Real-time monitoring of antibody glycosylation site occupancy by in situ Raman spectroscopy during bioreactor CHO cell cultures

Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

This paper reviews the key building blocks needed to develop a mechanistic model for use as an operational production tool. The Chinese Hamster Ovary (CHO) cell, one of the most widely used hosts for antibody production in the pharmaceutical industry, is considered as a case study. CHO cell metabolism is characterized by two main phases, exponential growth followed by a stationary phase with strong protein production. This process presents an appropriate degree of complexity to outline the modeling strategy. The paper is organized into four main steps: (1) CHO systems and data collection; (2) metabolic analysis; (3) formulation of the mathematical model; and finally, (4) numerical solution, calibration, and validation. The overall approach can build a predictive model of target variables. According to the literature, one of the main current modeling challenges lies in understanding and predicting the spontaneous metabolic shift. Possible candidates for the trigger of the metabolic shift include the concentration of lactate and carbon dioxide. In our opinion, ammonium, which is also an inhibiting product, should be further investigated. Finally, the expected progress in the emerging field of hybrid modeling, which combines the best of mechanistic modeling and machine learning, is presented as a fascinating breakthrough. Note that the modeling strategy discussed here is a general framework that can be applied to any bioprocess.

Pioneering Just-in-Time (JIT) Strategy for Accelerating Raman Method Development and Implementation for Biologic Continuous Manufacturing

Raman spectroscopy is a popular process analytical technology (PAT) tool that has been increasingly used to monitor and control the monoclonal antibody (mAb) manufacturing process. Although it allows the characterization of a variety of quality attributes by developing chemometric models, a large quantity of representative data is required, and hence, the model development process can be time-consuming. In recent years, the pharmaceutical industry has been expediting new drug development in order to achieve faster delivery of life-changing drugs to patients. The shortened development timelines have impacted the Raman application, as less time is allowed for data collection. To address this problem, an innovative Just-in-Time (JIT) strategy is proposed with the goal of reducing the time needed for Raman model development and ensuring its implementation. To demonstrate its capabilities, a proof-of-concept study was performed by applying the JIT strategy to a biologic continuous process for producing monoclonal antibody products. Raman spectroscopy and online two-dimensional liquid chromatography (2D-LC) were integrated as a PAT analyzer system. Raman models of antibody titer and aggregate percentage were calibrated by chemometric modeling in real-time. The models were also updated in real-time using new data collected during process monitoring. Initial Raman models with adequate performance were established using data collected from two lab-scale cell culture batches and subsequently updated using one scale-up batch. The JIT strategy is capable of accelerating Raman method development to monitor and guide the expedited biologics process development.

On-line targeted metabolomics for real-time monitoring of relevant compounds in fermentation processes

Fermentation monitoring is a powerful tool for bioprocess development and optimization. On-line metabolomics is a technology that is starting to gain attention as a bioprocess monitoring tool, allowing the direct measurement of many compounds in the fermentation broth at a very high time resolution. In this work, targeted on-line metabolomics was used to monitor 40 metabolites of interest during three Escherichia coli succinate production fermentation experiments every 5 min with a triple quadrupole mass spectrometer. This allowed capturing high-time resolution biological data that can provide critical information for process optimization. For nine of these metabolites, simple univariate regression models were used to model compound concentration from their on-line mass spectrometry peak area. These on-line metabolomics univariate models performed comparably to vibrational spectroscopy multivariate partial least squares regressions models reported in the literature, which typically are much more complex and time consuming to build. In conclusion, this work shows how on-line metabolomics can be used to directly monitor many bioprocess compounds of interest and obtain rich biological and bioprocess data.

