A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line monitoring of Chinese hamster ovary cell cultivations – Part I

A new two‐dimensional fluorescence sensor system was developed for in‐line monitoring of mammalian cell cultures. Fluorescence spectroscopy allows for the detection and quantification of naturally occurring intra‐ and extracellular fluorophores in the cell broth. The fluorescence signals correlate to the cells’ current redox state and other relevant process parameters. Cell culture pretests with twelve different excitation wavelengths showed that only three wavelengths account for a vast majority of spectral variation. Accordingly, the newly developed device utilizes three high‐power LEDs as excitation sources in combination with a back‐thinned CCD‐spectrometer for fluorescence detection. This setup was first tested in a lab design of experiments study with process relevant fluorophores proving its suitability for cell culture monitoring with LOD in the μg/L range. The sensor was then integrated into a CHO‐K1 cell culture process. The acquired fluorescence spectra of several batches were evaluated using multivariate methods. The resulting batch evolution models were challenged in deviating and “golden batch” validation runs. These first tests showed that the new sensor can trace the cells’ metabolic state in a fast and reliable manner. Cellular distress is quickly detected as a deviation from the “golden batch”.     

Data reconciliation of concentration estimates from mid‐infrared and dielectric spectral measurements for improved on‐line monitoring of bioprocesses

Real‐time data reconciliation of concentration estimates of process analytes and biomass in microbial fermentations is investigated. A Fourier‐transform mid‐infrared spectrometer predicting the concentrations of process metabolites is used in parallel with a dielectric spectrometer predicting the biomass concentration during a batch fermentation of the yeast Saccharomyces cerevisiae. Calibration models developed off‐line for both spectrometers suffer from poor predictive capability due to instrumental and process drifts unseen during calibration. To address this problem, the predicted metabolite and biomass concentrations, along with off‐gas analysis and base addition measurements, are reconciled in real‐time based on the closure of mass and elemental balances. A statistical test is used to confirm the integrity of the balances, and a non‐negativity constraint is used to guide the data reconciliation algorithm toward positive concentrations. It is verified experimentally that the proposed approach reduces the standard error of prediction without the need for additional off‐line analysis. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

In‐situ monitoring of Saccharomyces cerevisiae ITV01 bioethanol process using near‐infrared spectroscopy NIRS and chemometrics

The application feasibility of in‐situ or in‐line monitoring of S. cerevisiae ITV01 alcoholic fermentation process, employing Near‐Infrared Spectroscopy (NIRS) and Chemometrics, was investigated. During the process in a bioreactor, in the complex analytical matrix, biomass, glucose, ethanol and glycerol determinations were performed by a transflection fiber optic probe immersed in the culture broth and connected to a Near‐Infrared (NIR) process analyzer. The NIR spectra recorded between 800 and 2,200 nm were pretreated using Savitzky‐Golay smoothing and second derivative in order to perform a partial least squares regression (PLSR) and generate the calibration models. These calibration models were tested by external validation and then used to predict concentrations in batch alcoholic fermentations. The standard errors of calibration (SEC) for biomass, ethanol, glucose and glycerol were 0.212, 0.287, 0.532, and 0.296 g/L and standard errors of prediction (SEP) were 0.323, 0.369, 0.794, and 0.507 g/L, respectively. Calibration and validation criteria were defined and evaluated in order to generate robust and reliable models for an alcoholic fermentation process matrix. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:510–517, 2016     

A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line bioprocess monitoring of Chinese hamster ovary cell cultivations—Part II

This study was performed in order to evaluate a new LED‐based 2D‐fluorescence spectrometer for in‐line bioprocess monitoring of Chinese hamster ovary (CHO) cell culture processes. The new spectrometer used selected excitation wavelengths of 280, 365, and 455 nm to collect spectral data from six 10‐L fed‐batch processes. The technique provides data on various fluorescent compounds from the cultivation medium as well as from cell metabolism. In addition, scattered light offers information about the cultivation status. Multivariate data analysis tools were applied to analyze the large data sets of the collected fluorescence spectra. First, principal component analysis was used to accomplish an overview of all spectral data from all six CHO cultivations. Partial least square regression models were developed to correlate 2D‐fluorescence spectral data with selected critical process variables as offline reference values. A separate independent fed‐batch process was used for model validation and prediction. An almost continuous in‐line bioprocess monitoring was realized because 2D‐fluorescence spectra were collected every 10 min during the whole cultivation. The new 2D‐fluorescence device demonstrates the significant potential for accurate prediction of the total cell count, viable cell count, and the cell viability. The results strongly indicated that the technique is particularly capable to distinguish between different cell statuses inside the bioreactor. In addition, spectral data provided information about the lactate metabolism shift and cellular respiration during the cultivation process. Overall, the 2D‐fluorescence device is a highly sensitive tool for process analytical technology applications in mammalian cell cultures.     

