Cole–Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass

Enhancing real-time cell culture process monitoring through the integration of advanced machine learning techniques: A comparative analysis of Raman and capacitance spectroscopies

Machine learning (ML) techniques have emerged as an important tool improving the capabilities of online process monitoring and control in cell culture process for biopharmaceutical manufacturing. A variety of advanced ML algorithms have been evaluated in this study for cell growth monitoring using spectroscopic tools, including Raman and capacitance spectroscopies. While viable cell density can be monitored real-time in the cell culture process, online monitoring of cell viability has not been well established. A thorough comparison between the advanced ML techniques and traditional linear regression method (e.g., Partial Least Square regression) reveals a significant improvement in accuracy with the leading ML algorithms (e.g., 31.7% with Random Forest regressor), addressing the unmet need of continuous monitoring viability in a real time fashion. Both Raman and capacitance spectroscopies have demonstrated success in viability monitoring, with Raman exhibiting superior accuracy compared to capacitance. In addition, the developed methods have shown better accuracy in a relatively higher viability range (>90%), suggesting a great potential for early fault detection during cell culture manufacturing. Further study using ML techniques for VCD monitoring also showed an increased accuracy (27.3% with Raman spectroscopy) compared to traditional linear modeling. The successful integration of ML techniques not only amplifies the potential of process monitoring but also makes possible the development of advanced process control strategies for optimized operations and maximized efficiency.

Real-Time VCC Monitoring and Forecasting in HEK-Cell-Based rAAV Vector Production Using Capacitance Spectroscopy

Recombinant adeno-associated virus (rAAV) vector production is a complex process in which the robust cultivation of human embryonic kidney cells (HEK293) plays a critical role in generating high-quality viral vectors. Tracking the viable cell concentration (VCC) during upstream production is essential for process monitoring and for implementing actions that ensure optimal process management. The advent of inline capacitance probes has introduced a crucial process analytical technology (PAT) tool for real-time VCC measurement. Here, we present the development and application of a method for real-time monitoring of VCC in HEK293-based rAAV vector production. In a first step, BioPAT Viamass probes were used to record capacitance data of individual 10 L rAAV-8 batches within a frequency range of 50 kHz-20 MHz. Based on the capacitance data, a linear single-frequency model and an orthogonal partial least square (OPLS) multifrequency model for VCC prediction were developed. Subsequently, these models were deployed inline, and predictions were exposed into BioPAT MFCS bioprocess control software, enabling real-time VCC monitoring in subsequent rAAV-8 production batches. In addition, the continuous VCC signal was used as input for an exponential cell growth model that was deployed inline to provide accurate real-time forecasting of the transfection time point. To the best of our knowledge, this is the first example of inline deployment of VCC and Time-Till-Transfection predictive models to the bioprocess control system for real-time monitoring and forecasting of these parameters in HEK-cell-based transient rAAV vector production.

In-line prediction of viability and viable cell density through machine learning-based soft sensor modeling and an integrated systems approach: An industrially relevant PAT case study

The biopharmaceutical industry is shifting toward employing digital analytical tools for improved understanding of systems biology data and production of quality products. The implementation of these technologies can streamline the manufacturing process by enabling faster responses, reducing manual measurements, and building continuous and automated capabilities. This study discusses the use of soft sensor models for prediction of viability and viable cell density (VCD) in CHO cell culture processes by using in-line optical density and permittivity sensors. A significant innovation of this study is the development of a simplified empirical model and adoption of an integrated systems approach for in-line viability prediction. The initial evaluation of this viability model demonstrated promising accuracy with 96% of the residuals within a ±5% error limit and a Final Day mean absolute percentage error of ≤5% across various scales and process conditions. This model was integrated with a VCD prediction model utilizing Gaussian Process Regressor with Matern Kernel (nu = 0.5), selected from over a hundred advanced machine learning techniques. This VCD prediction model had an R2 of 0.92 with 89% predictions within ±10% error and significantly outperformed the commonly used partial least squares regression models. The results validated the use of these models for real-time in-line prediction of viability and VCD and highlighted the potential to substantially reduce reliance on labor-intensive discrete offline measurements. The integration of these innovative technologies aligns with regulatory guidelines and establishes a foundation for further advancements in the biomanufacturing industry, promising improved process control, efficiency, and compliance with quality standards.

