Assessment of near-infrared spectral information for rapid monitoring of bioprocess quality

Statistically-aided development of protein A affinity chromatography for enhancing recovery and controlling quality of a monoclonal antibody

Protein A chromatography is widely used for isolation of monoclonal antibodies (mAbs) from cell culture components. In this study, the effect of different process parameters of the Protein A purification namely, binding pH, elution pH, flow rate, neutralization pH and tween concentration, on the concentration and quality of the purified mAb were evaluated. Using design of experiments approach, the critical process parameters of protein A chromatography were identified and experimentally optimized. Their impact on quality attributes, such as size variants and charge variants, of the mAb was studied. Multivariate data analysis was subsequently performed using multiple linear regression and partial least squares regression methods. It was observed that the elution pH primarily governed the concentration of the purified mAb and the content of monomers and aggregates, while the tween concentration primarily influenced the main peak of the charge variants. This is the first study that evaluates the impact of tween concentration in buffers on the protein A chromatography purification step. These studies helped in identifying the design space and defining the target robust and optimal setpoints of the responses, which were subsequently verified experimentally. These setpoints not only passed the target criteria but also resulted in the highest recoveries during the investigation. Through this statistically-aided approach, an optimized and robust protein A chromatography process was rationally developed for purification of mAbs, while achieving the desired product quality. This study highlights the influence of multiple parameters of the protein A purification process on critical quality attributes of mAbs, such as the size and charge variants, which has been a very scarcely explored area.

In-situ electrochemical analysis of microbial activity

Microbes have a wide range of metabolic capabilities available that makes them industrially useful organisms. Monitoring these metabolic processes is a crucial component in efficient industrial application. Unfortunately, monitoring these metabolic processes can often be invasive and time consuming and expensive, especially within an anaerobic environment. Electrochemical techniques, such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) offer a non-invasive approach to monitor microbial activity and growth. EIS and CV were used to monitor Clostridium phytofermentans, an anaerobic and endospore-forming bacterium. C. phytofermentans ferments a wide range of sugars into hydrogen, acetate, and ethanol as fermentation by-products. For this study, both traditional microbiological and electrochemical techniques were used to monitor the growth of C. phytofermentans and the formation of fermentation products. An irreversible reduction peak was observed using CV beginning at mid-logarithmic phase of growth. This peak was associated with C. phytofermentans and not the spent medium and was indicative of a decrease in carbon and energy sources to the cells. Additionally, EIS analysis during growth provided information related to increased charge transfer resistance of the culture also as a function of carbon and energy source depletion. Results demonstrate that CV and EIS are useful tools in the monitoring the physiological status of bioprocesses.

Downstream process development strategies for effective bioprocesses: Trends, progress, and combinatorial approaches

The biopharmaceutical industry is at a turning point moving toward a more customized and patient-oriented medicine (precision medicine). Straightforward routines such as the antibody platform process are extended to production processes for a new portfolio of molecules. As a consequence, individual and tailored productions require generic approaches for a fast and dedicated purification process development. In this article, different effective strategies in biopharmaceutical purification process development are reviewed that can analogously be used for the new generation of antibodies. Conventional approaches based on heuristics and high-throughput process development are discussed and compared to modern technologies such as multivariate calibration and mechanistic modeling tools. Such approaches constitute a good foundation for fast and effective process development for new products and processes, but their full potential becomes obvious in a correlated combination. Thus, different combinatorial approaches are presented, which might become future directions in the biopharmaceutical industry.

Combined shifted-excitation Raman difference spectroscopy and support vector regression for monitoring the algal production of complex polysaccharides

The applicability of shifted-excitation Raman difference spectroscopy (SERDS) in combination with signal regression analysis as an alternative and non-invasive approach for monitoring the cultivation of phototrophic microorganisms producing complex molecules of pharmaceutical relevance in a bioreactor is demonstrated. As a model system, the cultivation of the red unicellular algae Porphyridium purpureum is used for focusing on the segregation of sulfated exopolysaccharides (EPS) which exhibit antiviral activity. The spectroscopic results obtained by partial linear least squares regression (PLSR) and by nonlinear support vector regression (SVR) are discussed against the corresponding results from the reference analytics based on the phenol-sulfuric acid assay. The SERDS-approach turns out to have strong potential as a non-invasive tool for online-monitoring of biotechnological processes.

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.

