Market access of gene therapies across Europe, USA, and Canada: challenges, trends, and solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Challenges blocking market access of GTMPs are highly interrelated. • Developers should seek support and early joint interactions with regulators and payers. • Conditional marketing authorization and reimbursement mechanisms should be explored. • RWE infrastructure and requirements should be developed on an international level. • Efficient innovative pricing and payment models should be implemented. A limited number of gene therapy medicinal products (GTMPs) have received marketing authorization (MA), of which some have been withdrawn, and even less have gained reimbursement. Many challenges that complicate GTMP market access can occur across multiple jurisdictions and decision-making contexts, but some reimbursement challenges are specific to jurisdictions. The importance of these challenges will vary according to the specific therapy being developed, the country where market access is sought, and the efforts made by developers, regulators and payers to implement solutions to overcome these barriers. This review could alert developers to challenges associated with GTMP MA and how to address them. This review can inform gene therapy developers on challenges that can be encountered when seeking market access. Moreover, it provides an overview of trends among challenges and potential solutions.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it