Likewise, regenerating organs sense injury extents to instruct appropriate replacement growth. Fish fins exemplify both phenomena through their tremendous diversity of form and remarkably robust regeneration. The classic zebrafish mutant longfint2 develops and regenerates dramatically elongated fins and underlying ray skeleton. We show longfint2 chromosome 2 overexpresses the ether-a-go-go-related voltage-gated potassium channel kcnh2a. Genetic disruption of kcnh2a in cis rescues longfint2, indicating longfint2 is a regulatory kcnh2a allele. We find longfint2 fin overgrowth originates from prolonged outgrowth periods by showing Kcnh2a chemical inhibition during late stage regeneration fully suppresses overgrowth. Cell transplantations demonstrate longfint2-ectopic kcnh2a acts tissue autonomously within the fin intra-ray mesenchymal lineage. Temporal inhibition of the Ca2+-dependent phosphatase calcineurin indicates it likewise entirely acts late in regeneration to attenuate fin outgrowth. Epistasis experiments suggest longfint2-expressed Kcnh2a inhibits calcineurin output to supersede growth cessation signals. We conclude ion signaling within the growth-determining mesenchyme lineage controls fin size by tuning outgrowth periods rather than altering positional information or cell-level growth potency.

Overcoming challenges of HERG potassium channel liability through rational design: Eag1 inhibitors for cancer treatment

Two decades of research have proven the relevance of ion channel expression for tumor progression in virtually every indication, and it has become clear that inhibition of specific ion channels will eventually become part of the oncology therapeutic arsenal. However, ion channels play relevant roles in all aspects of physiology, and specificity for the tumor tissue remains a challenge to avoid undesired effects. Eag1 (K_V 10.1) is a voltage-gated potassium channel whose expression is very restricted in healthy tissues outside of the brain, while it is overexpressed in 70% of human tumors. Inhibition of Eag1 reduces tumor growth, but the search for potent inhibitors for tumor therapy suffers from the structural similarities with the cardiac HERG channel, a major off-target. Existing inhibitors show low specificity between the two channels, and screenings for Eag1 binders are prone to enrichment in compounds that also bind HERG. Rational drug design requires knowledge of the structure of the target and the understanding of structure-function relationships. Recent studies have shown subtle structural differences between Eag1 and HERG channels with profound functional impact. Thus, although both targets' structure is likely too similar to identify leads that exclusively bind to one of the channels, the structural information combined with the new knowledge of the functional relevance of particular residues or areas suggests the possibility of selective targeting of Eag1 in cancer therapies. Further development of selective Eag1 inhibitors can lead to first-in-class compounds for the treatment of different cancers.

Insights into the Molecular Inhibition of the Oncogenic Channel KV10.1 by Globular Toxins

Inhibition of the expression of the human ether-à-go-go (hEAG1 or hK_V10.1) channel is associated with a dramatic reduction in the growth of several cancerous tumors. The modulation of this channel's activity is a promising target for the development of new anticancer drugs. Although some small molecules have shown inhibitory activity against K_V10.1, their lack of specificity has prevented their use in humans. In vitro studies have recently identified a limited number of peptide toxins with proven specificity in their hK_V10.1 channel inhibitory effect. These peptide toxins have become desirable candidates to use as lead compounds to design more potent and specific hK_V10.1 inhibitors. However, the currently available studies lack the atomic resolution needed to characterize the molecular features that favor their binding to hK_V10.1. In this work, we present the first attempt to locate the possible hK_V10.1 binding sites of the animal peptide toxins APETx4, Aa1a, Ap1a, and k-hefutoxin 1, all of which described as hK_V10.1 inhibitors. Our studies incorporated homology modeling to construct a robust three-dimensional (3D) model of hK_V10.1, applied protein docking, and multiscale molecular dynamics techniques to reveal in atomic resolution the toxin-channel interactions. Our approach suggests that some peptide toxins bind in the outer vestibule surrounding the pore of hK_V10.1; it also identified the channel residues Met397 and Asp398 as possible anchors that stabilize the binding of the evaluated toxins. Finally, a description of the possible mechanism for inhibition and gating is presented.

