A systematic review of the effects of CYP2D6 phenotypes on risperidone treatment in children and adolescents
Bibliographic record
Abstract
The second generation antipsychotic drug risperidone is widely used in the field of child and adolescent psychiatry to treat conditions associated with disruptive behavior, aggression and irritability, such as autism spectrum disorders. While risperidone can provide symptomatic relief for many patients, there is considerable individual variability in the therapeutic response and side-effect profile of the medication. One well established biological factor that contributes to these individual differences is genetic variation in the cytochrome P450 enzyme 2D6. The 2D6 enzyme metabolizes risperidone and therefore affects drug levels and dosing. In the present review, we summarize the current literature on 2D6 variants and their effects on risperidone responses, specifically in children and adolescents. Relevant articles were identified through systematic review, and after irrelevant articles were discarded, ten studies were included in the review. Most prospective studies were well controlled, but often did not have a large enough sample size to make robust statements about rarer variants, including those categorized as ultra-rapid and poor metabolizers. Individual studies demonstrated a role for different genetic variants in risperidone drug efficacy, pharmacokinetics, hyperprolactinemia, weight gain, extrapyramidal symptoms and drug-drug interactions. Where studies overlapped in measurements, there was typically a consensus between results. These findings indicate that the value of 2D6 genotyping in the youth population treated with risperidone requires further study, in particular with the less common variants.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".