Cell Culture Process Monitoring Research Articles

Improving Raman-Based Models for Real-Time Monitoring the CHO Cell Culture Process with Effective Variable Selection Strategies

Research has shown that Raman spectroscopy could be applied to monitor various components in mammalian cell culture in real time. In the process of application, it is necessary to ensure the performance of the Raman-based model. The variable selection strategy is an effective method that significantly influences the model performance and simplification. In this study, different variable selection strategies were evaluated, and the optimal variable selection strategy was determined for monitoring the CHO cell culture process. Firstly, a wide variety of spectral regions involving the Raman fingerprinting region and the C-H stretching region were investigated. Secondly, six different variable selection algorithms were meticulously assessed. Thirdly, the combination of different variable selection algorithms was used to improve model performance and simplify the model. Finally, the monitoring of cell culture processes was implemented. The findings underscored that commonly used spectral regions could improve the model performance but could not simplify the model well. Moving-window partial least square (MWPLS), genetic algorithm (GA), and random frog (RF) are more suitable for Raman modeling of the cell culture process, but they must be used after the spectral region selection. The combination of three variable selection algorithms (MWPLS-GA-RF) improved the model’s performance by 16–70% by selecting 30–60 variables, effectively simplifying the model. For glucose, lactate, viable cell density, and ammonium ion, real-time monitoring was performed well. This study will be helpful for researchers to select suitable variable selection strategies for building models for the real-time monitoring of cell culture.

Enhancing cross‐scale Raman in‐line monitoring capability of cell culture process in large‐scale manufacturing

AbstractThis study introduces an approach to enhance the Raman calibration model cross‐scale prediction capabilities in cell cultures. Our investigation centers on the improvement of Raman calibration models along with the scaling‐up of mammalian cell culture processes. Initially, we observed that integrating data from a 50 L run into the original dataset at lab‐scale was an effective strategy for improving prediction accuracy on 1000 L run. Further investigation indicated that this improvement could be attributed to the similarity between the spectra of 50 L with 1000 L. Subsequent investigation unveiled that the spectral variations primarily stemmed from differences between the instruments. This was verified through a direct comparison of two Raman analyzers. Then two pairwise spectral transformation methods, direct standardization (DS) and piecewise DS, both exhibited superior performance in predicting. This research underscores the importance of Raman spectral transfer method to alleviate instrument‐to‐instrument variations in cross‐scale monitoring of cell culture processes.

Integration of just-in-time learning with variational autoencoder for cell culture process monitoring based on Raman spectroscopy.

Protein production in the biopharmaceutical industry necessitates the utilization of multiple analytical techniques and control methodologies to ensure both safety and consistency. To facilitate real-time monitoring and control of cell culture processes, Raman spectroscopy has emerged as a versatile analytical technology. This technique, categorized as a Process Analytical Technology, employs chemometric models to establish correlations between Raman signals and key variables of interest. One notable approach for achieving real-time monitoring is through the application of just-in-time learning (JITL), an industrial soft sensor modeling technique that utilizes Raman signals to estimate process variables promptly. The conventional Raman-based JITL method relies on the K-nearest neighbor (KNN) algorithm with Euclidean distance as the similarity measure. However, it falls short of addressing the impact of data uncertainties. To rectify this limitation, this study endeavors to integrate JITL with a variational autoencoder (VAE). This integration aims to extract dominant Raman features in a nonlinear fashion, which are expressed as multivariate Gaussian distributions. Three experimental runs using different cell lines were chosen to compare the performance of the proposed algorithm with commonly utilized methods in the literature. The findings indicate that the VAE-JITL approach consistently outperforms partial least squares, convolutional neural network, and JITL with KNN similarity measure in accurately predicting key process variables.

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 0.22gL-1 for glucose in the range of 1.66-3.53gL-1 , 0.08gL-1 for lactate in the range of 0.15-1.19gL-1 , 0.31 E6 cellsmL-1 for VCD in the range of 0.96-5.68 E6 cellsmL-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.

Spectroscopic models for real‐time monitoring of cell culture processes using spatiotemporal just‐in‐time Gaussian processes

AbstractSpectroscopic methods play an instrumental role in the implementation of the U.S. Food and Drug Administration outlined process analytical technology for biopharmaceutical manufacturing. Industrial spectroscopic calibration models are typically developed in an offline setting using traditional methods, such as partial least squares and principal component regression. Apart from the limiting performances of these conventional models under time‐varying operating conditions, these methods require access to extensive historical data, which are seldom available in biopharmaceutical manufacturing. In this article, we propose a novel spatiotemporal just‐in‐time learning (ST‐JITL) based spectroscopic model calibration platform for automatic training and maintenance of calibration models using routine batch data. The proposed ST‐JITL framework uses Gaussian processes (GPs) for local model calibration. A GP model not only exhibits superior performance over traditional methods but also provides credibility intervals around the model predictions. The efficacy of the ST‐JITL based model calibration platform is demonstrated in predicting the critical performance parameters of an industrial cell culture process.

