An international comparative analysis and roadmap to sustainable biosimilar markets
Bibliographic record
Abstract
Background: Although biosimilar uptake has increased (at a variable pace) in many countries, there have been recent concerns about the long-term sustainability of biosimilar markets. The aim of this manuscript is to assess the sustainability of policies across the biosimilar life cycle in selected countries with a view to propose recommendations for supporting biosimilar sustainability. Methods: The study conducted a comparative analysis across 17 countries from North America, South America, Asia-Pacific, Europe and the Gulf Cooperation Council. Biosimilar policies were identified and their sustainability was assessed based on country-specific reviews of the scientific and grey literature, validation by industry experts and 23 international and local non-industry experts, and two advisory board meetings with these non-industry experts. Results: Given that European countries tend to have more experience with biosimilars and more developed policy frameworks, they generally have higher sustainability scores than the other selected countries. Existing approaches to biosimilar manufacturing and R&D, policies guaranteeing safe and high-quality biosimilars, exemption from the requirement to apply health technology assessment to biosimilars, and initiatives counteracting biosimilar misconceptions are considered sustainable. However, biosimilar contracting approaches, biosimilar education and understanding can be ameliorated in all selected countries. Also, similar policies are sometimes perceived to be sustainable in some markets, but not in others. More generally, the sustainability of the biosimilar landscape depends on the nature of the healthcare system and existing pharmaceutical market access policies, the experience with biosimilar use and policies. This suggests that a general biosimilar policy toolkit that ensures sustainability does not exist, but varies from country to country. Conclusion: This study proposes a set of elements that should underpin sustainable biosimilar policy development over time in a country. At first, biosimilar policies should guarantee the safety and quality of biosimilars, healthy levels of supply and a level of cost savings. As a country gains experience with biosimilars, policies need to optimise uptake and combat any misconceptions about biosimilars. Finally, a country should implement biosimilar policies that foster competition, expand treatment options and ensure a sustainable market environment.
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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 | 0.057 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".