Potent antibody therapeutics by design

Precision targeting in oncology: The future of conjugated drugs

Coupled drugs, especially antibody-coupled drugs (ADCs), are a hot topic in oncology. As the development of ADCs has progressed, different coupling modes have emerged, inspired by their structural design have emerged. Technological advances have led to interweaving and collision of old and new concepts of coupled drugs, and have even challenged the concepts and techniques of coupled drugs at this stage. For example, antibody-oligonucleotide conjugates are a new class of chimeric biomolecules synthesized by coupling oligonucleotides with monoclonal antibodies through linkers, offering precise targeting and improved pharmacokinetic properties. This study aimed to elucidate the mechanism of action of coupled drugs and their current development status in antitumor therapy to provide better strategies for antitumor therapy.

A broadly neutralizing human monoclonal antibody generated from transgenic mice immunized with HCMV particles limits virus infection and proliferation

Human cytomegalovirus (HCMV) is a β-herpesvirus that poses severe disease risk for immunocompromised patients who experience primary infection or reactivation. Development and optimization of safe and effective anti-HCMV therapeutics is of urgent necessity for the prevention and treatment of HCMV-associated diseases in diverse populations. The use of neutralizing monoclonal antibodies (mAbs) to limit HCMV infection poses a promising therapeutic strategy, as anti-HCMV mAbs largely inhibit infection by targeting virion glycoprotein complexes. In contrast, the small-molecule compounds currently approved for patients (e.g., ganciclovir, letermovir, and maribavir) target later stages of the HCMV life cycle. Here, we present a broadly neutralizing human mAb, designated 1C10, elicited from a VelocImmune mouse immunized with infectious HCMV particles. Clone 1C10 neutralizes infection after virion binding to cells by targeting gH/gL envelope complexes and potently reduces infection of diverse HCMV strains in fibroblast, trophoblast, and epithelial cells. Antibody competition assays found that 1C10 recognizes a region of gH associated with broad neutralization and binds to soluble pentamer in the low nanomolar range. Importantly, 1C10 treatment significantly reduced virus proliferation in both fibroblast and epithelial cells. Further, the combination treatment of mAb 1C10 with ganciclovir reduced HCMV infection and proliferation in a synergistic manner. This work characterizes a neutralizing human mAb for potential use as a HCMV treatment, as well as a possible therapeutic strategy utilizing combination-based treatments targeting disparate steps of the viral life cycle. Collectively, the findings support an antibody-based therapy to effectively treat patients at risk for HCMV-associated diseases.

IMPORTANCE

Human cytomegalovirus is a herpesvirus that infects a large proportion of the population and can cause significant disease in diverse patient populations whose immune systems are suppressed or compromised. The development and optimization of safe anti-HCMV therapeutics, especially those that have viral targets and inhibition mechanisms different from current HCMV treatments, are of urgent necessity to better public health. Human monoclonal antibodies (mAbs) that prevent HCMV entry of cells were identified by immunizing transgenic mice and screened for broad and effective neutralization capability. Here, we describe one such mAb, which was found to target gH/gL envelope complexes and effectively limit HCMV infection and dissemination. Further, administration of the antibody in combination with the antiviral drug ganciclovir inhibited HCMV in a synergistic manner, highlighting this approach and the use of anti-HCMV mAbs more broadly, as a potential therapeutic strategy for the treatment of diverse patient populations.

Decoupling Protein Concentration and Aggregate Content Using Diffusion and Water NMR

Protein-based biopharmaceutical drugs, such as monoclonal antibodies, account for the majority of the best-selling drugs globally in recent years. For bioprocesses, key performance indicators are the concentration and aggregate level for the product being produced. In water NMR (wNMR), the use of the water transverse relaxation rate [R2(1H2O)] has been previously used to determine protein concentration and aggregate level; however, it cannot be used to separate between them without using an additional technique. This work shows that it is possible to "decouple" these two key characteristics by recording the water diffusion coefficient [D(1H2O)] in conjunction with R2(1H2O), even in the event of overlap in either D(1H2O) or R2(1H2O). This method is demonstrated on three different systems, following appropriate D(1H2O) or R2(1H2O) calibration data acquisition for a protein of interest. Our method highlights the potential use of benchtop NMR as an at-line process analytical technique.

H1N1 nanobody development and therapeutic efficacy verification in H1N1-challenged mice

Influenza A virus causes numerous deaths and infections worldwide annually. Therefore, we have considered nanobodies as a potential treatment for patients with severe cases of influenza. We developed a nanobody that was expected to have protective efficacy against the A/California/04/2009 (CA/04; pandemic 2009 flu strain) and evaluated its therapeutic efficacy against CA/04 in mice experiments. This nanobody was derived from the immunization of the alpaca, and the inactivated CA/04 virus was used as an immunogen. We successfully generated a nanobody library through bio-panning, phage ELISA, and Bio-layer interferometry. Moreover, we confirmed that administering nanobodies after lethal doses of CA/04 reduced viral replication in the lungs and influenza-induced clinical signs in mice. These research findings will help to develop nanobodies as viral therapeutics for CA/04 and other infectious viruses.

Pin1-catalyzed conformational regulation after phosphorylation: A distinct checkpoint in cell signaling and drug discovery

Protein phosphorylation is one of the most common mechanisms regulating cellular signaling pathways, and many kinases and phosphatases are proven drug targets. Upon phosphorylation, protein functions can be further regulated by the distinct isomerase Pin1 through cis-trans isomerization. Numerous protein targets and many important roles have now been elucidated for Pin1. However, no tools are available to detect or target cis and trans conformation events in cells. The development of Pin1 inhibitors and stereo- and phospho-specific antibodies has revealed that cis and trans conformations have distinct and often opposing cellular functions. Aberrant conformational changes due to the dysregulation of Pin1 can drive pathogenesis but can be effectively targeted in age-related diseases, including cancers and neurodegenerative disorders. Here, we review advances in understanding the roles of Pin1 signaling in health and disease and highlight conformational regulation as a distinct signal transduction checkpoint in disease development and treatment.

Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks

Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such as in disease diagnosis and treatment. However, there are currently no reliable methods for predicting the antibodies that recognize a specific antigen region (or epitope) and, conversely, epitopes that recognize the binding region of a given antibody (or paratope). To fill this gap, we developed ImaPEp, a machine learning-based tool for predicting the binding probability of paratope-epitope pairs, where the epitope and paratope patches were simplified into interacting two-dimensional patches, which were colored according to the values of selected features, and pixelated. The specific recognition of an epitope image by a paratope image was achieved by using a convolutional neural network-based model, which was trained on a set of two-dimensional paratope-epitope images derived from experimental structures of antibody-antigen complexes. Our method achieves good performances in terms of cross-validation with a balanced accuracy of 0.8. Finally, we showcase examples of application of ImaPep, including extensive screening of large libraries to identify paratope candidates that bind to a selected epitope, and rescoring and refining antibody-antigen docking poses.

Concentration-Dependent Diffusion of Monoclonal Antibodies: Underlying Mechanisms of Anomalous Diffusion

The development, storage, transport, and subcutaneous delivery of highly concentrated monoclonal antibody formulations pose significant challenges due to the high solution viscosity and low diffusion of the antibody molecules in crowded environments. These issues often stem from the self-associating behavior of the antibody molecules, potentially leading to aggregation. In this work, we used a dissipative particle dynamics-based coarse-grained model to investigate the diffusion behavior of IgG1 antibody molecules in aqueous solutions with 15 and 32 mM NaCl and antibody concentrations ranging from 10 to 400 mg/mL. We determined the coarse-grained interaction parameters by matching the calculated structure factor with the computational and experimental data from the literature. Our results indicate Fickian diffusion for antibody concentrations of 10 and 25 mg/mL and anomalous diffusion for concentrations exceeding 50 mg/mL. The anomalous diffusion was observed for ∼0.33 to 0.4 μs, followed by Fickian diffusion for all antibody concentrations. We observed a strong linear correlation between the diffusion behavior of the antibody molecules (diffusion coefficient D and anomalous diffusion exponent α) and the amount of aggregates present in the solution and between the amount of aggregates and the Coulomb interaction energy. The investigation of underlying mechanisms for anomalous diffusion revealed that in crowded environments at high antibody concentrations, the attractive interaction between electrostatically complementary regions of the antibody molecules could further bring the neighboring molecules closer to one another, ultimately resulting in aggregate formation. Further, the Coulomb attraction can continue to draw more molecules together, forming larger aggregates.

Targeting cell surface glucose-regulated protein 94 in gastric cancer with an anti-GRP94 human monoclonal antibody

Gastric cancer (GC), a leading cause of cancer-related mortality, remains a significant challenge despite recent therapeutic advancements. In this study, we explore the potential of targeting cell surface glucose-regulated protein 94 (GRP94) with antibodies as a novel therapeutic approach for GC. Our comprehensive analysis of GRP94 expression across various cancer types, with a specific focus on GC, revealed a substantial overexpression of GRP94, highlighting its potential as a promising target. Through in vitro and in vivo efficacy assessments, as well as toxicological analyses, we found that K101.1, a fully human monoclonal antibody designed to specifically target cell surface GRP94, effectively inhibits GC growth and angiogenesis without causing in vivo toxicity. Furthermore, our findings indicate that K101.1 promotes the internalization and concurrent downregulation of cell surface GRP94 on GC cells. In conclusion, our study suggests that cell surface GRP94 may be a potential therapeutic target in GC, and that antibody-based targeting of cell surface GRP94 may be an effective strategy for inhibiting GRP94-mediated GC growth and angiogenesis. [BMB Reports 2024; 57(4): 188-193].

An innovative antibody fusion protein targeting PD-L1, VEGF and TGF-β with enhanced antitumor efficacies

Immunosuppressive pathways in the tumor microenvironment (TME) are inextricably linked to tumor progression. Mono-therapeutics of immune checkpoint inhibitors (ICIs, e.g. antibodies against programmed cell death protein-1/programmed cell death ligand-1, PD-1/PD-L1) is prone to immune escape while combination therapeutics tends to cause high toxicity and side effects. Therefore, using multi-functional molecules to target multiple pathways simultaneously is becoming a new strategy for cancer therapies. Here, we developed a trifunctional fusion protein, DR30206, composed of Bevacizumab (an antibody against VEGF), and a variable domain of heavy chain of heavy chain antibody (VHH) against PD-L1 and the extracellular domain (ECD) protein of TGF-β receptor II (TGF-β RII), which are fused to the N- and C-terminus of Bevacizumab, respectively. The original intention of DR30206 design was to enhance the immune responses pairs by targeting PD-L1 while inhibiting VEGF and TGF-β in the TME. Our data demonstrated that DR30206 exhibits high antigen-binding affinities and efficient blocking capabilities, the principal drivers of efficacy in antibody therapy. Furthermore, the capability of eliciting antibody-dependent cellular cytotoxicity (ADCC) and mixed lymphocyte reaction (MLR) provides a greater possibility to enhance the immune response. Finally, in vivo experiments showed that the antitumor activity of DR30206 was superior to those of monoclonal antibody of PD-L1 or VEGF, PD-L1 and TGF-β bispecific antibody or the combination inhibition of PD-L1 and VEGF. Our findings suggest there is a great potential for DR30206 to become a therapeutic for the treatment of multiple cancer types, especially lung cancer, colon adenocarcinoma and breast carcinoma.

Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling

In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.

Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy

High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 427 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training or other optimization may further improve performance.

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.

