Development Of Therapeutic Antibodies Research Articles

Affinity-Resolved Size Exclusion Chromatography Coupled to Mass Spectrometry: A Novel Tool to Study the Attribute-and-Function Relationship in Therapeutic Monoclonal Antibodies.

Assessment of critical quality attributes (CQAs) is an important aspect during the development of therapeutic monoclonal antibodies (mAbs). Attributes that affect either the target binding or Fc receptor engagement may have direct impacts on the drug safety and efficacy and thus are considered as CQAs. Native size exclusion chromatography (SEC)-based competitive binding assay has recently been reported and demonstrated significant benefits compared to conventional approaches for CQA identification, owing to its faster turn-around and higher multiplexity. Expanding on the similar concept, we report the development of a novel affinity-resolved size exclusion chromatography-mass spectrometry (AR-SEC-MS) method for rapid CQA evaluation in therapeutic mAbs. This method features wide applicability, fast turn-around, high multiplexity, and easy implementation. Using the well-studied Fc gamma receptor III-A (FcγRIIIa) and Fc interaction as a model system, the effectiveness of this method in studying the attribute-and-function relationship was demonstrated. Further, two case studies were detailed to showcase the application of this method in assessing CQAs related to antibody target binding, which included unusual N-linked glycosylation in a bispecific antibody and Met oxidation in a monospecific antibody, both occurring within the complementarity-determining regions (CDRs).

Affinity-Resolved Size Exclusion Chromatography Coupled to Mass Spectrometry: A Novel Tool to Study the Attribute-and-Function Relationship in Therapeutic Monoclonal Antibodies.

Assessment of critical quality attributes (CQAs) is an important aspect during the development of therapeutic monoclonal antibodies (mAbs). Attributes that affect either the target binding or Fc receptor engagement may have direct impacts on the drug safety and efficacy and thus are considered as CQAs. Native size exclusion chromatography (SEC)-based competitive binding assay has recently been reported and demonstrated significant benefits compared to conventional approaches for CQA identification, owing to its faster turn-around and higher multiplexity. Expanding on the similar concept, we report the development of a novel affinity-resolved size exclusion chromatography-mass spectrometry (AR-SEC-MS) method for rapid CQA evaluation in therapeutic mAbs. This method features wide applicability, fast turn-around, high multiplexity, and easy implementation. Using the well-studied Fc gamma receptor III-A (FcγRIIIa) and Fc interaction as a model system, the effectiveness of this method in studying the attribute-and-function relationship was demonstrated. Further, two case studies were detailed to showcase the application of this method in assessing CQAs related to antibody target binding, which included unusual N-linked glycosylation in a bispecific antibody and Met oxidation in a monospecific antibody, both occurring within the complementarity-determining regions (CDRs).

Two-dimensional high-throughput on-cell screening of immunoglobulins against broad antigen repertoires

Identifying high-affinity antibodies in human serum is challenging due to extremely low number of circulating B cells specific to the desired antigens. Delays caused by a lack of information on the immunogenic proteins of viral origin hamper the development of therapeutic antibodies. We propose an efficient approach allowing for enrichment of high-affinity antibodies against pathogen proteins with simultaneous epitope mapping, even in the absence of structural information about the pathogenic immunogens. To screen therapeutic antibodies from blood of recovered donors, only pathogen transcriptome is required to design an antigen polypeptide library, representing pathogen proteins, exposed on the bacteriophage surface. We developed a two-dimensional screening approach enriching lentiviral immunoglobulin libraries from the convalescent or vaccinated donors against bacteriophage library expressing the overlapping set of polypeptides covering the spike protein of SARS-CoV-2. This platform is suitable for pathogen-specific immunoglobulin enrichment and allows high-throughput selection of therapeutic human antibodies.

