Evidence map›Paper›PMID 34755105›Full record

ArticleInternational journal of pharmaceutics: X2021

X-ray imaging: A potential enabler of automated particulate detection and cake-structure analysis in lyophilized products?

Stephan Sacher, Johannes Poms, Michael Dekner, Sabine Wallner-Mang, Martin Vogt, Johannes G Khinast, Robert Schennach

Abstract read
In one paragraph

Article in International journal of pharmaceutics: X, 2021. 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. Review
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.

Stephan SacherResearch Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/2, 8010 Graz, Austria.
Johannes PomsResearch Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/2, 8010 Graz, Austria.
Michael DeknerBaxter AG (part of Takeda), Industriestraße 67, 1220 Wien, Austria.
Sabine Wallner-MangBaxter AG (part of Takeda), Industriestraße 67, 1220 Wien, Austria.
Martin VogtSyntegon Technology GmbH, Postfach 1127, 71301 Waiblingen, Germany.
Johannes G KhinastResearch Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/2, 8010 Graz, Austria.
Robert SchennachInstitute of Solid State Physics, Graz University of Technology, Petersgasse 16/2, 8010 Graz, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The presence of particulate matter in parenteral products is a major concern since it affects the patients' safety and is one of the main reasons for product recalls. Conventional quality control is based on a visual inspection, which is a labour-intensive task. Limited to clear solutions and the surface of lyophilised products, it cannot be applied to opaque containers. This study assesses the application of X-ray imaging for detecting the particulate matter in a pharmaceutical lyophilized product. The most common types of particulates (i.e., steel, glass, lyo stopper, polymers and organics in different size classes) were intentionally spiked in vials. After optimizing all relevant parameters of the X-ray set-up, all classes of particulates were detected. At the same time, due to contrast enhancement, the inherent structures of lyophilized cake became obvious. This work addresses the potential and limits of X-ray technology in that regard, paving the way for automated image-based particulate matter detection. Moreover, this paper discusses using this approach to predict critical quality attributes (CQAs) of the drug product based on the cake structure attributes.

Indexed as

Lyophilized cake structureLyophilized productsParenteralsParticulate matterParticulate matter detectionX-ray imaging

Identifiers

PMID34755105
PMCPMC8556755

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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.