Engineered antigen-binding fragments for enhanced crystallization of antibody:antigen complexes

The atomic-resolution structural information that X-ray crystallography can provide on the binding interface between a Fab and its cognate antigen is highly valuable for understanding the mechanism of interaction. However, many Fab:antigen complexes are recalcitrant to crystallization, making the endeavor a considerable effort with no guarantee of success. Consequently, there have been significant steps taken to increase the likelihood of Fab:antigen complex crystallization by altering the Fab framework. In this investigation, we applied the surface entropy reduction strategy coupled with phage-display technology to identify a set of surface substitutions that improve the propensity of a human Fab framework to crystallize. In addition, we showed that combining these surface substitutions with previously reported Crystal Kappa and elbow substitutions results in an extraordinary improvement in Fab and Fab:antigen complex crystallizability, revealing a strong synergistic relationship between these sets of substitutions. Through comprehensive Fab and Fab:antigen complex crystallization screenings followed by structure determination and analysis, we defined the roles that each of these substitutions play in facilitating crystallization and how they complement each other in the process.

Strategies and rules for tuning TCR-derived therapy

Manipulation of T cells has revolutionized cancer immunotherapy. Notably, the use of T cells carrying engineered T cell receptors (TCR-T) offers a favourable therapeutic pathway, particularly in the treatment of solid tumours. However, major challenges such as limited clinical response efficacy, off-target effects and tumour immunosuppressive microenvironment have hindered the clinical translation of this approach. In this review, we mainly want to guide TCR-T investigators on several major issues they face in the treatment of solid tumours after obtaining specific TCR sequences: (1) whether we have to undergo affinity maturation or not, and what parameter we should use as a criterion for being more effective. (2) What modifications can be added to counteract the tumour inhibitory microenvironment to make our specific T cells to be more effective and what is the safety profile of such modifications? (3) What are the new forms and possibilities for TCR-T cell therapy in the future?

Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes

Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html.

Novel NKG2D-directed bispecific antibodies enhance antibody-mediated killing of malignant B cells by NK cells and T cells

The activating receptor natural killer group 2, member D (NKG2D) represents an attractive target for immunotherapy as it exerts a crucial role in cancer immunosurveillance by regulating the activity of cytotoxic lymphocytes. In this study, a panel of novel NKG2D-specific single-chain fragments variable (scFv) were isolated from naïve human antibody gene libraries and fused to the fragment antigen binding (Fab) of rituximab to obtain [CD20×NKG2D] bibodies with the aim to recruit cytotoxic lymphocytes to lymphoma cells. All bispecific antibodies bound both antigens simultaneously. Two bibody constructs, [CD20×NKG2D#3] and [CD20×NKG2D#32], efficiently activated natural killer (NK) cells in co-cultures with CD20+ lymphoma cells. Both bibodies triggered NK cell-mediated lysis of lymphoma cells and especially enhanced antibody-dependent cell-mediated cytotoxicity (ADCC) by CD38 or CD19 specific monoclonal antibodies suggesting a synergistic effect between NKG2D and FcγRIIIA signaling pathways in NK cell activation. The [CD20×NKG2D] bibodies were not effective in redirecting CD8+ T cells as single agents, but enhanced cytotoxicity when combined with a bispecific [CD19×CD3] T cell engager, indicating that NKG2D signaling also supports CD3-mediated T cell activation. In conclusion, engagement of NKG2D with bispecific antibodies is attractive to directly activate cytotoxic lymphocytes or to support their activation by monoclonal antibodies or bispecific T cell engagers. As a perspective, co-targeting of two tumor antigens may allow fine-tuning of antibody cancer therapies. Our proposed combinatorial approach is potentially applicable for many existing immunotherapies but further testing in different preclinical models is necessary to explore the full potential.

Enhanced antitumor and anti-metastasis by VEGFR2-targeted doxorubicin immunoliposome synergy with NK cell activation

Liposomal doxorubicin exhibits stronger drug accumulation at the tumor site due to the Enhanced Permeability and Retention (EPR) effect. However, the prognosis for the patient is poor due to this drug's lack of targeting and tumor metastasis during treatment. Vascular epidermal growth factor receptor (VEGFR2) plays an important role in angiogenesis and cancer metastasis. To enhance antitumor efficacy of PEGylated liposomal doxorubicin, we constructed a VEGFR2-targeted and doxorubicin-loaded immunoliposome (Lipo-DOX-C00) by conjugating a VEGFR2-specific, single chain antibody fragment to DSPE-PEG2000-MAL, and then we inserted the antibody-conjugated polymer into liposomal doxorubicin (Lipo-DOX). The immunoliposome was formed uniformly with high affinity for VEGFR2. In vitro, Lipo-DOX-C00 enhanced doxorubicin internalization into LLC and 4T1 cells compared with non-conjugated, liposomal doxorubicin. In vivo, Lipo-DOX-C00 delivered DOX to tumor tissues effectively, which exhibited an improved antitumor and anti-metastasis efficacy in both LLC subcutaneous tumor models and 4T1 tumor models. In addition, the combined therapy of a VEGFR2-MICA bispecific antibody (JZC01) and Lipo-DOX-C00 achieved enhanced inhibition of cancer growth and metastasis due to activation of the immune system. Our study provides a promising approach to clinical application of liposomal doxorubicin.

Bispecific antibodies in cancer therapy: Target selection and regulatory requirements

In recent years, the development of bispecific antibodies (bsAbs) has been rapid, with many new structures and target combinations being created. The boom in bsAbs has led to the successive issuance of industry guidance for their development in the US and China. However, there is a high degree of similarity in target selection, which could affect the development of diversity in bsAbs. This review presents a classification of various bsAbs for cancer therapy based on structure and target selection and examines the advantages of bsAbs over monoclonal antibodies (mAbs). Through database research, we have identified the preferences of available bsAbs combinations, suggesting rational target selection options and warning of potential wastage of medical resources. We have also compared the US and Chinese guidelines for bsAbs in order to provide a reference for their development.

