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DOI 10.1080/17576180.2026.2712128

Медицинская библиотека · Bioanalysis · 2026

Advanced assay design for characterizing anti-drug antibody responses in clinical serum samples.

Lotz Gregor P, Sickert Denise, Staack Roland F, Jordan Gregor

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Аннотация

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.

Для цитирования: Lotz Gregor P, Sickert Denise, Staack Roland F, Jordan Gregor. Advanced assay design for characterizing anti-drug antibody responses in clinical serum samples.. Bioanalysis. 2026. DOI: 10.1080/17576180.2026.2712128.

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