Inhibitory and Stimulatory Effects of Selective Serotonin Reuptake Inhibitors on Cytochrome P450 2D6-mediated Dopamine Formation from p-Tyramine
Bibliographic record
Abstract
PURPOSE: The effects of selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine and paroxetine on dopamine formation from p-tyramine, mediated by cytochrome P450 (CYP) 2D6.2 (Arg296Cys, Ser486Thr) and CYP2D6.10 (Pro34Ser, Ser486Thr), were compared with their effects on CYP2D6.1 (wild type)-mediated dopamine formation, to investigate the influence of a CYP2D6 polymorphism on neuroactive amine metabolism in the brain. METHODS: The Michaelis constants (Km) and maximal velocity (Vmax) values of dopamine formation mediated by CYP2D6.1, CYP2D6.2, and CYP2D6.10 (expressed in recombinant Escherichia coli), and inhibition constants (Ki) of the SSRIs toward dopamine formation catalyzed by the CYP2D6 variants were estimated. RESULTS: The Km values for CYP2D6.2 and CYP2D6.10 decreased at lower fluoxetine concentrations, while the Vmax values for all CYP2D6 variants increased, indicating that fluoxetine stimulated dopamine formation. Conversely, paroxetine competitively inhibited dopamine formation mediated by CYP2D6.1, CYP2D6.2, and CYP2D6.10 with Ki values of 0.47, 1.33, and 31.3 µM, respectively. CONCLUSIONS: These results suggest that the inhibition/stimulation of CYP2D6-mediated dopamine formation by these SSRIs would be affected by CYP2D6 polymorphisms in the brain.
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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 | 0.000 |
| 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".