Capacitance sensors in cell-based bioprocesses: online monitoring of biomass and more

Biocapacitance measurement has emerged as a widely used technique for monitoring bioprocesses that involve living cells. Over time, hardware and software developments have enabled this method to move from food towards biopharma industries for improved characterisation, monitoring and control of the bioprocess, even in strictly regulated production environments. In alignment with the general trends in biopharma towards new modalities such as virus-based and cell-based therapies, biocapacitance measurement is entering this area and provides new opportunities for process development and control. Based on the recent progress, the authors strongly believe that even though biocapacitance measurement is a mature, established technology for online biomass monitoring, the nearest future will bring its new and exciting developments and applications that will enhance bioprocess understanding and bring new solutions for enhanced process understanding, monitoring and control.

Online deployment of an O-PLS model for dielectric spectroscopy-based inline monitoring of viable cell concentrations in Chinese hamster ovary cell perfusion cultivations

Viable cell concentration (VCC) is an essential parameter that is required to support the efficient cultivation of mammalian cells. Although commonly determined using at-line or off-line analytics, in-line capacitance measurements represent a suitable alternative method for the determination of VCC. In addition, these latter efforts are complimentary with the Food and Drug Administration's initiative for process analytical technologies (PATs). However, current applications for online determination of the VCC often rely on single frequency measurements and corresponding linear regression models. It has been reported that this may be insufficient for application at all stages of a mammalian cell culture processes due to changes in multiple cell parameters over time. Alternatively, dielectric spectroscopy, measuring capacitance at multiple frequencies, in combination with multivariate mathematical models, has proven to be more robust. However, this has only been applied for retrospective data analysis. Here, we present the implementation of an O-PLS model for the online processing of multifrequency capacitance signals and the on-the-fly integration of the models' VCC results into a supervisory control and data acquisition (SCADA) system commonly used for cultivation observation and control. This system was evaluated using a Chinese hamster ovary (CHO) cell perfusion process.

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.

Two-methods approach to follow up biomass by impedance spectroscopy: Bacillus thuringiensis fermentations as a study model

Impedance spectroscopy is used for the characterization of electrochemical systems as well as for the monitoring of bioprocesses. However, the data obtained using this technique allow multiple interpretations, depending on the methodology implemented. Hence, it is necessary to establish a robust methodology to reliably follow-up biomass in fermentations. In the present work, two methodological approaches, mainly used for the characterization of electrochemical systems, were employed to characterize and determine a frequency that allows the monitoring of biomass in Bacillus thuringiensis fermentations by impedance spectroscopy. The first approach, based on a conventional analysis, revealed a single distribution with a characteristic frequency of around 2 kHz. In contrast, the second approach, based on the distribution of relaxation times, gave three distributions (A, B, and C). The C distribution, found near 9 kHz, was more related to the microbial biomass than the distribution at 2 kHz using the equivalent circuits. The time course of the B. thuringiensis fermentation was followed; bacilli, spores, glucose, and acid and base consumption for pH were determined out of line; and capacitance at 9 kHz was monitored. The correlation between the time course data and the capacitance profile indicated that the monitoring of B. thuringiensis at 9 kHz mainly corresponds to extracellular activity and, in a second instance, to the cellular concentration. These results show that it is necessary to establish a robust and reliable methodology to monitor fermentation processes by impedance spectroscopy, and the distribution of relaxation times was more appropriate. KEY POINTS: • Application of impedance spectroscopy for bioprocess monitoring • Low-frequency monitoring of biomass in fermentations • Analysis of impedance data by two methodological approaches.

Cole-Cole modeling of real-time capacitance data for estimation of cell physiological properties in recombinant Escherichia coli cultivation

Real-time estimation of physiological properties of the cell during recombinant protein production would ensure enhanced process monitoring. In this study, we explored the application of dielectric spectroscopy to track the fed-batch phase of recombinant Escherichia coli cultivation for estimating the physiological properties, viz. cell diameter and viable cell concentration (VCC). The scanning capacitance data from the dielectric spectroscopy were pre-processed using moving average (MA). Later, it was modelled through a nonlinear theoretical Cole-Cole model and further solved using a global evolutionary genetic algorithm (GA). The parameters obtained from the GA were further applied for the estimation of the aforementioned physiological properties. The offline cell diameter and cell viability data were obtained from particle size analyzer and flow cytometry measurements to validate the Cole-Cole model. The offline VCC was calculated from the cell viability % from flow cytometry data and dry cell weight concentration (DCW). The Cole-Cole model predicted the cell diameter and VCC with an error of 1.03% and 7.72%, respectively. The proposed approach can enable the operator to take real-time process decisions in order to achieve desired productivity and product quality. This article is protected by copyright. All rights reserved.