Hemodialysis monitoring using mid‐ and near‐infrared spectroscopy with partial least squares regression

Blood constituents such as urea, glucose, lactate, phosphate and creatinine are of high relevance in monitoring the process of detoxification in ambulant dialysis treatment. In the present work, 2 different vibrational spectroscopic techniques are used to determine those molecules quantitatively in artificial dialysate solutions. The goal of the study is to compare the performance of near‐infrared (NIR) and mid‐infrared (MIR) spectroscopy in hyphenation with partial least squares regression (PLSR) directly by using the same sample set. The results show that MIR spectroscopy is better suited to analyze the analytes of interest. Multilevel multifactor design is used to cover the relevant concentration variations during dialysis. MIR spectroscopy coupled to a multi reflection attenuated total reflection (ATR) cell enables reliable prediction of all target analytes. In contrast, the NIR spectroscopic method does not give access to all 5 components but only to urea and glucose. For both methods, coefficients of determination greater or equal to 0.86 can be achieved in the test‐set validation process for urea and glucose. Lactate, phosphate and creatinine perform well in the MIR with R² ≥ 0.95 using test‐set validation.      

In‐line monitoring of amino acids in mammalian cell cultures using raman spectroscopy and multivariate chemometrics models

The application of PAT for in‐line monitoring of biopharmaceutical manufacturing operations has a central role in developing more robust and consistent processes. Various spectroscopic techniques have been applied for collecting real‐time data from cell culture processes. Among these, Raman spectroscopy has been shown to have advantages over other spectroscopic techniques, especially in aqueous culture solutions. Measurements of several process parameters such as glucose, lactate, glutamine, glutamate, ammonium, osmolality and VCD using Raman‐based chemometrics models have been reported in literature. The application of Raman spectroscopy, coupled with calibration models for amino acid measurement in cell cultures, has been assessed. The developed models cover four amino acids important for cell growth and production: tyrosine, tryptophan, phenylalanine and methionine. The chemometrics models based on Raman spectroscopy data demonstrate the significant potential for the quantification of tyrosine, tryptophan and phenylalanine. The model for methionine would have to be further refined to improve quantification.     

In situ near infrared spectroscopy for analyte‐specific monitoring of glucose and ammonium in streptomyces coelicolor fermentations

There are many challenges associated with in situ collection of near infrared (NIR) spectra in a fermentation broth, particularly for highly aerated and agitated fermentations with filamentous organisms. In this study, antibiotic fermentation by the filamentous bacterium Streptomyces coelicolor was used as a model process. Partial least squares (PLS) regression models were calibrated for glucose and ammonium based on NIR spectra collected in situ. To ensure that the models were calibrated based on analyte‐specific information, semisynthetic samples were used for model calibration in addition to data from standard batches. Thereby, part of the inherent correlation between the analytes could be eliminated. The set of semisynthetic samples were generated from fermentation broth from five separate fermentations to which different amounts of glucose, ammonium, and biomass were added. This method has previously been used off line but never before in situ. The use of semisynthetic samples along with validation on an independent batch provided a critical and realistic evaluation of analyte‐specific models based on in situ NIR spectroscopy. The prediction of glucose was highly satisfactory resulting in a RMSEP of 1.1 g/L. The prediction of ammonium based on NIR spectra collected in situ was not satisfactory. A comparison with models calibrated based on NIR spectra collected off line suggested that this is caused by signal attenuation in the optical fibers in the region above 2,000 nm; a region which contains important absorption bands for ammonium. For improved predictions of ammonium in situ, it is suggested to focus efforts on enhancing the signal in that particular region. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Real‐time monitoring prefrontal activities during online video game playing by functional near‐infrared spectroscopy

