Raman-based PAT for VLP precipitation: systematic data diversification and preprocessing pipeline identification

Virus-like particles (VLPs) are a promising class of biopharmaceuticals for vaccines and targeted delivery. Starting from clarified lysate, VLPs are typically captured by selective precipitation. While VLP precipitation is induced by step-wise or continuous precipitant addition, current monitoring approaches do not support the direct product quantification, and analytical methods usually require various, time-consuming processing and sample preparation steps. Here, the application of Raman spectroscopy combined with chemometric methods may allow the simultaneous quantification of the precipitated VLPs and precipitant owing to its demonstrated advantages in analyzing crude, complex mixtures. In this study, we present a Raman spectroscopy-based Process Analytical Technology (PAT) tool developed on batch and fed-batch precipitation experiments of Hepatitis B core Antigen VLPs. We conducted small-scale precipitation experiments providing a diversified data set with varying precipitation dynamics and backgrounds induced by initial dilution or spiking of clarified Escherichia coli-derived lysates. For the Raman spectroscopy data, various preprocessing operations were systematically combined allowing the identification of a preprocessing pipeline, which proved to effectively eliminate initial lysate composition variations as well as most interferences attributed to precipitates and the precipitant present in solution. The calibrated partial least squares models seamlessly predicted the precipitant concentration with R 2 of 0.98 and 0.97 in batch and fed-batch experiments, respectively, and captured the observed precipitation trends with R 2 of 0.74 and 0.64. Although the resolution of fine differences between experiments was limited due to the observed non-linear relationship between spectral data and the VLP concentration, this study provides a foundation for employing Raman spectroscopy as a PAT sensor for monitoring VLP precipitation processes with the potential to extend its applicability to other phase-behavior dependent processes or molecules.

Quantitative and qualitative analysis of cell culture media powders for mammalian cells by Raman microscopy

Cell culture media are essential for large-scale recombinant protein production using mammalian cell cultures. The composition and quality of media significantly impact cell growth and product formation. Analyzing media poses challenges due to complex compositions and undisclosed exact compositions. Traditional methods like NMR and chromatography offer sensitivity but require time-consuming sample preparation and lack spatial information. Raman chemical mapping characterizes solids, but its use in cell culture media analysis is limited so far. We present a chemometric evaluation for Raman maps to qualify and quantify media components, evaluate powder homogeneity, and perform lot-to-lot comparisons. Three lots of a marketed cell culture media powder were measured with Raman mapping technique. Chemometrics techniques have outlined a strategy to extract information from complex data. First, a spectral library has been structured. In addition to the 23 spectra for presumed ingredients, we obtained another 9 pure components with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Then the Spectral Angle Mapper-Orthogonal Projection (SAM-OP) algorithm revealed whether references actually occur in the mapped media powders. Finally, a quantification was provided by Classical Least Squares (CLS) modelling. Quantities of 18 significant amino acids mostly correlated with the reference method. The proposed method can be generally applied even for such complicated samples. Leveraging Raman mapping and innovative chemometric methods enhance recombinant protein production by improving the understanding of the spatial distribution and composition of cell culture media in mammalian cell cultivations.

An Investigation into the Metabolic Differences between Conventional and High Seeding Density Fed-Batch Cell Cultures by Applying a Segmented Modeling Approach

The conventional fed-batch process characterized by a low titer currently challenges pharmaceutical development. Process optimization by applying a perfusion process in the pre-stage and subsequent production phase at a high seeding density (HSD) can meet this challenge. In this study, we employed a simplified approach based on measured experiments, namely segmented modeling, to systematically analyze an HSD fed-batch process compared to a standard process. A comparison indicated that the metabolic phases of HSD processes are not only shifted in time, but metabolite trends show an altered metabolism. In an extended study, we integrated the intracellular fluxes determined by a metabolic flux analysis into the segmented modeling approach. Compared to using only extracellular rates, similar phases are identified, and this highlights the reliability of phase identification modeling using extracellular rates only. Furthermore, the segmented linear regression approach is used to create a model that describes cellular behavior and that can be used to predict potential improvements in the feeding strategy and in harvest viability. Here, overfeeding was eliminated and a significantly higher titer was achieved. This work provides insights into the overall metabolic changes in the HSD process and paves the way towards the optimization of the feeding regime.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review

