Robust depth filter sizing for centrate clarification

Lutz, Herb; Chefer, Kate; Felo, Michael; Cacace, Benjamin; Hove, Sarah; Wang, Bin; Blanchard, Mark; Oulundsen, George; Piper, R.D.; Zhao, Xiaoyang

doi:10.1002/btpr.2188

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…The impact of pore size distribution of the depth filter membrane across the different depth filter lots was found to be minimal on the depth filter loading capacity and hence lot to lot depth filter variability was not considered. However in a different case, the lot to lot variation in depth filter pore size distribution may result in significant variability and hence needs to be considered . These limitations can be removed by improving the robustness of the neural network using a larger dataset from at‐scale depth filtration.…”

Section: Conclusion and Recommendationmentioning

confidence: 99%

“…In reality, however, depth filter sizing depends on a variety of process factors and raw material attributes including the scale of operation, lot to lot variation in the filter, variation in operating flux, and feed variability . Lutz et al were able to distinguish between systematic differences of size from stochastic differences of harvest lots, filter lots, and backgrounds . To compensate for the impact of process variability and filter scaling, scaling is typically performed using sizing safety factors that could be as high as 1.5 .…”

Section: Introductionmentioning

confidence: 99%

“…Lutz et al were able to distinguish between systematic differences of size from stochastic differences of harvest lots, filter lots, and backgrounds . To compensate for the impact of process variability and filter scaling, scaling is typically performed using sizing safety factors that could be as high as 1.5 . While the present methodology works, it is economically inefficient and environmentally unfriendly (depth filters are typically single use).…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network (ANN)‐based prediction of depth filter loading capacity for filter sizing

Agarwal

Rathore

Hadpe

et al. 2016

Biotechnology Progress

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This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016.

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Section: Conclusion and Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial neural network (ANN)‐based prediction of depth filter loading capacity for filter sizing

Agarwal

Rathore

Hadpe

et al. 2016

Biotechnology Progress

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“…Depth filters have been traditionally made from cellulose fiber backbone, a porous filter‐aid such as diatomaceous earth and an ionic charged resin binder 7 . Recently manufactures have begun moving away from natural materials to ensure better consistency during filter manufacture 8,9 and future supply as DE is a finite resource. In comparison to membranes used in bioprocessing that are specified with a single nominal retention rating, depth filters are often given a range instead given their wide pore structure 10 …”

Section: Introductionmentioning

confidence: 99%

Analysis of fouling and breakthrough of process related impurities during depth filtration using confocal microscopy

Parau

Johnson

Pullen

et al. 2022

Biotechnology Progress

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Titer improvement has driven process intensification in mAb manufacture. However, this has come with the drawback of high cell densities and associated process related impurities such as cell debris, host cell protein (HCP), and DNA. This affects the capacity of depth filters and can lead to carryover of impurities to protein A chromatography leading to early resin fouling. New depth filter materials provide the opportunity to remove more process related impurities at this early stage in the process. Hence, there is a need to understand the mechanism of impurity removal within these filters. In this work, the secondary depth filter Millistak+ X0HC (cellulose and diatomaceous earth) is compared with the X0SP (synthetic), by examining the breakthrough of DNA and HCP. Additionally, a novel method was developed to image the location of key impurities within the depth filter structure under a confocal microscope. Flux, tested at 75, 100, and 250 LMH was found to affect the maximal throughput based on the max pressure of 30 psi, but no significant changes were seen in the HCP and DNA breakthrough. However, a drop in cell culture viability, from 87% to 37%, lead to the DNA breakthrough at 10% decreasing from 81 to 55 L/m2 for X0HC and from 105 to 47 L/m2 for X0SP. The HCP breakthrough was not affected by cell culture viability or filter type. The X0SP filter has a 30%–50% higher max throughput depending on viability, which can be explained by the confocal imaging where the debris and DNA are distributed differently in the layers of the filter pods, with more of the second tighter layer being utilized in the X0SP.

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“…Scale‐up was performed using an experimentally determined safety factor that accounted for the different flow distributions and hold‐up volumes at different scales of operation. Lutz et al (2015) evaluated the capacity of a lenticular depth filtration media in both lab‐, pilot‐, and commercial‐scale modules from Millipore. The scaling factor, defined as the ratio of the normalized filter area (in m 2 filter per volume processed) at large scale to that at small scale, was 0.8 between mini‐capsules and Pods and 0.9 between large stacks and Pods.…”

Section: Introductionmentioning

confidence: 99%

Scale‐up issues for commercial depth filters in bioprocessing

Nejatishahidein

Kim

Jung

et al. 2022

Biotech & Bioengineering

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Significant increases in cell density and product titer have led to renewed interest in the application of depth filtration for initial clarification of cell culture fluid in antibody production. The performance of these depth filters will depend on the local pressure and velocity distribution within the filter capsule, but these are very difficult to probe experimentally, leading to challenges in both process design and scale‐up. We have used a combination of carefully designed experimental studies and computational fluid dynamics (CFD) to examine these issues in both lab‐scale (SupracapTM 50) and pilot‐scale (StaxTM) depth filter modules, both employing dual‐layer lenticular PDH4 media containing diatomaceous earth. The SupracapTM 50 showed a more rapid increase in transmembrane pressure and a more rapid DNA breakthrough during filtration of a Chinese Hamster Ovary cell culture fluid. These results were explained using CFD calculations which showed very different flow distributions within the modules. CFD predictions were further validated using measurements of the residence time distribution and dye binding in both the lab‐scale and pilot‐plant modules. These results provide important insights into the factors controlling the performance and scale‐up of these commercially important depth filters as well as a framework that can be broadly applied to develop more effective depth filters and depth filtration processes.

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Robust depth filter sizing for centrate clarification

Cited by 10 publications

References 4 publications

Artificial neural network (ANN)‐based prediction of depth filter loading capacity for filter sizing

Artificial neural network (ANN)‐based prediction of depth filter loading capacity for filter sizing

Analysis of fouling and breakthrough of process related impurities during depth filtration using confocal microscopy

Scale‐up issues for commercial depth filters in bioprocessing

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