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Automated Tomographic Assessment of Structural Defects of Freeze-Dried Pharmaceuticals

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Bibliographic Details
Title: Automated Tomographic Assessment of Structural Defects of Freeze-Dried Pharmaceuticals
Authors: Patric Müller, Achim Sack, Jens Dümler, Michael Heckel, Tim Wenzel, Teresa Siegert, Sonja Schuldt-Lieb, Henning Gieseler, Thorsten Pöschel
Source: AAPS PharmSciTech. 25
Publication Status: Preprint
Publisher Information: Springer Science and Business Media LLC, 2024.
Publication Year: 2024
Subject Terms: Quality Control, Chemistry, Pharmaceutical, FOS: Physical sciences, Physics - Applied Physics, Robotics, 02 engineering and technology, Applied Physics (physics.app-ph), Condensed Matter - Soft Condensed Matter, 3. Good health, Machine Learning, Automation, 03 medical and health sciences, Freeze Drying, 0302 clinical medicine, Pharmaceutical Preparations, Freeze Drying/methods [MeSH], Pharmaceutical Preparations/chemistry [MeSH], Quality Control [MeSH], Tomography, X-Ray Computed/methods [MeSH], non-destructive inspection, X-ray tomography, Machine Learning [MeSH], freeze-drying, Robotics/methods [MeSH], Technology, Pharmaceutical/methods [MeSH], Chemistry, Pharmaceutical/methods [MeSH], lyopohilisate, Research Article, Automation/methods [MeSH], Technology, Pharmaceutical, Soft Condensed Matter (cond-mat.soft), Tomography, X-Ray Computed, 0210 nano-technology
Description: The topology and surface characteristics of lyophilisates significantly impact the stability and reconstitutability of freeze-dried pharmaceuticals. Consequently, visual quality control of the product is imperative. However, this procedure is not only time-consuming and labor-intensive but also expensive and prone to errors. In this paper, we present an approach for fully automated, non-destructive inspection of freeze-dried pharmaceuticals, leveraging robotics, computed tomography, and machine learning.
Document Type: Article
Language: English
ISSN: 1530-9932
DOI: 10.1208/s12249-024-02833-7
DOI: 10.48550/arxiv.2404.11867
Access URL: https://pubmed.ncbi.nlm.nih.gov/38918304
http://arxiv.org/abs/2404.11867
https://repository.publisso.de/resource/frl:6497075
Rights: CC BY
Accession Number: edsair.doi.dedup.....1d410d9229eed313e12e9ea44192f654
Database: OpenAIRE
Description
Abstract:The topology and surface characteristics of lyophilisates significantly impact the stability and reconstitutability of freeze-dried pharmaceuticals. Consequently, visual quality control of the product is imperative. However, this procedure is not only time-consuming and labor-intensive but also expensive and prone to errors. In this paper, we present an approach for fully automated, non-destructive inspection of freeze-dried pharmaceuticals, leveraging robotics, computed tomography, and machine learning.
ISSN:15309932
DOI:10.1208/s12249-024-02833-7