Effect of Technically Relevant X-Ray Doses on the Structure and Function of Alcohol Dehydrogenase and Hen Egg-White Lysozyme

Schaden, Lisa-Marie; Wimmer-Teubenbacher, Miriam; Poms, Johannes; Laggner, Peter; Lohner, Karl; Sacher, Stephan; Khinast, Johannes G.; Salar-Behzadi, Sharareh

doi:10.1007/s11095-018-2417-2

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…During a total exposure time of 4 s the dose was 8.4 mGy. In a study with two model proteins irradiation doses of up to 25 Gy did not cause significant changes in the activity and structure of proteins ( Schaden et al, 2018 ). However, the dose level must be considered in pharmaceutical applications on the commercial scale.…”

Section: Resultsmentioning

confidence: 99%

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

Sacher

Poms

Dekner

et al. 2021

International Journal of Pharmaceutics: X

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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.

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Section: Resultsmentioning

confidence: 99%