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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".