Advances in bioreactor control for production of biotherapeutic products

Enhancing complex bioprocess learning through simulation technology and hybrid teaching: A case study in university education

The utilization of computer simulators in university education is progressively being embraced to offer students a practical exposure to industrial bioprocesses. Bioreactor computer simulators hold various advantages over conventional laboratory experiments, such as cost-effectiveness and enhanced safety. The research objective is to assess the effectiveness of integrating bioreactor computer simulators into hybrid teaching to promote active and collaborative learning experiences and evaluate their impact on student participation and understanding. A hybrid strategy combining synchronous, face-to-face, and online teaching has been implemented to enhance the teaching-learning processes in the Industrial Bioprocesses course for Biochemistry students. The simulation software BIOSTAT®T Yeast was used. This software models the production of ethanol with Saccharomyces cerevisiae through batch cultivation and the determination of the kLa value of a bioreactor. In the first simulation activity, students analyzed the software response based on parameter values input by the instructor, while in the second simulation activity, students autonomously used the computer simulator under the primary oversight of the instructor. The survey results indicate that the pedagogical innovation was positively received and significantly motivating for the students. Comparing student satisfaction surveys between the two simulation activities suggests that fostering student autonomy and engagement through simulation technology can improve satisfaction and learning outcomes in bioprocess education.

Systems Engineering Approach to Modeling and Analysis of Chronic Obstructive Pulmonary Disease Part II: Extension for Variable Metabolic Rates

Recently, we developed a systems engineering model of the human cardiorespiratory system [Kurian et al. ACS Omega2023, 8 (23), 20524-20535. DOI: 10.1021/acsomega.3c00854] based on existing models of physiological processes and adapted it for chronic obstructive pulmonary disease (COPD)-an inflammatory lung disease with multiple manifestations and one of the leading causes of death in the world. This control engineering-based model is extended here to allow for variable metabolic rates established at different levels of physical activity. This required several changes to the original model: the model of the controller was enhanced to include the feedforward loop that is responsible for cardiorespiratory control under varying metabolic rates (activity level, characterized as metabolic equivalent of the task-Rm-and normalized to one at rest). In addition, a few refinements were made to the cardiorespiratory mechanics, primarily to introduce physiological processes that were not modeled earlier but became important at high metabolic rates. The extended model is verified by analyzing the impact of exercise (Rm > 1) on the cardiorespiratory system of healthy individuals. We further formally justify our previously proposed adaptation of the model for COPD patients through sensitivity analysis and refine the parameter tuning through the use of a parallel tempering stochastic global optimization method. The extended model successfully replicates experimentally observed abnormalities in COPD-the drop in arterial oxygen tension and dynamic hyperinflation under high metabolic rates-without being explicitly trained on any related data. It also supports the prospects of remote patient monitoring in COPD.

Development of an in-line Raman analytical method for commercial-scale CHO cell culture process monitoring: Influence of measurement channels and batch number on model performance

The mammalian cell culture process is a key step in commercial therapeutic protein production and needs to be monitored and controlled due to its complexity. Raman spectroscopy has been reported for cell culture process monitoring by analysis of many important parameters. However, studies on in-line Raman monitoring of the cell culture process were mainly conducted on small or pilot scale. Developing in-line Raman analytical methods for commercial-scale cell culture process monitoring is more challenging. In this study, an in-line Raman analytical method was developed for monitoring glucose, lactate, and viable cell density (VCD) in the Chinese hamster ovary (CHO) cell culture process during commercial production of biosimilar adalimumab (1500 L). The influence of different Raman measurement channels was considered to determine whether to merge data from different channels for model development. Raman calibration models were developed and optimized, with minimum root mean square error of prediction of 0.22 g L-1 for glucose in the range of 1.66-3.53 g L-1 , 0.08 g L-1 for lactate in the range of 0.15-1.19 g L-1 , 0.31 E6 cells mL-1 for VCD in the range of 0.96-5.68 E6 cells mL-1 on test sets. The developed analytical method can be used for cell culture process monitoring during manufacturing and meets the analytical purpose of this study. Further, the influence of the number of batches used for model calibration on model performance was also studied to determine how many batches are needed basically for method development. The proposed Raman analytical method development strategy and considerations will be useful for monitoring of similar bioprocesses.

Biomanufacturing of glycosylated antibodies: Challenges, solutions, and future prospects

Traditionally, recombinant protein production has been done in several expression hosts of bacteria, fungi, and majorly CHO (Chinese Hamster Ovary) cells; few have high production costs and are susceptible to harmful toxin contamination. Green algae have the potential to produce recombinant proteins in a more sustainable manner. Microalgal diversity leads to offer excellent opportunities to produce glycosylated antibodies. An antibody with humanized glycans plays a crucial role in cellular communication that works to regulate cells and molecules, to control disease, and to stimulate immunity. Therefore, it becomes necessary to understand the role of abiotic factors (light, temperature, pH, etc.) in the production of bioactive molecules and molecular mechanisms of product synthesis from microalgae which would lead to harnessing the potential of algal bio-refinery. However, the potential of microalgae as the source of bio-refinery has been less explored. In the present review, omics approaches for microalgal engineering, methods of humanized glycoproteins production focusing majorly on N-glycosylation pathways, light-based regulation of glycosylation machinery, and production of antibodies with humanized glycans in microalgae with a major emphasis on modulation of post-translation machinery of microalgae which might play a role in better understanding of microalgal potential as a source for antibody production along with future perspectives.