In situ Raman spectroscopy for simultaneous monitoring of multiple process parameters in mammalian cell culture bioreactors

Cell culture product quality attribute prediction using convolutional neural networks and Raman spectroscopy

Advanced process control in the biopharmaceutical industry often lacks real-time measurements due to resource constraints. Raman spectroscopy and Partial Least Squares (PLS) models are often used to monitor bioprocess cultures in real-time. In spite of the ease of training, the accuracy of the PLS model is impacted if it is not used to predict quality attributes for the cell lines it is trained on. To address this issue, a deep convolutional neural network (CNN) is proposed for offline modeling of metabolites using Raman spectroscopy. By utilizing asymmetric least squares smoothing to adjust Raman spectra baselines, a generic training data set is created by amalgamating spectra from various cell lines and operating conditions. This data set, combined with their derivatives, forms a two-dimensional model input. The CNN model is developed and validated for predicting different quality variables against measurements from various continuous and fed-batch experimental runs. Validation results confirm that the deep CNN model is an accurate generic model of the process to predict real-time quality attributes, even in experimental runs not included in the training data. This model is robust and versatile, requiring no recalibration when deployed at different sites to monitor various cell lines and experimental runs.

Pioneering Just-in-Time (JIT) Strategy for Accelerating Raman Method Development and Implementation for Biologic Continuous Manufacturing

Raman spectroscopy is a popular process analytical technology (PAT) tool that has been increasingly used to monitor and control the monoclonal antibody (mAb) manufacturing process. Although it allows the characterization of a variety of quality attributes by developing chemometric models, a large quantity of representative data is required, and hence, the model development process can be time-consuming. In recent years, the pharmaceutical industry has been expediting new drug development in order to achieve faster delivery of life-changing drugs to patients. The shortened development timelines have impacted the Raman application, as less time is allowed for data collection. To address this problem, an innovative Just-in-Time (JIT) strategy is proposed with the goal of reducing the time needed for Raman model development and ensuring its implementation. To demonstrate its capabilities, a proof-of-concept study was performed by applying the JIT strategy to a biologic continuous process for producing monoclonal antibody products. Raman spectroscopy and online two-dimensional liquid chromatography (2D-LC) were integrated as a PAT analyzer system. Raman models of antibody titer and aggregate percentage were calibrated by chemometric modeling in real-time. The models were also updated in real-time using new data collected during process monitoring. Initial Raman models with adequate performance were established using data collected from two lab-scale cell culture batches and subsequently updated using one scale-up batch. The JIT strategy is capable of accelerating Raman method development to monitor and guide the expedited biologics process development.

Feasibility and performance of cross-clone Raman calibration models in CHO cultivation

Raman spectroscopy is widely used in monitoring and controlling cell cultivations for biopharmaceutical drug manufacturing. However, its implementation for culture monitoring in the cell line development stage has received little attention. Therefore, the impact of clonal differences, such as productivity and growth, on the prediction accuracy and transferability of Raman calibration models is not yet well described. Raman OPLS models were developed for predicting titer, glucose and lactate using eleven CHO clones from a single cell line. These clones exhibited diverse productivity and growth rates. The calibration models were evaluated for clone-related biases using clone-wise linear regression analysis on cross validated predictions. The results revealed that clonal differences did not affect the prediction of glucose and lactate, but titer models showed a significant clone-related bias, which remained even after applying variable selection methods. The bias was associated with clonal productivity and lead to increased prediction errors when titer models were transferred to cultivations with productivity levels outside the range of their training data. The findings demonstrate the feasibility of Raman-based monitoring of glucose and lactate in cell line development with high accuracy. However, accurate titer prediction requires careful consideration of clonal characteristics during model development.

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.

Precision fermentation with mass spectrometry-based spent media analysis

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.