The release of the FDA's guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

Multi-attribute Raman spectroscopy (MARS) for monitoring product quality attributes in formulated monoclonal antibody therapeutics

Rapid release of biopharmaceutical products enables a more efficient drug manufacturing process. Multi-attribute methods that target several product quality attributes (PQAs) at one time are an essential pillar of the rapid-release strategy. The novel, high-throughput, and nondestructive multi-attribute Raman spectroscopy (MARS) method combines Raman spectroscopy, design of experiments, and multivariate data analysis (MVDA). MARS allows the measurement of multiple PQAs for formulated protein therapeutics without sample preparation from a single spectroscopic scan. Variable importance in projection analysis is used to associate the chemical and spectral basis of targeted PQAs, which assists in model interpretation and selection. This study shows the feasibility of MARS for the measurement of both protein purity-related and formulation-related PQAs; measurements of protein concentration, osmolality, and some formulation additives were achieved by a generic multiproduct model for various protein products containing the same formulation components. MARS demonstrates the potential to be a powerful methodology to improve the efficiency of biopharmaceutical development and manufacturing, as it features fast turnaround time, good robustness, less human intervention, and potential for automation.

Comparison of UV- and Raman-based monitoring of the Protein A load phase and evaluation of data fusion by PLS models and CNNs

A promising application of Process Analytical Technology to the downstream process of monoclonal antibodies (mAbs) is the monitoring of the Protein A load phase as its control promises economic benefits. Different spectroscopic techniques have been evaluated in literature with regard to the ability to quantify the mAb concentration in the column effluent. Raman and Ultraviolet (UV) spectroscopy are among the most promising techniques. In this study, both were investigated in an in-line setup and directly compared. The data of each sensor were analyzed independently with Partial-Least-Squares (PLS) models and Convolutional Neural Networks (CNNs) for regression. Furthermore, data fusion strategies were investigated by combining both sensors in hierarchical PLS models or in CNNs. Among the tested options, UV spectroscopy alone allowed for the most precise and accurate prediction of the mAb concentration. A Root Mean Square Error of Prediction (RMSEP) of 0.013 g L^-1 was reached with the UV-based PLS model. The Raman-based PLS model reached an RMSEP of 0.232 g L^-1 . The different data fusion techniques did not improve the prediction accuracy above the prediction accuracy of the UV-based PLS model. Data fusion by PLS models seems meritless when combining a very accurate sensor with a less accurate signal. Furthermore, the application of CNNs for UV and Raman spectra did not yield significant improvements in the prediction quality. For the presented application, linear regression techniques seem to be better suited compared with advanced nonlinear regression techniques, like, CNNs. In summary, the results support the application of UV spectroscopy and PLS modeling for future research and development activities aiming to implement spectroscopic real-time monitoring of the Protein A load phase.

Development of a rapid polarized total synchronous fluorescence spectroscopy (pTSFS) method for protein quantification in a model bioreactor broth

Protein quantification during bioprocess monitoring is essential for biopharmaceutical manufacturing and is complicated by the complex chemical composition of the bioreactor broth. Here we present the early-stage development and optimization of a polarized total synchronous fluorescence spectroscopy (pTSFS) method for protein quantification in a hydrolysate-protein model (mimics clarified bioreactor broth samples) using a standard benchtop laboratory fluorometer. We used UV transmitting polarizers to provide wider range pTSFS spectra for screening of the four different TSFS spectra generated by the measurement: parallel (||), perpendicular (⊥), unpolarized (T) intensity spectra and anisotropy maps. TSFS_|| (parallel polarized) measurements were the best for protein quantification compared to standard unpolarized measurements and the Bradford assay. This was because TSFS_|| spectra had a better analyte signal to noise ratio (SNR), due to the anisotropy of protein emission. This meant that protein signals were better resolved from the background emission of small molecule fluorophores in the cell culture media. SNR of >5000 was achieved for concentrations of bovine serum albumin/yeastolate 1.2/10 g L^-1 with TSFS_|| . Optimization using genetic algorithm and interval partial least squares based variable selection enabled reduction of spectral resolution and number of excitation wavelengths required without degrading performance. This enables fast (<3.5 min) online/at-line measurements, and the method had an LOD of 0.18 g L^-1 and high accuracy with a predictive error of <9%.

