<i>CYP2D6</i> and Antipsychotic Treatment Outcomes in Children and Youth: A Systematic Review
Bibliographic record
Abstract
Objective: To systematically review the impact of CYP2D6 genetic variation on antipsychotic pharmacokinetics, efficacy, and adverse drug reactions among children and youth. Method: The published literature was systematically searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations and critically evaluated using standardized tools and consensus criteria. Results: A total of 20 eligible studies comprising 1078 children and youth were evaluated. The included studies were of fair to moderate quality and included mostly males, individuals of European ancestry, and those treated with risperidone. CYP2D6 poor metabolizers (PMs) were consistently shown to have increased concentrations of risperidone relative to normal metabolizers (NMs). PMs were also consistently shown to have a greater propensity to experience antipsychotic (primarily risperidone) associated adverse drug reactions relative to NMs. However, robust evidence for an association between CYP2D6 and efficacy was less apparent. Conclusion and Clinical Significance: The current knowledge base suggests that CYP2D6 genetic variation has an appreciable impact on antipsychotic pharmacokinetics and the propensity for adverse drug reactions, particularly among children receiving risperidone treatment. However, several limitations with the current literature (e.g., sample sizes, study design, sample heterogeneity) should be addressed in future studies. Assuming that future studies support the link between CYP2D6 genetic variation and antipsychotic outcomes, we would anticipate an increase in the implementation of CYP2D6 -guided antipsychotic drug selection and dose optimization in child and adolescent psychiatric services.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".