Rapid At-line Early Cell Death Quantification using Capacitance Spectroscopy

Design and validation of a custom-made system to measure transepithelial electrical impedance in human corneas preserved in active storage machine

Corneal epithelial barrier represents one of the major limitations to ocular drug delivery and can be explored non-invasively through the evaluation of its electrical properties. Human corneas stored in active storage machine (ASM) could represent an interesting physiological model to explore transcorneal drug penetration. We designed a new system adapted to human corneas preserved in ASM to explore corneal epithelial barrier function ex-vivo. A bipolar set-up including Ag/AgCl electrodes adaptors to fit the corneal ASM and a dedicated software was designed and tested on freshly excised porcine corneas (n = 59) and human corneas stored 14 days in ASM (n = 6). Porcine corneas presented significant and proportional decrease in corneal impedance in response to increasing-size epithelial ulcerations and acute exposure to benzalkonium chloride (BAC) 0.01 and 0.05%. Human corneas stored 14 days in ASM presented a significant increase in corneal impedance associated with the restoration of a multi-layer epithelium and an enhanced expression of tight junctions markers zonula occludens 1, claudin 1 and occludin. These results support the relevance of the developed approach to pursue the exploration and development of human corneas stored in ASM as a physiological pharmacological model.

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.

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.

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.

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.

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.

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.

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.

Impedance-based non-invasive assay for ocular damage prediction on in vitro 3D reconstructed human corneal epithelium

Reconstructed human cornea-like epithelium (RhCE) holds unprecedented promise for toxicological analyses and the replacement of animal use. However, current standards to evaluate potential ocular irritancy present a major downfall, the need to invasively alter tissue samples to evaluate cell viability. In this study, the applicability of impedance analysis was validated by monitoring the change in cell capacitance during tissue maturation and before and after chemical application using coupled electrodes. Our results indicate that cell maturation on RhCE models can be evaluated during model production using capacitance sensing offering a faster and simpler quality control criteria for RhCE model usability. Additionally, cell capacitance resulted to be more sensitive in detecting slight cell damages than methods based on cell metabolism, and when integrated into OECD-approved testing strategies, capacitance sensing performed as good as currently accepted methodologies displaying 66% sensitivity, 100% specificity and 83% accuracy when evaluated at 300 Hz. In summary, a quantitative analysis to predict in vivo ocular irritation based on changes in RhCE capacitance by impedance spectroscopy is suggested. This methodology represents a non-invasive and non-destructive alternative that would enable the monitoring of reversible effects or repeated dose toxicity.