Pharmacogenetics‐Guided Advances in Antipsychotic Treatment
Bibliographic record
Abstract
Pharmacogenetics (PGx) research over the past 2 decades has produced extensive evidence for the influence of genetic factors on the efficacy and tolerability of antipsychotic treatment. However, the application of these findings to optimize treatment outcomes for patients in clinical practice has been limited. This paper presents a meta-review of key PGx findings related to antipsychotic response and common adverse effects, including antipsychotic-induced weight gain, tardive dyskinesia (TD), and clozapine-induced agranulocytosis (CIAG), and highlights advances and challenges in clinical implementation. Most robust findings from candidate gene and genomewide association studies were reported for associations between polymorphisms in CYP2D6 and exposure and response to specific antipsychotics. As a result, product labels and guidelines from various PGx expert groups have provided selection and dosing recommendations based on CYP2D6 metabolizer phenotypes for commonly prescribed antipsychotics. Other interesting genetic targets include DRD2 for antipsychotic response, SLC18A2 for TD, and the human leukocyte antigen (HLA) genes, HLA-DQB1 and HLA-B, for CIAG. Well-designed studies using large, well-characterized samples that leverages international collaborations are needed to validate previous findings, as well as discover new genetic variants involved in antipsychotic response and adverse effects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".