In situ Diazonium Salt Formation and Photochemical Aryl-Aryl Coupling in Continuous Flow Monitored by Inline NMR Spectroscopy.

A novel class of diazonium salts is introduced for the photochemical aryl-aryl coupling to produce (substituted) biphenyls. As common diazonium tetrafluoroborate salts fail, soluble and safe aryl diazonium trifluoroacetates are applied. In this mild synthesis route no catalysts are required to generate an aryl-radical by irradiation with UV-A light (365 nm). This reactive species undergoes direct C-H arylation at an arene, forming the product in reasonable reaction times. With the implementation of a continuous flow setup in a capillary photoreactor 13 different biphenyl derivatives are successfully synthesized. By integrating an inline 19F-NMR benchtop spectrometer, samples are reliably quantified as the fluorine-substituents act as a probe. Here, real-time NMR spectroscopy is a perfect tool to monitor the continuously operated system, which produces fine chemicals of industrial relevance even in a multigram scale.

Supervised and unsupervised machine learning approaches for monitoring subvisible particles within an aluminum-salt adjuvanted vaccine formulation.

Suspensions of protein antigens adsorbed to aluminum-salt adjuvants are used in many vaccines and require mixing during vial filling operations to prevent sedimentation. However, the mixing of vaccine formulations may generate undesirable particles that are difficult to detect against the background of suspended adjuvant particles. We simulated the mixing of a suspension containing a protein antigen adsorbed to an aluminum-salt adjuvant using a recirculating peristaltic pump and used flow imaging microscopy to record images of particles within the pumped suspensions. Supervised convolutional neural networks (CNNs) were used to analyze the images and create "fingerprints" of particle morphology distributions, allowing detection of new particles generated during pumping. These results were compared to those obtained from an unsupervised machine learning algorithm relying on variational autoencoders (VAEs) that were also used to detect new particles generated during pumping. Analyses of images conducted by applying both supervised CNNs and VAEs found that rates of generation of new particles were higher in aluminum-salt adjuvant suspensions containing protein antigen than placebo suspensions containing only adjuvant. Finally, front-face fluorescence measurements of the vaccine suspensions indicated changes in solvent exposure of tryptophan residues in the protein that occurred concomitantly with new particle generation during pumping.

Robust platform for inline Raman monitoring and control of perfusion cell culture.

Perfusion cell culture has been gaining increasing popularity for biologics manufacturing due to benefits such as smaller footprint, increased productivity, consistent product quality and manufacturing flexibility, cost savings, and so forth. Process Analytics Technologies tools are highly desirable for effective monitoring and control of long-running perfusion processes. Raman has been widely investigated for monitoring and control of traditional fed batch cell culture process. However, implementation of Raman for perfusion cell culture has been very limited mainly due to challenges with high-cell density and long running times during perfusion which cause extremely high fluorescence interference to Raman spectra and consequently it is exceedingly difficult to develop robust chemometrics models. In this work, a platform based on Raman measurement of permeate has been proposed for effective analysis of perfusion process. It has been demonstrated that this platform can effectively circumvent the fluorescence interference issue while providing rich and timely information about perfusion dynamics to enable efficient process monitoring and robust bioreactor feed control. With the highly consistent spectral data from cell-free sample matrix, development of chemometrics models can be greatly facilitated. Based on this platform, Raman models have been developed for good measurement of several analytes including glucose, lactate, glutamine, glutamate, and permeate titer. Performance of Raman models developed this way has been systematically evaluated and the models have shown good robustness against changes in perfusion scale and variations in permeate flowrate; thus models developed from small lab scale can be directly transferred for implementation in much larger scale of perfusion. With demonstrated robustness, this platform provides a reliable approach for automated glucose feed control in perfusion bioreactors. Glucose model developed from small lab scale has been successfully implemented for automated continuous glucose feed control of perfusion cell culture at much larger scale.

Simultaneous prediction of 16 quality attributes during protein A chromatography using machine learning based Raman spectroscopy models.

