Anti-drug antibody assay validation and cut-point analysis: a practical guide for the non-statistician

Anti-drug antibody (ADA) assays are central to immunogenicity assessment of therapeutic proteins, yet their validation remains challenging because key decisions depend on statistical distributions rather than calibrator-based concentration estimates. Cut-point studies are particularly vulnerable to misunderstanding around matrix-lot selection, balanced experimental design, independent observations, outlier handling, data transformation, statistical-method selection, and fixed, floating, or dynamic implementation. This review translates current regulatory and industry recommendations into a practical workflow for bioanalytical scientists without formal statistical training. It explains how to select treatment-naive samples, design and organize cut-point studies, interpret statistical reports, establish screening, confirmatory, and titer cut points, and link positive controls and system-suitability criteria to finalized decision thresholds. It also addresses provisional early-development cut points, target-population suitability, post-outlier information loss, drug tolerance, lifecycle reassessment, discordant titer behavior, and longitudinal interpretation of ADA with pharmacokinetic, pharmacodynamic, efficacy, and safety data. The objective is to help bioanalytical scientists recognize common sources of bias, challenge inappropriate assumptions, and implement scientifically defensible immunogenicity assessments.