The putative lithium-mimetic ebselen reduces impulsivity in rodent models
Bibliographic record
Abstract
Background: Deficits in impulse control feature in many psychiatric conditions including bipolar disorder, suicidality and addictions. Lithium lowers impulsivity in clinical populations and decreases pathological gambling in experimental medicine studies, but suffers from adverse effects, poor compliance and a low therapeutic index. Aims: Recently we identified that the neuroprotective agent ebselen, which is reportedly safe in humans, inhibited inositol monophosphatase (IMPase), a candidate lithium mechanism. Ebselen also reduced 5-HT receptor (5-HT 2A ) function which predicts impulsivity lowering properties. Here we investigated the effect of ebselen in rat models of impulsive behaviour. Methods: Ebselen was tested in two models of impulsivity with human analogues: the five-choice serial reaction time task (5-CSRTT) and rodent gambling task (rGT). The main outcome measures were premature responses (5-CSRTT and rGT) and choice behaviour (rGT), which model motor impulsivity and choice impulsivity, respectively. Results: At doses that decreased 5-HT 2A receptor function (DOI-induced wet dog shakes), ebselen decreased premature responding in the 5-CSRTT both in the absence and presence of cocaine. The 5-HT 2A receptor antagonist MDL 100,907 also reduced premature responding in the 5-CSRTT although not in the presence of cocaine. In the rGT ebselen showed a tendency to reduce premature responding but had no effect on choice behaviour. Conclusions: These findings suggest that ebselen preferentially reduces motor impulsivity over choice impulsivity, and that inhibition of 5-HT 2A receptor function is a contributing mechanism. Collectively, these data support the repurposing of ebselen as an anti-impulsive treatment and fast-tracking to clinical trials in patient groups characterised by poor impulse control.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".