Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China.
Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.
Biotechnol Bioeng. 2024 Jun;121(6):1876-1888. doi: 10.1002/bit.28696. Epub 2024 Mar 17.
Regulatory authorities recommend using residence time distribution (RTD) to address material traceability in continuous manufacturing. Continuous virus filtration is an essential but poorly understood step in biologics manufacturing in respect to fluid dynamics and scale-up. Here we describe a model that considers nonideal mixing and film resistance for RTD prediction in continuous virus filtration, and its experimental validation using the inert tracer NaNO. The model was successfully calibrated through pulse injection experiments, yielding good agreement between model prediction and experiment ( 0.90). The model enabled the prediction of RTD with variations-for example, in injection volumes, flow rates, tracer concentrations, and filter surface areas-and was validated using stepwise experiments and combined stepwise and pulse injection experiments. All validation experiments achieved 0.97. Notably, if the process includes a porous material-such as a porous chromatography material, ultrafilter, or virus filter-it must be considered whether the molecule size affects the RTD, as tracers with different sizes may penetrate the pore space differently. Calibration of the model with NaNO enabled extrapolation to RTD of recombinant antibodies, which will promote significant savings in antibody consumption. This RTD model is ready for further application in end-to-end integrated continuous downstream processes, such as addressing material traceability during continuous virus filtration processes.
监管机构建议使用停留时间分布（RTD）来解决连续制造中的物料可追溯性问题。连续病毒过滤是生物制品制造中在流体动力学和放大方面理解不足但至关重要的步骤。在这里，我们描述了一个模型，该模型考虑了非理想混合和膜阻力，用于连续病毒过滤中的 RTD 预测，并使用惰性示踪剂 NaNO 进行了实验验证。该模型通过脉冲注入实验成功进行了校准，模型预测与实验之间具有良好的一致性（ 0.90）。该模型能够预测具有变化的 RTD，例如在注射体积、流速、示踪剂浓度和过滤表面积方面的变化，并通过逐步实验和逐步与脉冲注射相结合的实验进行了验证。所有验证实验均达到 0.97。值得注意的是，如果该过程包含多孔材料（例如多孔色谱材料、超滤器或病毒过滤器），则必须考虑分子大小是否会影响 RTD，因为不同大小的示踪剂可能会以不同的方式穿透孔隙空间。使用 NaNO 对模型进行校准，可实现对重组抗体 RTD 的外推，这将显著节省抗体的消耗。该 RTD 模型已准备好进一步应用于端到端集成的连续下游工艺，例如在连续病毒过滤工艺中解决物料可追溯性问题。
