Making Advanced Therapies Affordable and Accessible: Two Strategic Approaches
Bibliographic record
Abstract
This article explores two complementary strategies for addressing the affordability and access challenges facing advanced therapies. As high development costs and limited market access have led to the withdrawal of several therapies, the article examines how these barriers create 'valleys of death' that prevent innovation from reaching patients. Through the case of Glybera and other examples, it outlines a rehabilitative approach focused on reforming current systems through improved reimbursement schemes, regulatory streamlining, and more efficient manufacturing. It also presents a transformative approach that embeds affordability and access from the beginning of the research and development process, encouraging local innovation, equitable intellectual property practices, and adaptive regulatory frameworks. Lessons from the COVID-19 vaccine experience demonstrate the real-world potential of these strategies to ensure that promising therapies are not only developed, but also equitably delivered worldwide.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".