The Utility of CYP2D6 and CYP2C19 Variants to Guide Pharmacological Treatment in Complex Unipolar Major Depression: A Pilot Longitudinal Study
Bibliographic record
Abstract
Major depression is a frequent condition which variably responds to treatment. In view of its high prevalence, the presence of treatment resistance in major depression significantly impacts on quality of life. Tailoring pharmacological treatment based on genetic polymorphisms is a current trend to personalizing pharmacological treatment in patients with major depressive disorders. Current guidelines for the use of genetic tests in major depression issued by the Clinical Pharmacogenomics Implementation Consortium (CPIC) are based on CYP2D6 and CYP2C19 polymorphisms which constitute the strongest evidence for pharmacogenomic guided treatment. There is evidence of increased clinical response to pharmacological treatment in major depression although largely in non-treatment resistant patients from Western countries. In this study, well characterised participants (N = 15) with complex, largely treatment resistant unipolar major depression were investigated, and clinical improvement was measured at baseline and at week-8 after the pharmacogenomics-guided treatment with the Montgomery Åsberg Depression Rating Scale (MÅDRS). Results suggested a statistically significant improvement (p = 0.01) of 16% at endpoint in the whole group and a larger effect in case of changes in medication regime (28%, p = 0.004). This small but appreciable effect can be understood in the context of the level of treatment resistance in the group. To our knowledge, this is the first study from the Middle East demonstrating the feasibility of this approach in the treatment of complex major depressive disorders.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".