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Emerging analytical tools for biopharmaceuticals: A critical review of cutting-edge technologies
1 Research Scientist III, Analytical R&D, Amneal Pharmaceuticals, NJ, USA MS in analytical chemistry.
2 Research Scientist III, Analytical R&D, Amneal Pharmaceuticals, NJ, USA MS in pharmaceutical manufacturing engineering.
Review
International Journal of Science and Research Archive, 2024, 12(02), 3017-3032.
Article DOI: 10.30574/ijsra.2024.12.2.1345
Publication history:
Received on 12 June 2024; revised on 22 July 2024; accepted on 25 July 2024
Abstract:
Fast developments in biopharmaceutical product lines, such as monoclonal antibodies, recombinant proteins, e-therapies, and cell-based treatments, require novel analysis tools for product quality, performance, and regulatory requirements. The insufficient capabilities of conventional analysis techniques to handle biologics complexities produced novel state-of-the-art methods for product characterization, quality control systems, and online process monitoring systems. The review explores modern biopharmaceutical analytics through HRMS, advanced chromatographic tools and PAT applications, AI-driven analysis, single-cell instrumentation, and structural characterization methods incorporating Cryo-EM and NMR spectroscopy. The sections explain fundamental concepts and implementations, positive aspects, and obstacles regarding these technologies in present-day biopharmaceutical advancement activities. The second part of the article examines regulatory standards alongside estimated patterns in the evolution of biopharmaceutical analytics, which requires automation for precise performance enhancement. The evaluation in this review provides comprehensive information about new analytical tools to assist researchers, industry professionals, and regulatory bodies when adopting innovative solutions for modern biopharmaceutical developments.
Keywords:
Biopharmaceutical Analysis; High-Resolution Mass Spectrometry; Chromatography; Process Analytical Technologies; Artificial Intelligence; Machine Learning; Single-Cell Analysis
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
