Dopamine D<sub>2/3</sub>Receptor Occupancy Following Dose Reduction Is Predictable With Minimal Plasma Antipsychotic Concentrations: An Open-Label Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Population pharmacokinetics can predict antipsychotic blood concentrations at a given time point prior to a dosage change. Those predicted blood concentrations could be used to estimate the corresponding dopamine D2/3 receptors (D2/3R) occupancy by antipsychotics based on the tight relationship between blood and brain pharmacokinetics. However, this 2-step prediction has never been tested. METHODS: Two blood samples were collected at separate time points from 32 clinically stable outpatients with schizophrenia (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; mean ± SD age: 60.1 ± 7.3 years) to measure plasma concentrations of olanzapine or risperidone at baseline. Then, subjects underwent a dose reduction of olanzapine or risperidone and completed a [(11)C]-raclopride positron emission tomography scan to measure D2/3R occupancy in the putamen. The plasma concentration at the time of the scan was predicted with the 2 samples based on population pharmacokinetic model, using NONMEM. D2/3R occupancy was then estimated by incorporating the predicted plasma concentration in a hyperbole saturation model. The predicted occupancy was compared to the observed value. RESULTS: The mean (95% CI) prediction errors for the prediction of D2/3R occupancy were -1.76% (-5.11 to 1.58) for olanzapine and 0.64% (-6.18 to 7.46) for risperidone. The observed and predicted D2/3R occupancy levels were highly correlated (r = 0.67, P = .001 for olanzapine; r = 0.67, P = .02 for risperidone). CONCLUSIONS: D2/3R occupancy levels can be predicted from blood drug concentrations collected prior to dosage change. Although this 2-step model is subject to a small degree of error, it could be used to select oral doses aimed at achieving optimal D2/3R occupancy on an individual basis.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".