Viable cell density on-line auto-control in perfusion cell culture aided by in-situ Raman spectroscopy

Different modeling approaches for inline biochemical monitoring over the VLP-making upstream stages using Raman spectroscopy

This work aimed to set inline Raman spectroscopy models to monitor biochemically (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, and ammonium) all upstream stages of a virus-like particle-making process. Linear (Partial least squares, PLS; Principal components regression, PCR) and nonlinear (Artificial neural networks, ANN; supported vector machine, SVM) modeling approaches were assessed. The nonlinear models, ANN and SVM, were the more suitable models with the lowest absolute errors. The mean absolute error of the best models within the assessed parameter ranges for viable cell density (0.01-8.83 × 106 cells/mL), cell viability (1.3-100.0 %), glucose (5.22-10.93 g/L), lactate (18.6-152.7 mg/L), glutamine (158-1761 mg/L), glutamate (807.6-2159.7 mg/L), and ammonium (62.8-117.8 mg/L) were 1.55 ± 1.37 × 106 cells/mL (ANN), 5.01 ± 4.93 % (ANN), 0.27 ± 0.22 g/L (SVM), 4.7 ± 2.6 mg/L (SVM), 51 ± 49 mg/L (ANN), 57 ± 39 mg/L (SVM) and 2.0 ± 1.8 mg/L (ANN), respectively. The errors achieved, and best-fitted models were like those for the same bioprocess using offline data and others, which utilized inline spectra for mammalian cell lines as a host.

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.

Enhancing real-time cell culture monitoring: Automated Raman model optimization with Taguchi method

Raman spectroscopy has found widespread usage in monitoring cell culture processes both in research and practical applications. However, commonly, preprocessing methods, spectral regions, and modeling parameters have been chosen based on experience or trial-and-error strategies. These choices can significantly impact the performance of the models. There is an urgent need for a simple, effective, and automated approach to determine a suitable procedure for constructing accurate models. This paper introduces the adoption of a design of experiment (DoE) method to optimize partial least squares models for measuring the concentration of different components in cell culture bioreactors. The experimental implementation utilized the orthogonal test table L25(56). Within this framework, five factors were identified as control variables for the DoE method: the window width of Savitzky-Golay smoothing, the baseline correction method, the order of preprocessing steps, spectral regions, and the number of latent variables. The evaluation method for the model was considered as a factor subject to noise. The optimal combination of levels was determined through the signal-to-noise ratio response table employing Taguchi analysis. The effectiveness of this approach was validated through two cases, involving different cultivation scales, different Raman spectrometers, and different analytical components. The results consistently demonstrated that the proposed approach closely approximated the global optimum, regardless of data set size, predictive components, or the brand of Raman spectrometer. The performance of models recommended by the DoE strategy consistently surpassed those built using raw data, underscoring the reliability of models generated through this approach. When compared to exhaustive all-combination experiments, the DoE approach significantly reduces calculation times, making it highly practical for the implementation of Raman spectroscopy in bioprocess monitoring.

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.

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.

Model-based control system design to manage process parameters in mammalian cell culture for biopharmaceutical manufacturing

To enhance the robustness and flexibility of biopharmaceutical manufacturing, a paradigm shift toward methods of continuous processing, such as perfusion, and fundamental technologies for high-throughput process development are being actively investigated. The continuous upstream process must establish an advanced control strategy to ensure a "State of Control" before operation. Specifically, feedforward and feedback control must address the complex fluctuations that occur during the culture process and maintain critical process parameters in appropriate states. However, control system design for industry-standard mammalian cell culture processes is still often performed in a laborious trial-and-error manner. This paper provides a novel control approach in which controller specifications to obtain desired control characteristics can be determined systematically by combining a culture model with control theory. In the proposed scheme, control conditions, such as PID parameters, can be specified mechanistically based on process understanding and control requirements without qualitative decision making or specific preliminary experiments. The effectiveness of the model-based control algorithm was verified by control simulations assuming perfusion Chinese hamster ovary culture. As a tool to assist in the development of control strategies, this study will reduce the high operational workload that is a serious problem in continuous culture and facilitate the digitalization of bioprocesses.

