Real-time amino acid and glucose monitoring system for the automatic control of nutrient feeding in CHO cell culture using raman spectroscopy

NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

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

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

Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor

As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.

Development of generic metabolic Raman calibration models using solution titration in aqueous phase and data augmentation for in-line cell culture analysis

This study presents a novel approach for developing generic metabolic Raman calibration models for in-line cell culture analysis using glucose and lactate stock solution titration in an aqueous phase and data augmentation techniques. First, a successful set-up of the titration method was achieved by adding glucose or lactate solution at several different constant rates into the aqueous phase of a bench-top bioreactor. Subsequently, the in-line glucose and lactate concentration were calculated and interpolated based on the rate of glucose and lactate addition, enabling data augmentation and enhancing the robustness of the metabolic calibration model. Nine different combinations of spectra pretreatment, wavenumber range selection, and number of latent variables were evaluated and optimized using aqueous titration data as training set and a historical cell culture data set as validation and prediction set. Finally, Raman spectroscopy data collected from 11 historical cell culture batches (spanning four culture modes and scales ranging from 3 to 200 L) were utilized to predict the corresponding glucose and lactate values. The results demonstrated a high prediction accuracy, with an average root mean square errors of prediction of 0.65 g/L for glucose, and 0.48 g/L for lactate. This innovative method establishes a generic metabolic calibration model, and its applicability can be extended to other metabolites, reducing the cost of deploying real-time cell culture monitoring using Raman spectroscopy in bioprocesses.

Adaptive modeling optimized by the data fusion strategy: Real-time dying cell percentage prediction using capacitance spectroscopy

The previous research showcased a partial least squares (PLS) regression model accurately predicting cell death percentages using in-line capacitance spectra. The current study advances the model accuracy through adaptive modeling employing a data fusion approach. This strategy enhances prediction performance by incorporating variables from the Cole-Cole model, conductivity and its derivatives over time, and Mahalanobis distance into the predictor matrix (X-matrix). Firstly, the Cole-Cole model, a mechanistic model with parameters linked to early cell death onset, was integrated to enhance prediction performance. Secondly, the inclusion of conductivity and its derivatives over time in the X-matrix mitigated prediction fluctuations resulting from abrupt conductivity changes during process operations. Thirdly, Mahalanobis distance, depicting spectral changes relative to a reference spectrum from a previous time point, improved model adaptability to independent test sets, thereby enhancing performance. The final data fusion model substantially decreased root-mean squared error of prediction (RMSEP) by around 50%, which is a significant boost in prediction accuracy compared to the prior PLS model. Robustness against reference spectrum selection was confirmed by consistent performance across various time points. In conclusion, this study illustrates that the data fusion strategy substantially enhances the model accuracy compared to the previous model relying solely on capacitance spectra.

An automated high inoculation density fed-batch bioreactor, enabled through N-1 perfusion, accommodates clonal diversity and doubles titers

An important consideration for biopharmaceutical processes is the cost of goods (CoGs) of biotherapeutics manufacturing. CoGs can be reduced by dramatically increasing the productivity of the bioreactor process. In this study, we demonstrate that an intensified process which couples a perfused N-1 seed reactor and a fully automated high inoculation density (HID) N stage reactor substantially increases the bioreactor productivity as compared to a low inoculation density (LID) control fed-batch process. A panel of six CHOK1SV GS-KO® CHO cell lines expressing three different monoclonal antibodies was evaluated in this intensified process, achieving an average 85% titer increase and 132% space-time yield (STY) increase was demonstrated when comparing the 12-day HID process to a 15-day LID control process. These productivity increases were enabled by automated nutrient feeding in both the N-1 and N stage bioreactors using in-line process analytical technologies (PAT) and feedback control. The N-1 bioreactor utilized in-line capacitance to automatically feed the bioreactor based on a capacitance-specific perfusion rate (CapSPR). The N-stage bioreactor utilized in-line Raman spectroscopy to estimate real-time concentrations of glucose, phenylalanine, and methionine, which are held to target set points using automatic feed additions. These automated feeding methodologies were shown to be generalizable across six cell lines with diverse feed requirements. We show this new process can accommodate clonal diversity and reproducibly achieve substantial titer uplifts compared to traditional cell culture processes, thereby establishing a baseline technology platform upon which further increases bioreactor productivity and CoGs reduction can be achieved.

Development of an in-line Raman analytical method for commercial-scale CHO cell culture process monitoring: Influence of measurement channels and batch number on model performance

The mammalian cell culture process is a key step in commercial therapeutic protein production and needs to be monitored and controlled due to its complexity. Raman spectroscopy has been reported for cell culture process monitoring by analysis of many important parameters. However, studies on in-line Raman monitoring of the cell culture process were mainly conducted on small or pilot scale. Developing in-line Raman analytical methods for commercial-scale cell culture process monitoring is more challenging. In this study, an in-line Raman analytical method was developed for monitoring glucose, lactate, and viable cell density (VCD) in the Chinese hamster ovary (CHO) cell culture process during commercial production of biosimilar adalimumab (1500 L). The influence of different Raman measurement channels was considered to determine whether to merge data from different channels for model development. Raman calibration models were developed and optimized, with minimum root mean square error of prediction of 0.22 g L-1 for glucose in the range of 1.66-3.53 g L-1 , 0.08 g L-1 for lactate in the range of 0.15-1.19 g L-1 , 0.31 E6 cells mL-1 for VCD in the range of 0.96-5.68 E6 cells mL-1 on test sets. The developed analytical method can be used for cell culture process monitoring during manufacturing and meets the analytical purpose of this study. Further, the influence of the number of batches used for model calibration on model performance was also studied to determine how many batches are needed basically for method development. The proposed Raman analytical method development strategy and considerations will be useful for monitoring of similar bioprocesses.