Automatic real‐time calibration, assessment, and maintenance of generic Raman models for online monitoring of cell culture processes

Raman spectroscopy is a multipurpose analytical technology that has found great utility in real-time monitoring and control of critical performance parameters of cell culture processes. As a process analytical technology (PAT) tool, the performance of Raman spectroscopy relies on chemometric models that correlate Raman signals to the parameters of interest. The current calibration techniques yield highly specific models that are reliable only on the operating conditions they are calibrated in. Furthermore, once models are calibrated, it is typical for the model performance to degrade over time due to various recipe changes, raw material variability, and process drifts. Maintaining the performance of industrial Raman models is further complicated due to the lack of a systematic approach to assessing the performance of Raman models. In this article, we propose a real-time just-in-time learning (RT-JITL) framework for automatic calibration, assessment, and maintenance of industrial Raman models. Unlike traditional models, RT-JITL calibrates generic models that can be reliably deployed in cell culture experiments involving different modalities, cell lines, media compositions, and operating conditions. RT-JITL is a first fully integrated and fully autonomous platform offering a self-learning approach for calibrating and maintaining industrial Raman models. The efficacy of RT-JITL is demonstrated on experimental studies involving real-time predictions of various cell culture performance parameters, such as metabolite concentrations, viability, and viable cell density. RT-JITL framework introduces a paradigm shift in the way industrial Raman models are calibrated, assessed, and maintained, which to the best of authors' knowledge, have not been done before.

A machine‐learning approach to calibrate generic Raman models for real‐time monitoring of cell culture processes

The manufacture of biotherapeutic proteins consists of complex upstream unit operations requiring multiple raw materials, analytical techniques, and control strategies to produce safe and consistent products for patients. Raman spectroscopy is a ubiquitous multipurpose analytical technique in biopharmaceutical manufacturing for real-time predictions of critical parameters in cell culture processes. The accuracy of Raman spectroscopy relies on chemometric models that need to be carefully calibrated. The existing calibration procedure is nontrivial to implement as it necessitates executing multiple carefully designed experiments for generating relevant calibration sets. Further, existing procedure yields calibration models that are reliable only in operating conditions they were calibrated in. This creates a unique challenge in clinical manufacturing where products have limited production history. In this paper, a novel machine-learning procedure based on just-in-time learning (JITL) is proposed to calibrate Raman models. Unlike traditional techniques, JITL-based generic Raman models can be reliably used for different modalities, cell lines, culture media, and operating conditions. The accuracy of JITL-based generic models is demonstrated on several validation studies involving real-time predictions of critical cell culture performance parameters, such as glucose, glutamate, glutamine, ammonium, lactate, sodium, calcium, viability, and viable cell density. The proposed JITL framework introduces a paradigm shift in the way industrial Raman models are calibrated, which to the best of authors' knowledge have not been done before.

High-Throughput, Automated Protein A Purification Platform with Multiattribute LC-MS Analysis for Advanced Cell Culture Process Monitoring.

The levels of many product related variants observed during the production of monoclonal antibodies are dependent on control of the manufacturing process, especially the cell culture process. However, it is difficult to characterize samples pulled from the bioreactor due to the low levels of product during the early stages of the process and the high levels of interfering reagents. Furthermore, analytical results are often not available for several days, which slows the process development cycle and prevents "real time" adjustments to the manufacturing process. To reduce the delay and enhance our ability to achieve quality targets, we have developed a low-volume, high-throughput, and high-content analytical platform for at-line product quality analysis. This workflow includes an automated, 96-well plate protein A purification step to isolate antibody product from the cell culture fermentation broth, followed by rapid, multiattribute LC-MS analysis. We have demonstrated quantitative correlations between particular process parameters with the levels of glycosylated and glycated species in a series of small scale experiments, but the platform could be used to monitor other attributes and applied across the biopharmaceutical industry.

Microarray-based MALDI-TOF mass spectrometry enables monitoring of monoclonal antibody production in batch and perfusion cell cultures

Cell culture process monitoring in monoclonal antibody (mAb) production is essential for efficient process development and process optimization. Currently employed online, at line and offline methods for monitoring productivity as well as process reproducibility have their individual strengths and limitations. Here, we describe a matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS)-based on a microarray for mass spectrometry (MAMS) technology to rapidly monitor a broad panel of analytes, including metabolites and proteins directly from the unpurified cell supernatant or from host cell culture lysates. The antibody titer is determined from the intact antibody mass spectra signal intensity relative to an internal protein standard spiked into the supernatant. The method allows a semi-quantitative determination of light and heavy chains. Intracellular mass profiles for metabolites and proteins can be used to track cellular growth and cell productivity.

