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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".