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New Platform for High-Throughput Surface Plasmon Resonance (SPR) Analysis for Antibody Screening and Characterization

PEGS, Boston, MA, USA
April 25, 2016

The discovery of monoclonal antibody drugs is a complex and costly exercise. Reducing product cycle time and improving the lead quality early in the R&D process is essential for successful development of new biotherapeutic drugs. This requires earlier and more detailed evaluation of increasingly large panels of biomolecules, derived from any display, hybridoma, or B cell technology.

Surface Plasmon Resonance (SPR) and related technologies, e.g., Biolayer Interferometry (BLI), are routinely used to efficiently study macromolecular interactions such as antigen-antibody binding. Instruments such as Biacore and Octet systems are increasingly applied in high-throughput screening for ranking antibody lead candidates according to their association-dissociation rate constants and binding affinity. Moreover, SPR technologies are applied in various areas of antibody lead characterization, developability assessment, and quality control, such as for epitope binning, profiling of binding kinetics, and stress testing. 

However, the increased throughput has created a bottleneck in the application of SPR technology: reproducible data analysis and effortless interpretation and utilization of results for decision-making. With different instruments used by multiple groups, harmonizing and integrating SPR data analysis processes, not only within one laboratory but also across the organization, has become a major challenge.

Here, we present a new platform for streamlining and standardizing SPR data analysis workflows for large-molecule R&D applications across instruments and groups. Our system enables parallel processing of large numbers of samples to scale up SPR-based assessment of biologicals such as antibodies, ADCs, and bispecifics. We have developed tailored solutions for processing, analyzing, visualizing, and interpreting SPR data. We present concrete use cases from SPR applications in antibody R&D and show how our system enables informed decision-making to identify the most promising candidate molecules.

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