Data-driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins

Recently, the advancement in process analytical technology and artificial intelligence (AI) has enabled the generation of enormous culture data sets from biomanufacturing processes that produce various recombinant therapeutic proteins (RTPs), such as monoclonal antibodies (mAbs). Thus, now it is very important to exploit them for the enhanced reliability, efficiency, and consistency of the RTP-producing culture processes and for the reduced incipient or abrupt faults. It is achievable by AI-based data-driven models (DDMs), which allow us to correlate biological and process conditions and cell culture states. In this work, we provide practical guidelines for choosing the best combination of model elements to design and implement successful DDMs for given hypothetical in-line data sets during mAb-producing Chinese hamster ovary cell culture, as such enabling us to forecast dynamic behaviors of culture performance such as viable cell density, mAb titer as well as glucose, lactate and ammonia concentrations. To do so, we created DDMs that balance computational load with model accuracy and reliability by identifying the best combination of multistep ahead forecasting strategies, input features, and AI algorithms, which is potentially applicable to implementation of interactive DDM within bioprocess digital twins. We believe this systematic study can help bioprocess engineers start developing predictive DDMs with their own data sets and learn how their cell cultures behave in near future, thereby rendering proactive decision possible.

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.

Halfway to Automated Feeding of Chinese Hamster Ovary Cells

This paper presents a comprehensive study on the development of models and soft sensors required for the implementation of the automated bioreactor feeding of Chinese hamster ovary (CHO) cells using Raman spectroscopy and chemometric methods. This study integrates various methods, such as partial least squares regression and variable importance in projection and competitive adaptive reweighted sampling, and highlights their effectiveness in overcoming challenges such as high dimensionality, multicollinearity and outlier detection in Raman spectra. This paper emphasizes the importance of data preprocessing and the relationship between independent and dependent variables in model construction. It also describes the development of a simulation environment whose core is a model of CHO cell kinetics. The latter allows the development of advanced control algorithms for nutrient dosing and the observation of the effects of different parameters on the growth and productivity of CHO cells. All developed models were validated and demonstrated to have a high robustness and predictive accuracy, which were reflected in a 40% reduction in the root mean square error compared to established methods. The results of this study provide valuable insights into the practical application of these methods in the field of monitoring and automated cell feeding and make an important contribution to the further development of process analytical technology in the bioprocess industry.

Excitation-Emission Matrix Fluorescence Spectroscopy Coupled with PARAFAC Modeling for Viability Prediction of Cells

Cell-based sensors and assays have great potential in bioanalysis, drug discovery screening, and biochemical mechanisms research. The cell viability tests should be fast, safe, reliable, and time- and cost-effective. Although methods stated as "gold standards", such as MTT, XTT, and LDH assays, usually fulfill these assumptions, they also show some limitations. They can be time-consuming, labor-intensive, and prone to errors and interference. Moreover, they do not enable the observation of the cell viability changes in real-time, continuously, and nondestructively. Therefore, we propose an alternative method of viability testing: native excitation-emission matrix fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC), which is especially advantageous for cell monitoring due to its noninvasiveness and nondestructiveness and because there is no need for labeling and sample preparation. We demonstrate that our approach provides accurate results with even better sensitivity than the standard MTT test. With PARAFAC, it is possible to study the mechanism of the observed cell viability changes, which can be directly linked to increasing/decreasing fluorophores in the cell culture medium. The resulting parameters of the PARAFAC model are also helpful in establishing a reliable regression model for accurate and precise determination of the viability in A375 and HaCaT-adherent cell cultures treated with oxaliplatin.

Development of Raman Calibration Model Without Culture Data for In-Line Analysis of Metabolites in Cell Culture Media

In this study, we developed a method to build Raman calibration models without culture data for cell culture monitoring. First, Raman spectra were collected and then analyzed for the signals of all the mentioned analytes: glucose, lactate, glutamine, glutamate, ammonia, antibody, viable cells, media, and feed agent. Using these spectral data, the specific peak positions and intensities for each factor were detected. Next, according to the design of the experiment method, samples were prepared by mixing the above-mentioned factors. Raman spectra of these samples were collected and were used to build calibration models. Several combinations of spectral pretreatments and wavenumber regions were compared to optimize the calibration model for cell culture monitoring without culture data. The accuracy of the developed calibration model was evaluated by performing actual cell culture and fitting the in-line measured spectra to the developed calibration model. As a result, the calibration model achieved sufficiently good accuracy for the three components, glucose, lactate, and antibody (root mean square errors of prediction, or RMSEP = 0.23, 0.29, and 0.20 g/L, respectively). This study has presented innovative results in developing a culture monitoring method without using culture data, while using a basic conventional method of investigating the Raman spectra of each component in the culture media and then utilizing a design of experiment approach.

