Using Bayesian Modeling to Optimize Antipsychotic Dosage in Clinical Practice
Bibliographic record
Abstract
Aim A robust and user-friendly software tool was developed for the prediction of dopamine D2 receptor occupancy (RO) in patients with schizophrenia treated with either olanzapine or risperidone. This tool can facilitate clinician exploration of the impact of treatment strategies on RO using sparse plasma concentration measurements. Methods Previously developed population pharmacokinetic (PPK) models for olanzapine and risperidone were combined with a PD model for D2 receptor occupancy (RO) and implemented in the R programming language. MAP Bayesian estimation was used to provide predictions of plasma concentration and receptor occupancy and based on sparse PK measurements. Results The average (standard deviation) response times of the tools were 2.8 (3.1) and 5.3 (4.3) seconds for olanzapine and risperidone, respectively. The mean error (95% confidence interval) and root mean squared error (RMSE, 95% CI) of predicted versus observed concentrations were 3.73 ng/mL (-2.42 – 9.87) and 10.816 (6.71 – 14.93) for olanzapine, and 0.46 ng/mL (-4.56 – 5.47) and 6.68 (3.57 – 9.78) for risperidone and its active metabolite (9-OH risperidone). Mean error and RMSE of RO were -1.47% (-4.65 – 1.69) and 5.80 (3.89 – 7.72) for olanzapine and -0.91% (-7.68 – 5.85) and 8.87 (4.56 – 13.17) for risperidone. Conclusion Treatment of schizophrenia with antipsychotics offers unique challenges and requires careful monitoring to establish the optimal dosing regimen. Our monitoring software predicts RO in a reliable and accurate form.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".