Orphan drug incentives in the pharmacogenomic context: policy responses in the US and Canada
Bibliographic record
Abstract
Advances in pharmacogenomic research and increasing industry interest in personalized medicine have important implications for the way that orphan drug policies are interpreted and applied. Concerns have been raised about the potential impact of pharmacogenomics and new genomic technologies on our understanding of how disease categories are delineated, and subsequently, how the concept of rare disease should be defined for the purposes of orphan drug policies. This article considers whether orphan drug legislation can be drafted in a way that will maximize benefits and minimize concerns relating to the impact of pharmacogenomics on orphan drug research and development. After reviewing the issues that may arise at the intersection of orphan drug policies and pharmacogenomics, this article will discuss the potential impact of pharmacogenomics at two critical points: orphan designation and approval of the drug product. At each of these points, the relevant aspects of current US orphan drug legislation are examined, focusing on the extent to which recent amendments may address concerns that have been raised previously. This analysis will then provide the foundation for a critical review and recommendations regarding the proposed new Canadian orphan drug framework.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".