Cardiac hERG K+ Channel as Safety and Pharmacological Target

The human ether-á-go-go related gene (hERG, KCNH2) encodes the pore-forming subunit of the potassium channel responsible for a fast component of the cardiac delayed rectifier potassium current (I_Kr). Outward I_Kr is an important determinant of cardiac action potential (AP) repolarization and effectively controls the duration of the QT interval in humans. Dysfunction of hERG channel can cause severe ventricular arrhythmias and thus modulators of the channel, including hERG inhibitors and activators, continue to attract intense pharmacological interest. Certain inhibitors of hERG channel prolong the action potential duration (APD) and effective refractory period (ERP) to suppress premature ventricular contraction and are used as class III antiarrhythmic agents. However, a reduction of the hERG/I_Kr current has been recognized as a predominant mechanism responsible for the drug-induced delayed repolarization known as acquired long QT syndromes (LQTS), which is linked to an increased risk for "torsades de pointes" (TdP) ventricular arrhythmias and sudden cardiac death. Many drugs of different classes and structures have been identified to carry TdP risk. Hence, assessing hERG/I_Kr blockade of new drug candidates is mandatory in the drug development process according to the regulatory agencies. In contrast, several hERG channel activators have been shown to enhance I_Kr and shorten the APD and thus might have potential antiarrhythmic effects against pathological LQTS. However, these activators may also be proarrhythmic due to excessive shortening of APD and the ERP.

Benzimidazole based hybrids against complex diseases: A catalogue of the SAR profile

The fused heterocyclic ring system has been recognized as a privileged structure that is used as a template in medicinal chemistry for drug discovery. Benzimidazole is one of the common scaffolds found in several natural products such as histidine, purines, and an integral part of vitamin B12. This hetero-aromatic bicyclic ring system acts as a pharmacophore in various drugs of therapeutic interest and has a broad spectrum of activity. Literature reports suggest that diversely substituted benzimidazoles possess distinct pharmacological profiles with multi-targeting potential, thereby, an indispensable anchor for the development of novel therapeutic agents against complex diseases such as cancer, malaria, inflammatory disorders, microbial diseases, hypertension, etc. Thus, lots of efforts have been diverted towards exploring the therapeutic potential of benzimidazoles. Despite great efforts made by the research community, still, some multi-factorial diseases continue to progress due to their complex pathophysiology. Under these sets of circumstances, there is a need to explore this nucleus for hybrid designing with multi-targeting potential against complex diseases. Benzimidazole-based hybrids have been reported to treat multifactorial diseases, making it a scaffold of interest for various pharmaceutical companies and research groups. In this write-up, we shed light on the recent pharmacological profiles, various designing strategies, and structure-activity relationships (SAR) of different benzimidazole-based hybrids.

A dual-fluorophore sensor approach for ratiometric fluorescence imaging of potassium in living cells

