Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Lab on a Chip: Bioreactors miniaturization for rapid optimization of biomedical processes and its impact on SARS-CoV-2 diagnosis

Lab on a Chip (LoC) as part of Microbioreactors (MBRs) constitute an emergent technology to carry out micro-bioprocesses based on microfluidics research. In this review, the usefulness of LoCs is exposed since its inception, demonstrating that it is a multidisciplinary research field, gathering different science branches to develop this technology. As a result, a beneficial point of advancement is reached, producing useful consumables for humanity. Some of the described LoCs throughout this work are also used to detect infectious diseases caused by bacteria or viruses, allowing accelerated studies on emerging or high-impact diseases, such as COVID-19. Here are also displayed with an updated panorama, different strategies to improve the use, applications in the biomedical field, and spread of these devices aimed at their availability to solve social problems.

Traces matter: Targeted optimization of monoclonal antibody N-glycosylation based on/by implementing automated high-throughput trace element screening

The use of high-throughput systems in cell culture process optimization offers various opportunities in biopharmaceutical process development. Here we describe the potential for acceleration and enhancement of product quality optimization and de novo bioprocess design regarding monoclonal antibody N-glycosylation by using an iterative statistical Design of Experiments (DoE) strategy based on our automated microtiter plate-based system for suspension cell culture. In our example, the combination of an initial screening of trace metal building blocks with a comprehensive DoE-based screening of 13 different trace elemental ions at three concentration levels in one run revealed most effective levers for N-glycan processing and biomass formation. Obtained results served to evaluate optimal concentration ranges and the right supplementation timing of relevant trace elements at shake flask and 2 L bioreactor scale. This setup identified manganese, copper, zinc, and iron as major factors. Manganese and copper acted as inverse key players in N-glycosylation, showing a positive effect of manganese and a negative effect of copper on glycan maturation in a zinc-dependent manner. Zinc and iron similarly improved cell growth and biomass formation. These findings allowed determining optimal concentration ranges for all four trace elements to establish control on desired product quality attributes regarding premature afucosylated and mature galactosylated glycan species. Our results demonstrates the power of combining robotics with DoE screening to enhance product quality optimization and to improve process understanding, thus, enabling targeted product quality control.

When Is an In Silico Representation a Digital Twin? A Biopharmaceutical Industry Approach to the Digital Twin Concept

Digital twins (DTs) are expected to render process development and life-cycle management much more cost-effective and time-efficient. A DT definition, a brief retrospect on their history and expectations for their deployment in today's business environment, and a detailed financial assessment of their attractive economic benefits are provided in this chapter. The argument that restrictive guidelines set forth by regulatory agencies would hinder the adoption of DTs in the (bio)pharmaceutical industry is revisited, concluding that those companies who collaborate with the agencies to further their technical capabilities will gain significant competitive advantage. The analyzed process development examples show high methodological readiness levels but low systematic adoption of technology. Given the technical feasibilities, financial opportunities, and regulatory encouragement, concerns regarding intellectual property and data sharing, though required to be taken into account, will at best delay an industry-wide adoption of DTs. In conclusion, it is expected that a strategic investment in DTs now will gain an advantage over competition that will be difficult to overcome by late adopters.

Multivariate data analysis of growth medium trends affecting antibody glycosylation

Use of multivariate data analysis for the manufacturing of biologics has been increasing due to more widespread use of data-generating process analytical technologies (PAT) promoted by the US FDA. To generate a large dataset on which to apply these principles, we used an in-house model CHO DG44 cell line cultured in automated micro bioreactors alongside PAT with four commercial growth media focusing on antibody quality through N-glycosylation profiles. Using univariate analyses, we determined that different media resulted in diverse amounts of terminal galactosylation, high mannose glycoforms, and aglycosylation. Due to the amount of in-process data generated by PAT instrumentation, multivariate data analysis was necessary to ascertain which variables best modeled our glycan profile findings. Our principal component analysis revealed components that represent the development of glycoforms into terminally galacotosylated forms (G1F and G2F), and another that encompasses maturation out of high mannose glycoforms. The partial least squares model additionally incorporated metabolic values to link these processes to glycan outcomes, especially involving the consumption of glutamine. Overall, these approaches indicated a tradeoff between cellular productivity and product quality in terms of the glycosylation. This work illustrates the use of multivariate analytical approaches that can be applied to complex bioprocessing problems for identifying potential solutions.

Impacts of intentional mycoplasma contamination on CHO cell bioreactor cultures

Mycoplasma contamination events in biomanufacturing facilities can result in loss of production and costly cleanups. Mycoplasma may survive in mammalian cell cultures with only subtle changes to the culture and may penetrate the 0.2 µm filters often used in the primary clarification of harvested cell culture fluid. Culture cell‐based and indicator cell‐based assays that are used to detect mycoplasma are highly sensitive but can take up to 28 days to complete and cannot be used for real‐time decision making during the biomanufacturing process. To support real‐time measurements of mycoplasma contamination, there is a push to explore nucleic acid testing. However, cell‐based methods measure growth or colony forming units and nucleic acid testing measures genome copy number; this has led to ambiguity regarding how to compare the sensitivity of the methods. In addition, the high risk of conducting experiments wherein one deliberately spikes mycoplasma into bioreactors has dissuaded commercial groups from performing studies to explore the multiple variables associated with the upstream effects of a mycoplasma contamination in a manufacturing setting. Here we studied the ability of Mycoplasma arginini to persist in a single‐use, perfusion rocking bioreactor system containing a Chinese hamster ovary (CHO) DG44 cell line expressing a model monoclonal immunoglobulin G1 (IgG1) antibody. We examined M. arginini growth and detection by culture methods, as well as the effects of M. arginini on mammalian cell health, metabolism, and productivity. We compared process parameters and controls normally measured in bioreactors including dissolved oxygen, gas mix, and base addition to maintain pH, to examine parameter changes as potential indicators of contamination. Our work showed that M. arginini affects CHO cell growth profile, viability, nutrient consumption, oxygen use, and waste production at varying timepoints after M. arginini introduction to the culture. Importantly, how the M. arginini contamination impacts the CHO cells is influenced by the concentration of CHO cells and rate of perfusion at the time of M. arginini spike. Careful evaluation of dissolved oxygen, pH control parameters, ammonia, and arginine over time may be used to indicate mycoplasma contamination in CHO cell cultures in a bioreactor before a read‐out from a traditional method.