Generative data augmentation and automated optimization of convolutional neural networks for process monitoring

Chemometric modeling for spectral data is considered a key technology in biopharmaceutical processing to realize real-time process control and release testing. Machine learning (ML) models have been shown to increase the accuracy of various spectral regression and classification tasks, remove challenging preprocessing steps for spectral data, and promise to improve the transferability of models when compared to commonly applied, linear methods. The training and optimization of ML models require large data sets which are not available in the context of biopharmaceutical processing. Generative methods to extend data sets with realistic in silico samples, so-called data augmentation, may provide the means to alleviate this challenge. In this study, we develop and implement a novel data augmentation method for generating in silico spectral data based on local estimation of pure component profiles for training convolutional neural network (CNN) models using four data sets. We simultaneously tune hyperparameters associated with data augmentation and the neural network architecture using Bayesian optimization. Finally, we compare the optimized CNN models with partial least-squares regression models (PLS) in terms of accuracy, robustness, and interpretability. The proposed data augmentation method is shown to produce highly realistic spectral data by adapting the estimates of the pure component profiles to the sampled concentration regimes. Augmenting CNNs with the in silico spectral data is shown to improve the prediction accuracy for the quantification of monoclonal antibody (mAb) size variants by up to 50% in comparison to single-response PLS models. Bayesian structure optimization suggests that multiple convolutional blocks are beneficial for model accuracy and enable transfer across different data sets. Model-agnostic feature importance methods and synthetic noise perturbation are used to directly compare the optimized CNNs with PLS models. This enables the identification of wavelength regions critical for model performance and suggests increased robustness against Gaussian white noise and wavelength shifts of the CNNs compared to the PLS models.

Feasibility and performance of cross-clone Raman calibration models in CHO cultivation

Raman spectroscopy is widely used in monitoring and controlling cell cultivations for biopharmaceutical drug manufacturing. However, its implementation for culture monitoring in the cell line development stage has received little attention. Therefore, the impact of clonal differences, such as productivity and growth, on the prediction accuracy and transferability of Raman calibration models is not yet well described. Raman OPLS models were developed for predicting titer, glucose and lactate using eleven CHO clones from a single cell line. These clones exhibited diverse productivity and growth rates. The calibration models were evaluated for clone-related biases using clone-wise linear regression analysis on cross validated predictions. The results revealed that clonal differences did not affect the prediction of glucose and lactate, but titer models showed a significant clone-related bias, which remained even after applying variable selection methods. The bias was associated with clonal productivity and lead to increased prediction errors when titer models were transferred to cultivations with productivity levels outside the range of their training data. The findings demonstrate the feasibility of Raman-based monitoring of glucose and lactate in cell line development with high accuracy. However, accurate titer prediction requires careful consideration of clonal characteristics during model development.

Current and future technologies for monitoring cultured meat: A review

The high population growth rate, massive animal food consumption, fast economic progress, and limited food resources could lead to a food crisis in the future. There is a huge requirement for dietary proteins including cultured meat is being progressed to fulfill the need for meat-derived proteins in the diet. However, production of cultured meat requires monitoring numerous bioprocess parameters. This review presents a comprehensive overview of various widely adopted techniques (optical, spectroscopic, electrochemical, capacitive, FETs, resistive, microscopy, and ultrasound) for monitoring physical, chemical, and biological parameters that can improve the bioprocess control in cultured meat. The methods, operating principle, merits/demerits, and the main open challenges are reviewed with the aim to support the readers in advancing knowledge on novel sensing systems for cultured meat applications.

Use of single analytic tool to quantify both absolute N-glycosylation and glycan distribution in monoclonal antibodies

Recombinant proteins represent almost half of the top selling therapeutics-with over a hundred billion dollars in global sales-and their efficacy and safety strongly depend on glycosylation. In this study, we showcase a simple method to simultaneously analyze N-glycan micro- and macroheterogeneity of an immunoglobulin G (IgG) by quantifying glycan occupancy and distribution. Our approach is linear over a wide range of glycan and glycoprotein concentrations down to 25 ng/mL. Additionally, we present a case study demonstrating the effect of small molecule metabolic regulators on glycan heterogeneity using this approach. In particular, sodium oxamate (SOD) decreased Chinese hamster ovary (CHO) glucose metabolism and reduced IgG glycosylation by 40% through upregulating reactive oxygen species (ROS) and reducing the UDP-GlcNAc pool, while maintaining a similar glycan profile to control cultures. Here, we suggest glycan macroheterogeneity as an attribute should be included in bioprocess screening to identify process parameters that optimize culture performance without compromising antibody quality.