Potassium is the most abundant intracellular metal in the body, playing vital roles in regulating intracellular fluid volume, nutrient transport, and cell-to-cell communication through nerve and muscle contraction. On the other hand, aberrant alterations in K⁺ homeostasis contribute to a diverse array of diseases spanning cardiovascular and neurological disorders to diabetes to kidney disease to cancer. There is an unmet need for studies of K⁺ physiology and pathology owing to the large differences in intracellular versus extracellular K⁺ concentrations ([K⁺]_intra = 150 mM, [K⁺]_extra = 3–5 mM). With a relative dearth of methods to reliably measure dynamic changes in intracellular K⁺ in biological specimens that meet the dual challenges of low affinity and high selectivity for K⁺, particularly over Na⁺, currently available fluorescent K⁺ sensors are largely optimized with high-affinity receptors that are more amenable for extracellular K⁺ detection. We report the design, synthesis, and biological evaluation of Ratiometric Potassium Sensor 1 (RPS-1), a dual-fluorophore sensor that enables ratiometric fluorescence imaging of intracellular potassium in living systems. RPS-1 links a potassium-responsive fluorescent sensor fragment (PS525) with a low-affinity, high-selectivity crown ether receptor for K⁺ to a potassium-insensitive reference fluorophore (Coumarin 343) as an internal calibration standard through ester bonds. Upon intracellular delivery, esterase-directed cleavage splits these two dyes into separate fragments to enable ratiometric detection of K⁺. RPS-1 responds to K⁺ in aqueous buffer with high selectivity over competing metal ions and is sensitive to potassium ions at steady-state intracellular levels and can respond to decreases or increases from that basal set point. Moreover, RPS-1 was applied for comparative screening of K⁺ pools across a panel of different cancer cell lines, revealing elevations in basal intracellular K⁺ in metastatic breast cancer cell lines vs. normal breast cells. This work provides a unique chemical tool for the study of intracellular potassium dynamics and a starting point for the design of other ratiometric fluorescent sensors based on two-fluorophore approaches that do not rely on FRET or related energy transfer designs.

We report a dual-fluorophore approach for ratiometric fluorescent imaging of K⁺ levels in live cells. Intracellular esterases cleave RPS-1 to detach the K⁺-responsive fluorophore (PS525) from its internal standard (Coumarin 343).

Torsadogenic potential of a novel remyelinating drug clemastine for multiple sclerosis assessed in the rabbit proarrhythmia model

We assessed the torsadogenic effects of a novel remyelinating drug clemastine for multiple sclerosis using an in vivo proarrhythmia model of acute atrioventricular block rabbit, since the drug has been demonstrated to suppress the human ether-á-go-go related gene (hERG) K⁺ channels. Bradycardia was induced by atrioventricular node ablation in isoflurane-anesthetized New Zealand White rabbits (n = 5), and the ventricle was electrically driven at 60 beats/min throughout the experiment, except when extrasystoles appeared. Intravenous administration of clinically relevant dose of 0.03 mg/kg of clemastine and 10-times higher dose of 0.3 mg/kg hardly affected the QT interval or duration of the monophasic action potential (MAP) of the ventricle. Additional administration of clemastine at 3 mg/kg significantly increased the QT interval, MAP duration and the short-term variability of repolarization. Meanwhile, the premature ventricular contractions with R on T phenomenon were observed in 3 out of 5 animals, and torsades de pointes arrhythmias were detected in 1 out of 5 animals. These results suggest that the torsadogenic potential of clemastine is obviously observed in the acute atrioventricular block rabbit, which will not appear within the prescribed dose for multiple sclerosis.

Current development of integrated web servers for preclinical safety and pharmacokinetics assessments in drug development

In drug development, preclinical safety and pharmacokinetics assessments of candidate drugs to ensure the safety profile are a must. While in vivo and in vitro tests are traditionally used, experimental determinations have disadvantages, as they are usually time-consuming and costly. In silico predictions of these preclinical endpoints have each been developed in the past decades. However, only a few web-based tools have integrated different models to provide a simple one-step platform to help researchers thoroughly evaluate potential drug candidates. To efficiently achieve this approach, a platform for preclinical evaluation must not only predict key ADMET (absorption, distribution, metabolism, excretion and toxicity) properties but also provide some guidance on structural modifications to improve the undesired properties. In this review, we organized and compared several existing integrated web servers that can be adopted in preclinical drug development projects to evaluate the subject of interest. We also introduced our new web server, Virtual Rat, as an alternative choice to profile the properties of drug candidates. In Virtual Rat, we provide not only predictions of important ADMET properties but also possible reasons as to why the model made those structural predictions. Multiple models were implemented into Virtual Rat, including models for predicting human ether-a-go-go-related gene (hERG) inhibition, cytochrome P450 (CYP) inhibition, mutagenicity (Ames test), blood-brain barrier penetration, cytotoxicity and Caco-2 permeability. Virtual Rat is free and has been made publicly available at https://virtualrat.cmdm.tw/.