Cell culture monitoring via an auto-sampler and an integrated multi-functional off-line analyzer

Mammalian cell-based bioprocesses are used extensively for production of therapeutic proteins. Off-line monitoring of such cultivations via manual sampling is often labor-intensive and can introduce operator-dependent error into the process. An integrated multi-functional off-line analyzer, the BioProfile FLEX (NOVA Biomedical, Waltham MA) has been developed, which combines the functionality of three off-line analyzers (a cell counter, an osmometer, and a gas/electrolyte & nutrient/metabolite bio-profile analyzer) into one device. In addition, a novel automated sampling system has also been developed that allows the BioProfile FLEX to automatically analyze the culture conditions in as many as ten bioreactors. This is the first report on the development and function of this integrated analyzer and an auto-sampler prototype for monitoring of mammalian cell cultures. Evaluation of the BioProfile FLEX was conducted in two separate laboratories and involved two BioProfile FLEX analyzers and two sets of reference analyzers (Nova BioProfile 400, Beckman-Coulter Vi-Cell AS, and Advanced Instruments Osmometer 3900), 13 CHO cell lines and over 20 operators. In general, BioProfile FLEX measurements were equivalent to those obtained using reference analyzers, and the auto-sampler did not alter the samples it provided to the BioProfile FLEX. These results suggest that the system has the potential to dramatically reduce the manual labor involved in monitoring mammalian cell bioprocesses without altering the quality of the data obtained, and integration with a bioreactor control system will allow feedback control of parameters previously available only for off-line monitoring.

Nondestructive determination of compound amoxicillin powder by NIR spectroscopy with the aid of chemometrics

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enables nondestructive analysis of solid samples without time-consuming sample preparation methods. A new method for the nondestructive determination of compound amoxicillin powder drug via NIR spectroscopy combined with an improved neural network model based on principal component analysis (PCA) and radial basis function (RBF) neural networks is investigated. The PCA technique is applied to extraction relevant features from lots of spectra data in order to reduce the input variables of the RBF neural networks. Various optimum principal component analysis--radial basis function (PCA-RBF) network models based on conventional spectra and preprocessing spectra (standard normal variate (SNV) and multiplicative scatter correction (MSC)) have been established and compared. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations are also used, which are compared with PCA-RBF neural networks. Experiment results show that the proposed PCA-RBF method is more efficient than PCR and PLS multivariate calibrations. And the PCA-RBF approach with SNV preprocessing spectra is found to provide the best performance.

Monitoring of substrate and product concentrations in acetic fermentation processes for onion vinegar production by NIR spectroscopy: value addition to worthless onions

Wastes and by-products of the onion-processing industry pose an increasing disposal and environmental problem and represent a loss of valuable sources of nutrients. The present study focused on the production of vinegar from worthless onions as a potential valorisation route which could provide a viable solution to multiple disposal and environmental problems, simultaneously offering the possibility of converting waste materials into a useful food-grade product and of exploiting the unique properties and health benefits of onions. This study deals specifically with the second and definitive step of the onion vinegar production process: the efficient production of vinegar from onion waste by transforming onion ethanol, previously produced by alcoholic fermentation, into acetic acid via acetic fermentation. Near-infrared spectroscopy (NIRS), coupled with multivariate calibration methods, has been used to monitor the concentrations of both substrates and products in acetic fermentation. Separate partial least squares (PLS) regression models, correlating NIR spectral data of fermentation samples with each kinetic parameter studied, were developed. Wavelength selection was also performed applying the iterative predictor weighting-PLS (IPW-PLS) method in order to only consider significant spectral features in each model development to improve the quality of the final models constructed. Biomass, substrate (ethanol) and product (acetic acid) concentration were predicted in the acetic fermentation of onion alcohol with high accuracy using IPW-PLS models with a root-mean-square error of the residuals in external prediction (RMSEP) lower than 2.5% for both ethanol and acetic acid, and an RMSEP of 6.1% for total biomass concentration (a very satisfactory result considering the relatively low precision and accuracy associated with the reference method used for determining the latter). Thus, the simple and reliable calibration models proposed in this study suggest that they could be implemented in routine applications to monitor and predict the key species involved in the acetic fermentation of onion alcohol, allowing the onion vinegar production process to be controlled in real time.

