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Record W1994686939 · doi:10.1186/1744-8603-10-3

“To patent or not to patent? the case of Novartis’ cancer drug Glivec in India”

2014· article· en· W1994686939 on OpenAlexaff
Ravinder Gabble, Jillian Clare Köhler

Bibliographic record

VenueGlobalization and Health · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsCentre for Global Health ResearchPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsSupreme courtMedicinePatentabilityLawsuitAppealBosutinibLawGeneric drugPolitical scienceIntellectual propertyImatinibNilotinibDrugMyeloid leukemiaPatent lawPharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: Glivec (imatinib mesylate), produced by the pharmaceutical company Novartis, is prescribed in the case of chronic myeloid leukemia, one of the most common blood cancers in eastern countries. After more than a decade of legal battles surrounding its patentability, the Supreme Court of India gave its final decision on April 1st of 2013, rejecting the appeal of the Swiss giant drug manufacturer. In 2006, the Indian Patent Office first refused Glivec's patent under Section 3(d) of the Indian Patent Act arguing that it was only a modified version of an existing drug, Imatinib, and therefore that the drug was not innovative. Novartis replied filing legal challenges against the Indian government but the final verdict in April of 2013 ends the battle. Indeed, the Supreme Court stated that even if the bioavailability of the drug was improved, it did not demonstrate enhanced efficacy and that Glivec was not patentable. METHODS: The research primarily focused on journal, newspaper and magazine articles relevant to the time frame of the lawsuit (from 1994 to 2013) as well as news searches through Google, Factiva, ProQuest, PubMed, and YouTube where press articles from court verdicts were obtained by using the following keywords: "India", "Novartis", "Glivec", "Patent", "Novartis Case", and "Supreme Court of India". The data sources were interpreted and analyzed according to the authors' own prior knowledge and understanding of the exigencies of the TRIPS Agreement. RESULTS: This case illuminates how India is interpreting international law to fit domestic public health needs. CONCLUSIONS: The Novartis case arguably sets an important precedent for the global pharmaceutical industry and ideally will help improve access to lifesaving medicines in the developing world by demanding that patient health needs supersede commercial interests. The Supreme Court of India's decision may affect the interpretation of the article of the TRIPS Agreement, which states members shall be free to determine the appropriate method of implementing the provisions of this Agreement within their own legal system and practice.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0090.017
Science and technology studies0.0060.006
Scholarly communication0.0090.004
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.158
GPT teacher head0.367
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations58
Published2014
Admission routes1
Has abstractyes

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