Estimated Dopamine D2 Receptor Occupancy and Remission in Schizophrenia
Bibliographic record
Abstract
In treating schizophrenia, 65% to 80% occupancy of dopamine D₂ receptors optimizes therapeutic efficacy while minimizing risks of extrapyramidal symptoms and cognitive impairments. However, it is unclear as to whether it is necessary to keep D₂ receptor occupancy within this therapeutic window to maintain clinical response. The data set from phase 1 of the Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE) trial was reappraised. Thirty patients receiving risperidone (12 patients), olanzapine (12 patients), or ziprasidone (6 patients) fulfilled the following definition of remission and were included: a score of 3 or less on the 8 specific items in the Positive and Negative Syndrome Scale (ie, P1, P2, P3, N1, N4, N6, G5, and G9; adopted from Andreasen et al, 2005) at the initial assessment and months 1, 2, and 6. Peak and trough D₂ receptor occupancy levels at month 6 were estimated from plasma antipsychotic concentrations using population pharmacokinetic analysis and our D₂ prediction model. Estimated mean ± SD peak and trough D₂ receptor occupancy levels at month 6 were 70.3% ± 9.8% and 60.5% ± 20.2%, respectively; among these individuals, 46.7% (14 patients) did not achieve continuous blockade of 65% or greater (ie, trough D₂ occupancy of <65%). In conclusion, approximately half of patients with remission did not achieve continuous blockade of estimated D₂ receptor occupancy 5% or greater. These results extend our previous findings and suggest that sustained D₂ receptor occupancy greater than 65% may not always be necessary for the maintenance treatment of schizophrenia.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".