Structural and interaction analysis of human lipocalin-type prostaglandin D synthase with the poorly water-soluble drug NBQX

Lipocalin-type prostaglandin D synthase (L-PGDS) is a secretory lipid-transporter protein that was shown to bind a wide variety of hydrophobic ligands in vitro. Exploiting this function, we previously examined the feasibility of using L-PGDS as a novel delivery vehicle for poorly water-soluble drugs. However, the mechanism by which human L-PGDS binds to poorly water-soluble drugs is unclear. In this study, we determined the solution structure of human L-PGDS and investigated the mechanism of L-PGDS binding to 6-nitro-7-sulfamoyl-benzo[f]quinoxalin-2,3-dione (NBQX), an α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor antagonist. NMR experiments showed that human L-PGDS has an eight-stranded antiparallel β-barrel structure that forms a central cavity, a short 310 -helix and two α-helices. Titration with NBQX was monitored using 1 H-15 N HSQC spectroscopy. At higher NBQX concentrations, some cross-peaks of the protein exhibited fast-exchanging shifts with a curvature, indicating at least two binding sites. These residues were located in the upper portion of the cavity. Singular value decomposition analysis revealed that human L-PGDS has two NBQX binding sites. Large chemical shift changes were observed in the H2-helix and A-, B-, C-, D-, H- and I-strands and H2-helix upon NBQX binding. Calorimetric experiments revealed that human L-PGDS binds two NBQX molecules with dissociation constants of 46.7 μm for primary binding and 185.0 μm for secondary binding. Molecular docking simulations indicated that these NBQX binding sites are located within the β-barrel. These results provide new insights into the interaction between poorly water-soluble drugs and human L-PGDS as a drug carrier.

Evaluation of AlphaFold Antibody-Antigen Modeling with Implications for Improving Predictive Accuracy

High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 429 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. We show the importance of bound-like component modeling in complex assembly accuracy, and that the current version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training may further improve its performance.

Bispecific antibody targeting TGF-β and PD-L1 for synergistic cancer immunotherapy

The PD-1/PD-L1 signaling pathway plays a crucial role in cancer immune evasion, and the use of anti-PD-1/PD-L1 antibodies represents a significant milestone in cancer immunotherapy. However, the low response rate observed in unselected patients and the development of therapeutic resistance remain major obstacles to their clinical application. Accumulating studies showed that overexpressed TGF-β is another immunosuppressive factor apart from traditional immune checkpoints. Actually, the effects of PD-1 and TGF-β pathways are independent and interactive, which work together contributing to the immune evasion of cancer cell. It has been verified that blocking TGF-β and PD-L1 simultaneously could enhance the efficacy of PD-L1 monoclonal antibody and overcome its treatment resistance. Based on the bispecific antibody or fusion protein technology, multiple bispecific and bifunctional antibodies have been developed. In the preclinical and clinical studies, these updated antibodies exhibited potent anti-tumor activity, superior to anti-PD-1/PD-L1 monotherapies. In the review, we summarized the advances of bispecific antibodies targeting TGF-β and PD-L1 in cancer immunotherapy. We believe these next-generation immune checkpoint inhibitors would substantially alter the cancer treatment paradigm, especially in anti-PD-1/PD-L1-resistant patients.

Mechanisms of Resistance to Antibody-Drug Conjugates

The treatment of cancer patients has dramatically changed over the past decades with the advent of monoclonal antibodies, immune-checkpoint inhibitors, bispecific antibodies, and innovative T-cell therapy. Antibody-drug conjugates (ADCs) have also revolutionized the treatment of cancer. Several ADCs have already been approved in hematology and clinical oncology, such as trastuzumab emtansine (T-DM1), trastuzumab deruxtecan (T-DXd), and sacituzumab govitecan (SG) for the treatment of metastatic breast cancer, and enfortumab vedotin (EV) for the treatment of urothelial carcinoma. The efficacy of ADCs is limited by the emergence of resistance due to different mechanisms, such as antigen-related resistance, failure of internalization, impaired lysosomal function, and other mechanisms. In this review, we summarize the clinical data that contributed to the approval of T-DM1, T-DXd, SG, and EV. We also discuss the different mechanisms of resistance to ADCs, as well as the ways to overcome this resistance, such as bispecific ADCs and the combination of ADCs with immune-checkpoint inhibitors or tyrosine-kinase inhibitors.

The Dawn of a New Era: Targeting the "Undruggables" with Antibody-Based Therapeutics

The high selectivity and affinity of antibodies toward their antigens have made them a highly valuable tool in disease therapy, diagnosis, and basic research. A plethora of chemical and genetic approaches have been devised to make antibodies accessible to more "undruggable" targets and equipped with new functions of illustrating or regulating biological processes more precisely. In this Review, in addition to introducing how naked antibodies and various antibody conjugates (such as antibody-drug conjugates, antibody-oligonucleotide conjugates, antibody-enzyme conjugates, etc.) work in therapeutic applications, special attention has been paid to how chemistry tools have helped to optimize the therapeutic outcome (i.e., with enhanced efficacy and reduced side effects) or facilitate the multifunctionalization of antibodies, with a focus on emerging fields such as targeted protein degradation, real-time live-cell imaging, catalytic labeling or decaging with spatiotemporal control as well as the engagement of antibodies inside cells. With advances in modern chemistry and biotechnology, well-designed antibodies and their derivatives via size miniaturization or multifunctionalization together with efficient delivery systems have emerged, which have gradually improved our understanding of important biological processes and paved the way to pursue novel targets for potential treatments of various diseases.

Antibody-Drug Conjugates for Lung Cancer: Payloads and Progress

Antibody Drug Conjugates (ADCs) are a novel class of therapeutic that structurally comprise an antibody directed at a tumor epitope connected via a linker to a cytotoxic payload that have shown significant antitumor activity across a range of malignancies including lung cancer. In this article we review the pharmacology of ADCs, describe results of trials with ADCs directed at targets in lung cancer including Trophoblast cell-surface antigen 2(TROP2), HER3, MET, Carcinoembryonic antigen-related cell adhesion molecular 5(CECAM-5) and HER2. Trastuzumab Deruxtecan (also known as DS-8201a or T-DXd) an ADC directed at HER2 recently became the first ADC to receive FDA approval in lung cancer, on the basis of its activity in tumors with HER2 mutations, demonstrated in the Destiny-Lung01 and Lung02 trials.