Several key technologies for advancing biopharmaceutical manufacturing depend on the successful implementation of process analytical technologies that can monitor multiple product quality attributes in a continuous in-line setting. Raman spectroscopy is an emerging technology in the biopharma industry that promises to fit this strategic need, yet its application is not widespread due to limited success for predicting a meaningful number of quality attributes. In this study, we addressed this very problem by demonstrating new capabilities for preprocessing Raman spectra using a series of Butterworth filters. The resulting increase in the number of spectral features is paired with a machine learning algorithm and laboratory automation hardware to drive the automated collection and training of a calibration model that allows for the prediction of 16 different product quality attributes in an in-line mode. The demonstrated ability to generate these Raman-based models for in-process product quality monitoring is the breakthrough to increase process understanding by delivering product quality data in a continuous manner. The implementation of this multiattribute in-line technology will create new workflows within process development, characterization, validation, and control.

Bioprocess in-line monitoring and control using Raman spectroscopy and Indirect Hard Modeling (IHM).

Process in-line monitoring and control are crucial to optimize the productivity of bioprocesses. A frequently applied Process Analytical Technology (PAT) tool for bioprocess in-line monitoring is Raman spectroscopy. However, evaluating bioprocess Raman spectra is complex and calibrating state-of-the-art statistical evaluation models is effortful. To overcome this challenge, we developed an Indirect Hard Modeling (IHM) prediction model in a previous study. The combination of Raman spectroscopy and the IHM prediction model enables non-invasive in-line monitoring of glucose and ethanol mass fractions during yeast fermentations with significantly less calibration effort than comparable approaches based on statistical models. In this study, we advance this IHM-based approach and successfully demonstrate that the combination of Raman spectroscopy and IHM is capable of not only bioprocess monitoring but also bioprocess control. For this purpose, we used this combination's in-line information as input of a simple on-off glucose controller to control the glucose mass fraction in Saccharomyces cerevisiae fermentations. When we performed two of these fermentations with different predefined glucose set points, we achieved similar process control quality as approaches using statistical models, despite considerably smaller calibration effort. Therefore, this study reaffirms that the combination of Raman spectroscopy and IHM is a powerful PAT tool for bioprocesses.

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.

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.

Opportunities and challenges of ultrasonic diagnostic techniques for plant-based food monitoring: principle, machine system, and application strategies.

Plant-based food consumption has increased substantially owing to its positive effects on human and global health. However, ensuring the quality and safety of plant-based foods remains a challenge. Diagnostic ultrasonic technology is widely used for rapid and nondestructive determination owing to its ability to penetrate optically opaque materials, strong directivity, rapid detection capabilities, low equipment costs, and ease of operation. This review provides a comprehensive understanding of diagnostic ultrasonic technology by summarizing the principles of food characterization, factors that influence detection accuracy and methods to mitigate their impact, composition of ultrasonic machine systems, and application of diagnostic ultrasound for monitoring plant-based foods. The detection principle of ultrasonic technology is based on empirical equations that establish a relationship between the ultrasonic and physicochemical indicators of food. To improve the detection accuracy, a compensation mechanism for the temperature and pressure should be established, measurement distances should be set in the far-field region, and liquid samples should be degassed. Furthermore, the sample platform design and the choice of detection mode depend on the nature of the food. Combining ultrasonic technology with machine learning techniques presents promising prospects for real-time process monitoring in the food and beverage industries.

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.

Application of PAT Probes in Aluminum Phosphate Adjuvant Manufacturing.

PURPOSE: This study is focused on monitoring process parameters and quality attributes of aluminum phosphate (AlPO4) using multiple in-line probes incorporated into an industrial-scale adjuvant suspension manufacturing unit. METHODS: The manufacturing of aluminum adjuvant suspension was monitored at manufacturing scale using conductivity, turbidity, infrared, and particle sizing and count probes to follow the continuous evolution of particle formation and size distribution, and the reaction kinetics during the synthesis of AlPO4. RESULTS: The data showed that AlPO4 forms large particles at the early stages of mixing, followed by a decrease in size and then stabilization towards the later stages of mixing and pH adjustment. The results provided a complementary view of process events and assisted in optimizing several parameters, e.g., flow rate of reactants AlCl3 and Na3PO4 solutions, mixing rate, pH, and conductivity of AlPO4, as well as adjuvant quality attribute such as particle size, thus streamlining and shortening the process development stage. CONCLUSION: The results of this study showed the usefulness of the in-line probes to automate continuous assessment of AlPO4 batch-to-batch consistency during in-house adjuvant production at the industrial scale.