Development of an inexpensive Raman-compatible substrate for the construction of a microarray screening platform

Biomaterial microarrays are being developed to facilitate identifying the extrinsic cues that elicit stem cell fate decisions to self-renew, differentiate and remain quiescent. Raman microspectroscopy, often combined with multivariate analysis techniques such as partial least square-discriminant analysis (PLS-DA), could enable the non-invasive identification of stem cell fate decisions made in response to extrinsic cues presented at specific locations on these microarrays. Because existing biomaterial microarrays are not compatible with Raman microspectroscopy, here, we develop an inexpensive substrate that is compatible with both single-cell Raman spectroscopy and the chemistries that are often used for biomaterial microarray fabrication. Standard deposition techniques were used to fabricate a custom Raman-compatible substrate that supports microarray construction. We validated that spectra from living cells on functionalized polyacrylamide (PA) gels attached to the custom Raman-compatible substrate are comparable to spectra acquired from a more expensive commercially available substrate. We also showed that the spectra acquired from individual living cells on functionalized PA gels attached to our custom substrates were of sufficient quality to enable accurate identification of cell phenotypes using PLS-DA models of the cell spectra. We demonstrated this by using cells from laboratory lines (CHO and transfected CHO cells) as well as adult stem cells that were freshly isolated from mice (long-term and short-term hematopoietic stem cells). The custom Raman-compatible substrate reported herein may be used as an inexpensive substrate for constructing biomaterial microarrays that enable the use of Raman microspectroscopy to non-invasively identify the fate decisions of stem cells in response to extrinsic cues.

Colorimetric and Physico-Chemical Property Relationships of Chemically Defined Media Powders Used in the Production of Biotherapeutics

Growth of mammalian cells in the production of biotherapeutics often require the benefits of chemically defined media (CDM). Storage, handling and stability advantages of CDM powders govern the preponderance of their use across the industry. Physico-chemical property lot-to-lot variation of these multicomponent powders, however, continues to be a challenge. Process imposed degradation of amino acids and vitamins, for example, can influence cell density, specific titer, and the quality profile of the molecule expressed due to the lack of process understanding and suitable mitigation controls. Such degradation can materialize in either their manufacture or in downstream media dissolution steps. Colorimetry, in lieu of visual appearance, can be an effective surveillance method for the direct assessment of CDM quality as color change is indicative of chemical-physical variations. This work describes a series of studies aimed to establish relationships between quantitative color change and physico-chemical attribute variation of glucose-free and glucose-based powders. The results illustrate color change is indicative of amino acid glycation, vitamin degradation and particle size shifts. These relationships enable a colorimetric control strategy for the sensitive and rapid detection of relevant CDM variation to drive additional targeted assessments to improve the productivity and robustness of cell culture processes.

