Bibliographic record
Abstract
OBJECTIVE: During the past decades, increasing efforts have been invested in studies to unravel the influence of genetic factors on antipsychotic (AP) dosage, treatment response, and occurrence of adverse effects. These studies aimed to improve clinical care by predicting outcome of treatment with APs and thus allowing for individualized treatment strategies. We highlight most important findings obtained through both candidate gene and genome-wide association studies, including pharmacokinetic and pharmacodynamic factors. METHODS: We reviewed studies on pharmacogenetics of AP response and adverse effects published on PubMed until early 2012. Owing to the high number of published studies, we focused our review on findings that have been replicated in independent studies or are supported by meta-analyses. RESULTS: Most robust findings were reported for associations between polymorphisms of the cytochrome P450 system, the dopamine and the serotonin transmitter systems, and dosage, treatment response, and adverse effects, such as AP-induced weight gain or tardive dyskinesia. These associations were either detected for specific medications or for classes of APs. CONCLUSION: First promising and robust results show that pharmacogenetics bear promise for a widespread use in future clinical practice. This will likely be achieved by developing algorithms that will include many genetic variants. However, further investigation is warranted to replicate and validate previous findings, as well as to identify new genetic variants involved in AP response and for replication of existing findings.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".