The National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) has announced two requests for information (RFIs) related to universal connectivity and federated learning from data-science experts from across the industry.

The gathered insights will be used to bolster NIIMBL’s Big Data program and inform strategic planning for possible future projects. Both RFIs asks submissions to detail the technical approach of a proposed solution as well as a cost estimate and an overview of how the proposing organization can support development.

"Enhanced data integration and predictive modeling are essential to help biopharmaceutical manufacturers make data-driven, analytics-based decisions to optimize manufacturing processes. These two RFIs will help NIIMBL's Big Data program tackle these challenges for the industry, said Roger Hart, NIIMBL senior fellow and Big Data program lead.

"Through collaboration within NIIMBL programs, members identify shared challenges and prioritize potential solutions," Hart told BioProcess Insider. "Projects aligned with those potential solutions are implemented using collective resources to raise community capabilities. Resulting aligned capabilities enhance the community’s ability to further collaborate using collective data to realize benefits for business stakeholders and patients."

NIIMBL hopes that information gathered from the Big Data program will aid its goal of accelerating the development and adoption of data-driven innovation and standards to increase the speed and resilience of biopharmaceutical manufacturing.

The Universal Connectivity for In-process Analytics in Biopharmaceutical Manufacturing RFI seeks information on potential solutions for a universal connectivity system for at-line and/or inline analytical instrumentation. Such a system would be built around the convergence and integration of operational and IT systems based on Industrial Internet of Things (IIoT) principles. The submission deadline is August 30, 2024.

Meanwhile, the Hybrid Model Federated Learning RFI requests information on innovative approaches that combine federated learning with hybrid models that are specifically tailored for biopharmaceutical manufacturing processes. The RFI seeks to enhance predictive analytics and improve data privacy and security. The submission deadline is September 24, 2024.