Comparing Onset of Biosimilar Versus Generic Competition in the United States
Bibliographic record
Abstract
We sought to compare expected and observed biosimilar and generic entry dates among new drugs approved by the US Food and Drug Administration (FDA) between 2000 and 2012. We defined expected biosimilar and generic entry dates as the later of the expiration of the key patent term or statutory exclusivity (12 years for biologics, 5 years for small molecule drugs not indicated for a rare disease, and 7 years for small molecule drugs indicated for a rare disease; plus 6 months if a pediatric extension had been granted). For drugs with expected entry prior to 2019, we calculated the proportion with observed biosimilar or generic entry. The expected biosimilar entry dates were estimated to be a median of 12.3 years (interquartile range (IQR) 12.0-14.0, n = 60) after FDA approval. The 12-year biologic statutory exclusivity period comprised 98% of the median expected protection period. By contrast, expected generic entry was estimated to be a median of 12.2 years (IQR 8.4-14.0, n=268), or 7.2 years after the 5-year small molecule statutory exclusivity (59% of the total expected market protection period). By 2019, observed biosimilar entry occurred in 12% of cases (3/25) and observed generic entry in 65% (101/155). We concluded that expected US market exclusivity periods are similar for biologic and small molecule drugs. Statutory exclusivity plays a more substantial role in market exclusivity protection for biologics. Biosimilar competition, currently lagging behind generic competition, will likely increase as the biosimilar market becomes established.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".