Towards the integration of pharmacogenetics in psychiatry
Bibliographic record
Abstract
PURPOSE OF REVIEW: The implementation of pharmacogenetic testing in psychiatry is underway but is not yet standard protocol. Barriers to pharmacogenetics becoming standard practice are the lack of translation of evidence-based recommendations and standardization of genetic testing panels. As for the latter, there are currently no regulatory standards related to the gene and allele content of testing panels used to derive medication selection and dosing advice. To address these barriers, we summarize the current gene-drug interaction knowledgebase and proposed a minimum gene and allele set for pharmacogenetic testing in psychiatry. RECENT FINDINGS: The Pharmacogenomics Knowledgebase has cataloged 448 gene-drug interactions relevant to psychiatry based on the current scientific literature, drug labels, and pharmacogenetic-based implementation guidelines. A majority of these interactions involved two cytochrome P450 enzymes (CYP2D6 and CYP2C19) and antidepressant medications, however, CYP2C9, HLA-A, and HLA-B are relevant to mood stabilizers/anticonvulsants. SUMMARY: On the basis of evidence base, we proposed a minimum gene and allele set for pharmacogenetic testing in psychiatry that includes 16 variant alleles within five genes (CYP2C9, CYP2C19, CYP2D6, HLA-A, HLA-B). The intent is to assist clinicians in judging the gene and allele content of pharmacogenetic tests and to facilitate pharmacogenetic testing as a standard protocol and companion tool for psychotropic medication selection and dosing.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".