Recent methods for measuring dopamine D3 receptor occupancy in vivo: importance for drug development
Bibliographic record
Abstract
There is considerable interest in developing highly selective dopamine (DA) D3 receptor ligands for a variety of mental health disorders. DA D3 receptors have been implicated in Parkinson's disease, schizophrenia, anxiety, depression, and substance use disorders. The most concrete evidence suggests a role for the D3 receptor in drug-seeking behaviors. D3 receptors are a subtype of D2 receptors, and traditionally the functional role of these two receptors has been difficult to differentiate. Over the past 10-15 years a number of compounds selective for D3 over D2 receptors have been developed. However, translating these findings into clinical research has been difficult as many of these compounds cannot be used in humans. Therefore, the functional data involving the D3 receptor in drug addiction mostly comes from pre-clinical studies. Recently, with the advent of [(11)C]-(+)-PHNO, it has become possible to image D3 receptors in the human brain with increased selectivity and sensitivity. This is a significant innovation over traditional methods such as [(11)C]-raclopride that cannot differentiate between D2 and D3 receptors. The use of [(11)C]-(+)-PHNO will allow for further delineation of the role of D3 receptors. Here, we review recent evidence that the role of the D3 receptor has functional importance and is distinct from the role of the D2 receptor. We then introduce the utility of analyzing [(11)C]-(+)-PHNO binding by region of interest. This novel methodology can be used in pre-clinical and clinical approaches for the measurement of occupancy of both D3 and D2 receptors. Evidence that [(11)C]-(+)-PHNO can provide insights into the function of D3 receptors in addiction is also presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".