Methodology for real-time,multianalyte monitoring of fermentations using an in-situ mid-infrared sensor

An in-situ, mid-infrared sensor was used to monitor the major analyte concentrations involved in the cultivation of Gluconacetobacter xylinus and the production of gluconacetan, a food-grade exopolysaccharide. To predict the analyte concentrations, three different sets of standard spectra were used to develop calibration models, applying partial least-squares regression. It was possible to build a valid calibration model to predict the 700 spectra collected during the complete time course of the cultivation, using only 12 spectra collected every 10 h as standards. This model was used to reprocess the concentration profiles from 0 to 15 g/L of nine different analytes with a mean standard error of validation of 0.23 g/L. However, this calibration model was not suitable for real-time monitoring as it was probably based on non-specific spectral features, which were correlated only with the measured analyte concentrations. Valid calibration models capable of real-time monitoring could be established by supplementing the set of 12 fermentation spectra with 42 standards of measured analytes. A pulse of 5 g/L ethanol showed the robustness of the model to sudden disturbances. The prediction of the models drifted, however, toward the end of the fermentation. The most robust calibration model was finally obtained by the addition of 34 standard spectra of non-measured analytes. Although the spectra did not contain analyte-specific information, it was believed that this addition would increase the variability space of the calibration model. Therefore, an expanded calibration model containing 88 spectra was used to monitor, in real time, the concentration profiles of fructose, acetic acid, ethanol and gluconacetan and allowed standard errors of prediction of 1.11, 0.37, 0.22, and 0.79 g/L, respectively.     

Determination of the enantiomeric excess of an M3 antagonist drug substance by chemometric analysis of the IR spectra of different guest-host complexes

A novel approach for the potential on-line determination of the enantiomeric excess (ee) of an M3 antagonist drug substance combining attenuated total reflectance infrared (ATR-IR) spectroscopy, guest-host complexes, and chemometric data analysis is described. Chiral recognition through a formation of diastereomeric complexes was measured by ATR-IR. Small changes on the IR spectra reflect the interaction between the guest (M3) and host (chiral selector). These changes are measured as a function of M3 enantiomer excess. The standard error of prediction is 1.3 ee%. The prediction results based on the IR method were in good agreement with the gravimetric method. The robustness of the calibration model was evaluated by varying the concentration of the chiral selector, the pH of the solution, and the organic solvents. The stability of the calibration model was also demonstrated through measuring different sets of samples on different days.     

Fluorescence-based soft-sensor for monitoring beta-lactoglobulin and alpha-lactalbumin solubility during thermal aggregation

A soft-sensor for monitoring solubility of native-like alpha-lactalbumin (alpha-LA) and beta-lactoglobulin (beta-LG) and their aggregation behavior following heat treatment of mixtures under different treatment conditions was developed using fluorescence spectroscopy data regressed with a multivariate Partial Least Squares (PLS) regression algorithm. PLS regression was used to correlate the concentrations of alpha-LA and beta-LG to the fluorescence spectra obtained for their mixtures. Data for the calibration and validation of the soft sensor was derived from fluorescence spectra. The process of thermal induced aggregation of beta-LG and alpha-LA protein in mixtures, which involves the disappearance of native-like proteins, was studied under various treatment conditions including different temperatures, pH, total initial protein concentration and proportions of alpha-LA and beta-LG. It was demonstrated that the multivariate regression models used could effectively deconvolute multi-wavelength fluorescence spectra collected under a variety of process conditions and provide a fairly accurate quantification of respective native-like proteins despite the significant overlapping between their emission profiles. It was also demonstrated that a PLS model can be used as a black-box prediction tool for estimating protein aggregation when combined with simple mass balances.     

基于傅里叶变换近红外光谱实时分析1,3-丙二醇发酵过程生物量的在线监测方法

生物量是反映生物发酵过程进展的重要参数,对生物量进行实时监测可用于对发酵过程的调控优化。为克服目前主要采用的离线方法检测生物量时间滞后和人工测量误差较大等缺点,本研究针对1,3-丙二醇发酵过程设计了一个基于傅里叶变换近红外光谱实时分析技术的生物量在线监测实验平台,通过对实时采集光谱预处理以及敏感光谱段分析,应用偏最小二乘算法,建立了1,3-丙二醇发酵过程生物量变化的动态预测模型。以底物甘油浓度为60 g/L和40 g/L的发酵过程作为外部验证实验,分析得到模型的预测均方根误差分别为0.341 6和0.274 3,结果表明所建立的模型具有较好的实时预测能力,能够实现对1,3-丙二醇发酵过程中生物量的有效在线监测。     