Real time monitoring of bioreactor mAb IgG3 cell culture process dynamics via Fourier transform infrared spectroscopy: Implications for enabling cell culture process analytical technology

Compared to small molecule process analytical technology (PAT) applications, biotechnology product PAT applications have certain unique challenges and opportunities. Understanding process dynamics of bioreactor cell culture process is essential to establish an appropriate process control strategy for biotechnology product PAT applications. Inline spectroscopic techniques for real time monitoring of bioreactor cell culture process have the distinct potential to develop PAT approaches in manufacturing biotechnology drug products. However, the use of inline Fourier transform infrared (FTIR) spectroscopic techniques for bioreactor cell culture process monitoring has not been reported. In this work, real time inline FTIR Spectroscopy was applied to a lab scale bioreactor mAb IgG3 cell culture fluid biomolecular dynamic model. The technical feasibility of using FTIR Spectroscopy for real time tracking and monitoring four key cell culture metabolites (including glucose, glutamine, lactate, and ammonia) and protein yield at increasing levels of complexity (simple binary system, fully formulated media, actual bioreactor cell culture process) was evaluated via a stepwise approach. The FTIR fingerprints of the key metabolites were identified. The multivariate partial least squares (PLS) calibration models were established to correlate the process FTIR spectra with the concentrations of key metabolites and protein yield of in-process samples, either individually for each metabolite and protein or globally for all four metabolites simultaneously. Applying the 2nd derivative pre-processing algorithm to the FTIR spectra helps to reduce the number of PLS latent variables needed significantly and thus simplify the interpretation of the PLS models. The validated PLS models show promise in predicting the concentration profiles of glucose, glutamine, lactate, and ammonia and protein yield over the course of the bioreactor cell culture process. Therefore, this work demonstrated the technical feasibility of real time monitoring of the bioreactor cell culture process via FTIR spectroscopy. Its implications for enabling cell culture PAT were discussed.

Advances in process monitoring tools for cell culture bioprocesses

The productivity of cell culture manufacturing for biologics has increased momentously in the past decades. Increasingly, the process research efforts are devoted into improving product quality and consistency. Consistent process performance and successful implementation of quality by design practice requires well‐utilized process analytical technology. This review summarizes recent progress and current status of bioprocess monitoring. Many sensors for bioprocess monitoring have been available for decades while new ones, especially spectrometric sensors, are making their way into cell culture bioprocesses. On‐line sampling devices have grown mature in the past decade thus making many instruments traditionally used for off‐line analysis available for at‐line use. With a general trend of using better defined medium for cell cultivation and increasing emphasis of process analytical tools, the spectrometric methods are also making headway in cell culture process monitoring. The integration of those sensing technologies will be important to advance the real‐time monitoring of the state of cellular physiology for the control for process consistency and product quality.

Non-invasive flow cytometric monitoring of pH i in cell culture processes using EGFP

We have described a new method for monitoring of cell culture processes using green fluorescent protein (GFP) fluorescent intensity. GFP has been used as a non-invasive fluorescent reporter for various cellular processes. In this study, enhanced (EGFP) was found to be a very sensitive indicator of pH i in in vitro cell culture, and responded rapidly to extracellular pH (pH e) changes. EGFP transfected cells were evaluated for pH i changes by flow cytometry, by measuring EGFP fluorescent intensity, and compared to that of the pH-sensitive fluoroprobe, SNARF. EGFP intensity was found to reflect pH i values of cells at different pH e in the presence of nigericin and was affected by the addition of HCl and NaOH. Significant changes in pH i were detected at different stages of batch culture and when using different cell density and media composition. The EGFP assay can be used to minimise the perturbation of cells and processes under study, thus leading to accurate information about the physiological state of single cells in a population. The results establish the application of EGFP as a non-invasive indicator of pH i for monitoring of mammalian cell culture processes.

Determination of Recombinant Protein in Animal Cell Culture Supernatant by Near-Infrared Spectroscopy

A method is described for the measurement of recombinant protein in animal cell culture supernatants with near-infrared (NIR) absorbance spectroscopy. Human antithrombin III is determined in CHO (Chinese hamster ovary)-T1 medium, both fresh and taken from CHO cell culture. NIR spectra are collected over the frequency range of 1000–2500 nm. A multivariate calibration model is generated by correlating spectral features with concentration values using partial least-squares regression. Separate test sets are used to evaluate the computed model. Protein measurement is established over the range 0.1–5 μg/mL with a standard error of prediction of less than 0.5 μg/mL. This result underlines the analytical applicability of NIR spectroscopy for animal cell culture process monitoring.

Cell culture process monitoring and control?a key to process optimization

Cell culture process monitoring and control?a key to process optimization