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.

Near-infrared spectroscopy based monitoring of all 20 amino acids in mammalian cell culture broth

The biopharmaceutical industry extensively employs Chinese hamster ovary (CHO) cell culture for monoclonal antibody production. Amino acids represent an essential source of nutrients in all CHO cell culture media, and their concentration is known to significantly impact cell viability, titre, and monoclonal antibody critical quality attributes. In this study, a robust Fourier transform near-infrared spectroscopy (FT-NIR) based quantification method has been developed for of all 20 amino acids (0-24 mM), as well as concentrations of glucose (0-6.7 mg mL^-1), lactate (0-2.7 mg mL^-1), and trastuzumab (0-2.5 mg mL^-1) in the CHO cell culture. Near infra-red absorbance spectrum in the range of 4000-11,000 cm^-1 were acquired, and spectra pre-processing through smoothening and derivatives were employed to enhance key characteristic signals. High-performance liquid chromatography with pre-column derivatization was used as the orthogonal analytical tool for quantification. Principal component analysis and partial least squares regression were employed for region selection and calibration model development, respectively. The results demonstrate that a good calibration statistic with the acceptable coefficient of determinations for both calibration (R_c² = 0.94-0.99) and prediction (R_p² = 0.83-0.98) could be achieved, along with high RPD values (>3) for all components except alanine (2.4). The external validation study also exhibited a satisfactory outcome (R_EV² = 0.89-0.99, RMSE = 0.04-1.04), validating the model's ability to predict the concentrations of the respective species. The calibration models were successfully applied for at-line monitoring of two perfusion runs on a 10 L scale. To our knowledge, this is the first application where NIR spectroscopy-based measurement of all 20 amino acids in mammalian cell culture samples has been demonstrated. The proposed tool can play a critical role as biopharma manufacturers implement continuous processing as well as for facilitating process analytical technology-based control of mammalian cell culture processes.

Biochemical monitoring throughout all stages of rabies virus-like particles production by Raman spectroscopy using global models

This work aimed to quantify growth and biochemical parameters (viable cell density, Xv; cell viability, CV; glucose, lactate, glutamine, glutamate, ammonium, and potassium concentrations) in upstream stages to obtain rabies virus-like particles (rabies VLP) from insect cell-baculovirus system using on-line and off-line Raman spectra to calibrate global models with minimal experimental data. Five cultivations in bioreactor were performed. The first one comprised the growth of uninfected Spodoptera frugiperda (Sf9) cells, the second and third runs to obtain recombinant baculovirus (rBV) bearing Rabies G glycoprotein and matrix protein, respectively. The fourth one involved the generation of rabies VLP from rBVs and the last one was a repetition of the third one with cell inoculum infected by rBV. The spectra were acquired through a Raman spectrometer with a 785-nm laser source. The fitted Partial Least Square models for nutrients and metabolites were comparable with those previously reported for mammalian cell lines (Relative error < 15 %). However, the use of this chemometrics approach for Xv and CV was not as accurate as it was for other parameters. The findings from this work established the basis for bioprocess Raman spectroscopical monitoring using insect cells for VLP manufacturing, which are gaining ground in the pharmaceutical industry.

rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing

Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist's perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model.

Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring

In recent years, the bioprocessing industry has experienced significant growth and is increasingly emerging as an important economic sector. Here, efficient process management and constant control of cellular growth are essential. Good product quality and yield can only be guaranteed with high cell density and high viability. Whereas the on-line measurement of physical and chemical process parameters has been common practice for many years, the on-line determination of viability remains a challenge and few commercial on-line measurement methods have been developed to date for determining viability in industrial bioprocesses. Thus, numerous studies have recently been conducted to develop sensors for on-line viability estimation, especially in the field of optical spectroscopic sensors, which will be the focus of this review. Spectroscopic sensors are versatile, on-line and mostly non-invasive. Especially in combination with bioinformatic data analysis, they offer great potential for industrial application. Known as soft sensors, they usually enable simultaneous estimation of multiple biological variables besides viability to be obtained from the same set of measurement data. However, the majority of the presented sensors are still in the research stage, and only a few are already commercially available.

