Time-limited reimbursement and Temporary Access Process for early access to oncology treatments in Canada: a perspective based on the epcoritamab experience
Bibliographic record
Abstract
For years, Canadians have faced long wait times for access to new medicines. These delays are largely attributed to complex health technology assessments, extended price negotiations and protracted provincial listing decisions. To address these challenges, in November 2023, Canada's Drug Agency (CDA) introduced its first early access program - the time-limited reimbursement recommendation (TLR) - aimed at accelerating the reimbursement of promising drugs undergoing Health Canada's Notice of Compliance with Conditions (NOC/c) process. In conjunction, the pan-Canadian Pharmaceutical Alliance developed the Temporary Access Process (pTAP) to support price negotiations for drugs that go through CDA's TLR pathway. AbbVie corporation was the first company to participate in the TLR and pTAP processes with EPKINLY (epcoritamab) - a novel treatment for advanced lymphoma. On 18 June 2024, EPKINLY became the first therapy in Canada to receive a positive CDA TLR recommendation and on 19 July 2024, AbbVie and the pan-Canadian Pharmaceutical Alliance successfully concluded pTAP negotiations. As of 1 November 2024, EPKINLY was listed in nine provinces, achieving a 10.7 month faster time-to-patient than the average time for the standard process, which is significant and meaningful to patients. This achievement demonstrates the potential of the TLR and pTAP processes to improve medicine access timelines for patients. However, an analysis of drugs that received NOC/c status from Health Canada between 2020 and 2024 reveals that very few drugs would have met the current strict eligibility criteria required to benefit from the TLR, limiting the potential benefits of these programs. While TLR and pTAP are promising initiatives, refinements are needed to maximize their impact and ensure faster access to life-saving therapies for Canadian patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".