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     

Real‐time monitoring of ethanol production during Pichia stipitis NRRL Y‐7124 alcoholic fermentation using transflection near infrared spectroscopy

The application of in situ near‐infrared spectroscopy monitoring of xylose metabolizing yeast such as Pichia stipitis for ethanol production with semisynthetic media, applying chemometrics, was investigated. During the process in a bioreactor, biomass, glucose, xylose, ethanol, acetic acid, and glycerol determinations were performed by a transflection probe immersed in the culture broth and connected to a near‐infrared process analyzer. Wavelength windows in near‐infrared spectra recorded between 800 and 2200 nm were pretreated using Savitzky–Golay smoothing, second derivative and multiplicative scattering correction in order to perform a partial least squares regression and generate the calibration models. These calibration models were tested by external validation (78 samples). Calibration and validation criteria were defined and evaluated in order to generate robust and reliable models for an alcoholic fermentation process matrix. Moreover, regressions coefficients (β) and variable influence in the projection plots were used to assess the results. A novelty is the use of β versus VIP dispersion plots to determine which vectors have more influence on the response in order to improve process comprehension and operability. Validated models were used in a real‐time monitoring during P. stipitis NRRL Y7124 semisynthetic media fermentations.     

Monitoring a high cell density recombinant Pichia pastoris fed-batch bioprocess using transmission and reflectance near infrared spectroscopy

The use of near infrared spectroscopy (NIRS) was investigated in the context of an efficient high cell density fed-batch industrial Pichia pastoris bioprocess for the production of a therapeutic mammalian protein. This process represented a considerable challenge from the viewpoint of using NIRS to model key analytes because it involved two carbon sources (glycerol and methanol) added at differing rates and times, used a chemically complex medium, and showed a change in liquid phase behaviour due to cell growth. Models for biomass, glycerol, methanol and product were constructed. Different methods of spectral collection and mathematical procedures were used relative to which analyte in the fermentation matrix was being modelled and the rationale behind the model building is clearly described. Regardless of the mode of spectral collection it was essential to consider the changes in modelled analyte concentration relative to changes in other spectral contributors (analytes). The study considerably extends the use of NIRS in fermentation processes to high cell density complex industrial production processes, and comments on how this further developments the technology towards routine in situ NIRS monitoring of bioprocesses.     

Advanced monitoring and control of pharmaceutical production processes with Pichia pastoris by using Raman spectroscopy and multivariate calibration methods

This contribution includes an investigation of the applicability of Raman spectroscopy as a PAT analyzer in cyclic production processes of a potential Malaria vaccine with Pichia pastoris. In a feasibility study, Partial Least Squares Regression (PLSR) models were created off‐line for cell density and concentrations of glycerol, methanol, ammonia and total secreted protein. Relative cross validation errors RMSE_cvrel range from 2.87% (glycerol) to 11.0% (ammonia). In the following, on‐line bioprocess monitoring was tested for cell density and glycerol concentration. By using the nonlinear Support Vector Regression (SVR) method instead of PLSR, the error RMSE_Prel for cell density was reduced from 5.01 to 2.94%. The high potential of Raman spectroscopy in combination with multivariate calibration methods was demonstrated by the implementation of a closed loop control for glycerol concentration using PLSR. The strong nonlinear behavior of exponentially increasing control disturbances was met with a feed‐forward control and adaptive correction of control parameters. In general the control procedure works very well for low cell densities. Unfortunately, PLSR models for glycerol concentration are strongly influenced by a correlation with the cell density. This leads to a failure in substrate prediction, which in turn prevents substrate control at cell densities above 16 g/L.     

On-line measurement of the substrate concentrations in <Emphasis Type="Italic">Pichia pastoris</Emphasis> fermentations using FT-IR/ATR

The glycerol and methanol concentrations in Pichia pastoris fermentations were measured on-line using Fourier transform infrared spectroscopy and an attenuated total reflection probe. Partial least squares regression was used to obtain calibration models. The models were regressed on synthetic multi-component spectra and semi-synthetic fermentation broth spectra. These were obtained by spectral addition. The accuracy for the on-line measurement of glycerol, given as standard error of prediction (SEP), was determined to 0.68 g/l, and the SEP of methanol was 0.13 g/l. We show how reliable calibration models are obtained relatively effortlessly by replacing extensive sampling from the reactor with simple mathematical manipulations of the model regression spectra.     

