Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review

NMR coupled with multivariate data analysis for monitoring the degradation of a formulated therapeutic monoclonal antibody

The function of a protein is directly coupled to its higher-order structure (HOS). Deviations in this critical quality attribute (CQA) may be linked to a decrease in the efficacy and/or safety of the final therapeutic product. High-resolution nuclear magnetic resonance (NMR) spectroscopy has been recently highlighted for protein HOS characterization, thanks to its ability to capture small changes at the molecular and structural levels (primary, secondary, tertiary and quaternary structures). The present study was carried out to demonstrate the ability of NMR (1D 1H and 2D 1H-13C experiments) coupled with multivariate data analysis (PCA and PLS regression) to monitor the degradation of a formulated mAb-like protein and for the simultaneous quantification of related CQAs, i.e., potency, purity and impurities (aggregates and fragments). The results indicate that this approach could be applied to mitigate product quality risks during development, manufacturing and stability studies of mAb therapeutics.

Rapid and high accuracy identification of culture medium by CNN of Raman spectra

Culture media are widely used for biological research and production. It is essential for the growth of microorganisms, cells, or tissues. It includes complex components like carbohydrates, proteins, vitamins, and minerals. The media's consistency is key for predictable outcomes in biology applications. However, traditional methods of analyzing media are costly and time-consuming by using chromatography or mass spectrometry. This study introduces an innovative approach using optimized convolutional neural networks (CNN) combined with Raman spectroscopy to identify culture media. Samples of culture media from different models and batches are prepared for identification experiment. Raman spectra of each culture media samples are captured with unique molecular vibrations and rotations by Raman spectrometer rapidly. After preprocessing of sample data, Raman spectra are input to CNN for identification training and validation. An optimized CNN with more layers is designed to enhance the identify ability for Raman spectra. In experiment, it compared the performance of PCA-SVM, the original CNN, and an optimized CNN for media identification. The PCA-SVM achieved high accuracy and precision rates of 99.19% and 98.39% respectively. The original CNN achieved an accuracy of 71.89% due to limited training dataset. The optimized CNN model achieved a perfect accuracy rate of 100% in identifying different culture media. To avoid overfitting risk, additional external test is performed with optimized CNN. The result confirmed that optimized CNN offering effectiveness in identifying media from different models and batches, with strong generalization ability. The findings in study may offer an efficient and cost-effective method for pharmaceutical companies, to ensure the consistency of culture media.

Raman-Enabled Predictions of Protein Content and Metabolites in Biopharmaceutical Saccharomyces cerevisiae Fermentations

Raman spectroscopy, a robust and non-invasive analytical method, has demonstrated significant potential for monitoring biopharmaceutical production processes. Its ability to provide detailed information about molecular vibrations makes it ideal for the detection and quantification of therapeutic proteins and critical control parameters in complex biopharmaceutical mixtures. However, its application in Saccharomyces cerevisiae fermentations has been hindered by the inherent strong fluorescence background from the cells. This fluorescence interferes with Raman signals, compromising spectral data accuracy. In this study, we present an approach that mitigates this issue by deploying Raman spectroscopy on cell-free media samples, combined with advanced chemometric modeling. This method enables accurate prediction of protein concentration and key process parameters, fundamental for the control and optimization of biopharmaceutical fermentation processes. Utilizing variable importance in projection (VIP) further enhances model robustness, leading to lower relative root mean squared error of prediction (RMSEP) values across the six targets studied. Our findings highlight the potential of Raman spectroscopy for real-time, on-line monitoring and control of complex microbial fermentations, thereby significantly enhancing the efficiency and quality of S. cerevisiae-based biopharmaceutical production.

Monitoring small-scale bioreactor studies for media development using polarized total synchronous fluorescence spectroscopy (pTSFS) and synchronous light scattering (SyLS)

Biopharmaceutical process development often involves the use of small-scale bioreactors (SSBR) for optimizing media formulations and process conditions during scale up to commercial scale production. Two key process parameters (CPP) used in SSBR studies are protein titre and viable cell density (VCD). Here, we explore the efficacy of parallel polarized total synchronous fluorescence spectroscopy (TSFS||) and Synchronous Light Scattering (SyLS||) to qualitatively monitor these CPPs and quantitatively predict titre and VCD for a large-scale cell culture media optimization SSBR study. The study involved 71 different media formulations (50+ components each), and the bioprocess was run for 13 days or more. Samples were extracted at set times (Day 0, 3, 9, and 13) and clarified by centrifugation. TSFS|| spectra showed significant emission changes along with increased light scatter over the course of the bioprocess. SyLS|| measurements strongly correlated with particle size data obtained from Dynamic Light Scattering but did not correlate well with VCD probably because of the centrifugation-based sample preparation. Statistical and principal component analysis (PCA) of the pTSFS data showed that spectral variation was greater between media formulations than due to the evolving bioprocess. This prevented the development of accurate global prediction models for media performance (e.g., predicting product titre at day 9 from media spectra measured at day 0). However, classification methods were successfully used to select media subsets with better quantitative prediction accuracy based on spectral similarities. A practical binary (high/low performance) classification model based on Support Vector Machines was generated for media formulation screening. Combining emission and scatter measurements with multivariate data analysis provides a more holistic, multi-attribute bioprocess monitoring method that minimizes the need to use different offline analytical methods. This methodology can be used to monitor process trajectories and deviations, and ultimately be used to predict bioprocess CPPs when implemented on production scale processes where there is much less compositional variation in the media. We believe this SSBR-pTSFS/SyLS approach will provide a valuable resource to develop the design/parameter space for in-process monitoring at production scale from early-stage process/media development studies.

