Bibliographic record
Abstract
This review updates the existing knowledge suggesting a role for the D3 receptor in schizophrenia and drug addiction. The D3 receptor is expressed in brain regions controlling reward, emotions, and motivation. Antipsychotics bind in vitro to the D3 receptor with similar affinity as to the D2 receptor, and occupancy of D3 receptors in vivo by these compounds given acutely at clinical dosage have been demonstrated in Positron Emission Tomography (PET) studies. The D3 receptor modulates glutamatergic pathways from the prefrontal cortex to subcortical areas, either directly by interacting with N-methyl-D-aspartate (NMDA) receptors in the nucleus accumbens, or indirectly by controlling dopamine release from ventral tegmental area neurons. In animals, D3 receptor antagonists reverse behavioral manifestations of NMDA receptor blockade and improve cognitive performances in various paradigms. Two D3 receptor-selective compounds have reached clinical trials in schizophrenia, with negative results seemingly due to insufficient target engagement; the results with a third compound, F17464, have not been disclosed yet. There is converging evidence that D3 receptors do not control the reinforcing effects of drugs of abuse (with the exception of alcohol under low requirement), but rather affects the motivation to take the drugs under high requirement, reactivity to drug-associated cues, and drug-seeking behaviors triggered by stimuli associated with relapse in humans. D3 receptor expression measured by PET is upregulated in humans with various drug addictions. A single administration of the D3 receptor-selective antagonist, GSK598809, in humans transiently alleviated craving in smokers after overnight abstinence. The clinical development of D3-selective compounds will benefit from initial assessment of target engagement through the use of PET.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".