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.

WAVE-based intensified perfusion cell culture for fast process development

OBJECTIVE

This study was to evaluate the feasibility of using a rocking type bioreactor system, specifically the WAVE 25, in an intensified perfusion culture (IPC) mode for monoclonal antibody (mAb) production in Chinese hamster ovary (CHO) cell line.

METHODS

A disposable perfusion bag with floating membrane was used in the IPC process. An automated filter switching system was employed to continuously clarify the harvested post-membrane culture fluid. The overall cell culture performance, product titer, and quality were compared to those of a typical IPC conducted in a bench-top glass bioreactor.

RESULTS

The results showed that the overall trends of cell culture performance, product titer (accumulated harvest volumetric titer) were similar to those of the typical IPC conducted in the glass bioreactor, while the purity related quality were slightly better than the typical run. Furthermore, with the automated filter switching system, the harvested post-membrane culture fluid could be continuously clarified, making it suitable for downstream continuous chromatography.

CONCLUSION

The study demonstrated the feasibility of using the WAVE-based rocking type bioreactor in the N stage IPC process, which increases the flexibility in adopting IPC process. The results suggest that the rocking type bioreactor system could be a viable alternative to traditional stirred tank bioreactors for perfusion culture in the biopharmaceutical industry.

Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control

Algal cultivations are strongly influenced by light and dark cycles. In this study, genome-scale metabolic models were applied to optimize nutrient supply during alternating light and dark cycles of Chlorella vulgaris. This approach lowered the glucose requirement by 75% and nitrate requirement by 23%, respectively, while maintaining high final biomass densities that were more than 80% of glucose-fed heterotrophic culture. Furthermore, by strictly controlling glucose feeding during the alternating cycles based on model-input, yields of biomass, lutein, and fatty acids per gram of glucose were more than threefold higher with cycling compared to heterotrophic cultivation. Next, the model was incorporated into open-loop and closed-loop control systems and compared with traditional fed-batch systems. Closed-loop systems which incorporated a feed-optimizing algorithm increased biomass yield on glucose more than twofold compared to standard fed-batch cultures for cycling cultures. Finally, the performance was compared to conventional proportional-integral-derivative (PID) controllers. Both simulation and experimental results exhibited superior performance for genome-scale model process control (GMPC) compared to traditional PID systems, reducing the overall measured value and setpoint error by 80% over 8 h. Overall, this approach provides researchers with the capability to enhance nutrient utilization and productivity of cell factories systematically by combining genome-scale models and controllers into an integrated platform with superior performance to conventional fed-batch and PID methodologies.

Upstream continuous processing: recent advances in production of biopharmaceuticals and challenges in manufacturing

Upstream continuous processing, or most commonly perfusion processing, for biopharmaceutical production, is emerging as a feasible and viable manufacturing approach. Development in production of recombinant therapeutic proteins as well as viral vectors, vaccines, and cell therapy products, has numerous research publications that came out in previous years. Recent research areas are in perfusion-operation strategies maximizing and controlling bioreactor cell density, adding feed solution designed to supplement basal medium feed stream, combining cell line engineering with bioreactor conditions such as hypoxia, and implementing online process monitoring of cell density by capacitance sensor and metabolites by Raman spectroscopy. Perfusion applications are not limited to production process alone but include other upstream areas where high cell density process is essential such as in cell bank preparation, N-1 seed bioreactor, and combination with intensified fed-batch production process. This review covers recent advances in continuous processing over the last two years for biopharmaceutical production.

Non-invasive real-time monitoring of cell concentration and viability using Doppler ultrasound

Bioprocess optimization towards higher productivity and better quality control relies on real-time process monitoring tools to measure process and culture parameters. Cell concentration and viability are among the most important parameters to be monitored during bioreactor operations that are typically determined using optical methods on an extracted sample. In this paper, we have developed an online non-invasive sensor to measure cell concentration and viability based on Doppler ultrasound. An ultrasound transducer is mounted outside the bioreactor vessel and emits a high frequency tone burst (15 MHz) through the vessel wall. Acoustic backscatter from cells in the bioreactor depends on cell concentration and viability. The backscattered signal is collected through the same transducer and analyzed using multivariate data analysis (MVDA) to characterize and predict the cell culture properties. We have developed accurate MVDA models to predict the Chinese hamster ovary (CHO) cell concentration in a broad range from 0.1 × 10⁶ cells/mL to 100 × 10⁶ cells/mL, and cell viability from 3% to 99%. The non-invasive monitoring is ideal for single use bioreactor and the in-situ measurements removes the burden for offline sampling and dilution steps. This method can be similarly applied to other suspension cell culture modalities.