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.

Advancing Raman model calibration for perfusion bioprocesses using spiked harvest libraries

BACKGROUND

Raman spectroscopy has gained popularity to monitor multiple process indicators simultaneously in biopharmaceutical processes. However, robust and specific model calibration remains a challenge due to insufficient analyte variability to train the models and high cross-correlation of various media components and artifacts throughout the process.

MAIN METHODS

A systematic Raman calibration workflow for perfusion processes enabling highly specific and fast model calibration was developed. Harvest libraries consisting of frozen harvest samples from multiple CHO cell culture bioreactors collected at different process times were established. Model calibration was subsequently performed in an offline setup using a flow cell by spiking process harvest with glucose, raffinose, galactose, mannose, and fructose.

MAJOR RESULTS

In a screening phase, Raman spectroscopy was proven capable not only to distinguish sugars with similar chemical structures in perfusion harvest but also to quantify them independently in process-relevant concentrations. In a second phase, a robust and highly specific calibration model for simultaneous glucose (RMSEP = 0.32 g/L) and raffinose (RMSEP = 0.17 g/L) real-time monitoring was generated and verified in a third phase during a perfusion process.

IMPLICATION

The proposed novel offline calibration workflow allowed proper Raman peak decoupling, reduced calibration time from months down to days, and can be applied to other analytes of interest including lactate, ammonia, amino acids, or product titer. This article is protected by copyright. All rights reserved.

Generic Chemometric Models for Metabolite Concentration Prediction Based on Raman Spectra

Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.

A Feedback Control Strategy for a Fed-Batch Monoclonal Antibody Production Process Utilising Infrequent and Irregular Sampled Measurements

The ability to take non-invasive Raman measurements presents a unique opportunity to use one Raman probe across multiple vessels in parallel, reducing costs but making measurements infrequent. Under these conditions, infrequent and irregular feedback signals can result in poor closed-loop control performance. This study addressed the issue of infrequent and irregular Raman measurements using a linear dynamic model developed from interpolated data to predict more frequent measurements of the controlled variable. The simulated monoclonal antibody production was sampled hourly with white noise added to the simulated glucose concentration to replicate real Raman measurements. The hourly samples were interpolated into 15 min intervals and a linear dynamic model was developed to predict the glucose concentration at 15 min intervals. These predicted values were then used in a feedback control loop by using model predictive control or a conventional proportional and integral controller to control the glucose concentration at 15 min sampling intervals. For setpoint tracking, the model predictive control reduced the integral of absolute errors to 14,600 from 15,900 (with a 1 h sampling time) or 8.2% reduction. With adaptive model predictive control, the integral of absolute errors was reduced from 14,500 (1 h sampling time) to 14,200 for setpoint tracking and from 13,500 (1 h sampling time) to 13,300 for disturbance rejection. A final comparison demonstrated that the proposed method can also cope with random variations in the sampling time.

Automated Data Generation for Raman Spectroscopy Calibrations in Multi-Parallel Mini Bioreactors

Raman spectroscopy is an analytical technology for the simultaneous measurement of important process parameters, such as concentrations of nutrients, metabolites, and product titer in mammalian cell culture. The majority of published Raman studies have concentrated on using the technique for the monitoring and control of bioreactors at pilot and manufacturing scales. This research presents a novel approach to generating Raman models using a high-throughput 250 mL mini bioreactor system with the following two integrated analysis modules: a prototype flow cell enabling on-line Raman measurements and a bioanalyzer to generate reference measurements without a significant time-shift, compared to the corresponding Raman measurement. Therefore, spectral variations could directly be correlated with the actual analyte concentrations to build reliable models. Using a design of experiments (DoE) approach and additional spiked samples, the optimized workflow resulted in robust Raman models for glucose, lactate, glutamine, glutamate and titer in Chinese hamster ovary (CHO) cell cultures producing monoclonal antibodies (mAb). The setup presented in this paper enables the generation of reliable Raman models that can be deployed to predict analyte concentrations, thereby facilitating real-time monitoring and control of biologics manufacturing.

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.

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.

Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes

The growing biopharmaceutical industry has reached a level of maturity that allows for the monitoring of numerous key variables for both process characterization and outcome predictions. Sensors were historically used in order to maintain an optimal environment within the reactor to optimize process performance. However, technological innovation has pushed towards on-line in situ continuous monitoring of quality attributes that could previously only be estimated off-line. These new sensing technologies when coupled with software models have shown promise for unique fingerprinting, smart process control, outcome improvement, and prediction. All this can be done without requiring invasive sampling or intervention on the system. In this paper, the state-of-the-art sensing technologies and their applications in the context of cell culture monitoring are reviewed with emphasis on the coming push towards industry 4.0 and smart manufacturing within the biopharmaceutical sector. Additionally, perspectives as to how this can be leveraged to improve both understanding and outcomes of cell culture processes are discussed.

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.

Excitation–emission matrix fluorescence spectroscopy for cell viability testing in UV-treated cell culture

Monitoring of cells viability is essential in a number of biomedical applications, including cell-based sensors, cell-based microsystems, and cell-based assays. The use of spectroscopic techniques for such purposes is especially advantageous since they are non-invasive, label-free, and non-destructive. However, such an approach must include chemometric analysis of the data to assess the information on cells viability. In the presented article we demonstrate, that excitation–emission matrix (EEM) fluorescence spectroscopy can be applied for reliable determination of cells viability due to the high correlation of EEM fluorescence data with the MTT test data. A375 cells (malignant melanoma) were exposed to UV radiation as a physical stress factor, resulting in a decrease of viability up to ca. 20%, confirmed by the standard MTT test. They were also characterized by means of EEM fluorescence spectroscopy coupled with unfolded partial least squares (UPLS) regression. Statistical evaluation revealed high accordance of the two methods of viability testing in terms of accuracy, precision, and correlation. The presented results are very promising for the development of spectroscopic soft sensors that can be applied for drug screening, biocompatibility testing, tissue engineering, and pharmacodynamic studies.

Excitation-emission matrix fluorescence spectroscopy can be applied for label-free and non-destructive determination of cells viability, which is promising methodology for drug screening, biocompatibility testing, or pharmacodynamic studies.

Spectral Markers for T Cell Death and Apoptosis-A Pilot Study on Cell Therapy Drug Product Characterization Using Raman Spectroscopy

Application of Raman spectroscopy as a T cell characterization tool supporting cell therapy drug product development has been evaluated. Statistically significant correlations between a set of Raman signals and established flow cytometry markers associated with apoptosis of T cells detected during drug product cryopreservation are presented in this study. Our study results demonstrate the potential of Raman spectroscopy for label-free measurements of T cell characteristics relevant to cell therapy product design and process control.

Comparison of UV- and Raman-based monitoring of the Protein A load phase and evaluation of data fusion by PLS models and CNNs

A promising application of Process Analytical Technology to the downstream process of monoclonal antibodies (mAbs) is the monitoring of the Protein A load phase as its control promises economic benefits. Different spectroscopic techniques have been evaluated in literature with regard to the ability to quantify the mAb concentration in the column effluent. Raman and Ultraviolet (UV) spectroscopy are among the most promising techniques. In this study, both were investigated in an in-line setup and directly compared. The data of each sensor were analyzed independently with Partial-Least-Squares (PLS) models and Convolutional Neural Networks (CNNs) for regression. Furthermore, data fusion strategies were investigated by combining both sensors in hierarchical PLS models or in CNNs. Among the tested options, UV spectroscopy alone allowed for the most precise and accurate prediction of the mAb concentration. A Root Mean Square Error of Prediction (RMSEP) of 0.013 g L^-1 was reached with the UV-based PLS model. The Raman-based PLS model reached an RMSEP of 0.232 g L^-1 . The different data fusion techniques did not improve the prediction accuracy above the prediction accuracy of the UV-based PLS model. Data fusion by PLS models seems meritless when combining a very accurate sensor with a less accurate signal. Furthermore, the application of CNNs for UV and Raman spectra did not yield significant improvements in the prediction quality. For the presented application, linear regression techniques seem to be better suited compared with advanced nonlinear regression techniques, like, CNNs. In summary, the results support the application of UV spectroscopy and PLS modeling for future research and development activities aiming to implement spectroscopic real-time monitoring of the Protein A load phase.

