On-line prediction of the glucose concentration of CHO cell cultivations by NIR and Raman spectroscopy: Comparative scalability test with a shake flask model system

Monitoring of Nutrients, Metabolites, IgG Titer, and Cell Densities in 10 L Bioreactors Using Raman Spectroscopy and PLS Regression Models

Background: Chinese hamster ovary (CHO) cell metabolism is complex, influenced by nutrients like glucose and glutamine and metabolites such as lactate. Real-time monitoring is necessary for optimizing culture conditions and ensuring consistent product quality. Raman spectroscopy has emerged as a robust process analytical technology (PAT) tool due to its non-invasive, in situ capabilities. This study evaluates Raman spectroscopy for monitoring key metabolic parameters and IgG titer in CHO cell cultures. Methods: Raman spectroscopy was applied to five 10 L-scale CHO cell cultures. Partial least squares (PLS) regression models were developed from four batches, including one with induced cell death, to enhance robustness. The models were validated against blind test sets. Results: PLS models exhibited high predictive accuracy (R2 > 0.9). Glucose and IgG titer predictions were reliable (RMSEP = 0.51 g/L and 0.12 g/L, respectively), while glutamine and lactate had higher RMSEP due to lower concentrations. Specific Raman bands contributed to the specificity of glucose, lactate, and IgG models. Predictions for viable (VCD) and total cell density (TCD) were less accurate due to the absence of direct Raman signals. Conclusions: This study confirms Raman spectroscopy's potential for real-time, in situ bioprocess monitoring without manual sampling. Chemometric analysis enhances model robustness, supporting automated control systems. Raman data could enable continuous feedback regulation of critical nutrients like glucose, ensuring consistent critical quality attributes (CQAs) in biopharmaceutical production.

Exploration of linear and interpretable models for quantification of cell parameters via contactless short-wave infrared hyperspectral sensing

The development of optical sensors for label-free quantification of cell parameters has numerous uses in the biomedical arena. However, using current optical probes requires the laborious collection of sufficiently large datasets that can be used to calibrate optical probe signals to true metabolite concentrations. Further, most practitioners find it difficult to confidently adapt black box chemometric models that are difficult to troubleshoot in high-stakes applications such as biopharmaceutical manufacturing. Replacing optical probes with contactless short-wave infrared (SWIR) hyperspectral cameras allows efficient collection of thousands of absorption signals in a handful of images. This high repetition allows for effective denoising of each spectrum, so interpretable linear models can quantify metabolites. To illustrate, an interpretable linear model called L-SLR is trained using small datasets obtained with a SWIR HSI camera to quantify fructose, viable cell density (VCD), glucose, and lactate. The performance of this model is also compared to other existing linear models, namely Partial Least Squares (PLS) and Non-negative Matrix Factorization (NMF). Using only 50% of the dataset for training, reasonable test performance of mean absolute error (MAE) and correlations (r2) are achieved for glucose (r2 = 0.88, MAE = 37 mg/dL), lactate (r2 = 0.93, MAE = 15.08 mg/dL), and VCD (r2 = 0.81, MAE = 8.6 × 105 cells/mL). Further, these models are also able to handle quantification of a metabolite like fructose in the presence of high background concentration of similar metabolite with almost identical chemical interactions in water like glucose. The model achieves reasonable quantification performance for large fructose level (100-1000 mg/dL) quantification (r2 = 0.92, MAE = 25.1 mg/dL) and small fructose level (< 60 mg/dL) concentrations (r2 = 0.85, MAE = 4.97 mg/dL) in complex media like Fetal Bovine Serum (FBS). Finally, the model provides sparse interpretable weight matrices that hint at the underlying solution changes that correlate to each cell parameter prediction.

