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Biomanufacturing

Overview

The NIST Biomanufacturing Program draws from a broad array of unique, interdisciplinary expertise, resources, and facilities including

  • biophysical characterization of biotherapeutics including colloidal and rheological measurements
  • state-of-the-art mass spectrometry, NMR, and cryo-electron microscopy capabilities for characterizing biotherapeutics
  • world-class neutron and X-ray scattering facilities to determine biotherapeutic stability and structure

We work closely with stakeholders from across the biopharmaceutical community including industry, FDA, universities, and equipment vendors to identify key measurement problems. NIST holds an annual Biopharmaceutical Measurement Roundtable consisting of members of the industry for feedback on the relevance of its research activities, and to learn of current and emerging measurement needs.

NIST is a trusted, impartial third party that promotes cross-industry collaboration and open data sharing.

Program Portfolio

Foundational measurement science on protein structure, cell biology, and emerging measurement technologies (scattering methods, NMR, HDX-MS, cryo-EM, optical methods) described in over 100 publications.

NIST Engagement with industry through

Measurement standards and data:

RM 8671 - NISTmAb, Humanized IgG1κ Monoclonal AntibodySRM 8634 - Ethylene Tetrafluoroethylene for Particle Size Distribution and Morphology,  glycan reference materials and data, cell culture for protein reference material development, and mass spectrometry reference data.

News and Updates

Projects and Programs

Tissue Engineering Measurands

Ongoing
Cell viability is often measured as a quality metric of tissue engineered medical products. However, there are a number of inherent assumptions about cell

NISTCHO

Ongoing
An open-access, living reference material for biomanufacturing innovation and collaboration

NIST Genome Editing Program

Ongoing
The NIST Genome Editing Program develops standards, methods, tools, technology, and community norms to advance the reliability of genome editing technology and

Publications

Interlaboratory Attribute Analytics Metrics from the MAM Consortium Round Robin Study

Author(s)
Trina Mouchahoir, John E. Schiel, Rich Rogers, N. Alan Heckert, Benjamin Place, Aaron Ammerman, Xiaoxiao Li, Tom Robinson, Brian Schmidt, Chris M. Chumsae, Xinbi Li, Anton V. Manuilov, Bo Yan, Gregory O. Staples, Da Ren, Alexander J. Veach, Dongdong Wang, Wael Yared, Zoran Sosic, Yan Wang, Li Zang, Anthony M. Leone, Peiran Liu, Richard Ludwig, Li Tao, Wei Wu, Ahmet Cansizoglu, Andrew Hanneman, Greg W. Adams, Irina Perdivara, Hunter Walker, Margo Wilson, Arnd Brandenburg, Nick DeGraan-Weber, Stefano Gotta, Joe Shambaugh, Melissa Alvarez, X. Christopher Yu, Li Cao, Chun Shao, Andrew Mahan, Hirsh Nanda, Kristen Nields, Nancy Nightlinger, Ben Niu, Jihong Wang, Wei Xu, Gabriella Leo, Nunzio Sepe, Yan-Hui Liu, Bhumit A. Patel, Douglas Richardson, Yi Wang, Daniela Tizabi, Oleg V. Borisov, Yali Lu, Ernest L. Maynard, Albrecht Gruhler, Kim F. Haselmann, Thomas N. Krogh, Carsten P. Sönksen, Simon Letarte, Sean Shen, Kristin Boggio, Keith Johnson, Wenqin Ni, Himakshi Patel, David Ripley, Jason C. Rouse, Ying Zhang, Carly Daniels, Andrew Dawdy, Olga Friese, Thomas W. Powers, Justin B. Sperry, Josh Woods, Eric Carlson, K. Ilker Sen, St John Skilton, Michelle Busch, Anders Lund, Martha Stapels, Xu Guo, Sibylle Heidelberger, Harini Kaluarachchi, Sean McCarthy, John Kim, Jing Zhen, Ying Zhou, Sarah Rogstad, Xiaoshi Wang, Jing Fang, Weibin Chen, Ying Qing Yu, John G. Hoogerheide, Rebecca Scott, Hua Yuan
The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process

Awards