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Automation of cellular therapy product manufacturing: results of a split validation comparing CD34 selection of peripheral blood stem cell apheresis product with a semi-manual vs. an automatic…

Overview of attention for article published in Journal of Translational Medicine, March 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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Title
Automation of cellular therapy product manufacturing: results of a split validation comparing CD34 selection of peripheral blood stem cell apheresis product with a semi-manual vs. an automatic procedure
Published in
Journal of Translational Medicine, March 2016
DOI 10.1186/s12967-016-0826-8
Pubmed ID
Authors

Christiane Hümmer, Carolin Poppe, Milica Bunos, Belinda Stock, Eva Wingenfeld, Volker Huppert, Juliane Stuth, Kristina Reck, Mike Essl, Erhard Seifried, Halvard Bonig

Abstract

Automation of cell therapy manufacturing promises higher productivity of cell factories, more economical use of highly-trained (and costly) manufacturing staff, facilitation of processes requiring manufacturing steps at inconvenient hours, improved consistency of processing steps and other benefits. One of the most broadly disseminated engineered cell therapy products is immunomagnetically selected CD34+ hematopoietic "stem" cells (HSCs). As the clinical GMP-compliant automat CliniMACS Prodigy is being programmed to perform ever more complex sequential manufacturing steps, we developed a CD34+ selection module for comparison with the standard semi-automatic CD34 "normal scale" selection process on CliniMACS Plus, applicable for 600 × 10(6) target cells out of 60 × 10(9) total cells. Three split-validation processings with healthy donor G-CSF-mobilized apheresis products were performed; feasibility, time consumption and product quality were assessed. All processes proceeded uneventfully. Prodigy runs took about 1 h longer than CliniMACS Plus runs, albeit with markedly less hands-on operator time and therefore also suitable for less experienced operators. Recovery of target cells was the same for both technologies. Although impurities, specifically T- and B-cells, were 5 ± 1.6-fold and 4 ± 0.4-fold higher in the Prodigy products (p = ns and p = 0.013 for T and B cell depletion, respectively), T cell contents per kg of a virtual recipient receiving 4 × 10(6) CD34+ cells/kg was below 10 × 10(3)/kg even in the worst Prodigy product and thus more than fivefold below the specification of CD34+ selected mismatched-donor stem cell products. The products' theoretical clinical usability is thus confirmed. This split validation exercise of a relatively short and simple process exemplifies the potential of automatic cell manufacturing. Automation will further gain in attractiveness when applied to more complex processes, requiring frequent interventions or handling at unfavourable working hours, such as re-targeting of T-cells.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Germany 1 1%
Ireland 1 1%
Unknown 73 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 20%
Other 11 14%
Student > Ph. D. Student 9 12%
Student > Master 7 9%
Student > Postgraduate 5 7%
Other 11 14%
Unknown 18 24%
Readers by discipline Count As %
Immunology and Microbiology 11 14%
Agricultural and Biological Sciences 11 14%
Biochemistry, Genetics and Molecular Biology 9 12%
Engineering 7 9%
Medicine and Dentistry 7 9%
Other 11 14%
Unknown 20 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 August 2016.
All research outputs
#3,700,906
of 23,344,526 outputs
Outputs from Journal of Translational Medicine
#602
of 4,117 outputs
Outputs of similar age
#57,848
of 301,294 outputs
Outputs of similar age from Journal of Translational Medicine
#10
of 74 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,117 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 301,294 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.