Harnessing Raman spectroscopy for cell therapy bioprocessing

Cell therapy manufacturing requires precise monitoring of critical parameters to ensure product quality, consistency and to facilitate the implementation of cost-effective processes. While conventional analytical methods offer limited real-time insights, integration of process analytical technology tools such as Raman spectroscopy in bioprocessing has the potential to drive efficiency and reliability during the manufacture of cell-based therapies while meeting stringent regulatory requirements. The non-destructive nature of Raman spectroscopy, combined with its ability to be integrated on-line with scalable platforms, allows for continuous data acquisition, enabling real-time correlations between process parameters and critical quality attributes. Herein, we review the role of Raman spectroscopy in cell therapy bioprocessing and discuss how simultaneous measurement of distinct parameters and attributes, such as cell density, viability, metabolites and cell identity biomarkers can streamline on-line monitoring and facilitate adaptive process control. This, in turn, enhances productivity and mitigates process-related risks. We focus on recent advances integrating Raman spectroscopy across various manufacturing stages, from optimizing culture media feeds to monitoring bioprocess dynamics, covering downstream applications such as detection of co-isolated contaminating cells, cryopreservation, and quality control of the drug product. Finally, we discuss the potential of Raman spectroscopy to revolutionize current practices and accelerate the development of advanced therapy medicinal products.

Raman and NIR spectroscopy-based real-time monitoring of the membrane filtration process of a recombinant protein for the diagnosis of SARS-CoV-2

This research shows the detailed comparison of Raman and near-infrared (NIR) spectroscopy as Process Analytical Technology tools for the real-time monitoring of a protein purification process. A comprehensive investigation of the application and model development of Raman and NIR spectroscopy was carried out for the real-time monitoring of a process-related impurity, imidazole, during the tangential flow filtration of Receptor-Binding Domain (RBD) of the SARS-CoV-2 Spike protein. The fast development of Raman and NIR spectroscopy-based calibration models was achieved using offline calibration data, resulting in low calibration and cross-validation errors. Raman model had an RMSEC of 1.53 mM, and an RMSECV of 1.78 mM, and the NIR model had an RMSEC of 1.87 mM and an RMSECV of 2.97 mM. Furthermore, Raman models had good robustness when applied in an inline measurement system, but on the contrary NIR spectroscopy was sensitive to the changes in the measurement environment. By utilizing the developed models, inline Raman and NIR spectroscopy were successfully applied for the real-time monitoring of a process-related impurity during the membrane filtration of a recombinant protein. The results enhance the importance of implementing real-time monitoring approaches for the broader field of diagnostic and therapeutic protein purification and underscore its potential to revolutionize the rapid development of biological products.

Comprehensive modeling of cell culture profile using Raman spectroscopy and machine learning

Chinese hamster ovary (CHO) cells are widely utilized in the production of antibody drugs. To ensure the production of large quantities of antibodies that meet the required specifications, it is crucial to monitor and control the levels of metabolites comprehensively during CHO cell culture. In recent years, continuous analysis methods employing on-line/in-line techniques using Raman spectroscopy have attracted attention. While these analytical methods can nondestructively monitor culture data, constructing a highly accurate measurement model for numerous components is time-consuming, making it challenging to implement in the rapid research and development of pharmaceutical manufacturing processes. In this study, we developed a comprehensive, simple, and automated method for constructing a Raman model of various components measured by LC-MS and other techniques using machine learning with Python. Preprocessing and spectral-range optimization of data for model construction (partial least square (PLS) regression) were automated and accelerated using Bayes optimization. Subsequently, models were constructed for each component using various model construction techniques, including linear regression, ridge regression, XGBoost, and neural network. This enabled the model accuracy to be improved compared with PLS regression. This automated approach allows continuous monitoring of various parameters for over 100 components, facilitating process optimization and process monitoring of CHO cells.

