Adenosine 2A Receptor Antagonists for the Treatment of Motor Symptoms in Parkinson's Disease
Bibliographic record
Abstract
Abstract Background Treatment of motor fluctuations in Parkinson's disease ( PD ) remains an unmet challenge. Adenosine 2A (A 2A ) receptors are located along the indirect pathway and represent a potential target to enhance l ‐3,4‐dihydroxyphenylalanine ( l ‐ DOPA ) antiparkinsonian action. Methods This article summarizes the preclinical and clinical literature on A 2A antagonists in PD , with a specific focus on their effect on off time, on time, and dyskinesia. Findings Several A 2A receptor antagonists have been tested in preclinical studies and clinical trials. In preclinical studies, A 2A antagonists enhanced l ‐ DOPA antiparkinsonian action without exacerbating dyskinesia, but A 2A antagonists were generally administered in combination with a subthreshold dose of l ‐ DOPA , which is different to the paradigms used in clinical trials, where A 2A antagonists were usually added to an optimal antiparkinsonian regimen. In clinical settings, A 2A antagonists generally reduced duration of off time, by as much as 25% in some studies. The effect of on time duration is less clear, and in a few studies an exacerbation of dyskinesia was reported. Two A 2A antagonists have been tested in phase III settings: istradefylline and preladenant. Istradefylline was effective in two phase III trials, but ineffective in another; the drug has been commercially available in Japan since 2013. In contrast, preladenant was ineffective in a phase III trial and the drug was discontinued. A phase III study with tozadenant will begin in 2015; the drug was effective at reducing off time in a phase II b study. Other A 2A antagonists are in development at the preclinical and early clinical levels.
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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 | 0.000 |
| 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 | 0.000 | 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".