Multivariate data analysis in cell gene therapy manufacturing

The emergence of cell gene therapy (CGT) as a safe and efficacious treatment for numerous severe inherited and acquired human diseases has led to growing interest and investment in new CGT products. The most successful of these have been autologous viral vector-based treatments. The development of viral vector manufacturing processes and ex vivo patient cell processing capabilities is a pressing issue in the advancement of autologous viral vector-based CGT treatments. In viral vector production, scale-up is a critical task due to the limited scalability of traditional laboratory systems and the demand for high volumes of viral vector manufactured in accordance with current good manufacturing practice. Ex vivo cell processing methods require optimisation and automation before they can be scaled out, and several other manufacturing challenges are prevalent such as high levels of raw material and process variability, difficulty characterising complex materials, and a lack of knowledge of critical process parameters and their effect on critical quality attributes of the viral vector and cell drug products. Multivariate data analysis (MVDA) has been leveraged successfully in a variety of applications in the chemical and biochemical industries, including for tasks such as bioprocess monitoring, identification of critical process parameters and assessment of process variability and comparability during process development, scale-up and technology transfer. Henceforth, MVDA is reviewed here as a suitable tool for tackling some of the challenges faced in the development of CGT manufacturing processes. A summary of some key CGT manufacturing challenges is provided along with a review of MVDA applications to mammalian and microbial processes, and an exploration of the potential benefits, requirements and pre-requisites of MVDA applications in the development of CGT manufacturing processes.

Towards robust cell culture processes - Unraveling the impact of media preparation by spectroscopic online monitoring

Biopharmaceutical manufacturing processes can be affected by variability in cell culture media, e.g. caused by raw material impurities. Although efforts have been made in industry and academia to characterize cell culture media and raw materials with advanced analytics, the process of industrial cell culture media preparation itself has not been reported so far. Within this publication, we first compare mid-infrared and two-dimensional fluorescence spectroscopy with respect to their suitability as online monitoring tools during cell culture media preparation, followed by a thorough assessment of the impact of preparation parameters on media quality. Through the application of spectroscopic methods, we can show that media variability and its corresponding root cause can be detected online during the preparation process. This methodology is a powerful tool to avoid batch failure and is a valuable technology for media troubleshooting activities. Moreover, in a design of experiments approach, including additional liquid chromatography-mass spectrometry analytics, it is shown that variable preparation parameters such as temperature, power input and preparation time can have a strong impact on the physico-chemical composition of the media. The effect on cell culture process performance and product quality in subsequent fed-batch processes was also investigated. The presented results reveal the need for online spectroscopic methods during the preparation process and show that media variability can already be introduced by variation in media preparation parameters, with a potential impact on scale-up to a commercial manufacturing process.

Confocal Raman spectroscopy: Evaluation of a non-invasive technique for the detection of topically applied ketorolac tromethamine in vitro and in vivo

Current information about the pharmacokinetics of an ocular drug can only be achieved by invasive sampling. However, confocal Raman spectroscopy bears the potential to quantify drug concentrations non-invasively. In this project, we evaluated the detection and quantification of ocular ketorolac tromethamine levels with confocal Raman spectroscopy after topical administration. Confocal Raman spectroscopy and high-performance liquid chromatography (HPLC) were compared in terms of sensitivity of detection. Enucleated pig eyes were treated with different concentrations of ketorolac. Hereafter, ketorolac concentrations in the aqueous humor of pig eyes were analyzed by confocal Raman spectroscopy and HPLC. Subsequently, twelve rabbits were treated with Acular™ for four weeks. At several time points, ketorolac concentrations in aqueous humor of the rabbits were measured by confocal Raman spectroscopy followed by drawing an aqueous humor sample for HPLC analysis. In ketorolac treated pig eyes, both ex vivo Raman spectroscopy as well as HPLC were able to detect ketorolac in a broad concentration range. However, in vivo confocal Raman spectroscopy in rabbits was unable to detect ketorolac in contrast to HPLC. To conclude, confocal Raman spectroscopy has the capacity to detect ketorolac tromethamine in vitro, but currently lacks sensitivity for in vivo detection.

