Evidence map›Paper›PMID 36837652›Full record

ArticleMembranes2023

Scaling Up of Steric Exclusion Membrane Chromatography for Lentiviral Vector Purification.

Jennifer Julia Labisch, Richard Paul, G Philip Wiese, Karl Pflanz

Abstract read
In one paragraph

Article in Membranes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jennifer Julia LabischLab Essentials Applications Development, Sartorius Stedim Biotech GmbH, August-Spindler-Straße 11, 37079 Göttingen, Germany.ORCID 0000-0001-8580-0602
Richard PaulLab Essentials Applications Development, Sartorius Stedim Biotech GmbH, August-Spindler-Straße 11, 37079 Göttingen, Germany.ORCID 0000-0003-4465-464X
G Philip WieseLab Essentials Applications Development, Sartorius Stedim Biotech GmbH, August-Spindler-Straße 11, 37079 Göttingen, Germany.ORCID 0000-0002-1653-976X
Karl PflanzLab Essentials Applications Development, Sartorius Stedim Biotech GmbH, August-Spindler-Straße 11, 37079 Göttingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lentiviral vectors (LVs) are widely used in clinical trials of gene and cell therapy. Low LV stability incentivizes constant development and the improvement of gentle process steps. Steric exclusion chromatography (SXC) has gained interest in the field of virus purification but scaling up has not yet been addressed. In this study, the scaling up of lentiviral vector purification by SXC with membrane modules was approached. Visualization of the LVs captured on the membrane during SXC showed predominant usage of the upper membrane layer. Furthermore, testing of different housing geometries showed a strong influence on the uniform usage of the membrane. The main use of the first membrane layer places a completely new requirement on the scaling of the process and the membrane modules. When transferring the SXC process to smaller or larger membrane modules, it became apparent that scaling of the flow rate is a critical factor that must be related to the membrane area of the first layer. Performing SXC at different scales demonstrated that a certain critical minimum surface area-dependent flow rate is necessary to achieve reproducible LV recoveries. With the presented scaling approach, we were able to purify 980 mL LVs with a recovery of 68%.

Indexed as

depletion potentiallentiviral vector purificationmembrane chromatographypolyethylene glycolscaling up of membrane modulessteric exclusion chromatography

Identifiers

PMID36837652
PMCPMC9958935

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.