Real-time monitoring and control of CHO cell apoptosis by in situ multifrequency scanning dielectric spectroscopy

Sensitivity of bulk electrical impedance spectroscopy (bio-capacitance) probes to cell and culture properties: Study on CHO cell cultures

Bulk electrical impedance spectroscopy (bio-capacitance) probes, hold significant promise for real-time cell monitoring in bioprocesses. Focusing on Chinese hamster ovary (CHO) cells, we present a sensitivity analysis framework to assess the impact of cell and culture properties on the complex permittivity spectrum, εmix, and its associated parameters, permittivity increment, Δε, critical frequency, fc, and Cole-Cole parameter, α, measured by bio-capacitance probes. Our sensitivity analysis showed that Δε is highly sensitive to cell size and concentration, making it suitable for estimating biovolume during the exponential growth phase, whereas fc provides information about cumulative changes in cell size, membrane permittivity, and cytoplasm conductivity during the transition to death phase. The analysis indicated that specific information about cell membrane permittivity or internal conductivity cannot be extracted from εmix spectrum. Based on the sensitivity analysis, we proposed two alternative parameters for monitoring cells in bioprocesses: Δε1 MHz and Δε1 MHz/Δε0.3 MHz, using measurements at 300 kHz, 1 MHz, and 10 MHz. Δε1 MHz is suitable for estimating viable cell density during the exponential growth phase due to its lower sensitivity to cell size. Δε1 MHz/Δε0.3 MHz can replace fc due to similar sensitivities to cell size and dielectric properties. These frequencies are within most bio-capacitance probes' optimal operation range, eliminating the need for low-frequency electrode polarization and high-frequency stray capacitances corrections. Experimental measurements on CHO cells confirmed the results of sensitivity analysis.

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.

Adaptive modeling optimized by the data fusion strategy: Real-time dying cell percentage prediction using capacitance spectroscopy

The previous research showcased a partial least squares (PLS) regression model accurately predicting cell death percentages using in-line capacitance spectra. The current study advances the model accuracy through adaptive modeling employing a data fusion approach. This strategy enhances prediction performance by incorporating variables from the Cole-Cole model, conductivity and its derivatives over time, and Mahalanobis distance into the predictor matrix (X-matrix). Firstly, the Cole-Cole model, a mechanistic model with parameters linked to early cell death onset, was integrated to enhance prediction performance. Secondly, the inclusion of conductivity and its derivatives over time in the X-matrix mitigated prediction fluctuations resulting from abrupt conductivity changes during process operations. Thirdly, Mahalanobis distance, depicting spectral changes relative to a reference spectrum from a previous time point, improved model adaptability to independent test sets, thereby enhancing performance. The final data fusion model substantially decreased root-mean squared error of prediction (RMSEP) by around 50%, which is a significant boost in prediction accuracy compared to the prior PLS model. Robustness against reference spectrum selection was confirmed by consistent performance across various time points. In conclusion, this study illustrates that the data fusion strategy substantially enhances the model accuracy compared to the previous model relying solely on capacitance spectra.

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.

A Bioreactor-Based Yellow Fever Virus-like Particle Production Process with Integrated Process Analytical Technology Based on Transient Transfection

Yellow Fever (YF) is a severe disease that, while preventable through vaccination, lacks rapid intervention options for those already infected. There is an urgent need for passive immunization techniques using YF-virus-like particles (YF-VLPs). To address this, we successfully established a bioreactor-based production process for YF-VLPs, leveraging transient transfection and integrating Process Analytical Technology. A cornerstone of this approach was the optimization of plasmid DNA (pDNA) production to a yield of 11 mg/L using design of experiments. Glucose, NaCl, yeast extract, and a phosphate buffer showed significant influence on specific pDNA yield. The preliminary work for VLP-production in bioreactor showed adjustments to the HEK cell density, the polyplex formation duration, and medium exchanges effectively elevated transfection efficiencies. The additive Pluronic F-68 was neutral in its effects, and anti-clumping agents (ACA) adversely affected the transfection process. Finally, we established the stirred-tank bioreactor process with integrated dielectric spectroscopy, which gave real-time insight in relevant process steps, e.g., cell growth, polyplex uptake, and harvest time. We confirmed the presence and integrity of YF-VLP via Western blot, imaging flow cytometry measurement, and transmission electron microscopy. The YF-VLP production process can serve as a platform to produce VLPs as passive immunizing agents against other neglected tropical diseases.

