Predicting Dopamine D2 Receptor Occupancy From Plasma Levels of Antipsychotic Drugs
Bibliographic record
Abstract
Measuring dopamine D₂ receptor occupancy levels using positron emission tomography (PET) is still widely unavailable. The objective of this study was to evaluate the accuracy of predicting D2 occupancy from the antipsychotic plasma level in patients with schizophrenia. Positron emission tomographic studies that measured plasma levels of antipsychotics and their corresponding D₂ occupancy levels were identified, using MEDLINE and EMBASE (last search: March 2010). Antipsychotics that were investigated in a total of 20 subjects or more were included. All data points for each antipsychotic were fit to a one-site binding model to estimate the total plasma concentration of each antipsychotic associated with a 50% occupancy (ED₅₀) of brain D₂ receptors. The mean prediction error and the root mean squared prediction error were used to measure the predictive performance of individual D₂ receptor occupancies from plasma drug levels derived from a one-site occupancy model using an ED₅₀ value calculated for each data point. A total of 34 treatment arms from 23 studies involving 281 subjects were included. The mean (95% confidence interval) prediction errors and root squared prediction errors were as low as 0.0 (-1.8 to 1.8) and 8.9 (7.6-10.2) for risperidone (n = 98); 0.0 (-3.5 to 3.5) and 15.1 (12.9-17.3) for clozapine (n = 75); -0.1 (-1.2 to 1.2), 0.0 (-1.9 to 1.9), and 4.6 (3.5-5.8) for olanzapine (n = 42); 0.1 (-3.4 to 3.5) and 9.9 (7.3-12.5) for haloperidol (n = 35); and -0.1 (-3.3 to 3.1) and 12.3 (8.8-15.7) for ziprasidone (n = 31), respectively. These findings suggest that D₂ occupancy of antipsychotics could be estimated with a high degree of accuracy using widely available plasma 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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".