Genetics of antipsychotic drug outcome and implications for the clinician: into the limelight
Bibliographic record
Abstract
Background and purposeAntipsychotics (APs) are the primary method of treatment for schizophrenia and other psychotic disorders. Unfortunately, lengthy trial-and-error approaches are typically required to find the optimal medication and dosage due to a large interindividual variability with outcome to AP treatment. The literature has shown abundant evidence for a genetic component in individuals’ responses to APs. Pharmacogenetic studies analyze specific genetic markers and their association with symptom improvement and occurrence of side effects with APs. This research aims to optimize AP drug treatment by usage of predictive testing and to personalize medicine.Recent findingsThis review will highlight the most consistent findings in pharmacogenetics of APs and will update the reader on the clinical implications. This will include how genetic variants modulate AP drug levels, side effects, and therapeutic symptom improvement (i.e. response) to AP treatment.SummarySeveral promising findings were obtained implicating gene variants of the dopamine receptor genes in addition to gene variants of serotonin receptors for response and common side effects. Notably, effect sizes appear to be particularly high in the genetics of side effects compared to response. One example is antipsychotic-induced weight gain where the leptin, HTR2C and in particular the melanocortin-4-receptor (MC4R) genes have been implicated in weight gain in children and adolescents. Consistent findings were also obtained for genes implicated in tardive dyskinesia and agranulocytosis. However, the most clinically relevant findings pertain to genes involved in drug metabolism such as the CYP2D6 and CYP2C19 genes which have been included in the first genetic test kits such as the Amplichip® CYP450 Test and more recently the DMET™ Plus Panel, the Genecept™ Assay, the Genomas HILOmet PhyzioType™ System, and the GeneSight® Test.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".