Fc engineering by monoclonal mammalian cell display for improved affinity and selectivity towards FcγRs

Abstract Fc optimization can significantly enhance therapeutic efficacy of monoclonal antibodies. However, existing Fc engineering approaches are sub-optimal with noted limitations, such as inappropriate glycosylation, polyclonal libraries, and utilizing fragment but not full-length IgG display. Applying cell cycle arrested recombinase-mediated cassette exchange, this study constructed high-quality monoclonal Fc libraries in CHO cells, displayed full-length IgG on cell surface, and preformed ratiometric fluorescence activated cell sorting (FACS) with the antigen and individual FcγRs. Identified Fc variants were quantitatively evaluated by flow cytometry, ELISA, kinetic and steady-state binding affinity measurements, and cytotoxicity assays. An error-prone Fc library focusing on the hinge-CH2 region was constructed in CHO cells with a functional diversity of 7.5 × 106. Panels of novel Fc variants with enhanced affinity and selectivity for FcγRs were isolated. Particularly, clone 2a-10 (G236E/K288R/K290W/K320M) showed increased binding strength towards FcγRIIa-131R and 131H allotypes with kinetic dissociation constants (KD-K) of 140 nM and 220 nM, respectively, while reduced binding strength towards FcγRIIb compared to WT Fc; clone 2b-1 (K222I/V302E/L328F/K334E) had KD-K of 180 nM towards FcγRIIb; clone 3a-2 (P247L/K248E/K334I) exhibited KD-K of 190 nM and 100 nM towards FcγRIIIa-176F and 176 V allotypes, respectively, and improved potency of 2.0 ng/ml in ADCC assays. Key mutation hotspots were identified, including P247 for FcγRIIIa, K290 for FcγRIIa, and K334 for FcγRIIb bindings. Discovery of Fc variants with enhanced affinity and selectivity towards individual FcγR and the identification of novel mutation hotspots provide valuable insights for further Fc optimization and serve as a foundation for advancing antibody therapeutics development.

Comprehensive Overview of Broadly Neutralizing Antibodies against SARS-CoV-2 Variants.

Currently, SARS-CoV-2 has evolved into various variants, including the numerous highly mutated Omicron sub-lineages, significantly increasing immune evasion ability. The development raises concerns about the possibly diminished effectiveness of available vaccines and antibody-based therapeutics. Here, we describe those representative categories of broadly neutralizing antibodies (bnAbs) that retain prominent effectiveness against emerging variants including Omicron sub-lineages. The molecular characteristics, epitope conservation, and resistance mechanisms of these antibodies are further detailed, aiming to offer suggestion or direction for the development of therapeutic antibodies, and facilitate the design of vaccines with broad-spectrum potential.

In vivo mRNA expression of a multi-mechanistic mAb combination protects against Staphylococcus aureus infection

Single monoclonal antibodies (mAbs) can be expressed in vivo through gene delivery of their mRNA formulated with liponanoparticles (mRNA/LNP). However, delivery of a mAb combination could be challenging due to risk of heavy and light variable chain mispairing. We evaluated the pharmacokinetics of a three mAb combination against Staphylococcus aureus first in single chain variable fragment scFv-Fc and then in immunoglobulin G 1 (IgG1) format in mice. Intravenous delivery of each mRNA/LNP or the trio (1 mg/kg each) induced functional antibody expression after 24 hours (10-100 μg/ml) with 64 to 78% cognate-chain paired IgG expression after 3 days, and an absence of non-cognate chain pairing for scFv-Fc. We did not observe reduced neutralizing activity for each mAb compared to the level of expression of chain-paired mAbs. Delivery of the trio mRNA protected mice in a S. aureus induced dermonecrosis model. Intravenous administration of the three mRNA in non-human primates achieved peak serum IgG levels ranging between 2.9-13.7 μg/ml with a half-life of 11.8-15.4 days. These results suggest nucleic acid delivery of mAb combinations holds promise and may be a viable option to streamline development of therapeutic antibodies.

Molecular basis for antibody recognition of multiple drug–peptide/MHC complexes

The HapImmuneTM platform exploits covalent inhibitors as haptens for creating major histocompatibility complex (MHC)-presented tumor-specific neoantigens by design, combining targeted therapies with immunotherapy for the treatment of drug-resistant cancers. A HapImmune antibody, R023, recognizes multiple sotorasib-conjugated KRAS(G12C) peptides presented by different human leukocyte antigens (HLAs). This high specificity to sotorasib, coupled with broad HLA-binding capability, enables such antibodies, when reformatted as T cell engagers, to potently and selectively kill sotorasib-resistant KRAS(G12C) cancer cells expressing different HLAs upon sotorasib treatment. The loosening of HLA restriction could increase the patient population that can benefit from this therapeutic approach. To understand the molecular basis for its unconventional binding capability, we used single-particle cryogenic electron microscopy to determine the structures of R023 bound to multiple sotorasib-peptide conjugates presented by different HLAs. R023 forms a pocket for sotorasib between the VH and VL domains, binds HLAs in an unconventional, angled way, with VL making most contacts with them, and makes few contacts with the peptide moieties. This binding mode enables the antibody to accommodate different hapten-peptide conjugates and to adjust its conformation to different HLAs presenting hapten-peptides. Deep mutational scanning validated the structures and revealed distinct levels of mutation tolerance by sotorasib- and HLA-binding residues. Together, our structural information and sequence landscape analysis reveal key features for achieving MHC-restricted recognition of multiple hapten-peptide antigens, which will inform the development of next-generation therapeutic antibodies.

AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences.

The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen-antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen-antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody-antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.

Accurate prediction of antibody function and structure using bio-inspired antibody language model.

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% nonredundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials. The BALMFold structure prediction server is freely available at https://beamlab-sh.com/models/BALMFold.

DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability

Therapeutic antibody development faces challenges due to high viscosities and aggregation tendencies. The spatial charge map (SCM) and spatial aggregation propensity (SAP) are computational techniques that aid in predicting viscosity and aggregation, respectively. These methods rely on structural data derived from molecular dynamics (MD) simulations, which are computationally demanding. DeepSCM, a deep learning surrogate model based on sequence information to predict SCM, was recently developed to screen high-concentration antibody viscosity. This study further utilized a dataset of 20,530 antibody sequences to train a convolutional neural network deep learning surrogate model called Deep Spatial Properties (DeepSP). DeepSP directly predicts SAP and SCM scores in different domains of antibody variable regions based solely on their sequences without performing MD simulations. The linear correlation coefficient between DeepSP scores and MD-derived scores for 30 properties achieved values between 0.76 and 0.96 with an average of 0.87. DeepSP descriptors were employed as features to build machine learning models to predict the aggregation rate of 21 antibodies, and the performance is similar to the results obtained from the previous study using MD simulations. This result demonstrates that the DeepSP approach significantly reduces the computational time required compared to MD simulations. The DeepSP model enables the rapid generation of 30 structural properties that can also be used as features in other research to train machine learning models for predicting various antibody stability using sequences only. DeepSP is freely available as an online tool via https://deepspwebapp.onrender.com and the codes and parameters are freely available at https://github.com/Lailabcode/DeepSP.

Prediction of polyspecificity from antibody sequence data by machine learning.

Antibodies are generated with great diversity in nature resulting in a set of molecules, each optimized to bind a specific target. Taking advantage of their diversity and specificity, antibodies make up for a large part of recently developed biologic drugs. For therapeutic use antibodies need to fulfill several criteria to be safe and efficient. Polyspecific antibodies can bind structurally unrelated molecules in addition to their main target, which can lead to side effects and decreased efficacy in a therapeutic setting, for example via reduction of effective drug levels. Therefore, we created a neural-network-based model to predict polyspecificity of antibodies using the heavy chain variable region sequence as input. We devised a strategy for enriching antibodies from an immunization campaign either for antigen-specific or polyspecific binding properties, followed by generation of a large sequencing data set for training and cross-validation of the model. We identified important physico-chemical features influencing polyspecificity by investigating the behaviour of this model. This work is a machine-learning-based approach to polyspecificity prediction and, besides increasing our understanding of polyspecificity, it might contribute to therapeutic antibody development.

Rapid development of double-hit mRNA antibody cocktail against orthopoxviruses

The Orthopoxvirus genus, especially variola virus (VARV), monkeypox virus (MPXV), remains a significant public health threat worldwide. The development of therapeutic antibodies against orthopoxviruses is largely hampered by the high cost of antibody engineering and manufacturing processes. mRNA-encoded antibodies have emerged as a powerful and universal platform for rapid antibody production. Herein, by using the established lipid nanoparticle (LNP)-encapsulated mRNA platform, we constructed four mRNA combinations that encode monoclonal antibodies with broad neutralization activities against orthopoxviruses. In vivo characterization demonstrated that a single intravenous injection of each LNP-encapsulated mRNA antibody in mice resulted in the rapid production of neutralizing antibodies. More importantly, mRNA antibody treatments showed significant protection from weight loss and mortality in the vaccinia virus (VACV) lethal challenge mouse model, and a unique mRNA antibody cocktail, Mix2a, exhibited superior in vivo protection by targeting both intracellular mature virus (IMV)-form and extracellular enveloped virus (EEV)-form viruses. In summary, our results demonstrate the proof-of-concept production of orthopoxvirus antibodies via the LNP-mRNA platform, highlighting the great potential of tailored mRNA antibody combinations as a universal strategy to combat orthopoxvirus as well as other emerging viruses.