A growing body of literature has suggested that video game playing can induce functional and structural plasticity of the brain. The underlying mechanisms, however, remain poorly understood. In this study, functional near‐infrared spectroscopy (fNIRS) was used to record prefrontal activities in 24 experienced game players when they played a massively multiplayer online battle arena video game, League of Legends (LOL), under naturalistic conditions. It was observed that game onset was associated with significant activations in the ventrolateral prefrontal cortex (VLPFC) and concomitant deactivations in the dorsolateral prefrontal cortex (DLPFC) and frontal pole area (FPA). Game events, such as slaying an enemy and being slain by an enemy evoked region‐specific time‐locked hemodynamic/oxygenation responses in the prefrontal cortex (PFC). It was proposed that the VLPFC activities during LOL playing are likely responses to visuo‐motor task load of the game, while the DLPFC/FPA activities may be involved in the constant shifts of attentional states and allocation of cognitive resources required by game playing. The present study demonstrated that it is feasible to use fNIRS to monitor real‐time prefrontal activity during online video game playing.      

Raman spectroscopy and chemometrics for on‐line control of glucose fermentation by Saccharomyces cerevisiae

This work presents the use of Raman spectroscopy and chemometrics for on‐line control of the fermentation process of glucose by Saccharomyces cerevisiae. In a first approach, an on‐line determination of glucose, ethanol, glycerol, and cells was accomplished using multivariate calibration based on partial least squares (PLS). The PLS models presented values of root mean square error of prediction (RMSEP) of 0.53, 0.25, and 0.02% for glucose, ethanol and glycerol, respectively, and RMSEP of 1.02 g L^?1 for cells. In a second approach, multivariate control charts based on multiway principal component analysis (MPCA) were developed for detection of fermentation fault‐batch. Two multivariate control charts were developed, based on the squared prediction error (Q) and Hotelling's T². The use of the Q control chart in on‐line monitoring was efficient for detection of the faults caused by temperature, type of substrate and contamination, but the T² control chart was not able to monitor these faults. On‐line monitoring by Raman spectroscopy in conjunction with chemometric procedures allows control of the fermentative process with advantages in relation to reference methods, which require pretreatment, manipulation of samples and are time consuming. Also, the use of multivariate control charts made possible the detection of faults in a simple way, based only on the spectra of the system. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

pH prediction and control in bioprocesses using mid-infrared spectroscopy

An on-line pH monitoring method based on mid-infrared spectroscopy relevant to bioprocesses is presented. This approach is non-invasive and does not require the addition of indicators or dyes, since it relies on the analysis of species of common buffers used in culture media, such as phosphate buffer. Starting with titrations of phosphoric and acetic acid solutions over almost the entire pH range (2-12), it was shown that the infrared spectra of all samples can be expressed as a linear combination of the molar absorbance of the acids and their deprotonated forms. In other words, pH had no direct influence on the molar infrared spectra themselves, but only on deprotonation equilibria. Accurate prediction (standard error of prediction for pH < 0.15 pH units) was achieved by taking into account the non-ideal behavior of the solutions, using the Debye-Hückel theory to estimate the activity coefficients. Batch cultures of E. coli were chosen as a case study to show how this approach can be applied to bioprocess monitoring. The discrepancy between the spectroscopic prediction and the conventional electrochemical probe never exceeded 0.12 pH units, and the technique was fast enough to implement a feedback controller to maintain the pH constant during cultivation.     

Application of near‐infrared (NIR) spectroscopy for screening of raw materials used in the cell culture medium for the production of a recombinant therapeutic protein

Control of raw materials based on an understanding of their impact on product attributes has been identified as a key aspect of developing a control strategy in the Quality by Design (QbD) paradigm. This article presents a case study involving use of a combined approach of Near‐infrared (NIR) spectroscopy and Multivariate Data Analysis (MVDA) for screening of lots of basal medium powders based on their impact on process performance and product attributes. These lots had identical composition as per the supplier and were manufactured at different scales using an identical process. The NIR/MVDA analysis, combined with further investigation at the supplier site, concluded that grouping of medium components during the milling and blending process varied with the scale of production and media type. As a result, uniformity of blending, impurity levels, chemical compatibility, and/or heat sensitivity during the milling process for batches of large‐scale media powder were deemed to be the source of variation as detected by NIR spectra. This variability in the raw materials was enough to cause unacceptably large variability in the performance of the cell culture step and impact the attributes of the resulting product. A combined NIR/MVDA approach made it possible to finger print the raw materials and distinguish between good and poor performing media lots. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

In situ Raman spectroscopy for simultaneous monitoring of multiple process parameters in mammalian cell culture bioreactors

