An overview of biosimilars approvals by thirteen regulatory authorities: A cross nationalcomparison
Bibliographic record
Abstract
Biosimilars are biological medicines highly similar to a previously licensed reference product and their licensing is expected to improve access to biological therapies. This study aims to present an overview of biosimilars approval by thirteen regulatory authorities (RA). The study is a cross-national comparison of regulatory decisions involving biosimilars in Argentina, Australia, Brazil, Chile, Canada, Colombia, Europe, Hungary, Guatemala, Italy, Mexico, Peru and United States. We examined publicly available documents containing information regarding the approval of biosimilars and investigated the publication of public assessment reports for registration applications, guidelines for biosimilars licensing, and products approved. Data extraction was conducted by a network of researchers and regulatory experts. All the RA had issued guidance documents establishing the requirements for the licensing of biosimilars. However, only three RA had published public assessment reports for registration applications. In total, the investigated jurisdictions had from 19 to 78 biosimilars approved, most of them licensed from 2018 to 2020. In spite of the advance in the number of products in recent years, some challenges still persist. Limited access to information regarding the assessment of biosimilars by RA can affect confidence, which may ultimately impact adoption of these products in practice.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.028 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".