Inline Raman spectroscopy as process analytical technology for SARS-CoV-2 VLP production

The present work focused on inline Raman spectroscopy monitoring of SARS-CoV-2 VLP production using two culture media by fitting chemometric models for biochemical parameters (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, ammonium, and viral titer). For that purpose, linear, partial least square (PLS), and nonlinear approaches, artificial neural network (ANN), were used as correlation techniques to build the models for each variable. ANN approach resulted in better fitting for most parameters, except for viable cell density and glucose, whose PLS presented more suitable models. Both were statistically similar for ammonium. The mean absolute error of the best models, within the quantified value range for viable cell density (375,000-1,287,500 cell/mL), cell viability (29.76-100.00%), glucose (8.700-10.500 g/), lactate (0.019-0.400 g/L), glutamine (0.925-1.520 g/L), glutamate (0.552-1.610 g/L), viral titer (no virus quantified-7.505 log10 PFU/mL) and ammonium (0.0074-0.0478 g/L) were, respectively, 41,533 ± 45,273 cell/mL (PLS), 1.63 ± 1.54% (ANN), 0.058 ± 0.065 g/L (PLS), 0.007 ± 0.007 g/L (ANN), 0.007 ± 0.006 g/L (ANN), 0.006 ± 0.006 g/L (ANN), 0.211 ± 0.221 log10 PFU/mL (ANN), and 0.0026 ± 0.0026 g/L (PLS) or 0.0027 ± 0.0034 g/L (ANN). The correlation accuracy, errors, and best models obtained are in accord with studies, both online and offline approaches while using the same insect cell/baculovirus expression system or different cell host. Besides, the biochemical tracking throughout bioreactor runs using the models showed suitable profiles, even using two different culture media.

Artifacts and Anomalies in Raman Spectroscopy: A Review on Origins and Correction Procedures

Raman spectroscopy, renowned for its unique ability to provide a molecular fingerprint, is an invaluable tool in industry and academic research. However, various constraints often hinder the measurement process, leading to artifacts and anomalies that can significantly affect spectral measurements. This review begins by thoroughly discussing the origins and impacts of these artifacts and anomalies stemming from instrumental, sampling, and sample-related factors. Following this, we present a comprehensive list and categorization of the existing correction procedures, including computational, experimental, and deep learning (DL) approaches. The review concludes by identifying the limitations of current procedures and discussing recent advancements and breakthroughs. This discussion highlights the potential of these advancements and provides a clear direction for future research to enhance correction procedures in Raman spectral analysis.

Fluorescence diffuse optical monitoring of bioreactors: a hybrid deep learning and model-based approach for tomography

Biosynthesis in bioreactors plays a vital role in many applications, but tools for accurate in situ monitoring of the cells are still lacking. By engineering the cells such that their conditions are reported through fluorescence, it is possible to fill in the gap using fluorescence diffuse optical tomography (fDOT). However, the spatial accuracy of the reconstruction can still be limited, due to e.g. undersampling and inaccurate estimation of the optical properties. Utilizing controlled phantom studies, we use a two-step hybrid approach, where a preliminary fDOT result is first obtained using the classic model-based optimization, and then enhanced using a neural network. We show in this paper using both simulated and phantom experiments that the proposed method can lead to a 8-fold improvement (Intersection over Union) of fluorescence inclusion reconstruction in noisy conditions, at the same speed of conventional neural network-based methods. This is an important step towards our ultimate goal of fDOT monitoring of bioreactors.

Multivariate data analysis on multisensor measurement for inline process monitoring of adenovirus production in HEK293 cells

In the era of Biopharma 4.0, process digitalization fundamentally requires accurate and timely monitoring of critical process parameters (CPPs) and quality attributes. Bioreactor systems are equipped with a variety of sensors to ensure process robustness and product quality. However, during the biphasic production of viral vectors or replication-competent viruses for gene and cell therapies and vaccination, current monitoring techniques relying on a single working sensor can be affected by the physiological state change of the cells due to infection/transduction/transfection step required to initiate production. To address this limitation, a multisensor (MS) monitoring system, which includes dual-wavelength fluorescence spectroscopy, dielectric signals, and a set of CPPs, such as oxygen uptake rate and pH control outputs, was employed to monitor the upstream process of adenovirus production in HEK293 cells in bioreactor. This system successfully identified characteristic responses to infection by comparing variations in these signals, and the correlation between signals and target critical variables was analyzed mechanistically and statistically. The predictive performance of several target CPPs using different multivariate data analysis (MVDA) methods on data from a single sensor/source or fused from multiple sensors were compared. An MS regression model can accurately predict viable cell density with a relative root mean squared error (rRMSE) as low as 8.3% regardless of the changes occurring over the infection phase. This is a significant improvement over the 12% rRMSE achieved with models based on a single source. The MS models also provide the best predictions for glucose, glutamine, lactate, and ammonium. These results demonstrate the potential of using MVDA on MS systems as a real-time monitoring approach for biphasic bioproduction processes. Yet, models based solely on the multiplicity and timing of infection outperformed both single-sensor and MS models, emphasizing the need for a deeper mechanistic understanding in virus production prediction.

Biomedical applications, perspectives and tag design concepts in the cell - silent Raman window

Spectroscopic studies increasingly employ Raman tags exhibiting a signal in the cell - silent region of the Raman spectrum (1800-2800 cm-1), where bands arising from biological molecules are inherently absent. Raman tags bearing functional groups which contain a triple bond, such as alkyne and nitrile or a carbon-deuterium bond, have a distinct vibrational frequency in this region. Due to the lack of spectral background and cell-associated bands in the specific area, the implementation of those tags can help overcome the inherently poor signal-to-noise ratio and presence of overlapping Raman bands in measurements of biological samples. The cell - silent Raman tags allow for bioorthogonal imaging of biomolecules with improved chemical contrast and they have found application in analyte detection and monitoring, biomarker profiling and live cell imaging. This review focuses on the potential of the cell - silent Raman region, reporting on the tags employed for biomedical applications using variants of Raman spectroscopy.

Accelerating attribute-focused process and product development through the development and deployment of autonomous process analytical technology platform system

Enabling real-time monitoring and control of the biomanufacturing processes through product quality insights continues to be an area of focus in the biopharmaceutical industry. The goal is to manufacture products with the desired quality attributes. To realize this rigorous attribute-focused Quality by Design approach, it is critical to support the development of processes that consistently deliver high-quality products and facilitate product commercialization. Time delays associated with offline analytical testing can limit the speed of process development. Thus, developing and deploying analytical technology is necessary to accelerate process development. In this study, we have developed the micro sequential injection process analyzer and the automatic assay preparation platform system. These innovations address the unmet need for an automatic, online, real-time sample acquisition and preparation platform system for in-process monitoring, control, and release of biopharmaceuticals. These systems can also be deployed in laboratory areas as an offline analytical system and on the manufacturing floor to enable rapid testing and release of products manufactured in a good manufacturing practice environment.

