Lecanemab, Donanemab, and Emerging Disease Modifying Therapies for Alzheimer Disease: Clinical Evidence and Primary Care Implications in Canada
Bibliographic record
Abstract
Introduction: Alzheimer's disease is a progressive neurodegenerative disorder with current treatment limited to symptomatic relief and no alteration in disease progression. Advances in the DMTs (Disease-modifying-therapies) may alter the progression if used in early phase of the disease. Objective: To review and summarize the current evidence on advancements in DMTs in AD; review their regulatory status in Canada; Examine risk vs. benefit and explore implications for Family Physicians. Methods: Narrative review of recent randomized controlled trials. regulatory status and submissions, and Canadian policy documents up to mid-2025. Sources include PubMed, clinical trial registries, Health Canada Notices, Alzheimer's Society of Canada materials. Results: Monoclonal antibodies such as lecanemab and donanemab show reduction in amyloid plaque and slowing of cognitive and functional decline (modest) in early AD (mild cognitive impairment or mild dementia). In contrast, Aducanumab while initially promising ended up showing mixed results and due to inconsistent clinical outcomes, it is not approved by Health Canada at present. Tau‑targeting and anti‑inflammatory agents are under investigation but have not yet demonstrated definitive benefit. Key risks include amyloid‑related imaging abnormalities (ARIA), infusion reactions, cost, and lack of data for long term safety. Canadian health system challenges include lack of access to biomarker testing and imaging, Lack of healthcare specialists, and reimbursement frameworks. Conclusion: Using DMTs in the early phases of AD offers significant promise in altering disease progression. However, Family Physicians needs to thoroughly evaluate the risk to benefit ratio, ensure careful patient selection, and work in close collaboration with specialists. Furthermore, ensuring the healthcare system is ready— with access to proper testing facilities, coverage models, and support systems—is essential for the safe and equitable implementation of these therapies.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".