Effects of norquetiapine, the active metabolite of quetiapine, on cloned hERG potassium channels

Second-generation antipsychotic quetiapine blocks voltage-dependent potassium channels in coronary arterial smooth muscle cells

Voltage-dependent K+ (Kv) channels play an important role in restoring the membrane potential to its resting state, thereby maintaining vascular tone. In this study, native smooth muscle cells from rabbit coronary arteries were used to investigate the inhibitory effect of quetiapine, an atypical antipsychotic agent, on Kv channels. Quetiapine showed a concentration-dependent inhibition of Kv channels, with an IC50 of 47.98 ± 9.46 μM. Although quetiapine (50 μM) did not alter the steady-state activation curve, it caused a negative shift in the steady-state inactivation curve. The application of 1 and 2 Hz train steps in the presence of quetiapine significantly increased the inhibition of Kv current. Moreover, the recovery time constants from inactivation were prolonged in the presence of quetiapine, suggesting that its inhibitory action on Kv channels is use (state)-dependent. The inhibitory effects of quetiapine were not significantly affected by pretreatment with Kv1.5, Kv2.1, and Kv7 subtype inhibitors. Based on these findings, we conclude that quetiapine inhibits Kv channels in both a concentration- and use (state)-dependent manner. Given the physiological significance of Kv channels, caution is advised in the use of quetiapine as an antipsychotic due to its potential side effects on cardiovascular Kv channels.

Impact of quetiapine on ion channels and contractile dynamics in rat ventricular myocyte

Antipsychotic drugs often lead to adverse effects, including those related to the cardiovascular system. Of these, quetiapine is known to cause significant changes in the QT interval although the underlying mechanism remains mysterious, prompting us to examine its effects on cardiac electrophysiological properties. Therefore, we investigated the effect of quetiapine on contraction, action potential (AP), and the associated membrane currents such as L-type Ca2+ and K+ using the whole-cell patch clamp method to examine its impacts on isolated rat ventricular myocytes. Our results showed that (1) quetiapine reduces cell contractility in a concentration-dependent manner and (2) leads to a significant prolongation in the duration of AP in isolated ventricular myocytes. This effect was both concentration and frequency-dependent; (3) quetiapine significantly decreased the Ca2+, transient outward K+, and steady-state K+ currents. However, only high concentration of quetiapine (100 μM) could significantly change the activation and reactivation kinetics of L-type Ca2+ channels. This study demonstrates that QT extension induced by quetiapine is mainly associated with the prolongation of AP. Moreover, quetiapine caused a significant decrease in contractile force and excitability of ventricular myocytes by suppressing Ca2+ and K+ currents.

Effects of the antipsychotic quetiapine on sleep and breathing: a review of clinical findings and potential mechanisms

Quetiapine is an antipsychotic medication indicated for schizophrenia and bipolar disorder. However, quetiapine also has hypnotic properties and as such is increasingly being prescribed at low doses 'off-label' in people with insomnia symptoms. Pharmacologically, in addition to its dopaminergic properties, quetiapine also modulates multiple other transmitter systems involved in sleep/wake modulation and potentially breathing. However, very little is known about the impact of quetiapine on obstructive sleep apnoea (OSA), OSA endotypes including chemosensitivity, and control of breathing. Given that many people with insomnia also have undiagnosed OSA, it is important to understand the effects of quetiapine on OSA and its mechanisms. Accordingly, this concise review covers the existing knowledge on the effects of quetiapine on sleep and breathing. Further, we highlight the pharmacodynamics of quetiapine and its potential to alter key OSA endotypes to provide potential mechanistic insight. Finally, an agenda for future research priorities is proposed to fill the current key knowledge gaps.

Quetiapine-Related Deaths: In Search of a Surrogate Endpoint

Quetiapine is a second-generation antipsychotic drug available for two and half decades. Due to increased misuse, prescription outside the approved indications, and availability on the black market, it is being encountered in medicolegal autopsies more frequently. For instance, it has been linked to increased mortality rates, most likely due to its adverse effects on the cardiovascular system. Its pharmacokinetic features and significant postmortem redistribution challenge traditional sampling in forensic toxicology. Therefore, a systematic literature review was performed, inclusive of PubMed, the Web of Science-core collection, and the Scopus databases; articles were screened for the terms "quetiapine", "death", and "autopsy" to reevaluate each matrix used as a surrogate endpoint in the forensic toxicology of quetiapine-related deaths. Ultimately, this review considers the results of five studies that were well presented (more than two matrices, data available for all analyses, for instance). The highest quetiapine concentrations were usually measured in the liver tissue. As interpreted by their authors, the results of the considered studies showed a strong correlation between some matrices, but, unfortunately, the studies presented models with poor goodness of fit. The distribution of quetiapine in distinct body compartments/tissues showed no statistically significant relationship with the length of the postmortem interval. Furthermore, this study did not confirm the anecdotal correlation of peripheral blood concentrations with skeletal muscle concentrations. Otherwise, there was no consistency regarding selecting an endpoint for analysis.