Method development for quantitative monitoring of monoclonal antibodies in upstream cell-culture process samples with limited sample preparation - An evaluation of various capillary coatings

Monoclonal antibodies (mAbs) have become an important class of biopharmaceuticals used for the treatment of various diseases. Their quantification during the manufacturing process is important. In this work, a capillary zone electrophoresis (CZE) method was developed for the monitoring of the mAb concentration during cell-culture processes. CZE method development rules are outlined, particularly discussing various capillary coatings, such as a neutral covalent polyvinyl alcohol coating, a dynamic successive multiple ionic-polymer coating, and dynamic coatings using background electrolyte additives such as triethanolamine (T-EthA) and triethylamine. The dynamic T-EthA coating resulted in most stable electro-osmotic flows and most efficient peak shapes. The method is validated over the range 0.1-10 mg/ml, with a linear range of 0.08-1.3 mg/ml and an extended range of 1-10 mg/ml by diluting samples in the latter concentration range 10-fold in water. The intraday precision and accuracy were 2%-12% and 88%-107%, respectively, and inter-day precision and accuracy were 4%-9% and 93%-104%, respectively. The precision and accuracy of the lowest concentration level (0.08 mg/ml) were slightly worse and still well in scope for monitoring purposes. The presented method proved applicable for analysing in-process cell-culture samples from different cell-culture processes and is possibly well suited as platform method.

Molecular Dynamics Simulation for Investigating Antigen-Antibody Interaction

Molecular dynamics (MD) simulation is a computational method which elucidates the protein dynamics. Following analyses characterize the dynamics and structural change as well as interaction energy. To characterize the protein structure effectively, the internal angular coordinates are often useful. Directional analysis provides the averages and variances of those coordinates in a mathematically rigorous way. Here, we describe not only a standard MD simulation procedure for the antigen-antibody system but also an umbrella sampling method following a multistep targeted MD simulation (US/mTMD), which is useful for evaluating the free energy profile along the antigen-antibody dissociation coordinate.

Elarekibep (PRS-060/AZD1402), a new class of inhaled Anticalin medicine targeting IL-4Ra for type 2 endotype asthma

BACKGROUND

Type 2 endotype asthma is driven by IL-4 and IL-13 signaling via IL-4Ra, which is highly expressed on airway epithelium, airway smooth muscle, and immunocytes in the respiratory mucosa, suggesting potential advantages of an inhalable antagonist. Lipocalin 1 (Lcn1), a 16 kDa protein abundant in human periciliary fluid, has a robust drug-like structure well suited to protein engineering, but it has never been used to make an inhaled Anticalin protein therapeutic.

OBJECTIVES

We sought to reengineer Lcn1 into an inhalable IL-4Ra antagonist and assess its pharmacodynamic/kinetic profile.

METHODS

Lcn1 was systematically modified by directed protein mutagenesis yielding a high-affinity, slowly dissociating, long-acting full antagonist of IL-4Ra designated PRS-060 with properties analogous to dupilumab, competitively antagonizing IL-4Ra-dependent cell proliferation, mucus induction, and eotaxin expression in vitro. Because PRS-060 displayed exquisite specificity for human IL-4Ra, with no cross-reactivity to rodents or higher primates, we created a new triple-humanized mouse model substituting human IL-4Ra, IL-4, and IL-13 at their correct syntenic murine loci to model clinical dosing.

RESULTS

Inhaled PRS-060 strongly suppressed acute allergic inflammation indexes in triple-humanized mice with a duration of action longer than its bulk clearance, suggesting that it may act locally in the lung.

CONCLUSION

Lcn1 can be reengineered into the Anticalin antagonist PRS-060 (elarekibep), exemplifying a new class of inhaled topical, long-acting therapeutic drugs with the potential to treat type 2 endotype asthma.

Machine learning methods for protein-protein binding affinity prediction in protein design

Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design.

Bispecific antibodies-effects of point mutations on CH3-CH3 interface stability

A new format of therapeutic proteins is bispecific antibodies, in which two different heavy chains heterodimerize to obtain two different binding sites. Therefore, it is crucial to understand and optimize the third constant domain (CH3-CH3) interface to favor heterodimerization over homodimerization, and to preserve the physicochemical properties, as thermal stability. Here, we use molecular dynamics simulations to investigate the dissociation process of 19 CH3-CH3 crystal structures that differ from each other in few point mutations. We describe the dissociation of the dimeric interface as a two-steps mechanism. As confirmed by a Markov state model, apart from the bound and the dissociated state, we observe an additional intermediate state, which corresponds to an encounter complex. The analysis of the interdomain contacts reveals key residues that stabilize the interface. We expect that our results will improve the understanding of the CH3-CH3 interface interactions and thus advance the developability and design of new antibodies formats.

Targeting TRAIL Death Receptors in Triple-Negative Breast Cancers: Challenges and Strategies for Cancer Therapy

The tumor necrosis factor (TNF) superfamily member TNF-related apoptosis-inducing ligand (TRAIL) induces apoptosis in cancer cells via death receptor (DR) activation with little toxicity to normal cells or tissues. The selectivity for activating apoptosis in cancer cells confers an ideal therapeutic characteristic to TRAIL, which has led to the development and clinical testing of many DR agonists. However, TRAIL/DR targeting therapies have been widely ineffective in clinical trials of various malignancies for reasons that remain poorly understood. Triple negative breast cancer (TNBC) has the worst prognosis among breast cancers. Targeting the TRAIL DR pathway has shown notable efficacy in a subset of TNBC in preclinical models but again has not shown appreciable activity in clinical trials. In this review, we will discuss the signaling components and mechanisms governing TRAIL pathway activation and clinical trial findings discussed with a focus on TNBC. Challenges and potential solutions for using DR agonists in the clinic are also discussed, including consideration of the pharmacokinetic and pharmacodynamic properties of DR agonists, patient selection by predictive biomarkers, and potential combination therapies. Moreover, recent findings on the impact of TRAIL treatment on the immune response, as well as novel strategies to address those challenges, are discussed.

