A.1 Repurposing Ambroxol as a disease-modifying treatment for Parkinson’s disease dementia: A phase 2, randomized, double blind placebo-controlled trial
Bibliographic record
Abstract
Background: Currently there are no disease modifying treatment for Synucleinopathies including Parkinson’s disease Dementia (PDD). Carrying a mutation in the GBA gene (beta-glucocerebrosidase/ GCAse) is a leading risk factor for synucleinopathies. Raising activity GCAse lowers α-synuclein levels in cells and animal models. Ambroxol is a pharmacological chaperone for GCAse and can raise GCAse levels. Our goal is to test Ambroxol as a disease-modifying treatment in PDD. Methods: We randomized fifty-five individuals with PDD to Ambroxol 1050mg/day, 525mg/day, or placebo for 52 weeks. Primary outcome measures included safety, Alzheimer’s disease Assessment Scale-cognitive (ADAS-Cog) subscale and the Clinician’s Global Impression of Change (CGIC). Secondary outcomes included pharmacokinetics, cognitive and motor outcomes and and plasma and CSF biomarkers. Results: Ambroxol was well tolerated. There were 7 serious adverse events (SAEs) none deemed related to Ambroxol. GCase activity was increased in white blood cells by ~1.5 fold. There were no differences between groups on primary outcome measures. Patients receiving high dose Ambroxol appeared better on the Neuropsychiatric Inventory. GBA carriers appeared to improve on some cognitive tests. pTau 181 was reduced in CSF. Conclusions: Ambroxol was safe and well-tolerated in PDD. Ambroxol may improve biomarkers and cognitive outcomes in GBA1 mutation carrie.rs Ambroxol improved some biomarkerss. ClinicalTrials.gov NCT02914366
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".