Predicting Plasma Concentration of Risperidone Associated With Dosage Change
Bibliographic record
Abstract
BACKGROUND: Due to high interindividual variability in peripheral pharmacokinetic parameters, dosing of antipsychotics relies on clinical trial and error. This blind process of upward or downward clinical dose titration carries a risk of relapse and adverse effects in the treatment of schizophrenia. Using population pharmacokinetic methods, the authors therefore sought to predict plasma concentrations of risperidone (RIS) plus 9-hydroxyrisperidone (9-OH-RIS) before a dosage change. METHODS: Two plasma samples were collected at 2 separate given time points for the measurement of RIS and 9-OH-RIS concentrations from 50 patients with schizophrenia or schizoaffective disorder maintained on risperidone (mean ± SD age = 56 ± 15 years; 39 men). After an oral risperidone dose adjustment, a third sample was collected. The plasma concentration of the third sample was individually predicted in a blinded fashion with the 2 baseline plasma concentrations before dose adjustment and clinical and demographic information, using the mixed-effects model with NONMEM that was derived from the data of the Clinical Antipsychotic Trials in Intervention Effectiveness study. RESULTS: The mean (95% confidence interval) prediction errors (in ng/mL) were as low as 0.0 (-1.3 to 1.4) for RIS and 1.0 (-1.1 to 3.0) for 9-OH-RIS. The observed and predicted concentrations of RIS and 9-OH-RIS were highly correlated (r = 0.96, P < 0.0001 and r = 0.92, P < 0.0001, respectively). CONCLUSIONS: Antipsychotic plasma concentrations can be predicted before risperidone dose adjustment. In light of the known relationship between plasma drug concentration, dopamine D2 receptor occupancy, and clinical effects, our results confirm that individualized dosing with the measurement of antipsychotic plasma concentrations has the potential for bedside clinical application.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".