FLORENCE, Italy: A programme for European Coordinated Research on Long-term Information and Communication Technologies (ICT) and ICT-based scientific challenges, CHIST-ERA annually calls for research proposals on key emerging topics. In 2017, the brief was ‘Big Data and process modelling for smart industry’.
The call was addressed by a consortium composed by two Industrial partners—PQE Group (Italy) and Istituto De Angeli-Fareva (Italy)—and three Academic Partners—National College of Ireland (Ireland), University of Thessaly (Greece) and Polytechnic University of Valencia (Spain).
The Consortium brings together state-of-the-art expertise and capacity for developing and promoting new approaches for Smart Pharmaceutical MaNufacturIng (SPuMoNI) in order to support the Pharmaceutical industry using leading computational and data quality techniques. It combines partners with a track record in pharmaceutical production systems, cloud computing, blockchain technologies, and data quality.
Presented at the CHIST-ERA Projects Seminar as part of the EU Presidency 2019 activities in Bucharest last April, SPuMoNI receives financial support from the European Union and is worth over 1.1 M Euro.
The Pharmaceutical industry is producing significant amounts of data through manufacturing lines increasingly automated via pervasive sensors and devices. Manufacturing line data sources are heterogeneous with various embedded systems controlling the different processes involved in the production of medicaments.
Data Integrity and end-to-end traceability have become a key point to be compliant with the different international regulations and guidelines. As an example, in order to release a medicine batch number, it is necessary to ensure that all the data produced is compliant with the ALCOA principles (Attributable, Legible, Contemporaneous, Original and Accurate). Auditable computerised systems are therefore the key to pharma production lines, since the industry is becoming increasingly regulated for product quality and patient health purposes. As systems are generating data in various formats, data must be dynamically analysed to ensure the quality and compliance of the overall process. The idea of this project is to systematically assess all data produced by computerised production systems in representative pharma environments: (i) design data quality assessment models based on the Data Quality dimensions agreed by the European Institute for Innovation Through Health Data, including rules derived from regulatory documents; and, (ii) identify behaviour patterns of data probability distributions over time and among the manufacturing sources to identify outliers, i.e. data behavioural patterns which can violate ALCOA premises.
To this end, there will be a semi-autonomous data quality control decision support system aiding pharma manufacturing companies to reduce the effort of analysing compliance data. Finally, a system prototype demonstration in an operational environment will be evaluated using industry-grade real pharmaceutical manufacturing data sets and streams coupled with best pharma industry practices.
“We are very glad to be the coordinator of this international project with a great responsibility in terms of data governance. For this reason, we are defining the rules that this data has to follow in order to be compliant with different global regulations.”
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