Bispecific Antibodies: A Novel Approach for the Treatment of Solid Tumors

Advancement in sequencing technologies allows for the identification of molecular pathways involved in tumor progression and treatment resistance. Implementation of novel agents targeting these pathways, defined as targeted therapy, significantly improves the prognosis of cancer patients. Targeted therapy also includes the use of monoclonal antibodies (mAbs). These drugs recognize specific oncogenic proteins expressed in cancer cells. However, as with many other types of targeting agents, mAb-based therapy usually fails in the long-term control of cancer progression due to the development of resistance. In many cases, resistance is caused by the activation of alternative pathways involved in cancer progression and the development of immune evasion mechanisms. To overcome this off-target resistance, bispecific antibodies (bsAbs) were developed to simultaneously target differential oncogenic pathway components, tumor-associated antigens (TAA) and immune regulatory molecules. As a result, in the last few years, several bsAbs have been tested or are being tested in cancer patients. A few of them are currently approved for the treatment of some hematologic malignancies but no bsAbs are approved in solid tumors. In this review, we will provide an overview of the state-of-the-art of bsAbs for the treatment of solid malignancies outlining their classification, design, main technologies utilized for production, mechanisms of action, updated clinical evidence and potential limitations.

Current landscape and future directions of bispecific antibodies in cancer immunotherapy

Recent advances in cancer immunotherapy using monoclonal antibodies have dramatically revolutionized the therapeutic strategy against advanced malignancies, inspiring the exploration of various types of therapeutic antibodies. Bispecific antibodies (BsAbs) are recombinant molecules containing two different antigens or epitopes identifying binding domains. Bispecific antibody-based tumor immunotherapy has gained broad potential in preclinical and clinical investigations in a variety of tumor types following regulatory approval of newly developed technologies involving bispecific and multispecific antibodies. Meanwhile, a series of challenges such as antibody immunogenicity, tumor heterogeneity, low response rate, treatment resistance, and systemic adverse effects hinder the application of BsAbs. In this review, we provide insights into the various architecture of BsAbs, focus on BsAbs' alternative different mechanisms of action and clinical progression, and discuss relevant approaches to overcome existing challenges in BsAbs clinical application.

Role of antibody engineering in generation of derivatives starting from MOv19 MAb: 40 years of biological/therapeutic tools against folate receptor alfa

In the 1980s, we developed and characterized numerous murine monoclonal antibodies (MAbs) directed against human tumor-associated antigens. This mini review is focused on the generation of derivatives of an anti-folate receptor α (FRα) MAbs, named MOv19, exploiting the antibody-engineering progresses in the last 40 years. The FRα location on the luminal surface of proliferating epithelial cells, inaccessible to circulation, versus its over-expression in the entire surface of numerous carcinomas suggested a role for anti-FRα MAbs in the diagnosis and/or treatment of solid tumors. Presently, two MOv19 derivatives are in clinical trials: a chimeric resurfaced version in an antibody-drug conjugate format (SORAYA trial, 2022) and the murine scFv in a second generation chimeric antigen receptor, CAR-T (Phase Ia, 2021). MOv19 and its derivatives could be considered a relevant example that well-characterized anti-tumor murine Mabs and antibody engineering could be combined to generate useful therapeutic tools.

Development of a human phage display-derived anti-PD-1 scFv antibody: an attractive tool for immune checkpoint therapy

Background

The PD-1 checkpoint pathway plays a major role in tumor immune evasion and the development of the tumor microenvironment. Clinical studies show that therapeutic antibodies blocking the PD-1 pathway can restore anti-tumor or anti-virus immune responses by the reinvigoration of exhausted T cells. Because of the promising results of anti-PD-1 monoclonal antibodies in cancer treatment, autoimmune disorders, and infectious diseases, the PD-1 has emerged as an encouraging target for different diseases.

Results

In the present study, we employed a human semi-synthetic phage library for isolation of some scFvs against the extracellular domain of PD-1 protein by panning process. After the panning, a novel anti-PD-1 scFv (SS107) was found that exhibited specific binding to PD-1 antigen and stimulated Jurkat T cells. The selected anti-PD-1 scFv could restore the production of IL-2 and IFN-γ by Jurkat T cells that were co-cultured with PD-L1 positive tumor cells.

Conclusion

This anti-PD-1 scFv with high specificity and the ability to reactivate exhausted T cells has the potential to be developed as an anti-cancer agent or to be used in combination with other therapeutic approaches.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12896-022-00752-8.

Development of a Monoclonal scFv against Cytotoxin to Neutralize Cytolytic Activity Induced by Naja atra Venom on Myoblast C2C12 Cells

The Taiwanese cobra, Naja atra, is a clinically significant species of snake observed in the wild in Taiwan. Victims bitten by N. atra usually experience severe pain and local tissue necrosis. Although antivenom is available for treatment of cobra envenomation, its neutralization potency against cobra-induced necrosis is weak, with more than 60% of cobra envenoming patients developing tissue necrosis after antivenom administration. The present study found that cytotoxin (CTX) is a key component of N. atra venom responsible for cytotoxicity against myoblast cells. Anti-CTX IgY was generated in hens, and the spleens of these hens were used to construct libraries for the development of single chain variable fragments (scFv). Two anti-CTX scFv, S1 and 2S7, were selected using phage display technology and biopanning. Both polyclonal IgY and monoclonal scFv S1 reacted specifically with CTX in cobra venom. In a cell model assay, the CTX-induced cytolytic effect was inhibited only by monoclonal scFv S1, not by polyclonal IgY. Moreover, the neutralization potency of scFv S1 was about 3.8 mg/mg, approximately three times higher than that of conventional freeze-dried neurotoxic antivenom (FNAV). Collectively, these results suggest that scFv S1 can effectively neutralize CTX-induced cytotoxicity and, when combined with currently available antivenom, can improve the potency of the latter, thereby preventing tissue damage induced by cobra envenoming.

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.