A multivariate calibration procedure for UV/VIS spectrometric monitoring of BHK‐21 cell metabolism and growth

Monitoring mammalian cell culture with UV–vis spectroscopy has not been widely explored. The aim of this work was to calibrate Partial Least Squares (PLS) models from off‐line UV–vis spectral data in order to predict some nutrients and metabolites, as well as viable cell concentrations for mammalian cell bioprocess using phenol red in culture medium. The BHK‐21 cell line was used as a mammalian cell model. Spectra of samples taken from batches performed at different dissolved oxygen concentrations (10, 30, 50, and 70% air saturation), in two bioreactor configurations and with two strategies to control pH were used to calibrate and validate PLS models. Glutamine, glutamate, glucose, and lactate concentrations were suitably predicted by means of this strategy. Especially for glutamine and glucose concentrations, the prediction error averages were lower than 0.50 ± 0.10 mM and 2.21 ± 0.16 mM, respectively. These values are comparable with those previously reported using near infrared and Raman spectroscopy in conjunction with PLS. However, viable cell concentration models need to be improved. The present work allows for UV–vis at‐line sensor development, decrease cost related to nutrients and metabolite quantifications and establishment of fed‐batch feeding schemes. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 30:241–248, 2014     

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     

On-line infrared spectroscopy for bioprocess monitoring

One of the major aims of bioprocess engineering is the real-time monitoring of important process variables. This is the basis of precise process control and is essential for high productivity as well as the exact documentation of the overall production process. Infrared spectroscopy is a powerful analytical technique to analyze a wide variety of organic compounds. Thus, infrared sensors are ideal instruments for bioprocess monitoring. The sensors are non-invasive, have no time delay due to sensor response times, and have no influence on the bioprocess itself. No sampling is necessary, and several components can be analyzed simultaneously. In general, the direct monitoring of substrates, products, metabolites, as well as the biomass itself is possible. In this review article, insights are provided into the different applications of infrared spectroscopy for bioprocess monitoring and the complex data interpretation. Different analytical techniques are presented as well as example applications in different areas.     

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     

Chemometric modelling with two-dimensional fluorescence data for Claviceps purpurea bioprocess characterization

Modern bioprocess control requires fast data acquisition and in-time evaluation of bioprocess variables. On-line fluorescence spectroscopy and the application of chemometric methods accomplish these goals. In order to demonstrate how time-consuming off-line analysis methods can be replaced for bioprocess monitoring, fluorescence measurements were performed during different cultivations of the fungus Claviceps purpurea. To predict process variables like biomass, protein, and alkaloid concentrations, chemometric models were developed on the basis of the acquired fluorescence spectra. The results of these investigations are presented and the applicability of this approach for bioprocess monitoring is discussed.     

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.      

Mid‐infrared spectroscopy‐based analysis of mammalian cell culture Parameters

Within the framework of process analytical technology, infrared spectroscopy (IR) has been used for characterization of biopharmaceutical production processes. Although noninvasive attenuated total reflection (ATR) spectroscopy can be regarded as gold standard within IR‐based process analytics, simpler and more cost‐effective mid‐infrared (MIR) instruments might improve acceptability of this technique for high‐level monitoring of small scale experiments as well as for academia where financial restraints impede the use of costly equipment. A simple and straightforward at‐line mid‐IR instrument was used to monitor cell viability parameters, activity of lactate dehydrogenase (LDH), amount of secreted antibody, and concentration of glutamate and lactate in a Chinese hamster ovary cell culture process, applying multivariate prediction models, including only 25–28 calibration samples per model. Glutamate amount could be predicted with high accuracy (R² 0.91 for independent test‐set) while antibody concentration achieved good prediction for concentrations >0.4 mg L^?1. Prediction of LDH activity was accurate except for the low activity regime. The model for lactate monitoring was only moderately good and requires improvements. Relative cell viability between 20 and 95% could be predicted with low error (8.82%) in comparison to reference methods. An initial model for determining the number of nonviable cells displayed only acceptable accuracy and requires further improvement. In contrast, monitoring of viable cell number showed better accuracy than previously published ATR‐based results. These results prove the principal suitability of less sophisticated MIR instruments to monitor multiple parameters in biopharmaceutical production with relatively low investments and rather fast calibration procedures. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:578–584, 2015     

Real‐time monitoring of high‐gravity corn mash fermentation using in situ raman spectroscopy

In situ Raman spectroscopy was employed for real‐time monitoring of simultaneous saccharification and fermentation (SSF) of corn mash by an industrial strain of Saccharomyces cerevisiae. An accurate univariate calibration model for ethanol was developed based on the very strong 883 cm^?1 C–C stretching band. Multivariate partial least squares (PLS) calibration models for total starch, dextrins, maltotriose, maltose, glucose, and ethanol were developed using data from eight batch fermentations and validated using predictions for a separate batch. The starch, ethanol, and dextrins models showed significant prediction improvement when the calibration data were divided into separate high‐ and low‐concentration sets. Collinearity between the ethanol and starch models was avoided by excluding regions containing strong ethanol peaks from the starch model and, conversely, excluding regions containing strong saccharide peaks from the ethanol model. The two‐set calibration models for starch (R² = 0.998, percent error = 2.5%) and ethanol (R² = 0.999, percent error = 2.1%) provide more accurate predictions than any previously published spectroscopic models. Glucose, maltose, and maltotriose are modeled to accuracy comparable to previous work on less complex fermentation processes. Our results demonstrate that Raman spectroscopy is capable of real time in situ monitoring of a complex industrial biomass fermentation. To our knowledge, this is the first PLS‐based chemometric modeling of corn mash fermentation under typical industrial conditions, and the first Raman‐based monitoring of a fermentation process with glucose, oligosaccharides and polysaccharides present. Biotechnol. Bioeng. 2013; 110: 1654–1662. © 2013 Wiley Periodicals, Inc.     

