Evidence level for pharmacogenetic testing in antidepressant treatment: a systematic review
Bibliographic record
Abstract
RATIONALE: Preemptive pharmacogenetic (PGx) testing offers a promising approach to personalized antidepressant treatment by identifying genetic variations influencing drug metabolism. By focusing on CYP2D6 and CYP2C19 genes, this strategy aims to improve treatment response, minimize adverse effects, and optimize dosing in patients with depression. OBJECTIVES AND METHODS: This systematic review evaluates the effectiveness of preemptive PGx testing, primarily for CYP2D6 and CYP2C19, in enhancing antidepressant treatment outcomes. A comprehensive search of databases, including PubMed and Embase, was conducted to identify relevant studies. The review included randomized controlled trials and meta-analyses that assessed PGx testing in relation to treatment response and remission. Data on clinical outcomes were extracted and analyzed. RESULTS: PGx testing led to improved antidepressant response rates and remission at 8- and 12-week follow-ups compared to treatment-as-usual (TAU). However, where data were available, benefits were less pronounced after six months of follow-up. The findings suggest that PGx testing plays an important role in achieving earlier remission, while TAU requires a longer time to achieve remission. CONCLUSION: Preemptive pharmacogenetic testing for CYP2D6 and CYP2C19 could enhance early antidepressant treatment outcomes, offering a valuable tool for personalized medicine. Further research is required to explore implementation challenges in diverse clinical settings.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".