Sustainable approaches for drug repurposing in rare diseases: recommendations from the IRDiRC Task Force
Bibliographic record
Abstract
Drug repurposing represents a real opportunity to address unmet needs and improve the lives of rare disease patients. It is often presented as a faster, safer and cheaper path for bringing drugs into new indications. However, several economic, regulatory and scientific barriers can impede the successful repurposing of drugs for rare diseases. The International Rare Diseases Research Consortium (IRDiRC) set up the Task Force on Sustainable Models in Drug Repurposing with the objective of identifying key factors for achieving sustainable repurposing approaches in rare diseases. In order to help inform expert opinion, the Task Force investigated six cases of medicinal products repurposed into new rare indications and four cases of ongoing development programs. A questionnaire addressing the major steps of the repurposing approach was developed by the Task Force and sent to contact points of the organizations. In addition, interviews were conducted with the relevant organization representatives to conduct a deeper dive into the sustainability of the repurposing approach for each of the selected cases. Based on the collective experience of the members of the Task Force and the output from the questionnaires/interviews, we have identified ten key factors that should be considered by those embarking on repurposing projects. These factors include the identification of unmet patient needs and partnership with patients, collection of evidence concerning disease prevalence, patient numbers, drug pharmacology and disease etiology, drug industrial property status, off-label or compounding use, data from past clinical studies and needs for extended non-clinical and clinical studies. The development of a collaborative funding framework and early discussion with regulators and payers are additional factors to implement early in the development of sustainable drug repurposing projects.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".