Evidence map›Paper›PMID 41059077›Full record

ArticleFrontiers in medical technology2025

Feasibility analysis of automated cleaning in biopharmaceutical production using cleaning-in-place concepts from food production.

Ferdinand Groten, Chris Henze, Matthias Joppa, Laura Herbst, Bastian Nießing, Marc Mauermann, Robert H Schmitt

Abstract read
In one paragraph

Article in Frontiers in medical technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Ferdinand GrotenDepartment for Bio-Adaptive Production, Fraunhofer Institute for Production Technology IPT, Aachen, Germany.
Chris HenzeFraunhofer-Institute for Process Engineering and Packaging IVV, Dresden, Germany.
Matthias JoppaFraunhofer-Institute for Process Engineering and Packaging IVV, Dresden, Germany.
Laura HerbstDepartment for Bio-Adaptive Production, Fraunhofer Institute for Production Technology IPT, Aachen, Germany.
Bastian NießingDepartment for Bio-Adaptive Production, Fraunhofer Institute for Production Technology IPT, Aachen, Germany.
Marc MauermannFraunhofer-Institute for Process Engineering and Packaging IVV, Dresden, Germany.
Robert H SchmittDepartment for Bio-Adaptive Production, Fraunhofer Institute for Production Technology IPT, Aachen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In biopharmaceutical production involving cells, cell-derived products, or tissues, the cleaning of surfaces that come into direct or indirect contact with the product is currently performed mostly by hand as the initial step in decontamination. This manual approach leads to production inefficiencies, reduced reproducibility of decontamination processes, and product losses. In food production, automated processes are preferred for the decontamination of interior product contact surfaces. This article studies the feasibility of adapting the automated cleaning-in-place concepts used in the food industry to biopharmaceutical production. The focus is on spray cleaning processes and validation by cleaning simulation. An existing automated cell production platform is used as a case study for validation. The results indicate that modifying an existing platform to support cleaning-in-place presents significant challenges. However, the article outlines general design guidelines for developing new biopharmaceutical production platforms that can accommodate automated cleaning.

Indexed as

automationbiopharmaceutical industryCIPcleaningdecontaminationfood production

Identifiers

PMID41059077
PMCPMC12497818

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.