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     

Process analytical technology tools for perfusion cell culture

During cell cultivation processes for the production of biopharmaceuticals, good process performance and good product quality can be ensured by online monitoring of critical process parameters (e.g. temperature, pH, or dissolved oxygen). These data can be used in real‐time for process control, as suggested by the process analytical technology (PAT) initiative. Today, solutions for real‐time monitoring of parameters such as concentrations of cells, main nutrients, and metabolism by‐products are developing, but applications of these more complex tools in industrial settings are still limited. Here, we evaluated the use of dielectric spectroscopy (DS) and near‐infrared spectroscopy (NIRS) as PAT tools for a perfusion PER.C6^® cultivation process. We showed that DS enabled predictions of viable cell density from the cultivation vessel, with a root mean square error of prediction (RMSEP) of 4.4% of the calibration range. Additionally, predictions of glucose and lactate concentrations from the cultivation vessel (RMSEP of 10 and 14%, respectively) and from the perfusion stream (RMSEP of 12 and 10%, respectively) were achieved with NIRS. We also showed that the perfusion stream offers great opportunities for noninvasive, yet frequent process monitoring. Accurate online monitoring of critical process parameters with PAT tools is the essential first step toward increased control of process output.     

Deprotonation of glutamic acid induced by weak magnetic field: an FTIR-ATR study

It has been claimed that weak extremely low frequency electromagnetic fields (ELF‐EMFs) can affect biochemical reactions and a wide‐ranging body of literature is available on this topic. Nevertheless, the physical nature of these effects remains largely unknown. We investigated the influence of ELF‐EMF on glutamic acid solutions using Fourier transform infrared‐attenuated total reflectance (FTIR‐ATR) spectra. Samples were exposed for 10, 20, or 30 min to a weak EMF generated by Helmoltz coils, and then placed in a spectrometer. After exposure, those solutions that had a pH lower than the isoelectric point tended to show a shift toward the deprotonation of the carboxylic group, while solutions having a pH greater than the isoelectric point showed the deprotonation of the residual amine group. Moreover, at low pH values, we also detected a shift of the δ_antisym band of the amine. The effects lasted a few minutes after exposure before the native configuration was restored. The spectral modifications were observed after each independent exposure to EMFs, and the same effects were seen by varying the frequencies in the range of 0–7 kHz. Therefore, the hypothesis of the existence of a resonant frequency that has been proposed elsewhere cannot be supported by the results of this study. The most surprising characteristic of this effect is the long‐lasting nature of the perturbation, which is hard to be explained in terms of short‐living excitations in biological matter. Bioelectromagnetics 32:218–225, 2011. © 2010 Wiley‐Liss, Inc.     

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     

Prediction of the Quantity and Purity of an Antibody Capture Process in Real Time

Regulatory recommendations for quality by design instead of quality by testing raise increasing interest in new sensor technologies. An online monitoring system for downstream processes is developed, which is based on an array of online detectors. Besides standard detectors (UV, pH, and conductivity), our chromatographic workstation is equipped with a fluorescence and a mid‐infrared spectrometer, a light scattering, and a refractive index detector. The combination of these sensors enables the prediction of specific protein concentration and various purity attributes, such as high molecular weight impurities, DNA and host cell protein content during the elution phase of a chromatographic antibody capture process. Prediction models solely based on online signals are set up providing real‐time predictions. No mechanistic models or information about the chromatographic runs is used. These predictions allow online pooling decisions replacing time‐ and labor‐intensive laboratory measurements. Different process variations, such as changes in the column load or elution buffer, are introduced to test the predictive power of the models. Extrapolation of the models worked well when the column load is changed, whereas model adjustment is necessary when the elution conditions are changed considerably.     

Direct spectroscopic (FTIR) detection of intraspecific binary contaminations in yeast cultures

Fourier transform infrared spectroscopy has proved to be a good method to identify and characterize microorganisms. This technique has been proposed as a tool to determine the level of contamination in binary mixtures of strains belonging to different species and even to diverse kingdoms, showing a good linear relationship between spectral outputs and contamination levels. The monitoring of intraspecific contamination is a critical point in both laboratory practice and industrial monitoring, but it is challenged by the difficulty to discriminate between very similar cultures belonging to the same species. In this paper we considered binary intraspecific mixtures of strains belonging to three species ( Saccharomyces cerevisiae, Debaryomyces hansenii and Rhodotorula minuta ). Results showed that contaminated and pure cultures can be discriminated on the basis of their infrared spectra and that different spectral areas respond to the contamination according to the species under test. Moreover, some spectral areas change linearly with the increase of contaminants, giving the possibility of using this procedure for preliminary estimations of the contamination in addition to the even more important opportunity to indicate the presence of contaminants of the same species at low levels in fermentation cultures.     

