Development of a Method That Eliminates False-Positive Results due to Nerve Growth Factor Interference in the Assessment of Fulranumab Immunogenicity
Journal Title: The AAPS Journal - Year 2014, Vol 16, Issue 3
Abstract
Fulranumab, a human IgG2 monoclonal antibody that neutralizes nerve growth factor (NGF), is currently in development for the treatment of pain. Our initial immunogenicity test method was found to be prone to NGF interference, leading to a high apparent incidence of anti-drug antibody (ADA) in phase 1 studies. The ADA immunoassay comprised a homogeneous bridging electrochemiluminescence (ECL) format with biotin and ruthenium-labeled fulranumab bound together (“bridged”) by ADA in test samples for detection. In this assay, NGF produced a false-positive signal due to its ability to bridge fulranumab molecules. Thus, we developed a specificity assay to eliminate the NGF false-positive results. We encountered the challenge of eliminating drug interference as well as drug target interference, and discovered that the acid-dissociation-based pretreatment of samples used for mitigating drug interference dramatically increased drug target interference. Several strategies were investigated to eliminate the NGF interference; yet only one strategy specifically removed NGF and produced true fulranumab-specific ADA results by using competitive inhibition with fulranumab and utilizing an alternative NGF binding antibody to eliminate NGF interference. Using this new method, we confirmed that the high apparent anti-fulranumab antibody incidence (>60%) in clinical study samples was in fact due to fulranumab-bound NGF released during the acid-dissociation step of the ADA testing method. We conclude that our revised method accurately identifies anti-fulranumab antibodies by incorporating steps to eliminate fulranumab and NGF interference. We advise that acid-dissociation pretreatment must not be universally applied to improve ADA assays without investigating its bioanalytical risks versus benefits.
Authors and Affiliations
Sheng Dai, Allen Schantz, Adrienne Clements-Egan, Michael Cannon, Gopi Shankar
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