Estimating the Potential Impact of CYP2C19 and CYP2D6 Genetic Testing on Protocol-Based Care for Depression in Canada and the United States
Bibliographic record
Abstract
The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) algorithm is the most recognized protocol-based care approach for moderate to severe depression. However, its implementation results in one-third of individuals receiving modest to no symptom remission. One possible explanation is the inter-individual differences in antidepressant metabolism due to CYP2C19 and CYP2D6genetic variation. Here, we aimed to determine the potential benefit of pairing CYP2C19 and CYP2D6testing with the five-step STAR*D algorithm. To estimate the proportion of individuals that could benefit from CYP2C19 and CYP2D6 testing, we simulated the STAR*D algorithm using ethnicity-specific phenotype (e.g., metabolizer status) frequencies published by the Clinical Pharmacogenetics Implementation Consortium and census data from the Canada and the US. We found that up to one-third of the US and Canadian populations being treated for depression could benefit from the addition of CYP2C19and CYP2D6 genetic testing. The potential benefit varied for each step of the algorithm and for each province, territory, and state. CYP2C19 genotyping had the greatest potential impact within the first two steps of the algorithm, while CYP2D6 genotyping had the most notable impact in Steps 3, 4, and 5. Our findings suggest the implementation of CYP2C19and CYP2D6 genetic testing alongside the STAR*D treatment algorithm may improve depression treatment outcomes in Canada and the US.
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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 | 0.028 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".