Biological patent thickets and delayed access to biosimilars, an American problem
Bibliographic record
Abstract
Our study seeks to determine whether patent thickets covering biologic drugs are responsible for delayed biosimilar market entry. We compare patent assertions against the same biosimilar drugs across three countries. On average nine to twelve times more patents were asserted against biosimilars in the United States than in Canada and the United Kingdom. Biosimilars also enter the Canadian and UK markets more quickly than they do in the United States following regulatory approval. Later market entry is not a problem when the brand name drug company is asserting high quality patents (i.e. patents covering significant advances). Consequently, we drilled down into the U.S. patent portfolio of one major biologic, Abbvie's Humira drug, and found that it was made up of roughly 80% non-patentably distinct (duplicative) patents linked together by terminal disclaimers, which is permitted under United States Patent and Trademark Office (USPTO) rules. In contrast, there were far less non-duplicative European patents that covered Humira. Patent thickets can allow brand name drug companies to delay biosimilar entry by relying on the high cost of challenging many duplicative patents instead of the quality of their underlying patents. Accordingly, we suggest several policy interventions that may thin these biologic patent thickets.
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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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".