How social pharmaceutical innovations are addressing problems of availability, accessibility and affordability of drugs for rare diseases
Bibliographic record
Abstract
BACKGROUND: The current organization of the pharmaceutical innovation system poses three major challenges for rare disease patients in terms of availability, accessibility and affordability of treatments. While some changes have emerged in the European Union to address some of these challenges, their impacts are not experienced uniformly across member states nor around the world. We have observed niche initiatives that are actively working to address those challenges within their local contexts. In a position paper in this journal, we characterized such initiatives as “social pharmaceutical innovation” (or SPIN): novel collaborations involving diverse sets of actors that break with conventional pharmaceutical innovation practices to develop interventions that address unmet societal needs of rare disease patients and that are not primarily market driven. RESULTS: Here we report on 15 cases of SPIN across Brazil, Canada, France and the Netherlands that we studied through semi-structured qualitative interviews (n = 151) with players involved in those cases. Our findings show how SPIN initiatives are reconfiguring pharmaceutical innovation networks to include a wider range of actors in redistributed and differentiated roles within innovation processes. Further, we find that SPINs are associated with changes in the ways data is gathered (often in clinical contexts rather than in conventional trials), and how evidence is assembled to improve access to the treatments. Finally, we demonstrate how SPINs are providing new routes for patients to access treatments for rare diseases, often at more affordable prices. CONCLUSIONS: While promising, SPINs are not perfect solutions for rare disease patients or the broader challenges to the pharmaceutical innovation system. SPINs are specific solutions adapted to the particulars of local framing, institutions, national policy and care contexts of rare diseases, and should be developed as such. Our findings support these recommendations for SPIN: use local knowledge and expertise in crafting SPINs; develop comprehensive strategies for data governance, access and ownership; and explore new economic models to recoup investments and/or sustain future initiatives. We invite collaboration on these topics and emerging SPIN initiatives so as to support efforts at addressing challenges of availability, accessibility and affordability of treatments for rare diseases patients.
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.052 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".