P-Glycoprotein-Mediated Interaction Is a Risk Factor for QT Prolongation in Concomitant Use of Antipsychotics and SSRIs as P-Glycoprotein-Mediated Inhibitors: Analysis of the Japanese Adverse Drug Event Report Database

The inhibition of human ether-a-go-go-related gene (hERG) channels is a known cause of QT prolongation triggered by antipsychotic drugs. Our previous studies suggest that P-glycoprotein (P-gp)-mediated drug interactions may lead to increased gastrointestinal absorption of pimozide and its accumulation in cardiomyocytes, thereby enhancing the inhibitory effect of hERG channels. There is a paucity of epidemiological studies examining the risk of QT prolongation by antipsychotic drugs in terms of P-gp-mediated interactions with concomitant drugs. Therefore, using the Japanese Adverse Event Reporting Database, we investigated whether the risk of QT prolongation triggered by antipsychotic drugs associated with hERG inhibition is affected by the concomitant use of selective serotonin reuptake inhibitors (SSRIs) associated with P-gp inhibition. The results showed that the frequency of QT prolongation increased when the antipsychotic drugs quetiapine and sulpiride, which are P-gp substrates, were combined with SSRIs with P-gp inhibition. In contrast, no association with QT prolongation was observed in patients on non-P-gp-substrate antipsychotics, irrespective of the P-gp inhibitory effect of the concomitant SSRI. These results suggest that P-gp-mediated interactions are a risk factor for antipsychotic-induced QT prolongation. There is a need for further investigation into the risks of specific drug combinations.

In silico prediction of hERG blockers using machine learning and deep learning approaches

The human ether-à-go-go-related gene (hERG) is associated with drug cardiotoxicity. If the hERG channel is blocked, it will lead to prolonged QT interval and cause sudden death in severe cases. Therefore, it is important to evaluate the hERG-blocking property of compounds in early drug discovery. In this study, a dataset containing 4556 compounds with IC50 values determined by patch clamp techniques on mammalian lineage cells was collected, and hERG blockers and non-blockers were distinguished according to three single thresholds and two binary thresholds. Four machine learning (ML) algorithms combining four molecular fingerprints and molecular descriptors as well as graph convolutional neural networks (GCNs) were used to construct a series of binary classification models. The results showed that the best models varied for different thresholds. The ML models implemented by support vector machine and random forest performed well based on Morgan fingerprints and molecular descriptors, with AUCs ranging from 0.884 to 0.950. GCN showed superior prediction performance with AUCs above 0.952, which might be related to its direct extraction of molecular features from the original input. Meanwhile, the classification of binary threshold was better than that of single threshold, which could provide us with a more accurate prediction of hERG blockers. At last, the applicability domain for the model was defined, and seven structural alerts that might generate hERG blockage were identified by information gain and substructure frequency analysis. Our work would be beneficial for identifying hERG blockers in chemicals.

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.

Quetiapine and other antipsychotics combined with opioids in legal autopsy cases: A random finding or cause of fatal outcome?

Opioid poisoning is a frequent cause of death in drug addicts and occurs with opioid treatment. Quetiapine is often found in forensic autopsies and may increase the risk of fatal opioid poisoning by enhancing sedation, respiratory depression, hypotension and QT prolongation. We systematically searched for studies of acute toxicity of quetiapine or other antipsychotics combined with morphine or methadone. Case reports describing toxicity of quetiapine combined with morphine or methadone were also included. We retrieved one human study that observed pharmacokinetic interaction between quetiapine and methadone, and 16 other human studies. Fourteen investigated the combination of droperidol and morphine in treatment doses, and some indicated an additive sedative effect. Five animal studies with acepromazine in combination with morphine or methadone were located and indicated an additive effect on sedation and hypotension. Six forensic case reports in which death could have been caused solely by quetiapine, the opioid, or other drugs were found. Thus, acute toxicity of quetiapine combined with morphine or methadone has not been studied. Because of quetiapine's effects on alpha-adrenoceptors, muscarinic and histamine receptors, human ether-a-go-go-channels and methadone kinetics, we suggest further research to clarify if the indicated additive effects of opioids and droperidol or acepromazine are also true for quetiapine.

