Genetic differences in cytochrome P450 enzymes and antidepressant treatment response
Bibliographic record
Abstract
AIMS: Antidepressant response varies between patients, possibly due to differences in the rate cytochrome P450 enzymes metabolise antidepressants into inactive compounds. Drug metabolism rates are influenced by common variants in the genes encoding these enzymes. However, it remains unclear whether treatment outcomes can be predicted by either CYP450 genotype or antidepressant serum concentration. METHODS: In GENDEP (a pharmacogenetic study of depressed individuals treated with either escitalopram or nortriptyline), serum concentrations of antidepressants and their primary metabolite were measured after eight weeks treatment and variants in CYP2D6 and CYP2C19 were genotyped. RESULTS: Amongst patients taking escitalopram (n=223), the genotype CYP2C19 was significantly associated with escitalopram serum concentrations and desmethylescitalopram:escitalopram ratio. For those taking nortriptyline (n=161), the CYP2D6 genotype was significantly associated with nortriptyline and 10-hydroxynortriptyline serum concentrations and 10-hydroxynortriptyline:nortrip-tyline ratio. CYP450 genotypes conferring greater enzyme activity were linked to lower drug serum concentrations and higher metabolite:drug ratios. Nonetheless, no significant association was found between either CYP450 genotype or antidepressant serum concentration and treatment response. CONCLUSIONS: While there is a significant relationship between the CYP450 genotype and serum concentrations of escitalopram and nortriptyline, the genotypes are not predictive of differences in treatment response for either drug. Furthermore, differences in antidepressant serum concentrations are not associated with variability in treatment response.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".