At-line raman spectroscopy and design of experiments for robust monitoring and control of miniature bioreactor cultures

The biopharmaceutical industry is moving toward a more quality by design (QbD) approach that seeks to increase product and process understanding and process control. Miniature bioreactor systems offer a high-throughput method enabling the assessment of numerous process variables in a controlled environment. However, the number of off/at-line samples that can be taken is restricted due to the small working volume of each vessel. This limitation may be resolved through the use of Raman spectroscopy due to its ability to obtain multianalyte data from small sample volumes fast. It can, however, be challenging to implement this technique for this application due to the complexity of the sample matrix and that analytes are often present in low concentration. Here, we present a design of experiments (DOE) approach to generate samples for calibrating robust multivariate predictive models measuring glucose, lactate, ammonium, viable cell concentration (VCC) and product concentration, for unclarified cell culture that improves the daily monitoring of each miniature bioreactor vessel. Furthermore, we demonstrate how the output of the glucose and VCC models can be used to control the glucose and main nutrient feed rate within miniature bioreactor cultures to within qualified critical limits set for larger scale vessels. The DOE approach used to generate the calibration sample set is shown to result in models more robust to process changes than by simply using samples taken from the “typical” process. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2740, 2019.     

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

In this study, the application of Raman spectroscopy to the simultaneous quantitative determination of glucose, glutamine, lactate, ammonia, glutamate, total cell density (TCD), and viable cell density (VCD) in a CHO fed‐batch process was demonstrated in situ in 3 L and 15 L bioreactors. Spectral preprocessing and partial least squares (PLS) regression were used to correlate spectral data with off‐line reference data. Separate PLS calibration models were developed for each analyte at the 3 L laboratory bioreactor scale before assessing its transferability to the same bioprocess conducted at the 15 L pilot scale. PLS calibration models were successfully developed for all analytes bar VCD and transferred to the 15 L scale. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012     

On-line near infrared monitoring of glycerol-boosted anaerobic digestion processes: evaluation of process analytical technologies

A study of NIR as a tool for process monitoring of thermophilic anaerobic digestion boosted by glycerol has been carried out, aiming at developing simple and robust Process Analytical Technology modalities for on-line surveillance in full scale biogas plants. Three 5 L laboratory fermenters equipped with on-line NIR sensor and special sampling stations were used as a basis for chemometric multivariate calibration. NIR characterisation using Transflexive Embedded Near Infra-Red Sensor (TENIRS) equipment integrated into an external recurrent loop on the fermentation reactors, allows for representative sampling, of the highly heterogeneous fermentation bio slurries. Glycerol is an important by-product from the increasing European bio-diesel production. Glycerol addition can boost biogas yields, if not exceeding a limiting 5-7 g L(-1) concentration inside the fermenter-further increase can cause strong imbalance in the anaerobic digestion process. A secondary objective was to evaluate the effect of addition of glycerol, in a spiking experiment which introduced increasing organic overloading as monitored by volatile fatty acids (VFA) levels. High correlation between on-line NIR determinations of glycerol and VFA contents has been documented. Chemometric regression models (PLS) between glycerol and NIR spectra needed no outlier removals and only one PLS-component was required. Test set validation resulted in excellent measures of prediction performance, precision: r(2) = 0.96 and accuracy = 1.04, slope of predicted versus reference fitting. Similar prediction statistics for acetic acid, iso-butanoic acid and total VFA proves that process NIR spectroscopy is able to quantify all pertinent levels of both volatile fatty acids and glycerol.     

Real time monitoring of multiple parameters in mammalian cell culture bioreactors using an in-line Raman spectroscopy probe

The FDA's process analytical technology initiative encourages drug manufacturers to apply innovative ideas to better understand their processes. There are many challenges to applying these techniques to monitor mammalian cell culture bioreactors for biologics manufacturing. These include the ability to monitor multiple components in complex medium formulations non-invasively and in-line. We report results that demonstrate, for the first time, the technical feasibility of the in-line application of Raman spectroscopy for monitoring a mammalian cell culture bioreactor. A Raman probe was used for the simultaneous prediction of culture parameters including glutamine, glutamate, glucose, lactate, ammonium, viable cell density, and total cell density.     

Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture

The Food and Drug Administration (FDA) initiative of Process Analytical Technology (PAT) encourages the monitoring of biopharmaceutical manufacturing processes by innovative solutions. Raman spectroscopy and the chemometric modeling tool partial least squares (PLS) have been applied to this aim for monitoring cell culture process variables. This study compares the chemometric modeling methods of Support Vector Machine radial (SVMr), Random Forests (RF), and Cubist to the commonly used linear PLS model for predicting cell culture components—glucose, lactate, and ammonia. This research is performed to assess whether the use of PLS as standard practice is justified for chemometric modeling of Raman spectroscopy and cell culture data. Model development data from five small-scale bioreactors (2 × 1 L and 3 × 5 L) using two Chinese hamster ovary (CHO) cell lines were used to predict against a manufacturing scale bioreactor (2,000 L). Analysis demonstrated that Cubist predictive models were better for average performance over PLS, SVMr, and RF for glucose, lactate, and ammonia. The root mean square error of prediction (RMSEP) of Cubist modeling was acceptable for the process concentration ranges of glucose (1.437 mM), lactate (2.0 mM), and ammonia (0.819 mM). Interpretation of variable importance (VI) results theorizes the potential advantages of Cubist modeling in avoiding interference of Raman spectral peaks. Predictors/Raman wavenumbers (cm⁻¹) of interest for individual variables are X1139–X1141 for glucose, X846–X849 for lactate, and X2941–X2943 for ammonia. These results demonstrate that other beneficial chemometric models are available for use in monitoring cell culture with Raman spectroscopy.     

Generic Raman‐based calibration models enabling real‐time monitoring of cell culture bioreactors

Raman‐based multivariate calibration models have been developed for real‐time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO‐based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1004–1013, 2015     

Hybrid modeling framework for process analytical technology: application to Bordetella pertussis cultures

In the process analytical technology (PAT) initiative, the application of sensors technology and modeling methods is promoted. The emphasis is on Quality by Design, online monitoring, and closed-loop control with the general aim of building in product quality into manufacturing operations. As a result, online high-throughput process analyzers find increasing application and therewith high amounts of highly correlated data become available online. In this study, an hybrid chemometric/mathematical modeling method is adopted for data analysis, which is shown to be advantageous over the commonly used chemometric techniques in PAT applications. This methodology was applied to the analysis of process data of Bordetella pertussis cultivations, namely online data of near-infrared, (NIR), pH, temperature and dissolved oxygen, and off-line data of biomass, glutamate, and lactate concentrations. The hybrid model structure consisted of macroscopic material balance equations in which the specific reactions rates are modeled by nonlinear partial least square (PLS). This methodology revealed a significant higher statistical confidence in comparison to PLSs, translated in a reduction of mean squared prediction errors (e.g., individual root mean squared prediction errors calibration/validation obtained through the hybrid model for the concentrations of lactate: 0.8699/0.7190 mmol/L; glutamate: 0.6057/0.2917 mmol/L; and biomass: 0.0520/0.0283 OD; and obtained through the PLS model for the concentrations of lactate: 1.3549/1.0087 mmol/L; glutamate: 0.7628/0.3504 mmol/L; and biomass: 0.0949/0.0412 OD). Moreover, the analysis of loadings and scores in the hybrid approach revealed that process features can, as for PLS, be extracted by the hybrid method.     

Real‐time monitoring of antibody glycosylation site occupancy by in situ Raman spectroscopy during bioreactor CHO cell cultures

The glycosylation of therapeutic monoclonal antibodies (mAbs), a known critical quality attribute, is often greatly modified during the production process by animal cells. It is essential for biopharmaceutical industries to monitor and control this glycosylation. However, current glycosylation characterization techniques involve time‐ and labor‐intensive analyses, often carried out at the end of the culture when the product is already synthesized. This study proposes a novel methodology for real‐time monitoring of antibody glycosylation site occupancy using Raman spectroscopy. It was first observed in CHO cell batch culture that when low nutrient concentrations were reached, a decrease in mAb glycosylation was induced, which made it essential to rapidly detect this loss of product quality. By combining in situ Raman spectroscopy with chemometric tools, efficient prediction models were then developed for both glycosylated and nonglycosylated mAbs. By comparing variable importance in projection profiles of the prediction models, it was confirmed that Raman spectroscopy is a powerful method to distinguish extremely similar molecules, despite the high complexity of the culture medium. Finally, the Raman prediction models were used to monitor batch and feed‐harvest cultures in situ. For the first time, it was demonstrated that the concentrations of glycosylated and nonglycosylated mAbs could be successfully and simultaneously estimated in real time with high accuracy, including their sudden variations due to medium exchanges. Raman spectroscopy can thus be considered as a promising PAT tool for feedback process control dedicated to on‐line optimization of mAb quality. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:486–493, 2018     