Online monitoring of hiPSC expansion and hepatic differentiation in 3D culture by dielectric spectroscopy

Hepatocyte-like cells derived from human-induced pluripotent stem cells (hiPSC-HLC) are expected to have important applications in drug screening and regenerative medicine. However, hiPSC-HLC are difficult to produce on a large-scale to obtain relevant numbers for such applications. The aim of this study was to implement a novel integrated strategy for scalable production of hiPSC-HLC and demonstrate the applicability of dielectric spectroscopy to monitor hiPSC expansion/differentiation processes. We cultured hiPSC as three-dimensional (3D) aggregates in stirred-tank bioreactors (STB) operated in perfusion with an in situ capacitance probe. Dissolved oxygen concentration and dilution rate were controlled along the process and after 5 days of cell expansion, the hepatic differentiation was integrated in sequential steps for 28 days. The hiPSC were able to grow as 3D aggregates and the expression of hepatic markers and albumin production after differentiation confirmed that hepatocyte differentiation improved when compared to 2D culture. These hiPSC-HLC exhibited functional characteristics of hepatocytes including glycogen storage and drug metabolization capacity. Our results also show a good correlation between the cell permittivity measured online and the aggregate biovolume measured by standard offline methods, demonstrating for the first time the potential of dielectric spectroscopy to monitor hiPSC expansion and differentiation in STB.

Dielectric Spectroscopy to Improve the Production of rAAV Used in Gene Therapy

The insect cell-baculovirus expression vector system is an established method for large scale recombinant adeno-associated virus (rAAV) production, largely due to its scalability and high volumetric productivities. During rAAV production it is critical to monitor process parameters such as Spodoptera frugiperda (Sf9) cell concentration, infection timing, and cell harvest viabilities since they can have a significant influence on rAAV productivity and product quality. Herein we developed the use of dielectric spectroscopy as a process analytical technology (PAT) tool used to continuously monitor the production of rAAV in 2 L stirred tank bioreactors, achieving enhanced control over the production process. This study resulted in improved manufacturing robustness through continuous monitoring of cell culture parameters, eliminating sampling needs, increasing the accuracy of infection timing, and reliably estimating the time of harvest. To increase the accuracy of baculovirus infection timing, the cell growth/permittivity model was coupled to a feedback loop with real-time monitoring. This system was able to predict baculovirus infection timing up to 24 h in advance for greatly improved accuracy of infection and ensuring consistent high rAAV productivities. Furthermore, predictive models were developed based on the dielectric measurements of the culture. These multiple linear regression-based models resulted in correlation coefficients (Q2) of 0.89 for viable cell concentration, 0.97 for viability, and 0.92 for cell diameter. Finally, models were developed to predict rAAV titer providing the capability to distinguish in real time between high and low titer production batches.

Control of Specific Growth Rate in Fed-Batch Bioprocesses: Novel Controller Design for Improved Noise Management

Accurate control of the specific growth rate (µ) of microorganisms is dependent on the ability to quantify the evolution of biomass reliably in real time. Biomass concentration can be monitored online using various tools and methods, but the obtained signal is often very noisy and unstable, leading to inaccuracies in the estimation of μ. Furthermore, controlling the growth rate is challenging as the process evolves nonlinearly and is subject to unpredictable disturbances originating from the culture’s metabolism. In this work, a novel feedforward-feedback controller logic is presented to counter the problem of noise and oscillations in the control variable and to address the exponential growth dynamics more effectively. The controller was tested on fed-batch cultures of Kluyveromyces marxianus, during which μ was estimated in real time from online biomass concentration measurements obtained with dielectric spectroscopy. It is shown that the specific growth rate can be maintained at different setpoint values with an average root mean square control error of 23 ± 6%.

Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors

Viable cell concentration (VCC) is one of the most important process attributes during mammalian cell cultivations. Current state-of-the-art measurements of VCC comprise offline methods which do not allow for continuous process data. According to the FDA's process analytical technology initiative, process monitoring and control should be applied to gain process understanding and to ensure high product quality. In this work, the use of an inline capacitance probe to monitor online VCCs of a mammalian CHO cell culture process in small-scale bioreactors (250 mL) was investigated. Capacitance sensors using single frequency are increasingly common for biomass monitoring. However, the single-frequency signal corresponds to the cell polarization that represents the viable cell volume. Therefore single-frequency measurements are dependent on cell diameter changes. Measuring the capacitance across various frequencies (frequency scanning) can provide information about the VCC and cope with changing cell diameter. Applying multivariate data analysis on the frequency scanning data successfully enabled direct online monitoring of VCCs in this study. The multivariate model was trained with data from 5 standard cultivations. The model provided a prediction of VCCs with relative errors from 5.5 to 11%, which is a good agreement with the acceptance criterion based on the offline reference method accuracy (approximately 10% relative error) and strongly improved compared with single-frequency results (16 to 23% relative error). Furthermore, robustness trials were conducted to demonstrate the model's predictive ability under challenging conditions. The process deviations in regard to dilution steps and feed variations were detected immediately in the online prediction of the VCC with relative errors between 6.7 and 13.2%. Thus in summary, the presented method on capacitance frequency scanning demonstrates its suitability for process monitoring and control that can save batches, time, and cost. Graphical abstract.

High level extracellular production of recombinant human interferon alpha 2b in glycoengineered Pichia pastoris: culture medium optimization, high cell density cultivation and biological characterization

AIMS

The present study was aimed at design of experiments (DoE)- and artificial intelligence-based culture medium optimization for high level extracellular production of a novel recombinant human interferon alpha 2b (huIFNα2b) in glycoengineered Pichia pastoris and its characterization.

METHODS AND RESULTS

The artificial neural network-genetic algorithm model exhibited improved huIFNα2b production and better predictability compared to response surface methodology. The optimized medium exhibited a fivefold increase in huIFNα2b titre compared to the complex medium. A maximum titre of huIFNα2b (436 mg l^-1 ) was achieved using the optimized medium in the bioreactor. Real-time capacitance data from dielectric spectroscopy were utilized to model the growth kinetics with unstructured models. Biological characterization by antiproliferative assay proved that the purified recombinant huIFNα2b was biologically active, exhibiting growth inhibition on breast cancer cell line.

CONCLUSIONS

Culture medium optimization resulted in enhanced production of huIFNα2b in glycoengineered P. pastoris at both shake flask and bioreactor level. The purified huIFNα2b was found to be N-glycosylated and biologically active.

SIGNIFICANCE AND IMPACT OF THE STUDY

DoE-based medium optimization strategy significantly improved huIFNα2b production. The antiproliferative activity of huIFNα2b substantiates its potential scope for application in cancer therapy.

A Novel Application for Low Frequency Electrochemical Impedance Spectroscopy as an Online Process Monitoring Tool for Viable Cell Concentrations

New approaches in process monitoring during industrial fermentations are not only limited to classical pH, dO₂ and offgas analysis, but use different in situ and online sensors based on different physical principles to determine biomass, product quality, lysis and far more. One of the very important approaches is the in situ accessibility of viable cell concentration (VCC). This knowledge provides increased efficiency in monitoring and controlling strategies during cultivations. Electrochemical impedance spectroscopy—EIS—is used to monitor biomass in a fermentation of E. coli BL21(DE3), producing a recombinant protein using a fed batch-based approach. Increases in the double layer capacitance (C_dl), determined at frequencies below 1 kHz, are proportional to the increase of biomass in the batch and fed batch phase, monitored in offline and online modes for different cultivations. A good correlation of C_dl with cell density is found and in order to get an appropriate verification of this method, different state-of-the-art biomass measurements are performed and compared. Since measurements in this frequency range are largely determined by the double layer region between the electrode and media, rather minor interferences with process parameters (aeration, stirring) are to be expected. It is shown that impedance spectroscopy at low frequencies is a powerful tool for cultivation monitoring.

