Multi-attribute Raman Spectroscopy (MARS) for Monitoring Product Quality Attributes in Formulated Monoclonal Antibody Therapeutics

Overview

Journal MAbs

Specialty Allergy & Immunology

Date 2021 Dec 29

PMID 34965193

Citations 7

Authors

Bingchuan Wei

Nicholas Woon

Lu Dai

Raphael Fish

Michelle Tai

Winode Handagama

Ashley Yin

Jia Sun

Andrew Maier

Dana McDaniel

Elvira Kadaub

Jessica Yang

Miguel Saggu

Ann Woys

Oxana Pester

Danny Lambert

Alex Pell

Zhiqi Hao

Gordon Magill

Jack Yim

Jefferson Chan

Lindsay Yang

Frank Macchi

Christian Bell

Galahad Deperalta

Yan Chen

Affiliations

Soon will be listed here.

Abstract

Rapid release of biopharmaceutical products enables a more efficient drug manufacturing process. Multi-attribute methods that target several product quality attributes (PQAs) at one time are an essential pillar of the rapid-release strategy. The novel, high-throughput, and nondestructive multi-attribute Raman spectroscopy (MARS) method combines Raman spectroscopy, design of experiments, and multivariate data analysis (MVDA). MARS allows the measurement of multiple PQAs for formulated protein therapeutics without sample preparation from a single spectroscopic scan. Variable importance in projection analysis is used to associate the chemical and spectral basis of targeted PQAs, which assists in model interpretation and selection. This study shows the feasibility of MARS for the measurement of both protein purity-related and formulation-related PQAs; measurements of protein concentration, osmolality, and some formulation additives were achieved by a generic multiproduct model for various protein products containing the same formulation components. MARS demonstrates the potential to be a powerful methodology to improve the efficiency of biopharmaceutical development and manufacturing, as it features fast turnaround time, good robustness, less human intervention, and potential for automation.

Citing Articles

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Comprehensive modeling of cell culture profile using Raman spectroscopy and machine learning.

Tanemura H, Kitamura R, Yamada Y, Hoshino M, Kakihara H, Nonaka K Sci Rep. 2023; 13(1):21805.

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Application of Raman Spectroscopy to Dynamic Binding Capacity Analysis.

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