Evidence map›Paper›PMID 40150783›Full record

ArticleBioengineering (Basel, Switzerland)2025

Direct Consideration of Process History During Intensified Design of Experiments Planning Eases Interpretation of Mammalian Cell Culture Dynamics.

Samuel Kienzle, Lisa Junghans, Stefan Wieschalka, Katharina Diem, Ralf Takors, Nicole Erika Radde, Marco Kunzelmann, Beate Presser, Verena Nold

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Samuel KienzleDevelopment Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorferstraße 65, 88397 Biberach an der Riß, Germany.ORCID 0000-0002-6816-0401
Lisa JunghansDevelopment Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorferstraße 65, 88397 Biberach an der Riß, Germany.
Stefan WieschalkaDevelopment Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorferstraße 65, 88397 Biberach an der Riß, Germany.
Katharina DiemDevelopment Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorferstraße 65, 88397 Biberach an der Riß, Germany.
Ralf TakorsInstitute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany.ORCID 0000-0001-5837-6906
Nicole Erika RaddeInstitute for Stochastics and Applications, University of Stuttgart, Wankelstr. 5, 70563 Stuttgart, Germany.ORCID 0000-0002-5145-0058
Marco KunzelmannDevelopment Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorferstraße 65, 88397 Biberach an der Riß, Germany.ORCID 0000-0003-3005-5718
Beate PresserDevelopment Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorferstraße 65, 88397 Biberach an der Riß, Germany.ORCID 0009-0007-6653-7950
Verena NoldDevelopment Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorferstraße 65, 88397 Biberach an der Riß, Germany.ORCID 0000-0003-0302-0966

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intra-experimental factor setting shifts in intensified design of experiments (iDoE) enhance understanding of bioproduction processes by capturing their dynamics and are thus essential to fulfill quality by design (QbD) ambitions. Determining the influence of process history on the cellular responses, often referred to as memory effect, is fundamental for accurate predictions. However, the current iDoE designs do not explicitly consider nor quantify the influence of process history. Therefore, we propose the one-factor-multiple-columns (OFMC)-format for iDoE planning. This format explicitly describes stage-dependent factor effects and potential memory effects as across-stage interactions (ASIs) during a bioprocess. To illustrate its utility, an OFMC-iDoE that considers the characteristic growth phases during a fed-batch process was planned. Data were analyzed using ordinary least squares (OLS) regression as previously described via stage-wise analysis of the time series and compared to direct modeling of end-of-process outcomes enabled by the OFMC-format. This article aims to provide the reader with a framework on how to plan and model iDoE data and highlights how the OFMC-format simplifies planning, and data acquisition, eases modeling and gives a straightforward quantification of potential memory effects. With the proposed OFMC-format, optimization of bioprocesses can leverage which factor settings are most beneficial in which state of the mammalian culture and thus elevate performance and quality to the next level.

Indexed as

dynamic process optimizationmammalian bioprocess dynamicsquality by designshifted factor settingsstatistical design of experiment

Identifiers

PMID40150783
PMCPMC11939677

What OpenQuestion holds

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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.