Twelve Years of Drug Prioritization to Help Accelerate Disease Modification Trials in Parkinson’s Disease: The International Linked Clinical Trials Initiative
Bibliographic record
Abstract
In 2011, the UK medical research charity Cure Parkinson's set up the international Linked Clinical Trials (iLCT) committee to help expedite the clinical testing of potentially disease modifying therapies for Parkinson's disease (PD). The first committee meeting was held at the Van Andel Institute in Grand Rapids, Michigan in 2012. This group of PD experts has subsequently met annually to assess and prioritize agents that may slow the progression of this neurodegenerative condition, using a systematic approach based on preclinical, epidemiological and, where possible, clinical data. Over the last 12 years, 171 unique agents have been evaluated by the iLCT committee, and there have been 21 completed clinical studies and 20 ongoing trials associated with the initiative. In this review, we briefly outline the iLCT process as well as the clinical development and outcomes of some of the top prioritized agents. We also discuss a few of the lessons that have been learnt, and we conclude with a perspective on what the next decade may bring, including the introduction of multi-arm, multi-stage clinical trial platforms and the possibility of combination therapies for PD.
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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 | 0.059 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| 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".