With the current biotherapeutic market dominated by antibody molecules, bispecific antibodies represent a key component of the next-generation of antibody therapy. Bispecific antibodies can target two different antigens at the same time, such as simultaneously binding tumor cell receptors and recruiting cytotoxic immune cells. Structural diversity has been fast-growing in the bispecific antibody field, creating a plethora of novel bispecific antibody scaffolds, which provide great functional variety. Two common formats of bispecific antibodies on the market are the single-chain variable fragment (scFv)-based (no Fc fragment) antibody and the full-length IgG-like asymmetric antibody. Unlike the conventional monoclonal antibodies, great production challenges with respect to the quantity, quality, and stability of bispecific antibodies have hampered their wider clinical application and acceptance. In this review, we focus on these two major bispecific types and describe recent advances in the design, production, and quality of these molecules, which will enable this important class of biologics to reach their therapeutic potential.

Constraint-based modeling has been applied to analyze metabolism of numerous organisms via flux balance analysis and genome-scale metabolic models, including mammalian cells such as the Chinese hamster ovary (CHO) cells—the principal cell factory platform for therapeutic protein production. Unfortunately, the application of genome-scale model methodologies using the conventional biomass objective function is challenged by the presence of overly-restrictive constraints, including essential amino acid exchange fluxes that can lead to improper predictions of growth rates and intracellular flux distributions. In this study, these constraints are found to be reliably predicted by an “essential nutrient minimization” approach. After modifying these constraints with the predicted minimal uptake values, a series of unconventional objective functions are applied to minimize each individual non-essential nutrient uptake rate, revealing useful insights about metabolic exchange rates and flows across different cell lines and culture conditions. This unconventional uptake-rate objective functions (UOFs) approach is able to distinguish metabolic differences between three distinct CHO cell lines (CHO-K1, -DG44, and -S) not directly observed using the conventional biomass growth maximization solutions. Further, a comparison of model predictions with experimental data from literature correctly correlates with the specific CHO-DG44-derived cell line used experimentally, and the corresponding dual prices provide fruitful information concerning coupling relationships between nutrients. The UOFs approach is likely to be particularly suited for mammalian cells and other complex organisms which contain multiple distinct essential nutrient inputs, and may offer enhanced applicability for characterizing cell metabolism and physiology as well as media optimization and biomanufacturing control.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus utilizes the extensively glycosylated spike (S) protein protruding from the viral envelope to bind to angiotensin-converting enzyme-related carboxypeptidase (ACE2) as its primary receptor to mediate host-cell entry. Currently, the main recombinant S protein production hosts are Chinese hamster ovary (CHO) and human embryonic kidney (HEK) cells. In this study, a recombinant S protein truncated at the transmembrane domain and engineered to express a C-terminal trimerization motif was transiently produced in CHO and HEK cell suspensions. To further evaluate the sialic acid linkages presenting on S protein, a two-step amidation process, employing dimethylamine and ammonium hydroxide reactions in a solid support system, was developed to differentially modify the sialic acid linkages on the glycans and glycopeptides from the S protein. The process also adds a charge to Asp and Glu which aids in ionization. We used MALDI-TOF and LC-MS/MS with electron-transfer/higher-energy collision dissociation (EThcD) fragmentation to determine global and site-specific N-linked glycosylation patterns. We identified 21 and 19 out of the 22 predicted N-glycosites of the SARS-CoV-2 S proteins produced in CHO and HEK, respectively. It was found that the N-glycosite at 1,158 position (N1158) and at 122, 282 and 1,158 positions (N122, N282 and N1158) were absent on S from CHO and HEK cells, respectively. The structural mapping of glycans of recombinant human S proteins reveals that CHO-Spike exhibits more complex and higher sialylation (α2,3-linked) content while HEK-Spike exhibits more high-mannose and a small amount of α2,3- and α2,6-linked sialic acids. The N74 site represents the most abundant glycosite on both spike proteins. The relatively higher amount of high-mannose abundant sites (N17, N234, N343, N616, N709, N717, N801, and N1134) on HEK-Spike suggests that glycan-shielding may differ among the two constructs. HEK-Spike can also provide different host immune system interaction profiles based on known immune system active lectins. Collectively, these data underscore the importance of characterizing the site-specific glycosylation of recombinant human spike proteins from HEK and CHO cells in order to better understand the impact of the production host on this complex and important protein used in research, diagnostics and vaccines.

Nutrient availability is critical for growth of algae and other microbes used for generating valuable biochemical products. Determining the optimal levels of nutrient supplies to cultures can eliminate feeding of excess nutrients, lowering production costs and reducing nutrient pollution into the environment. With the advent of omics and bioinformatics methods, it is now possible to construct genome-scale models that accurately describe the metabolism of microorganisms. In this study, a genome-scale model of the green alga Chlorella vulgaris (iCZ946) was applied to predict feeding of multiple nutrients, including nitrate and glucose, under both autotrophic and heterotrophic conditions. The objective function was changed from optimizing growth to instead minimizing nitrate and glucose uptake rates, enabling predictions of feed rates for these nutrients. The metabolic model control (MMC) algorithm was validated for autotrophic growth, saving 18% nitrate while sustaining algal growth. Additionally, we obtained similar growth profiles by simultaneously controlling glucose and nitrate supplies under heterotrophic conditions for both high and low levels of glucose and nitrate. Finally, the nitrate supply was controlled in order to retain protein and chlorophyll synthesis, albeit at a lower rate, under nitrogen-limiting conditions. This model-driven cultivation strategy doubled the total volumetric yield of biomass, increased fatty acid methyl ester (FAME) yield by 61%, and enhanced lutein yield nearly 3 fold compared to nitrogen starvation. This study introduces a control methodology that integrates omics data and genome-scale models in order to optimize nutrient supplies based on the metabolic state of algal cells in different nutrient environments. This approach could transform bioprocessing control into a systems biology-based paradigm suitable for a wide range of species in order to limit nutrient inputs, reduce processing costs, and optimize biomanufacturing for the next generation of desirable biotechnology products.

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