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

Raman spectroscopy is a suitable monitoring technique for CHO cultivations. However, a thorough discussion of peaks, bands, and region assignments to key metabolites and culture attributes, and the interpretability of produced calibrations is scarce. That understanding is vital for the long-term predictive ability of monitoring models, and to facilitate lifecycle management that comply with regulatory guidelines. Several fed-batch lab-scale mAb mammalian cultivations were carried out, with in situ Raman spectroscopy used for process state estimation and attribute monitoring. The goal was to evaluate its use as a process analytical technology (PAT) tool to detect residual glucose and lactate levels, understand their dynamics and interconversion, and eventually estimate key performance culture and product quality attributes. Glucose and lactate models were optimized up to 0.31 g L^-1 with 3 Latent Variables (LVs) and 0.19 g L^-1 (2 LVs) accuracy, respectively. Glutamine and product titer models, were not specific and accurate enough, even though indirect calibrations were obtained with a RMSEP of 0.12 g L^-1 (4 LVs) and 0.29 g L^-1 (5 LVs), respectively. A critical discussion and details about the extensive work done in calibration development and optimization are provided. Namely, considering a risk-based selection of variability sources impacting sample spectra, executing designed experiments with spiked cultivations, and using advanced chemometric procedures for variable selection and model cross validation. A strategy is presented to evaluation Raman spectroscopy as a reliable PAT technology fit-for industrial use. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:659-670, 2018.

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.

Model-Based Methods in the Biopharmaceutical Process Lifecycle

Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.

Calibration, standardization, and quantitative analysis of multidimensional fluorescence (MDF) measurements on complex mixtures (IUPAC Technical Report)

This IUPAC Technical Report describes and compares the currently applied methods for the calibration and standardization of multi-dimensional fluorescence (MDF) spectroscopy data as well as recommendations on the correct use of chemometric methods for MDF data analysis. The paper starts with a brief description of the measurement principles for the most important MDF techniques and a short introduction to the most important applications. Recommendations are provided for instrument calibration, sample preparation and handling, and data collection, as well as the proper use of chemometric data analysis methods.

Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review

The production of active pharmaceutical ingredients (APIs) is currently undergoing its biggest transformation in a century. The changes are based on the rapid and dramatic introduction of protein- and macromolecule-based drugs (collectively known as biopharmaceuticals) and can be traced back to the huge investment in biomedical science (in particular in genomics and proteomics) that has been ongoing since the 1970s. Biopharmaceuticals (or biologics) are manufactured using biological-expression systems (such as mammalian, bacterial, insect cells, etc.) and have spawned a large (>€35 billion sales annually in Europe) and growing biopharmaceutical industry (BioPharma). The structural and chemical complexity of biologics, combined with the intricacy of cell-based manufacturing, imposes a huge analytical burden to correctly characterize and quantify both processes (upstream) and products (downstream). In small molecule manufacturing, advances in analytical and computational methods have been extensively exploited to generate process analytical technologies (PAT) that are now used for routine process control, leading to more efficient processes and safer medicines. In the analytical domain, biologic manufacturing is considerably behind and there is both a huge scope and need to produce relevant PAT tools with which to better control processes, and better characterize product macromolecules. Raman spectroscopy, a vibrational spectroscopy with a number of useful properties (nondestructive, non-contact, robustness) has significant potential advantages in BioPharma. Key among them are intrinsically high molecular specificity, the ability to measure in water, the requirement for minimal (or no) sample pre-treatment, the flexibility of sampling configurations, and suitability for automation. Here, we review and discuss a representative selection of the more important Raman applications in BioPharma (with particular emphasis on mammalian cell culture). The review shows that the properties of Raman have been successfully exploited to deliver unique and useful analytical solutions, particularly for online process monitoring. However, it also shows that its inherent susceptibility to fluorescence interference and the weakness of the Raman effect mean that it can never be a panacea. In particular, Raman-based methods are intrinsically limited by the chemical complexity and wide analyte-concentration-profiles of cell culture media/bioprocessing broths which limit their use for quantitative analysis. Nevertheless, with appropriate foreknowledge of these limitations and good experimental design, robust analytical methods can be produced. In addition, new technological developments such as time-resolved detectors, advanced lasers, and plasmonics offer potential of new Raman-based methods to resolve existing limitations and/or provide new analytical insights.

