The G‐protein biased partial κ opioid receptor agonist 6′‐GNTI blocks hippocampal paroxysmal discharges without inducing aversion
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: With a prevalence of 1-2%, epilepsies belong to the most frequent neurological diseases worldwide. Although antiepileptic drugs are available since several decades, the incidence of patients that are refractory to medication is still over 30%. Antiepileptic effects of κ opioid receptor (κ receptor) agonists have been proposed since the 1980s. However, their clinical use was hampered by dysphoric side effects. Recently, G-protein biased κ receptor agonists were developed, suggesting reduced aversive effects. EXPERIMENTAL APPROACH: We investigated the effects of the κ receptor agonist U-50488H and the G-protein biased partial κ receptor agonist 6'-GNTI in models of acute seizures and drug-resistant temporal lobe epilepsy and in the conditioned place avoidance (CPA) test. Moreover, we performed slice electrophysiology to understand the functional mechanisms of 6'-GNTI. KEY RESULTS: As previously shown for U-50488H, 6'-GNTI markedly increased the threshold for pentylenetetrazole-induced seizures. All treated mice displayed reduced paroxysmal activity in response to U-50488H (20 mg·kg(-1) ) or 6'-GNTI (10-30 nmoles) treatment in the mouse model of intra-hippocampal injection of kainic acid. Single cell recordings on hippocampal pyramidal cells revealed enhanced inhibitory signalling as potential mechanisms causing the reduction of paroxysmal activity. Effects of 6'-GNTI were blocked in both seizure models by the κ receptor antagonist 5'-GNTI. Moreover, 6'-GNTI did not induce CPA, a measure of aversive effects, while U-50488H did. CONCLUSIONS AND IMPLICATIONS: Our data provide the proof of principle that anticonvulsant/antiseizure and aversive effects of κ receptor activation can be pharmacologically separated in vivo.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".