Neutralizing Antibody Sample Testing and Report Harmonization
Bibliographic record
Abstract
Immunogenicity testing and characterization is an important part of understanding the immune response to administration of a protein therapeutic. Neutralizing antibody (NAb) assays are used to characterize a positive anti-drug antibody (ADA) response. Harmonization of reporting of NAb assay performance and results enables efficient communication and expedient review by industry and health authorities. Herein, a cross-industry group of NAb assay experts have harmonized NAb assay reporting recommendations and provided a bioanalytical report (BAR) submission editable template developed to facilitate agency filings. This document addresses key bioanalytical reporting gaps and provides a report structure for documenting clinical NAb assay performance and results. This publication focuses on the content and presentation of the NAb sample analysis report including essential elements such as the method, critical reagents and equipment, data analysis, study samples, and results. The interpretation of immunogenicity data, including the evaluation of the impact of NAb on safety, exposure, and efficacy, is out of scope of this publication.
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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 | 0.205 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.018 |
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".