Biosimilar monoclonal antibodies: the scientific basis for extrapolation
Bibliographic record
Abstract
INTRODUCTION: Biosimilars are biologic products that receive authorization based on an abbreviated regulatory application containing comparative quality and nonclinical and clinical data that demonstrate similarity to a licensed biologic product. Extrapolation of safety and efficacy has emerged as an important way to simplify biosimilar development. Regulatory authorities have generally reached the consensus that extrapolation of similarity from one indication to other approved indications of the reference product can be permitted if it is scientifically justified. AREAS COVERED: Recently, the first biosimilar, biosimilar infliximab (Remsima/Inflectra) to the innovator monoclonal antibody infliximab (Remicade), was approved in the European Union, Canada and South Korea; the USA subsequently approved its first biosimilar, a less complex molecule (filgrastim-sndz). Based on two clinical trials of biosimilar infliximab in patients with rheumatoid arthritis and ankylosing spondylitis, the European Medicines Agency allowed extrapolation to all eight approved indications for innovator infliximab, whereas Health Canada did not permit extrapolation to the indications for ulcerative colitis and Crohn's disease. These differing decisions on extrapolation of indications for biosimilar infliximab highlight important unanswered regulatory and scientific questions. Here, we propose substantive scientific considerations for indication extrapolation. EXPERT OPINION: The preclinical and clinical criteria that are currently required to merit indication extrapolation have not been rigorously evaluated.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".