Decomposition profile data analysis of multiple drug effects identifies endoplasmic reticulum stress-inducing ability as an unrecognized factor

Chemicals have multiple effects in biological systems. Because their on-target effects dominate the output, their off-target effects are often overlooked and can sometimes cause dangerous adverse events. Recently, we developed a novel decomposition profile data analysis method, orthogonal linear separation analysis (OLSA), to analyse multiple effects. In this study, we tested whether OLSA identified the ability of drugs to induce endoplasmic reticulum (ER) stress as a previously unrecognized factor. After analysing the transcriptome profiles of MCF7 cells treated with different chemicals, we focused on a vector characterized by well-known ER stress inducers, such as ciclosporin A. We selected five drugs predicted to be unrecognized ER stress inducers, based on their inducing ability scores derived from OLSA. These drugs actually induced X-box binding protein 1 splicing, an indicator of ER stress, in MCF7 cells in a concentration-dependent manner. Two structurally different representatives of the five test compounds exhibited similar results in HepG2 and HuH7 cells, but not in PXB primary hepatocytes derived from human-liver chimeric mice. These results indicate that our decomposition strategy using OLSA uncovered the ER stress-inducing ability of drugs as an unrecognized effect, the manifestation of which depended on the background of the cells.

Astemizole Sensitizes Adrenocortical Carcinoma Cells to Doxorubicin by Inhibiting Patched Drug Efflux Activity

Adrenocortical carcinoma (ACC) presents a high risk of relapse and metastases with outcomes not improving despite extensive research and new targeted therapies. We recently showed that the Hedgehog receptor Patched is expressed in ACC, where it strongly contributes to doxorubicin efflux and treatment resistance. Here, we report the identification of a new inhibitor of Patched drug efflux, the anti-histaminergic drug astemizole. We show that astemizole enhances the cytotoxic, proapoptotic, antiproliferative and anticlonogenic effects of doxorubicin on ACC cells at concentrations of astemizole or doxorubicin that are not effective by themselves. Our results suggest that a low concentration of astemizole sensitizes ACC cells to doxorubicin, which is a component of the standard treatment for ACC composed of etoposide, doxorubicin, cisplatin and mitotane (EDPM). Patched uses the proton motive force to efflux drugs. This makes its function specific to cancer cells, thereby avoiding toxicity issues that are commonly observed with inhibitors of ABC multidrug transporters. Our data provide strong evidence that the use of astemizole or a derivative in combination with EDPM could be a promising therapeutic option for ACC by increasing the treatment effectiveness at lower doses of EDPM, which would reduce the severe side effects of this regimen.

Novel Therapeutic Approaches of Ion Channels and Transporters in Cancer

The expression and function of many ion channels and transporters in cancer cells display major differences in comparison to those from healthy cells. These differences provide the cancer cells with advantages for tumor development. Accordingly, targeting ion channels and transporters have beneficial anticancer effects including inhibition of cancer cell proliferation, migration, invasion, metastasis, tumor vascularization, and chemotherapy resistance, as well as promoting apoptosis. Some of the molecular mechanisms associating ion channels and transporters with cancer include the participation of oxidative stress, immune response, metabolic pathways, drug synergism, as well as noncanonical functions of ion channels. This diversity of mechanisms offers an exciting possibility to suggest novel and more effective therapeutic approaches to fight cancer. Here, we review and discuss most of the current knowledge suggesting novel therapeutic approaches for cancer therapy targeting ion channels and transporters. The role and regulation of ion channels and transporters in cancer provide a plethora of exceptional opportunities in drug design, as well as novel and promising therapeutic approaches that may be used for the benefit of cancer patients.

