Application of Big-Data technologies and approaches to improve speed, efficiency, and quality of biopharmaceutical manufacturing

Biopharmaceutical manufacturing is witnessing transformative shifts driven by the implementation of big data technologies which is in turn enabling a more diverse approach to bioprocess engineering. The pursuit of greater speed, efficiency, and quality in manufacturing processes has placed increased emphasis on digital strategies that enhance every stage, from process design and technology transfer to supply-chain management and quality assurance. Traditionally, data management in these domains has been limited by isolated datasets and inflexible structures, restricting both the exploitation and sharing of valuable information.Recent studies, however, have demonstrated how advanced dataflows, innovative management solutions, robust interoperability frameworks, and powerful modelling and simulation tools can elevate the entire bioprocessing workflow. Unique challenges have also arisen, such as the integration of AI in highly regulated settings and the imperative to safeguard patient and proprietary data, particularly when seeking to create communal value datasets accessible across organizational boundaries.This Research Topic aims to foster deeper exploration of how big-data driven approaches are actively reshaping biopharmaceutical process engineering to achieve scalable, efficient, and high-quality bioproduct manufacturing. The goal is to identify both opportunities and barriers associated with deploying big data tools across all industrial domains of bioprocessing in order to facilitate innovation and knowledge to enable delivery of the best possible manufacturing processes.Key objectives include illuminating best practices for secure data management, advancing the use of AI and machine learning in compliance-heavy biopharmaceutical manufacturing environments, and critically evaluating strategies for balancing data protection with the creation of shared industry resources and pre-competitive knowledge. Contributors are encouraged to address both technical advancements and regulatory or ethical considerations as they apply to real-world industrial settings.The scope of this Research Topic targets the intersection of bioprocess and biopharmaceutical engineering and big data technologies, focusing on their impact on industrialized biomanufacturing processes and data ecosystems. Submissions that address either foundational and applied questions or cross-cutting themes are welcome, provided a clear connection to bioprocess engineering is established. To gather further insights, we welcome articles addressing, but not limited to, the following themes:· Data flow and structure for process design, technology transfer, manufacturing, quality control, supply chain, facility and equipment.· Interoperable data management solutions linking connectivity, schema syntax, and semantics for bioprocessing to enable maturation of data to information and knowledge.· Modelling, simulation, and digital twins in bioprocess optimization· Deployment of AI and machine learning in regulated manufacturing environments· Strategies for secure sharing of patient and proprietary data· Creation and utilization of communal value datasets in competitive industries· Digital and data-driven innovations in supply-chain, facilities, and equipment managementIt is of particular importance to the Guest Editor Team that it is the responsibility of the authors to ensure the validity of their work and their references. Appendix: Article types may include Original Research, Review, Perspective, Technology Report, and Methods articles. Topic editors: Mudassir Mohammad Rashid, Dirk Tischler, David Roush Source: https://www.frontiersin.org/research-topics/79594/application-of-big-data-technologies-and-approaches-to-improve-speed-efficiency-and-quality-of-biopharmaceutical-manufacturing