Protocol for a pharmacogenetic study of antidepressants: characterization of drug-metabolizing profiles of cytochromes CYP2D6 and CYP2C19 in a Sardinian population of patients with major depressive disorder
Bibliographic record
Abstract
The effectiveness of antidepressants shows high interindividual variability ranging from full symptomatologic remission to treatment-resistant depression. Many factors can determine the variation in the clinical response, but a fundamental role is played by genetic variation within the genes encoding for the enzymes most involved in the metabolism of antidepressant drugs: the CYP2D6 and CYP2C19 isoforms of the cytochrome P450 system. This study is poised to clarify whether the different metabolizing phenotypes related to CYP2D6 and CYP2C19 could have an impact on the clinical efficacy of antidepressants and whether the frequency of these phenotypes of metabolization shows differences in the population of Sardinian patients compared to other Caucasian populations. The sample is being recruited from patients followed-up and treated at the Psychiatric Unit of the Department of Medical Science and Public Health, University of Cagliari and the University Hospital Agency of Cagliari (Italy). The study design includes three approaches: (1) a pharmacogenetic analysis of 80 patients diagnosed with MDD resistant to antidepressant treatment compared to 80 clinically responsive or remitted patients; (2) a prospective arm (N = 30) of the study where we will test the impact of genetic variation within the CYP2D6 and CYP2C19 genes on clinical response to antidepressants and on their serum levels and (3) the assessment of the socio-economic impact of antidepressant therapies, and estimation of the cost-effectiveness of the pharmacogenetic test based on CYP genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.071 | 0.027 |
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".