Online monitoring of protein refolding in inclusion body processing using intrinsic fluorescence.

Inclusion bodies (IBs) are protein aggregates formed as a result of overexpression of recombinant protein in E. coli. The formation of IBs is a valuable strategy of recombinant protein production despite the need for additional processing steps, i.e., isolation, solubilization and refolding. Industrial process development of protein refolding is a labor-intensive task based largely on empirical approaches rather than knowledge-driven strategies. A prerequisite for knowledge-driven process development is a reliable monitoring strategy. This work explores the potential of intrinsic tryptophan and tyrosine fluorescence for real-time and in situ monitoring of protein refolding. In contrast to commonly established process analytical technology (PAT), this technique showed high sensitivity with reproducible measurements for protein concentrations down to 0.01 g L - 1 . The change of protein conformation during refolding is reflected as a shift in the position of the maxima of the tryptophan and tyrosine fluorescence spectra as well as change in the signal intensity. The shift in the peak position, expressed as average emission wavelength of a spectrum, was correlated to the amount of folding intermediates whereas the intensity integral correlates to the extent of aggregation. These correlations were implemented as an observation function into a mechanistic model. The versatility and transferability of the technique were demonstrated on the refolding of three different proteins with varying structural complexity. The technique was also successfully applied to detect the effect of additives and process mode on the refolding process efficiency. Thus, the methodology presented poses a generic and reliable PAT tool enabling real-time process monitoring of protein refolding.

Micro simulated moving bed chromatography-mass spectrometry as a continuous on-line process analytical tool.

Continuous manufacturing is becoming increasingly important in the (bio-)pharmaceutical industry, as more product can be produced in less time and at lower costs. In this context, there is a need for powerful continuous analytical tools. Many established off-line analytical methods, such as mass spectrometry (MS), are hardly considered for process analytical technology (PAT) applications in biopharmaceutical processes, as they are limited to at-line analysis due to the required sample preparation and the associated complexity, although they would provide a suitable technique for the assessment of a wide range of quality attributes. In this study, we investigated the applicability of a recently developed micro simulated moving bed chromatography system (µSMB) for continuous on-line sample preparation for MS. As a test case, we demonstrate the continuous on-line MS measurement of a protein solution (myoglobin) containing Tris buffer, which interferes with ESI-MS measurements, by continuously exchanging this buffer with a volatile ammonium acetate buffer suitable for MS measurements. The integration of the µSMB significantly increases MS sensitivity by removing over 98% of the buffer substances. Thus, this study demonstrates the feasibility of on-line µSMB-MS, providing a versatile PAT tool by combining the detection power of MS for various product attributes with all the advantages of continuous on-line analytics.

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.

Development of automated metabolite control using mid-infrared probe for bioprocesses and vaccine manufacturing.

Automation of metabolite control in fermenters is fundamental to develop vaccine manufacturing processes more quickly and robustly. We created an end-to-end process analytical technology and quality by design-focused process by replacing manual control of metabolites during the development of fed-batch bioprocesses with a system that is highly adaptable and automation-enabled. Mid-infrared spectroscopy with an attenuated total reflectance probe in-line, and simple linear regression using the Beer-Lambert Law, were developed to quantitate key metabolites (glucose and glutamate) from spectral data that measured complex media during fermentation. This data was digitally connected to a process information management system, to enable continuous control of feed pumps with proportional-integral-derivative controllers that maintained nutrient levels throughout fed-batch stirred-tank fermenter processes. Continuous metabolite data from mid-infrared spectra of cultures in stirred-tank reactors enabled feedback loops and control of the feed pumps in pharmaceutical development laboratories. This improved process control of nutrient levels by 20-fold and the drug substance yield by an order of magnitude. Furthermore, the method is adaptable to other systems and enables soft sensing, such as the consumption rate of metabolites. The ability to develop quantitative metabolite templates quickly and simply for changing bioprocesses was instrumental for project acceleration and heightened process control and automation. ONE-SENTENCE SUMMARY: Intelligent digital control systems using continuous in-line metabolite data enabled end-to-end automation of fed-batch processes in stirred-tank reactors.

