Senior Associate Scientist II Seismic Therapeutic, United States
Disclosure(s):
Rasika Harshe, Masters Degree: No relevant disclosure to display
Introduction/Rationale: Using proprietary Invisi-B and Invisi-T machine learning tools, our IMPACT platform allows us to remove B and T cell epitopes from novel drugs to make them invisible to the immune system while retaining optimal drug-like properties. Our platform enables parallelization, thereby eliminating the need for numerous cycles of protein engineering and significantly reducing the discovery time to identify top candidates. Assessment of pre-existing antibodies to novel bacterial derived biotherapeutics is a key aspect of our platform that helps to inform our engineering and is an important consideration for understanding pre-existing immunity.
Methods: Here, we describe the development and optimization of a bridging assay for highly sensitive and specific detection of pre-existing antibodies to a bacterial derived Protease-Fc fusion. Initially, we optimized the biotinylation and Sulfo-Tag labeling of our biotherapeutic molecule to adapt it for routine use in our assay.
Results: Human serum samples were assessed and the ratio of signal responses compared to negative controls was used to identify positive or negative samples. Titration of serum samples was performed to understand levels and prevalence of pre-existing antibodies to different biotherapeutics.
Conclusion: This type of optimized analytical assay allows for sensitive and reliable detection of pre-existing antibodies to novel biotherapeutics, which can also be used to assess the removal of B cell epitopes for novel molecules.
