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

【Author】 Leal, Fatima; Chis, Adriana E.; Caton, Simon; Gonzalez-Velez, Horacio; Garcia-Gomez, Juan M.; Dura, Marta; Sanchez-Garcia, Angel; Saez, Carlos; Karageorgos, Anthony; Gerogiannis, Vassilis C.; Xenakis, Apostolos; Lallas, Efthymios; Ntounas, Theodoros; Vasileiou, Eleni; Mountzouris, Georgios; Otti, Barbara; Pucci, Penelope; Papini, Rossano; Cerrai, David; Mier, Mariola

【Source】BIG DATA RESEARCH

【影响因子】3.739

【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 EUfunded 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. (C) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license.

【Keywords】ALCOA; Blockchain; Data anaytics; Data quality; Intelligent agents; Smart contracts

【发表时间】2021 2022-05-15

【收录时间】2022-01-02

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