Risperidone in Schizophrenia: Is There a Role for Therapeutic Drug Monitoring?
Bibliographic record
Abstract
BACKGROUND: Although risperidone is commonly used in the acute and maintenance treatment of schizophrenia, the role of therapeutic drug monitoring has yet to be elucidated. The purpose of this review was to determine whether risperidone warrants therapeutic drug monitoring in patients with schizophrenia. METHODS: Using a previously published nine-step decision-making algorithm, the available evidence was examined to determine whether therapeutic drug monitoring of risperidone is warranted in adult schizophrenic patients. RESULTS: Analytical methods used to quantify risperidone, 9-hydroxyrisperidone, and the combined active moiety are specific, sensitive, accurate, and precise; however, the therapeutic range for risperidone has not yet been established. Relationships between risperidone dose and plasma concentrations of risperidone, 9-hydroxyrisperidone, or the active moiety have not yet been demonstrated. A clear correlation between plasma risperidone concentrations and therapeutic response has also not been shown. Intrinsic interpatient variability, genetically based differences in drug metabolism, and metabolic drug interactions may all influence the variability in plasma concentrations. Patients with schizophrenia on risperidone for prevention of relapse take the medication for a sufficient duration of therapy to benefit from therapeutic drug monitoring; however, the routine use of risperidone concentrations in all patients with schizophrenia is not likely to have a significant impact on the clinical decision-making process or provide more information than clinical judgment alone. CONCLUSIONS: The routine use of risperidone levels does not seem warranted in all patients with schizophrenia. Clinical end points (ie, response and toxicity) should be monitored by assessing changes in symptoms and emergence of adverse effects, especially extrapyramidal symptoms. Therapeutic drug monitoring of risperidone may be beneficial in certain circumstances, including assessing potential noncompliance and supporting compliance, ruling out therapeutic failure as a result of low drug concentrations, and identifying and managing drug interactions, adverse effects, and use in special populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".