Lessons Learnt from the Second Generation of Anti-Amyloid Monoclonal Antibodies Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Alzheimer disease (AD) is a chronic neurodegenerative disorder with complex pathophysiology that affects over 50 million people worldwide. Most drug therapies, to date, have focused on targeting the amyloid-beta (Aβ) pathway, but clinical outcomes of anti-Aβ antibodies have been unsuccessful and unable to meet their primary endpoints. Similar trends have also been observed in treatments that target the tau pathway. SUMMARY: This paper reviews recent anti-Aβ passive monotherapies, since Bapineuzumab, that have progressed to phase 3 clinical trials. Specifically, we discuss the 4 clinical trial programs of Solanezumab (targets Aβ monomers), Aducanumab (targets Aβ oligomers and plaques), Crenezumab (targets Aβ oligomers), and Gantenerumab (targets Aβ fibrils) which are all exogenous monoclonal antibodies. We conclude with potential reasons for why they have not met their primary endpoints and discuss lessons learnt from these trials. Key Message: Future disease-modifying trials (DMTs) for AD should be conducted in asymptomatic, Aβ-positive individuals. Moreover, potential additive and/or synergistic benefits focusing on anti-Aβ and anti-tau drug combinations merit further investigation.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".