表征(材料科学)
粒径
纳米粒子跟踪分析
悬挂(拓扑)
粒子(生态学)
分散性
微粒
纳米技术
关键质量属性
化学
材料科学
生物系统
数学
小RNA
生物化学
海洋学
有机化学
物理化学
同伦
微泡
生物
纯数学
基因
地质学
作者
Daniela Stadler,Constanze Helbig,Klaus Wuchner,J FRANK,Klaus Richter,Andrea Hawe,Tim Menzen
标识
DOI:10.1016/j.ejpb.2024.114340
摘要
Lentiviral vectors (LVVs) are used as a starting material to generate chimeric antigen receptor (CAR) T cells. Therefore, LVVs need to be carefully analyzed to ensure safety, quality, and potency of the final product. We evaluated orthogonal and complementary analytical techniques for their suitability to characterize particulate matter (impurities and LVVs) in pharmaceutical LVV materials at development stage derived from suspension and adherent manufacturing processes. Microfluidic resistive pulse sensing (MRPS) with additional manual data fitting enabled the assessment of mode diameters for particles in the expected LVV size range in material from adherent production. LVV material from a suspension process, however, contained substantial amounts of particulate impurities which blocked MRPS cartridges. Sedimentation-velocity analytical ultracentrifugation (SV-AUC) resolved the LVV peak in material from adherent production well, whereas in more polydisperse samples from suspension production, presence of particulate impurities masked a potential signal assignable to LVVs. In interferometric light microscopy (ILM) and nanoparticle tracking analysis (NTA), lower size detection limits close to ∼ 70 nm resulted in an apparent peak in particle size distributions at the expected size for LVVs emphasizing the need to interpret these data with care. Interpretation of data from dynamic light scattering (DLS) was limited by insufficient size resolution and sample polydispersity. In conclusion, the analysis of LVV products manufactured at pharmaceutical scale with current state-of-the-art physical (nano)particle characterization techniques was challenging due to the presence of particulate impurities of heterogeneous size. Among the evaluated techniques, MRPS and SV-AUC were most promising yielding acceptable results at least for material from adherent production.
科研通智能强力驱动
Strongly Powered by AbleSci AI