hERG-Att: Self-attention-based deep neural network for predicting hERG blockers

A voltage-gated potassium channel encoded by the human ether-à-go-go-related gene (hERG) regulates cardiac action potential, and it is involved in cardiotoxicity with compounds that inhibit its activity. Therefore, the screening of hERG channel blockers is a mandatory step in the drug discovery process. The screening of hERG blockers by using conventional methods is inefficient in terms of cost and efforts. This has led to the development of many in silico hERG blocker prediction models. However, constructing a high-performance predictive model with interpretability on hERG blockage by certain compounds is a major obstacle. In this study, we developed the first, attention-based, interpretable model that predicts hERG blockers and captures important hERG-related compound substructures. To do that, we first collected various datasets, ranging from public databases to publicly available private datasets, to train and test the model. Then, we developed a precise and interpretable hERG blocker prediction model by using deep learning with a self-attention approach that has an appropriate molecular descriptor, Morgan fingerprint. The proposed prediction model was validated, and the validation result showed that the model was well-optimized and had high performance. The test set performance of the proposed model was significantly higher than that of previous fingerprint-based conventional machine learning models. In particular, the proposed model generally had high accuracy and F1 score thereby, representing the model's predictive reliability. Furthermore, we interpreted the calculated attention score vectors obtained from the proposed prediction model and demonstrated the important structural patterns that are represented in hERG blockers. In summary, we have proposed a powerful and interpretable hERG blocker prediction model that can reduce the overall cost of drug discovery by accurately screening for hERG blockers and suggesting hERG-related substructures.

Structures Illuminate Cardiac Ion Channel Functions in Health and in Long QT Syndrome

The cardiac action potential is critical to the production of a synchronized heartbeat. This electrical impulse is governed by the intricate activity of cardiac ion channels, among them the cardiac voltage-gated potassium (Kv) channels KCNQ1 and hERG as well as the voltage-gated sodium (Nav) channel encoded by SCN5A. Each channel performs a highly distinct function, despite sharing a common topology and structural components. These three channels are also the primary proteins mutated in congenital long QT syndrome (LQTS), a genetic condition that predisposes to cardiac arrhythmia and sudden cardiac death due to impaired repolarization of the action potential and has a particular proclivity for reentrant ventricular arrhythmias. Recent cryo-electron microscopy structures of human KCNQ1 and hERG, along with the rat homolog of SCN5A and other mammalian sodium channels, provide atomic-level insight into the structure and function of these proteins that advance our understanding of their distinct functions in the cardiac action potential, as well as the molecular basis of LQTS. In this review, the gating, regulation, LQTS mechanisms, and pharmacological properties of KCNQ1, hERG, and SCN5A are discussed in light of these recent structural findings.

Lerisetron Analogues with Antimalarial Properties: Synthesis, Structure-Activity Relationship Studies, and Biological Assessment

A phenotypic whole cell high-throughput screen against the asexual blood and liver stages of the malaria parasite identified a benzimidazole chemical series. Among the hits were the antiemetic benzimidazole drug Lerisetron 1 (IC₅₀ NF54 = 0.81 μM) and its methyl-substituted analogue 2 (IC₅₀ NF54 = 0.098 μM). A medicinal chemistry hit to lead effort led to the identification of chloro-substituted analogue 3 with high potency against the drug-sensitive NF54 (IC₅₀ NF54 = 0.062 μM) and multidrug-resistant K1 (IC₅₀ K1 = 0.054 μM) strains of the human malaria parasite Plasmodium falciparum. Compounds 2 and 3 gratifyingly showed in vivo efficacy in both Plasmodium berghei and P. falciparum mouse models of malaria. Cardiotoxicity risk as expressed in strong inhibition of the human ether-a-go-go-related gene (hERG) potassium channel was identified as a major liability to address. This led to the synthesis and biological assessment of around 60 analogues from which several compounds with improved antiplasmodial potency, relative to the lead compound 3, were identified.

