Occupancy of Serotonin Transporters by Paroxetine and Citalopram During Treatment of Depression: A [<sup>11</sup>C]DASB PET Imaging Study
Bibliographic record
Abstract
OBJECTIVE: Selective serotonin reuptake inhibitors are commonly used to treat major depression; however, the percentage of serotonin (5-HT) transporter (5-HTT) sites occupied during clinical dosing is unknown. This study measured the proportion of 5-HTT sites blocked during paroxetine and citalopram treatment of depression and assessed the relationship between serum paroxetine levels and the proportion of 5-HTT sites blocked. METHOD: Twelve medication-free depressed patients completed a 6-week trial of either paroxetine (N=8) or citalopram (N=4). Striatal 5-HTT binding potential was measured with [(11)C]DASB and positron emission tomography, before and after 4 weeks of treatment. The binding potential is proportional to receptor density. Striatal 5-HTT binding potential was measured twice in six healthy subjects and once in 11 healthy subjects. RESULTS: A significant decrease in striatal 5-HTT binding potential was found after either treatment, compared to changes found over a 4-week period in healthy subjects. For patients treated with 20 mg/day of paroxetine (N=7), the mean proportion of 5-HTT sites occupied was 83%. For patients treated with 20 mg/day of citalopram (N=4), the mean 5-HTT occupancy was 77%. 5-HTT occupancy increased in a nonlinear relationship with serum levels of paroxetine such that a plateau of occupancy around 85% occurred for serum paroxetine levels greater than 28 microg/liter. CONCLUSIONS: During treatment with clinical doses of paroxetine or citalopram, approximately 80% of 5-HTT receptors are occupied. This change in 5-HTT binding potential is greater than the known physiological range of changes in 5-HTT binding potential but may be necessary for some therapeutic effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".