Contributions towards variable temperature shielding for compact NMR instruments.

The application of compact NMR instruments to hot flowing samples or exothermically reacting mixtures is limited by the temperature sensitivity of permanent magnets. Typically, such temperature effects directly influence the achievable magnetic field homogeneity and hence measurement quality. The internal-temperature control loop of the magnet and instruments is not designed for such temperature compensation. Passive insulation is restricted by the small dimensions within the magnet borehole. Here, we present a design approach for active heat shielding with the aim of variable temperature control of NMR samples for benchtop NMR instruments using a compressed airstream which is variable in flow and temperature. Based on the system identification and surface temperature measurements through thermography, a model predictive control was set up to minimise any disturbance effect on the permanent magnet from the probe or sample temperature. This methodology will facilitate the application of variable-temperature shielding and, therefore, extend the application of compact NMR instruments to flowing sample temperatures that differ from the magnet temperature.

Reaction monitoring via benchtop nuclear magnetic resonance spectroscopy: A practical comparison of on-line stopped-flow and continuous-flow sampling methods.

The ability for nuclear magnetic resonance (NMR) spectroscopy to provide quantitative, structurally rich information makes this spectroscopic technique an attractive reaction monitoring tool. The practicality of NMR for this type of analysis has only increased in the recent years with the influx of commercially available benchtop NMR instruments and compatible flow systems. In this study, we aim to compare 19F NMR reaction profiles acquired under both on-line continuous-flow and stopped-flow sampling methods, with modern benchtop NMR instrumentation, and two reaction systems: a homogeneous imination reaction and a biphasic activation of a carboxylic acid to acyl fluoride. Reaction trends with higher data density can be acquired with on-line continuous-flow analyses, and this work highlights that representative reaction trends can be acquired without any correction when monitoring resonances with a shorter spin-lattice relaxation time (T1), and with the used flow conditions. On-line stopped-flow analyses resulted in representative reaction trends in all cases, including the monitoring of resonances with a long T1, without the need of any correction factors. The benefit of easier data analysis, however, comes with the cost of time, as the fresh reaction solution must be flowed into the NMR system, halted, and time must be provided for spins to become polarized in the instrument's external magnetic field prior to spectral measurement. Results for one of the reactions were additionally compared with the use of a high-field NMR.

Design of experiments for micellar electrokinetic chromatography method development for the monitoring of water-soluble vitamins in cell culture medium.

Biopharmaceutical production takes place in complex processes which should be thoroughly understood. Therefore, the iConsensus project focuses on developing a monitoring platform integrating several process analytical technology tools for integrated, automated monitoring of the biopharmaceutical process. Water-soluble vitamin monitoring using (microchip) capillary electrophoresis (CE) is part of this platform. This work comprises the development of conventional CE methods as the first part towards integrated vitamin monitoring. The vitamins were divided based on their physical-chemical properties to develop two robust methods. Previously, a method for the analysis of cationic vitamins (pyridoxine, pyridoxal, pyridoxamine, thiamine and nicotinamide) in cell culture medium was developed. This work focused on the development of a micellar electrokinetic chromatography method for anionic and neutral vitamins (riboflavin, d-calcium pantothenate, biotin, folic acid, cyanocobalamin and ascorbic acid). By employing multivariate design of experiments, the background electrolyte (BGE) could be optimised within one experiment testing only 11 BGEs. The optimised BGE conditions were 200 mM borate with 77 mM sodium dodecyl sulphate at a pH of 8.6. Using this BGE, all above-mentioned cationic, anionic and neutral vitamins could be separated in clean samples. In cell culture medium, most anionic and neutral vitamins could be separated. Combining the two methods allows for analysis of cationic, anionic and neutral vitamins in cell culture medium samples. The next step towards integrated vitamin monitoring includes transfer to microchip CE. Due to the lack of fast and reliable methods for vitamin monitoring, the developed capillary methods could be valuable as stand-alone at-line process analytical technology solutions as well.

