Disease‐modifying strategies for Parkinson's disease
Bibliographic record
Abstract
Parkinson's disease (PD) is an increasingly prevalent and progressively disabling neurodegenerative disease. The impact of PD on patients and their families as well as its burden on health care systems could be substantially reduced by disease-modifying therapies that slow the rate of neurodegeneration or stop the disease process. Multiple agents have been studied in clinical trials designed to assess disease modification in PD, but all have failed. Over the last 3 years, clinical trials investigating the potential of adeno-associated virus serotype 2 (AAV)-neuturin, coenzyme Q10, creatine, pramipexole, and pioglitazone reported negative findings or futility. Despite these disappointments, progress has been made by expanding our understanding of molecular pathways involved in PD to reveal new targets, and by developing novel animal models of PD for preclinical studies. Currently, at least eight ongoing clinical trials are testing the promise of isradipine, caffeine, nicotine, glutathione, AAV2-glial cell-line derived neurotrophic factor (GDNF), as well as active and passive immunization against α-synuclein (α-Syn). In this review, we summarize the clinical trials of disease-modifying therapies for PD that were published since 2013 as well as clinical trials currently in progress. We also discuss promising approaches and ongoing challenges in this area of PD research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".