Single dose varenicline may trigger epileptic activity

Interaction of Varenicline with Classic Antiseizure Medications in the Mouse Maximal Electroshock-Induced Seizure Model

Varenicline (VAR) is a partial agonist of brain α4β2 nicotinic acetylcholine receptors recommended as a first line pharmacotherapy for smoking cessation. The aim of this study was to examine whether VAR affects the protective activity of four classic antiseizure medications, i.e., carbamazepine (CBZ), phenobarbital (PB), phenytoin (PHT), and valproate (VPA) on maximal electroshock (MES)-induced seizures, which may serve as an experimental model of human-generalized tonic-clonic seizures in mice. VAR administered intraperitoneally (i.p.) at a subthreshold dose of 0.5 mg/kg decreased the protective activity of CBZ against MES-induced convulsions, increasing its median effective dose (ED50) from 10.92 ± 1.0 to 18.15 ± 1.73 mg/kg (p < 0.01). The effect of VAR was dose-dependent because a lower dose of VAR (0.25 mg/kg) failed to antagonize the protective activity of CBZ. VAR administered at the subthreshold dose of 0.5 mg/kg had no impact on the protective activity of PB, PHT, and VPA in the mouse MES model. The inhibitory effect of VAR on the protective activity of CBZ against tonic-clonic convulsions most likely resulted from the pharmacodynamic mechanism(s) and was not associated with the changes in total brain concentrations of CBZ. VAR-evoked alterations in the anticonvulsive activity of CBZ may be of serious concern for epileptic tobacco smokers.

Molecular Mingling: Multimodal Predictions of Ligand Promiscuity in Pentameric Ligand-Gated Ion Channels

Background: Human pentameric ligand-gated ion channels (pLGICs) comprise nicotinic acetylcholine receptors (nAChRs), 5-hydroxytryptamine type 3 receptors (5-HT3Rs), zinc-activated channels (ZAC), γ-aminobutyric acid type A receptors (GABAARs) and glycine receptors (GlyRs). They are recognized therapeutic targets of some of the most prescribed drugs like general anesthetics, anxiolytics, smoking cessation aids, antiemetics and many more. Currently, approximately 100 experimental structures of pLGICs with ligands bound exist in the protein data bank (PDB). These atomic-level 3D structures enable the generation of a comprehensive binding site inventory for the superfamily and the in silico prediction of binding site properties. Methods: A panel of high throughput in silico methods including pharmacophore screening, conformation analysis and descriptor calculation was applied to a selection of allosteric binding sites for which in vitro screens are lacking. Variant abundance near binding site forming regions and computational docking complement the approach. Results: The structural data reflects known and novel binding sites, some of which may be unique to individual receptors, while others are broadly conserved. The membrane spanning domain, comprising four highly conserved segments, contains ligand interaction sites for which in vitro assays suitable for high throughput screenings are critically lacking. This is also the case for structurally more variable novel sites in the extracellular domain. Our computational results suggest that the phytocannabinoid Δ9-tetrahydrocannabinol (Δ9-THC) can utilize multiple pockets which are likely to exist on most superfamily members. Conclusion: With this study, we explore the potential for polypharmacology among pLGICs. Our data suggest that ligands can display two forms of promiscuity to an extent greater than what has been realized: 1) Ligands can interact with homologous sites in many members of the superfamily, which bears toxicological relevance. 2) Multiple pockets in distinct localizations of individual receptor subtypes share common ligands, which counteracts efforts to develop selective agents. Moreover, conformational states need to be considered for in silico drug screening, as certain binding sites display considerable flexibility. In total, this work contributes to a better understanding of polypharmacology across pLGICs and provides a basis for improved structure guided in silico drug development and drug derisking.

Raster plots machine learning to predict the seizure liability of drugs and to identify drugs

In vitro microelectrode array (MEA) assessment using human induced pluripotent stem cell (iPSC)-derived neurons holds promise as a method of seizure and toxicity evaluation. However, there are still issues surrounding the analysis methods used to predict seizure and toxicity liability as well as drug mechanisms of action. In the present study, we developed an artificial intelligence (AI) capable of predicting the seizure liability of drugs and identifying drugs using deep learning based on raster plots of neural network activity. The seizure liability prediction AI had a prediction accuracy of 98.4% for the drugs used to train it, classifying them correctly based on their responses as either seizure-causing compounds or seizure-free compounds. The AI also made concentration-dependent judgments of the seizure liability of drugs that it was not trained on. In addition, the drug identification AI implemented using the leave-one-sample-out scheme could distinguish among 13 seizure-causing compounds as well as seizure-free compound responses, with a mean accuracy of 99.9 ± 0.1% for all drugs. These AI prediction models are able to identify seizure liability concentration-dependence, rank the level of seizure liability based on the seizure liability probability, and identify the mechanism of the action of compounds. This holds promise for the future of in vitro MEA assessment as a powerful, high-accuracy new seizure liability prediction method.

The risk of varenicline-induced seizure among those who have attempted to quit smoking using pharmacotherapy

OBJECTIVE

Varenicline is an effective smoking cessation agent; however, its use is limited because of black box warnings issued by regulatory agencies in the U.S. and Australia. The U.S. Food and Drug Administration updated the label for varenicline in 2015 to warn about the risk of varenicline-induced seizures. The objective of this study was to examine the risk of seizure associated with varenicline use.

METHODS

A nested case-control study was performed using IMS LifeLink PharMetrics Plus administrative claims data (2009-2015). The outcome was presumptive seizures. All smokers making an attempt to quit smoking and having no recent seizure events were included in the nest. Cases and controls were matched (1:4) on age (±5 years), sex, index date (±30 days), event date, and duration of enrollment. An exposure period of 90 days preceding the event date was used. Chi-square tests were used to compare the characteristics of cases and controls. Conditional logistic regression was conducted to determine if an association between presumptive seizures and varenicline use exists.

RESULTS

Our final sample was comprised of 1342 cases and 5368 controls. The adjusted analysis showed that odds of a seizure for patients with a varenicline prescription were 1.09 (confidence interval [CI] = 0.88-1.36) times those of patients with no varenicline exposure.

CONCLUSIONS

This study did not find a significant association between varenicline and increased risk of presumptive seizures. These findings raise questions regarding the necessity for a warning label for increased risk of seizures associated with varenicline.

Nephrotoxic effects of varenicline as the most effective drug used for smoking cessation: a preliminary experimental study

PURPOSE

Varenicline is a new most effective drug for smoking cessation. Its effect on kidney functions remains unclear. This study purposed to investigate whether varenicline causes nephrotoxicity in rats.

METHODS

Fifteen rats were randomly assigned to three groups: control, 0.0125 mg kg(-1) varenicline and 0.025 mg kg(-1) varenicline (single dose for 3 days, i.p.). Before and after experimental period, serum neutrophil gelatinase-associated lipocalin, creatinine and urea levels were measured. Total oxidant and antioxidant status were measured in kidney homogenates. Histological examination was performed in kidney.

RESULTS

The nephrotoxic effects of varenicline were detected by histopathological and biochemical examinations in the varenicline treatment groups. No change was observed in the control group.

CONCLUSIONS

These findings firstly indicate that a 3-day varenicline treatment causes nephrotoxic effects in rats.