Who Ever Said It Would Be Easy? Reflecting on Two Clinical Trials Targeting <scp>α‐Synuclein</scp>
Bibliographic record
Abstract
Two recent, high-profile manuscripts reported negative results with two parallel approaches of passive immunization targeting α-synuclein in a population of patients with early Parkinson's disease (PD). These phase II studies failed to show a bona fide disease-modifying neuroprotective effect on PD progression, despite the evidence that these antibodies effectively bind native α-synuclein in human serum. Here, we discuss the possible reasons that could help explain the lack of clinical efficacy. In particular, we highlight (1) the wealth of evidence supporting the notion of α-synuclein as a valid therapeutic target; (2) the lack of evidence of target engagement in the aforementioned studies, especially of the elusive oligomeric species, the likely culprits in disease pathogenesis and/or its propagation; (3) the limitations, especially in terms of timing passive immunization, of preclinical models, where the same α-synuclein antibodies succeeded in mitigating disease manifestations; (4) the consideration of possibly intervening at an even earlier stage of disease in future trials; and (5) the multitude of strategies beyond passive immunization that could be used to combat α-synuclein-mediated neurodegeneration, if in the end the current approach is not fruitful. Overall, our perception is that converging developments in the field, among them novel bioassays and biomarkers, improved cellular and animal models and objective measurements of motor activities integrated into clinical trials, if further optimized, will gradually move the momentum of the field forward. This, to better test the concept of whether α-synuclein-targeting therapies can indeed deliver the "holy grail" of neuroprotection to the benefit of the PD community. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".