Design of experiments and design space approaches in the pharmaceutical bioprocess optimization

The optimization of pharmaceutical bioprocesses suffers from several challenges like complexity, upscaling costs, regulatory approval, leading to the risk of delivering substandard drugs to patients. Bioprocess is very complex and requires the evaluation of multiple components that need to be monitored and controlled in order to attain the desired state when the process ends. Statistical design of experiments (DoE) is a powerful tool for optimizing bioprocesses because it plays a critical role in the quality by design strategy as it is useful in exploring the experimental domain and providing statistics of interest that enable scientists to understand the impact of critical process parameters on the critical quality attributes. This review summarizes selected publications in which DoE methodology was used to optimize bioprocess. The main objective of the critical review was to clearly demonstrate potential benefits of using the DoE and design space methodologies in bioprocess optimization.

Continuous optical in-line glucose monitoring and control in CHO cultures contributes to enhanced metabolic efficiency while maintaining darbepoetin alfa product quality

Great efforts are directed towards improving productivity, consistency and quality of biopharmaceutical processes and products. One particular area is the development of new sensors for continuous monitoring of critical bioprocess parameters by using online or in-line monitoring systems. Recently, we developed a glucose biosensor applicable in single-use, in-line and long-term glucose monitoring in mammalian cell bioreactors. Now, we integrated this sensor in an automated glucose monitoring and feeding system capable of maintaining stable glucose levels, even at very low concentrations. We compared this fed-batch feedback system at both low (< 1 mM) and high (40 mM) glucose levels with traditional batch culture methods, focusing on glycosylation and glycation of the recombinant protein darbepoetin alfa (DPO) produced by a CHO cell line. We evaluated cell growth, metabolite and product concentration under different glucose feeding strategies and show that continuous feeding, even at low glucose levels, has no harmful effects on DPO quantity and quality. We conclude that our system is capable of tight glucose level control throughout extended bioprocesses and has the potential to improve performance where constant maintenance of glucose levels is critical.

Process analytics 4.0: A paradigm shift in rapid analytics for biologics development

Analytical testing of product quality attributes and process parameters during the biologics development (Process analytics) has been challenging due to the rapid growth of biomolecules with complex modalities to support unmet therapeutic needs. Thus, the expansion of the process analytics tool box for rapid analytics with the deployment of cutting-edge technologies and cyber-physical systems is a necessity. We introduce the term, Process Analytics 4.0; which entails not only technology aspects such as process analytical technology (PAT), assay automation, and high-throughput analytics, but also cyber-physical systems that enable data management, visualization, augmented reality, and internet of things (IoT) infrastructure for real time analytics in process development environment. This review is exclusively focused on dissecting high-level features of PAT, automation, and data management with some insights into the business aspects of implementing during process analytical testing in biologics process development. Significant technological and business advantages can be gained with the implementation of digitalization, automation, and real time testing. A systematic development and employment of PAT in process development workflows enable real time analytics for better process understanding, agility, and sustainability. Robotics and liquid handling workstations allow rapid assay and sample preparation automation to facilitate high-throughput testing of attributes and molecular properties which are otherwise challenging to monitor with PAT tools due to technological and business constraints. Cyber-physical systems for data management, visualization, and repository must be established as part of Process Analytics 4.0 framework. Furthermore, we review some of the challenges in implementing these technologies based on our expertise in process analytics for biopharmaceutical drug substance development.

Developments and opportunities in continuous biopharmaceutical manufacturing

Today’s biologics manufacturing practices incur high costs to the drug makers, which can contribute to high prices for patients. Timely investment in the development and implementation of continuous biomanufacturing can increase the production of consistent-quality drugs at a lower cost and a faster pace, to meet growing demand. Efficient use of equipment, manufacturing footprint, and labor also offer the potential to improve drug accessibility. Although technological efforts enabling continuous biomanufacturing have commenced, challenges remain in the integration, monitoring, and control of traditionally segmented unit operations. Here, we discuss recent developments supporting the implementation of continuous biomanufacturing, along with their benefits.

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.

