Interest of locally weighted regression to overcome nonlinear effects during in situ NIR monitoring of CHO cell culture parameters and antibody glycosylation

Abstract

Animal cell culture processes have become the standard platform to produce therapeutic proteins such as recombinant monoclonal antibodies (mAb). Since the mAb quality could be subject to significant changes depending on manufacturing process conditions, real time monitoring and control systems are required to ensure mAb specifications mainly glycosylation and patient safety. Up to now, real time monitoring glycosylation of proteins has received scarce attention. In this article, the use of near infrared (NIR) to monitor mAb glycosylation has been reported for the first time.Whereas monitoring models are mainly constructed using linear partial least squares regressions (PLSR), evidences presented in this study indicate nonlinearity relationship between in situ captured spectra and compound concentrations, compromising the PLSR performances. A novel and simple approach was proposed to fit nonlinearity using the locally weighted regression (LWR). The LWR models were found to be more appropriate for handling information contained in spectra so that real time monitoring of cultures were accurately performed. Moreover, for the first time, the LWR calibration models allowed mAb glycosylation to be monitored, in a real time manner, by using in situ NIR spectroscopy. These results represent a further step toward developing active-control feedback of animal cell processes, particularly for ensuring properties of biologics.in situ near infrared spectroscopy, locally weighted regression-LWR, monoclonal antibody-mAb glycosylation, partial least squares regression-PLSR, real time monitoring

Holographic Imaging of Insect Cell Cultures: Online Non-Invasive Monitoring of Adeno-Associated Virus Production and Cell Concentration

“…Most cell culture monitoring methods employing label-free methodologies are based on spectroscopic techniques, which have been widely used for cell culture process monitoring. Examples include the use of dielectric spectroscopy and turbidimetry/light scattering probes for the determination of cell concentration [4,5], as well as the use of Raman [6,7], infrared [8] and fluorescence [9] spectroscopy, which allow the quantification of metabolites based on direct spectra quantification, but also the indirect determination of cell concentration and product formation based on chemometric analysis.…”

Portable Analytical Techniques for Monitoring Volatile Organic Chemicals in Biomanufacturing Processes: Recent Advances and Limitations

“…Clustering methods seek to describe a dataset into groups, or clusters; classification methods attempt to predict information about an unknown sample based on previously acquired data. Common statistical methods for biomanufacturing monitoring include: principal component analysis (PCA) (Albrecht et al, 2018 ; Chen et al, 2020 ) linear discriminant analysis (LDA) (Wang et al, 2012 ; Silva et al, 2017 ), partial least square (PLS) regression/discrimination analysis (Kammies et al, 2016 ; Matthews et al, 2016 ; McCartney et al, 2019 ; Pontius et al, 2020 ; Zavala-Ortiz et al, 2020 ), and artificial neural network (ANN) (López et al, 2017 ; Oyetunde et al, 2018 ).…”

“…Common statistical methods for biomanufacturing monitoring include: principal component analysis (PCA) (Albrecht et al, 2018;Chen et al, 2020) linear discriminant analysis (LDA) (Wang et al, 2012;Silva et al, 2017), partial least square (PLS) regression/discrimination analysis (Kammies et al, 2016;Matthews et al, 2016;McCartney et al, 2019;Pontius et al, 2020;Zavala-Ortiz et al, 2020), and artificial neural network (ANN) (López et al, 2017;Oyetunde et al, 2018). Most e-noses for volatile gas measurement generally rely on the adsorption of gas molecules to the surface of sensors.…”

“…It is well-known that the cultivation of a particular cell culture produces a diverse set of volatile organic chemicals (VOCs) as featured metabolites. A wide variety of standard analytical techniques have been applied to the determination of the identities and contents of different gaseous targets, including gas chromatography (GC) (Langejuergen et al, 2015 ; Lavra et al, 2015 ; Tang et al, 2017 ), mass spectrometry (MS) (Schmidberger and Huber, 2013 ; Chingin et al, 2014 ; Chippendale et al, 2014 ; Schmidberger et al, 2014 ), near-infrared (NIR) spectroscopy (Cimander and Mandenius, 2002 ; Zhang et al, 2009 ; Nespeca et al, 2017 ; Zavala-Ortiz et al, 2020 ), and multi-wavelength fluorimetry (Faassen and Hitzmann, 2015 ; Rowland-Jones et al, 2017 ). Most of those methods, however, suffer from one or more of the drawbacks below: demand on bulk instrumentation, lack of portability, high cost, and long-term sample preparation and analysis.…”

Fiberoptic-Coupled Spectrofluorometer with Array Detection as a Process Analytical Chemistry Tool for Continuous Flow Monitoring of Fluoroquinolone Antibiotics

“…PAT has many applications in the pharmaceutical and antibiotics manufacturing [12][13][14][15][16][17][18][19][20][21][22][23][24][25], chemical [26,27], petrochemical [28], and food industries [29][30][31][32][33]. In addition, there are many recent progress in realtime monitoring of cultivations in bioreactors and cell culture process [34][35][36][37][38][39][40][41], fermentation [42,43] and biological process [44], and electrochemical [45,46] and protein purification [47]. Using PAT enables us to get a deeper understanding of the process.…”