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Baiaolin Lecture | Evaluating the Risk of Sub Visible Particles in Monoclonal Antibodies: Insights from QCM-D, Machine Learning, and Computer Simulation Analysis
Date: 2025-12-19Read: 0
Report Content Introduction
This study utilized the dissipative quartz crystal microbalance (QCM-D) interface characterization technique, combined with computational simulations and experimental measurements of physicochemical properties, to investigate the significant differences in the formation of sub visible particles (SVP) of different monoclonal antibodies under interface stress.
We conducted forced degradation experiments in borosilicate glass and high-density polyethylene containers, and ranked the SVP risks of 15 monoclonal antibodies by shaking and stirring. The data shows that the adsorption kinetics of antibodies at the solid-liquid interface are strongly correlated with stirring induced SVP, while the correlation with oscillation induced SVP is weak.
In addition, we analyzed the SVP morphology in flow imaging microscopy images using self supervised machine learning and found that although the surface chemical properties of the two container materials were different, the SVP morphology produced under stirring conditions was highly similar, while oscillation led to the formation of unique particle morphology.
In summary, this study demonstrates the practical value of QCM-D and computer models in evaluating the exploitability of monoclonal antibodies and the tendency of interface mediated SVP formation, providing an effective strategy for reducing SVP related risks in the development of biological therapeutic drugs.
Dr. Wang Yibo is currently working in the formulation design and development department of AstraZeneca in the United StatesServe as a postdoctoral researcher, utilizing their expertise in chemistry and data science to drive progress in biopharmaceutical research.
Dr. Wang Yibo graduated from the University of Virginia with a Ph.D. in Chemistry (specializing in super-resolution microscopy imaging) and a Master's degree in Data Science.
During his doctoral studies, he developed innovative solutions focused on bacterial biofilm imaging, image segmentation, and tracking; During the postdoctoral stage, the focus is on analyzing and solving the developmental feasibility of monoclonal antibodies (mAbs) and the root cause analysis of sub visible particles (SVPs) by combining machine learning and biophysical characterization methods.
Dr. Wang Yibo's postdoctoral work at AstraZeneca demonstrates his commitment to expanding the boundaries of scientific exploration and driving innovation in biopharmaceuticals through interdisciplinary approaches.
Report time:
January 8th, 2026 (Thursday) at 10am(Beijing time)
Report platform:
Tencent Meeting (conference number will be notified separately after registration)
QSense QCM-DTechnical Introduction:
QSense Dissipative quartz crystal microbalance technology（QCM-D）It is a surface interface sensitive method that can provide real-time, online, and in situ insights into molecular surface interactions at the nanoscale.
QSenseTechnology provides a powerful, reproducible, and rapid method for evaluating the stability and material compatibility of biopharmaceuticals. It can detect the interactions between biopharmaceuticals and materials related to manufacturing, storage, and management, evaluate the surface adsorption of antibodies and excipients, proactively identify potential incompatibility or stability issues that may occur at a certain time and cause, and indicate actual results in advance. This allows you to proactively mitigate risks and achieve a smoother development process.
利用QSensetechnologyYou can study:
What is the adsorption capacity of antibodies on different surface materials? How are molecules arranged on the surface of materials? What surfaces may cause compatibility issues? How do drug excipients affect antibody adsorption? ConcentrationpHHow do changes in value, temperature, surface material, or surfactant type affect adsorption capacity?
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