DOTAD: A Database of Therapeutic Antibody Developability.

The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.

Abstract 2821: Development of therapeutic monoclonal antibodies against DKK1 peptide-HLA-A2 complex to treat human cancers

Abstract Immunotherapy is a promising option for cancer treatment. Targeted immunotherapy with monoclonal antibodies (mAbs) is an effective and safe method for the treatment of malignancies. In recent years, efforts have been made to identify potential therapeutic mAbs by defining alternative or novel target antigens. We reported that Dickkopf-1 (DKK1) is a good target for immunotherapy of human cancers based on its wide expression in different cancers but not in normal tissues. As DKK1 is a secreted protein, mAbs directly binding to DKK1 may have limited effects on cancer cells in vivo. Our previous studies demonstrated that DKK1 peptide (such as P20 and P66v, which bind with HLA-A*0201 (HLA-A2) molecule) specific cytotoxic T cells specifically kill myeloma and other cancer cells that express DKK1 and HLA-A2, but not HLA-A2+ normal cells, indicating that DKK1+ tumor cells naturally express these peptides, in the context of HLA-A2 molecules, on their surface. To develop cancer therapeutic antibodies, DKK1 peptide P20-HLA-A2 monomer was synthesized and used to immunize mice. Hybridomas secreting mAbs recognizing cell surface-expressed DKKl P20-HLA-A2 complex (DKK1-A2) were obtained and analyzed. In this study, we generated novel (DKK1-A2) mAbs recognizing DKK1 (P20) peptide in the context of human HLA-A*0201 (HLA-A2) molecules (DKK1-A2 complexes) that are naturally expressed by HLA-A2+DKK1+ cancer cells. These mAbs bound to HLA-A2+DKK1+ hematologic (myeloma, lymphoma, and leukemia) and solid (breast, pancreatic, and prostate) cancer cells and directly induced apoptosis in the cancer cells by activating the caspase-9 cascade. DKK1-A2 mAbs also effectively lysed cancer cells in vitro by mediating CDC and ADCC activities. Furthermore, DKK1-A2 mAbs were therapeutic against established hematologic and solid cancers in their xenograft mouse models. To enhance the therapeutic efficacy of DKK1-A2 mAbs, we investigated the effect of combination treatment of DKK1-A2 mAbs with lenalidomide. Our results showed that there is an additive therapeutic effect between DKK1-A2 mAbs and lenalidomide in vivo, underscoring a potential clinical development strategy for combining DKK1-A2 mAb with lenalidomide to treat cancer patients. As DKK1 is not detected in most human tissues, DKK1-A2 mAbs did not bind to or kill HLA-A2+ blood cells in vitro nor caused tissue damage in vivo in tumor-free or tumor-bearing HLA-A2-transgenic mice. Thus, our study suggests that DKK1-A2 mAbs may be a promising therapeutic agent to treat human cancers and warrants further preclinic studies to determine their translatability to clinic. Citation Format: Jianfei Qian, Wang Qiang, Liuling Xiao, Wei Xiong, Miao Xian, Chuanchao Zhang, Yabo Li, Qing Yi. Development of therapeutic monoclonal antibodies against DKK1 peptide-HLA-A2 complex to treat human cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2821.

Abstract 89: Comprehensive cell based bioassay approaches for characterization, and clinical development of therapeutic agonistic antibodies for cancer immunotherapy and autoimmune diseases

Abstract Agonistic antibodies are emerging as promising therapeutics for targeting immune checkpoints, particularly those within the TNF Receptor Superfamily, as they play a crucial role in regulating immune cell function, especially in the context of cancer treatment. In addition, there is also a growing focus on the potential of agonistic antibodies to selectively activate co-inhibitory checkpoint receptors. This offers a promising avenue for suppressing inflammation in autoimmune diseases and restoring tolerance in graft-vs-host disease (GvHD) while avoiding the complications of global immunosuppression. While a substantial number of clinical trials are currently underway for oncology and autoimmune indications, there is a notable scarcity of commercially available assays to support the clinical development of this class of therapeutics, impacting their time-to-clinic. Existing assays frequently fail to accurately assess the agonistic activity of antibodies as they are unable to replicate the necessary physiologically relevant conditions. In this context, we present several case studies that highlight the pivotal role of cell-based bioassay approaches developed by Eurofins DiscoverX in driving the characterization and clinical development of agonistic antibody therapeutics targeting various checkpoint receptors. Additionally, we discuss our data demonstrating the indispensable role of FcyR-mediated antibody clustering in establishing the otherwise elusive agonistic activity of antibodies. Citation Format: Venkatesh Chari, Jennifer Lin-Jones, Luhan Yang, Jane E. Lamerdin, Gaurav Agrawal. Comprehensive cell based bioassay approaches for characterization, and clinical development of therapeutic agonistic antibodies for cancer immunotherapy and autoimmune diseases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 89.