Characterization of mammalian cell culture raw materials by combining spectroscopy and chemometrics

Two of the primary issues with characterizing the variability of raw materials used in mammalian cell culture, such as wheat hydrolysate, is that the analyses of these materials can be time consuming, and the results of the analyses are not straightforward to interpret. To solve these issues, spectroscopy can be combined with chemometrics to provide a quick, robust and easy to understand methodology for the characterization of raw materials; which will improve cell culture performance by providing an assessment of the impact that a given raw material will have on final product quality. In this study, four spectroscopic technologies: near infrared spectroscopy, middle infrared spectroscopy, Raman spectroscopy, and fluorescence spectroscopy were used in conjunction with principal component analysis to characterize the variability of wheat hydrolysates, and to provide evidence that the classification of good and bad lots of raw material is possible. Then, the same spectroscopic platforms are combined with partial least squares regressions to quantitatively predict two cell culture critical quality attributes (CQA): integrated viable cell density and IgG titer. The results showed that near infrared (NIR) spectroscopy and fluorescence spectroscopy are capable of characterizing the wheat hydrolysate's chemical structure, with NIR performing slightly better; and that they can be used to estimate the raw materials' impact on the CQAs. These results were justified by demonstrating that of all the components present in the wheat hydrolysates, six amino acids: arginine, glycine, phenylalanine, tyrosine, isoleucine and threonine; and five trace elements: copper, phosphorus, molybdenum, arsenic and aluminum, had a large, statistically significant effect on the CQAs, and that NIR and fluorescence spectroscopy performed the best for characterizing the important amino acids. It was also found that the trace elements of interest were not characterized well by any of the spectral technologies used; however, the trace elements were also shown to have a less significant effect on the CQAs than the amino acids. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers, 33:1127-1138, 2017.

Differentiating Pseudomonas sp. strain ADP cells in suspensions and biofilms using Raman spectroscopy and scanning electron microscopy

High quality spectra of Pseudomonas sp. strain ADP in the planktonic and biofilm state were obtained using Raman microspectroscopy. These spectra enabled the identification of key differences between free and biofilm cells in the fingerprint region of Raman spectra in the nucleic acid, carbohydrate, and protein regions. Scanning electron microscopy (SEM) enabled detailed visualization of ADP biofilm with confirmation of associated extracellular matrix structure. Following extraction and Raman analysis of extracellular polymeric substances, Raman spectral differences between free and biofilm cells were largely attributed to the contribution of extracellular matrix components produced in mature biofilms. Raman spectroscopy complemented with SEM proves to be useful in distinguishing physiological properties among cells of the same species. Graphical Abstract Raman spectroscopy complemented with SEM proves to be useful in distinguishing physiological properties among cells of the same species.

Spectroscopic sensors for in-line bioprocess monitoring in research and pharmaceutical industrial application

The use of spectroscopic sensors for bioprocess monitoring is a powerful tool within the process analytical technology (PAT) initiative of the US Food and Drug Administration. Spectroscopic sensors enable the simultaneous real-time bioprocess monitoring of various critical process parameters including biological, chemical, and physical variables during the entire biotechnological production process. This potential can be realized through the combination of spectroscopic measurements (UV/Vis spectroscopy, IR spectroscopy, fluorescence spectroscopy, and Raman spectroscopy) with multivariate data analysis to obtain relevant process information out of an enormous amount of data. This review summarizes the newest results from science and industry after the establishment of the PAT initiative and gives a critical overview of the most common in-line spectroscopic techniques. Examples are provided of the wide range of possible applications in upstream processing and downstream processing of spectroscopic sensors for real-time monitoring to optimize productivity and ensure product quality in the pharmaceutical industry.

Label-free live cell imaging by Confocal Raman Microscopy identifies CHO host and producer cell lines

As a possible viable and non-invasive method to identify high producing cells, Confocal Raman Microscopy was shown to be able to differentiate CHO host cell lines and derivative production clones. Cluster analysis of spectra and their derivatives was able to differentiate between different producer cell lines and a host, and also distinguished between an intracellular region of high lipid and protein content that in structure resembles the Endoplasmic Reticulum. This ability to identify the ER may be a major contributor to the identification of high producers. PCA enabled the discrimination even of host cell lines and their subclones with inherently higher production capacity. The method is thus a promising option that may contribute to early, non-invasive identification of high potential candidates during cell line development and possibly could also be used for proof of identity of established production clones.

