Differential Potency of Venlafaxine, Paroxetine, and Atomoxetine to Inhibit Serotonin and Norepinephrine Reuptake in Patients With Major Depressive Disorder
Bibliographic record
Abstract
BACKGROUND: Venlafaxine is a dual serotonin (5-HT) and norepinephrine reuptake inhibitor. The specific dose at which it begins to efficiently engage the norepinephrine transporter (NET) remained to be determined. Paroxetine is generally considered as a selective 5-HT reuptake inhibitor but exhibits some affinity for NET. Atomoxetine is a NET inhibitor but also has some affinity for the 5-HT reuptake transporter (SERT). METHODS: This study examined the effects of forced titration of venlafaxine from 75 to 300 mg/d, paroxetine from 20 to 50 mg/d, or atomoxetine from 25 to 80 mg/d in 32 patients with major depressive disorder. Inhibition of SERT was estimated using the depletion of whole-blood 5-HT. Inhibition of NET was assessed using the attenuation of the systolic blood pressure produced by i.v. injections of tyramine. RESULTS: All 3 medications significantly reduced 5-HT levels at the initiating regimens: venlafaxine and paroxetine by approximately 60% and atomoxetine by 16%. The 3 subsequent regimens of venlafaxine and paroxetine reduced 5-HT levels by over 90%, but the highest dose of atomoxetine only reached a 40% inhibition. Atomoxetine dose dependently inhibited the tyramine pressor response from the lowest dose, venlafaxine from 225 mg/d, and paroxetine left it unaltered throughout. CONCLUSION: These results confirm that venlafaxine and paroxetine are potent SERT inhibitors over their usual therapeutic range but that venlafaxine starts inhibiting NET only at 225 mg/d, whereas paroxetine remains selective for SERT up to 50 mg/d. Atomoxetine dose dependently inhibits NET from a low dose but does not inhibit SERT to a clinically relevant degree.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".