[<sup>11</sup>C]‐DASB, a tool for in vivo measurement of SSRI‐induced occupancy of the serotonin transporter: PET characterization and evaluation in cats
Bibliographic record
Abstract
The in vivo pharmacological profile of [(11)C]-DASB, a new radioligand developed for in vivo imaging of the serotonin transporter (SERT), was evaluated in the cat brain using positron emission tomography (PET). The in vivo distribution of [(11)C]-DASB binding was consistent with the known distribution of SERT sites in the cat brain in vitro with high uptakes of radioactivity in the midbrain and thalamus, intermediate levels in striatum, and modest to low levels of radioactivity in the neocortex and cerebellum, respectively. [(11)C]-DASB binding potential (BP) values ranged from about 0.2 in the neocortex to 2.2 in the midbrain. Radioligand binding in all brain regions except cerebellum was markedly reduced following pretreatment with fluoxetine and citalopram, but was unaffected by pretreatment with GBR12909, maprotiline, and haloperidol, indicating specificity of [(11)C]-DASB binding to the SERT. Two cats were each examined using PET and [(11)C]-DASB in a longitudinal fashion (from 30 min and up to 24 days) following a single i.v. dose of: 1) fluoxetine, and 2) citalopram at different dosages. Both drugs induced similar degrees of SERT occupancy at 30 min postinjection (approximately 90%). A comparison of citalopram and fluoxetine pharmacokinetics in the same animal and at the same dosage (1 mg/kg) showed that citalopram SERT occupancy and plasma half-lives were 9 times and 14 times shorter, respectively, than those of fluoxetine and norfluoxetine. In addition, studies performed after injection of the monoamine oxidase inhibitor tranylcypromine suggested that high levels of synaptic serotonin may compete with [(11)C]-DASB for binding on the SERT. These studies indicate that [(11)C]-DASB is a suitable PET radioligand for measuring drug occupancy of the SERT in vivo and has potential for monitoring in vivo changes in serotonin levels.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".