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Application of the Quality‐by‐Design (QbD) Approach for Erythropoietin Alpha Purification
Author(s) -
Kim Tae Kyu,
Seo KwangSeok,
Kwon SangOh,
Little Thomas A.,
Kim Mijung,
Kim ChanWha
Publication year - 2019
Publication title -
bulletin of the korean chemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.237
H-Index - 59
ISSN - 1229-5949
DOI - 10.1002/bkcs.11737
Subject(s) - quality by design , critical quality attributes , biopharmaceutical , process engineering , yield (engineering) , design of experiments , quality (philosophy) , process analytical technology , computer science , statistics , engineering , mathematics , materials science , work in process , microbiology and biotechnology , operations management , chemical engineering , particle size , philosophy , epistemology , metallurgy , biology
This study was aimed at process characterization and improving quality of purification of erythropoietin α, a biopharmaceutical agent. In biopharmaceutical manufacturing, quality should always be targeted to ensure safety and efficacy. Design‐of‐experiments–based approaches have been explored to rapidly and efficiently achieve an optimized yield and an increased understanding of a product and process variables affecting the product's critical quality attributes in the biopharmaceutical industry; this system is known as the quality‐by‐design approach. Changes in three critical process parameters—buffer pH, flow rate, and loading amount—were evaluated. Process characterization was conducted on a scaled‐down model previously validated by comparison with data from a large‐scale production facility. Seven critical quality attributes—relative aggregate content, host cell protein, host cell deoxynucleotides, endotoxin, Z ‐value (N‐glycan score), relative content of charge isomers, and step yield—were analyzed. Multivariate regression analysis was performed to establish statistical prediction models for performance indicators and quality attributes; accordingly, we constructed contour plots and conducted a Monte Carlo simulation to clarify the design space. As a result of the optimization analysis of the purification process, it was confirmed that proven acceptance ranges were optimized as follows: loading amount (mg/mL) 0.4–4.0, buffer pH 7.0–8.0, and flow rate (mL/min) 0.5–1.6.