Fedegari is the global expert in sterilisation and bio-decontamination solutions. Its in-depth expertise is therefore a tangible asset in life science-related sectors such as the pharmaceutical and biotech industries.

Currently, there are no systematic studies or guidelines correlating the surface properties of materials used for primary packaging of drugs and the processes of washing, depyrogenation and sterilisation of these materials in relation to certain reference pharmaceutical formulations. To date, knowledge related to this correlation is empirical and heavily dependent on the experience of those involved in defining the process. The aim of this work is to develop an artificial intelligence-based knowledge management platform that allows the prediction of the behaviour of packaging subjected to typical aseptic or sterile production processes.

Il progetto prevede una fase di raccolta e caratterizzazione, la gestione e l’analisi del risultato con creazione del database oltre all’adozione di metodologie di Intelligenza Artificiale. In particolare, lo studio e lo sviluppo di modelli e algoritmi di supporto al Knowledge Management per la gestione dell’interazione tra i materiali di imballaggio medicale, i trattamenti sugli stessi ed il prodotto farmaceutico.

Data analytics

Graph Neural Netowork (GNN)

Digitalization

Use of AI techniques to predict the best interaction between process-product and primary packaging.

Use of digital technologies to optimise production and operational processes.

Reduction in processing times and environmental impact.

The proposal to use AI techniques for managing knowledge relating to interactions between pharmaceutical products, primary packaging materials and the treatments required for the same does not exist in the current industry scenario and may be further developed in other areas of application.