Economic analysis of opportunities to accelerate Alzheimer's disease research and development
Bibliographic record
Abstract
The development of disease-modifying treatments for Alzheimer's disease (AD) faces a number of barriers. Among these are the lack of surrogate biomarkers, the exceptional size and duration of clinical trials, difficulties in identifying appropriate populations for clinical trials, and the limitations of monotherapies in addressing such a complex multifactorial disease. This study sets out to first estimate the consequent impact on the expected cost of developing disease-modifying treatments for AD and then to estimate the potential benefits of bringing together industry, academic, and government stakeholders to co-invest in, for example, developing better biomarkers and cognitive assessment tools, building out advanced registries and clinical trial-readiness cohorts, and establishing clinical trial platforms to investigate combinations of candidate drugs and biomarkers from the portfolios of multiple companies. Estimates based on interviews with experts on AD research and development suggest that the cost of one new drug is now $5.7 billion (95% confidence interval (CI) $3.7-9.5 billion) and could be reduced to $2.0 billion (95% CI $1.5-2.9 billion). The associated acceleration in the arrival of disease-modifying treatments could reduce the number of case years of dementia by 7.0 million (95% CI 4.4-9.4 million) in the United States from 2025 through 2040.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".