Recent Advances in Biologic Therapeutic N-Glycan Preparation Techniques and Analytical Methods for Facilitating Biomanufacturing Automation

N-glycosylation is a post-translational modification that occurs during the production of monoclonal antibody (mAb) therapeutics. During production of mAb based therapeutics the use of various hosts and cell culture additives attribute to glycan heterogeneity. The safety and efficacy of monoclonal antibodies with mechanism of actions that utilize Fc effector functions can be negatively impacted by glycan heterogeneity and thus is often considered a critical quality attribute (CQA). In this mini review, we discuss recent advances in mAb sample preparation specifically focused on denaturation, enzymatic processing, and released glycans derivatization methods. Additionally, we review the recent advances in characterization of released and intact N-glycans using chromatography, capillary electrophoresis, and mass spectrometry techniques with a focus on rapid, automated approaches that support analysis of glycosylation profiles of biopharmaceuticals. We delve into advances within sample preparation techniques that allow for rapid and robust sample preparation as well as how these techniques are being used for innovative at-line high-throughput screening and process analytical technology (PAT). The future of biomanufacturing is focused on decreasing process costs while increasing process understanding and quality for novel biologic candidates and biosimilars. Therefore, advances in PAT for biotherapeutics will positively influence current manufacturing practices and enable further bioprocess automation.

Progress toward rapid, at-line N-glycosylation detection and control for recombinant protein expression

Proteins continue to represent a large fraction of the therapeutics market, reaching over a hundred billion dollars in market size globally. One key feature of protein modification that can affect both structure and function is the addition of glycosylation following protein folding, leading to regulatory requirements for the accurate assessment of protein attributes, including glycan structures. The non-template-driven, innately heterogeneous N-glycosylation process thus requires accurate detection to robustly generate protein therapies. A challenge exists in the timely detection of protein glycosylation without labor-intensive manipulation. In this article, we discuss progress toward N-glycoprotein control, focusing on novel control strategies and the advancement of rapid, high-throughput analysis methods.

Continuous manufacturing of monoclonal antibodies: Automated downstream control strategy for dynamic handling of titer variations

Handling long-term dynamic variability in harvest titer is a critical challenge in continuous downstream manufacturing. This challenge is becoming increasingly important with the advent of high-titer clones and modern upstream perfusion processes where the titer can vary significantly across the course of a campaign. In this paper, we present a strategy for real-time, dynamic adjustment of the entire downstream train, including capture chromatography, viral inactivation, depth filtration, polishing chromatography, and single-pass formulation, to accommodate variations in titer from 1-7 g/L. The strategy was tested in real time in a continuous downstream purification process of 36 h duration with induced titer variations. The dynamic control strategy leverages real-time NIR-based concentration sensors in the harvest material to continuously track the titer, integrated with an in-house Python-based control system that operates a BioSMB for carrying out capture and polishing chromatography, as well as a series of pumps and solenoid valves for carrying out viral inactivation and formulation. A set of 9 different methods, corresponding to the different harvest titers have been coded onto the Python controller. The methods have a varying number of chromatography columns (3-6 for Protein A and 2-10 for CEX), designed to ensure proper scheduling and optimize productivity across the entire titer variation space. The approach allows for a wide range of titers to be processed on a single integrated setup without having to change equipment or to re-design each time. The strategy also overcomes a key unexplored challenge in continuous processing, namely hand-shaking the downstream train to upstream conditions with long-term titer variability while maintaining automated operation with high productivity and robustness.