Study of pellet formation of filamentous fungi Rhizopus oryzae using a multiple logistic regression model

Fungal pellet formation is an important topic of fermentation research. It has been reported that many factors such as agitation, medium nutrients, pH, polymer additives, and inoculum size influence the formation of fungal pellets. However, a few studies on the effects of all of these factors on fungal pellet formation have been reported. This paper conducted a comprehensive investigation using a completely randomized design (CRD) on a filamentous fungus, Rhizopus oryzae NRRL 395, in order to discover the effects of the above factors on fungal pellet formation. In addition, other factors, such as addition of biodegradable polymers and spore storage time that have not been reportedly studied were examined and their effects on pellet formation were investigated. A multiple logistic regression model was established to predict the probability of pellet formation using the above factors and their interactions as predictor variables. Model building and diagnostics were obtained using the Statistical Analysis System (SAS 9.0) program. The model developed in this study can be used to predict the pellet formation of other R. oryzae strains as well.

Near infrared spectroscopy for bioprocess monitoring and control: current status and future trends

The development of Near Infrared Spectroscopy has paralleled that of the PC, and the application of NIR in many industries has undergone explosive growth in recent years. This has been particularly apparent in the area of microbial and cell culture system monitoring and control. Potentially, NIR offers the prospect of real-time control of the physiology of cultured cells in fermenters, leading to marked improvements in authenticity, purity and production efficiency. Despite this, NIR is not yet as widely applied within the bioprocessing industry as its potential might suggest. This review critically evaluates the development of this rapidly moving area as it pertains to microbial and cell culture system control and highlights the critical stages in the development of the technology. It indicates the work that must still be carried out if the full potential of NIR is to be exploited in making proteins, hormones and antibiotics by the fermentation route. The review comes at a particularly timely moment when NIR stands on the threshold of widespread acceptance in bioprocessing. This is the ideal moment to assess what the technology can offer the microbiologist, and where it may develop in the future.

Improvement of bioprocess monitoring: development of novel concepts

The advancement of bioprocess monitoring will play a crucial role to meet the future requirements of bioprocess technology. Major issues are the acceleration of process development to reduce the time to the market and to ensure optimal exploitation of the cell factory and further to cope with the requirements of the Process Analytical Technology initiative. Due to the enormous complexity of cellular systems and lack of appropriate sensor systems microbial production processes are still poorly understood. This holds generally true for the most microbial production processes, in particular for the recombinant protein production due to strong interaction between recombinant gene expression and host cell metabolism. Therefore, it is necessary to scrutinise the role of the different cellular compartments in the biosynthesis process in order to develop comprehensive process monitoring concepts by involving the most significant process variables and their interconnections. Although research for the development of novel sensor systems is progressing their applicability in bioprocessing is very limited with respect to on-line and in-situ measurement due to specific requirements of aseptic conditions, high number of analytes, drift, and often rather low physiological relevance. A comprehensive survey of the state of the art of bioprocess monitoring reveals that only a limited number of metabolic variables show a close correlation to the currently explored chemical/physical principles. In order to circumvent this unsatisfying situation mathematical methods are applied to uncover "hidden" information contained in the on-line data and thereby creating correlations to the multitude of highly specific biochemical off-line data. Modelling enables the continuous prediction of otherwise discrete off-line data whereby critical process states can be more easily detected. The challenging issue of this concept is to establish significant on-line and off-line data sets. In this context, online sensor systems are reviewed with respect to commercial availability in combination with the suitability of offline analytical measurement methods. In a case study, the aptitude of the concept to exploit easily available online data for prediction of complex process variables in a recombinant E. coli fed-batch cultivation aiming at the improvement of monitoring capabilities is demonstrated. In addition, the perspectives for model-based process supervision and process control are outlined.

The effect of preprocessing methods in reducing interfering variability from near-infrared measurements of creams

This work is part of a study in which the possibility of NIR combined with some chemometrical methods is investigated as a suitable technique to classify clinical study samples of a cream. In this study, the influence of different preprocessing methods on the removal of spectral variations due to some variance sources has been investigated. The applied preprocessing methods are standard normal variate (SNV), detrend correction, offset correction, and first and second derivation. The investigated variance sources are different batches of ingredients, different samples of the same batch, different days and different positions of the sample cup in the sample drawer of the instrument. A nested ANOVA design has been applied in order to quantify the variances introduced by these variance sources. Since ANOVA is a univariate technique, the necessary variable (wavelength) selection has been performed by the Fisher criterion. The best results, i.e. largest reduction of interfering variability and clearest distinction between different clinical study samples, are obtained with the second derivative spectra.