Capacitance spectroscopy enables real-time monitoring of early cell death in mammalian cell culture

BACKGROUND/AIMS

Previous work developed a quantitative model using capacitance spectroscopy in an at-line setup to predict the dying cell percentage measured from a flow cytometer. This work aimed to transfer the at-line model to monitor lab-scale bioreactors in real-time, waiving the need for frequent sampling and enabling precise controls.

METHODS AND RESULTS

Due to the difference between the at-line and in-line capacitance probes, direct application of the at-line model resulted in poor accuracy and high prediction bias. A new model with a variable range and offering similar spectral shape across all probes was first constructed, improving prediction accuracy. Moreover, the global calibration method included the variance of different probes and scales in the model, reducing prediction bias. External parameter orthogonalization, a preprocessing method, also mitigated the interference from feeding, which further improved model performance. The root-mean-square error of prediction of the final model was 6.56% (8.42% of the prediction range) with an R² of 92.4%.

CONCLUSION

The culture evolution trajectory predicted by the in-line model captured the cell death and alarmed cell death onset earlier than the trypan blue exclusion test. Additionally, the incorporation of at-line spectra following orthogonal design into the calibration set was shown to generate calibration models that are more robust than the calibration models constructed using the in-line spectra only. This is advantageous, as at-line spectral collection is easier, faster, and more material-sparing than in-line spectra collection.

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.

Rapid At-line Early Cell Death Quantification using Capacitance Spectroscopy

Cell death is one of the failure modes of mammalian cell culture. Apoptosis is a regulated cell death process mainly observed in cell culture. Timely detection of apoptosis onset allows opportunities for preventive controls that ensure high productivity and consistent product quality. Capacitance spectroscopy captures the apoptosis-related cellular properties changes and thus quantifies the percentage of dying cells. This work demonstrated a quantification model that measures the percentage of apoptotic cells using a capacitance spectrometer in an at-line setup. When predicting the independent test set collected from bench-scale bioreactors, the root-mean-squared error of prediction (RMSEP) was 8.8% (equivalent to 9.9% of the prediction range). The predicted culture evolution trajectory aligned with measured values from the flow cytometer. Furthermore, this method alarms cell death onset earlier than the traditional viability test, i.e., trypan blue exclusion test. Comparing to flow cytometry (the traditional early cell death detection method), this method is rapid, simple, and less labor-intensive. Additionally, this at-line setup can be easily transferred between scales (e.g., lab-scale for development to manufacturing-scale), which benefits process transfers between facilities, scale-up, and other process transitions. This article is protected by copyright. All rights reserved.

Taking the pulse of bioprocesses: at-line and in-line monitoring of mammalian cell cultures

Real-time and near real-time monitoring of cell culture processes are critical to the evolving process analytical technology (PAT) paradigm for upstream bioprocessing. The responses measured from these analytical instruments can enable rapid feedback to perturbations that can otherwise lead to batch failures. Historically, real-time monitoring of bioreactor processes has been relegated to parameters such as pH, dissolved oxygen, and temperature. Other analytical results, such as cell growth and metabolites, are provided through manual daily sampling. In order to reduce sample error and increase throughput, real-time and near real-time instruments have been developed. Here we discuss recent advances in these technologies. This article aims to focus on other developing at-line and in-line technologies that enable monitoring of bioreactor processes, including dielectric spectroscopy, NIR, off-gas spectrometry, integrated at-line HPLC, and nanofluidic devices for monitoring cell growth and health, metabolites, titer, and product quality.

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 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).

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.