Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
The NIST Monoclonal Antibody (NISTmAb).
A new method for monitoring biopharmaceutical product quality was recently put to the test by The National Institute of Standards and Technology (NIST) and 28 laboratories representing the biopharmaceutical industry, instrument and software vendors, and the federal government. The results of this interlaboratory study were recently published in the Journal of the American Society for Mass Spectrometry.
The new multi-attribute method, or MAM, is an emerging, mass spectrometry-based technique for monitoring product quality and detecting multiple types of potential impurities in biopharmaceutical products with a single-step test.
Participants evaluated MAM using the NIST Monoclonal Antibody (NISTmAb) Reference Material 8671 as a model test sample. They were given several altered NISTmAb samples and asked to report any differences found between those and the unaltered reference material.
The study showed that participating labs were able to successfully identify several types of alterations in the samples using MAM, including impurities and chemical transformations that have to be tightly controlled when producing biopharmaceutical products.
The study also identified the current capabilities and challenges associated with MAM and outlines best practices that can improve reliability.
“The ultimate goal is to speed the time-to-market for life-saving medicines through more efficient quality control methods,” said NIST research chemist Trina Mouchahoir. “And this study provides a roadmap for getting there.”
Paper: Trina Mouchahoir, John E. Schiel, Rich Rogers, Alan Heckert, Benjamin J. 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, Helena Maria Barysz, Michael Jahn, Ben Niu, Jihong Wang, 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, and Hua Yuan. New Peak Detection Performance Metrics from the MAM Consortium Interlaboratory Study. Journal of the American Society for Mass Spectrometry. Published 12 March 2021. DOI: https://doi.org/10.1021/jasms.0c00415