Giving the cells what they need when they need it: Biosensor-based feeding control

"Giving the cells exactly what they need, when they need it" is the core idea behind the proposed bioprocess control strategy: operating bioprocess based on the physiological behavior of the microbial population rather than exclusive monitoring of environmental parameters. We are envisioning to achieve this through the use of genetically encoded biosensors combined with online flow cytometry (FCM) to obtain a time-dependent "physiological fingerprint" of the population. We developed a biosensor based on the glnA promoter (glnAp) and applied it for monitoring the nitrogen-related nutritional state of Escherichia coli. The functionality of the biosensor was demonstrated through multiple cultivation runs performed at various scales-from microplate to 20 L bioreactor. We also developed a fully automated bioreactor-FCM interface for on-line monitoring of the microbial population. Finally, we validated the proposed strategy by performing a fed-batch experiment where the biosensor signal is used as the actuator for a nitrogen feeding feedback control. This new generation of process control, -based on the specific needs of the cells, -opens the possibility of improving process development on a short timescale and therewith, the robustness and performance of fermentation processes.

Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants

BACKGROUND

Charge related heterogeneities of monoclonal antibody (mAb) based therapeutic products are increasingly being considered as a critical quality attribute (CQA). They are typically estimated using analytical cation exchange chromatography (CEX), which is time consuming and not suitable for real time control. Raman spectroscopy coupled with artificial intelligence (AI) tools offers an opportunity for real time monitoring and control of charge variants.

OBJECTIVE

We present a process analytical technology (PAT) tool for on-line and real-time charge variant determination during process scale CEX based on Raman spectroscopy employing machine learning techniques.

METHOD

Raman spectra are collected from a reference library of samples with distribution of acidic, main, and basic species from 0-100% in a mAb concentration range of 0-20 g/L generated from process-scale CEX. The performance of different machine learning techniques for spectral processing is compared for predicting different charge variant species.

RESULT

A convolutional neural network (CNN) based model was successfully calibrated for quantification of acidic species, main species, basic species, and total protein concentration with R2 values of 0.94, 0.99, 0.96 and 0.99, respectively, and the Root Mean Squared Error (RMSE) of 0.1846, 0.1627, and 0.1029 g/L, respectively, and 0.2483 g/L for the total protein concentration.

CONCLUSION

We demonstrate that Raman spectroscopy combined with AI-ML frameworks can deliver rapid and accurate determination of product related impurities. This approach can be used for real time CEX pooling decisions in mAb production processes, thus enabling consistent charge variant profiles to be achieved.

Multi-dimensional technology - Recent advances and applications for biotherapeutic characterization

This review provides an overview of the latest advancements and applications in multi-dimensional liquid chromatography coupled with mass spectrometry (mD-LC-MS), covering aspects such as inter-laboratory studies, digestion strategy, trapping column, and multi-level analysis. The shift from an offline to an online workflow reduces sample processing artifacts, analytical variability, analysis time, and the labor required for data acquisition. Over the past few years, this technique has demonstrated sufficient maturity for application across a diverse range of complex products. Moreover, there is potential for this strategy to evolve into an integrated process analytical technology tool for the real-time monitoring of monoclonal antibody quality. This review also identifies emerging trends, including its application to new modalities, the possibility of evaluating biological activity within the mD-LC set-up, and the consideration of multi-dimensional capillary electrophoresis as an alternative to mD-LC. As mD-LC-MS continues to evolve and integrate emerging trends, it holds the potential to shape the next generation of analytical tools, offering exciting possibilities for enhanced characterization and monitoring of complex biopharmaceutical products.

Raman spectroscopy and its plasmon-enhanced counterparts: A toolbox to probe protein dynamics and aggregation

Protein unfolding and aggregation are often correlated with numerous diseases such as Alzheimer's, Parkinson's, Huntington's, and other debilitating neurological disorders. Such adverse events consist of a plethora of competing mechanisms, particularly interactions that control the stability and cooperativity of the process. However, it remains challenging to probe the molecular mechanism of protein dynamics such as aggregation, and monitor them in real-time under physiological conditions. Recently, Raman spectroscopy and its plasmon-enhanced counterparts, such as surface-enhanced Raman spectroscopy (SERS) and tip-enhanced Raman spectroscopy (TERS), have emerged as sensitive analytical tools that have the potential to perform molecular studies of functional groups and are showing significant promise in probing events related to protein aggregation. We summarize the fundamental working principles of Raman, SERS, and TERS as nondestructive, easy-to-perform, and fast tools for probing protein dynamics and aggregation. Finally, we highlight the utility of these techniques for the analysis of vibrational spectra of aggregation of proteins from various sources such as tissues, pathogens, food, biopharmaceuticals, and lastly, biological fouling to retrieve precise chemical information, which can be potentially translated to practical applications and point-of-care (PoC) devices. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > Diagnostic Nanodevices Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Validation of the cell culture monitoring using a Raman spectroscopy calibration model developed with artificially mixed samples and investigation of model learning methods using initial batch data

The development of calibration models using Raman spectra data has long been challenged owing to the substantial time and cost required for robust data acquisition. To reduce the number of experiments involving actual incubation, a calibration model development method was investigated by measuring artificially mixed samples. In this method, calibration datasets were prepared using spectra from artificially mixed samples with adjusted concentrations based on design of experiments. The precision of these calibration models was validated using the actual cell culture sample. The results showed that when the culture conditions were unchanged, the root mean square error of prediction (RMSEP) of glucose, lactate, and antibody concentrations was 0.34, 0.33, and 0.25 g/L, respectively. Even when variables such as cell line or culture media were changed, the RMSEPs of glucose, lactate, and antibody concentrations remained within acceptable limits, demonstrating the robustness of the calibration models with artificially mixed samples. To further improve accuracy, a model training method for small datasets was also investigated. The spectral pretreatment conditions were optimized using error heat maps based on the first batch of each cell culture condition and applied these settings to the second and third batches. The RMSEPs improved for glucose, lactate, and antibody concentration, with values of 0.44, 0.19, and 0.18 g/L under constant culture conditions, 0.37, 0.12, and 0.12 g/L for different cell lines, and 0.26, 0.40, and 0.12 g/L when the culture media was changed. These results indicated the efficacy of calibration modeling with artificially mixed samples for actual incubations under various conditions.

