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Record W4280621377 · doi:10.1377/hlthaff.2021.01874

Patents And Regulatory Exclusivities On Inhalers For Asthma And COPD, 1986–2020

2022· article· en· W4280621377 on OpenAlexaff
William B. Feldman, Doni Bloomfield, Reed F. Beall, Aaron S. Kesselheim

Bibliographic record

VenueHealth Affairs · 2022
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversity of Calgary
FundersNational Institutes of HealthArnold VenturesNational Heart, Lung, and Blood InstituteGilead SciencesBlue Cross Blue Shield of Massachusetts
KeywordsInhalerBrand namesFood and drug administrationMedicineAsthmaBusinessPulmonary diseaseProduct (mathematics)COPDAdvertisingMarketingPharmacology

Abstract

fetched live from OpenAlex

Inhalers are the mainstay of treatment for asthma and chronic obstructive pulmonary disease (COPD). These products face limited generic competition in the US and remain expensive. To better understand the strategies that brand-name inhaler manufacturers have employed to preserve their market dominance, we analyzed all patents and regulatory exclusivities granted to inhalers approved by the Food and Drug Administration between 1986 and 2020. Of the sixty-two inhalers approved, fifty-three were brand-name products, and these brand-name products had a median of sixteen years of protection from generic competition. Only one inhaler contained an ingredient with a new mechanism of action. More than half of all patents were on the inhaler devices, not the active ingredients or other aspects of these drug-device combinations. Manufacturers augmented periods of brand-name market exclusivity by moving active ingredients from one inhaler device into another ("device hops"). The median time from approval of an originator product to the last-to-expire patent or regulatory exclusivity of branded follow-ons was twenty-eight years (across device hops on fourteen originator products). Regulatory and patent reform is critical to ensure that the rewards bestowed on brand-name inhaler manufacturers better reflect the added clinical benefit of new products.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.021
GPT teacher head0.281
Teacher spread0.260 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations31
Published2022
Admission routes1
Has abstractyes

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