Comprehensive analysis and characterization of glycan pairing in therapeutic antibodies and Fc-containing biotherapeutics: Addressing current limitations and implications for N-glycan impact.

N-glycosylation of the Fc part is a (critical) quality attribute of therapeutic antibodies and Fc-containing biotherapeutics, that impacts their stability, immunogenicity, pharmacokinetics, and effector functions. Current glycosylation analysis methods focus on the absolute amounts of glycans, neglecting the apparent glycan distribution over the entirety of proteins. The combination of the two Fc N-glycans, herein referred to as glyco-pair, therefore remains unknown, which is a major drawback for N-glycan impact assessment. This study presents a comprehensive workflow for the analysis and characterization of Fc N-glycan pairing in biotherapeutics, addressing the limitations of current glycosylation analysis methods. The applicability of the method across various biotherapeutic proteins including antibodies, bispecific antibody formats, and a Fc-Fusion protein is demonstrated, and the impact of method conditions on glycan pairing analysis is highlighted. Moreover, the influence of the molecular format, Fc backbone, production process, and cell line on glycan pairing pattern was investigated. The results underscore the significance of comprehensive glycan pairing analysis to accurately assess the impact of N-glycans on important product quality attributes of therapeutic antibodies and Fc-containing biotherapeutics.

Chromatographic single-step purification of tagless proteins using gp41-1 split inteins.

The current trend in biopharmaceutical drug manufacturing is towards increasing potency and complexity of products such as peptide scaffolds, oligonucleotides and many more. Therefore, a universal affinity purification step is important in order to meet the requirements for cost and time efficient drug production. By using a self-splicing intein affinity tag, a purification template is generated that allows for a universal chromatographic affinity capture step to generate a tagless target protein without the use of proteases for further tag removal. This study describes the successful implementation of gp41-1-based split inteins in a chromatographic purification process for, e.g., E. coli-derived targets. The tagless target is generated in a single-step purification run. The on-column cleavage is induced by triggering a simple pH change in the buffer conditions without the need for additives such as Zn2+ or thiols. This system has proven to be reusable for at least ten purification cycles that use 150 mM H3PO4 as the cleaning agent.

Identification of process conditions influencing protein aggregation in Chinese hamster ovary cell culture.

Protein aggregation of monoclonal antibodies (mAbs) is a common phenomenon associated with the production of these biopharmaceuticals. These aggregates can lead to adverse side effects in patients upon administration, thus expensive downstream processing steps to remove the higher molecular weight species are inevitable. A preferable approach is to reduce the level of aggregation during bioprocessing by a careful adjustment of critical process parameters. Recently, new analytical methods enabled characterization of mAb aggregation during bioprocessing of mammalian cells. Furthermore, rapid and efficient bioprocess optimization has been performed using design of experiments (DoE) strategies. In this work, we describe a DoE-based approach for the analysis of process parameters and cell culture additives influencing protein aggregation in Chinese hamster ovary (CHO) cell cultures. Important bioprocess variables influencing the aggregation of mAb and host cell proteins were identified in initial screening experiments. Response surface modeling was further applied in order to find optimal conditions for the reduction of protein aggregation during cell culture. It turned out that a temperature-shift to 31 °C, osmolality above 420 mOsm/kg, agitation at 100 rpm and 0.04% (w/v) antifoam significantly reduced the level of aggregates without substantial detrimental effects on cell culture performance in our model system. Finally, the aggregation reducing conditions were verified and applied to another production system using a different bioprocess medium and another CHO cell line producing another mAb. Our results show that protein aggregation can be controlled during cell culture and helps to improve bioprocessing of mAbs, by giving insights into the protein aggregation at its origin in mammalian cell culture.

Reduced modeling of signal transduction - a modular approach.

BACKGROUND: Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen) was developed which allows an automatic rule-based set-up of mechanistic model equations. In many cases these models can be reduced by an exact domain-oriented lumping technique. However, the resulting models can still consist of a very large number of differential equations. RESULTS: We introduce a new reduction technique, which allows building modularized and highly reduced models. Compared to existing approaches further reduction of signal transduction networks is possible. The method also provides a new modularization criterion, which allows to dissect the model into smaller modules that are called layers and can be modeled independently. Hallmarks of the approach are conservation relations within each layer and connection of layers by signal flows instead of mass flows. The reduced model can be formulated directly without previous generation of detailed model equations. It can be understood and interpreted intuitively, as model variables are macroscopic quantities that are converted by rates following simple kinetics. The proposed technique is applicable without using complex mathematical tools and even without detailed knowledge of the mathematical background. However, we provide a detailed mathematical analysis to show performance and limitations of the method. For physiologically relevant parameter domains the transient as well as the stationary errors caused by the reduction are negligible. CONCLUSION: The new layer based reduced modeling method allows building modularized and strongly reduced models of signal transduction networks. Reduced model equations can be directly formulated and are intuitively interpretable. Additionally, the method provides very good approximations especially for macroscopic variables. It can be combined with existing reduction methods without any difficulties.