Pharmacogenetics of Antipsychotic Drug Treatment: Update and Clinical Implications
Bibliographic record
Abstract
Numerous genetic variants have been shown to be associated with antipsychotic response and adverse effects of schizophrenia treatment. However, the clinical application of these findings is limited. The aim of this narrative review is to summarize the most recent publications and recommendations related to the genetics of antipsychotic treatment and shed light on the clinical utility of pharmacogenetics/pharmacogenomics (PGx). We reviewed the literature on PGx studies with antipsychotic drugs (i.e., antipsychotic response and adverse effects) and commonly used commercial PGx tools for clinical practice. Publications and reviews were included with emphasis on articles published between January 2015 and April 2018. We found 44 studies focusing on antipsychotic response and 45 studies on adverse effects (e.g., antipsychotic-induced weight gain, movement disorders, hormonal abnormality, and clozapine-induced agranulocytosis/granulocytopenia), albeit with mixed results. Overall, several gene variants related to antipsychotic response and adverse effects in the treatment of patients with schizophrenia have been reported, and several commercial pharmacogenomic tests have become available. However, further well-designed investigations and replication studies in large and well-characterized samples are needed to facilitate the application of PGx findings to clinical practice.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".