Dietrich Annabelle, Beckert Nicole, Hubbuch Jürgen
Karlsruhe Institute of Technology (KIT), Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Fritz-Haber-Weg 2, Karlsruhe, 76131, Germany.
Karlsruhe Institute of Technology (KIT), Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Fritz-Haber-Weg 2, Karlsruhe, 76131, Germany.
J Colloid Interface Sci. 2025 Sep 15;694:137663. doi: 10.1016/j.jcis.2025.137663. Epub 2025 Apr 24.
Although cross-flow filtration (CFF) is a time- and resource-efficient purification method, CFF has rarely been explored for lipid nanoparticle (LNP) purification as a scalable alternative to dialysis. CFF-based processing allows for buffer exchange by diafiltration (DF) and setting a target product concentration by ultrafiltration (UF). Herein, we investigate the effect of CFF-based processing on LNP characteristics and process performance by performing a parameter study through the variation of selected membrane-related and operational parameters. We used a pre-dilution approach prior to LNP purification to reduce the ethanol content while maintaining LNP characteristics. Taking advantage of the integration potential of CFF for process analytical technology (PAT), we successfully established size monitoring for LNPs by integrating at-line dynamic light scattering (DLS), providing near real-time process insights. During processing, we observed an increase in LNP size and a change in their size distribution, dependent on processing time but not on the varied process parameters. Following comprehensive off-line analyses, all other LNP characteristics remained constant and final lipid recoveries were achieved in the range of 86-89% for all CFF processes. Long-term, the CFF-purified LNPs showed a lower increase in size compared to the dialyzed LNPs during storage of 14 days. Lastly, examination of purified LNP behavior during sterile filtration revealed changes in particle size in the upper size range. In general, we provide comprehensive insights into CFF-based LNP processing and its impact on LNP characteristics and process performance. Such studies are expected to contribute to the understanding of CFF-based LNP processing and their future application for size-controlled LNP production.
尽管错流过滤（CFF）是一种节省时间和资源的纯化方法，但作为透析的可扩展替代方法，CFF在脂质纳米颗粒（LNP）纯化方面很少被探索。基于CFF的处理允许通过渗滤（DF）进行缓冲液交换，并通过超滤（UF）设定目标产品浓度。在此，我们通过改变选定的膜相关参数和操作参数进行参数研究，来研究基于CFF的处理对LNP特性和工艺性能的影响。在LNP纯化之前，我们采用预稀释方法来降低乙醇含量，同时保持LNP特性。利用CFF对过程分析技术（PAT）的集成潜力，我们通过集成在线动态光散射（DLS）成功建立了LNP的尺寸监测，提供了近实时的过程洞察。在处理过程中，我们观察到LNP尺寸增加及其尺寸分布发生变化，这取决于处理时间，而不取决于变化的工艺参数。经过全面的离线分析，所有其他LNP特性保持不变，所有CFF工艺的最终脂质回收率在86-89%范围内。长期来看，在14天的储存期内，与透析后的LNP相比，CFF纯化的LNP尺寸增加较少。最后，对无菌过滤过程中纯化LNP行为的检查揭示了较大尺寸范围内颗粒尺寸的变化。总的来说，我们提供了对基于CFF的LNP处理及其对LNP特性和工艺性能影响的全面洞察。此类研究有望有助于理解基于CFF的LNP处理及其未来在尺寸可控LNP生产中的应用。
