An overview of biosimilars and non-biologic complex drugs in Europe, the United States, and Canada and their relevance to multiple sclerosis
Bibliographic record
Abstract
The advent of biological medicines has significantly transformed the landscapes of many disease spaces and improved the lives of millions around the world. However, the structural complexity and sensitivity of such products result in a high price tag, adding to already financially strained healthcare systems. As these and other expensive complex drugs lose market exclusivity, stakeholders eagerly await the arrival of lower cost alternatives, such as biosimilars and subsequent entry non-biological complex drugs (NBCDs). Nevertheless, stakeholders remain uncertain about key issues which have resulted in heterogeneous reimbursement policies and varying levels of biosimilar uptake and subsequent entry NBCD approval processes between different markets. With the imminent introduction of both subsequent entry NBCDs and biosimilars for multiple sclerosis (MS), it is important to get a better understanding of this new class of products and how healthcare systems have been adapting to their use. This article defines biosimilars and subsequent entry NBCDs and provides an overview of how these products have been introduced in Europe, the United States, and Canada from a regulatory, health technology, and reimbursement perspective. In addition, this article briefly explores the potential impact and outlook of biosimilar and NBCD products related to MS.
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.
How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".