Role of global public sector research in discovering new drugs and vaccines
Bibliographic record
Abstract
Analysis of international public-sector contributions to Food and Drug Administration (FDA)-approved drugs and vaccines allows for a more thorough examination of the global biomedical innovation ecosystem by institution of origin. Using new and existing methods, we have identified 364 FDA-approved drugs and vaccines approved from 1973 to 2016 discovered in whole or in part by Public Sector Research Institutions (PSRIs) worldwide. We identified product-specific intellectual property contributions to FDA-approved small molecule and biologic drugs and vaccines from the FDA Orange Book, our peer network, published studies, and three new sources: reports of medical product manufacturers' payments to physicians and teaching hospitals under The Sunshine Act of 2010, a paper by Kneller and 64 royalty monetization transactions by academic institutions and/or their faculty that one of us (AS) maintains. We include a total of 293 drugs discovered either wholly by a US PSRI or jointly by a U.S. and a non-U.S. PSRI. 119 FDA-approved drugs and vaccines were discovered by PSRIs outside the U.S. Of these, 71 were solely discovered outside the US, while 48 also involved intellectual property contributions by US PSRIs. In the context of the global public sector landscape, the US dominates drug discovery, accounting for two-thirds of these drugs and many of the important, innovative vaccines introduced over the past 30 years. Contributions by Canada, UK, Germany, Belgium, Japan, and others each amount to 5.4% or less of the total. Supplementary Information: The online version contains supplementary material available at 10.1007/s10961-023-10007-z.
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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 | 0.054 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.029 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".