Commercialization of cell and gene therapy in Canada: Current landscape, challenges and opportunities
Bibliographic record
Abstract
• Canada lags other jurisdictions, such as the USA and EU, in CGT commercialization despite recent approval trends. • Regulatory ambiguity and lack of orphan drug incentives limit CGT development. • Traditional health technology assessment (HTA) approaches in Canada struggle to capture CGTs’ long-term value. • Implementing real-world evidence and decentralized manufacturing could improve domestic commercialization. • Adopting global policy models could improve CGT access, equity, and innovation in Canada. Cell and gene therapies (CGTs) offer transformative treatments for certain genetic diseases and cancers, with a growing number of global approvals. Yet Canadian commercialization lags behind the USA and Europe. This review identifies key barriers and proposes policy solutions informed by international examples. A policy-oriented environmental scan was conducted using regulatory documents, peer-reviewed literature, government reports, and industry publications. Barriers and solutions are organized into four domains: regulation, manufacturing, pricing and reimbursement, and access and equity. Despite recent investment downturns in this therapeutic area, Canada’s approval of its first clustered regularly interspaced short palindromic repeats (CRISPR)-based therapy and chimeric antigen receptor (CAR) T-cell expansion suggest future growth. Strategic reforms could improve domestic CGT innovation, affordability, and equitable access.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".