Modelling the opportunity for cost-savings or patient access with biosimilar adalimumab and tocilizumab: a European perspective
Bibliographic record
Abstract
OBJECTIVES: Biosimilars improve patient access by providing cost-effective treatment options. This study assessed the potential for savings and expanded patient access with increased use of two biosimilar disease modifying anti-rheumatic drugs (DMARDs): (a) approved adalimumab biosimilars and (b) the first tocilizumab biosimilar, representing an established biosimilar field and a recent biosimilar entrant in France, Germany, Italy, Spain, and the United Kingdom (UK). METHODS: Separate ex-ante analyses were conducted for each country, parameterized using country-specific list prices, unit volumes annually, and market shares for each therapy. Discounting scenarios of 10%, 20%, and 30% were tested for tocilizumab. Outputs included direct cost-savings associated with drug acquisition or the incremental number of patients that could be treated if savings were redirected. Two biosimilar conversion scenarios were tested. RESULTS: Savings associated with a 100% conversion to adalimumab biosimilar ranged from €10.5 to €187 million (UK and Germany, respectively), or an additional 1,096 to 19,454 patients that could be treated using the cost-savings. Introduction of a tocilizumab biosimilar provided savings up to €29.3 million in the most conservative scenario. Exclusive use of tocilizumab biosimilars (at a 30% discount) could increase savings to €28.8 to €113 million or expand access to an additional 43% of existing tocilizumab users across countries. CONCLUSION: This study demonstrates the benefits that can be realized through increased biosimilar adoption, not only in an untapped tocilizumab market, but also through incremental increases in well-established markets such as adalimumab. As healthcare budgets continue to face downwards pressure globally, strategies to increase biosimilar market share could prove useful to help manage financial constraints.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".