An Overview of Pharmacogenetics in Psychotropic Drugs
Bibliographic record
Abstract
There is considerable variation in the individualized response to psychotropic drug therapies, which include antidepressants, antipsychotics and mood stabilizers. It has been proposed that the wide interindividual variability in psychotropic drug-response may be attributable to genetic variants. Thus pharmacogenetics may help to accurately predict response to psychotropic treatment, and may be used as guidelines in selecting an appropriate psychotropic treatment in order to maximize drug efficacy and minimize drug toxicity. Although the clinical utility of psychiatric pharmacogenetics is very promising, its adoption in clinical practice has been slow. This resistance may stem from sometimes conflicting findings among pharmacogenetic studies. The failure to replicate these findings may result from a lack of high-quality studies and unresolved methodological issues. In this review we will address methodological and statistical challenges in pharmacogenetic studies and summarize the current pharmacogenetic literature on psychotropic drug-response. Keywords: Antidepressants, antipsychotics, lithium, mood stabilizers, pharmacogenetics, risperidone, selective serotonin re-uptake inhibitors (SSRIs).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".