过滤(数学)
可扩展性
吞吐量
缩放比例
滤波器(信号处理)
计算机科学
常量(计算机编程)
材料科学
机械
错流过滤
化学
数学
物理
电信
生物化学
统计
几何学
数据库
膜
无线
计算机视觉
程序设计语言
作者
Jannik Dippel,Sebastian Handt,Björn Hansmann,Thomas Loewe
标识
DOI:10.5731/pdajpst.2019.011254
摘要
Scalability of filter throughput in normal flow filtration runs is an important consideration in the development of biopharmaceutical downstream processes. Depending on the filtration mode used, filter device geometry can significantly affect scalability. In this study, scaling of different polyethersulfone sterilizing-grade filters was performed in two filtration modes—at constant flow and at constant pressure—using a particulate model solution as well as a cell-free mAb solution as a representative example. Both filtration methods were compared regarding their practicability as well as their scalability of the final filter throughput and the filtration time. The pressure-dependent filter fouling that occurred with the mAb solution and the model solution showed that using different pressures for small- and process-scale filtration runs could potentially influence the predicted filter capacity. Overall, good scalability of the final filter throughput was determined for filters ranging from small-scale flat disc filters (4.5 cm2) to large pleated filter assemblies (5.4 m2) in filtration runs at constant flow as well as for filter capsules (0.6 m2) of up to 10” for filtration runs at constant pressure. Moreover, at constant flow, the filtration time could be accurately predicted because it was determined by the adjusted flow rate. However, at constant pressure, potential resistances in process-scale devices can result in lower fluid fluxes and, hence, a longer unpredictable filtration time compared with small-scale filter elements. This paper introduces a novel scaling method performed at constant pressure that compensates for the pressure losses resulting from process-scale device resistances. Improved scalability regarding final filter throughput and filtration time are shown here with this scaling method compared with scaling at constant pressure. Therefore, this study provides information essential in decision-making to achieve optimal scaling within biopharmaceutical process development.
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