Gantenerumab: an anti-amyloid monoclonal antibody with potential disease-modifying effects in early Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: This review describes the research and development process of gantenerumab, a fully human anti-amyloid monoclonal antibody in development to treat early symptomatic and asymptomatic Alzheimer's disease (AD). Anti-amyloid monoclonal antibodies can substantially reverse amyloid plaque pathology and may modify the course of the disease by slowing or stopping its clinical progression. Several molecules targeting amyloid have failed in clinical development due to drug-related factors (e.g., treatment-limiting adverse events, low potency, poor brain penetration), study design/methodological issues (e.g., disease stage, lack of AD pathology confirmation), and other factors. The US Food and Drug Administration's approval of aducanumab, an anti-amyloid monoclonal antibody as the first potential disease-modifying therapy for AD, signaled the value of more than 20 years of drug development, adding to the available therapies the first nominal success since cholinesterase inhibitors and memantine were approved. BODY: Here, we review over 2 decades of gantenerumab development in the context of scientific discoveries in the broader AD field. Key learnings from the field were incorporated into the gantenerumab phase 3 program, including confirmed amyloid positivity as an entry criterion, an enriched clinical trial population to ensure measurable clinical decline, data-driven exposure-response models to inform a safe and efficacious dosing regimen, and the use of several blood-based biomarkers. Subcutaneous formulation for more pragmatic implementation was prioritized as a key feature from the beginning of the gantenerumab development program. CONCLUSION: The results from the gantenerumab phase 3 programs are expected by the end of 2022 and will add critical information to the collective knowledge on the search for effective AD treatments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".