Trends in Biotechnology
ForumSpecial Issue: Therapeutic BiomanufacturingAdvancing Mass Spectrometry Technology in cGMP Environments
Section snippets
Analytical Testing in the Biotech Industry
In order to control drug product quality and to optimize manufacturing process, an array of analytical assays, including compendial assays, bioassays, impurity assays, and purity assays, are used to measure the physical properties, biological activities, process- and product-related impurities, and purity of therapeutic proteins in the biotech industry. Purity and product-related impurity assays are crucial because they provide entity information of therapeutic proteins, and commonly used
Advancing MS in Current Good Manufacturing Practice (cGMP) Environments
The cGMP is the main regulatory standard set by the FDA and other regulatory agencies to ensure pharmaceutical quality. The cGMP regulations assure the identity, purity, and quality of pharmaceutical products by controlling the manufacturing operations, including analytical testing. The ‘current’ part of cGMP requires pharmaceutical companies to comply with the regulations by using modern technologies to achieve higher pharmaceutical quality.
As part of the Pharmaceutical Quality for the 21st
Multi-Attribute Method (MAM)
Recently, Amgen developed a quantitative LC-MS based MAM that has been used for drug product characterization, process characterization, as well as cGMP release and stability testing [5., 6., 7.]. Unlike conventional analytical assays, which track changes in peak size and peak shape of whole/partial proteins, MAM tracks the actual changes on specific CQAs at the amino acid level.
MAM is an attribute-specific LC-MS-based peptide mapping method that is composed of three parts: (i) sample
Considerations for MAM cGMP Implementation
Before an analytical assay can be implemented in cGMP environments, it needs to demonstrate that the assay is scientifically sound and fit for its intended purposes. MAM is used for purity and identification assays for cGMP testing, so it needs to comply with regulatory guidelines on analytical method validation, which include method accuracy, precision, specificity, linearity, detection/quantitation limit (LOD/LOQ), range, robustness, and system suitability [9].
Introducing a novel analytical
Concluding Remarks and Future Directions
Automated sample preparation systems not only improve the consistency, but also enable MAM as a process analytical technology (PAT) tool to monitor and control CQAs of therapeutic proteins during process development and manufacturing. Current software for data analysis on the market are semiautomatic; artificial intelligence (AI)-powered software can dramatically reduce the data processing time and increase the accuracy and consistency of data analysis. The combination of the future development
Acknowledgments
The author thanks Izydor Apostol, Jette Wypych, Tura Camilli, Le Zhang, Mike Abernathy, Alex Veach, Melissa Sato, Ting Song, Sabrina Benchaar, Xiaoyan Guan, Lan Li, Jing Yan, Jiu-Li Song, Tamer Eris, Tom Dillon, Pavel Bondarenko, Jason Richardson, Richard Wu, Zhongqi Zhang, Chris Clarke, Rowena Cruz, Margaret Ricci, Trent Munroe, Les Miranda, Linda Narhi, Rohini Deshpande, Chetan Goudar, and Rich Rogers for inspirational discussions and helpful suggestions; FDA emerging technology team (ETT)
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2023, Journal of Pharmaceutical SciencesCitation Excerpt :QbD is a frame work built around health authority expectations, where quality of biotherapeutics must be built into the process used to produce and test.1,2 Analytical method lifecycle management typically begins with an analytical target profile (ATP) followed by method development, phase-appropriate validation, method transfer, and implementation for routine testing.1-4 Optimization occurs throughout the method's lifecycle starting in development and moving to method robustness, once there is a better understanding of experimental parameters.