GL-II-73, a Positive Allosteric Modulator of α5GABAA Receptors, Reverses Dopamine System Dysfunction Associated with Pilocarpine-Induced Temporal Lobe Epilepsy
Bibliographic record
Abstract
Although seizures are a hallmark feature of temporal lobe epilepsy (TLE), psychiatric comorbidities, including psychosis, are frequently associated with TLE and contribute to decreased quality of life. Currently, there are no defined therapeutic protocols to manage psychosis in TLE patients, as antipsychotic agents may induce epileptic seizures and are associated with severe side effects and pharmacokinetic and pharmacodynamic interactions with antiepileptic drugs. Thus, novel treatment strategies are necessary. Several lines of evidence suggest that hippocampal hyperactivity is central to the pathology of both TLE and psychosis; therefore, restoring hippocampal activity back to normal levels may be a novel therapeutic approach for treating psychosis in TLE. In rodent models, increased activity in the ventral hippocampus (vHipp) results in aberrant dopamine system function, which is thought to underlie symptoms of psychosis. Indeed, we have previously demonstrated that targeting α5-containing γ-aminobutyric acid receptors (α5GABAARs), an inhibitory receptor abundant in the hippocampus, with positive allosteric modulators (PAMs), can restore dopamine system function in rodent models displaying hippocampal hyperactivity. Thus, we posited that α5-PAMs may be beneficial in a model used to study TLE. Here, we demonstrate that pilocarpine-induced TLE is associated with increased VTA dopamine neuron activity, an effect that was completely reversed by intra-vHipp administration of GL-II-73, a selective α5-PAM. Further, pilocarpine did not alter the hippocampal α5GABAAR expression or synaptic localization that may affect the efficacy of α5-PAMs. Taken together, these results suggest augmenting α5GABAAR function as a novel therapeutic modality for the treatment of psychosis in TLE.
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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) | 0.001 | 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".