Preconditioning by ultra-low dose of tramadol reduces the severity of tramadol-induced seizure: Contribution of glutamate receptors

Metabotropic glutamate receptors (mGluRs) in epileptogenesis: an update on abnormal mGluRs signaling and its therapeutic implications

Epilepsy is a neurological disorder characterized by high morbidity, high recurrence, and drug resistance. Enhanced signaling through the excitatory neurotransmitter glutamate is intricately associated with epilepsy. Metabotropic glutamate receptors (mGluRs) are G protein-coupled receptors activated by glutamate and are key regulators of neuronal and synaptic plasticity. Dysregulated mGluR signaling has been associated with various neurological disorders, and numerous studies have shown a close relationship between mGluRs expression/activity and the development of epilepsy. In this review, we first introduce the three groups of mGluRs and their associated signaling pathways. Then, we detail how these receptors influence epilepsy by describing the signaling cascades triggered by their activation and their neuroprotective or detrimental roles in epileptogenesis. In addition, strategies for pharmacological manipulation of these receptors during the treatment of epilepsy in experimental studies is also summarized. We hope that this review will provide a foundation for future studies on the development of mGluR-targeted antiepileptic drugs.

Investigation of the Mechanisms of Tramadol-Induced Seizures in Overdose in the Rat

Tramadol overdose is frequently associated with the onset of seizures, usually considered as serotonin syndrome manifestations. Recently, the serotoninergic mechanism of tramadol-attributed seizures has been questioned. This study’s aim was to identify the mechanisms involved in tramadol-induced seizures in overdose in rats. The investigations included (1) the effects of specific pretreatments on tramadol-induced seizure onset and brain monoamine concentrations, (2) the interaction between tramadol and γ-aminobutyric acid (GABA)A receptors in vivo in the brain using positron emission tomography (PET) imaging and 11C-flumazenil. Diazepam abolished tramadol-induced seizures, in contrast to naloxone, cyproheptadine and fexofenadine pretreatments. Despite seizure abolishment, diazepam significantly enhanced tramadol-induced increase in the brain serotonin (p < 0.01), histamine (p < 0.01), dopamine (p < 0.05) and norepinephrine (p < 0.05). No displacement of 11C-flumazenil brain kinetics was observed following tramadol administration in contrast to diazepam, suggesting that the observed interaction was not related to a competitive mechanism between tramadol and flumazenil at the benzodiazepine-binding site. Our findings do not support the involvement of serotoninergic, histaminergic, dopaminergic, norepinephrine or opioidergic pathways in tramadol-induced seizures in overdose, but they strongly suggest a tramadol-induced allosteric change of the benzodiazepine-binding site of GABAA receptors. Management of tramadol-poisoned patients should take into account that tramadol-induced seizures are mainly related to a GABAergic pathway.

Effects of naloxone and diazepam on blood glucose levels in tramadol overdose using generalized estimating equation (GEE) model; (an experimental study)

BACKGROUND

Tramadol is a synthetic opioid and poisoning is increasing around the world day by day. Various treatments are applied for tramadol poisoning. Due to the unknown effects of tramadol poisoning and some of its treatments on blood glucose levels, this study was conducted to investigate the overdose of tramadol and its common treatments (naloxone, diazepam), and their combination on blood glucose levels in male rats.

METHODS

This study was conducted in 45 male Wistar rats. The animals were randomly divided into five groups of 9. They received a 75 mg/kg dose of tramadol alone with naloxone, diazepam, and a combination of both of these two drugs. On the last day, animals' tail vein blood glucose levels (BGL) were measured using a glucometer at different times, including before the tramadol injection (baseline) and 1 hour, 3 hours, and 6 hours after wards. The rats were anesthetized and sacrificed 24 h after the last injection. Blood samples were then taken, and the serum obtained was used to verify the fasting glucose concentration. Data were analyzed using SPSS software at a significance level of 0.05 using a one-way analysis of variance (ANOVA) and a generalized estimating equation (GEE).

RESULTS

According to the GEE model results, the diazepam-tramadol and naloxone-diazepam-tramadol groups showed blood glucose levels five units higher than the tramadol group (p < 0.05). The diazepam-tramadol group had significantly higher blood glucose levels than the naloxone-tramadol group (p < 0.05). The mean blood glucose levels before the intervention, 3 hours and 6 hours after the injection of tramadol did not differ between the groups, but the blood glucose levels 1 hour after the injection of tramadol in the group of naloxone-tramadol were significantly lower than in the control group (p < 0.05). Blood glucose levels did not differ between the groups 24 h after injection of tramadol.

CONCLUSION

The results of the present study showed tramadol overdose does not affect blood glucose levels. The diazepam-tramadol combination and the diazepam-naloxone-tramadol combination caused an increase in blood glucose levels.

Histograms of Frequency-Intensity Distribution Deep Learning to Predict the Seizure Liability of Drugs in Electroencephalography

Detection of seizures as well as that of seizure auras is effective in improving the predictive accuracy of seizure liability of drugs. Whereas electroencephalography has been known to be effective for the detection of seizure liability, no established methods are available for the detection of seizure auras. We developed a method for detecting seizure auras through machine learning using frequency-characteristic images of electroencephalograms. Histograms of frequency-intensity distribution prepared from electroencephalograms of rats analyzed during seizures induced with 4-aminopyridine (6 mg/kg), strychnine (3 mg/kg), and pilocarpine (400 mg/kg), were used to create an artificial intelligence (AI) system that learned the features of frequency-characteristic images during seizures. The AI system detected seizure states learned in advance with 100% accuracy induced even by convulsants acting through different mechanisms, and the risk of seizure before a seizure was detected in general observation. The developed AI system determined that the unlearned convulsant Tramadol (150 mg/kg) was the risk of seizure and the negative compounds aspirin and vehicle were negative. Moreover, the AI system detected seizure liability even in electroencephalography data associated with the use of 4-aminopyridine (3 mg/kg), strychnine (1 mg/kg), and pilocarpine (150 mg/kg), which did not induce seizures detectable in general observation. These results suggest that the AI system developed herein is an effective means for electroencephalographic detection of seizure auras, raising expectations for its practical use as a new analytical method that allows for the sensitive detection of seizure liability of drugs that has been overlooked previously in preclinical studies.