Assessment of Neutralizing Antibody Activity in Clinical Studies: Use of Surrogate Measurements Instead of Stand-alone Assays
Bibliographic record
Abstract
Neutralizing antibodies (NAbs) to protein therapeutics have traditionally been assumed to be the most impactful subset of anti-drug-antibodies (ADA). NAbs can block the biotherapeutic from engaging its target impacting efficacy and may also cause serious safety events. Stand-alone NAb assays have been employed to detect neutralizing responses, often with reconfigured versions of other assays. These methods have historically been implemented in registrational trials for all molecules, and in early-stage studies for high risk biotherapeutics. However, data has demonstrated that NAb response and ADA magnitude are highly correlated. Additionally, the use of other markers to identify clinically relevant immunogenicity, such as apparent impact on pharmacokinetics (PK) or pharmacodynamics (PD), has been increasing. This manuscript reviews the available data on clinically meaningful immunogenic responses to biologics and proposes a risk-based strategy to determine if and when to employ a stand-alone NAb assay. For molecules with a high risk of safety consequences of immunogenicity (e.g., biological mimics) a NAb assay is recommended. However, for lower-safety risk molecules a stand-alone NAb assay does not enhance the interpretation of clinical data and is likely not needed. A combination of other assessments including ADA status, magnitude and persistence, PK, and PD (and efficacy) can be used as a surrogate for NAb assay data. Integration of data from all clinical evaluations is recommended by Health Authorities and can provide a more accurate overall assessment of neutralizing activity. This approach identifies clinically impactful downstream readouts of neutralizing activity without the need for a stand-alone NAb assay.
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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 | 0.151 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".