The challenge of indication extrapolation for infliximab biosimilars
Bibliographic record
Abstract
A biosimilar is intended to be highly similar to a reference biologic such that any differences in quality attributes (i.e., molecular characteristics) do not affect safety or efficacy. Achieving this benchmark for biologics, especially large glycoproteins such as monoclonal antibodies, is challenging given their complex structure and manufacturing. Regulatory guidance on biosimilars issued by the U.S. Food and Drug Administration, Health Canada and European Medicines Agency indicates that, in addition to a demonstration of a high degree of similarity in quality attributes, a reduced number of nonclinical and clinical comparative studies can be sufficient for approval. Following a tiered approach, clinical studies are required to address concerns about possible clinically significant differences that remain after laboratory and nonclinical evaluations. Consequently, a critical question arises: can clinical studies that satisfy concerns regarding safety and efficacy in one condition support "indication extrapolation" to other conditions? This question will be addressed by reviewing the case of a biosimilar to infliximab that was approved recently in South Korea, Europe, and Canada for multiple indications through extrapolation. The principles discussed should also apply to biosimilars of other monoclonal antibodies that are approved to treat multiple distinct conditions.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".