The global intellectual property ecosystem for insulin and its public health implications: an observational study
Bibliographic record
Abstract
BACKGROUND: Lack of access to insulin and poor health outcomes are issues for both low and high income countries. This has been accompanied by a shift from relatively inexpensive human insulin to its more expensive analogs, marketed by three to four main global players. Nonetheless, patent-based market exclusivities are beginning to expire there for the first generation insulin analogs. This paper adds a global dimension to information on the U.S. patent landscape for insulin by reviewing the patent status of insulins with emphasis on the situation outside the US and Europe. METHODS: Using the term "insulin", we searched for patents listed on the United States Food and Drug Administration's (USFDA) Orange Book and the Canadian Online Drug Product Database Online Query and its Patent Register. With this information, we expanded the search globally using the World Intellectual Property Organization (WIPO) PatentScope database, the European Patent Office's INPADOC database and various country-specific Patent Offices. RESULTS: Patent protected insulins marketed in the U.S. and other countries are facing an imminent patent-expiration "cliff' yet the three companies that dominate the global insulin market are continuing to file for patents in and outside the U.S, but very rarely in Africa. Only a few local producers in the so-called "pharmerging" markets (e.g., Brazil, India, China) are filing for global patent protection on their own insulins. There is moderate, but statistically significant association between patent filings and diabetes disease burden. CONCLUSIONS: The global market dominance by a few companies of analog over human insulin will likely continue even though patents on the current portfolio of insulin analogs will expire very soon. Multinationals are continuing to file for more insulin patents in the bigger markets with large disease burdens and a rapidly emerging middle class. Off-patent human insulins can effectively manage diabetes. A practical way forward would be find (potential) generic manufacturers globally and nudge them towards opportunities to diversify their national insulin markets with acceptable off-patent products for export.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".