NEWARK, Del., October 21, 2025 — The National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) is pleased to announce awards for two projects to support Big Data Program initiatives on modeling and simulation. The awards stem from two Requests for Applications (RFAs) NIIMBL released in May 2025.
The 12-month projects are scheduled to begin in December.
The projects address critical industry needs for interoperable computer models to simulate conditions and attributes during upstream and downstream biopharmaceutical manufacturing processes at various scales. Computer models can be used to augment laboratory experiments to create more robust data that enable more efficient, flexible, and consistent manufacturing processes.
To address a critical component of upstream processing, a project led by Jeffrey Chalmers of The Ohio State University and Michael Betenbaugh of Johns Hopkins University will develop interoperable Python-based software modules to simulate scale-dependent glycosylation profiles in Chinese Hamster Ovary (CHO) cell culture. The platform will help manufacturers predict how fluid movement in bioreactors of various scales impact product quality. The platform will help NIIMBL partners build digital twins, accelerate process development, validate glycosylation robustness, and inform scale-up decisions.
The second project, led by Nicholas Vecchiarello of the University of Virginia, will use interoperable modules to simulate buffer-induced pH shifts during downstream operations. The computational framework will integrate molecular-level characterization of the protein mixture with mechanistic process modeling to comprehensively represent the coupled interactions among proteins, ligands, buffers, and salt electrolytes. The framework is suitable for multiple chromatographic modes as well as a wide range of buffer and electrolyte conditions. The project aims to make high-fidelity, mechanistically grounded simulation tools more accessible to the broader bioprocess community.
“These projects represent a major leap forward in the use of data assets and modeling in biomanufacturing,” said Roger Hart, NIIMBL Senior Fellow and Big Data Program Lead. “By enabling predictive modeling of glycosylation and pH shifts across scales and conditions, we’re equipping manufacturers with powerful tools to accelerate development, improve product consistency, and make smarter scale-up decisions.”
The project is sponsored by the NIIMBL Big Data Program, a collaboration between industry, academia, and government agencies to accelerate the development and adoption of data-driven innovation and standards to increase the speed and resilience of biopharmaceutical manufacturing.
To learn more about NIIMBL, including how to be a member, visit NIIMBL.org.
About NIIMBL
The National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) is a public-private partnership whose mission is to accelerate biopharmaceutical innovation, support the development of standards that enable more efficient and rapid manufacturing capabilities, and educate and train a world-leading biopharmaceutical manufacturing workforce, fundamentally advancing U.S. competitiveness in this industry. NIIMBL is part of Manufacturing USA®, a network of federally sponsored manufacturing innovation institutes, and is funded through a cooperative agreement with the National Institute of Standards and Technology (NIST) in the U.S. Department of Commerce with additional support from its members.
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