Method development for quantitative monitoring of monoclonal antibodies in upstream cell-culture process samples with limited sample preparation - An evaluation of various capillary coatings.

Monoclonal antibodies (mAbs) have become an important class of biopharmaceuticals used for the treatment of various diseases. Their quantification during the manufacturing process is important. In this work, a capillary zone electrophoresis (CZE) method was developed for the monitoring of the mAb concentration during cell-culture processes. CZE method development rules are outlined, particularly discussing various capillary coatings, such as a neutral covalent polyvinyl alcohol coating, a dynamic successive multiple ionic-polymer coating, and dynamic coatings using background electrolyte additives such as triethanolamine (T-EthA) and triethylamine. The dynamic T-EthA coating resulted in most stable electro-osmotic flows and most efficient peak shapes. The method is validated over the range 0.1-10 mg/ml, with a linear range of 0.08-1.3 mg/ml and an extended range of 1-10 mg/ml by diluting samples in the latter concentration range 10-fold in water. The intraday precision and accuracy were 2%-12% and 88%-107%, respectively, and inter-day precision and accuracy were 4%-9% and 93%-104%, respectively. The precision and accuracy of the lowest concentration level (0.08 mg/ml) were slightly worse and still well in scope for monitoring purposes. The presented method proved applicable for analysing in-process cell-culture samples from different cell-culture processes and is possibly well suited as platform method.

Early detection and metabolic pathway identification of T cell activation by in-process intracellular mass spectrometry.

BACKGROUND AIMS: In-process monitoring and control of biomanufacturing workflows remains a significant challenge in the development, production, and application of cell therapies. New process analytical technologies must be developed to identify and control the critical process parameters that govern ex vivo cell growth and differentiation to ensure consistent and predictable safety, efficacy, and potency of clinical products. METHODS: This study demonstrates a new platform for at-line intracellular analysis of T-cells. Untargeted mass spectrometry analyses via the platform are correlated to conventional methods of T-cell assessment. RESULTS: Spectral markers and metabolic pathways correlated with T-cell activation and differentiation are detected at early time points via rapid, label-free metabolic measurements from a minimal number of cells as enabled by the platform. This is achieved while reducing the analytical time and resources as compared to conventional methods of T-cell assessment. CONCLUSIONS: In addition to opportunities for fundamental insight into the dynamics of T-cell processes, this work highlights the potential of in-process monitoring and dynamic feedback control strategies via metabolic modulation to drive T-cell activation, proliferation, and differentiation throughout biomanufacturing.

Real-time detection of mAb aggregates in an integrated downstream process.

The implementation of continuous processing in the biopharmaceutical industry is hindered by the scarcity of process analytical technologies (PAT). To monitor and control a continuous process, PAT tools will be crucial to measure real-time product quality attributes such as protein aggregation. Miniaturizing these analytical techniques can increase measurement speed and enable faster decision-making. A fluorescent dye (FD)-based miniaturized sensor has previously been developed: a zigzag microchannel which mixes two streams under 30 s. Bis-ANS and CCVJ, two established FDs, were employed in this micromixer to detect aggregation of the biopharmaceutical monoclonal antibody (mAb). Both FDs were able to robustly detect aggregation levels starting at 2.5%. However, the real-time measurement provided by the microfluidic sensor still needs to be implemented and assessed in an integrated continuous downstream process. In this work, the micromixer is implemented in a lab-scale integrated system for the purification of mAbs, established in an ÄKTA™ unit. A viral inactivation and two polishing steps were reproduced, sending a sample of the product pool after each phase directly to the microfluidic sensor for aggregate detection. An additional UV sensor was connected after the micromixer and an increase in its signal would indicate that aggregates were present in the sample. The at-line miniaturized PAT tool provides a fast aggregation measurement, under 10 min, enabling better process understanding and control.