A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line bioprocess monitoring of Chinese hamster ovary cell cultivations—Part II

This study was performed in order to evaluate a new LED‐based 2D‐fluorescence spectrometer for in‐line bioprocess monitoring of Chinese hamster ovary (CHO) cell culture processes. The new spectrometer used selected excitation wavelengths of 280, 365, and 455 nm to collect spectral data from six 10‐L fed‐batch processes. The technique provides data on various fluorescent compounds from the cultivation medium as well as from cell metabolism. In addition, scattered light offers information about the cultivation status. Multivariate data analysis tools were applied to analyze the large data sets of the collected fluorescence spectra. First, principal component analysis was used to accomplish an overview of all spectral data from all six CHO cultivations. Partial least square regression models were developed to correlate 2D‐fluorescence spectral data with selected critical process variables as offline reference values. A separate independent fed‐batch process was used for model validation and prediction. An almost continuous in‐line bioprocess monitoring was realized because 2D‐fluorescence spectra were collected every 10 min during the whole cultivation. The new 2D‐fluorescence device demonstrates the significant potential for accurate prediction of the total cell count, viable cell count, and the cell viability. The results strongly indicated that the technique is particularly capable to distinguish between different cell statuses inside the bioreactor. In addition, spectral data provided information about the lactate metabolism shift and cellular respiration during the cultivation process. Overall, the 2D‐fluorescence device is a highly sensitive tool for process analytical technology applications in mammalian cell cultures.     

Enabling PAT in insect cell bioprocesses: In situ monitoring of recombinant adeno-associated virus production by fluorescence spectroscopy

The process analytical technology (PAT) initiative shifted the bioprocess development mindset towards real-time monitoring and control tools to measure relevant process variables online, and acting accordingly when undesirable deviations occur. Online monitoring is especially important in lytic production systems in which released proteases and changes in cell physiology are likely to affect product quality attributes, as is the case of the insect cell-baculovirus expression vector system (IC-BEVS), a well-established system for production of viral vectors and vaccines. Here, we applied fluorescence spectroscopy as a real-time monitoring tool for recombinant adeno-associated virus (rAAV) production in the IC-BEVS. Fluorescence spectroscopy is simple, yet sensitive and informative. To overcome the strong fluorescence background of the culture medium and improve predictive ability, we combined artificial neural network models with a genetic algorithm-based approach to optimize spectra preprocessing. We obtained predictive models for rAAV titer, cell viability and cell concentration with normalized root mean squared errors of 7%, 4%, and 7%, respectively, for leave-one-batch-out cross-validation. Our approach shows fluorescence spectroscopy allows real-time determination of the best time of harvest to maintain rAAV infectivity, an important quality attribute, and detection of deviations from the golden batch profile. This methodology can be applied to other biopharmaceuticals produced in the IC-BEVS, supporting the use of fluorescence spectroscopy as a versatile PAT tool.     

Nondestructive near-infrared spectroscopic measurement of multiple analytes in undiluted samples of serum-based cell culture media.

An adaptive calibration procedure is used to build selective multivariate calibration models for the measurement of glucose, lactate, glutamine, and ammonia in undiluted serum-based cell culture media. This adaptive procedure removes metabolism-induced covariance between these analytes in a series of calibration samples collected during the cultivation of PC-3 human prostate cancer cells. Partial least-squares calibration models are generated from single-beam near-infrared (NIR) spectra collected over the 4800- to 4200-cm(-1) combination spectral range. Calibration models were generated with both the full spectral range and optimized spectral ranges. In both cases, the number of model factors was optimized and model validity was determined by comparing analyte concentrations predicted from a series of independent and unaltered samples that were obtained during a subsequent cultivation of the PC-3 cells. Similar analytical performance was achieved with fewer model factors when the optimized spectral range was used. The lowest standard errors of prediction were 0.82, 0.94, 0.55, and 0.76 mM for glucose, lactate, glutamine, and ammonia, respectively. Different spectral ranges were optimal for each analyte and the optimized spectral range coincided with the distinguishing spectral features of the analyte. The results of this study demonstrate that NIR spectroscopy can be used effectively in the off-line measurement of important nutrients (glucose and glutamine) and byproducts (lactate and ammonia) in a serum-based animal cell culture medium.     