Drug compound screening in single and integrated multi-organoid body-on-a-chip systems

Current practices in drug development have led to therapeutic compounds being approved for widespread use in humans, only to be later withdrawn due to unanticipated toxicity. These occurrences are largely the result of erroneous data generated by in vivo and in vitro preclinical models that do not accurately recapitulate human physiology. Herein, a human primary cell- and stem cell-derived 3D organoid technology is employed to screen a panel of drugs that were recalled from market by the FDA. The platform is comprised of multiple tissue organoid types that remain viable for at least 28 days, in vitro. For many of these compounds, the 3D organoid system was able to demonstrate toxicity. Furthermore, organoids exposed to non-toxic compounds remained viable at clinically relevant doses. Additional experiments were performed on integrated multi-organoid systems containing liver, cardiac, lung, vascular, testis, colon, and brain. These integrated systems proved to maintain viability and expressed functional biomarkers, long-term. Examples are provided that demonstrate how multi-organoid 'body-on-a-chip' systems may be used to model the interdependent metabolism and downstream effects of drugs across multiple tissues in a single platform. Such 3D in vitro systems represent a more physiologically relevant model for drug screening and will likely reduce the cost and failure rate associated with the approval of new drugs.

Ion Channel Dysregulation in Head and Neck Cancers: Perspectives for Clinical Application

Head and neck cancers are a highly complex and heterogeneous group of malignancies that involve very diverse anatomical structures and distinct aetiological factors, treatments and clinical outcomes. Among them, head and neck squamous cell carcinomas (HNSCC) are predominant and the sixth most common cancer worldwide with still low survival rates. Omic technologies have unravelled the intricacies of tumour biology, harbouring a large diversity of genetic and molecular changes to drive the carcinogenesis process. Nonetheless, this remarkable heterogeneity of molecular alterations opens up an immense opportunity to discover novel biomarkers and develop molecular-targeted therapies. Increasing evidence demonstrates that dysregulation of ion channel expression and/or function is frequently and commonly observed in a variety of cancers from different origin. As a consequence, the concept of ion channels as potential membrane therapeutic targets and/or biomarkers for cancer diagnosis and prognosis has attracted growing attention. This chapter intends to comprehensively and critically review the current state-of-art ion channel dysregulation specifically focusing on head and neck cancers and to formulate the major challenges and research needs to translate this knowledge into clinical application. Based on current reported data, various voltage-gated potassium (K_v) channels (i.e. K_v3.4, K_v10.1 and K_v11.1) have been found frequently aberrantly expressed in HNSCC as well as precancerous lesions and are highlighted as clinically and biologically relevant features in both early stages of tumourigenesis and late stages of disease progression. More importantly, they also emerge as promising candidates as cancer risk markers, tumour markers and potential anti-proliferative and anti-metastatic targets for therapeutic interventions; however, the oncogenic properties seem to be independent of their ion-conducting function.

Improving prediction of torsadogenic risk in the CiPA in silico model by appropriately accounting for clinical exposure