Current and future technologies for monitoring cultured meat: A review

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

An assessment of the impact of Raman based glucose feedback control on CHO cell bioreactor process development

Process analytical technology (PAT) tools such as Raman Spectroscopy have become established tools for real time measurement of CHO cell bioreactor process variables and are aligned with the QbD approach to manufacturing. These tools can have a significant impact on process development if adopted early, creating an end-to-end PAT/QbD focused process. This study assessed the impact of Raman based feedback control on early and late phase development bioreactors by using a Raman based PLS model and PAT management system to control glucose in two CHO cell line bioreactor processes. The impact was then compared to bioreactor processes which used manual bolus fed methods for glucose feed delivery. Process improvements were observed in terms of overall bioreactor health, product output and product quality. Raman controlled batches for Cell Line 1 showed a reduction in glycation of 43.4% and 57.9%, respectively. Cell Line 2 batches with Raman based feedback control showed an improved growth profile with higher VCD and viability and a resulting 25% increase in overall product titer with an improved glycation profile. The results presented here demonstrate that Raman spectroscopy can be used in both early and late-stage process development and design for consistent and controlled glucose feed delivery.

From Spatial-Temporal Multiscale Modeling to Application: Bridging the Valley of Death in Industrial Biotechnology

The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed.

Fast-Training Deep Learning Algorithm for Multiplex Quantification of Mammalian Bioproduction Metabolites via Contactless Short-Wave Infrared Hyperspectral Sensing

Within the biopharmaceutical sector, there exists the need for a contactless multiplex sensor, which can accurately detect metabolite levels in real time for precise feedback control of a bioreactor environment. Reported spectral sensors in the literature only work when fully submerged in the bioreactor and are subject to probe fouling due to a cell debris buildup. The use of a short-wave infrared (SWIR) hyperspectral (HS) cam era allows for efficient, fully contactless collection of large spectral datasets for metabolite quantification. Here, we report the development of an interpretable deep learning system, a convolution metabolite regression (CMR) approach that detects glucose and lactate concentrations using label-free contactless HS images of cell-free spent media samples from Chinese hamster ovary (CHO) cell growth flasks. Using a dataset of <500 HS images, these CMR algorithms achieved a competitive test root-mean-square error (RMSE) performance of glucose quantification within 27 mg/dL and lactate quantification within 20 mg/dL. Conventional Raman spectroscopy probes report a validation performance of 26 and 18 mg/dL for glucose and lactate, respectively. The CMR system trains within 10 epochs and uses a convolution encoder with a sparse bottleneck regression layer to pick the best-performing filters learned by CMR. Each of these filters is combined with existing interpretable models to produce a metabolite sensing system that automatically removes spurious predictions. Collectively, this work will advance the safe and efficient adoption of contactless deep learning sensing systems for fine control of a variety of bioreactor environments.

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.

A shallow convolutional neural network with elastic nets for blood glucose quantitative analysis using Raman spectroscopy

In this paper, a one-dimensional shallow convolutional neural network structure combined with elastic nets (1D-SCNN-EN) was firstly proposed to predict the glucose concentration of blood by Raman spectroscopy. A total of 106 different blood glucose spectra were obtained by Fourier transform (FT) Raman spectroscopy. The one-dimensional shallow convolutional neural network, with elastic nets added to the full connected layer, was presented to capture multiple deep features and reduce the complexity of the model. The 1D-SCNN-EN model has a better performance than conventional approaches (partial least squares and support vector machine). The root mean squared error of calibration (RMSEC), the root mean squared error of prediction (RMSEP), the determination coefficient of prediction (R_P²), and the residual predictive deviation of prediction (RPD) were 0.10262, 0.11210, 0.99403, and 12.94601, respectively. The experiment results showed that the 1D-SCNN-EN model has a higher prediction accuracy and stronger robustness than the other regression models. The overall studies indicated that the 1D-SCNN-EN model looked promising for predict the glucose concentration of blood by Raman spectroscopy when the sample size is small.