On-line untargeted metabolomics monitoring of an E. coli succinate fermentation process

The real‐time monitoring of metabolites (RTMet) is instrumental for the industrial production of biobased fermentation products. This study shows the first application of untargeted on‐line metabolomics for the monitoring of undiluted fermentation broth samples taken automatically from a 5 L bioreactor every 5 min via flow injection mass spectrometry. The travel time from the bioreactor to the mass spectrometer was 30 s. Using mass spectrometry allows, on the one hand, the direct monitoring of targeted key process compounds of interest and, on the other hand, provides information on hundreds of additional untargeted compounds without requiring previous calibration data. In this study, this technology was applied in an Escherichia coli succinate fermentation process and 886 different m/z signals were monitored, including key process compounds (glucose, succinate, and pyruvate), potential biomarkers of biomass formation such as (R)‐2,3‐dihydroxy‐isovalerate and (R)‐2,3‐dihydroxy‐3‐methylpentanoate and compounds from the pentose phosphate pathway and nucleotide metabolism, among others. The main advantage of the RTMet technology is that it allows the monitoring of hundreds of signals without the requirement of developing partial least squares regression models, making it a perfect tool for bioprocess monitoring and for testing many different strains and process conditions for bioprocess development.

Analytical Similarity Assessment of Biosimilars: Global Regulatory Landscape, Recent Studies and Major Advancements in Orthogonal Platforms

Biopharmaceuticals are one of the fastest-growing sectors in the biotechnology industry. Within the umbrella of biopharmaceuticals, the biosimilar segment is expanding with currently over 200 approved biosimilars, globally. The key step towards achieving a successful biosimilar approval is to establish analytical and clinical biosimilarity with the innovator. The objective of an analytical biosimilarity study is to demonstrate a highly similar profile with respect to variations in critical quality attributes (CQAs) of the biosimilar product, and these variations must lie within the range set by the innovator. This comprises a detailed comparative structural and functional characterization using appropriate, validated analytical methods to fingerprint the molecule and helps reduce the economic burden towards regulatory requirement of extensive preclinical/clinical similarity data, thus making biotechnological drugs more affordable. In the last decade, biosimilar manufacturing and associated regulations have become more established, leading to numerous approvals. Biosimilarity assessment exercises conducted towards approval are also published more frequently in the public domain. Consequently, some technical advancements in analytical sciences have also percolated to applications in analytical biosimilarity assessment. Keeping this in mind, this review aims at providing a holistic view of progresses in biosimilar analysis and approval. In this review, we have summarized the major developments in the global regulatory landscape with respect to biosimilar approvals and also catalogued biosimilarity assessment studies for recombinant DNA products available in the public domain. We have also covered recent advancements in analytical methods, orthogonal techniques, and platforms for biosimilar characterization, since 2015. The review specifically aims to serve as a comprehensive catalog for published biosimilarity assessment studies with details on analytical platform used and critical quality attributes (CQAs) covered for multiple biotherapeutic products. Through this compilation, the emergent evolution of techniques with respect to each CQA has also been charted and discussed. Lastly, the information resource of published biosimilarity assessment studies, created during literature search is anticipated to serve as a helpful reference for biopharmaceutical scientists and biosimilar developers.

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.

On the Use of Surface Plasmon Resonance-Based Biosensors for Advanced Bioprocess Monitoring

Biomanufacturers are being incited by regulatory agencies to transition from a quality by testing framework, where they extensively test their product after their production, to more of a quality by design or even quality by control framework. This requires powerful analytical tools and sensors enabling measurements of key process variables and/or product quality attributes during production, preferably in an online manner. As such, the demand for monitoring technologies is rapidly growing. In this context, we believe surface plasmon resonance (SPR)-based biosensors can play a role in enabling the development of improved bioprocess monitoring and control strategies. The SPR technique has been profusely used to probe the binding behavior of a solution species with a sensor surface-immobilized partner in an investigative context, but its ability to detect binding in real-time and without a label has been exploited for monitoring purposes and is promising for the near future. In this review, we examine applications of SPR that are or could be related to bioprocess monitoring in three spheres: biotherapeutics production monitoring, vaccine monitoring, and bacteria and contaminant detection. These applications mainly exploit SPR’s ability to measure solution species concentrations, but performing kinetic analyses is also possible and could prove useful for product quality assessments. We follow with a discussion on the limitations of SPR in a monitoring role and how recent advances in hardware and SPR response modeling could counter them. Mainly, throughput limitations can be addressed by multi-detection spot instruments, and nonspecific binding effects can be alleviated by new antifouling materials. A plethora of methods are available for cell growth and metabolism monitoring, but product monitoring is performed mainly a posteriori. SPR-based biosensors exhibit potential as product monitoring tools from early production to the end of downstream processing, paving the way for more efficient production control. However, more work needs to be done to facilitate or eliminate the need for sample preprocessing and to optimize the experimental protocols.

Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process

The Quality by Design (QbD) approach to the production of therapeutic monoclonal antibodies (mAbs) emphasizes an understanding of the production process ensuring product quality is maintained throughout. Current methods for measuring critical quality attributes (CQAs) such as glycation and glycosylation are time and resource intensive, often, only tested offline once per batch process. Process analytical technology (PAT) tools such as Raman spectroscopy combined with chemometric modeling can provide real time measurements process variables and are aligned with the QbD approach. This study utilizes these tools to build partial least squares (PLS) regression models to provide real time monitoring of glycation and glycosylation profiles. In total, seven cell line specific chemometric PLS models; % mono-glycated, % non-glycated, % G0F-GlcNac, % G0, % G0F, % G1F, and % G2F were considered. PLS models were initially developed using small scale data to verify the capability of Raman to measure these CQAs effectively. Accurate PLS model predictions were observed at small scale (5 L). At manufacturing scale (2000 L) some glycosylation models showed higher error, indicating that scale may be a key consideration in glycosylation profile PLS model development. Model robustness was then considered by supplementing models with a single batch of manufacturing scale data. This data addition had a significant impact on the predictive capability of each model, with an improvement of 77.5% in the case of the G2F. The finalized models show the capability of Raman as a PAT tool to deliver real time monitoring of glycation and glycosylation profiles at manufacturing scale.

Practical monitoring technologies for cells and substrates in biomanufacturing

Precise control over bioreactor operation is desired for optimal productivity and product quality, and there is an increased drive to automation in biomanufacturing. All of these goals require sensors, not only of the basic parameters of temperature, pH, and dissolved oxygen, but of the biomass and substrate concentrations, which directly determine the outcome of the bioprocess. While there are many innovative sensing concepts for biomass and substrate concentrations, this review focuses on sensors that are in-line with the bioreactor, providing data continuously without the removal of sample from the system. The discussion emphasizes the requirements of industry for these sensors, including performance, ease of use, and cost. As the bioeconomy grows, advances in sensing technologies will be needed to achieve the automation of the future for a wider array of bioreactors.

White paper on high-throughput process development for integrated continuous biomanufacturing

Continuous manufacturing is an indicator of a maturing industry, as can be seen by the example of the petrochemical industry. Patent expiry promotes a price competition between manufacturing companies, and more efficient and cheaper processes are needed to achieve lower production costs. Over the last decade, continuous biomanufacturing has had significant breakthroughs, with regulatory agencies encouraging the industry to implement this processing mode. Process development is resource and time consuming and, although it is increasingly becoming less expensive and faster through high-throughput process development (HTPD) implementation, reliable HTPD technology for integrated and continuous biomanufacturing is still lacking and is considered to be an emerging field. Therefore, this paper aims to illustrate the major gaps in HTPD and to discuss the major needs and possible solutions to achieve an end-to-end Integrated Continuous Biomanufacturing, as discussed in the context of the 2019 Integrated Continuous Biomanufacturing conference. The current HTPD state-of-the-art for several unit operations is discussed, as well as the emerging technologies which will expedite a shift to continuous biomanufacturing.