Towards developing a protein infrared spectra databank (PISD) for proteomics research

Fourier transform infrared (FTIR) spectroscopy is an attractive tool for proteomics research as it can be used to rapidly characterize protein secondary structure in aqueous solution. Recently, a number of secondary structure prediction methods based on reference sets of FTIR spectra from proteins with known structure from X-ray crystallography have been suggested. These prediction methods, often referred to as pattern recognition based approaches, demonstrated good prediction accuracy using some error measure, e.g., the standard error of prediction (SEP). However, to avoid possible adverse effects from differences in recording, the analysis has been mostly based on reference sets of FTIR spectra from proteins recorded in one laboratory only. As a result, these studies were based on reference sets of FTIR spectra from a limited number of proteins. Pattern recognition based approaches, however, rely on reference sets of FTIR spectra from as many proteins as possible representing all possible band shape variation to be related to the diversity of protein structural classes. Hence, if we want to build reliable pattern recognition based systems to support proteomics research, which are capable of making good predictions from spectral data of any unknown protein, one common goal should be to build a comprehensive protein infrared spectra databank (PISD) containing FTIR spectra of proteins of known structure. We have started the process of developing a comprehensive PISD composed of spectra recorded in different laboratories. As part of this work, here we investigate possible effects on prediction accuracy achieved by a neural network analysis when using reference sets composed of FTIR spectra from different laboratories. Surprisingly low magnitude of difference in SEPs throughout all our experiments suggests that FTIR spectra recorded in different laboratories may be safely combined into one reference set with only minor deterioration of prediction accuracy in the worst case.     

Thermodynamic and structural analysis of homodimeric proteins: model of β-lactoglobulin

The energetics of protein homo-oligomerization was analyzed in detail with the application of a general thermodynamic model. We have studied the thermodynamic aspects of protein-protein interaction employing β-lactoglobulin A from bovine milk at pH=6.7 where the protein is mainly in its dimeric form. We performed differential calorimetric scans at different total protein concentration and the resulting thermograms were analyzed with the thermodynamic model for oligomeric proteins previously developed. The thermodynamic model employed, allowed the prediction of the sign of the enthalpy of dimerization, the analysis of complex calorimetric profiles without transitions baselines subtraction and the obtainment of the thermodynamic parameters from the unfolding and the association processes and the compared with association parameters obtained with Isothermal Titration Calorimetry performed at different temperatures. The dissociation and unfolding reactions were also monitored by Fourier-transform infrared spectroscopy and the results indicated that the dimer of β-lactoglobulin (N(2)) reversibly dissociates into monomeric units (N) which are structurally distinguishable by changes in their infrared absorbance spectra upon heating. Hence, it is proposed that β-lactoglobulin follows the conformational path induced by temperature:N(2)?2N?2D. The general model was validated with these results indicating that it can be employed in the study of the thermodynamics of other homo-oligomeric protein systems.     

Real-time update of calibration model for better monitoring of batch processes using spectroscopy

In order to reduce the large calibration matrix usually required for calibrating multiwavelength optical sensors, a simple algorithm based on the addition in process of new standards is proposed. A small calibration model, based on 14 standards, is periodically updated by spectra collected on-line during fermentation operation. Concentrations related to these spectra are reconciled into best-estimated values, by considering carbon and oxygen balances. Using this method, fructose, acetate, and gluconacetan were monitored during batch fermentations of Gluconacetobacter xylinus 12281 using mid-infrared spectroscopy. It is shown that this algorithm compensates for noncalibrated events such as production or consumption of by-products. The standard error of prediction (SEP) values were 0.99, 0.10, and 0.90 g/L for fructose, acetate, and gluconacetan, respectively. By contrast, without an updating of the calibration model, the SEP values were 2.46, 0.92, and 1.04 g/L for fructose, acetate, and gluconacetan, respectively. Using only 14 standards, it was therefore possible to approach the performance of an 88-standard-based calibration model having SEP values of 1.11, 0.37, and 0.79 g/L for fructose, acetate, and gluconacetan, respectively. Therefore, the proposed algorithm is a valuable approach to reduce the calibration time of multiwavelength optical sensors.     

A Fourier-transform infrared spectroscopic study of the phosphoserine residues in hen egg phosvitin and ovalbumin

A Fourier-transform infrared spectroscopic study of hen egg phosvitin and ovalbumin has been carried out. Bands arising from monoanionic and dianionic phosphate monoester [Shimanouchi, T., Tsuboi, M., & Kyogoku, Y. (1964) Adv. Chem. Phys. 8, 435-498] can be identified easily in the 1300-930 cm-1 region in spectra of solutions of O-phosphoserine and phosvitin, a highly phosphorylated protein. On the other hand, spectra of ovalbumin show a relatively strong absorption above 1000 cm-1 arising from the protein moiety. Below 1000 cm-1, a single band at 979 cm-1 is observed; this band is not present in spectra of dephosphorylated ovalbumin, and therefore, it has been assigned to the symmetric stretching of the phosphorylated Ser-68 and Ser-344 in the dianionic ionization state. In addition, bands arising from symmetric and antisymmetric stretchings of the monoanionic ionization state, and from the antisymmetric stretching of the dianionic state, can be detected above 1000 cm-1 in difference spectra of ovalbumin minus dephosphorylated ovalbumin. The effect of pH on the infrared spectra of O-phosphoserine, phosvitin, and ovalbumin is consistent with the phosphoserine residues undergoing ionization with pK values about 6. This study demonstrates that Fourier-transform infrared spectroscopy can be a useful technique to assess the ionization state of phosphoserine residues in proteins in solution.     