Size exclusion chromatography for screening yeastolate used in cell culture media

Yeastolate is often used as a media supplement in industrial mammalian cell culture or as a major media component for microbial fermentations. Yeastolate variability can significantly affect process performance, but analysis is technically challenging because of its compositional complexity. However, what may be adequate for manufacturing purposes is a fast, inexpensive screening method to identify molecular variance and provide sufficient information for quality control purposes, without characterizing all the molecular components. Here we used Size Exclusion Chromatography (SEC) and chemometrics as a relatively fast screening method for identifying lot-to-lot variance (with Principal Component Analysis, PCA) and investigated if Partial Least Squares, PLS, predictive models which correlated SEC data with process titer could be obtained. SEC provided a relatively fast measure of gross molecular size hydrolysate variability with minimal sample preparation and relatively simple data analysis. The sample set comprised of 18 samples from 12 unique source lots of an ultra-filtered yeastolate (10 kDa molecular weight cut-off) used in a mammalian cell culture process. SEC showed significant lot-to-lot variation, at 214 and 280 nm detection, with the most significant variation, that correlated with process performance, occurring at a retention time of ∼6 min. PCA and PLS regression correlation models provided fast identification of yeastolate variance and its process impact. The primary drawback is the limited column lifetime (<300 injections) caused by the complex nature of yeastolate and the presence of zinc. This limited long term reproducibility because these age-related, non-linear changes in chromatogram peak positions and shapes were very significant.

Enhanced bioprocess control to advance the manufacture of mesenchymal stromal cell-derived extracellular vesicles in stirred-tank bioreactors

Extracellular vesicles (EVs) derived from mesenchymal stromal cells (MSCs) act as signaling mediators of cellular responses. However, despite representing a promising alternative to cell-based therapies, clinical translation of EVs is currently limited by their lack of scalability and standardized bioprocessing. Herein, we integrated scalable downstream processing protocols with standardized expansion of large numbers of viable cells in stirred-tank bioreactors to improve EV production. Higher EV yields were linked to EV isolation by tangential flow filtration followed by size exclusion chromatography, rendering 5 times higher number of EVs comparatively to density gradient ultracentrifugation protocols. Additionally, when compared to static culture, EV manufacture in bioreactors resulted in 2.2 higher yields. Highlighting the role of operating under optimal cell culture conditions to maximize the number of EVs secreted per cell, MSCs cultured at lower glucose concentration favored EV secretion. While offline measurements of metabolites concentration can be performed, in this work, Raman spectroscopy was also applied to continuously track glucose levels in stirred-tank bioreactors, contributing to streamline the selection of optimal EV collection timepoints. Importantly, MSC-derived EVs retained their quality attributes and were able to stimulate angiogenesis in vitro, therefore highlighting their promising therapeutic potential.

Shedding light on reovirus assembly-Multimodal imaging of viral factories

Avian (ortho)reovirus (ARV), which belongs to Reoviridae family, is a major domestic fowl pathogen and is the causative agent of viral tenosynovitis and chronic respiratory disease in chicken. ARV replicates within cytoplasmic inclusions, so-called viral factories, that form by phase separation and thus belong to a wider class of biological condensates. Here, we evaluate different optical imaging methods that have been developed or adapted to follow formation, fluidity and composition of viral factories and compare them with the complementary structural information obtained by well-established transmission electron microscopy and electron tomography. The molecular and cellular biology aspects for setting up and following virus infection in cells by imaging are described first. We then demonstrate that a wide-field version of fluorescence recovery after photobleaching is an effective tool to measure fluidity of mobile viral factories. A new technique, holotomographic phase microscopy, is then used for imaging of viral factory formation in live cells in three dimensions. Confocal Raman microscopy of infected cells provides "chemical" contrast for label-free segmentation of images and addresses important questions about biomolecular concentrations within viral factories and other biological condensates. Optical imaging is complemented by electron microscopy and tomography which supply higher resolution structural detail, including visualization of individual virions within the three-dimensional cellular context.

Raman spectroscopy provides valuable process insights for cell-derived and cellular products

Two of the big challenges in modern bioprocesses are process economics and in-depth process understanding. Getting access to online process data helps to understand process dynamics and monitor critical process parameters (CPPs). This is an important part of the quality-by- design concept that was introduced to the pharmaceutical industry in the last decade. Raman spectroscopy has proven to be a versatile tool to allow noninvasive measurements and access to a broad spectrum of analytes. This information can then be used for enhanced process control strategies. This review article will focus on the latest applications of Raman spectroscopy in established protein production bioprocesses as well as show its potential for virus, cell therapy, and mRNA processes.

Development of Raman Calibration Model Without Culture Data for In-Line Analysis of Metabolites in Cell Culture Media

In this study, we developed a method to build Raman calibration models without culture data for cell culture monitoring. First, Raman spectra were collected and then analyzed for the signals of all the mentioned analytes: glucose, lactate, glutamine, glutamate, ammonia, antibody, viable cells, media, and feed agent. Using these spectral data, the specific peak positions and intensities for each factor were detected. Next, according to the design of the experiment method, samples were prepared by mixing the above-mentioned factors. Raman spectra of these samples were collected and were used to build calibration models. Several combinations of spectral pretreatments and wavenumber regions were compared to optimize the calibration model for cell culture monitoring without culture data. The accuracy of the developed calibration model was evaluated by performing actual cell culture and fitting the in-line measured spectra to the developed calibration model. As a result, the calibration model achieved sufficiently good accuracy for the three components, glucose, lactate, and antibody (root mean square errors of prediction, or RMSEP = 0.23, 0.29, and 0.20 g/L, respectively). This study has presented innovative results in developing a culture monitoring method without using culture data, while using a basic conventional method of investigating the Raman spectra of each component in the culture media and then utilizing a design of experiment approach.

