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AB1591 POLICY DRIVERS FOR MARKET PENETRATION OF ANTI-TNF BIOSIMILARS: MULTI-COUNTRY COMPARISONS

2023· article· en· W4379650819 on OpenAlexafffundabout
Wei Zhang, Daphne Guh, Huaibo Sun, Angela Tam, Nick Bansback, Aidan Hollis, Paul Grootendorst, Aslam H. Anis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsUniversity of TorontoUniversity of CalgaryCentre for Advancing Health OutcomesUniversity of British Columbia
FundersUCB PharmaJazz PharmaceuticalsNordic Pharma GroupMylanBiogenSanofiMerckCanadian Institutes of Health ResearchNovartisPfizerEli Lilly and CompanyBristol-Myers Squibb
KeywordsBiosimilarProcurementInfliximabMedicinePenetration (warfare)Market penetrationMarket shareBusinessMarketingOperations researchTumor necrosis factor alphaInternal medicineEngineering

Abstract

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Background 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. Objectives To evaluate the impact of policy measures on market penetration of anti-TNF biosimilars. Methods 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). Results 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. Conclusion 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. References [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. Frontiers in Pharmacology. 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. CMAJ. 2022;194(15):E556-E560. doi:10.1503/cmaj.211478 [3] NHS England. Commissioning Framework for Biological Medicines. NHS England; 2017. Accessed December 15, 2022. https://www.england.nhs.uk/wp-content/uploads/2017/09/biosimilar-medicines-commissioning-framework.pdf Acknowledgements We acknowledge the funding support the Canadian Institutes of Health Research Project Grant (PJT-178132). Disclosure of Interests 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.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.048
GPT teacher head0.344
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes3
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