In this study, the application of Raman spectroscopy to the simultaneous quantitative determination of glucose, glutamine, lactate, ammonia, glutamate, total cell density (TCD), and viable cell density (VCD) in a CHO fed‐batch process was demonstrated in situ in 3 L and 15 L bioreactors. Spectral preprocessing and partial least squares (PLS) regression were used to correlate spectral data with off‐line reference data. Separate PLS calibration models were developed for each analyte at the 3 L laboratory bioreactor scale before assessing its transferability to the same bioprocess conducted at the 15 L pilot scale. PLS calibration models were successfully developed for all analytes bar VCD and transferred to the 15 L scale. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

Implementation of proton transfer reaction‐mass spectrometry (PTR‐MS) for advanced bioprocess monitoring

We report on the implementation of proton transfer reaction‐mass spectrometry (PTR‐MS) technology for on‐line monitoring of volatile organic compounds (VOCs) in the off‐gas of bioreactors. The main part of the work was focused on the development of an interface between the bioreactor and an analyzer suitable for continuous sampling of VOCs emanating from the bioprocess. The permanently heated sampling line with an inert surface avoids condensation and interaction of volatiles during transfer to the PTR‐MS. The interface is equipped with a sterile sinter filter unit directly connected to the bioreactor headspace, a condensate trap, and a series of valves allowing for dilution of the headspace gas, in‐process calibration, and multiport operation. To assess the aptitude of the entire system, a case study was conducted comprising three identical cultivations with a recombinant E. coli strain, and the volatiles produced in the course of the experiments were monitored with the PTR‐MS. The high reproducibility of the measurements proved that the established sampling interface allows for reproducible transfer of volatiles from the headspace to the PTR‐MS analyzer. The set of volatile compounds monitored comprises metabolites of different pathways with diverse functions in cell physiology but also volatiles from the process matrix. The trends of individual compounds showed diverse patterns. The recorded signal levels covered a dynamic range of more than five orders of magnitude. It was possible to assign specific volatile compounds to distinctive events in the bioprocess. The presented results clearly show that PTR‐MS was successfully implemented as a powerful bioprocess‐monitoring tool and that access to volatiles emitted by the cells opens promising perspectives in terms of advanced process control. Biotechnol. Bioeng. 2012; 109: 3059–3069. © 2012 Wiley Periodicals, Inc.     

Process control in cell culture technology using dielectric spectroscopy

In the biopharmaceutical industry, mammalian and insect cells as well as plant cell cultures are gaining worldwide importance to produce biopharmaceuticals and as products themselves, for example in stem cell therapy. These highly sophisticated cell-based production processes need to be monitored and controlled to guarantee product quality and to satisfy GMP requirements. With the process analytical technology (PAT) initiative, requirements regarding process monitoring and control have changed and real-time in-line monitoring tools are now recommended. Dielectric spectroscopy (DS) can serve as a tool to satisfy some PAT requirements. DS has been used in the medical field for quite some time and it may allow real-time process monitoring of biological cell culture parameters. DS has the potential to enable process optimization, automation, cost reduction, and a more consistent product quality. Dielectric spectroscopy is reviewed here as a tool to monitor biochemical processes. Commercially available dielectric sensing systems are discussed. The potential of this technology is demonstrated through examples of current and potential future applications in research and industry for mammalian and insect cell culture.     

Simultaneous and rapid monitoring of biomass and biopolymer production by Sphingomonas paucimobilis using Fourier transform-near infrared spectroscopy

The application of Fourier Transform near infrared spectroscopy (FT-NIRS) to near real-time monitoring of polysaccharide and biomass concentration was investigated using a gellan-producing strain of Sphingomonas paucimobilis grown in a stirred tank reactor. Successful models for both biomass and gellan were constructed despite the physichochemical complexity of the viscous process fluid. Modelling of biomass proved more challenging than for gellan, partly because of the low range of biomass concentration but a model with a good correlation coefficient (0.94) was formulated based on second derivative spectra. The gellan model was highly satisfactory, with an excellent correlation coefficient (0.98), again based on second derivative spectra. No sample pre-treatment was required and all spectral scanning was carried out on whole broth. Additionally, both models should be robust in practice since both were formulated using low numbers of factors. Thus, the near real time simultaneous monitoring of gellan and biomass in this highly complex matrix using FT-NIRS potentially opens the way to greatly improved process control strategies.     