Process Analytical Technologies - Advances in bioprocess integration and future perspectives

Process Analytical Technology (PAT) instruments include analyzers capable of measuring physical and chemical process parameters and key attributes with the goal of optimizing process controls. PAT in the form of a probe or sensor is designed to integrate within the pharmaceutical manufacturing line and is coupled with computing equipment to perform chemometric modeling for result interpretation and multilayer statistical control of processes. PAT solutions are intended for understanding bioprocesses with a goal to control quality at all stages of product manufacturing and achieve quality by design (QbD). The goal of PAT implementation is to promote real-time release of products to decrease the cycle time and cost of production. This review focuses on the applications of PAT solutions at different stages of the manufacturing process for vaccine production, the advantages, challenges at present state, and the vision of the future development of biopharmaceutical industries.

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

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

Comparison of partial least square, artificial neural network, and support vector regressions for real-time monitoring of CHO cell culture processes using in situ near-infrared spectroscopy

The biopharmaceutical industry must guarantee the efficiency and biosafety of biological medicines, which are quite sensitive to cell culture process variability. Real-time monitoring procedures based on vibrational spectroscopy such as near-infrared (NIR) spectroscopy, are then emerging to support innovative strategies for retro-control of key parameters as substrates and by-product concentration. Whereas monitoring models are mainly constructed using partial least squares regression (PLSR), spectroscopic models based on artificial neural networks (ANNR) and support vector regression (SVR) are emerging with promising results. Unfortunately, analysis of their performance in cell culture monitoring has been limited. This study was then focused to assess their performance and suitability for the cell culture process challenges. PLSR had inferior values of the determination coefficient (R² ) for all the monitored parameters (i.e., 0.85, 0.93, and 0.98, respectively for the PLSR, SVR, and ANNR models for glucose). In general, PLSR had a limited performance while models based on ANNR and SVR have been shown superior due to better management of inter-batch heterogeneity and enhanced specificity. Overall, the use of SVR and ANNR for the generation of calibration models enhanced the potential of NIR spectroscopy as a monitoring tool.

Prediction of Aqueous Glucose Concentration Using Hyperspectral Imaging

Near infrared hyperspectral imaging (HSI) is an emerging optical imaging modality which boasts several advantages. Compared to conventional spectroscopy, HSI pro-vides thousands of spectral samples with embedded spatial information in a single image. This allows the collection of high quality and high volume spectral signals in a short time. In this paper, transmissive HSI combined with Partial Least Squares Regression (PLSR) was used to non-invasively predict aqueous glucose concentration. Aqueous glucose samples are prepared with concentration ranging from 0 - 1000 mg/dL at intervals of 100 mg/dL and 100 - 300 mg/dL at intervals of 20 mg/dL. Our results are validated using leave-one-concentration-out cross validation, and demonstrate the feasibility of the proposed aqueous glucose concentration detection method using the combination of HSI and PLSR.

The role of Raman spectroscopy in biopharmaceuticals from development to manufacturing

Biopharmaceuticals have revolutionized the field of medicine in the types of active ingredient molecules and treatable indications. Adoption of Quality by Design and Process Analytical Technology (PAT) frameworks has helped the biopharmaceutical field to realize consistent product quality, process intensification, and real-time control. As part of the PAT strategy, Raman spectroscopy offers many benefits and is used successfully in bioprocessing from single-cell analysis to cGMP process control. Since first introduced in 2011 for industrial bioprocessing applications, Raman has become a first-choice PAT for monitoring and controlling upstream bioprocesses because it facilitates advanced process control and enables consistent process quality. This paper will discuss new frontiers in extending these successes in upstream from scale-down to commercial manufacturing. New reports concerning the use of Raman spectroscopy in the basic science of single cells and downstream process monitoring illustrate industrial recognition of Raman’s value throughout a biopharmaceutical product’s lifecycle. Finally, we draw upon a nearly 90-year history in biological Raman spectroscopy to provide the basis for laboratory and in-line measurements of protein quality, including higher-order structure and composition modifications, to support formulation development.