Monitoring complex media fermentations with near-infrared spectroscopy: Comparison of different variable selection methods

Near-infrared spectroscopy (NIRS) is known to be a suitable technique for rapid fermentation monitoring. Industrial fermentation media are complex, both chemically (ill-defined composition) and physically (multiphase sample matrix), which poses an additional challenge to the development of robust NIRS calibration models. We investigated the use of NIRS for at-line monitoring of the concentration of clavulanic acid during an industrial fermentation. An industrial strain of Streptomyces clavuligerus was cultivated at 200-L scale for the production of clavulanic acid. Partial least squares (PLS) regression was used to develop calibration models between spectral and analytical data. In this work, two different variable selection methods, genetic algorithms (GA) and PLS-bootstrap, were studied and compared with models built using all the spectral variables. Calibration models for clavulanic acid concentration performed well both on internal and external validation. The two variable selection methods improved the predictive ability of the models up to 20%, relative to the calibration model built using the whole spectra.

Spectral analysis and fingerprinting for biomedia characterisation

Classical culture media, as well as domestic and/or industrial wastewater treated by biological processes, have a complex composition. The on-line and/or in situ determination of some substances is possible, but expensive, as sample collection and pre-treatment are often necessary with strict rules of sterility. More global methods can be used to detect rapidly "accidents" such as the appearance of an undesirable by-product in a fermentation broth or of a toxic substance in wastewater. These methods combine a "hard" part, for sensing, and a "soft" part, for data treatment. Among potential "hard" candidates, spectroscopy can be the basis for non-invasive and non-destructive measuring systems. Some of them have been already tested in situ: ultra-violet-visible, infra-red (mid or near), fluorescence (mono-dimensional, two-dimensional or synchronous), dielectric, while others, more sophisticated, such as mass spectrometry, coupled or not to pyrolysis, nuclear magnetic resonance and Raman spectroscopy, have been proposed. All these methods provide spectra, i.e. large sets of data, from which meaningful information should be rapidly extracted, either for analysis or fingerprinting. The recourse to data-mining techniques (the "soft" part) such as principal components analysis, projection on latent structures or artificial neural networks, is a necessary step for that task. A review of techniques, mostly based on spectroscopy, with examples taken in the bioengineering field in general is proposed.

Application of multi-wavelength fluorometry for on-line monitoring of an anaerobic digestion process

This work examined the use of multi-wavelength fluorometry for on-line monitoring of an anaerobic digestion process. Experiments were carried out in a laboratory-scale anaerobic digestor fed with either synthetic or agricultural (cheese factory) wastewater. An in-line fiber optic probe installed in the external recirculation loop of the reactor was used to acquire fluorescence spectra with an interval of 5-10 min. The spectra were compared with analytical measurements taken at the same time to develop regression models, which were then used to predict concentrations of chemical oxygen demand, volatile fatty acids, and other key process parameters. A comparison of partial least squares (PLS), nonlinear principal components regression, and step-wise regression models on an independent set of data showed that the PLS model gave the best prediction accuracy.

On-line fermentation monitoring by mid-infrared spectroscopy

A new method for on-line monitoring of fermentations using mid-infrared (MIR) spectroscopy has been developed. The method has been used to predict the concentrations of glucose and ethanol during a baker's yeast fermentations. A completely automated flow system was employed as an interface between the bioprocess under study and the Fourier transform infrared (FT-IR) spectrometer, which was equipped with a flow cell housing a diamond attenuated total reflection (ATR) element. By using the automated flow system, experimental problems related to adherence of CO(2) bubbles to the ATR surface, as well as formation of biofilms on the ATR surface, could be efficiently eliminated. Gas bubbles were removed during sampling, and by using rinsing steps any biofilm could be removed from the ATR surface. In this way, constant measuring conditions could be guaranteed throughout prolonged fermentation times (approximately 8 h). As a reference method, high-performance liquid chromatography (HPLC) with refractive index detection was used. The recorded data from different fermentations were modeled by partial least-squares (PLS) regression comparing two different strategies for the calibration. On the one hand, calibration sets were constructed from spectra recorded from either synthetic standards or from samples drawn during fermentation. On the other hand, spectra from fermentation samples and synthetic standards were combined to form a calibration set. Differences in the kinetics of the studied fermentation processes used for calibration and prediction, as well as the precision of the HPLC reference method, were identified as the main chemometric sources of error. The optimal PLS regression method was obtained using the mixed calibration set of samples from fermentations and synthetic standards. The root mean square errors of prediction in this case were 0.267 and 0.336 g/L for glucose and ethanol concentration, respectively.

