Novel data analysis methods to overcome cut point challenges and enable comprehensive assessment of antidrug binding activity in confirmatory assays

Confirmatory cut point has limited ability to make accurate classifications in immunogenicity assays

Aim: Competitive inhibition with excess unlabeled drug is used to confirm the presence of antidrug antibodies (ADA) in study samples. We evaluated specific and nonspecific responses from both drug-naive and drug-treated subjects to identify conditions required by the confirmatory assay to make accurate ADA classifications. Results: Nonspecific signal measured in drug-naive samples used to determine assay cut points was uniformly low and close to the screening cut point. Confirmatory assays performed on incurred study samples with nonspecific responses significantly above the level observed during cut point determination resulted in incorrect ADA classifications. Conclusion: Intensity of confirmatory response should be proportional to the screening response and therefore, to ensure accurate ADA classifications, the confirmatory responses cannot be considered as independent but need to be evaluated in relation to the screening responses.

Development of NanoLuc bridging immunoassay for detection of anti-drug antibodies

Anti-drug antibodies (ADAs) are generated in-vivo as an immune response to therapeutic antibody drugs and can significantly affect the efficacy and safety of the drugs. Hence, detection of ADAs is recommended by regulatory agencies during drug development process. A widely accepted method for measuring ADAs is "bridging" immunoassay and is frequently performed using enzyme-linked immunosorbent assay (ELISA) or electrochemiluminescence (ECL) platform developed by Meso Scale Discovery (MSD). ELISA is preferable due to widely available reagents and instruments and broad familiarity with the technology; however, MSD platform has gained wide acceptability due to a simpler workflow, higher sensitivity, and a broad dynamic range but requires proprietary reagents and instruments. We describe the development of a new bridging immunoassay where a small (19kDa) but ultra-bright NanoLuc luciferase enzyme is used as an antibody label and signal is luminescence. The method combines the convenience of ELISA format with assay performance similar to that of the MSD platform. Advantages of the NanoLuc bridging immunoassay are highlighted by using Trastuzumab and Cetuximab as model drugs and developing assays for detection of anti-Trastuzumab antibodies (ATA) and anti-Cetuximab antibodies (ACA). During development of the assay several aspects of the method were optimized including: (a) two different approaches for labeling drugs with NanoLuc; (b) sensitivity and dynamic range; and (c) compatibility with the acid dissociation step for improved drug tolerance. Assays showed high sensitivity of at least 1.0ng/mL, dynamic range of greater than four log orders, and drug tolerance of >500.

Recommendations for the development and validation of confirmatory anti-drug antibody assays

Identification and characterization of anti-drug antibodies is a critical component of biopharmaceutical drug development. The tiered approach for immunogenicity testing consists of screening, confirmatory, and characterization assays. Herein, we provide recommendations for confirmatory assays by expanding upon published guidance and present common practices across the industry. The authors recommend scientific approaches for development and validation of confirmatory assays using competition methods in ligand-binding assays, along with statistical formulae for routine use and validation. The paper will assist in understanding the confirmatory assay, and carefully implementing validation criteria a priori, as well as during sample analysis. These approaches represent the authors’ current knowledge and practices, with the aim that more uniform practices will be applied across the industry.

Statistical approaches for the determination of cut points in anti-drug antibody bioassays

Cut points in immunogenicity assays are used to classify future specimens into anti-drug antibody (ADA) positive or negative. To determine a cut point during pre-study validation, drug-naive specimens are often analyzed on multiple microtiter plates taking sources of future variability into account, such as runs, days, analysts, gender, drug-spiked and the biological variability of un-spiked specimens themselves. Five phenomena may complicate the statistical cut point estimation: i) drug-naive specimens may contain already ADA-positives or lead to signals that erroneously appear to be ADA-positive, ii) mean differences between plates may remain after normalization of observations by negative control means, iii) experimental designs may contain several factors in a crossed or hierarchical structure, iv) low sample sizes in such complex designs lead to low power for pre-tests on distribution, outliers and variance structure, and v) the choice between normal and log-normal distribution has a serious impact on the cut point. We discuss statistical approaches to account for these complex data: i) mixture models, which can be used to analyze sets of specimens containing an unknown, possibly larger proportion of ADA-positive specimens, ii) random effects models, followed by the estimation of prediction intervals, which provide cut points while accounting for several factors, and iii) diagnostic plots, which allow the post hoc assessment of model assumptions. All methods discussed are available in the corresponding R add-on package mixADA.