Patent Challenges And Litigation On Inhalers For Asthma And COPD
Bibliographic record
Abstract
Between 1986 and 2020 the Food and Drug Administration (FDA) approved fifty-three brand-name inhalers for asthma and chronic obstructive pulmonary disease (COPD), but by the end of 2022 only three of those inhalers faced independent generic competition. Manufacturers of brand-name inhalers have created long periods of market exclusivity by obtaining multiple patents, many on the delivery devices rather than the active ingredients, and by introducing new devices that contain old active ingredients. Limited generic competition for inhalers has raised questions about whether the Drug Price Competition and Patent Term Restoration Act of 1984, also known as the Hatch-Waxman Act, for challenging patents is adequately facilitating the entry of complex generic drug-device combinations. For the fifty-three brand-name inhalers approved during the period 1986-2020, generic manufacturers filed challenges authorized by the Hatch-Waxman Act, which are known as paragraph IV certifications, on only seven products (13 percent). The median time from FDA approval to first paragraph IV certification was fourteen years. Paragraph IV certifications resulted in approved generics for only two products, each of which experienced fifteen years of market exclusivity before generic approval. Reform of the generic drug approval system is critical to ensuring the timely availability of competitive markets for generic drug-device combinations such as inhalers.
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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 | 0.019 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.015 | 0.008 |
| 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".