Absorption Spectra Description for T-Cell Concentrations Determination and Simultaneous Measurements of Species during Co-Cultures

Advanced Therapy Medicinal Products are promising drugs for patients in therapeutic impasses. Their complex fabrication process implies regular quality controls to monitor cell concentration. Among the different methods available, optical techniques offer several advantages. Our study aims to measure cell concentration in real time in a potential closed-loop environment using white light spectroscopy and to test the possibility of simultaneously measuring concentrations of several species. By analyzing the shapes of the absorption spectra, this system allowed the quantification of T-cells with an accuracy of about 3% during 30 h of cultivation monitoring and 26 h of doubling time, coherent with what is expected for normal cell culture. Moreover, our system permitted concentration measurements for two species in reconstructed co-cultures of T-cells and Candida albicans yeasts. This method can now be applied to any single or co-culture, it allows real-time monitoring, and can be easily integrated into a closed system.

Productivity and quality improvement for a symmetric bispecific antibody through the application of intensified perfusion cell culture

Background

Aggregation, fragmentation, and low yield are issues frequently found during the cell culture process of bispecific antibodies (bsAbs), whose inherent complexity likely plays a role in causing these issues.

Methods

In this study, we made a head-to-head comparison between fed-batch cell culture and intensified perfusion cell culture with a symmetric bsAb case.

Results

In comparison with the fed-batch culture, a 6.6-fold improvement in integrated viable cell density and a 10.9-fold improvement in volumetric productivity were achieved with the intensified perfusion mode. In addition, a significant decrease in aggregation and fragmentation was observed with the intensified perfusion cell culture. Furthermore, product homogeneity was improved, which was reflected by the increased percentage of capillary isoelectric focusing main group. The quality improvement with intensified perfusion cell culture can be attributed to the shortened product retention in the bioreactor.

Conclusions

These findings suggest that intensified perfusion cell culture could be a better choice than traditional fed-batch especially for complex molecules like bsAbs. As this is a single case report, future studies on other cases are needed to further confirm the general applicability of this strategy.

N-1 Perfusion Platform Development Using a Capacitance Probe for Biomanufacturing

Fed-batch process intensification with a significantly shorter culture duration or higher titer for monoclonal antibody (mAb) production by Chinese hamster ovary (CHO) cells can be achieved by implementing perfusion operation at the N-1 stage for biomanufacturing. N-1 perfusion seed with much higher final viable cell density (VCD) than a conventional N-1 batch seed can be used to significantly increase the inoculation VCD for the subsequent fed-batch production (referred as N stage), which results in a shorter cell growth phase, higher peak VCD, or higher titer. In this report, we incorporated a process analytical technology (PAT) tool into our N-1 perfusion platform, using an in-line capacitance probe to automatically adjust the perfusion rate based on real-time VCD measurements. The capacitance measurements correlated linearly with the offline VCD at all cell densities tested (i.e., up to 130 × 10⁶ cells/mL). Online control of the perfusion rate via the cell-specific perfusion rate (CSPR) decreased media usage by approximately 25% when compared with a platform volume-specific perfusion rate approach and did not lead to any detrimental effects on cell growth. This PAT tool was applied to six mAbs, and a platform CSPR of 0.04 nL/cell/day was selected, which enabled rapid growth and maintenance of high viabilities for four of six cell lines. In addition, small-scale capacitance data were used in the scaling-up of N-1 perfusion processes in the pilot plant and in the GMP manufacturing suite. Implementing a platform approach based on capacitance measurements to control perfusion rates led to efficient process development of perfusion N-1 for supporting high-density CHO cell cultures for the fed-batch process intensification.

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.