In vivo Auto-tuning of Antibody-Drug Conjugate Delivery for Effective Immunotherapy using High-Avidity, Low-Affinity Antibodies.

Antibody-drug conjugates (ADCs) have experienced a surge in clinical approvals in the past five years. Despite this success, a major limitation to ADC efficacy in solid tumors is poor tumor penetration, which leaves many cancer cells untargeted. Increasing antibody doses or co-administering ADC with an unconjugated antibody can improve tumor penetration and increase efficacy when target receptor expression is high. However, it can also reduce efficacy in low-expression tumors where ADC delivery is limited by cellular uptake. This creates an intrinsic problem because many patients express different levels of target between tumors and even within the same tumor. Here, we generated High-Avidity, Low-Affinity (HALA) antibodies that can automatically tune the cellular ADC delivery to match the local expression level. Using HER2 ADCs as a model, HALA antibodies were identified with the desired HER2 expression-dependent competitive binding with ADCs in vitro . Multi-scale distribution of trastuzumab emtansine and trastuzumab deruxtecan co-administered with the HALA antibody were analyzed in vivo , revealing that the HALA antibody increased ADC tumor penetration in high-expression systems with minimal reduction in ADC uptake in low-expression tumors. This translated to greater ADC efficacy in immunodeficient mouse models across a range of HER2 expression levels. Furthermore, Fc-enhanced HALA antibodies showed improved Fc-effector function at both high and low expression levels and elicited a strong response in an immunocompetent mouse model. These results demonstrate that HALA antibodies can expand treatment ranges beyond high expression targets and leverage strong immune responses.

Reduction of monoclonal antibody viscosity using interpretable machine learning.

Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.

Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning.

Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.

Microsurgery Robots: Applications, Design, and Development.

Microsurgical techniques have been widely utilized in various surgical specialties, such as ophthalmology, neurosurgery, and otolaryngology, which require intricate and precise surgical tool manipulation on a small scale. In microsurgery, operations on delicate vessels or tissues require high standards in surgeons' skills. This exceptionally high requirement in skills leads to a steep learning curve and lengthy training before the surgeons can perform microsurgical procedures with quality outcomes. The microsurgery robot (MSR), which can improve surgeons' operation skills through various functions, has received extensive research attention in the past three decades. There have been many review papers summarizing the research on MSR for specific surgical specialties. However, an in-depth review of the relevant technologies used in MSR systems is limited in the literature. This review details the technical challenges in microsurgery, and systematically summarizes the key technologies in MSR with a developmental perspective from the basic structural mechanism design, to the perception and human-machine interaction methods, and further to the ability in achieving a certain level of autonomy. By presenting and comparing the methods and technologies in this cutting-edge research, this paper aims to provide readers with a comprehensive understanding of the current state of MSR research and identify potential directions for future development in MSR.

Chemical inhibitors of hexokinase-2 enzyme reduce lactate accumulation, alter glycosylation processing, and produce altered glycoforms in CHO cell cultures.

Chinese hamster ovary (CHO) cells, predominant hosts for recombinant biotherapeutics production, generate lactate as a major glycolysis by-product. High lactate levels adversely impact cell growth and productivity. The goal of this study was to reduce lactate in CHO cell cultures by adding chemical inhibitors to hexokinase-2 (HK2), the enzyme catalyzing the conversion of glucose to glucose 6-phosphate, and examine their impact on lactate accumulation, cell growth, protein titers, and N-glycosylation. Five inhibitors of HK2 enzyme at different concentrations were evaluated, of which 2-deoxy- d-glucose (2DG) and 5-thio- d-glucose (5TG) successfully reduced lactate accumulation with only limited impacts on CHO cell growth. Individual 2DG and 5TG supplementation led to a 35%-45% decrease in peak lactate, while their combined supplementation resulted in a 60% decrease in peak lactate. Inhibitor supplementation led to at least 50% decrease in moles of lactate produced per mol of glucose consumed. Recombinant EPO-Fc titers peaked earlier relative to the end of culture duration in supplemented cultures leading to at least 11% and as high as 32% increase in final EPO-Fc titers. Asparagine, pyruvate, and serine consumption rates also increased in the exponential growth phase in 2DG and 5TG treated cultures, thus, rewiring central carbon metabolism due to low glycolytic fluxes. N-glycan analysis of EPO-Fc revealed an increase in high mannose glycans from 5% in control cultures to 25% and 37% in 2DG and 5TG-supplemented cultures, respectively. Inhibitor supplementation also led to a decrease in bi-, tri-, and tetra-antennary structures and up to 50% lower EPO-Fc sialylation. Interestingly, addition of 2DG led to the incorporation of 2-deoxy-hexose (2DH) on EPO-Fc N-glycans and addition of 5TG resulted in the first-ever observed N-glycan incorporation of 5-thio-hexose (5TH). Six percent to 23% of N-glycans included 5TH moieties, most likely 5-thio-mannose and/or 5-thio-galactose and/or possibly 5-thio-N-acetylglucosamine, and 14%-33% of N-glycans included 2DH moieties, most likely 2-deoxy-mannose and/or 2-deoxy-galactose, for cultures treated with different concentrations of 5TG and 2DG, respectively. Our study is the first to evaluate the impact of these glucose analogs on CHO cell growth, protein production, cell metabolism, N-glycosylation processing, and formation of alternative glycoforms.