Mining the microbial metabolome: a new frontier for natural product lead discovery

Traditionally, natural products have been important sources of new leads for the pharmaceutical industry, but with discovery rates of novel structural classes in decline, the need to bioprospect alternate sources of chemical diversity is evident. Microbial genome sequencing projects have revealed the presence of 'silent' biosynthetic gene clusters where there is no current detectable product. Likewise, culture-independent techniques have provided access to the collective genomes of environmental microflora. Both sources of molecular diversity could encode potentially valuable metabolites. The ability to measure the entire complement of metabolites within microorganisms that are used as surrogate hosts to express such gene clusters will be crucial to the exploitation of these yet untapped reservoirs of metabolic diversity for future natural product drug discovery.

In-situ near infrared spectroscopy to monitor key analytes in mammalian cell cultivation

The use of in-situ near infrared spectroscopy (NIRS) as a tool for monitoring four key analytes in a CHO-K1 animal cell culture was investigated. Previous work using on-line NIRS to monitor bioprocesses has involved its application ex-situ where the analyzer is physically outside the fermentor, or to microbial bioprocesses. This novel application of NIRS to monitor analytes within an animal cell culture using a steam sterilizable in-situ fiber optic probe is very important for furthering the use of NIRS within the bioprocessing industry. The method of calibration used to develop the models involved the use of large data sets so that all likely variation in stoichiometry was incorporated within the models. Successful models for glucose, lactate, glutamine, and ammonia were built with Standard Error of Predictions (SEP's) of 0.072 (g/L), 0.0144 (g/L), 0.308 (mM), and 0.036 (mM), respectively of the total concentration range.

Optical sensor systems for bioprocess monitoring

Bioreactors are closed systems in which microorganisms can be cultivated under defined, controllable conditions that can be optimized with regard to viability, reproducibility, and product-oriented productivity. To drive the biochemical reaction network of the biological system through the desired reaction optimally, the complex interactions of the overall system must be understood and controlled. Optical sensors which encompass all analytical methods based on interactions of light with matter are efficient tools to obtain this information. Optical sensors generally offer the advantages of noninvasive, nondestructive, continuous, and simultaneous multianalyte monitoring. However, at this time, no general optical detection system has been developed. Since modern bioprocesses are extremely complex and differ from process to process (e.g., fungal antibiotic production versus mammalian cell cultivation), appropriate analytical systems must be set up from different basic modules, designed to meet the special demands of each particular process. In this minireview, some new applications in bioprocess monitoring of the following optical sensing principles will be discussed: UV spectroscopy, IR spectroscopy, Raman spectroscopy, fluorescence spectroscopy, pulsed terahertz spectroscopy (PTS), optical biosensors, in situ microscope, surface plasmon resonance (SPR), and reflectometric interference spectroscopy (RIF).

Use of at-line and in-situ near-infrared spectroscopy to monitor biomass in an industrial fed-batch Escherichia coli process

One of the key goals in bioprocess monitoring is to achieve real-time knowledge of conditions within the bioreactor, i.e., in-situ. Near-infrared spectroscopy (NIRS), with its ability to carry out multi-analyte quantification rapidly with little sample presentation, is potentially applicable in this role. In the present study, the application of NIRS to a complex, fed-batch industrial E. coli (RV308/PHKY531) process was investigated. This process undergoes a series of temperature changes and is vigorously agitated and aerated. These conditions can pose added challenges to in-situ NIRS. Using the measurement of a key analyte (biomass) as an illustration, the details of the relationship between the at-line and in-situ use of NIRS are considered from the viewpoint of both theory and practical application. This study shows that NIRS can be used both at-line and in-situ in order to achieve good predictive models for biomass. There are particular challenges imposed by in-situ operation (loss of wavelength regions and noise) which meant the need for signal optimisation studies. This showed that whilst the at-line modelling process may provide some useful information for the in-situ process, there were distinct differences. This study shows that the in-situ use of NIRS in a highly challenging matrix (similar to those encountered in current industrial practice) is possible, and thus extends previous works in the area.