ReviewBioanalysis2026
Advanced assay design for characterizing anti-drug antibody responses in clinical serum samples.
Review in Bioanalysis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monitoring anti-drug antibody (ADA) responses is critical for evaluating the safety and efficacy of protein therapeutics. While traditional three-tiered testing (screening, confirmation, titration) reliably identifies ADA incidence, onset and magnitude, modern, complex biologics necessitate adapting our assessment strategies. Consequently, advanced assay designs are required to capture the full biological and clinical impact of these immune responses. Structured to guide bioanalytical scientists through this paradigm shift, this review first explores the transition toward integrated functional assessments, detailing how "active" pharmacokinetic (PK) and pharmacodynamic (PD) assays, neutralizing antibody (nAb) testing, and domain-specific assays help align immunogenicity characterization with a drug's mechanism of action (MoA). Next, we highlight Model-Informed Assay Development (MIAD) as a transformative tool for optimizing drug tolerance and estimating ADA-Reagent-Drug complex (ARC) formation. We then outline a risk-based, fit-for-purpose (FFP) framework for developing these advanced assays. Furthermore, we provide practical considerations for reporting advanced characterization data in regulatory filings. Finally, we conclude by differentiating benign ADA release from active inflammatory responses driven by the interaction of complex biologics with antigen-presenting cells, and explore future perspectives, specifically how Systems Immunogenicity and Artificial Intelligence (AI) will transition the field from retrospective monitoring to predictive immunogenicity profiling.
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What Socratic holds
Registered trials
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 Socratic graph.