Any adverse event is reliant on three properties: the appropriate pharmacology to trigger the event, the appropriate exposure of compound, and intrinsic patient factors. Each alone is necessary but insufficient to predict the event. The Comprehensive in vitro Proarrhythmia Assessment (CiPA) initiative attempts to predict the risk of torsade de pointes (TdP) by focusing on an in-silico model with thresholds determined at modest multiples of the therapeutic exposure for the parent molecule. This emphasizes the pharmacologic properties necessary for TdP but does not account for situations where clinical exposure may be higher, or where hERG potassium channel active metabolites are involved. Could accounting for clinical worst-case scenarios and metabolites, as is already standard practice in thorough QTc studies, improve the prediction algorithm? Terfenadine, a drug classed as "Intermediate" risk by CiPA, was assessed differently in the in-silico model validation. The clinical concentration of terfenadine used for the model was the exposure in the presence of metabolic inhibition representing a 14 to 40-fold increase in exposure compared to the therapeutic plasma concentration. However, several other "Intermediate" risk compounds are also known to be sensitive to metabolic inhibition and/or to have therapeutically active major metabolites, some of which are known to block hERG. Risperidone and astemizole are relevant examples. If only parent exposure is used to calculate a therapeutic window, risperidone has a relatively large multiple between clinical exposure and the hERG potency. Using this exposure of risperidone, the drug borders the "Intermediate" and "Low/No" risk categories for the CiPA in-silico model's TdP metric. The desmethyl metabolite of astemizole likely contributes significantly to the effects on cardiac repolarization, being equipotent on hERG but circulating at much higher levels than parent. Recalculating the TdP metric and margin values for terfenadine, risperidone and astemizole using the unbound concentration normally associated with treatment and a clinical worst case changes the qNet metric to higher risk values and illustrates the potential benefit to the algorithm of consistently using a clinical high exposure scenario accounting for all "hERG-active species". This exercise suggests repeating the model qualification accounting for clinical exposures and metabolites under 'stressed' scenarios would improve prediction of the TdP risk.

PRC2 is high maintenance

As the process that silences gene expression ensues during development, the stage is set for the activity of Polycomb-repressive complex 2 (PRC2) to maintain these repressed gene profiles. PRC2 catalyzes a specific histone posttranslational modification (hPTM) that fosters chromatin compaction. PRC2 also facilitates the inheritance of this hPTM through its self-contained "write and read" activities, key to preserving cellular identity during cell division. As these changes in gene expression occur without changes in DNA sequence and are inherited, the process is epigenetic in scope. Mutants of mammalian PRC2 or of its histone substrate contribute to the cancer process and other diseases, and research into these aberrant pathways is yielding viable candidates for therapeutic targeting. The effectiveness of PRC2 hinges on its being recruited to the proper chromatin sites; however, resolving the determinants to this process in the mammalian case was not straightforward and thus piqued the interest of many in the field. Here, we chronicle the latest advances toward exposing mammalian PRC2 and its high maintenance.

Molecular Docking Guided Grid-Independent Descriptor Analysis to Probe the Impact of Water Molecules on Conformational Changes of hERG Inhibitors in Drug Trapping Phenomenon

Human ether a-go-go related gene (hERG) or KV11.1 potassium channels mediate the rapid delayed rectifier current (I_Kr) in cardiac myocytes. Drug-induced inhibition of hERG channels has been implicated in the development of acquired long QT syndrome type (aLQTS) and fatal arrhythmias. Several marketed drugs have been withdrawn for this reason. Therefore, there is considerable interest in developing better tests for predicting drugs which can block the hERG channel. The drug-binding pocket in hERG channels, which lies below the selectivity filter, normally contains K⁺ ions and water molecules. In this study, we test the hypothesis that these water molecules impact drug binding to hERG. We developed 3D QSAR models based on alignment independent descriptors (GRIND) using docked ligands in open and closed conformations of hERG in the presence (solvated) and absence (non-solvated) of water molecules. The ligand–protein interaction fingerprints (PLIF) scheme was used to summarize and compare the interactions. All models delineated similar 3D hERG binding features, however, small deviations of about ~0.4 Å were observed between important hotspots of molecular interaction fields (MIFs) between solvated and non-solvated hERG models. These small changes in conformations do not affect the performance and predictive power of the model to any significant extent. The model that exhibits the best statistical values was attained with a cryo_EM structure of the hERG channel in open state without water. This model also showed the best R² of 0.58 and 0.51 for the internal and external validation test sets respectively. Our results suggest that the inclusion of water molecules during the docking process has little effect on conformations and this conformational change does not impact the predictive ability of the 3D QSAR models.