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

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.     

The optimization of protein secondary structure determination with infrared and circular dichroism spectra.

We have used the circular dichroism and infrared spectra of a specially designed 50 protein database [Oberg, K.A., Ruysschaert, J.M. & Goormaghtigh, E. (2003) Protein Sci. 12, 2015-2031] in order to optimize the accuracy of spectroscopic protein secondary structure determination using multivariate statistical analysis methods. The results demonstrate that when the proteins are carefully selected for the diversity in their structure, no smaller subset of the database contains the necessary information to describe the entire set. One conclusion of the paper is therefore that large protein databases, observing stringent selection criteria, are necessary for the prediction of unknown proteins. A second important conclusion is that only the comparison of analyses run on circular dichroism and infrared spectra independently is able to identify failed solutions in the absence of known structure. Interestingly, it was also found in the course of this study that the amide II band has high information content and could be used alone for secondary structure prediction in place of amide I.     

Conformational studies of the copolymer poly(L-lysine,L-tyrosine) 1:1 in aqueous solution

The absorption and circular dichroism spectra of the 1:1 copolymer (L -Lys, L -Tyr)_n have been investigated in aqueous solutions at pH ranging from 3 to 13. The spectral patterns indicate that the fully charged polympholyte assumes a nonperiodic conformation on the acid and basic sides of the isoelectric point. At pH ranging from 9.2 to 11.6, the polymer is largely ordered and takes mostly an antiparallel β-structure as is shown by the infrared spectra in D₂O solutions. Moreover, the rotational strength of the L_a transition of tyrosyl is independent of the polymer conformation, whereas that of the L_b transition is strongly sensitive to it.     

On-line monitoring of human prostate cancer cells in a perfusion rotating wall vessel by near-infrared spectroscopy

PC-3 human prostate cancer cells have been cultivated in a rotating wall vessel in which glucose, lactate, and glutamine profiles were monitored noninvasively and in real time by near-infrared (NIR) spectroscopy. The calibration models were based on off-line spectra from tissue culture experiments described previously (Rhiel et al., Biotechnol Bioeng 77:73-82). Monitoring performance was improved by Fourier filtering of the spectra and initial off-set adjustment. The resulting standard errors of predictions were 0.95, 0.74, and 0.39 mM for glucose, lactate, and glutamine, respectively. The concentration of ammonia could not be accurately measured from the same spectra. In addition, metabolite uptake and production rates were determined for PC-3 prostate cancer cells during exponential growth in batch-mode cultivation. Cells grew with a doubling time of 21 h and consumed glucose and glutamine at rates of 6.8 and 1.8 x 10(-17) mol/cell.s, respectively. This resulted in lactate and ammonia production rates of 11.9 and 1.3 x 10(-17) mol/cell.s, respectively. Compared with other monitoring technologies, this technology has many advantages for spaceflights and stand-alone units; for instance, calibration can be performed at one time and then applied in a reagentless, low-maintenance way at a later time. The resulting concentration information can be incorporated into closed-loop control schemes, thereby leading to better in vitro models of in vivo behavior.     

Bioprocess in-line monitoring using Raman spectroscopy and Indirect Hard Modeling (IHM): A simple calibration yields a robust model

To increase the process productivity and product quality of bioprocesses, the in-line monitoring of critical process parameters is highly important. For monitoring substrate, metabolite, and product concentrations, Raman spectroscopy is a commonly used Process Analytical Technology (PAT) tool that can be applied in-situ and non-invasively. However, evaluating bioprocess Raman spectra with a robust state-of-the-art statistical model requires effortful model calibration. In the present study, we in-line monitored a glucose to ethanol fermentation by Saccharomyces cerevisiae (S. cerevisiae) using Raman spectroscopy in combination with the physics-based Indirect Hard Modeling (IHM) and showed successfully that IHM is an alternative to statistical models with significantly lower calibration effort. The IHM prediction model was developed and calibrated with only 16 Raman spectra in total, which did not include any process spectra. Nevertheless, IHM's root mean square errors of prediction (RMSEPs) for glucose (3.68 g/L) and ethanol (1.69 g/L) were comparable to the prediction quality of similar studies that used statistical models calibrated with several calibration batches. Despite our simple calibration, we succeeded in developing a robust model for evaluating bioprocess Raman spectra.