Scaling up human mesenchymal stem cell manufacturing using bioreactors for clinical uses

Human mesenchymal stem cells (hMSCs) are multipotent cells and an attractive therapeutic agent in regenerative medicine and intensive clinical research. Despite the great potential, the limitation that needs to be overcome is the necessity of ex vivo expansion because of insufficient number of hMSCs presented within adult organs and the high doses required for a transplantation. As a result, numerous research studies aim to provide novel expansion methods in order to achieve appropriate numbers of cells with preserved therapeutic quality. Bioreactor-based cell expansion provide high-level production of hMSCs in accordance with good manufacturing practice (GMP) and quality standards. This review summarizes current knowledge about the hMSCs manufacturing platforms with a main focus to the application of bioreactors for large-scale production of GMP-grade hMSCs.

Pursuit of the Ultimate Regression Model for Samarium(III), Europium(III), and LiCl Using Laser-Induced Fluorescence, Design of Experiments, and a Genetic Algorithm for Feature Selection

Laser-induced fluorescence spectroscopy, Raman scattering, and partial least squares regression models were optimized for the quantification of samarium (0-150 μg mL^-1), europium (0-75 μg mL^-1), and lithium chloride (0.1-12 M) with a transformational preprocessing strategy. Selecting combinations of preprocessing methods to optimize the prediction performance of regression models is frequently a major bottleneck for chemometric analysis. Here, we propose an optimization tool using an innovative combination of optimal experimental designs for selecting preprocessing transformation and a genetic algorithm (GA) for feature selection. A D-optimal design containing 26 samples (i.e., combinations of preprocessing strategies) and a user-defined design (576 samples) did not statistically lower the root mean square error of the prediction (RMSEP). The greatest improvement in prediction performance was achieved when a GA was used for feature selection. This feature selection greatly lowered RMSEP statistics by an average of 53%, resulting in the top models with percent RMSEP values of 0.91, 3.5, and 2.1% for Sm(III), Eu(III), and LiCl, respectively. These results indicate that preprocessing corrections (e.g., scatter, scaling, noise, and baseline) alone cannot realize the optimal regression model; feature selection is a more crucial aspect to consider. This unique approach provides a powerful tool for approaching the true optimum prediction performance and can be applied to numerous fields of spectroscopy and chemometrics to rapidly construct models.

Generic Chemometric Models for Metabolite Concentration Prediction Based on Raman Spectra

Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.

Effect of Paclitaxel Stereochemistry on X-ray-Triggered Release of Paclitaxel from CaWO4/Paclitaxel-Coloaded PEG-PLA Nanoparticles

For many locally advanced tumors, the chemotherapy-radiotherapy (CT-RT) combination ("chemoradiation") is currently the standard of care. Intratumoral (IT) CT-based chemoradiation has the potential to overcome the limitations of conventional systemic CT-RT (side effects). For maximizing the benefits of IT CT-RT, our laboratory has previously developed a radiation-controlled drug release formulation, in which anticancer drug paclitaxel (PTX) and radioluminescent CaWO₄ (CWO) nanoparticles (NPs) are co-encapsulated with poly(ethylene glycol)-poly(lactic acid) (PEG-PLA) block copolymers ("PEG-PLA/CWO/PTX NPs"). These PEG-PLA/CWO/PTX NPs enable radiation-controlled release of PTX and are capable of producing sustained therapeutic effects lasting for at least one month following a single IT injection. The present article focuses on discussing our recent finding about the effect of the stereochemical structure of PTX on the efficacy of this PEG-PLA/CWO/PTX NP formulation. Stereochemical differences in two different PTX compounds ("PTX-S" from Samyang Biopharmaceuticals and "PTX-B" from Biotang) were characterized by 2D heteronuclear/homonuclear NMR, Raman spectroscopy, and circular dichroism measurements. The difference in PTX stereochemistry was found to significantly influence their water solubility (WS); PTX-S (WS ≈ 4.69 μg/mL) is about 19 times more water soluble than PTX-B (WS ≈ 0.25 μg/mL). The two PTX compounds showed similar cancer cell-killing performances in vitro when used as free drugs. However, the subtle stereochemical difference significantly influenced their X-ray-triggered release kinetics from the PEG-PLA/CWO/PTX NPs; the more water-soluble PTX-S was released faster than the less water-soluble PTX-B. This difference was manifested in the IT pharmacokinetics and eventually in the survival percentages of test animals (mice) treated with PEG-PLA/CWO/PTX NPs + X-rays in an in vivo human tumor xenograft study; at short times (<1 month), concurrent PEG-PLA/CWO/PTX-S NPs produced a greater tumor-suppression effect, whereas PEG-PLA/CWO/PTX-B NPs had a longer-lasting radio-sensitizing effect. This study demonstrates the importance of the stereochemistry of a drug in a therapy based on a controlled release formulation.

A highly sensitive impedimetric sensor based on a MIP biomimetic for the detection of enrofloxacin

The benefits of molecularly imprinted polymer (MIP) technology in creating artificial receptors to replace natural counterparts have piqued the interest of numerous researchers in recent years. We propose a biomimetic enrofloxacin-MIP for enrofloxacin (ENRO) antigen detection using gold nanoparticles (AuNPs) and MIP methodologies in this study. A self-assembled monomer layer of aminothiophenol was used to immobilize a pre-formed complex of the anti-enrofloxacin monoclonal antibody and enrofloxacin antigen onto the surface of an AuNP coated screen-printed carbon ink electrode (SPCE). The poly-(aminothiophenol) layer thickness was adjusted to entrap and restrict enrofloxacin antigens near the surface. The imprinting and removal of the enrofloxacin antigen in the MIP film were strongly validated by the Raman spectra. The final mAb-MIP sensor had better sensitivity (302 Ω mL ng^-1) and a better detection limit (0.05 ng mL^-1) than self-assembled monolayer (SAM)-based immunosensors, which had 102 Ω mL ng^-1 and 0.1 ng mL^-1, respectively.

Automated Data Generation for Raman Spectroscopy Calibrations in Multi-Parallel Mini Bioreactors

Raman spectroscopy is an analytical technology for the simultaneous measurement of important process parameters, such as concentrations of nutrients, metabolites, and product titer in mammalian cell culture. The majority of published Raman studies have concentrated on using the technique for the monitoring and control of bioreactors at pilot and manufacturing scales. This research presents a novel approach to generating Raman models using a high-throughput 250 mL mini bioreactor system with the following two integrated analysis modules: a prototype flow cell enabling on-line Raman measurements and a bioanalyzer to generate reference measurements without a significant time-shift, compared to the corresponding Raman measurement. Therefore, spectral variations could directly be correlated with the actual analyte concentrations to build reliable models. Using a design of experiments (DoE) approach and additional spiked samples, the optimized workflow resulted in robust Raman models for glucose, lactate, glutamine, glutamate and titer in Chinese hamster ovary (CHO) cell cultures producing monoclonal antibodies (mAb). The setup presented in this paper enables the generation of reliable Raman models that can be deployed to predict analyte concentrations, thereby facilitating real-time monitoring and control of biologics manufacturing.

