Development of generic raman models for a GS‐KO^TM CHO platform process

Abstract

The monitoring and control of bioprocesses is of the utmost importance in order to provide a consistent, safe, and high-quality product for consumers. Current monitoring and control schemes rely on infrequent and time consuming offline sampling methods, which inherently leads to some variability in the process which may impact the product quality profile. As part of Lonza's dedication to process analytical technology (PAT) initiatives this study evaluated the ability to generate generic calibration models, which are independent of the cell line, using Raman probes to monitor changes in glucose, lactate, glutamate, ammonium, viable cell concentration (VCC), total cell concentration (TCC) and product concentration. Calibration models were developed from cell culture using two different CHOK1SV GS-KO cell lines producing different monoclonal antibodies (mAbs). Developed predictive models, measured changes in glucose, lactate, ammonium, VCC, and TCC with average prediction errors of 0.44, 0.23, 0.03 g L , 1.90 × 10 cells mL , and 1.85 × 10 cells mL , respectively over the course of cell culture with minimal cell line dependence. The development of these generic models allows the application of spectroscopic PAT techniques in clinical and commercial manufacturing environments, where processes are typically run once or twice in GMP manufacturing based on a common platform process. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:730-737, 2018.

Enabling PAT in insect cell bioprocesses: In situ monitoring of recombinant adeno‐associated virus production by fluorescence spectroscopy

“…Several spectroscopic techniques have been used for cell culture process and virus production monitoring, with Raman (Rangan et al, 2018;Santos, Kessler, Salou, Menezes, & Peinado, 2018;Webster, Hadley, Hilliard, Jaques, & Mason, 2018), near-infrared (Mercier et al, 2015;Rowland-Jones, van den Berg, Racher, Martin, & Jaques, 2017), dielectric (Kroll, Stelzer, & Herwig, 2017;Mercier et al, 2015;Nikolay, Léon, Schwamborn, Genzel, & Reichl, 2018;Petiot, Ansorge, Rosa-Calatrava, & Kamen, 2016) and fluorescence spectroscopy (Karakach et al, 2018;Schwab & Hesse, 2017) being the most widely used. All possess characteristics desirable for PAT: spectroscopic techniques are noninvasive, nondestructive, and able to provide rapid information from several components simultaneously (Ohadi, Aghamohseni, Legge, & Budman, 2014;Ohadi, Legge, & Budman, 2015;Rowland-Jones et al, 2017;Teixeira et al, 2011Teixeira et al, , 2009, including product quality attributes (Chopda, Pathak, Batra, Gomes, & Rathore, 2017;Li et al, 2019).…”

Comparison of time‐gated surface‐enhanced raman spectroscopy (TG‐SERS) and classical SERS based monitoring of Escherichia coli cultivation samples

“…Remarkably, the application of complex media might lead to different conclusions about the benefits of SERS pretreatment and Raman spectroscopy evaluation. Despite the advantages of SERS nanostructures to drastically enhance the Raman signal and quench fluorescence, a careful planned set‐up is necessary to carry out reproducible measurement results and neglect effects of signal variations due to aggregation . The combination of TG‐Raman with SERS is beneficial for a qualitative analysis .…”

“…Despite the advantages of SERS nanostructures to drastically enhance the Raman signal and quench fluorescence, a careful planned set-up is necessary to carry out reproducible measurement results and neglect effects of signal variations due to aggregation. 60,63 The combination of TG-Raman with SERS is beneficial for a qualitative analysis. 64 In this study, spectra derived from the TG-Raman with and without SERS resulted in well-discernable spectral peaks, which allow the identification of a much higher number of peaks compared to NIR-Raman.…”

Automatic real‐time calibration, assessment, and maintenance of generic Raman models for online monitoring of cell culture processes

“…Barring few real‐time measurements, such as pH, dissolved oxygen (DO), and temperature, much of the cell culture parameters, such as metabolite and product concentrations, cell growth, and other product quality attributes are monitored using offline methods (Santos, Kessler, Salou, Menezes, & Peinado, ). Generally, these offline methods require removal of cells from the bioreactor and are often labor‐intensive, require trained operators, and generate waste through the use of expensive and often toxic reagents and samples (Webster, Hadley, Hilliard, Jaques, & Mason, ). In addition, offline measurement occurs at infrequent intervals (e.g., every 12–24 hr) leading to limited process resolution and potentially missed shifts in cell metabolism that may be indicative of process changes or abnormal operations.…”

“…For example, Whelan et al () and Berry et al () proposed Raman models for metabolite concentrations that are independent of the volume of the bioreactor used in cell culture experiments. Similarly, Mehdizadeh et al () proposed Raman models that are generic with respect to cell lines and André et al () and Webster et al () proposed Raman models that are generic with respect to cell hosts.…”