Prasinezumab slows motor progression in rapidly progressing early-stage Parkinson’s disease
Bibliographic record
Abstract
Prasinezumab, a monoclonal antibody that binds aggregated α-synuclein, is being investigated as a potential disease-modifying therapy in early-stage Parkinson's disease. Although in the PASADENA phase 2 study, the primary endpoint (Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) sum of Parts I + II + III) was not met, prasinezumab-treated individuals exhibited slower progression of motor signs than placebo-treated participants (MDS-UPDRS Part III). We report here an exploratory analysis assessing whether prasinezumab showed greater benefits on motor signs progression in prespecified subgroups with faster motor progression. Prasinezumab's potential effects on disease progression were assessed in four prespecified and six exploratory subpopulations of PASADENA: use of monoamine oxidase B inhibitors at baseline (yes versus no); Hoehn and Yahr stage (2 versus 1); rapid eye movement sleep behavior disorder (yes versus no); data-driven subphenotypes (diffuse malignant versus nondiffuse malignant); age at baseline (≥60 years versus <60 years); sex (male versus female); disease duration (>12 months versus <12 months); age at diagnosis (≥60 years versus <60 years); motor subphenotypes (akinetic-rigid versus tremor-dominant); and motor subphenotypes (postural instability gait dysfunction versus tremor-dominant). In these subpopulations, the effect of prasinezumab on slowing motor signs progression (MDS-UPDRS Part III) was greater in the rapidly progressing subpopulations (for example, participants who were diffuse malignant or taking monoamine oxidase B inhibitors at baseline). This exploratory analysis suggests that, in a trial of 1-year duration, prasinezumab might reduce motor progression to a greater extent in individuals with more rapidly progressing Parkinson's disease. However, because this was a post hoc analysis, additional randomized clinical trials are needed to validate these findings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".