From Development to Qualification: A Robust C1q-Binding Assay
Webinar Date/Time: Wed, Nov 18, 2026 11:00 AM EST
Still analyzing C1q binding the hard way? Consider a novel biosensor-based approach for robust, reproducible results.
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Event Overview:
C1q protein binding to therapeutic antibodies can trigger the complement cascade and lead to targeted destruction of cancer cells by complement dependent cytotoxicity (CDC). Quantifying the interaction between a therapeutic antibody and C1q can give an indication of its CDC activity.
In this webinar Han Yang from WuXi Biologics, will discuss the development and qualification of a biolayer interferometry (BLI)-based method for assessing antibody-C1q binding. Using trastuzumab as a model IgG1, the webinar will cover key considerations for developing a reliable C1q-binding assay and how the Octet® BLI platform can be used to quantify binding affinity. Results across key qualification criteria, including specificity, precision, accuracy, linearity, and robustness, will be presented, demonstrating the suitability of the method for therapeutic antibody development and alignment with ICH guidelines.
Key Learning Objectives:
- Discuss how BLI enables rapid, label-free antibody-C1q interaction analysis
- Learn the fundamentals of assay design, orientation, and biosensor selection
- Explore assay performance across a range of key qualification parameters
Who Should Attend:
- Researchers working in analytical development
- Scientists working in biologic manufacturing and quality control
Speaker:
Han Yang, M.S.
Associate Director, Analytical Development
WuXi Biologics
Han Yang is an Associate Director in Analytical Development at WuXi Biologics, supporting biologics from discovery through CMC development and commercialization. She specializes in large-molecule analytics, biosensor characterization, biophysical analysis, and analytical strategies for therapeutic proteins and antibodies. Previously at Pfizer, she applied biosensor platforms to drive decision-making in drug discovery and candidate optimization.
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