Evidence that Formulations of the Selective MAO-B Inhibitor, Selegiline, which Bypass First-Pass Metabolism, also Inhibit MAO-A in the Human Brain
Bibliographic record
Abstract
Selegiline (L-deprenyl) is a selective, irreversible inhibitor of monoamine oxidase B (MAO-B) at the conventional dose (10 mg/day oral) that is used in the treatment of Parkinson's disease. However, controlled studies have demonstrated antidepressant activity for high doses of oral selegiline and for transdermal selegiline suggesting that when plasma levels of selegiline are elevated, brain MAO-A might also be inhibited. Zydis selegiline (Zelapar) is an orally disintegrating formulation of selegiline, which is absorbed through the buccal mucosa producing higher plasma levels of selegiline and reduced amphetamine metabolites compared with equal doses of conventional selegiline. Although there is indirect evidence that Zydis selegiline at high doses loses its selectivity for MAO-B, there is no direct evidence that it also inhibits brain MAO-A in humans. We measured brain MAO-A in 18 healthy men after a 28-day treatment with Zydis selegiline (2.5, 5.0, or 10 mg/day) and in 3 subjects receiving the selegiline transdermal system (Emsam patch, 6 mg/day) using positron emission tomography and the MAO-A radiotracer [(11)C]clorgyline. We also measured dopamine transporter (DAT) availability in three subjects from the 10 mg group. The 10 mg Zydis selegiline dose significantly inhibited MAO-A (36.9±19.7%, range 11-70%, p<0.007)) but not DAT; and while Emsam also inhibited MAO-A (33.2±28.9 (range 9-68%) the difference did not reach significance (p=0.10)) presumably because of the small sample size. Our results provide the first direct evidence of brain MAO-A inhibition in humans by formulations of selegiline, which are currently postulated but not verified to target brain MAO-A in addition to MAO-B.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".