Norquetiapine blocks the human cardiac sodium channel Nav1.5 in a state-dependent manner

Quetiapine, an atypical antipsychotic drug, is used for the treatment of schizophrenia and acute mania. Although a previous report showed that quetiapine blocked hERG potassium current, quetiapine has been considered relatively safe in terms of cardiovascular side effects. In the present study, we used the whole-cell patch-clamp technique to investigate the effect that quetiapine and its major metabolite norquetiapine can exert on human cardiac sodium channels (hNa_v1.5). The half-maximal inhibitory concentrations of quetiapine and norquetiapine at a holding potential of -90 mV near the resting potential of cardiomyocytes were 30 and 6 μM, respectively. Norquetiapine as well as quetiapine was preferentially bound in the inactivated state of the hNa_v1.5 channel. Norquetiapine slowed the recovery from inactivation of hNa_v1.5 and consequently induced strong use-dependent inhibition. Our results indicate that norquetiapine blocks hNa_v1.5 current in concentration-, state- and use-dependent manners, suggesting that the blockade of hNa_v1.5 current by norquetiapine may shorten the cardiac action potential duration and reduce the risk of QT interval prolongation induced by the inhibition of hERG potassium currents.

Approach to Evaluating QT Prolongation of Quetiapine Fumarate in Late Stage of Clinical Development Using Concentration-QTc Modeling and Simulation in Japanese Patients With Bipolar Disorder

PURPOSE

Quetiapine has been reported to prolong the QT interval, and has been used as a positive control in thorough QT studies. The objective of the present study was to evaluate, in the late stages of clinical development, the QT-prolongation effects of the extended-release (XR) formulation of quetiapine at the approved dose in Japanese patients with bipolar disorder, using concentration-QT modeling and simulation.

METHODS

Plasma concentrations of quetiapine and 4 of its metabolites (M1, M2, M4, and M5), and the QT interval corrected using the Fridericia formula (QTcF), were used for the concentration-QT analysis. Data from intensive electrocardiogram monitoring at predose and at 4, 6, 10, and 24 h after the administration of the last dose were pooled from a Phase I trial (6949-CL-0006) and from sparse sampling in late-stage clinical trials (6949-CL-0005, -0021, -0022, and -0023) in Japanese patients (N = 505). The upper limit of 1-sided 95% confidence interval (CI) of the changes from baseline in QTcF (ΔQTcF) at the geometric mean Cmax of a therapeutic dose of 300 mg once daily was predicted using a linear mixed-effects model, with the intercept as a random effect specifying a subject effect.

FINDINGS

For quetiapine and M2, but not M1, M4, or M5, positive slopes were observed between ΔQTcF and concentration. The predicted upper limits of the 1-sided 95% CIs did not exceed the regulatory threshold of 10 msec. Therefore, QTc prolongation is unlikely to be clinically relevant at the approved dose of quetiapine XR.

IMPLICATIONS

In this pooled data analysis of the QT-prolongation effects of the quetiapine XR, positive relationships between ΔQTcF and quetiapine and M2 concentrations were observed. However, the predicted upper limits of the 1-sided 95% CIs did not exceed 10 msec. Therefore, QTc prolongation is unlikely to be clinically relevant at the approved dose. ClinicalTrials.gov identifiers: NCT01725282, NCT01919008, NCT01725308, NCT01737268, and NCT02362412.

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs

Determining the target genes that interact with drugs—drug–target interactions—plays an important role in drug discovery. Identification of drug–target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug–target interactions. We constructed a drug–target heterogeneous network that contains the drug similarities based on the chemical structures of drugs, the target similarities based on target sequences, and the known drug–target interactions. The topological information of the network was captured by random walks to update the similarities between drugs or targets. The paths between drugs and targets could be divided into multiple categories, and the features of each category of paths were extracted. We constructed a prediction model based on gradient boosting decision trees. The model establishes multiple decision trees with the extracted features and obtains the interaction scores between drugs and targets. DTIGBDT is a method of ensemble learning, and it effectively reduces the impact of class imbalance. The experimental results indicate that DTIGBDT outperforms several state-of-the-art methods for drug–target interaction prediction. In addition, case studies on Quetiapine, Clozapine, Olanzapine, Aripiprazole, and Ziprasidone demonstrate the ability of DTIGBDT to discover potential drug–target interactions.