Three-Dimensional (3D) cell culture monitoring: Opportunities and challenges for impedance spectroscopy

Three-dimensional (3D) cell culture has developed rapidly over the past 5–10 years with the goal of better replicating human physiology and tissue complexity in the laboratory. Quantifying cellular responses is fundamental in understanding how cells and tissues respond during their growth cycle and in response to external stimuli. There is a need to develop and validate tools that can give insight into cell number, viability, and distribution in real-time, nondestructively and without the use of stains or other labelling processes. Impedance spectroscopy can address all of these challenges and is currently used both commercially and in academic laboratories to measure cellular processes in 2D cell culture systems. However, its use in 3D cultures is not straight forward due to the complexity of the electrical circuit model of 3D tissues. In addition, there are challenges in the design and integration of electrodes within 3D cell culture systems. Researchers have used a range of strategies to implement impedance spectroscopy in 3D systems. This review examines electrode design, integration, and outcomes of a range of impedance spectroscopy studies and multiparametric systems relevant to 3D cell cultures. While these systems provide whole culture data, impedance tomography approaches have shown how this technique can be used to achieve spatial resolution. This review demonstrates how impedance spectroscopy and tomography can be used to provide real-time sensing in 3D cell cultures, but challenges remain in integrating electrodes without affecting cell culture functionality. If these challenges can be addressed and more realistic electrical models for 3D tissues developed, the implementation of impedance-based systems will be able to provide real-time, quantitative tracking of 3D cell culture systems.     

Generic Raman‐based calibration models enabling real‐time monitoring of cell culture bioreactors

Raman‐based multivariate calibration models have been developed for real‐time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO‐based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1004–1013, 2015     

Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture

The Food and Drug Administration (FDA) initiative of Process Analytical Technology (PAT) encourages the monitoring of biopharmaceutical manufacturing processes by innovative solutions. Raman spectroscopy and the chemometric modeling tool partial least squares (PLS) have been applied to this aim for monitoring cell culture process variables. This study compares the chemometric modeling methods of Support Vector Machine radial (SVMr), Random Forests (RF), and Cubist to the commonly used linear PLS model for predicting cell culture components—glucose, lactate, and ammonia. This research is performed to assess whether the use of PLS as standard practice is justified for chemometric modeling of Raman spectroscopy and cell culture data. Model development data from five small-scale bioreactors (2 × 1 L and 3 × 5 L) using two Chinese hamster ovary (CHO) cell lines were used to predict against a manufacturing scale bioreactor (2,000 L). Analysis demonstrated that Cubist predictive models were better for average performance over PLS, SVMr, and RF for glucose, lactate, and ammonia. The root mean square error of prediction (RMSEP) of Cubist modeling was acceptable for the process concentration ranges of glucose (1.437 mM), lactate (2.0 mM), and ammonia (0.819 mM). Interpretation of variable importance (VI) results theorizes the potential advantages of Cubist modeling in avoiding interference of Raman spectral peaks. Predictors/Raman wavenumbers (cm⁻¹) of interest for individual variables are X1139–X1141 for glucose, X846–X849 for lactate, and X2941–X2943 for ammonia. These results demonstrate that other beneficial chemometric models are available for use in monitoring cell culture with Raman spectroscopy.     

Optimization of norovirus virus‐like particle production in Pichia pastoris using a real‐time near‐infrared bioprocess monitor

The production of norovirus virus‐like particles (NoV VLPs) displaying NY‐ESO‐1 cancer testis antigen in Pichia pastoris BG11 Mut⁺ has been enhanced through feed‐strategy optimization using a near‐infrared bioprocess monitor (RTBio^® Bioprocess Monitor, ASL Analytical, Inc.), capable of monitoring and controlling the concentrations of glycerol and methanol in real‐time. The production of NoV VLPs displaying NY‐ESO‐1 in P. pastoris has potential as a novel cancer vaccine platform. Optimization of the growth conditions resulted in an almost two‐fold increase in the expression levels in the fermentation supernatant of P. pastoris as compared to the starting conditions. We investigated the effect of methanol concentration, batch phase time, and batch to induction transition on NoV VLP‐NY‐ESO‐1 production. The optimized process included a glycerol transition phase during the first 2 h of induction and a methanol concentration set point of 4 g L^?1 during induction. Utilizing the bioprocess monitor to control the glycerol and methanol concentrations during induction resulted in a maximum NoV VP1‐NY‐ESO‐1 yield of 0.85 g L^?1. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:518–526, 2016