Pharmacogenomic Testing for Neuropsychiatric Drugs: Current Status of Drug Labeling, Guidelines for Using Genetic Information, and Test Options
Bibliographic record
Abstract
Advancements in pharmacogenomics have introduced an increasing number of opportunities to bring personalized medicine into clinical practice. Understanding how and when to use this technology to guide pharmacotherapy used to treat psychiatric and neurological (neuropsychiatric) conditions remains a challenge for many clinicians. Currently, guidelines exist to assist clinicians in the use of existing genetic information for drug selection and/or dosing for the tricyclic antidepressants, carbamazepine, and phenytoin. Additional language in the product labeling suggests that genetic information may also be useful for determining the starting and target doses, as well as drug interaction potential, for a number of other drugs. In this review, we outline the current status of pharmacogenomic testing for neuropsychiatric drugs as it pertains to information contained in drug labeling, consensus guidelines, and test panels, as well as considerations related to obtaining tests for patients.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".