Point mutations in SARS-CoV-2 variants induce long-range dynamical perturbations in neutralizing antibodies

Monoclonal antibodies are emerging as a viable treatment for the coronavirus disease 19 (COVID-19). However, newly evolved variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can reduce the efficacy of currently available antibodies and can diminish vaccine-induced immunity. Here, we demonstrate that the microscopic dynamics of neutralizing monoclonal antibodies can be profoundly modified by the mutations present in the spike proteins of the SARS-COV-2 variants currently circulating in the world population. The dynamical perturbations within the antibody structure, which alter the thermodynamics of antigen recognition, are diverse and can depend both on the nature of the antibody and on the spatial location of the spike mutation. The correlation between the motion of the antibody and that of the spike receptor binding domain (RBD) can also be changed, modulating binding affinity. Using protein-graph-connectivity networks, we delineated the mutant-induced modifications in the information-flow along allosteric pathway throughout the antibody. Changes in the collective dynamics were spatially distributed both locally and across long-range distances within the antibody. On the receptor side, we identified an anchor-like structural element that prevents the detachment of the antibodies; individual mutations there can significantly affect the antibody binding propensity. Our study provides insight into how virus neutralization by monoclonal antibodies can be impacted by local mutations in the epitope via a change in dynamics. This realization adds a new layer of sophistication to the efforts for rational design of monoclonal antibodies against new variants of SARS-CoV2, taking the allostery in the antibody into consideration.

Mutations in the new variants of SARS-CoV-2 spike protein modulates the dynamics of the neutralizing antibodies. Capturing such modulations from MD simulations and graph network model identifies the role of mutations in facilitating immune evasion.

Rational Design and Systemic Appraisal of an EGFR-Targeting Antibody-Drug Conjugate LR-DM1 for Pancreatic Cancer

By harnessing the payload DM1 and a monoclonal antibody LR004 through a noncleavable linker succinimidyl-4-(N-maleimidomethyl)-cyclohexane-1-carboxylate, we designed and evaluated an antibody-drug conjugate LR-DM1 with an appropriate drug-antibody ratio of 3.6. LR-DM1, which was targeted toward the epidermal growth factor receptor for pancreatic cancer, exhibited potent antiproliferation activity in vitro with a half-maximal inhibitory concentration value of 7.03 nM for Capan-2 cells. Particularly, it displayed prominent tumor growth inhibition in vivo under 20 mg/kg LR-DM1 dosage in a single administration or multiple administrations without apparent abnormality of pathological observation. Moreover, LR-DM1 possessed a relatively broad therapeutic index with a half-lethal dose above 300 mg/kg, which was over 15-fold higher than the highest administration dosage of 20 mg/kg. This initial study on LR-DM1 holds promise for further development of a new antibody drug conjugate that is transformative for treatment of patients concerned.

Self-Assembly of Oriented Antibody-Decorated Metal-Organic Framework Nanocrystals for Active-Targeting Applications

Antibody (Ab)-targeted nanoparticles are becoming increasingly important for precision medicine. By controlling the Ab orientation, targeting properties can be enhanced; however, to afford such an ordered configuration, cumbersome chemical functionalization protocols are usually required. This aspect limits the progress of Abs-nanoparticles toward nanomedicine translation. Herein, a novel one-step synthesis of oriented monoclonal Ab-decorated metal-organic framework (MOF) nanocrystals is presented. The crystallization of a zinc-based MOF, Zn₂ (mIM)₂ (CO₃ ), from a solution of Zn²⁺ and 2-methylimidazole (mIM), is triggered by the fragment crystallizable (Fc) region of the Ab. This selective growth yields biocomposites with oriented Abs on the MOF nanocrystals (MOF*Ab): the Fc regions are partially inserted within the MOF surface and the antibody-binding regions protrude from the MOF surface toward the target. This ordered configuration imparts antibody-antigen recognition properties to the biocomposite and shows preserved target binding when compared to the parental antibodies. Next, the biosensing performance of the system is tested by loading MOF*Ab with luminescent quantum dots (QD). The targeting efficiency of the QD-containing MOF*Ab is again, fully preserved. The present work represents a simple self-assembly approach for the fabrication of antibody-decorated MOF nanocrystals with broad potential for sensing, diagnostic imaging, and targeted drug delivery.

Development of a high-throughput method to screen novel antiviral materials

Respiratory infectious diseases pose a serious threat worldwide, and novel antiviral materials are highly demanded. Photocatalytic nanoparticles have been developed to inhibit indirect transmission of pathogens by acting as surface coating materials. During development of such antiviral materials, researchers use bacteriophages as model viruses due to their safety and experimental efficiency. Screening methods are used to identify potential antiviral materials, and better screening technologies will accelerate the discovery of antiviral treatments. In this study, we constructed a novel platform to evaluate antiviral activity of surface coating materials using the M13 bacteriophage and phagemid system derived from phage display technology. The evaluation results generated by this system for the two tested antiviral materials were comparable to those for the materials tested on the Qβ bacteriophage and influenza virus using traditional screening methods. The experimental system developed in this study provides rapid and effective screening and can be applied to the development of novel antiviral materials.

Triggered azobenzene-based prodrugs and drug delivery systems

Azobenzene-based molecules show unique trans-cis isomerization upon ultraviolet light irradiation, which induce the change of polarity, crystallinity, stability, and binding affinity with pharmacological target. Moreover, azobenzene is the substrate of azoreductase that is often overexpressed in many pathological sites, e.g. hypoxic solid tumor. Therefore, azobenzene can be a multifunctional molecule in material science, pharmaceutical science and biomedicine because of its sensitivity to light, hypoxia and certain enzymes, hence showing potential application in site-specific smart therapy. Herein we focus on the employment of azobenzene and its derivatives for engineering triggered prodrug and drug delivery systems, and provide an overview of photoswitchable azo-based prodrugs, the associated problems regarding ultraviolet light and reversible isomerization, as well as the potential solutions. We also present the advance of azo-bearing delivery vehicles wherein azobenzene act as the linker, capping agent, and building block, and discuss the corresponding mechanisms for controlled cargo release, endocytosis enhancement and sensitization of free radical cancer therapy.