A Novel Approach for Non-Invasive Continuous In-Line Control of Perfusion Cell Cultivations by Raman Spectroscopy

Continuous manufacturing is becoming more important in the biopharmaceutical industry. This processing strategy is favorable, as it is more efficient, flexible, and has the potential to produce higher and more consistent product quality. At the same time, it faces some challenges, especially in cell culture. As a steady state has to be maintained over a prolonged time, it is unavoidable to implement advanced process analytical technologies to control the relevant process parameters in a fast and precise manner. One such analytical technology is Raman spectroscopy, which has proven its advantages for process monitoring and control mostly in (fed-) batch cultivations. In this study, an in-line flow cell for Raman spectroscopy is included in the cell-free harvest stream of a perfusion process. Quantitative models for glucose and lactate were generated based on five cultivations originating from varying bioreactor scales. After successfully validating the glucose model (Root Mean Square Error of Prediction (RMSEP) of ∼0.2 g/L), it was employed for control of an external glucose feed in cultivation with a glucose-free perfusion medium. The generated model was successfully applied to perform process control at 4 g/L and 1.5 g/L glucose over several days, respectively, with variability of ±0.4 g/L. The results demonstrate the high potential of Raman spectroscopy for advanced process monitoring and control of a perfusion process with a bioreactor and scale-independent measurement method.

Online HPLC–HRMS Platform: The Next-Generation Process Analytical Technology Tool for Real-Time Monitoring of Antibody Quality Attributes in Biopharmaceutical Processes

Online monitoring of critical quality attributes (CQAs) directly within the bioreactor can provide the basis for advanced processing of therapeutics production, including automated real-time monitoring, feedback control process intensification, smart manufacturing, and real-time release testing. This paper presents recent developments in online high performance liquid chromatography–high-resolution mass spectrometry (HPLC–HRMS) platforms as a promising process analytical technology (PAT) tool for real-time monitoring of antibody quality attributes (QAs) in biopharmaceutical processes. This technology can be used to monitor multiple CQAs and process parameters during cell culture production, enabling real-time decisions.

Design of Experiments, Chemometrics, and Raman Spectroscopy for the Quantification of Hydroxylammonium, Nitrate, and Nitric Acid

Selecting optimal combinations of preprocessing methods is a major holdup for chemometric analysis. The analyst decides which method(s) to apply to the data, frequently by highly subjective or inefficient means, such as user experience or trial and error. Here, we present a user-friendly method using optimal experimental designs for selecting preprocessing transformations. We applied this strategy to optimize partial least square regression (PLSR) analysis of Stokes Raman spectra to quantify hydroxylammonium (0-0.5 M), nitric acid (0-1 M), and total nitrate (0-1.5 M) concentrations. The best PLSR model chosen by a determinant (D)-optimal design comprising 26 samples (i.e., combinations of preprocessing methods) was compared with PLSR models built with no preprocessing, a user-selected preprocessing method (i.e., trial and error), and a user-defined design strategy (576 samples). The D-optimal selection strategy improved PLSR prediction performance by more than 50% compared with the raw data and reduced the number of combinations by more than 95.5%.

Raman Spectroscopy and Its Modifications Applied to Biological and Medical Research

Nowadays, there is an interest in biomedical and nanobiotechnological studies, such as studies on carotenoids as antioxidants and studies on molecular markers for cardiovascular, endocrine, and oncological diseases. Moreover, interest in industrial production of microalgal biomass for biofuels and bioproducts has stimulated studies on microalgal physiology and mechanisms of synthesis and accumulation of valuable biomolecules in algal cells. Biomolecules such as neutral lipids and carotenoids are being actively explored by the biotechnology community. Raman spectroscopy (RS) has become an important tool for researchers to understand biological processes at the cellular level in medicine and biotechnology. This review provides a brief analysis of existing studies on the application of RS for investigation of biological, medical, analytical, photosynthetic, and algal research, particularly to understand how the technique can be used for lipids, carotenoids, and cellular research. First, the review article shows the main applications of the modified Raman spectroscopy in medicine and biotechnology. Research works in the field of medicine and biotechnology are analysed in terms of showing the common connections of some studies as caretenoids and lipids. Second, this article summarises some of the recent advances in Raman microspectroscopy applications in areas related to microalgal detection. Strategies based on Raman spectroscopy provide potential for biochemical-composition analysis and imaging of living microalgal cells, in situ and in vivo. Finally, current approaches used in the papers presented show the advantages, perspectives, and other essential specifics of the method applied to plants and other species/objects.

Raman spectroscopy-based adversarial network combined with SVM for detection of foodborne pathogenic bacteria

Raman spectroscopy combined with artificial intelligence algorithms have been widely explored and focused on in recent years for food safety testing. It is still a challenge to overcome the cumbersome culture process of bacteria and the need for a large number of samples, which hinder qualitative analysis, to obtain a high classification accuracy. In this paper, we propose a method based on Raman spectroscopy combined with generative adversarial network and multiclass support vector machine to classify foodborne pathogenic bacteria. 30,000 iterations of generative adversarial network are trained for three strains of bacteria, generative model G generates data similar to the actual samples, discriminant model D verifies the accuracy of the generated data, and 19 feature variables are obtained by selecting the feature bands according to the Raman spectroscopy pattern. Better classification results are obtained by optimising the parameters of the multi-class support vector machine, etc. Our detection and classification method not only solves the problem of needing a large number of samples as training set, but also improves the accuracy of the classification model. Therefore, this GAN-SVM classification model provides a new idea for the detection of bacteria based on Raman spectroscopy technology combined with artificial intelligence algorithms.

