The Global Landscape of Manufacturers of Follow-on Biologics: An Overview of Five Major Biosimilar Markets and 15 Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Current knowledge is limited about which manufacturers are active in the global field of biopharmaceutical product development and how many unique follow-on biologics are approved in global markets. OBJECTIVE: This study aimed to provide a cross-sectional overview of manufacturers of follow-on biologics approved in 15 large countries from different regions of the world, as well as in five major biosimilar markets with long established biosimilar frameworks. METHODS: We screened national drug databases to identify follow-on biologics and their manufacturers approved in 15 countries in Asia, Africa, Latin America and the rest of the world, as well as five major biosimilar markets: the European Union (including the UK), USA, Canada, Australia and Japan. RESULTS: This study identified a total of 304 follow-on biologics from different manufacturers for 18 active substance classes included in the analysis. Of these, 67 products are approved as biosimilars in at least one of the five major biosimilar markets. A total of 140 (46%) follow-on biologics are manufactured in India or China, of which only eight (seven from India and one from China) are approved as biosimilars in any of the five major biosimilar markets. This study found that the majority of follow-on biologics are only approved in the respective country of manufacturing. A small number of manufacturers, primarily from India and Argentina, supply their products to other regions in the world. As some countries have less stringent regulatory approaches for biosimilars, or have only recently implemented biosimilar guidance in line with World Health Organization standards, follow-on biologics could have been approved that would not be considered biosimilars according to the World Health Organization standards. CONCLUSIONS: With this study, we try to contribute to discussions on creating more transparency about global approvals of follow-on biologics and promoting access to high-quality biosimilars in countries around the world.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it