Emerging Challenges and Opportunities in Pharmaceutical Manufacturing and Distribution

The rise of personalised and highly complex drug product profiles necessitates significant advancements in pharmaceutical manufacturing and distribution. Efforts to develop more agile, responsive, and reproducible manufacturing processes are being combined with the application of digital tools for seamless communication between process units, plants, and distribution nodes. In this paper, we discuss how novel therapeutics of high-specificity and sensitive nature are reshaping well-established paradigms in the pharmaceutical industry. We present an overview of recent research directions in pharmaceutical manufacturing and supply chain design and operations. We discuss topical challenges and opportunities related to small molecules and biologics, dividing the latter into patient- and non-specific. Lastly, we present the role of process systems engineering in generating decision-making tools to assist manufacturing and distribution strategies in the pharmaceutical sector and ultimately embrace the benefits of digitalised operations.

Raman Spectroscopy to Monitor Post-Translational Modifications and Degradation in Monoclonal Antibody Therapeutics

Monoclonal antibodies (mAbs) represent a rapidly expanding market for biotherapeutics. Structural changes in the mAb can lead to unwanted immunogenicity, reduced efficacy, and loss of material during production. The pharmaceutical sector requires new protein characterization tools that are fast, applicable in situ and to the manufacturing process. Raman has been highlighted as a technique to suit this application as it is information-rich, minimally invasive, insensitive to water background and requires little to no sample preparation. This study investigates the applicability of Raman to detect Post-Translational Modifications (PTMs) and degradation seen in mAbs. IgG4 molecules have been incubated under a range of conditions known to result in degradation of the therapeutic including varied pH, temperature, agitation, photo, and chemical stresses. Aggregation was measured using size-exclusion chromatography, and PTM levels were calculated using peptide mapping. By combining principal component analysis (PCA) with Raman spectroscopy and circular dichroism (CD) spectroscopy structural analysis we were able to separate proteins based on PTMs and degradation. Furthermore, by identifying key bands that lead to the PCA separation we could correlate spectral peaks to specific PTMs. In particular, we have identified a peak which exhibits a shift in samples with higher levels of Trp oxidation. Through separation of IgG4 aggregates, by size, we have shown a linear correlation between peak wavenumbers of specific functional groups and the amount of aggregate present. We therefore demonstrate the capability for Raman spectroscopy to be used as an analytical tool to measure degradation and PTMs in-line with therapeutic production.

N-Glycosylation of IgG and IgG-Like Recombinant Therapeutic Proteins: Why Is It Important and How Can We Control It?

Regulatory bodies worldwide consider N-glycosylation to be a critical quality attribute for immunoglobulin G (IgG) and IgG-like therapeutics. This consideration is due to the importance of posttranslational modifications in determining the efficacy, safety, and pharmacokinetic properties of biologics. Given its critical role in protein therapeutic production, we review N-glycosylation beginning with an overview of the myriad interactions of N-glycans with other biological factors. We examine the mechanism and drivers for N-glycosylation during biotherapeutic production and the several competing factors that impact glycan formation, including the abundance of precursor nucleotide sugars, transporters, glycosidases, glycosyltransferases, and process conditions. We explore the role of these factors with a focus on the analytical approaches used to characterize glycosylation and associated processes, followed by the current state of advanced glycosylation modeling techniques. This combination of disciplines allows for a deeper understanding of N-glycosylation and will lead to more rational glycan control.

Control of IgG glycosylation by in situ and real-time estimation of specific growth rate of CHO cells cultured in bioreactor

The cell-specific growth rate (µ) is a critical process parameter for antibody production processes performed by animal cell cultures, as it describes the cell growth and reflects the cell physiological state. When there are changes in these parameters, which are indicated by variations of µ, the synthesis and the quality of antibodies are often affected. Therefore, it is essential to monitor and control the variations of µto assure the antibody production and achieve high product quality. In this study, a novel approach for on-line estimation of µ was developed based on the process analytical technology initiative by using an in situ dielectric spectroscopy. Critical moments, such as significant µ decreases, were successfully detected by this method, in association with changes in cell physiology as well as with an accumulation of nonglycosylated antibodies. Thus, this method was used to perform medium renewals at the appropriate time points, maintaining the values of µ close to its maximum. Using this method, we demonstrated that the physiological state of cells remained stable, the quantity and the glycosylation quality of antibodies were assured at the same time, leading to better process performances compared with the reference feed-harvest cell cultures carried out by using off-line nutrient measurements.