Abstract 7174: Pharmacokinetics and immunogenicity of anti-PD-1 antibody in humanized FcRn mouse models compared to immunocompetent mouse models

Abstract Introduction: The development and use of therapeutic antibodies in cancer therapy has increased significantly in recent years, led by the success of approved immune checkpoint blockade antibodies targeting PD-1 and PD-L1 axis. However, obtaining more clinically relevant pharmacokinetics (PK), safety and efficacy data for antibodies is still challenging in preclinical phase. Humanized FcRn (hFcRn) transgenic mouse model has a more faithful catabolism compared to WT mice, providing more accurate and predictable PK data of innovative antibody-based therapeutics at discovery stage[1]. In this study, we sought to investigate pharmacokinetics and immunogenicity characteristics of anti-PD-1 antibody in hFcRn transgenic mice and provide guidance for its differentiation of prediction value for drug PK and safety by comparing to WT mice. Methods: Non-tumor bearing hFcRn mice and wild type C57BL/6 mice were divided into 2 groups, 5 mice were enrolled into each group. Mice were administered anti-PD-1(Nivolumab) at 10 mg/kg single dose after grouping by intravenous (IV) injection. Blood samples at 12 timepoints (15min, 6h, 1, 2, 4, 7, 10, 14, 21, 28, 35, 42 days) post-dosing were collected for PK and Anti-Drug Antibody (ADA) analysis by MSD and ELISA. Results: Nivolumab clearance in hFcRn mice followed a typical linear profile with rapid distribution phase and slow elimination phase (t1/2 = 350 to 400 h). While the PK profile in C57BL/6 mice displayed non-linear clearance and very short elimination phase (t1/2 = 45 to 50 h). This data demonstrated that hFcRn mice have better correlated half-life with human (Nivolumab single dose by IV with 10mg/kg, t1/2 = 595 h) [2]. ADA analysis revealed that it was produced starting from Day 10 in wild type C57BL/6 mice and the frequency of ADA induced by Nivolumab in wild type mice (2/5) was higher than hFcRn mice (0/5). The variation of Nivolumab level was correlated with ADA production in wild type mice. This suggested hFcRn shows lower immunogenicity than wild type mice for human IgG drug and can potentially improve negative effect of ADA interference on PK evaluation. Conclusion: The result of this study demonstrated that the PK data in hFcRn mouse model was more predictive of human PK profile compared to wild type C57BL/6 mice. This advances innovation in antibody-based therapeutics and translational applications, with improved PK, safety and efficacy evaluation for preclinical human IgG drug development in cancer therapy.

Abstract 1876: Development of therapeutic antibodies targeting the GLUT-1 transporter to restrict cancer cell growth

