Biosimilars in immune‐mediated inflammatory diseases: initial lessons from the first approved biosimilar anti‐tumour necrosis factor monoclonal antibody
Bibliographic record
Abstract
The introduction of targeted biological therapies has revolutionised the management of immune-mediated inflammatory diseases (IMIDs) such as rheumatoid arthritis, ankylosing spondylitis, psoriasis and inflammatory bowel disease. Following treatment with these therapies, many patients experience significant improvements in different aspects of their disease, including symptoms, work productivity and other outcomes relevant for individuals and society. However, due to the complexity of biological drug development and manufacturing processes, the costs of these therapies are relatively high. Indeed, the financial burden on healthcare systems due to biological therapies is considerable and lack of patient access to effective treatment remains a concern in many parts of the world. As many reference biological therapies have now reached or are near to patent expiry, a number of 'biosimilar' drugs have been developed for use in various clinical settings, and some of these drugs are already in use in several countries. While the potential pharmacoeconomic benefits of cost-effective biosimilars seem clear, several issues have been raised regarding, for example, the definition of biosimilarity and the validity of indication extrapolation, as well as the 'switchability' and relative immunogenicity of biosimilars and their reference drugs. In this review, these issues will be discussed with reference to CT-P13, a biosimilar of the anti-tumour necrosis factor monoclonal antibody infliximab, which is approved in Europe and elsewhere for the treatment of various IMIDs. Other important issues, including those related to data collection during nonclinical and clinical development of biosimilars, are also discussed.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".