Monoamine Reuptake Inhibitors in Parkinson’s Disease
Bibliographic record
Abstract
The motor manifestations of Parkinson's disease (PD) are secondary to a dopamine deficiency in the striatum. However, the degenerative process in PD is not limited to the dopaminergic system and also affects serotonergic and noradrenergic neurons. Because they can increase monoamine levels throughout the brain, monoamine reuptake inhibitors (MAUIs) represent potential therapeutic agents in PD. However, they are seldom used in clinical practice other than as antidepressants and wake-promoting agents. This review article summarises all of the available literature on use of 50 MAUIs in PD. The compounds are divided according to their relative potency for each of the monoamine transporters. Despite wide discrepancy in the methodology of the studies reviewed, the following conclusions can be drawn: (1) selective serotonin transporter (SERT), selective noradrenaline transporter (NET), and dual SERT/NET inhibitors are effective against PD depression; (2) selective dopamine transporter (DAT) and dual DAT/NET inhibitors exert an anti-Parkinsonian effect when administered as monotherapy but do not enhance the anti-Parkinsonian actions of L-3,4-dihydroxyphenylalanine (L-DOPA); (3) dual DAT/SERT inhibitors might enhance the anti-Parkinsonian actions of L-DOPA without worsening dyskinesia; (4) triple DAT/NET/SERT inhibitors might exert an anti-Parkinsonian action as monotherapy and might enhance the anti-Parkinsonian effects of L-DOPA, though at the expense of worsening dyskinesia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".