Metabolic Control in Mammalian Fed-Batch Cell Cultures for Reduced Lactic Acid Accumulation and Improved Process Robustness

Biomass and cell-specific metabolic rates usually change dynamically over time, making the "feed according to need" strategy difficult to realize in a commercial fed-batch process. We here demonstrate a novel feeding strategy which is designed to hold a particular metabolic state in a fed-batch process by adaptive feeding in real time. The feed rate is calculated with a transferable biomass model based on capacitance, which changes the nutrient flow stoichiometrically in real time. A limited glucose environment was used to confine the cell in a particular metabolic state. In order to cope with uncertainty, two strategies were tested to change the adaptive feed rate and prevent starvation while in limitation: (i) inline pH and online glucose concentration measurement or (ii) inline pH alone, which was shown to be sufficient for the problem statement. In this contribution, we achieved metabolic control within a defined target range. The direct benefit was two-fold: the lactic acid profile was improved and pH could be kept stable. Multivariate Data Analysis (MVDA) has shown that pH influenced lactic acid production or consumption in historical data sets. We demonstrate that a low pH (around 6.8) is not required for our strategy, as glucose availability is already limiting the flux. On the contrary, we boosted glycolytic flux in glucose limitation by setting the pH to 7.4. This new approach led to a yield of lactic acid/glucose (Y L/G) around zero for the whole process time and high titers in our labs. We hypothesize that a higher carbon flux, resulting from a higher pH, may lead to more cells which produce more product. The relevance of this work aims at feeding mammalian cell cultures safely in limitation with a desired metabolic flux range. This resulted in extremely stable, low glucose levels, very robust pH profiles without acid/base interventions and a metabolic state in which lactic acid was consumed instead of being produced from day 1. With this contribution, we wish to extend the basic repertoire of available process control strategies, which will open up new avenues in automation technology and radically improve process robustness in both process development and manufacturing.

Universal Capacitance Model for Real-Time Biomass in Cell Culture

: Capacitance probes have the potential to revolutionize bioprocess control due to their safe and robust use and ability to detect even the smallest capacitors in the form of biological cells. Several techniques have evolved to model biomass statistically, however, there are problems with model transfer between cell lines and process conditions. Errors of transferred models in the declining phase of the culture range for linear models around +100% or worse, causing unnecessary delays with test runs during bioprocess development. The goal of this work was to develop one single universal model which can be adapted by considering a potentially mechanistic factor to estimate biomass in yet untested clones and scales. The novelty of this work is a methodology to select sensitive frequencies to build a statistical model which can be shared among fermentations with an error between 9% and 38% (mean error around 20%) for the whole process, including the declining phase. A simple linear factor was found to be responsible for the transferability of biomass models between cell lines, indicating a link to their phenotype or physiology.

Impedance sensing device enables early detection of pressure ulcers in vivo

When pressure is applied to a localized area of the body for an extended time, the resulting loss of blood flow and subsequent reperfusion to the tissue causes cell death and a pressure ulcer develops. Preventing pressure ulcers is challenging because the combination of pressure and time that results in tissue damage varies widely between patients, and the underlying damage is often severe by the time a surface wound becomes visible. Currently, no method exists to detect early tissue damage and enable intervention. Here we demonstrate a flexible, electronic device that non-invasively maps pressure-induced tissue damage, even when such damage cannot be visually observed. Using impedance spectroscopy across flexible electrode arrays in vivo on a rat model, we find that impedance is robustly correlated with tissue health across multiple animals and wound types. Our results demonstrate the feasibility of an automated, non-invasive 'smart bandage' for early detection of pressure ulcers.

On-chip microelectrode impedance analysis of mammalian cell viability during biomanufacturing

The characterization of cell viability is a challenging task in applied biotechnology, as no clear definition of cell death exists. Cell death is accompanied with a change in the electrical properties of the membrane as well as the cell interior. Therefore, changes in the physiology of cells can be characterized by monitoring of their dielectric properties. We correlated the dielectric properties of industrially used mammalian cells, sedimented over interdigitated microelectrodes, to the AC signal response across the chip. The voltage waveforms across the electrodes were processed to obtain the circuit impedance, which was used to quantify the changes in cell viability. We observed an initial decrease in impedance, after which it remained nearly constant. The results were compared with data from the dye exclusion viability test, the cell specific oxygen uptake rate, and the online viable cell density data from capacitance probes. The microelectrode technique was found to be sensitive to physiological changes taking place inside the cells before their membrane integrity is compromised. Such accurate determination of the metabolic status during this initial period, which turned out to be less well captured in the dye exclusion tests, may be essential for several biotechnology operations.