Comprehensive N- and O-Glycoproteomic Analysis of Multiple Chinese Hamster Ovary Host Cell Lines.

Glycoproteomic analysis of three Chinese hamster ovary (CHO) suspension host cell lines (CHO-K1, CHO-S, and CHO-Pro5) commonly utilized in biopharmaceutical settings for recombinant protein production is reported. Intracellular and secreted glycoproteins were examined. We utilized an immobilization and chemoenzymatic strategy in our analysis. Glycoproteins or glycopeptides were first immobilized through reductive amination, and the sialyl moieties were amidated for protection. The desired N- or O-glycans and glycopeptides were released from the immobilization resin by enzymatic or chemical digestion. Glycopeptides were studied by Orbitrap Liquid chromatography-mass spectrometry (LC/MS), and the released glycans were analyzed by Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF). Differences were detected in the relative abundances of N- and O-glycopeptide types, their resident and released glycans, and their glycoprotein complexity. Ontogeny analysis revealed key differences in features, such as general metabolic and biosynthetic pathways, including glycosylation systems, as well as distributions in cellular compartments. Host cell lines and subfraction differences were observed in both N- and O-glycan and glycoprotein pools. Differences were observed in sialyl and fucosyl glycan distributions. Key differences were also observed among glycoproteins that are problematic contaminants in recombinant antibody production. The differences revealed in this study should inform the choice of cell lines best suited for a particular bioproduction application.

Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.

Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.

The interplay of protein engineering and glycoengineering to fine-tune antibody glycosylation and its impact on effector functions.

The N-glycan pattern of an IgG antibody, attached at a conserved site within the fragment crystallizable (Fc) region, is a critical antibody quality attribute whose structural variability can also impact antibody function. For tailoring the Fc glycoprofile, glycoengineering in cell lines as well as Fc amino acid mutations have been applied. Multiple glycoengineered Chinese hamster ovary cell lines were generated, including defucosylated (FUT8KO), α-2,6-sialylated (ST6KI), and defucosylated α-2,6-sialylated (FUT8KOST6KI), expressing either a wild-type anti-CD20 IgG (WT) or phenylalanine to alanine (F241A) mutant. Matrix-assisted laser desorption ionization-time of flight mass spectrometry characterization of antibody N-glycans revealed that the F241A mutation significantly increased galactosylation and sialylation content and glycan branching. Furthermore, overexpression of recombinant human α-2,6-sialyltransferase resulted in a predominance of α-2,6-sialylation rather than α-2,3-sialylation for both WT and heavily sialylated F241A antibody N-glycans. Interestingly, knocking out α-1,6-fucosyltransferase (FUT8KO), which removed core fucose, lowered the content of N-glycans with terminal Gal and increased levels of terminal GlcNAc and Man5 groups on WT antibody. Further complement-dependent cytotoxicity (CDC) analysis revealed that, regardless of the production cells, WT antibody samples have higher cytotoxic CDC activity with more exposed Gal residues compared to their individual F241A mutants. However, the FUT8KO WT antibody, with a large fraction of bi-GlcNAc structures (G0), displayed the lowest CDC activity of all WT antibody samples. Furthermore, for the F241A mutants, a higher CDC activity was observed for α-2,6- compared to α-2,3-sialylation. Antibody-dependent cellular cytotoxicity (ADCC) analysis revealed that the defucosylated WT and F241A mutants showed enhanced in vitro ADCC performance compared to their fucosylated counterparts, with the defucosylated WT antibodies displaying the highest overall ADCC activity, regardless of sialic acid substitution. Moreover, the FcγRIIIA receptor binding by antibodies did not always correspond directly with ADCC result. This study demonstrates that glycoengineering and protein engineering can both promote and inhibit antibody effector functions and represent practical approaches for varying glycan composition and functionalities during antibody development.