Raman spectroscopic based chemometric models to support a dynamic capacitance based cell culture feeding strategy

Multiple process analytical technology (PAT) tools are now being applied in tandem for cell culture. Research presented used two in-line probes, capacitance for a dynamic feeding strategy and Raman spectroscopy for real-time monitoring. Data collected from eight batches at the 15,000 L scale were used to develop process models. Raman spectroscopic data were modelled using Partial Least Squares (PLS) by two methods-(1) use of the full dataset and (2) split the dataset based on the capacitance feeding strategy. Root mean square error of prediction (RMSEP) for the first model method of capacitance was 1.54 pf/cm and the second modelling method was 1.40 pf/cm. The second Raman method demonstrated results within expected process limits for capacitance and a 0.01% difference in total nutrient feed compared to the capacitance probe. Additional variables modelled using Raman spectroscopy were viable cell density (VCD), viability, average cell diameter, and viable cell volume (VCV).

Combining convolutional neural networks and on-line Raman spectroscopy for monitoring the Cornu Caprae Hircus hydrolysis process

Cornu Caprae Hircus (goat horn, GH) is one of the frequently used medicinal animal horns in traditional Chinese medicine (TCM). Hydrolysis is one of the key steps for GH pretreatment in pharmaceutical manufacturing. However, the physicochemical complexity of the hydrolysis samples imposes a challenge for hydrolysis process analysis and monitoring. In this study, convolutional neural networks (CNNs), one of the most popular deep learning methods, were used to develop quantitative calibration models based on on-line Raman spectroscopy for monitoring the GH hydrolysis process. Partial least squares (PLS) calibration models were also developed for model performance comparison. For CNN modeling, raw Raman spectra were used as inputs and hyperparameters in the CNN structure were optimized. Results show for four of the seven analytes, the optimized CNN models using raw spectra as inputs outperform the optimized PLS models developed with preprocessed spectra. Therefore, compared with the commonly used PLS algorithm, CNN modeling is also a practicable regression method and can be employed for the analytical purpose of this study. Models with better performance are expected to be obtained by improving the CNN model structure and using more effective hyperparameter optimization approaches in further studies. To the best of our knowledge, this is the first reported case study of combining CNNs and on-line Raman spectroscopy for a regression task.

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.

Applications of Raman spectroscopy in the development of cell therapies: state of the art and future perspectives

This comprehensive review article discusses current and future perspectives of Raman spectroscopy-based analyses of cell therapy processes and products.

Recent Progress in Lab-On-a-Chip Systems for the Monitoring of Metabolites for Mammalian and Microbial Cell Research

Lab-on-a-chip sensing technologies have changed how cell biology research is conducted. This review summarises the progress in the lab-on-a-chip devices implemented for the detection of cellular metabolites. The review is divided into two subsections according to the methods used for the metabolite detection. Each section includes a table which summarises the relevant literature and also elaborates the advantages of, and the challenges faced with that particular method. The review continues with a section discussing the achievements attained due to using lab-on-a-chip devices within the specific context. Finally, a concluding section summarises what is to be resolved and discusses the future perspectives.

Development of an on-line Raman spectral analytical method for monitoring and endpoint determination of the Cornu Caprae Hircus hydrolysis process

OBJECTIVES

Cornu Caprae Hircus (goat horn, GH), a medicinal animal horn, is frequently used in traditional Chinese medicine, and hydrolysis is one of the most important processes for GH pretreatment in pharmaceutical manufacturing. In this study, on-line Raman spectroscopy was applied to monitor the GH hydrolysis process by the development of partial least squares (PLS) calibration models for different groups of amino acids.

METHODS

Three steps were considered in model development. In the first step, design of experiments (DOE)-based preprocessing method selection was conducted. In the second step, the optimal spectral co-addition number was determined. In the third step, sample selection or reconstruction methods based on hierarchical clustering analysis (HCA) were used to extract or reconstruct representative calibration sets from the pool of hydrolysis process samples and investigated for their ability to improve model performance.

KEY FINDINGS

This study has shown the feasibility of using on-line Raman spectral analysis for monitoring the GH hydrolysis process based on the designed measurement system and appropriate model development steps.

CONCLUSIONS

The proposed Raman-based calibration models are expected to be used in GH hydrolysis process monitoring, leading to more rapid material information acquisition, deeper process understanding, more accurate endpoint determination and thus better product quality consistency.