Advances in recombinant antibody manufacturing

Since the first use of Chinese hamster ovary (CHO) cells for recombinant protein expression, production processes have steadily improved through numerous advances. In this review, we have highlighted several key milestones that have contributed to the success of CHO cells from the beginning of their use for monoclonal antibody (mAb) expression until today. The main factors influencing the yield of a production process are the time to accumulate a desired amount of biomass, the process duration, and the specific productivity. By comparing maximum cell densities and specific growth rates of various expression systems, we have emphasized the limiting parameters of different cellular systems and comprehensively described scientific approaches and techniques to improve host cell lines. Besides the quantitative evaluation of current systems, the quality-determining properties of a host cell line, namely post-translational modifications, were analyzed and compared to naturally occurring polyclonal immunoglobulin fractions from human plasma. In summary, numerous different expression systems for mAbs are available and also under scientific investigation. However, CHO cells are the most frequently investigated cell lines and remain the workhorse for mAb production until today.

Monitoring antibody aggregation in early drug development using Raman spectroscopy and perturbation-correlation moving windows

In this study, we demonstrate the sensitivity of two-dimensional perturbation-correlation moving windows (PCMW) to characterize conformational transitions in antibodies. An understanding of how physiochemical properties affect protein stability and instigate aggregation is essential for the engineering of antibodies. In order to establish the potential of PCMW as a technique for early identification of aggregation mechanisms during antibody development, five antibodies with varying propensity to aggregate were compared. Raman spectra were acquired, using a 532 nm excitation wavelength as the protein samples were heated from 56 to 78 °C and analyzed with PCMW. Initial principal component analysis confirmed a trend between the observed spectral variations and increasing temperature for all five samples. Analysis using PCMW revealed that when spectral variations were directly related to temperature, distinct differences in conformational changes could be determined between samples related to protein stability, providing a greater understanding of the aggregation mechanisms of problematic antibody variants.

Comprehensive, quantitative bioprocess productivity monitoring using fluorescence EEM spectroscopy and chemometrics

Using fluorescence excitation-emission matrix spectroscopy and chemometric methods we demonstrate an effective and rapid method for quantitative monitoring of a mammalian cell culture based manufacturing process.

Raman spectroscopy and multivariate analysis for the rapid discrimination between native-like and non-native states in freeze-dried protein formulations

This study investigates whether Raman spectroscopy combined with multivariate analysis (MVA) enables a rapid and direct differentiation between two classes of conformational states, i.e., native-like and non-native proteins, in freeze-dried formulations. A data set comprising of 99 spectra, both from native-like and various types of non-native freeze-dried protein formulations, was obtained by freeze-drying lactate dehydrogenase (LDH) as model protein under various conditions. Changes in the secondary structure in the solid freeze-dried proteins were determined through visual interpretation of the blank corrected second derivative amide I band in the ATR-FTIR spectra (further called FTIR spectra) and served as an independent reference to assign class labels. Exploratory analysis and supervised classification, using Principal Components Analysis (PCA) and Partial Least Squares - Linear Discriminant Analysis (PLS-LDA), respectively, revealed that Raman spectroscopy is with 95% accuracy able to correctly discriminate between native-like and non-native states in the tested freeze-dried LDH formulations. Backbone (i.e., amide III) and side chain sensitive spectral regions proved important for making the discrimination between both classes. As discrimination was not influenced by the spectral signals from the tested excipients, there was no need for blank corrections. The Raman model may allow direct and automated analysis of the investigated quality attribute, opening possibilities for a real time and in-line quality indication as a future step. However, the sensitivity of the method should be further investigated and where possible improved.