Advancing Quantitative ADA Detection Through Model Informed Assay Development (MIAD)

Immunogenicity testing for anti-drug antibodies (ADAs) is crucial in therapeutic protein development, yet current quasi-quantitative assays struggle to accurately measure ADAs when the antibodies have different binding strength (affinities) or due to heterogeneity of ADAs and residual drug interference. While traditional QC-based assay development is limited by the lack of representative ADA reference standards, we propose Model-Informed Assay Development (MIAD) as a transformative solution. MIAD mathematically simulates complex analyte-reagent interactions to identify optimal conditions for signal-generating analyte-reagent complex (ARC) formation, enabling scientifically sound assay optimization independent of positive controls. Our findings demonstrate that optimal sample dilution and reagent concentrations can overcome drug interference and improved detection of antibodies (ADAs) with different binding strengths. This work applies MIAD to address critical ADA assay challenges: drug tolerance and affinity-dependent detectability. We tested MIAD's prediction in three real world case studies and found strong agreement. Our findings show that optimized sample dilutions and reagent concentrations effectively overcome drug interference and affinity differences, enhancing ADA detectability and recovery. MIAD also helps understanding whether a hook-shaped curve is due to a prozone effect or drug interference, guiding the development of unbiased assays crucial for accurate S/N-based magnitude estimation.