Selective GABAergic treatment for panic? Investigations in experimental panic induction and panic disorder
Bibliographic record
Abstract
gamma-Aminobutyric acid (GABA) is the most important inhibitory neurotransmitter in the central nervous system (CNS). It exerts its rapid inhibitory action mostly through GABA(A) receptors, which are targets for benzodiazepines, barbiturates, neuroactive steroids and distinct anticonvulsive agents. There is considerable evidence that dysfunction of GABA(A) receptors or dysregulation of GABA concentrations in the CNS (or both) plays an important role in the pathophysiology of panic disorder. Currently, benzodiazepines are the only drugs directly targeting the GABA(A) receptors that are approved for the treatment of anxiety disorders. Because of their well-known anxiolytic effects, they are widely used in this setting, but side effects limit their use in long-term treatment. The question of whether drugs that selectively increase GABA concentrations in the CNS could improve symptoms of anxiety has been discussed. Recent investigations by our group have demonstrated that enhancement of endogenous GABA (through blockade of GABA transaminase by vigabatrin or through inhibition of GABA transporters by tiagabine) exerts anxiolytic effects on experimentally induced panic. Our studies in healthy volunteers have shown that both compounds lead to a significant reduction in panic symptoms elicited by cholecystokinin-tetrapeptide. Moreover, benzodiazepine-like effects on the activity of the hypothalamic-pituitary-adrenal axis have been observed in association with vigabatrin treatment. Small open studies in patients with panic disorder also showed an improvement in panic and anxiety with both compounds. This review summarizes our recent research on the effects of selective GABAergic treatment in experimentally induced panic and outlines the possible role of compounds targeting the GABA binding site of the GABA(A)-benzodiazepine receptor for the treatment of panic and anxiety.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".