Immunogenicity is a biological response to pharmacologic therapeutic intervention and therefore fits squarely within established definitions of a biomarker. Yet, unlike most biomarker assays, where analytical performance is tailored to context of use, anti-drug antibody (ADA) testing has converged on a largely uniform, three-tiered paradigm built around statistically derived cut points and titer reporting. This perspective argues that this approach arose from understandable, risk-averse responses to rare, high-impact safety events, combined with early analogies to the vaccine field, rather than from first principles thinking aligned with drug development needs.As a result, immunogenicity datasets are often reduced to binary classifications that discard biological context, inflate reported incidence, and complicate efforts to relate immune responses to clinically meaningful outcomes such as pharmacokinetics, pharmacodynamics, efficacy, or safety. Titer-based readouts, while appropriate for large vaccine-like responses, are frequently insufficiently granular to capture the full spectrum of response magnitudes relevant to contemporary biotherapeutic modalities.Reframing immunogenicity as a context-of-use-driven biomarker measurement leverages complete, continuous response profiles (e.g. screening-tier signal-to-noise) alongside pharmacokinetic/pharmacodynamic (PK/PD), and clinical outcomes to identify clinically relevant immunogenicity thresholds. This approach preserves nuance, improves interpretability and stakeholder communication, and focuses attention on clinical impact most relevant to patients and regulators.
Lauren Stevenson (2026) studied this question.