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Record W4388729178 · doi:10.1371/journal.pmed.1004309

Patents and regulatory exclusivities on FDA-approved insulin products: A longitudinal database study, 1986–2019

2023· article· en· W4388729178 on OpenAlexaff
T. Anders Olsen, Reed F. Beall, Ryan P. Knox, S. Sean Tu, Aaron S. Kesselheim, William B. Feldman

Bibliographic record

VenuePLoS Medicine · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsUniversity of Calgary
FundersNational Heart, Lung, and Blood InstituteWest Virginia UniversityArnold VenturesCommonwealth Fund
KeywordsMedicineFood and drug administrationApproved drugPharmacologyBusinessDrug

Abstract

fetched live from OpenAlex

BACKGROUND: Insulin is the primary treatment for type 1 and some type 2 diabetes but remains costly in the United States, even though it was discovered more than a century ago. High prices can lead to nonadherence and are often sustained by patents and regulatory exclusivities that limit competition on brand-name products. We sought to examine how manufacturers have used patents and regulatory exclusivities on insulin products approved from 1986 to 2019 to extend periods of market exclusivity. METHODS AND FINDINGS: We used the publicly available Food and Drug Administration (FDA) Approved Drug Products with Therapeutic Equivalence Evaluations (Orange Book) to identify all approved biosynthetic insulin products. Individual products approved under the same New Drug Application (NDA)-e.g., a vial and pen-were considered as separate products for the purposes of analysis. We recorded all patents and regulatory exclusivities listed in the Orange Book on each product and used Google Patents to extract the timing of patent application and whether patents were obtained on delivery devices or others aspects of the product. The primary outcome was the duration of expected protection, which was determined by subtracting the FDA approval date for each product from its last-to-expire patent or regulatory exclusivity (whichever occurred later). We performed a secondary analysis that considered overall protection on insulin lines-defined as groups of products approved under the same NDA with the same active ingredients manufactured by the same company. We also examined competition from follow-on insulin products-defined as products approved with the same active ingredients as originators but manufactured by different companies (approved via a specific drug approval pathway under section 505(b)(2) of the Food, Drug, and Cosmetic Act). During the study period, the FDA approved 56 individual products across 25 different insulin lines and 5 follow-ons across 3 different insulin lines. Thirty-three (59%) of the 56 products were drug-device combinations. Manufacturers of 9 products approved during the study period obtained patents filed after FDA approval that extended their duration of expected protection (by a median of 6 years). Approximately 63% of all patents on drug-device combinations approved during the study period were related to delivery devices. The median duration of expected protection on insulin products was 16.0 years, and the median protection on insulin lines was 17.6 years. An important limitation of our analysis is that manufacturers may continue to add patents on existing insulin products while competitors may challenge patents; therefore, periods of protection may change over time. CONCLUSIONS: Among several strategies that insulin manufacturers have employed to extend periods of market exclusivity on brand-name insulin products are filing patents after FDA approval and obtaining a large number of patents on delivery devices. Policy reforms are needed to promote timely competition in the pharmaceutical market and ensure that patients have access to low-cost drugs.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.163
GPT teacher head0.306
Teacher spread0.143 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2023
Admission routes1
Has abstractyes

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