Abstract Background: Upregulated glucose metabolism is one of the strategies cancer cells use to fuel their abnormal cell growth and division. Targeting the cancer-specific glucose transporter GLUT-1 is a promising approach to restrict glucose uptake and challenge the metabolic needs of tumor cells. Antibodies, as opposed to small molecule inhibitors, are an attractive modality to therapeutically target complex muti-pass membrane transporters. Here we report the development of antibodies that very specifically block the function of only GLUT-1. Methods: Monoclonal antibodies against GLUT-1 were generated using virus-like particles. For immunization, chickens were used as they are evolutionarily distant from mammals and can produce antibodies with prolonged CDR3-s in VH, which could facilitate their binding to the limited extracellular region of GLUT-1. Antibody discovery was performed with HybriFree B cell cloning technology followed by functional screens with 2-deoxyglucose uptake interference or cell proliferation measurements. Results: The discovered antibodies specifically bind to GLUT-1 with low nanomolar EC50 values and do not target other glucose transporters. The lead candidate, ICO-33, inhibits glucose uptake and rewires the metabolism of GLUT-1-dependent cancer cells to rely on oxidative phosphorylation. This results in a significantly synergistic cancer proliferation inhibition with ICO-33 and OXPHOS inhibitor combinations in doses that do not inhibit proliferation as single agents. ICO-33 and OXPHOS inhibitor treatment is well-tolerated and demonstrates a drastic tumor growth inhibition in in vivo colorectal and pancreatic cancer models. Conclusions: We describe the discovery of highly specific monoclonal antibodies targeting GLUT-1. We further demonstrate that the restriction of the much-needed glucose uptake by ICO-33 makes cancer cells highly susceptible to OXPHOS inhibitor co-therapy both in vitro and in vivo, validating the antibody as a promising candidate for clinical development. Citation Format: Siret Tahk, Kai Virumae, Korneelia Anton, Paule Hermet, Robin Pau, Mario Plaas, Maiken Abel, Denis Belitškin, Luciano Galdieri, Steven Garner, Jillian Krings, Kaleb Collver, Emily Schultz, Kaitlyne Powers, Alastair King, Francisca Neethling, Anu Planken, Mart Ustav, Joan Terya, Andres Mannik, Mart Ustav. Development of therapeutic antibodies targeting the GLUT-1 transporter to restrict cancer cell growth [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1876.

Abstract 6344: Engineering of ICAM-1 refractory antibodies for the development of therapeutic antibodies and antibody drug conjugates (ADCs) in ICAM-1 over-expressing cancers

Abstract Human cancers employ a number of mechanisms to evade host immune responses against novel antigens generated from aberrant over-expression, mutations and/or epigenetic alterations. Humoral immunity utilizes antibodies and immune-effector cells as well as molecular immune complexes involving the complement system to mediate the killing of dysregulated cancer cells. We refer to these anti-cancer mechanisms as Humoral Immuno-Oncology (HIO). HIO suppression is mediated by tumor-produced proteins called HIO factors. One such factor is CA125, which was previously shown to bind IgG-type antibodies and inhibit their immune-effector activities, including antibody dependent (ADCC) and complement dependent (CDC) cellular cytotoxicity. Using a combination of experimental screening and literature searches, a second protein produced by tumors and associated with a variety of cancer indications was discovered and found to be soluble ICAM-1 (sICAM-1). Through various functional studies we reported that sICAM-1 and membrane ICAM-1 (mICAM-1) are capable of binding IgG1-type antibodies that in turn inhibits their immune-effector activity. Through a combination of truncation and substitution mutagenesis, we identified a four amino acid motif within the CH3 domain of IgG1 essential for ICAM-1 binding, resulting in the inhibition of ADCC activity. While traditional glycine and alanine substitutions in this region abrogated ICAM-1 binding, these modifications caused tertiary structural changes in the Fc domain that resulted in loss of ADCC activity. Through a combinatorial amino acid substitution approach, we identified a four amino acid combination within the 407YSKL410 to 407FARV410 motif that resulted in antibodies refractory to sICAM-1 binding with robust ADCC activity. Additionally, isogenic wildtype and ICAM-1 knockdown target cell lines showed mICAM-1 inhibited target cell killing of a saporin antibody drug conjugate (ADC) and this was the direct result of mICAM-1 reducing antibody internalization, a requisite for maximal ADC target cell killing. The inhibitory activity of mICAM-1 could be overcome employing antibodies containing the 407FARV410 substitution. These findings highlight yet another mechanism by which tumors can suppress the host’s immune system for survival and offer new concepts for developing antibody-based therapies that can aid in the treatment of various cancer indications, especially those over-expressing ICAM-1. Moreover, the findings here offer diagnostic and therapeutic clinical design opportunities to improve upon existing approved immune-mediated therapies for which this factor is present. Citation Format: Brad Kline, Luigi Grasso, Nicolas Nicolaides. Engineering of ICAM-1 refractory antibodies for the development of therapeutic antibodies and antibody drug conjugates (ADCs) in ICAM-1 over-expressing cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6344.

