
Recorded webinar
Assessing subvisible particle risks in monoclonal antibodies: insights from quartz crystal microbalance with dissipation, machine learning, and in silico analysis
Throughout the lifecycle of biopharmaceutical development and manufacturing, monoclonal antibodies (mAbs) are subjected to diverse interfacial stresses and encounter various container surfaces. These interactions can cause the formation of subvisible particles (SVPs) that complicate developability and stability assessments of the drug products. This study leverages quartz crystal microbalance with dissipation (QCM-D), an interfacial characterization technique, as well as both in silico and experimentally measured physicochemical properties, to investigate the significant differences in SVP formation among different mAbs due to interfacial stresses. We conducted forced degradation experiments in borosilicate glass and high-density polyethylene containers, using agitation and stirring to rank 15 mAbs on SVP risks. Our data indicate that the kinetics of antibody adsorption to solid–liquid interfaces correlate strongly with SVP propensity in the stirring study yet show a weaker correlation with agitation-induced SVPs. In addition, SVP morphology was analyzed using self-supervised machine learning on flow imaging microscopy images. Despite the differing surface chemistry of the two container types, stirring resulted in similar SVP morphologies, in contrast to the unique morphologies produced by agitation. Collectively, our research demonstrates the utility of QCM-D and in silico models in evaluating mAb developability and their tendency to form interface-mediated SVPs, providing a strategy to mitigate risks associated with SVP formation in biotherapeutic development.
Webinar details
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Originally aired
November 20, 2025
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Length
40 min
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Presentation by
Dr Yibo Wang
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Technologies
QCM-D
Presentation by Dr Yibo Wang
Yibo Wang currently serves as a postdoctoral researcher in the department of Dosage Form Design and Development (DFDD) at AstraZeneca, where he applies his expertise in chemistry and data science to contribute to the advancement of biopharmaceutical research. Yibo graduated from University of Virginia holding a Ph.D. in Chemistry with a specialization in super-resolution microscopy and a master's degree in data science. His Ph.D. works focused on developing novel solutions for bacterial biofilm imaging, image segmentation, and tracking. His postdoc work encompasses the utilization of machine learning and biophysical characterization methods to analyze and address challenges in mAb developability and subvisible particle root cause analysis. Yibo's role as a postdoc at AstraZeneca reflects his commitment to pushing the boundaries of scientific exploration and leveraging interdisciplinary approaches to drive innovation in the field of biopharmaceuticals.