Detection of low numbers of bacterial cells in a pharmaceutical drug product using Raman spectroscopy and PLS-DA multivariate analysis

Fast and non-invasive approach to detect drug product (DP) samples with low numbers of bacteria within the primary packaging. The method combines Raman spectroscopy and partial least squared discriminant analysis (RS-PLS-DA).

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.

A Fully Integrated Online Platform For Real Time Monitoring Of Multiple Product Quality Attributes In Biopharmaceutical Processes For Monoclonal Antibody Therapeutics

In response to FDA's call for Quality by Design (QbD) in biopharmaceutical product development, the biopharmaceutical industry has been developing highly sensitive and specific technologies in the monitoring and controlling of product quality attributes for bioprocesses. We previously published the successful application of an off-line multi-attribute method (MAM) to monitor more than 20 critical quality attributes (CQA) with superior sensitivity for the upstream process. To further remove the hurdles of laborious process sampling and sample preparation associated with the offline method, we present here a fully integrated MAM based online platform for automated real time online process monitoring. This integrated system includes Modular Automated Sampling Technology (MAST) based aseptic sampling, multi-function Sequential Injection Analysis (SIA) sample preparation, UHPLC separation and high-resolution mass spectrometry (HRMS) analysis. Continuous automated daily monitoring of a 17-day cell culture process was successfully demonstrated for a model monoclonal antibody (mAb) molecule with similar specificity and sensitivity as we reported earlier. To the best of our knowledge, this is the first report of an end-to-end automated online MAM system, which would allow the MAM to be applied to routine bioprocess monitoring, potentially replacing multiple conventional low resolution and low sensitivity off-line methods. The online HPLC or HPLC/MS platform could be easily adapted to support other processing steps such as downstream purification with minimal software re-configuration.

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

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.

Influence of carbon dot synthetic parameters on photophysical and biological properties

Recently, carbon dots (CDs) have been widely investigated for biological applications in imaging. One-step hydrothermal synthesis is considered to be one of the most promising methods for the synthesis of CDs, due to its simple and rapid manipulation, flexible selection of ingredients, environmentally friendly conditions, and low-cost. A number of synthetic and post-synthetic parameters, including solvent, heating time, dopant quantity, and particle size distribution, play a crucial role in controlling the size and surface structure of CDs, which ultimately have influence on their photophysical and biological behavior. Despite the crucial role of each of these parameters in defining the yield and nature of synthesized CDs, they have not previously been rigorously optimized, particularly with respect to desired biological applications. Herein, we report our comprehensive optimization of the parameters employed for the hydrothermal synthesis of CDs to gain a better understanding of the effect of these parameters on optical properties, cytotoxicity, and cellular uptake efficiency. Furthermore, this work will open up new pathways toward the design of CDs with physiochemical properties tailored for specific biomedical applications such as bioimaging.

Application of ultraviolet, visible, and infrared light imaging in protein-based biopharmaceutical formulation characterization and development studies

Imaging is increasingly more utilized as analytical technology in biopharmaceutical formulation research, with applications ranging from subvisible particle characterization to thermal stability screening and residual moisture analysis. This review offers a comprehensive overview of analytical imaging for scientists active in biopharmaceutical formulation research and development, where it presents the unique information provided by the ultraviolet (UV), visible (Vis), and infrared (IR) sections in the electromagnetic spectrum. The main body of this review consists of an outline of UV, Vis, and IR imaging techniques for several (bio)physical properties that are commonly determined during protein-based biopharmaceutical formulation characterization and development studies. The review concludes with a future perspective of applied imaging within the field of biopharmaceutical formulation research.

Analysis of biopharmaceutical formulations by Time Domain Nuclear Magnetic Resonance (TD-NMR) spectroscopy: A potential method for detection of counterfeit biologic pharmaceuticals

¹H Time-Domain Nuclear Magnetic Resonance (TD-NMR) is used to characterize solutions of antibodies that simulate biologic pharmaceutical formulations. The results from these measurements are compared with those from solutions in which the concentration or identity of the antibody has been altered. TD-NMR is shown to be very sensitive to differences in the amount of antibody in solution, with the ability to detect variations in as low as 2 mg/mL. It is therefore capable, by comparison with data from known formulations, of determining whether a particular sample is likely to be of an authentic biologic formulation. This method expands on the previous use of HPLC, UV/VIS, Near-IR and High-Resolution NMR to detect adulterated pharmaceutical materials. While the sensitivity of the method is high, it is a fingerprinting methodology, illustrating differences but not elucidating their origin. The extracted relaxation times reflect the combined effect of all solutes (antibody, buffer components, etc.) on the solvent (water).

Process Analytical Technology for Precipitation Process Integration into Biologics Manufacturing towards Autonomous Operation—mAb Case Study

The integration of real time release testing into an advanced process control (APC) concept in combination with digital twins accelerates the process towards autonomous operation. In order to implement this, on the one hand, measurement technology is required that is capable of measuring relevant process data online, and on the other hand, a suitable model must be available to calculate new process parameters from this data, which are then used for process control. Therefore, the feasibility of online measurement techniques including Raman-spectroscopy, attenuated total reflection Fourier transformed infrared spectroscopy (ATR-FTIR), diode array detector (DAD) and fluorescence is demonstrated within the framework of the process analytical technology (PAT) initiative. The best result is achieved by Raman, which reliably detected mAb concentration (R2 of 0.93) and purity (R2 of 0.85) in real time, followed by DAD. Furthermore, the combination of DAD and Raman has been investigated, which provides a promising extension due to the orthogonal measurement methods and higher process robustness. The combination led to a prediction for concentration with a R2 of 0.90 ± 3.9% and for purity of 0.72 ± 4.9%. These data are used to run simulation studies to show the feasibility of process control with a suitable digital twin within the APC concept.

