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Estimation of raw material performance in mammalian cell culture using near infrared spectra combined with chemometrics approaches
Author(s) -
Lee Hae Woo,
Christie Andrew,
Liu Jun Jay,
Yoon Seongkyu
Publication year - 2012
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1002/btpr.1536
Subject(s) - chemometrics , raw material , partial least squares regression , biological system , principal component analysis , process engineering , biochemical engineering , computer science , environmental science , microbiology and biotechnology , chemistry , machine learning , artificial intelligence , biology , engineering , organic chemistry
Understanding variability in raw materials and their impacts on product quality is of critical importance in the biopharmaceutical manufacturing processes. For this purpose, several spectroscopic techniques have been studied for raw material characterization, providing fast and nondestructive ways to measure quality of raw materials. However, investigations of correlation between spectra of raw materials and cell culture performance have been scarce due to their complexity and uncertainty. In this study, near‐infrared spectra and bioassays of multiple soy hydrolysate lots manufactured by different vendors were analyzed using chemometrics approaches in order to address variability of raw materials as well as correlation between raw material properties and corresponding cell culture performance. Principal component analysis revealed that near‐infrared spectra of different soy lots contain enough physicochemical information about soy hydrolysates to allow identification of lot‐to‐lot variability as well as vendor‐to‐vendor differences. The identified compositional variability was further analyzed in order to estimate cell growth and protein production of two mammalian cell lines under the condition of varying soy dosages using partial least square regression combined with optimal variable selection. The performance of the resulting models demonstrates the potential of near‐infrared spectroscopy as a robust lot selection tool for raw materials while providing a biological link between chemical composition of raw materials and cell culture performance. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012