Feedback control of two supplemental feeds during fed-batch culture on a platform process using inline Raman models for glucose and phenylalanine concentration

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.

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.

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.

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.

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.

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.

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

An innovative, Raman spectroscopy-based monitoring and control system is introduced in this paper for designing dynamic feeding strategies that allow the maintenance of key cellular nutrients at an ideal level in Chinese hamster ovary cell culture. The Partial Least Squares calibration models built for glucose, lactate and 16 (out of 20) individual amino acids had very good predictive power with low root mean square errors values and high square correlation coefficients. The developed models used for real-time measurement of nutrient and by-product concentrations allowed us to gain better insight into the metabolic behavior and nutritional consumption of cells. To establish a more beneficial nutritional environment for the cells, two types of dynamic feeding strategies were used to control the delivery of two-part multi-component feed media according to the prediction of Raman models (glucose or arginine). As a result, instead of high fluctuations, the nutrients (glucose together with amino acids) were maintained at the desired level providing a more balanced environment for the cells. Moreover, the use of amino acid-based feeding control enabled to prevent the excessive nutrient replenishment and was economically beneficial by significantly reducing the amount of supplied feed medium compared to the glucose-based dynamic fed culture. This article is protected by copyright. All rights reserved.

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.