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Record W4411254450 · doi:10.1101/2025.06.11.25329435

Social and private value created through commercialization of Gilead Sciences’ innovative medicines for Hepatitis C and HIV: a cross-sectional analysis

2025· preprint· en· W4411254450 on OpenAlexaff
Paula Grazielle Chaves da Silva, Rena M. Conti, Fred D. Ledley

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsQuest University Canada
FundersBentley University
KeywordsCommercializationCross-sectional studyValue (mathematics)Human immunodeficiency virus (HIV)MedicinePolitical sciencePsychologySociologyVirologyComputer science

Abstract

fetched live from OpenAlex

Abstract Objectives This work assesses the value created for society and for private sector by Gilead Sciences, Inc. (Gilead) through commercialization of medicines for hepatitis C and HIV from 2012-2020. While Gilead has developed several groundbreaking medicines, the company has also been criticized for its drug pricing, patenting, and profits. This analysis posits the total value created by a medicine derives from the health benefit provided to those using these medicines. Design Cross-sectional analysis. Primary and Secondary outcome measures Health benefit from treatment for nine hepatitis C or HIV medicines (Quality-Adjusted Life Years [QALYs], $), number of individuals benefited, price paid, and Gilead Sciences’ financial report (10K). Results Gilead’s product sales generated 15.1 million in health benefit measured using QALYs for 23.7 million people. Expressed in monetary terms, these products created a total health value of $744.8 billion (US: $168.0; global: $576.8). This produced a residual health value, net price paid, of $544.6 billion (US: $35.9; global: $508.7) including $418.6 billion from hepatitis C medicines (US: $62.4; global: $356.2) and $125.9 billion from HIV medicines (US: -$26.6; global: $152.5). Total social value (US + global) created was $613.6 billion including residual health value ($544.6 billion), R&D ($34.2 billion), job creation ($9.7 billion), and social payments ($25.1 billion). Total private value was $193.5 billion including shareholder value ($85.7 billion) and network value (payments to commercial firms, $107.8 billion). Conclusions Social and private value differed between US and global sales and among diseases. This analysis highlights the scale of the value that can be created by modern medicines and the commercialization of pharmaceutical products applied to unmet medical needs. Assessing value creation at product level may guide corporate strategies and inform policies to promote both profit and public purpose. Strengths and limitations of this study “Total stakeholder value” approach considers the sum of value provided to different stakeholders Total health value measured from unit sales and health benefit to treated individuals Analysis based on market data and financial reports of manufacturers applicable to business strategy and policy Method applicable to assessing value created by individual drugs, classes of drugs, or companies Health benefit measured in QALYs are subject to limitations of this metric MeSh Headings “Hepatitis C” [MeSh Terms], “HIV Infections” [MeSh Terms], “Quality-Adjusted Life Years” [MeSh Terms], “Private Sector” [MeSh Terms], “Public Sector” [MeSh Terms]

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)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.142
GPT teacher head0.400
Teacher spread0.258 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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