NEWS<\/span><\/a><\/b><\/span><\/h5>\n[\/vc_column_text][vc_empty_space height=”60px”][vc_column_text css_animation=”none”]<\/p>\n
KNOWLEDGE4PHARMA Project: Fedegari, MADE, and the University of Pavia for the Optimization of Sterilization Processes<\/b><\/h2>\n
[\/vc_column_text][vc_empty_space height=”40px”][vc_column_text css_animation=”none”]In an ever-changing context, innovation and research are essential elements that drive process progress and ensure high standards of safety and sustainability.[\/vc_column_text][vc_empty_space height=”20px”][vc_empty_space height=”20px”][\/vc_column_inner][vc_column_inner width=”1\/12″][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row full_width=”stretch_row” equal_height=”yes” css_animation=”fadeInUp” css=”.vc_custom_1733409276965{margin-top: 60px !important;}” el_id=”content”][vc_column width=”1\/2″][vc_column_text css_animation=”none”]For this reason, Fedegari has joined the \u201cKNOWLEDGE4PHARMA \u2013 Pharmaceutical Knowledge Management for Primary Packaging Design and Development\u201d project, in partnership with MADE Competence Center and the University of Pavia. This initiative seeks to embed Artificial Intelligence in the development of advanced sterilization solutions, with the objective of converting empirical expertise about our systems into a structured, scalable, and predictive digital source.<\/p>\n
Specifically, the project focuses on developing a system model based on the study of the interaction between Ready-To-Fill (RTF) container surfaces, biotechnological drug products, and saturated steam sterilization processes, in order to identify the most suitable parameters according to specific combinations of materials and products.<\/p>\n
The study has laid the foundation for developing a Graph Neural Network (GNN), a mathematical model powered by a continuously updated dataset.[\/vc_column_text][vc_empty_space height=”40px”][\/vc_column][vc_column width=”1\/2″][vc_column_text css_animation=”none”]By leveraging Artificial Intelligence, the model enables the analysis of complex relationships and the prediction of system behavior. It precisely estimates interactions among materials, products, and process conditions, recommends ideal parameter configurations, and guides efficient and accurate decision-making in sterilization cycle development. This enhances process quality and safety, while reducing energy consumption and optimizing resource use.<\/p>\n
Through this initiative, Fedegari positions itself at the forefront of the evolution of the sterilization process, integrating its expertise with academic excellence and the potential of Artificial Intelligence to generate tangible value. This commitment is embodied in the development of advanced solutions for the pharmaceutical industry, aimed at anticipating and actively shaping the sector’s future evolution.[\/vc_column_text][vc_empty_space height=”40px”][\/vc_column][\/vc_row][vc_row full_width=”stretch_row_content_no_spaces” equal_height=”yes” css=”.vc_custom_1733409269281{background-color: #eaedf2 !important;}”][vc_column][vc_row_inner css=”.vc_custom_1576490623608{background-color: #c4161c !important;}”][vc_column_inner][vc_empty_space][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"
In an ever-changing context, innovation and research are essential elements that drive process progress and ensure high standards of safety and sustainability.<\/p>\n","protected":false},"author":1,"featured_media":24024,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[153],"tags":[154],"class_list":["post-24007","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-en","tag-news-en"],"yoast_head":"\n
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