Antibodies with Weakly Basic Isoelectric Points Minimize Trade-offs between Formulation and Physiological Colloidal Properties

The widespread interest in antibody therapeutics has led to much focus on identifying antibody candidates with favorable developability properties. In particular, there is broad interest in identifying antibody candidates with highly repulsive self-interactions in standard formulations (e.g., low ionic strength buffers at pH 5-6) for high solubility and low viscosity. Likewise, there is also broad interest in identifying antibody candidates with low levels of non-specific interactions in physiological solution conditions (PBS, pH 7.4) to promote favorable pharmacokinetic properties. To what extent antibodies that possess both highly repulsive self-interactions in standard formulations and weak non-specific interactions in physiological solution conditions can be systematically identified remains unclear and is a potential impediment to successful therapeutic drug development. Here, we evaluate these two properties for 42 IgG1 variants based on the variable fragments (Fvs) from four clinical-stage antibodies and complementarity-determining regions from 10 clinical-stage antibodies. Interestingly, we find that antibodies with the strongest repulsive self-interactions in a standard formulation (pH 6 and 10 mM histidine) display the strongest non-specific interactions in physiological solution conditions. Conversely, antibodies with the weakest non-specific interactions under physiological conditions display the least repulsive self-interactions in standard formulations. This behavior can be largely explained by the antibody isoelectric point, as highly basic antibodies that are highly positively charged under standard formulation conditions (pH 5-6) promote repulsive self-interactions that mediate high colloidal stability but also mediate strong non-specific interactions with negatively charged biomolecules at physiological pH and vice versa for antibodies with negatively charged Fv regions. Therefore, IgG1s with weakly basic isoelectric points between 8 and 8.5 and Fv isoelectric points between 7.5 and 9 typically display the best combinations of strong repulsive self-interactions and weak non-specific interactions. We expect that these findings will improve the identification and engineering of antibody candidates with drug-like biophysical properties.

Antibody–Drug Conjugates as an Emerging Therapy in Oncodermatology

Simple Summary

Currently, the therapeutic arsenal to fight cancers is extensive. Among these, antibody–drug conjugates (ADCs) consist in an antibody linked to a cytotoxic agent, allowing a specific delivery to tumor cells. ADCs are an emerging class of therapeutics, with twelve FDA- and EMA-approved drugs for hematological and solid cancers. In recent years, tremendous progress has been observed in therapeutic approaches for advanced skin cancer patients. ADCs appear as an emerging therapeutic option in oncodermatology. After providing an overview of ADC design and development, the goal of this article is to review the potential ADC indications in the field of oncodermatology.

Abstract

Antibody–drug conjugates (ADCs) are an emerging class of therapeutics, with twelve FDA- and EMA-approved drugs for hematological and solid cancers. Such drugs consist in a monoclonal antibody linked to a cytotoxic agent, allowing a specific cytotoxicity to tumor cells. In recent years, tremendous progress has been observed in therapeutic approaches for advanced skin cancer patients. In this regard, targeted therapies (e.g., kinase inhibitors) or immune checkpoint-blocking antibodies outperformed conventional chemotherapy, with proven benefit to survival. Nevertheless, primary and acquired resistances as well as adverse events remain limitations of these therapies. Therefore, ADCs appear as an emerging therapeutic option in oncodermatology. After providing an overview of ADC design and development, the goal of this article is to review the potential ADC indications in the field of oncodermatology.

Design of a Novel Fab-Like Antibody Fragment with Enhanced Stability and Affinity for Clinical use

With increasing interest in applying recombinant monoclonal antibodies (mAbs) in human medicine, engineered mAb fragments with reduced size and improved stability are in demand to overcome current limitations in clinical use. Herein, a novel Fab-like antibody fragment generated via an in silico-based engineering approach where the CH1 and CL domains of Fab are replaced by the IgG1 CH3 domains is described. This construct, designated as FabCH3, maintains the natural N-terminus and C-terminus of IgG antibody, can be expressed at a high level in bacterial cells and, importantly, exhibits much higher stability and affinity than the parental Fab when tested in a mesothelin-specific Fab m912, as well as a vascular endothelial growth factor A (VEGFA)-specific Fab Ranibizumab (in vivo). The high-resolution crystal structures of m912 FabCH3 and m912 Fab are determined, and the comparative analysis reveals more rigid structures in both constant domains and complementarity-determining regions of FabCH3, explaining its enhanced stability and affinity. Overall, the stabilized FabCH3 described in this report provides a versatile platform for engineering Fab-like antibody fragments with higher stability and antigen-binding affinity that can be used as a distinct class of antibody therapeutics.

Host receptor-targeted therapeutic approach to counter pathogenic New World mammarenavirus infections

Five New World mammarenaviruses (NWMs) cause life-threatening hemorrhagic fever (HF). Cellular entry by these viruses is mediated by human transferrin receptor 1 (hTfR1). Here, we demonstrate that an antibody (ch128.1/IgG1) which binds the apical domain of hTfR1, potently inhibits infection of attenuated and pathogenic NWMs in vitro. Computational docking of the antibody Fab crystal structure onto the known structure of hTfR1 shows an overlapping receptor-binding region shared by the Fab and the viral envelope glycoprotein GP1 subunit that binds hTfR1, and we demonstrate competitive inhibition of NWM GP1 binding by ch128.1/IgG1 as the principal mechanism of action. Importantly, ch128.1/IgG1 protects hTfR1-expressing transgenic mice against lethal NWM challenge. Additionally, the antibody is well-tolerated and only partially reduces ferritin uptake. Our findings provide the basis for the development of a novel, host receptor-targeted antibody therapeutic broadly applicable to the treatment of HF of NWM etiology.

Reduction of therapeutic antibody self-association using yeast-display selections and machine learning

Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties.Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CS-SINS: charge-stabilized self-interaction nanoparticle spectroscopy; FACS: fluorescence-activated cell sorting; Fab: fragment antigen binding; Fv: fragment variable; IgG: immunoglobulin; QD: quantum dot; PBS: phosphate-buffered saline; V_H: variable heavy; V_L: variable light.