The purpose of this study was to develop a biobetter version of recombinant human interferon-β 1a (rhIFN-β 1a) to improve its biophysical properties, such as aggregation, production and stability, and pharmacokinetic properties without jeopardizing its activity. To achieve this, we introduced additional glycosylation into rhIFN-β 1a via site-directed mutagenesis. Glycoengineering of rhIFN-β 1a resulted in a new molecular entity, termed R27T, which was defined as a rhIFN-β mutein with two N-glycosylation sites at 80th (original site) and at an additional 25th amino acid due to a mutation of Thr for Arg at position 27th of rhIFN-β 1a. Glycoengineering had no effect on rhIFN-β ligand-receptor binding, as no loss of specific activity was observed. R27T showed improved stability and had a reduced propensity for aggregation and an increased half-life. Therefore, hyperglycosylated rhIFN-β could be a biobetter version of rhIFN-β 1a with a potential for use as a drug against multiple sclerosis.
Most malignant tumors originate from epithelial tissues in which tight junctions mediate cell–cell interactions. Tight junction proteins, especially claudin-3 (CLDN3), are overexpressed in various cancers. Claudin-3 is exposed externally during tumorigenesis making it a potential biomarker and therapeutic target. However, the development of antibodies against specific CLDN proteins is difficult, because CLDNs are four-transmembrane domain proteins with high homology among CLDN family members and species. Here, we developed a human IgG1 monoclonal antibody (h4G3) against CLDN3 through scFv phage display using CLDN3-overexpressing stable cells and CLDN3-embedded lipoparticles as antigens. The h4G3 recognized the native conformation of human and mouse CLDN3 without cross-reactivity to other CLDNs. The binding kinetics of h4G3 demonstrated a sub-nanomolar affinity for CLDN3 expressed on the cell surface. The h4G3 showed antibody-dependent cellular cytotoxicity (ADCC) according to CLDN3 expression levels in various cancer cells by the activation of FcγRIIIa (CD16a). The biodistribution of h4G3 was analyzed by intravenous injection of fluorescence-conjugated h4G3 which showed that it localized to the tumor site in xenograft mice bearing CLDN3-expressing tumors. These results indicate that h4G3 recognizes CLDN3 specifically, suggesting its value for cancer diagnosis, antibody-drug conjugates, and potentially as a chimeric antigen receptor (CAR) for CLDN3-expressing pan-carcinoma.
The glycoengineering approach is used to improve biophysical properties of protein-based drugs, but its direct impact on binding affinity and kinetic properties for the glycoengineered protein and its binding partner interaction is unclear. Type I interferon (IFN) receptors, composed of IFNAR1 and IFNAR2, have different binding strengths, and sequentially bind to IFN in the dominant direction, leading to activation of signals and induces a variety of biological effects. Here, we evaluated receptor-binding kinetics for each state of binary and ternary complex formation between recombinant human IFN-β-1a and the glycoengineered IFN-β mutein (R27T) using the heterodimeric Fc-fusion technology, and compared biological responses between them. Our results have provided evidence that the additional glycan of R27T, located at the binding interface of IFNAR2, destabilizes the interaction with IFNAR2 via steric hindrance, and simultaneously enhances the interaction with IFNAR1 by restricting the conformational freedom of R27T. Consequentially, altered receptor-binding kinetics of R27T in the ternary complex formation led to a substantial increase in strength and duration of biological responses such as prolonged signal activation and gene expression, contributing to enhanced anti-proliferative activity. In conclusion, our findings reveal N-glycan at residue 25 of R27T is a crucial regulator of receptor-binding kinetics that changes biological activities such as long-lasting activation. Thus, we believe that R27T may be clinically beneficial for patients with multiple sclerosis.
Several data augmentation methods deploy unlabeled-in-distribution (UID) data to bridge the gap between the training and inference of neural networks. However, these methods have clear limitations in terms of availability of UID data and dependence of algorithms on pseudo-labels. Herein, we propose a data augmentation method to improve generalization in both adversarial and standard learning by using out-of-distribution (OOD) data that are devoid of the abovementioned issues. We show how to improve generalization theoretically using OOD data in each learning scenario and complement our theoretical analysis with experiments on CIFAR-10, CIFAR-100, and a subset of ImageNet. The results indicate that undesirable features are shared even among image data that seem to have little correlation from a human point of view. We also present the advantages of the proposed method through comparison with other data augmentation methods, which can be used in the absence of UID data. Furthermore, we demonstrate that the proposed method can further improve the existing state-of-the-art adversarial training.
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