Metabolic engineering challenges of extending N-glycan pathways in Chinese hamster ovary cells.

In mammalian cells, N-glycans may include multiple N-acetyllactosamine (poly-LacNAc) units that can play roles in various cellular functions and properties of therapeutic recombinant proteins. Previous studies indicated that ß-1,3-N-acetylglucosaminyltransferase 2 (B3GNT2) and ß-1,4-galactotransferase 1 (B4GALT1) are two of the primary glycosyltransferases involved in generating LacNAc units. In the current study, knocking out sialyltransferase genes slightly enhanced the LacNAc content (≥4 repeats per glycan) on recombinant EPO protein. Next, the role of single and dual-overexpression of B3GNT2 and B4GALT1 was explored in recombinant EPO-expressing Chinese hamster ovary (CHO) cells. While overexpression of B4GALT1 slightly enhanced the levels of large glycans on recombinant EPO, overexpression of B3GNT2 in EPO-expressing CHO cells significantly decreased the recombinant EPO LacNAc content, resulting in N-glycans terminating primarily with GlcNAc structures, a limited number of Gals, and nearly undetectable sialylation, which was also observed in sialyltransferases knock-out-B3GNT2 overexpression cell lines. Considering the nature of the binding domain motifs present on B3GNT2, which evolved from ß1,3-galactosyltransferases, its overexpression may have competed and inhibited endogenous ß1,4-galactosyltransferases for exposed GlcNAc residues on the N-glycans, resulting in premature termination of many N-glycans at GlcNAc. Furthermore, B3GNT2 overexpression enhanced intracellular UDP-GlcNAc and CMP-Neu5Ac content while slightly lowering UDP-Gal content. The presence of a sink for UDP-GlcNAc in the form of B3GNT2 with no disposition may have also elevated the intracellular levels of this nucleotide as well as its downstream product, CMP-Neu5Ac. Furthermore, we were unable to overexpress B4GALT1 at either the transcriptional or translational levels following initial B3GNT2 expression. Expression of B3GNT2 following initial expression of B4GALT1 was also problematic in that transcriptional and translational analysis indicated the accumulation of truncated B3GNT2 missing a section of the B3GNT2 trans-Golgi lumen domain while transmembrane and cytoplasmic domains were present. Given that glycosylation is a very complex intra-network process, the addition of one or more recombinant glycosyltransferases may have an unexpected influence on the expression and activities of glycosyltransferases, which can disrupt the nucleotide sugar levels and lead to unexpected modifications of the resulting N-glycan patterns.

Design and Production of Bispecific Antibodies.

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.

Characterization of intact glycopeptides reveals the impact of culture media on site-specific glycosylation of EPO-Fc fusion protein generated by CHO-GS cells.

With the increasing demand to provide more detailed quality attributes, more sophisticated glycan analysis tools are highly desirable for biopharmaceutical manufacturing. Here, we performed an intact glycopeptide analysis method to simultaneously analyze the site-specific N- and O-glycan profiles of the recombinant erythropoietin Fc (EPO-Fc) protein secreted from a Chinese hamster ovary glutamine synthetase stable cell line and compared the effects of two commercial culture media, EX-CELL (EX) and immediate advantage (IA) media, on the glycosylation profile of the target protein. EPO-Fc, containing the Fc region of immunoglobulin G1 (IgG1) fused to EPO, was harvested at Day 5 and 8 of a batch cell culture process followed by purification and N- and O-glycopeptide profiling. A mixed anion exchange chromatographic column was implemented to capture and enrich N-linked glycopeptides. Using intact glycopeptide characterization, the EPO-Fc was observed to maintain their individual EPO and Fc N-glycan characteristics in which the EPO region presented bi-, tri-, and tetra-branched N-glycan structures, while the Fc N-glycan displayed mostly biantennary glycans. EPO-Fc protein generated in EX medium produced more complex tetra-antennary N-glycans at each of the three EPO N-sites while IA medium resulted in a greater fraction of bi- and tri-antennary N-glycans at these same sites. Interestingly, the sialylation content decreased from sites 1-4 in both media while the fucosylation progressively increased with a maximum at the final IgG Fc site. Moreover, we observed that low amounts of Neu5Gc were detected and the content increased at the later sampling time in both EX and IA media. For O-glycopeptides, both media produced predominantly three structures, N1F1F0SOG0, N1H1F0S1G0, and N1H1F0S2G0, with lesser amounts of other structures. This intact glycopeptide method can decipher site-specific glycosylation profile and provide a more detailed characterization of N- and O-glycans present for enhanced understanding of the key product quality attributes such as media on recombinant proteins of biotechnology interest.