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

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

Discovery of a Lead Triphenylethanamine Cholesterol Ester Transfer Protein (CETP) Inhibitor

Lead optimization of the diphenylpyridylethanamine (DPPE) and triphenylethanamine (TPE) series of CETP inhibitors to improve their pharmaceutical profile is described. Polar groups at the N-terminus position in the DPPE series resulted in further improvement in potency and pharmaceutical properties concomitant with retaining the safety, efficacy, and pharmacokinetic (PK) profile. A structure-activity relationship observed in the DPPE series was extended to the corresponding analogs in the more potent TPE series, and further optimization resulted in the identification of 2-amino-N-((R)-1-(3-cyclopropoxy-4-fluorophenyl)-1-(3-fluoro-5-(1,1,2,2-tetrafluoroethoxy)phenyl)-2-phenylethyl)-4,4,4-trifluoro-3-hydroxy-3-(trifluoromethyl)butanamide (13). Compound 13 demonstrated no significant changes in either mean arterial blood pressure or heart rate in telemetry rats, had an excellent PK profile, and demonstrated robust efficacy in human CETP/apo-B-100 dual transgenic mice and in hamsters.

Analysis of time-course drug response in rat cardiomyocytes cultured on a pattern of islands

We previously reported the kinetics analysis of cardiomyocyte beating using scanning electrochemical microscopy (SECM). In this study, a stage-top incubator and a capillary micropipette (MP) for delivering drugs were assembled with an SECM instrument, and the responses of rat cardiomyocytes were analyzed under a culture environment after drug stimulation. When adenosine triphosphate (ATP) was delivered to synchronously beating cardiomyocytes, the beating acceleration effect of ATP was counteracted by the synchronously beating network in the culture dish. In contrast, cardiomyocytes cultured on a pattern of islands in a culture dish showed fluctuations in the duration of beating upon the addition of ATP. We also examined the effect of the cardiotoxic agent astemizole on cardiomyocytes and successfully detected motion fluctuations. Therefore, drug stimulation via MPs and beating measurement by SECM are effective routes for the evaluation of drug candidates through the analysis of time-course beating motion fluctuations of the cardiomyocytes.

Blockade of the Human Ether A-Go-Go-Related Gene (hERG) Potassium Channel by Fentanyl

The human ether-a-go-go-related gene (hERG) encodes the pore-forming subunit of the rapidly activating delayed rectifier potassium channel (I_Kr). Drug-mediated or medical condition-mediated disruption of hERG function is the primary cause of acquired long-QT syndrome, which predisposes affected individuals to ventricular arrhythmias and sudden death. Fentanyl abuse poses a serious health concern, with abuse and death rates rising over recent years. As fentanyl has a propensity to cause sudden death, we investigated its effects on the hERG channel. The effects of norfentanyl, the main metabolite, and naloxone, an antidote used in fentanyl overdose, were also examined. Currents of hERG channels stably expressed in HEK293 cells were recorded using the whole-cell voltage-clamp method. When hERG tail currents were analyzed upon -50 mV repolarization after a 50 mV depolarization, fentanyl and naloxone blocked hERG current (I_hERG) with IC₅₀ values of 0.9 and 74.3 μM, respectively, whereas norfentanyl did not block. However, fentanyl-mediated block of I_hERG was voltage dependent. When a voltage protocol that mimics a human ventricular action potential (AP) was used, fentanyl blocked I_hERG with an IC₅₀ of 0.3 μM. Furthermore, fentanyl (0.5 μM) prolonged AP duration and blocked I_Kr in ventricular myocytes isolated from neonatal rats. The concentrations of fentanyl used in this study were higher than seen with clinical use but overlap with postmortem overdose concentrations. Although mechanisms of fentanyl-related sudden death need further investigation, blockade of hERG channels may contribute to the death of individuals with high-concentration overdose or compromised cardiac repolarization.

Drug-likeness of linear pentamidine analogues and their impact on the hERG K+channel – correlation with structural features

The pentamidines with S atoms or sulfanilide groups in the linker have favorable drug-likeness parameters and low toxicity.