Evidence map›Paper›PMID 42345601›Full record

ArticleBioanalysis

Advancing beyond stand-alone NAb assays: perspectives and regulatory impact of applying integrated immunogenicity assessment in drug development.

Kirstee Martin, Marit Lichtfuss, Devangi Mehta

Abstract read
In one paragraph

Article in Bioanalysis. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kirstee MartinClinical Bioanalytics and Biomakers, CSL, Melbourne, Victoria, Australia.
Marit LichtfussClinical Bioanalytics and Biomakers, CSL, Melbourne, Victoria, Australia.
Devangi MehtaTranslational Sciences, Immunologix Laboratories, Tampa, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neutralizing antibody (NAb) activity assessment is a regulatory expectation in the clinical development of therapeutic proteins, traditionally addressed through standalone NAb assays. However, evolving regulatory guidance and industry experience increasingly recognize that integrated analyses of anti-drug antibodies (ADA), pharmacokinetics (PK), pharmacodynamics (PD), efficacy, and safety may provide a more clinically meaningful evaluation of neutralizing activity. This perspective examines the scientific and regulatory rationale for moving beyond routine reliance on standalone NAb assays and describes a risk-based, integrated immunogenicity assessment framework. Two case studies involving low-risk monoclonal antibodies-garadacimab and clazakizumab-are presented, in which validated standalone NAb assays were available but not relied upon for primary clinical interpretation due to limited sensitivity and lack of added clinical value. Instead, longitudinal ADA characterization integrated with PK and PD or efficacy data was used to assess clinically meaningful neutralizing activity. In both cases, this approach enabled clear differentiation between detectable but clinically irrelevant immunogenicity and immunogenicity with clinical consequence and was accepted by regulatory authorities. These examples illustrate how integrated immunogenicity assessments can replace standalone NAb assays while remaining scientifically rigorous, clinically informative, and aligned with regulatory expectations.

Indexed as

Antibodies, MonoclonalAntibodies, NeutralizingDrug DevelopmentAnimalsHumansAntibodies, MonoclonalAntibodies, Neutralizinganti-drug antibodies (ADA)Immunogenicityneutralizing antibodiespharmacodynamicspharmacokineticsregulatory impact

Identifiers

PMID42345601
PMCPMC13557571

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.