Cross‐scale predictive modeling of CHO cell culture growth and metabolites using Raman spectroscopy and multivariate analysis

Multi‐component, multi‐scale Raman spectroscopy modeling results from a monoclonal antibody producing CHO cell culture process including data from two development scales (3 L, 200 L) and a clinical manufacturing scale environment (2,000 L) are presented. Multivariate analysis principles are a critical component to partial least squares (PLS) modeling but can quickly turn into an overly iterative process, thus a simplified protocol is proposed for addressing necessary steps including spectral preprocessing, spectral region selection, and outlier removal to create models exclusively from cell culture process data without the inclusion of spectral data from chemically defined nutrient solutions or targeted component spiking studies. An array of single‐scale and combination‐scale modeling iterations were generated to evaluate technology capabilities and model scalability. Analysis of prediction errors across models suggests that glucose, lactate, and osmolality are well modeled. Model strength was confirmed via predictive validation and by examining performance similarity across single‐scale and combination‐scale models. Additionally, accurate predictive models were attained in most cases for viable cell density and total cell density; however, these components exhibited some scale‐dependencies that hindered model quality in cross‐scale predictions where only development data was used in calibration. Glutamate and ammonium models were also able to achieve accurate predictions in most cases. However, there are differences in the absolute concentration ranges of these components across the datasets of individual bioreactor scales. Thus, glutamate and ammonium PLS models were forced to extrapolate in cases where models were derived from small scale data only but used in cross‐scale applications predicting against manufacturing scale batches. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 31:566–577, 2015     

Process analytical technology case study,part III: Calibration monitoring and transfer

This is the third of a series of articles detailing the development of near-infrared spectroscopy methods for solid dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to develop a system for continuous calibration monitoring and formulate an appropriate strategy for calibration transfer. Indcators of high-flux noise (noise factor level) and wave-length uncertainty were developed. These measurements, in combination with Hotelling’s T² and Q residual, are used to continuously monitor instrument performance and model relevance. Four calibration transfer techniques were compared. Three established techniques, finite impulse response filtering, generalized least squares weighting, and piecewise direct standardization were evaluated. A fourth technique, baseline subtraction, was the most effective for calibration transfer. Using as few as 15 transfer samples, predictive capability of the analytical method was maintained across multiple instruments and major instrument maintenance.     

A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line monitoring of Chinese hamster ovary cell cultivations – Part I

A new two‐dimensional fluorescence sensor system was developed for in‐line monitoring of mammalian cell cultures. Fluorescence spectroscopy allows for the detection and quantification of naturally occurring intra‐ and extracellular fluorophores in the cell broth. The fluorescence signals correlate to the cells’ current redox state and other relevant process parameters. Cell culture pretests with twelve different excitation wavelengths showed that only three wavelengths account for a vast majority of spectral variation. Accordingly, the newly developed device utilizes three high‐power LEDs as excitation sources in combination with a back‐thinned CCD‐spectrometer for fluorescence detection. This setup was first tested in a lab design of experiments study with process relevant fluorophores proving its suitability for cell culture monitoring with LOD in the μg/L range. The sensor was then integrated into a CHO‐K1 cell culture process. The acquired fluorescence spectra of several batches were evaluated using multivariate methods. The resulting batch evolution models were challenged in deviating and “golden batch” validation runs. These first tests showed that the new sensor can trace the cells’ metabolic state in a fast and reliable manner. Cellular distress is quickly detected as a deviation from the “golden batch”.     

