Assessment of Neutralizing Antibody Activity in Clinical Studies: Use of Surrogate Measurements Instead of Stand-alone Assays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it