Raman-based dynamic feeding strategies using real-time glucose concentration monitoring system during adalimumab producing CHO cell cultivation

The use of Process Analytical Technology tools coupled with chemometrics has been shown great potential for better understanding and control of mammalian cell cultivations through real-time process monitoring. In-line Raman spectroscopy was utilized to determine the glucose concentration of the complex bioreactor culture medium ensuring real-time information for our process control system. This work demonstrates a simple and fast method to achieve a robust partial least squares calibration model under laboratory conditions in an early phase of the development utilizing shake flask and bioreactor cultures. Two types of dynamic feeding strategies were accomplished where the multi-component feed medium additions were controlled manually and automatically based on the Raman monitored glucose concentration. The impact of these dynamic feedings was also investigated and compared to the traditional bolus feeding strategy on cellular metabolism, cell growth, productivity, and binding activity of the antibody product. Both manual and automated dynamic feeding strategies were successfully applied to maintain the glucose concentration within a narrower and lower concentration range. Thus, besides glucose, the glutamate was also limited at low level leading to reduced production of inhibitory metabolites, such as lactate and ammonia. Consequently, these feeding control strategies enabled to provide beneficial cultivation environment for the cells. In both experiments, higher cell growth and prolonged viable cell cultivation were achieved which in turn led to increased antibody product concentration compared to the reference bolus feeding cultivation.

Direct optical detection of cell density and viability of mammalian cells by means of UV/VIS spectroscopy

The critical process parameters cell density and viability during mammalian cell cultivation are assessed by UV/VIS spectroscopy in combination with multivariate data analytical methods. This direct optical detection technique uses a commercial optical probe to acquire spectra in a label-free way without signal enhancement. For the cultivation, an inverse cultivation protocol is applied, which simulates the exponential growth phase by exponentially replacing cells and metabolites of a growing Chinese hamster ovary cell batch with fresh medium. For the simulation of the death phase, a batch of growing cells is progressively replaced by a batch with completely starved cells. Thus, the most important parts of an industrial batch cultivation are easily imitated. The cell viability was determined by the well-established method partial least squares regression (PLS). To further improve process knowledge, the viability has been determined from the spectra based on a multivariate curve resolution (MCR) model. With this approach, the progress of the cultivations can be continuously monitored solely based on an UV/VIS sensor. Thus, the monitoring of critical process parameters is possible inline within a mammalian cell cultivation process, especially the viable cell density. In addition, the beginning of cell death can be detected by this method which allows us to determine the cell viability with acceptable error. The combination of inline UV/VIS spectroscopy with multivariate curve resolution generates additional process knowledge complementary to PLS and is considered a suitable process analytical tool for monitoring industrial cultivation processes.

On-line glucose monitoring by near infrared spectroscopy during the scale up steps of mammalian cell cultivation process development

NIR spectroscopy is a non-destructive tool for in-situ, on-line bioprocess monitoring. One of its most frequent applications is the determination of metabolites during cultivation, especially glucose. Previous studies have usually investigated the applicability of Near Infrared (NIR) spectroscopy at one bioreactor scale but the effect of scale up was not explored. In this study, the complete scale up from shake flask (1 L) through 20 L, 100 L and 1000 L up to 5000 L bioreactor volume level was monitored with on-line NIR spectroscopy. The differences between runs and scales were examined using principal component analysis. The bioreactor runs were relatively similar regardless of scales but the shake flasks differed strongly from bioreactor runs. The glucose concentration throughout five 5000 L scale bioreactor runs were predicted by partial least squares regression models that were based on pre-processed spectra of bioreactor runs and combinations of them. The model that produced the lowest error of prediction (4.18 mM on a 29 mM concentration range) for all five runs in the prediction set was based on the combination of 20 L and 100 L data. This result demonstrated the capabilities and the limitations of an NIR system for glucose monitoring in mammalian cell cultivations.