Regulatory challenges with biosimilars: an update from 20 countries
Bibliographic record
Abstract
The World Health Organization (WHO) issued guidelines for the regulatory evaluation of biosimilars in 2009 and has provided considerable effort toward helping member states implement the evaluation principles in the guidelines into their regulatory practices. Despite this effort, a recent WHO survey (conducted in 2019-2020) has revealed four main remaining challenges: unavailable/insufficient reference products in the country; lack of resources; problems with the quality of some biosimilars (and even more with noninnovator products); and difficulties with the practice of interchangeability and naming of biosimilars. The following have been identified as opportunities/solutions for regulatory authorities to deal with the existing challenges: (1) exchange of information on products with other regulatory authorities and accepting foreign licensed and sourced reference products, hence avoiding conducting unnecessary (duplicate) bridging studies; (2) use of a "reliance" concept and/or joint review for the assessment and approval of biosimilars; (3) review and reassessment of the products already approved before the establishment of a regulatory framework for biosimilar approval; and (4) setting appropriate regulatory oversight for good pharmacovigilance, which is essential for the identification of problems with products and establishing the safety and efficacy of interchangeability of biosimilars.
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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 | 0.049 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".