Non-medical switching of biologicals/biosimilars: Canada, Europe and the US – a webinar report
Bibliographic record
Abstract
Introduction: Biosimilars are now key players in the global drugs market offering potentially more affordable treatment options with similar safety and efficacy. However, there are concerns about non-medical switching practices of originator biologicals/biosimilars in different regions. A webinar was held to discuss non-medical switching practices and to explore the importance of safeguarding the physician–patient relationship. Methods: The webinar was held by the Alliance for Safe Biologic Medicines (ASBM) in collaboration with the Generics and Biosimilars Initiative (GaBI) on 20 July 2022. It consisted of various expert speaker presentations followed by a Q&A with the panel. The audience also had the opportunity to ask questions online throughout the webinar. Results: Presentations discussed key concerns about non-medical switching practices of originator biological/biosimilar medicines. There was particular emphasis on the practices in Canada, Europe and the US. Further details of the presentations were discussed during the Q&A and clarifications were made via the concurrent online Q&A. Conclusions: The webinar enabled in-depth discussion of non-medical switching practices in Canada, Europe and the US. There was specific emphasis on the forced-switching policies adopted in Canada and their shortcomings. There was also discussion about the US’s interchangeability designation. Overall, it was highlighted that safeguarding the physician–patient relationship is key in decisions about biologicals prescribing, dispensing and reimbursement. This can be achieved through robust policy and regulation and upholding transparent practices.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".