Patents and Regulatory Exclusivities on GLP-1 Receptor Agonists
Bibliographic record
Abstract
Importance: Glucagon-like peptide 1 (GLP-1) receptor agonists were first approved for the treatment of type 2 diabetes in 2005. Demand for these drugs has increased rapidly in recent years, as indications have expanded, but they remain expensive. Objective: To analyze how manufacturers of brand-name GLP-1 receptor agonists have used the patent and regulatory systems to extend periods of market exclusivity. Evidence Review: The annual US Food and Drug Administration's (FDA) Approved Drug Products With Therapeutic Equivalence Evaluations was used to identify GLP-1 receptor agonists approved from 2005 to 2021 and to record patents and nonpatent statutory exclusivities listed for each product. Google Patents was used to extract additional data on patents, including whether each was obtained on the delivery device or another aspect of the product. The primary outcome was the duration of expected protection from generic competition, defined as the time elapsed from FDA approval until expiration of the last-to-expire patent or regulatory exclusivity. Findings: On the 10 GLP-1 receptor agonists included in the cohort, drug manufacturers listed with the FDA a median of 19.5 patents (IQR, 9.0-25.8) per product, including a median of 17 patents (IQR, 8.3-22.8) filed before FDA approval and 1.5 (IQR, 0-2.8) filed after FDA approval. Fifty-four percent of all patents listed on GLP-1 receptor agonists were on the delivery devices rather than active ingredients. Manufacturers augmented patent protection with a median of 2 regulatory exclusivities (IQR, 0-3) obtained at approval and 1 (IQR, 0.3-4.3) added after approval. The median total duration of expected protection after FDA approval, when accounting for both preapproval and postapproval patents and regulatory exclusivities, was 18.3 years (IQR, 16.0-19.4). No generic firm has successfully challenged patents on GLP-1 receptor agonists to gain FDA approval. Conclusions and Relevance: Patent and regulatory reform is needed to ensure timely generic entry of GLP-1 receptor agonists to the market.
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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 | 0.014 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".