AB1591 POLICY DRIVERS FOR MARKET PENETRATION OF ANTI-TNF BIOSIMILARS: MULTI-COUNTRY COMPARISONS
Bibliographic record
Abstract
<h3>Background</h3> Health systems across countries have used different policy measures (e.g. price discounts, tendering, mandating switches) to encourage the introduction of biosimilars but their differential impact on market penetration is unknown. <h3>Objectives</h3> To evaluate the impact of policy measures on market penetration of anti-TNF biosimilars. <h3>Methods</h3> Quarterly IQVIA MIDAS sales data from 2012-2021 for infliximab, etanercept and adalimumab in 5 countries (Canada, France, Germany, Italy and the United Kingdom (UK)) that used different policy tools were used. Biosimilar market penetration was measured by dividing the number of defined daily dose units (DDDs) of biosimilars sold by the total number of DDDs of the originator and biosimilars sold. Market penetration since the first sale date of the biosimilars was captured by product, setting (hospital vs. retail), and country. Policy impact was examined by comparing market penetration among different policy scenarios (Table 1). <h3>Results</h3> Biosimilars for all three anti-TNFs had higher volume share over time (Figure 1) in the UK than Germany, suggesting that higher prescribing quotas among new and existing patients increases the market penetration of biosimilars given comparable demand-side policies. Since some anti-TNFs are dispensed only in hospitals in some countries, the settings are somewhat different. In France, infliximab biosimilars in hospital setting had faster penetration than other anti-TNF biosimilars in retail setting, indicating that tendering works better than price-links to increase biosimilar uptake. However, infliximab biosimilars in France had slower penetration than in Italy despite the higher discount price-link but absent quotas in France. Similarly, biosimilars had higher volume share over time in UK than Italy, which also suggests that higher prescribing quotas have a greater impact than price-links. Without price-link and tendering measures, Canada had the slowest penetration. By the end of our study period, the limited rollout of mandatory switching policies (3/10 provinces) for existing patients may not be sufficient in fostering market penetration, despite widespread implementation of mandatory prescribing for new patients. <h3>Conclusion</h3> Through comparisons between and within countries, we found that higher prescribing quotas or switching for existing patients and tendering are the key policy drivers for market penetration of anti-TNF biosimilars. <h3>References</h3> [1] Vogler S, Schneider P, Zuba M, Busse R, Panteli D. Policies to Encourage the Use of Biosimilars in European Countries and Their Potential Impact on Pharmaceutical Expenditure. <i>Frontiers in Pharmacology</i>. 2021;12. Accessed December 15, 2022. https://www.frontiersin.org/articles/10.3389/fphar.2021.625296 [2] McClean AR, Law MR, Harrison M, Bansback N, Gomes T, Tadrous M. Uptake of biosimilar drugs in Canada: analysis of provincial policies and usage data. <i>CMAJ</i>. 2022;194(15):E556-E560. doi:10.1503/cmaj.211478 [3] NHS England. <i>Commissioning Framework for Biological Medicines</i>. NHS England; 2017. Accessed December 15, 2022. https://www.england.nhs.uk/wp-content/uploads/2017/09/biosimilar-medicines-commissioning-framework.pdf <h3>Acknowledgements</h3> We acknowledge the funding support the Canadian Institutes of Health Research Project Grant (PJT-178132). <h3>Disclosure of Interests</h3> Wei Zhang Grant/research support from: Pfizer, Tilray, and bioMérieux Canada for projects unrelated to the present study., Daphne Guh: None declared, Huiying Sun: None declared, Alexander Tam Consultant of: I was previously employed at a market access research company. In that capacity, I generated reports for health technology companies. Companies were: Novartis AG, Merck & Co., Pfzier Inc., Biogen Inc., Sanofi, Mylan N.V., Bristol-Myers Squibb, Jazz Pharmaceuticals, and Roche Diagnostics., Nick Bansback: None declared, Aidan Hollis: None declared, Paul Grootendorst Consultant of: I wrote expert reports on behalf of both of branded and generic drug companies. None were specifically related to drugs/devices for use in rheumatology., Grant/research support from: Sanofi co-sponsored (along with a public organization) a post- doctoral fellowship that I supervised., Aslam Anis Grant/research support from: I have previously received grants from Beijing Genomics Institute, AbbVie, Abbott Laboratories Ltd. and Sanofi-Aventis Canada Inc to conduct work unrelated to the present study.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".