TY - JOUR
T1 - Advances in on-line monitoring and control of mammalian cell cultures
T2 - Supporting the PAT initiative
AU - Teixeira, Ana Palma
AU - Oliveira, Rui
AU - Alves, Paula Maria
AU - Carrondo, Manuel José Teixeira
N1 - Funding Information:
Financial support for this work was provided by the Portuguese Fundação para a Ciência e Tecnologia through the project POCTI/BIO/57927/2004 and by E.C. contract Baculogenes (FP6-037541).
Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2009/11
Y1 - 2009/11
N2 - In recent years, much attention has been directed towards the development of global methods for on-line process monitoring, especially since the Food and Drug Administration (FDA) launched the Process Analytical Technology (PAT) guidance, stimulating biopharmaceutical companies to update their monitoring tools to ensure a pre-defined final product quality. The ideal technologies for biopharmaceutical processes should operate in situ, be non-invasive and generate on-line information about multiple key bioprocess and/or metabolic variables. A wide range of spectroscopic techniques based on in situ probes have already been tested in mammalian cell cultures, such as near infrared (NIR), mid infrared (MIR), 2D fluorescence and dielectric capacitance spectroscopy; similarly, the electronic nose technique based on chemical array sensors has been tested for in situ off-gas analysis of mammalian cell cultures. All these methods provide series of spectra, from which meaningful information must be extracted. In this sense, data mining techniques such as principal components regression (PCR), partial least squares (PLS) or artificial neural networks (ANN) have been applied to handle the dense flow of data generated from the real-time process analyzers. Furthermore, the implementation of feedback control methods would help to improve process performance and ultimately ensure reproducibility. This review discusses the suitability of several spectroscopic techniques coupled with chemometric methods for improved monitoring and control of mammalian cell processes.
AB - In recent years, much attention has been directed towards the development of global methods for on-line process monitoring, especially since the Food and Drug Administration (FDA) launched the Process Analytical Technology (PAT) guidance, stimulating biopharmaceutical companies to update their monitoring tools to ensure a pre-defined final product quality. The ideal technologies for biopharmaceutical processes should operate in situ, be non-invasive and generate on-line information about multiple key bioprocess and/or metabolic variables. A wide range of spectroscopic techniques based on in situ probes have already been tested in mammalian cell cultures, such as near infrared (NIR), mid infrared (MIR), 2D fluorescence and dielectric capacitance spectroscopy; similarly, the electronic nose technique based on chemical array sensors has been tested for in situ off-gas analysis of mammalian cell cultures. All these methods provide series of spectra, from which meaningful information must be extracted. In this sense, data mining techniques such as principal components regression (PCR), partial least squares (PLS) or artificial neural networks (ANN) have been applied to handle the dense flow of data generated from the real-time process analyzers. Furthermore, the implementation of feedback control methods would help to improve process performance and ultimately ensure reproducibility. This review discusses the suitability of several spectroscopic techniques coupled with chemometric methods for improved monitoring and control of mammalian cell processes.
KW - Chemometric methods
KW - Mammalian cell processes
KW - Monoclonal antibodies
KW - PAT
KW - Spectroscopic techniques
UR - http://www.scopus.com/inward/record.url?scp=70349949073&partnerID=8YFLogxK
U2 - 10.1016/j.biotechadv.2009.05.003
DO - 10.1016/j.biotechadv.2009.05.003
M3 - Review article
C2 - 19450676
VL - 27
SP - 726
EP - 732
JO - Biotechnology Advances
JF - Biotechnology Advances
SN - 0734-9750
IS - 6
ER -