Genetics of Common Antipsychotic-Induced Adverse Effects
Bibliographic record
Abstract
The effectiveness of antipsychotic drugs is limited due to accompanying adverse effects which can pose considerable health risks and lead to patient noncompliance. Pharmacogenetics (PGx) offers a means to identify genetic biomarkers that can predict individual susceptibility to antipsychotic-induced adverse effects (AAEs), thereby improving clinical outcomes. We reviewed the literature on the PGx of common AAEs from 2010 to 2015, placing emphasis on findings that have been independently replicated and which have additionally been listed to be of interest by PGx expert panels. Gene-drug associations meeting these criteria primarily pertain to metabolic dysregulation, extrapyramidal symptoms (EPS), and tardive dyskinesia (TD). Regarding metabolic dysregulation, results have reaffirmed HTR2C as a strong candidate with potential clinical utility, while MC4R and OGFR1 gene loci have emerged as new and promising biomarkers for the prediction of weight gain. As for EPS and TD, additional evidence has accumulated in support of an association with CYP2D6 metabolizer status. Furthermore, HSPG2 and DPP6 have been identified as candidate genes with the potential to predict differential susceptibility to TD. Overall, considerable progress has been made within the field of psychiatric PGx, with inroads toward the development of clinical tools that can mitigate AAEs. Going forward, studies placing a greater emphasis on multilocus effects will need to be conducted.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".