Abstract 5333: NeoMab: A novel platform for fully human therapeutic antibody development

Abstract Antibodies and antibody containing therapeutics, such as multivalent antibodies, chimeric antigen receptors (CARs), and antibody directed conjugates (ADCs), have been proven to be highly prominent in cancer treatment. However, challenges such as immunogenicity stemming from non-human or unnatural sequences and limitations in preclinical animal study systems still impede the rapid development of antibody drugs. To overcome these hurdles, GemPharmatech has created a novel antibody gene humanized model, the NeoMab mouse, to develop fully human antibodies. The NeoMab model was established via large genomic fragment knock-in technology, retaining native mouse constant region encoding genes and regulatory elements, while incorporating human variable gene repertoires into the endogenous loci. Developed in the BALB/c background, the NeoMab mouse carries fully functional MHC molecules, potentially enhancing antigen presentation. These mice sustain a competent immune system with a B cell development and maturation pattern, immune cell profile, and serum immunoglobulin levels comparable to those of BALB/c mice. NeoMab mice exhibit a robust humoral immune response, with a rapid increase in antigen-specific serum titers upon immunization using different antigens, such as recombined protein, plasmids, and overexpressing cell lines. Utilizing human V(D)J genes to encode immunoglobulins at a human-like frequency and diversity, antibodies derived from NeoMab mice demonstrate affinity and functional activity comparable to, or even surpassing, FDA-approved drugs. In conclusion, the NeoMab mouse model in the BALB/c background has been rigorously validated to elicit a robust response to specific antigen immunization, producing antibodies with high affinity, diversity, and functional activity. GemPharmatech's extensive drug-target gene humanized mouse library (comprising over 800 targets) and diverse disease models in oncology and other fields facilitate a seamless connection between in vitro antibody screening and in vivo animal studies. The integration of NeoMab, target humanization animals, and disease models enables GemPharmatech to provide an integrated solution, reducing time and costs while elevating the quality of antibody discovery in the field. Citation Format: Mingkun Zhang, Cunxiang Ju, Song Li, Junchao Peng, Jing Zhao, Xiang Gao. NeoMab: A novel platform for fully human therapeutic antibody development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5333.

Abstract 3107: SMASH: Single molecule antibody screening with high-throughput imaging system

Abstract Larger, more diverse antibody libraries are being screened in recent years. Despite enabling the high-throughput screening of extensive libraries, the process for identification and characterization of antibodies remains laborious and time-intensive. Here we report a platform that not only compiles hit finding and validation processes, but also provides high-resolution binding affinity as well as rank order among the library being screened. We streamlined the library construction and affinity screening via microscale transient expression in HEK293T cells with designed antibody sequence and direct quantification of antibody-antigen binding affinity which reduced the timeline from multiple weeks to a single week. We constructed the single-chain fragment antigen binding (scFab) libraries by replacing the duplex DNA fragment in CDR of interest while preserving the human framework of Fab. We have demonstrated that the SPID technique allows to detect antibody-antigen interactions from just dozens of pg of scFabs in total. This allowed us to bypass the conventional DNA cloning or even PCR amplification to achieve the minimum amount of DNA plasmids for expressing scFabs to be analyzed such as BLI and SPR. Therefore, we assembled the DNA plasmids through in vitro ligation with high efficiency and directly introduced it to HEK293T in 96-well microplate with microscale (200 ul) for transient transfection, which only takes three days for library generation. Next, we utilized SPID to quantify the number of single molecules between free and antigen-bound fractions of scFab, resulting in occupancy at the antibody-antigen binding equilibrium. This technique facilitated the determination of binding affinity (KD) for 200 antibody-antigen pairs within a 6-hour TAT encompassing both measurement and analysis. In total, we utilized 4,000 times less antibody for characterization while achieving enhanced sensitivity compared to both BLI and SPR methods. As a proof-of-concept, we generated a library consisting of 720 variants of Trastuzumab in scFab format to probe the landscape of CDRs affecting affinities against HER2 showing nM to low uM dynamic range of KD. Out of 720 analyzed variants, 5% were deleterious, 38% provided substantial changes, 40% showed little/no change, 17% enhanced affinity to HER2. To validate the robustness of the platform, we extensively expanded the library screening, assessing over 2,000 antibody variants for their affinity against more than three different therapeutic targets. To conclude, we demonstrated an efficient approach for monitoring changes in affinity resulting from single mutations in scFab. This rapid screening of site-saturated libraries would lead to a combinational variant library that our platform can screen. Ultimately, our platform aims to identify potential candidates for therapeutic antibody development throughput the phases of antibody discovery, maturation, and engineering. Citation Format: Jihye Jo, Changju Chun, Byeong-Kwon Sohn, Booyoung Yu, Jiyu Lee. SMASH: Single molecule antibody screening with high-throughput imaging system [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3107.