Evidence map›Paper›PMID 32625011›Full record

ArticleEngineering in life sciences2019

A statistical approach to improve compound screening in cell culture media.

Eduard Puente-Massaguer, Llorenç Badiella, Sonia Gutiérrez-Granados, Laura Cervera, Francesc Gòdia

Abstract read
In one paragraph

Article in Engineering in life sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

5 authors.

Eduard Puente-MassaguerDepartament d'Enginyeria Química Biològica i Ambiental Campus Bellaterra Cerdanyola del Vallès Universitat Autònoma de Barcelona Barcelona Spain.ORCID https://orcid.org/0000-0002-2816-7051
Llorenç BadiellaServei d'Estadística Aplicada Campus Bellaterra Cerdanyola del Vallès Universitat Autònoma de Barcelona Barcelona Spain.
Sonia Gutiérrez-GranadosDepartament d'Enginyeria Química Biològica i Ambiental Campus Bellaterra Cerdanyola del Vallès Universitat Autònoma de Barcelona Barcelona Spain.
Laura CerveraDepartament d'Enginyeria Química Biològica i Ambiental Campus Bellaterra Cerdanyola del Vallès Universitat Autònoma de Barcelona Barcelona Spain.
Francesc GòdiaDepartament d'Enginyeria Química Biològica i Ambiental Campus Bellaterra Cerdanyola del Vallès Universitat Autònoma de Barcelona Barcelona Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Chinese hamster ovary (CHO) cell line is widely used for the production of recombinant proteins due to its high growing capacity and productivity, as well as other cell lines derived later than CHO. Adapting cell culture media for each specific cell line is a key to exploit these features for cost effective and fast product generation. Media supplementation is generally addressed by means of one-factor-at-a-time or classical design of experiments approaches but these techniques may not be efficient enough in preliminary screening phases. In this study, a novel strategy consisting in folding over the Plackett-Burman design was used to increase cell growth and trastuzumab production of different CHO cell lines through supplementation with nonanimal recombinant compounds. Synergies between compounds could be detected with a reduced number of experiments by using this methodology in comparison to more conventional fractional factorial designs. In the particular case reported here, the sequential use of this modified Plackett-Burman in combination with a Box-Behnken design led to a 1.5-fold increase in cell growth (10 × 10

Indexed as

best‐fit Box‐BehnkenCHO cell lineDoEfolded‐over Plackett‐Burmanmedium optimizationtrastuzumab

Identifiers

PMID32625011
PMCPMC6999547

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.