Process monitoring of polysaccharide deketalization for vaccine bioconjugation development using in situ analytical methodology

Deep eutectic solvents as sustainable extraction media for extraction of polysaccharides from natural sources: Status, challenges and prospects

Deep eutectic solvents (DES) emerge as promising alternatives to conventional solvents, offering outstanding extraction capabilities, low toxicity, eco-friendliness, straightforward synthesis procedures, broad applicability, and impressive recyclability. DES are synthesized by combining two or more components through various synthesis procedures, such as heat-assisted mixing/stirring, grinding, freeze drying, and evaporation. Polysaccharides, as abundant natural materials, are highly valued for their biocompatibility, biodegradability, and sustainability. These versatile biopolymers can be derived from various natural sources such as plants, algae, animals, or microorganisms using diverse extraction techniques. This review explores the synthesis procedures of DES, their physicochemical properties, characterization analysis, and their application in polysaccharide extraction. The extraction optimization strategies, parameters affecting DES-based polysaccharide extraction, and separation mechanisms are comprehensively discussed. Additionally, this review provides insights into recently developed molecular guides for DES screening and the utilization of artificial neural networks for optimizing DES-based extraction processes. DES serve as excellent extraction media for polysaccharides from different sources, preserving their functional features. They are utilized both as extraction solvents and as supporting media to enhance the extraction abilities of other solvents. Continued research aims to improve DES-based extraction methods and achieve selective, energy-efficient processes to meet the demands of this expanding field.

In situ process analytical technology for real time viable cell density and cell viability during live-virus vaccine production

Viable cell density (VCD) and cell viability (CV) are key performance indicators of cell culture processes in biopharmaceutical production of biologics and vaccines. Traditional methods for monitoring VCD and CV involve offline cell counting assays that are both labor intensive and prone to high variability, resulting in sparse sampling and uncertainty in the obtained data. Process analytical technology (PAT) approaches offer a means to address these challenges. Specifically, in situ probe-based measurements of dielectric spectroscopy (also commonly known as capacitance) can characterize VCD and CV continuously in real time throughout an entire process, enabling robust process characterization. In this work, we propose in situ dielectric spectroscopy as a PAT tool for real time analysis of live-virus vaccine (LVV) production. Dielectric spectroscopy was collected across 25 discreet frequencies, offering a thorough evaluation of the proposed technology. Correlation of this PAT methodology to traditional offline cell counting assays was performed, in which VCD and CV were both successfully predicted using dielectric spectroscopy. Both univariate and multivariate data analysis approaches were evaluated for their potential to establish correlation between the in situ dielectric spectroscopy and offline measurements. Univariate analysis strategies are presented for optimal single frequency selection. Multivariate analysis, in the form of partial least squares (PLS) regression, produced significantly higher correlations between dielectric spectroscopy and offline VCD and CV data, as compared to univariate analysis. Specifically, by leveraging multivariate analysis of dielectric information from all 25 spectroscopic frequencies measured, PLS models performed significantly better than univariate models. This is particularly evident during cell death, where tracking VCD and CV have historically presented the greatest challenge. The results of this work demonstrate the potential of both single and multiple frequency dielectric spectroscopy measurements for enabling robust LVV process characterization, suggesting that broader application of in situ dielectric spectroscopy as a PAT tool in LVV processes can provide significantly improved process understanding. To the best of our knowledge, this is the first report of in situ dielectric spectroscopy with multivariate analysis to successfully predict VCD and CV in real time during live virus-based vaccine production.

Effects of slit dual-frequency ultrasound-assisted pulping on the structure, functional properties and antioxidant activity of Lycium barbarum proteins and in situ real-time monitoring process

To improve the protein dissolution rate and the quality of fresh Lycium barbarum pulp (LBP), we optimized the slit dual-frequency ultrasound-assisted pulping process, explored the dissolution kinetics of Lycium barbarum protein (LBPr), and established a near-infrared spectroscopy in situ real-time monitoring model for LBPr dissolution through spectral information analysis and chemometric methods. The results showed that under optimal conditions (dual-frequency 28-33 kHz, 300 W, 31 min, 40 °C, interval ratio 5:2 s/s), ultrasonic treatment not only significantly increased LBPr dissolution rate (increased by 71.48 %, p < 0.05), improved other nutrient contents and color, but also reduced the protein particle size, changed the amino acid composition ratio and protein structure, and increased the surface hydrophobicity, zeta potential, and free sulfhydryl content of protein, as well as the antioxidant activity of LBPr. In addition, ultrasonication significantly improved the functional properties of the protein, including thermal stability, foaming, emulsification and oil absorption capacity. Furthermore, the real-time monitoring model of the dissolution process was able to quantitatively predict the dissolution rate of LBPr with good calibration and prediction performance (Rc = 0.9835, RMSECV = 2.174, Rp = 0.9841, RMSEP = 1.206). These findings indicated that dual-frequency ultrasound has great potential to improve the quality of LBP and may provide a theoretical basis for the establishment of an intelligent control system in the industrialized production of LBP and the functional development of LBPr.

Machine Learning for Prediction, Classification, and Identification of Immobilized Enzymes for Biocatalysis

BACKGROUND

Enzyme immobilization is a beneficial component involved in biocatalytic strategies. Understanding and evaluating the enzyme immobilization system plays an important role in the successful development and implementation of the biocatalysis route. Ensuring the implementation of a successful enzyme immobilization process is vital for realizing a highly functioning and well suited biocatalytic process within pharmaceutical development.

AIM

To develop a method which can accurately and objectively identify and classify differences within enzyme immobilization systems, sample preparation methods, and data collection parameters.

METHODS

Raman hyperspectral imaging was used to obtain a total of eight spectral data sets from enzyme immobilization samples. Partial least squares discriminant analysis (PLS-DA) was used to classify and identify the samples based on their differences.

RESULTS

Several two-class, four-class, and eight-class PLS-DA models were built to classify the different sample data sets. All models reached between 92-100% accuracy after cross-validation and external validation, illustrating great success of the models for identifying differences between the samples.

CONCLUSION

Raman hyperspectral imaging with machine learning can be used to investigate, interpret, and classify different data collection parameters, sample preparation methods, and enzyme immobilization supports, providing crucial insight into enzyme immobilization process development.