High-throughput profiling of antibody self-association in multiple formulation conditions by PEG stabilized self-interaction nanoparticle spectroscopy

Affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) is an assay developed to monitor the propensity of antibody self-association, hence assessing its colloidal stability. It has been widely used by pharmaceutical companies to screen antibodies at the early discovery stages, aiming to flag potential issues with high concentration formulation. However, the original assay format is not suitable for certain formulation conditions, in particular histidine buffer. In addition, the previous data extrapolation method is suboptimal and cumbersome for processing large amounts of data (100s of molecules) in a high-throughput fashion. To address these limitations, we developed an assay workflow with two major improvements: 1) use of a stabilizing reagent to enable screening of a broader range of formulation conditions beyond phosphate-buffered saline, pH 7.4; and 2) inclusion of a novel algorithm and robust data processing schema that empowers streamlined data analysis. The optimized assay format expands the screening applicability to a wider range of formulation conditions critical for downstream development. Such capability is enhanced by a custom data management workflow for optimal data extraction, analysis, and automation. Our protocol and the R/Shiny application for analysis are publicly available and open-source to benefit the broader scientific community.

Automated summary

Affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) is a test method developed for evaluating the colloidal stability of antibodies, commonly used for screening antibodies in the early stages of pharmaceutical discovery. We have made improvements to the method by using a stabilizing reagent to broaden the range of formulation conditions that can be screened, and by introducing a new algorithm and data processing schema for streamlined data analysis. The optimized test format expands the screening applicability to critical formulation conditions and is supported by a custom data management workflow for efficient data extraction, analysis, and automation.