Biosimilar approval pathways: comparing the roles of five medicines regulators
Bibliographic record
Abstract
Biologics are playing an increasingly important role in health care globally but are placing a substantial burden on payers. The development of biosimilars-drugs that are highly similar to and have no clinically meaningful differences from originator biologics-is critical to improving the affordability and accessibility of these medications. Medicines regulators, however, have had varied success with biosimilars to date. We examined agency guidance documents, peer-reviewed articles, and gray literature related to biosimilars in Australia, Canada, the European Union, the United Kingdom, and the United States to evaluate variations in the approaches to biosimilar approval taken by their respective medicines regulators. We found that the medicines regulators take similar approaches to biosimilar approvals, but that differences in their policies and their jurisdiction's laws regarding testing requirements, indication extrapolation, exclusivities, and substitution may contribute to the varied successes of biosimilars observed. Policies supportive of product-specific guidance, extrapolation, shorter exclusivity periods, and substitution were correlated with greater success in biosimilar approval and uptake. As medicines regulators work to promote biosimilars, understanding the impact of these laws and policies is crucial. Reforms consistent with these policies can create regulatory environments more supportive of biosimilar approvals, promoting access to affordable biologics for patients globally.
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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 | 0.070 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".