Translocator Protein (18 kDa) as a Target for Novel Anxiolytics with a Favourable Side‐Effect Profile
Bibliographic record
Abstract
Anxiety disorders are frequent and highly disabling diseases with considerable socio-economic impact. In the treatment of anxiety disorders, benzodiazepines (BZDs) as direct modulators of the GABA(A) receptor are used as emergency medication because of their rapid onset of action. However, BZDs act also as sedatives and rather quickly induce tolerance and abuse liability associated with withdrawal symptoms. Antidepressants with anxiolytic properties are also applied as first line long-term treatment of anxiety disorders. However, the onset of action of antidepressants takes several weeks. Obviously, novel pharmacological approaches are needed that combine a rapid anxiolytic efficacy with the lack of tolerance induction, abuse liability and withdrawal symptoms. Neurosteroids are potent allosteric modulators of GABA(A) receptor function. The translocator protein (18 kDa) (TSPO) plays an important role for the synthesis of neurosteroids by promoting the transport of cholesterol from the outer to the inner mitochondrial membrane, which is the rate-limiting step in neurosteroidogenesis. Etifoxine not only exerts anxiolytic effects as a TSPO ligand by enhancing neurosteroidogenesis, but also acts as a weak direct GABA(A) receptor enhancer. The TSPO ligand XBD173 enhances GABAergic neurotransmission via the promotion of neurosteroidogenesis without direct effects at the GABA(A) receptor. XBD173 counteracts pharmacologically-induced panic in rodents in the absence of sedation and tolerance development. Also in humans, XBD173 displays antipanic activity and does not cause sedation and withdrawal symptoms after 7 days of treatment. XBD173 therefore appears to be a promising candidate for fast-acting anxiolytic drugs with less severe side-effects than BZDs. In this review, we focus on the pathophysiology of anxiety disorders and TSPO ligands as a novel pharmacological approach in the treatment of these disorders.
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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 | 0.001 |
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".