In‐line monitoring of amino acids in mammalian cell cultures using raman spectroscopy and multivariate chemometrics models

The application of PAT for in‐line monitoring of biopharmaceutical manufacturing operations has a central role in developing more robust and consistent processes. Various spectroscopic techniques have been applied for collecting real‐time data from cell culture processes. Among these, Raman spectroscopy has been shown to have advantages over other spectroscopic techniques, especially in aqueous culture solutions. Measurements of several process parameters such as glucose, lactate, glutamine, glutamate, ammonium, osmolality and VCD using Raman‐based chemometrics models have been reported in literature. The application of Raman spectroscopy, coupled with calibration models for amino acid measurement in cell cultures, has been assessed. The developed models cover four amino acids important for cell growth and production: tyrosine, tryptophan, phenylalanine and methionine. The chemometrics models based on Raman spectroscopy data demonstrate the significant potential for the quantification of tyrosine, tryptophan and phenylalanine. The model for methionine would have to be further refined to improve quantification.     

Process analytical technology tools for perfusion cell culture

During cell cultivation processes for the production of biopharmaceuticals, good process performance and good product quality can be ensured by online monitoring of critical process parameters (e.g. temperature, pH, or dissolved oxygen). These data can be used in real‐time for process control, as suggested by the process analytical technology (PAT) initiative. Today, solutions for real‐time monitoring of parameters such as concentrations of cells, main nutrients, and metabolism by‐products are developing, but applications of these more complex tools in industrial settings are still limited. Here, we evaluated the use of dielectric spectroscopy (DS) and near‐infrared spectroscopy (NIRS) as PAT tools for a perfusion PER.C6^® cultivation process. We showed that DS enabled predictions of viable cell density from the cultivation vessel, with a root mean square error of prediction (RMSEP) of 4.4% of the calibration range. Additionally, predictions of glucose and lactate concentrations from the cultivation vessel (RMSEP of 10 and 14%, respectively) and from the perfusion stream (RMSEP of 12 and 10%, respectively) were achieved with NIRS. We also showed that the perfusion stream offers great opportunities for noninvasive, yet frequent process monitoring. Accurate online monitoring of critical process parameters with PAT tools is the essential first step toward increased control of process output.     

In‐line monitoring of protein concentration with MIR spectroscopy during UFDF

Rapid increase of product titers in upstream processes has presented challenges for downstream processing, where purification costs increase linearly with the increase of the product yield. Hence, innovative solutions are becoming increasingly popular. Process Analytical Technology (PAT) tools, such as spectroscopic techniques, are on the rise due to their capacity to provide real‐time, precise analytics. This ensures consistent product quality and increased process understanding, as well as process control. Mid‐infrared spectroscopy (MIR) has emerged as a highly promising technique within recent years, owing to its ability to monitor several critical process parameters at the same time and unchallenging spectral analysis and data interpretation. For in‐line monitoring, Attenuated Total Reflectance—Fourier Transform Infrared Spectroscopy (ATR‐FTIR) is a method of choice, as it enables reliable measurements in a liquid environment, even though water absorption bands are present in the region of interest. Here, we present MIR spectroscopy as a monitoring tool of critical process parameters in ultrafiltration/diafiltration (UFDF). MIR spectrometer was integrated in the UFDF process in an in‐line fashion through a single‐use flow cell containing a single bounce silicon ATR crystal. The results indicate that the one‐point calibration algorithm applied to the MIR spectra, predicts highly accurate protein concentrations, as compared with validated offline analytical methods.     

An adaptive modeling approach using spiking-augmentation method to improve chemometric model performance in bioprocess monitoring

Intensified and continuous processes require fast and robust methods and technologies to monitor product titer for faster analytical turnaround time, process monitoring, and process control. The current titer measurements are mostly offline chromatography-based methods which may take hours or even days to get the results back from the analytical labs. Thus, offline methods will not meet the requirement of real time titer measurements for continuous production and capture processes. FTIR and chemometric based multivariate modeling are promising tools for real time titer monitoring in clarified bulk (CB) harvests and perfusate lines. However, empirical models are known to be vulnerable to unseen variability, specifically a FTIR chemometric titer model trained on a given biological molecule and process conditions often fails to provide accurate predictions of titer in another molecule under different process conditions. In this study, we developed an adaptive modeling strategy: the model was initially built using a calibration set of available perfusate and CB samples and then updated by augmenting spiking samples of the new molecules to the calibration set to make the model robust against perfusate or CB harvest of the new molecule. This strategy substantially improved the model performance and significantly reduced the modeling effort for new molecules.