The pharmacogenetics of antipsychotic-induced adverse events
Bibliographic record
Abstract
PURPOSE OF REVIEW: Antipsychotic drugs are effective in alleviating a variety of symptoms and are medication of first choice in schizophrenia. However, a substantial interindividual variability in side effects often requires a lengthy 'trial-and-error' approach until the right medication is found for the right patient. Genetic factors have long been hypothesized to be involved and identification of related gene variants could be used to predict and tailor drug treatment. RECENT FINDINGS: This review highlighting the most recent genetic findings was conducted on the two most common and most well-studied side effects: antipsychotic-induced weight gain and tardive dyskinesia. SUMMARY: Regarding weight gain, most promising and most consistent findings were obtained in the serotonergic system (HTR2C) and with hypothalamic leptin-melanocortin genes, in particular with one variant close to the melanocortin-4-receptor (MC4R) gene. With respect to tardive dyskinesia, most interesting findings were generally obtained in genes related to the dopaminergic system (dopamine receptors D2 and D3), and more recently with glutamatergic system genes. Overall, genetic studies have been successful in identifying strong findings, in particular for antipsychotic-induced weight gain and to some extent for tardive dyskinesia. Apart from the need for replication studies in larger and well-characterized samples, the next challenge will be to create predictive algorithms that can be used for clinical practice.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.005 | 0.002 |
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".