Commercial Pharmacogenetic Tests in Psychiatry: Do they Facilitate the Implementation of Pharmacogenetic Dosing Guidelines?
Bibliographic record
Abstract
Abstract Introduction Expert groups have created dosing guidelines to facilitate the implementation of pharmacogenetic knowledge into clinical practice and commercial pharmacogenetic tests are becoming increasingly accessible. However, the extent to which these commercial tests facilitate the implementation of dosing guidelines is not clear. Methods Gene-drug pairs included on 22 commercial pharmacogenetic test panels were extracted and cross-referenced with the 74 gene-drug pairs with dosing guidelines in the Pharmacogenetics Knowledgebase, with particular attention given to the 28 gene-drug pairs relevant to psychiatry. Results On average, 70% of the 28 gene-drug pairs most relevant to psychiatry were covered by the examined tests. Six gene-drug pairs (CYP2D6-venlafaxine, CYP2D6-paroxetine, CYP2D6-amitriptyline, CYP2C19-sertraline, CYP2C19-citalopram, CYP2C19-amitriptyline) were included by all tests. Gene-drug pairs included on less than half of the test panels included HLA-B-phenytoin (14%), HLA-A-carbamazepine (24%), HLA-B-carbamazepine (29%), and CYP2D6-zuclopenthixol (43%). Discussion Most commercial pharmacogenetic tests we examined are well-equipped to facilitate implementation of the majority of dosing guidelines relevant to psychiatry but are limited in their ability to facilitate implementation of the full spectrum of dosing guidelines currently available.
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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 | 0.058 | 0.266 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".