Monitoring mAb cultivations with in‐situ raman spectroscopy: The influence of spectral selectivity on calibration models and industrial use as reliable PAT tool

Abstract

Raman spectroscopy is a suitable monitoring technique for CHO cultivations. However, a thorough discussion of peaks, bands, and region assignments to key metabolites and culture attributes, and the interpretability of produced calibrations is scarce. That understanding is vital for the long-term predictive ability of monitoring models, and to facilitate lifecycle management that comply with regulatory guidelines. Several fed-batch lab-scale mAb mammalian cultivations were carried out, with in situ Raman spectroscopy used for process state estimation and attribute monitoring. The goal was to evaluate its use as a process analytical technology (PAT) tool to detect residual glucose and lactate levels, understand their dynamics and interconversion, and eventually estimate key performance culture and product quality attributes. Glucose and lactate models were optimized up to 0.31 g L with 3 Latent Variables (LVs) and 0.19 g L (2 LVs) accuracy, respectively. Glutamine and product titer models, were not specific and accurate enough, even though indirect calibrations were obtained with a RMSEP of 0.12 g L (4 LVs) and 0.29 g L (5 LVs), respectively. A critical discussion and details about the extensive work done in calibration development and optimization are provided. Namely, considering a risk-based selection of variability sources impacting sample spectra, executing designed experiments with spiked cultivations, and using advanced chemometric procedures for variable selection and model cross validation. A strategy is presented to evaluation Raman spectroscopy as a reliable PAT technology fit-for industrial use. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:659-670, 2018.

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

“…The general ability of non‐SERS Raman to monitor glucose and lactate concentration accurately, if compared with 2‐D fluorescence and NIR spectroscopy, was recently observed in samples of a cell culture process . A RMSEP of up to 0.31 g·L −1 was reported recently . AMP, however, SERS spectra were slightly superior to Raman spectra obtained with the NIR‐Raman.…”

“…Supporting Information Figure S5A,B). From a physiological point of view, the cAMP concentration in E. coli increases under limited substrate supply . cAMP is an important molecule to indicate the energetic state of a culture and the degree of starvation under typically applied fed‐batch conditions, in which the main nutrient is limiting .…”

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).…”

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

“…Stable production of biotherapeutic proteins from modified cell lines requires real‐time monitoring and control of cell culture processes to ensure optimal cell growth profiles and consistent product quality (Fan et al, ; Kim, Kim, & Lee, ; Torkashvand et al, ). 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, ).…”

“…Raman spectroscopy provides an alternative to manual sampling by delivering real‐time, model‐based predictions of performance parameters. Since its first reported use in 1999 for predicting glucose and ethanol concentrations in a yeast cultivation (Shaw et al, ), Raman spectroscopy has been widely adopted in biomanufacturing for real‐time monitoring and control of cell culture performance parameters, such as glucose, glutamine, glutamate, lactate, ammonium, viable cell density (VCD), and product concentrations (Abu‐Absi et al, ; Berry et al, ; Li, Ray, Leister, & Ryder, ; Santos et al, ; Whelan, Craven, & Glennon, ).…”