Glucose monitoring and adaptive feeding of mammalian cell culture in the presence of strong autofluorescence by near infrared Raman spectroscopy

Raman spectroscopy offers an attractive platform for real-time monitoring and control of metabolites and feeds in cell culture processes, including mammalian cell culture for biopharmaceutical production. However, specific cell culture processes may generate substantial concentrations of chemical species and byproducts with high levels of autofluorescence when excited with the standard 785 nm wavelength. Shifting excitation further toward the near-infrared allows reduction or elimination of process autofluorescence. We demonstrate such a reduction in a highly autofluorescent mammalian cell culture process. Using the Kaiser RXN2-1000 platform, which utilizes excitation at 993 nm, we developed multivariate glucose models in a cell culture process which was previously impossible using 785 nm excitation. Additionally, the glucose level in the production bioreactor was controlled entirely by Raman adaptive feeding, allowing for maintenance of glucose levels at an arbitrary set point for the duration of the culture. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1574-1580, 2018.

Influence of Incident Wavelength and Detector Material Selection on Fluorescence in the Application of Raman Spectroscopy to a Fungal Fermentation Process

Raman spectroscopy is a novel tool used in the on-line monitoring and control of bioprocesses, offering both quantitative and qualitative determination of key process variables through spectroscopic analysis. However, the wide-spread application of Raman spectroscopy analysers to industrial fermentation processes has been hindered by problems related to the high background fluorescence signal associated with the analysis of biological samples. To address this issue, we investigated the influence of fluorescence on the spectra collected from two Raman spectroscopic devices with different wavelengths and detectors in the analysis of the critical process parameters (CPPs) and critical quality attributes (CQAs) of a fungal fermentation process. The spectra collected using a Raman analyser with the shorter wavelength (903 nm) and a charged coupled device detector (CCD) was corrupted by high fluorescence and was therefore unusable in the prediction of these CPPs and CQAs. In contrast, the spectra collected using a Raman analyser with the longer wavelength (993 nm) and an indium gallium arsenide (InGaAs) detector was only moderately affected by fluorescence and enabled the generation of accurate estimates of the fermentation’s critical variables. This novel work is the first direct comparison of two different Raman spectroscopy probes on the same process highlighting the significant detrimental effect caused by high fluorescence on spectra recorded throughout fermentation runs. Furthermore, this paper demonstrates the importance of correctly selecting both the incident wavelength and detector material type of the Raman spectroscopy devices to ensure corrupting fluorescence is minimised during bioprocess monitoring applications.

Comparison of time-gated surface-enhanced raman spectroscopy (TG-SERS) and classical SERS based monitoring of Escherichia coli cultivation samples

The application of Raman spectroscopy as a monitoring technique for bioprocesses is severely limited by a large background signal originating from fluorescing compounds in the culture media. Here, we compare time-gated Raman (TG-Raman)-, continuous wave NIR-process Raman (NIR-Raman), and continuous wave micro-Raman (micro-Raman) approaches in combination with surface enhanced Raman spectroscopy (SERS) for their potential to overcome this limit. For that purpose, we monitored metabolite concentrations of Escherichia coli bioreactor cultivations in cell-free supernatant samples. We investigated concentration transients of glucose, acetate, AMP, and cAMP at alternating substrate availability, from deficiency to excess. Raman and SERS signals were compared to off-line metabolite analysis of carbohydrates, carboxylic acids, and nucleotides. Results demonstrate that SERS, in almost all cases, led to a higher number of identifiable signals and better resolved spectra. Spectra derived from the TG-Raman were comparable to those of micro-Raman resulting in well-discernable Raman peaks, which allowed for the identification of a higher number of compounds. In contrast, NIR-Raman provided a superior performance for the quantitative evaluation of analytes, both with and without SERS nanoparticles when using multivariate data analysis. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1533-1542, 2018.