Accelerating Oncology Drug Reimbursement in Canada: Impact of the CDA-AMC Time-Limited Recommendation and pCPA Temporary Access Process
Bibliographic record
Abstract
The complex pathway for new drug reimbursement in Canada has been well documented. Drugs with promising early efficacy data may receive a Notice of Compliance with Conditions (NOC/c) from Health Canada. For oncology drugs that receive NOC/c, the pathway through positive review by Canada’s Drug Agency (CDA-AMC) and subsequent public reimbursement can take over 500 days. To address this challenge, in September 2023, CDA-AMC announced a new Time-Limited Recommendation (TLR) category, and in parallel, the pan-Canadian Pharmaceutical Alliance (pCPA) developed a set of principles and conditions for a Temporary Access Process (pTAP). This accelerated access pathway, the first of its kind in Canada, enables patients with advanced diseases to gain timely access to promising therapies while managing the uncertainties and risks associated with early approvals. This report provides a first assessment of the impact of the TLR-pTAP process on the reimbursement timelines for oncology drugs approved with NOC/c. Methods: The time from NOC/c approvals for oncology drugs between 1 January 2023 to 31 December 2024, to first provincial listings, and the timelines of the Health Canada, CDA-AMC, and pCPA review processes, were collected and evaluated. Results: Nine oncology NOC/c were granted during the selected period, of which three products, Columvi, Akeega, and Epkinly, received provincial listings, and the median time from regulatory approvals to provincial listings is 509 days (IQ range 306–544 days). One drug, Epkinly, has elected to adopt the TLR-pTAP pathway. Compared to the conventional reimbursement pathway—including for the drug Columvi, whose therapeutic profile is similar to that of Epkinly—the new pathway reduced the time to first provincial listing by over 200 days. A stepwise analysis indicates that the most significant accelerator within the TLR-pTAP pathway is the pCPA’s prioritization and processing of the file in parallel to the CDA-AMC’s health technology assessment (HTA) review process, rather than subsequently. Electing to file the HTA submission pre-NOC could have further accelerated timelines. No acceleration in each agency’s review time was observed. Conclusions: Participation in the TLR-pTAP pathway can help mitigate concerns over uncertainties associated with novel therapies while providing timelier access for patients with life-threatening diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".