Novel nondopaminergic targets for motor features of Parkinson's disease: Review of recent trials
Bibliographic record
Abstract
Neurotransmitters other than dopamine are recognized as having modulatory roles within the basal ganglia and can influence the basal ganglia dopaminergic system to alter activity of the direct and indirect pathways. Many nondopaminergic neurotransmitter systems have been implicated in the mechanisms contributing to the motor features of Parkinson's disease (PD). Thus, it is now well established that neurotransmitter systems, including glutamatergic, GABAergic, cholinergic, noradrenergic, serotonergic, opioidergic, histaminergic, and adenosinergic systems, are affected in the pathogenesis of PD. Nondopaminergic neurotransmitter systems are thus targets for the development of novel therapies for motor symptoms and motor complications in PD. Over the last 5 years, more than 20 randomized, control trials (RCTs) in PD investigating drugs that target several of these nondopaminergic neurotransmitter systems for the treatment of motor features have been completed. There are at least 15 additional RCTs that are ongoing or planned. Here, we review these RCTs to highlight the potential nondopaminergic pharmacological therapies for treatment of motor features of PD. Nondopaminergic drugs are not expected to replace dopaminergic strategies, but further development of these drugs will likely yield novel approaches with positive clinical implications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 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".