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.

Influence of cell specific parameters in a dielectric spectroscopy conversion model used to monitor viable cell density in bioreactors

In the biopharmaceutical industry, the use of mammalian cells to produce therapeutic proteins is becoming increasingly widespread. Monitoring of these cultures via different analysis techniques is essential to ensure a good quality product while respecting good manufacturing practice (GMP) regulations. Process Analytical Technologies (PAT) tools provide real-time measurements of the physiological state of the culture and enable process automation. Dielectric spectroscopy is a PAT that can be used to monitor the viable cell concentration (VCC) of living cells after processing raw permittivity data. Several modeling approaches exist and estimate biomass with different accuracy. The accuracy of the Cole-Cole and Maxwell Wagner's equations are studied here in the determination of the VCC and cell radius in Chinese hamster ovary (CHO) culture. A sensitivity analysis performed on the parameters entering the equations highlighted the importance of the cell specific parameters such as internal conductivity (σi ) and membrane capacitance (Cm ) in the accuracy of the estimation of VCC and cell radius. The most accurate optimization method found to improve the accuracy involves in-process adjustments of Cm and σi in the model equations with samplings from the bioreactor. This combination of offline and in situ data improved the estimation precision of the VCC by 69% compared to a purely mechanistic model without offline adjustments.

Automated Raman feed-back control of multiple supplemental feeds to enable an intensified high inoculation density fed-batch platform process

Biologics manufacturing is increasingly moving toward intensified processes that require novel control strategies in order to achieve higher titers in shorter periods of time compared to traditional fed-batch cultures. In order to implement these strategies for intensified processes, continuous process monitoring is often required. To this end, inline Raman spectroscopy was used to develop partial least squares models to monitor changes in residual concentrations of glucose, phenylalanine and methionine during the culture of five different glutamine synthetase piggyBac® Chinese hamster ovary clones cultured using an intensified high inoculation density fed-batch platform process. Continuous monitoring of residual metabolite concentrations facilitated automated feed-rate adjustment of three supplemental feeds to maintain glucose, phenylalanine, and methionine at desired setpoints, while maintaining other nutrient concentrations at acceptable levels across all clones cultured on the high inoculation density platform process. Furthermore, all clones cultured on this process achieved high viable cell concentrations over the course of culture, indicating no detrimental impacts from the proposed feeding strategy. Finally, the automated control strategy sustained cultures inoculated at high cell densities to achieve product concentrations between 5 and 8.3 g/L over the course of 12 days of culture.

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.

Advances in bioreactor control for production of biotherapeutic products

Advanced control strategies are well established in chemical, pharmaceutical, and food processing industries. Over the past decade, the application of these strategies is being explored for control of bioreactors for manufacturing of biotherapeutics. Most of the industrial bioreactor control strategies apply classical control techniques, with the control system designed for the facility at hand. However, with the recent progress in sensors, machinery, and industrial internet of things, and advancements in deeper understanding of the biological processes, coupled with the requirement of flexible production, the need to develop a robust and advanced process control system that can ease process intensification has emerged. This has further fuelled the development of advanced monitoring approaches, modeling techniques, process analytical technologies, and soft sensors. It is seen that proper application of these concepts can significantly improve bioreactor process performance, productivity, and reproducibility. This review is on the recent advancements in bioreactor control and its related aspects along with the associated challenges. This study also offers an insight into the future prospects for development of control strategies that can be designed for industrial-scale production of biotherapeutic products.

Progress in fed-batch culture for recombinant protein production in CHO cells

Nearly 80% of the approved human therapeutic antibodies are produced by Chinese Hamster Ovary (CHO) cells. To achieve better cell growth and high-yield recombinant protein, fed-batch culture is typically used for recombinant protein production in CHO cells. According to the demand of nutrients consumption, feed medium containing multiple components in cell culture can affect the characteristics of cell growth and improve the yield and quality of recombinant protein. Fed-batch optimization should have a connection with comprehensive factors such as culture environmental parameters, feed composition, and feeding strategy. At present, process intensification (PI) is explored to maintain production flexible and meet forthcoming demands of biotherapeutics process. Here, CHO cell culture, feed composition in fed-batch culture, fed-batch culture environmental parameters, feeding strategies, metabolic byproducts in fed-batch culture, chemostat cultivation, and the intensified fed-batch are reviewed. KEY POINTS: • Fed-batch culture in CHO cells is reviewed. • Fed-batch has become a common technology for recombinant protein production. • Fed batch culture promotes recombinant protein production in CHO cells.