Logo
    • English
    • Ελληνικά
    • Deutsch
    • français
    • italiano
    • español
  • English 
    • English
    • Ελληνικά
    • Deutsch
    • français
    • italiano
    • español
  • Login
View Item 
  •   University of Thessaly Institutional Repository
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
  • View Item
  •   University of Thessaly Institutional Repository
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
Institutional repository
All of DSpace
  • Communities & Collections
  • By Issue Date
  • Authors
  • Titles
  • Subjects

Smart Pharmaceutical Manufacturing: Ensuring End-to-End Traceability and Data Integrity in Medicine Production

Thumbnail
Author
Leal F., Chis A.E., Caton S., González–Vélez H., García–Gómez J.M., Durá M., Sánchez–García A., Sáez C., Karageorgos A., Gerogiannis V.C., Xenakis A., Lallas E., Ntounas T., Vasileiou E., Mountzouris G., Otti B., Pucci P., Papini R., Cerrai D., Mier M.
Date
2021
Language
en
DOI
10.1016/j.bdr.2020.100172
Keyword
Blockchain
Embedded systems
Intelligent agents
Process control
ALCOA
Block-chain
Data anaytic
Data integrity
Data quality
Data set
End to end
Heterogeneous data
Pharmaceutical manufacturing
Production line
Smart contract
Elsevier Inc.
Metadata display
Abstract
Production lines in pharmaceutical manufacturing generate numerous heterogeneous data sets from various embedded systems which control the multiple processes of medicine production. Such data sets should arguably ensure end-to-end traceability and data integrity in order to release a medicine batch, which is uniquely identified and tracked by its batch number/code. Consequently, auditable computerised systems are crucial on pharmaceutical production lines, since the industry is becoming increasingly regulated for product quality and patient health purposes. This paper describes the EU-funded SPuMoNI project, which aims to ensure the quality of large amounts of data produced by computerised production systems in representative pharmaceutical environments. Our initial results include significant progress in: (i) end-to-end verification taking advantage of blockchain properties and smart contracts to ensure data authenticity, transparency, and immutability; (ii) data quality assessment models to identify data behavioural patterns that can violate industry practices and/or international regulations; and (iii) intelligent agents to collect and manipulate data as well as perform smart decisions. By analysing multiple sensors in medicine production lines, manufacturing work centres, and quality control laboratories, our approach has been initially evaluated using representative industry-grade pharmaceutical manufacturing data sets generated at an IT environment with regulated processes inspected by regulatory and government agencies. © 2021 The Author(s)
URI
http://hdl.handle.net/11615/75732
Collections
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ. [19735]
htmlmap 

 

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

LoginRegister (MyDspace)
Help Contact
DepositionAboutHelpContact Us
Choose LanguageAll of DSpace
EnglishΕλληνικά
htmlmap