Uptake of oncology-related biosimilars: a global analysis of usage data
Bibliographic record
Abstract
BACKGROUND: Biologics have greatly improved cancer management but are costly. Biosimilars cost less and have no clinically meaningful differences compared with reference products. However, they are not identical, leading to hesitation among clinicians and patients to use them. The objective of this study is to measure the uptake of oncology-related biosimilars versus reference products in the United States and countries with similar regulatory frameworks. METHODS: We conducted a cross-sectional sales analysis from 13 countries between October 2022 and September 2023 for 5 oncology-related biologics with biosimilars: bevacizumab, filgrastim, pegfilgrastim, rituximab, and trastuzumab. We used IQVIA MIDAS® data on country-level quarterly sales volume and value. RESULTS: Among the 13 countries, the United States ranked 10th in the proportion of oncology-related biosimilar uptake by units sold (75% vs median 86%) and spending (58% vs 76%). Biosimilar uptake in the United States was 84% for filgrastim (vs 95%), 83% for bevacizumab (vs 86%), 75% for rituximab (vs 93%), 70% for trastuzumab (vs 70%), and 44% for pegfilgrastim (vs 83%). The United States spent USD $8.4 billion on these biologics during the study period. European countries including Norway, Italy, and Sweden had the highest uptake, whereas New Zealand, Japan, and Belgium had the lowest. Across countries, biosimilar filgrastim had the highest uptake (95% of units) and trastuzumab the lowest (70%). CONCLUSIONS: Oncology-related biosimilar uptake in the United States was below average among included countries. Increasing biosimilar uptake may reduce spending, and savings can be reinvested into cancer care. Future research on time trends can help assess barriers and enablers of biosimilar uptake across countries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".