Monoamine Oxidase-A Occupancy by Moclobemide and Phenelzine: Implications for the Development of Monoamine Oxidase Inhibitors
Bibliographic record
Abstract
BACKGROUND: Monoamine oxidase inhibitors (MAOIs) are being developed for major depressive disorder, Alzheimer's, and Parkinson's Disease. Newer MAOIs have minimal sensitivity to tyramine, but a key limitation for optimizing their development is that standards for in vivo monoamine oxidase-A (MAO-A) occupancy in humans are not well established. The objectives were to determine the dose-occupancy relationship of moclobemide and the occupancy of phenelzine at typical clinical dosing. METHODS: Major depressive episode (MDE) subjects underwent [(11)C]harmine positron emission tomography scanning prior to and following 6 weeks of treatment with moclobemide or phenelzine. RESULTS: Mean brain MAO-A occupancies were 74.23±8.32% for moclobemide at 300-600 mg daily (n = 11), 83.75±5.52% for moclobemide at 900-1200 mg daily (n = 9), and 86.82±6.89% for phenelzine at 45-60 mg daily (n = 4). The regional dose-occupancy relationship of moclobemide fit a hyperbolic function [F(x) = a(x/[b + x]); F(1,18) = 5.57 to 13.32, p = 0.002 to 0.03, mean 'a': 88.62±2.38%, mean 'b': 69.88±4.36 mg]. Multivariate analyses of variance showed significantly greater occupancy of phenelzine (45-60mg) and higher-dose moclobemide (900-1200 mg) compared to lower-dose moclobemide [300-600 mg; F(7,16) = 3.94, p = 0.01]. CONCLUSIONS: These findings suggest that for first-line MDE treatment, daily moclobemide doses of 300-600mg correspond to a MAO-A occupancy of 74%, whereas for treatment-resistant MDE, either phenelzine or higher doses of moclobemide correspond to a MAO-A occupancy of at least 84%. Therefore, novel MAO inhibitor development should aim for similar thresholds. The findings provide a rationale in treatment algorithm design to raise moclobemide doses to inhibit more MAO-A sites, but suggest switching from high-dose moclobemide to phenelzine is best justified by binding to additional targets.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".