Near‐infrared spectroscopy for metabolite quantification and species identification

Near‐infrared (NIR) spectroscopy is a high‐throughput method to analyze the near‐infrared region of the electromagnetic spectrum. It detects the absorption of light by molecular bonds and can be used with live insects. In this study, we investigate the accuracy of NIR spectroscopy in determining triglyceride level and species of wild‐caught Drosophila. We employ the chemometric approach to produce a multivariate calibration model. The multivariate calibration model is the mathematical relationship between the changes in NIR spectra and the property of interest as determined by the reference analytical method. Once the calibration model was developed, we used an independent set to validate the accuracy of the calibration model. The optimized calibration model for triglyceride quantification yielded coefficients of determination of 0.73 for the calibration test set and 0.70 for the independent test set. Simultaneously, we used NIR spectroscopy to discriminate two species of Drosophila. Flies from independent sets were correctly classified into Drosophila melanogaster and Drosophila simulans with accuracy higher than 80%. These results suggest that NIRS has the potential to be used as a high‐throughput screening method to assess a live individual insect's triglyceride level and taxonomic status.     

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.     

Effects of temperature and glycerol and methanol‐feeding profiles on the production of recombinant galactose oxidase in Pichia pastoris

Optimization of protein production from methanol‐induced Pichia pastoris cultures is necessary to ensure high productivity rates and high yields of recombinant proteins. We investigated the effects of temperature and different linear or exponential methanol‐feeding rates on the production of recombinant Fusarium graminearum galactose oxidase (EC 1.1.3.9) in a P. pastoris Mut⁺ strain, under regulation of the AOX1 promoter. We found that low exponential methanol feeding led to 1.5‐fold higher volumetric productivity compared to high exponential feeding rates. The duration of glycerol feeding did not affect the subsequent product yield, but longer glycerol feeding led to higher initial biomass concentration, which would reduce the oxygen demand and generate less heat during induction. A linear and a low exponential feeding profile led to productivities in the same range, but the latter was characterized by intense fluctuations in the titers of galactose oxidase and total protein. An exponential feeding profile that has been adapted to the apparent biomass concentration results in more stable cultures, but the concentration of recombinant protein is in the same range as when constant methanol feeding is employed. © 2014 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 30:728–735, 2014     

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.     

On-line monitoring of recombinant bacterial cultures using multi-wavelength fluorescence spectroscopy

Multi-wavelength fluorescence spectroscopy was evaluated as a tool for on-line monitoring of recombinant Escherichia coli cultivations expressing human basic fibroblast growth factor (hFGF-2). The data sets for the various combinations of the excitation and emission spectra from batch cultivations were analyzed using principal component analysis. Chemometric models (the partial least squares method) were developed for correlating the fluorescence data and the experimentally measured variables such as the biomass and glucose concentrations as well as the carbon dioxide production rate. Excellent correlations were obtained for these variables for the calibration cultivations. The predictability of these models was further tested in batch and fed-batch cultivations. The batch cultivations were well predicted by the PLS models for biomass, glucose concentrations and carbon dioxide production rate (RMSEPs were respectively 5%, 7%, 9%). However, when tested for biomass concentrations in fed-batch cultivations (with final biomass three times higher than the highest calibration data) the models had good predictability at high growth rates (RMSEPs were 3% and 4%, respectively for uninduced and induced fed-batch cultivations), which was as good as for the batch cultivations used for developing the models (RMSEPs were 3% and 5%, respectively for uninduced and induced batch cultivations). The fed-batch cultivations performed at low growth rates exhibited much higher fluorescence for fluorophores such as flavin and NAD(P)H as compared to fed-batch cultivations at high growth rate. Therefore, the PLS models tended to over-predict the biomass concentrations at low growth rates. Obviously the cells changed their concentration of biogenic fluorophores depending on the growth rate. Although multi-wavelength fluorescence spectroscopy is a valuable tool for on-line monitoring of bioprocess, care must be taken to re-calibrate the PLS models at different growth rates to improve the accuracy of predictions.     

Biomass soft sensor for a Pichia pastoris fed-batch process based on phase detection and hybrid modeling

A common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data-driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO₂ in the off-gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% and R² of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of the P. pastoris bioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.