Fully automated peptide mapping multi-attribute method by liquid chromatography-mass spectrometry with robotic liquid handling system

The multi-attribute method (MAM) based on liquid chromatography (LC)-tandem mass spectrometry is emerging as a powerful tool to directly monitor multiple product quality attributes simultaneously. To better implement MAM, either for product characterization or for quality control (QC), there is a need for a robust, universal, and high-throughput workflow that can be broadly adopted in different laboratories with minimal barriers to implementation. Manual preparation of samples for MAM, however, is labor intensive and produces nontrivial variations across analysts and laboratories. We describe the development of a fully automated peptide mapping procedure with a high-throughput robotic liquid handling system to improve sample handling capacity and outcome reproducibility while saving analyst hands-on time. Our procedure features the automation of a "microdialysis" step, an efficient desalting approach prior to proteolytic digestion that optimizes digestion completeness and consistency each time. The workflow is completely hands-free and requires the analyst only to pre-normalize the sample concentrations and to load buffers and reagents at their designated positions on the robotic deck. The robotic liquid handler performs all the subsequent preparation steps and stores the digested samples on a chiller unit to await retrieval for further analysis. We also demonstrate that the manual and automated procedures are comparable with regard to protein sequence coverage, digestion completeness and consistency, and quantification of posttranslational modifications. Notably, in contrast to a previously reported automated sample preparation protocol that relied on customized accessories, all components in our automation procedure are commercial products that are readily available. In addition, we also present the high-throughput data analysis workflow by using Protein Metrics. The automation procedure can be applied cross-functionally in the biopharmaceutical industry and, given its practicality and reproducibility, can pave the way for MAM implementation in QC laboratories.

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%.

Analytical performance of Raman spectroscopy in assaying biochemical components in human serum

Chronic non-infectious diseases are important to research as they are the main causes of death in Brazil and worldwide. One very important chronic non-infectious disease is cardiovascular disease, whose risk factors (diabetes, dyslipidemia, and renal failure) can be detected through assessments of serum biochemical components. The objective of this study was to evaluate the analytical performance of Raman spectroscopy for analysis of lipid profile (total cholesterol, triglycerides, and HDL cholesterol), non-protein nitrogenous compounds (urea and creatinine), and glucose in 242 human serum samples. Models to discriminate and quantify the samples were developed using the predicted concentration by quantitative regression model based on partial least squares (PLS). The analytical error for the "leave-one-out" cross-validation based on the predicted PLS concentration was 10.5 mg/dL for total cholesterol, 21.4 mg/dL for triglyceride, 13.0 mg/dL for HDL cholesterol, 4.9 mg/dL for urea, 0.21 mg/dL for creatinine, and 15.4 mg/dL for glucose. The Kappa coefficient indicate very good agreement for cholesterol (0.83), good for triglyceride (0.77), urea (0.70) and creatinine (0.66), and fair for HDL cholesterol (0.38) and glucose (0.30). The results of the analytical performance demonstrated that Raman spectroscopy can be considered an important methodology to screen the population, especially for serum triglycerides and cholesterol.

Monitoring and control of E. coli cell integrity

Soluble expression of recombinant proteins in E. coli is often done by translocation of the product across the inner membrane (IM) into the periplasm, where it is retained by the outer membrane (OM). While the integrity of the IM is strongly coupled to viability and impurity release, a decrease in OM integrity (corresponding to increased "leakiness") leads to accumulation of product in the extracellular space, strongly impacting the downstream process. Whether leakiness is desired or not, differential monitoring and control of IM and OM integrity are necessary for an efficient E. coli bioprocess in compliance with the guidelines of Quality by Design and Process Analytical Technology. In this review, we give an overview of relevant monitoring tools, summarize the research on factors affecting E. coli membrane integrity and provide a brief discussion on how the available monitoring technology can be implemented in real-time control of E. coli cultivations.

Towards Autonomous Operation by Advanced Process Control—Process Analytical Technology for Continuous Biologics Antibody Manufacturing

Continuous manufacturing opens up new operation windows with improved product quality in contrast to documented lot deviations in batch or fed-batch operations. A more sophisticated process control strategy is needed to adjust operation parameters and keep product quality constant during long-term operations. In the present study, the applicability of a combination of spectroscopic methods was evaluated to enable Advanced Process Control (APC) in continuous manufacturing by Process Analytical Technology (PAT). In upstream processing (USP) and aqueous two-phase extraction (ATPE), Raman-, Fourier-transformed infrared (FTIR), fluorescence- and ultraviolet/visible- (UV/Vis) spectroscopy have been successfully applied for titer and purity prediction. Raman spectroscopy was the most versatile and robust method in USP, ATPE, and precipitation and is therefore recommended as primary PAT. In later process stages, the combination of UV/Vis and fluorescence spectroscopy was able to overcome difficulties in titer and purity prediction induced by overlapping side component spectra. Based on the developed spectroscopic predictions, dynamic control of unit operations was demonstrated in sophisticated simulation studies. A PAT development workflow for holistic process development was proposed.

Advanced control strategies for bioprocess chromatography: Challenges and opportunities for intensified processes and next generation products

Recent advances in process analytical technologies and modelling techniques present opportunities to improve industrial chromatography control strategies to enhance process robustness, increase productivity and move towards real-time release testing. This paper provides a critical overview of batch and continuous industrial chromatography control systems for therapeutic protein purification. Firstly, the limitations of conventional industrial fractionation control strategies using in-line UV spectroscopy and on-line HPLC are outlined. Following this, an evaluation of monitoring and control techniques showing promise within research, process development and manufacturing is provided. These novel control strategies combine rapid in-line data capture (e.g. NIR, MALS and variable pathlength UV) with enhanced process understanding obtained from mechanistic and empirical modelling techniques. Finally, a summary of the future states of industrial chromatography control systems is proposed, including strategies to control buffer formulation, product fractionation, column switching and column fouling. The implementation of these control systems improves process capabilities to fulfil product quality criteria as processes are scaled, transferred and operated, thus fast tracking the delivery of new medicines to market.

Facile All-Optical Method for In Situ Detection of Low Amounts of Ammonia

As a key precursor for nitrogenous compounds and fertilizer, ammonia affects our lives in numerous ways. Rapid and sensitive detection of ammonia is essential, both in environmental monitoring and in process control for industrial production. Here we report a novel and nonperturbative method that allows rapid detection of ammonia at low concentrations, based on the all-optical detection of surface-enhanced Raman signals. We show that this simple and affordable approach enables ammonia probing at selected regions of interest with high spatial resolution, making in situ and operando observations possible.

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Novel method for detection of ammonia at concentrations below 1 ppm in just under 1 s

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This approach allows local detection of ammonia amounts as low as 10⁴–10⁵ molecules

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Method for sensitive direct monitoring of catalytic/electrocatalytic processes

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The method allows following the dynamics of ammonia concentration change in real time