Recommendations for the Development and Validation of Neutralizing Antibody Assays in Support of Biosimilar Assessment
Bibliographic record
Abstract
The American Association of Pharmaceutical Scientists (AAPS) biosimilar focus group on nonclinical and clinical assays has developed this manuscript to guide the industry on best practices and testing strategies when developing neutralizing antibody (NAb) assays for biosimilar programs. The immunogenicity assessment to biosimilar and originator drug products is one of the key aspects of clinical programs for biosimilars to demonstrate biosimilarity. Establishing that there are no clinically meaningful differences in immune response between a proposed product and the originator product is a key element in the demonstration of biosimilarity. It is critical to collect, evaluate, and compare the safety and immunogenicity data from the clinical pharmacology, safety, and/or efficacy studies especially when the originator drug product is known to have potential for immune-mediated toxicity. This manuscript aims to provide a comprehensive review and recommendations on assay formats, critical reagents, approaches to method development, and validation of the neutralizing antibody assays in extrapolation within the scope of biosimilar drug development programs. Even if there are multiple options on the development and validation of NAb assays for biosimilar programs, the type of drug and its MoA will help determine the assay format and technical platform for NAb assessment (e.g., cell-based or non-cell-based assay). We recommend to always perform a one-assay approach as it is better to confirm the biosimilarity using one-assay for NAb. If a one-assay approach is not feasible, then a two-assay format may be used. This manuscript will provide all the details necessary to develop NAb assays for biosimilars.
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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 | 0.084 | 0.133 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.031 |
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".