Neutralizing Antibody Validation Testing and Reporting Harmonization
Bibliographic record
Abstract
Evolving immunogenicity assay performance expectations and a lack of harmonized neutralizing antibody validation testing and reporting tools have resulted in significant time spent by health authorities and sponsors on resolving filing queries. A team of experts within the American Association of Pharmaceutical Scientists' Therapeutic Product Immunogenicity Community across industry and the Food and Drug Administration addressed challenges unique to cell-based and non-cell-based neutralizing antibody assays. Harmonization of validation expectations and data reporting will facilitate filings to health authorities and are described in this manuscript. This team provides validation testing and reporting strategies and tools for the following assessments: (1) format selection; (2) cut point; (3) assay acceptance criteria; (4) control precision; (5) sensitivity including positive control selection and performance tracking; (6) negative control selection; (7) selectivity/specificity including matrix interference, hemolysis, lipemia, bilirubin, concomitant medications, and structurally similar analytes; (8) drug tolerance; (9) target tolerance; (10) sample stability; and (11) assay robustness.
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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 | 0.440 | 0.330 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".