A novel approach for using dielectric spectroscopy to predict viable cell volume (VCV) in early process development

Online monitoring of viable cell volume (VCV) is essential to the development, monitoring, and control of bioprocesses. The commercial availability of steam-sterilizable dielectricspectroscopy probes has enabled successful adoption of this technology as a key noninvasive method to measure VCV for cell-culture processes. Technological challenges still exist, however. For some cell lines, the technique’s accuracy in predicting the VCV from probepermittivity measurements declines as the viability of the cell culture decreases. To investigate the cause of this decrease in accuracy, divergences in predicted vs. actual VCV measurements were directly related to the shape of dielectric frequency scans collected during a cell culture. The changes in the shape of the beta dispersion, which are associated with changes in cell state, are quantified by applying a novel “area ratio” (AR) metric to frequency-scanning data from the dielectric-spectroscopy probes. The AR metric is then used to relate the shape of the beta dispersion to single-frequency permittivity measurements to accurately predict the offline VCV throughout an entire fed-batch run, regardless of cell state. This work demonstrates the possible feasibility of quantifying the shape of the beta dispersion, determined from frequency-scanning data, for enhanced measurement of VCV in mammalian cell cultures by applying a novel shape-characterization technique. In addition, this work demonstrates the utility of using changes in the shape of the beta dispersion to quantify cell health.

Real-time estimation of biomass and specific growth rate in physiologically variable recombinant fed-batch processes

The real-time measurement of biomass has been addressed since many years. The quantification of biomass in the induction phase of a recombinant bioprocess is not straight forward, since biological burden, caused by protein expression, can have a significant impact on the cell morphology and physiology. This variability potentially leads to poor generalization of the biomass estimation, hence is a very important issue in the dynamic field of process development with frequently changing processes and producer lines. We want to present a method to quantify “biomass” in real-time which avoids off-line sampling and the need for representative training data sets. This generally applicable soft-sensor, based on first principles, was used for the quantification of biomass in induced recombinant fed-batch processes. Results were compared with “state of the art” methods to estimate the biomass concentration and the specific growth rate µ. Gross errors such as wrong stoichiometric assumptions or sensor failure were detected automatically. This method allows for variable model coefficients such as yields in contrast to other process models, hence does not require prior experiments. It can be easily adapted to a different growth stoichiometry; hence the method provides good generalization, also for induced culture mode. This approach estimates the biomass (or anabolic bioconversion) in induced fed-batch cultures in real-time and provides this key variable for process development for control purposes.

Quantitative modeling of viable cell density, cell size, intracellular conductivity, and membrane capacitance in batch and fed-batch CHO processes using dielectric spectroscopy

Dielectric spectroscopy was used to analyze typical batch and fed-batch CHO cell culture processes. Three methods of analysis (linear modeling, Cole-Cole modeling, and partial least squares regression), were used to correlate the spectroscopic data with routine biomass measurements [viable packed cell volume, viable cell concentration (VCC), cell size, and oxygen uptake rate (OUR)]. All three models predicted offline biomass measurements accurately during the growth phase of the cultures. However, during the stationary and decline phases of the cultures, the models decreased in accuracy to varying degrees. Offline cell radius measurements were unsuccessfully used to correct for the deviations from the linear model, indicating that physiological changes affecting permittivity were occurring. The beta-dispersion was analyzed using the Cole-Cole distribution parameters Deltaepsilon (magnitude of the permittivity drop), f(c) (critical frequency), and alpha (Cole-Cole parameter). Furthermore, the dielectric parameters static internal conductivity (sigma(i)) and membrane capacitance per area (C(m)) were calculated for the cultures. Finally, the relationship between permittivity, OUR, and VCC was examined, demonstrating how the definition of viability is critical when analyzing biomass online. The results indicate that the common assumptions of constant size and dielectric properties used in dielectric analysis are not always valid during later phases of cell culture processes. The findings also demonstrate that dielectric spectroscopy, while not a substitute for VCC, is a complementary measurement of viable biomass, providing useful auxiliary information about the physiological state of a culture.