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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".