Effect of Cytochrome P450 (CYP) 2D6 Genetic Polymorphism on the Inhibitory Action of Antidepressants on CYP2D6-Mediated Dopamine Formation from p-Tyramine
Bibliographic record
Abstract
PURPOSE: The inhibitory effects of antidepressants, such as imipramine, desipramine, and fluvoxamine, on dopamine formation from p-tyramine catalyzed by cytochrome P450 (CYP) 2D6.2 (Arg296Cys, Ser486Thr) and CYP2D6.10 (Pro34Ser, Ser486Thr), were compared with those on dopamine formation catalyzed by CYP2D6.1 (wild type), to investigate the effect of a CYP2D6 polymorphism on neuroactive amine metabolism in the brain. METHODS: Inhibition constants (Ki) of the antidepressants toward dopamine formation catalyzed by CYP2D6.1, CYP2D6.2, and CYP2D6.10, which were expressed in recombinant Escherichia coli, were compared. RESULTS: Imipramine and desipramine competitively or non-competitively inhibited dopamine formation mediated by CYP2D6.1, CYP2D6.2, and CYP2D6.10 with Ki values of 3.9-4.9, 5.9-9.6, and 26.7-37.5 µM, respectively. The maximal velocity (Vmax) values for dopamine formation by all CYP2D6 variants gradually increased with increasing fluvoxamine concentrations up to 40-100 µM, indicating that fluvoxamine stimulated dopamine formation. CONCLUSIONS: These results suggest that the inhibition/stimulation of CYP2D6-mediated dopamine formation by these antidepressants would be affected by CYP2D6 polymorphism in the brain. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".