Engineering a High-Affinity Anti-IL-15 Antibody: Crystal Structure Reveals an α-Helix in VH CDR3 as Key Component of Paratope

Predicting class switch recombination in B-cells from antibody repertoire data

Statistical and machine learning methods have proved useful in many areas of immunology. In this paper, we address for the first time the problem of predicting the occurrence of class switch recombination (CSR) in B-cells, a problem of interest in understanding antibody response under immunological challenges. We propose a framework to analyze antibody repertoire data, based on clonal (CG) group representation in a way that allows us to predict CSR events using CG level features as input. We assess and compare the performance of several predicting models (logistic regression, LASSO logistic regression, random forest, and support vector machine) in carrying out this task. The proposed approach can obtain an unweighted average recall of71 % $71\%$ with models based on variable region descriptors and measures of CG diversity during an immune challenge and, most notably, before an immune challenge.

Screening Non-neutralizing Anti-idiotype Antibodies Against a Drug Candidate for Total Pharmacokinetic and Target Engagement Assay

Non-neutralizing anti-idiotype antibodies against a therapeutic monoclonal antibody (mAb) play a crucial role in the creation of total pharmacokinetic (PK) assays and total target engagement (TE) assays during both pre-clinical and clinical development. The development of these anti-idiotype antibodies is challenging. In this study, we utilized a hybridoma platform to produce a variety of anti-idiotype antibodies against GSK2857914, a humanized IgG1 anti-BCMA monoclonal antibody. The candidate clones were evaluated using surface plasmon resonance (SPR) and bio-layer interferometry (BLI) for binding affinity, binding profiling, matrix interference, and antibody pairing determination. We discovered that three anti-idiotype antibodies did not prevent BCMA from binding to GSK2857914. All three candidates demonstrated high binding affinities. One of the three exhibited minimal matrix inference and could pair with the other two candidates. Additionally, one of the three clones was biotinylated as a capture reagent for the total PK assay, and another was labeled with ruthenium as a detection reagent for both the total PK assay and total TE assay. The assay results clearly show that these reagents are genuine non-neutralizing anti-idiotypic antibodies and are suitable for total PK and TE assay development. Based on this and similar studies, we conclude that the hybridoma platform has a high success rate for generating non-neutralizing anti-idiotype antibodies. Our methodology for developing and characterizing non-neutralizing anti-idiotype antibodies to therapeutic antibodies can be generally applied to any antibody-based drug candidate's total PK and total TE assay development.

Counteracting Interleukin-15 to Elucidate Its Modes of Action in Physiology and Pathology

Interleukin (IL)-15 belongs to the common gamma-dependent cytokine family, along with IL-2, IL-4, IL-7, IL-9, and IL-21. IL-15 is crucial for the homeostasis of Natural Killer (NK) and memory CD8 T cells, and to fight against cancer progression. However, dysregulations of IL-15 expression could occur and participate in the emergence of autoimmune inflammatory diseases as well as hematological malignancies. It is therefore important to understand the different modes of action of IL-15 to decrease its harmful action in pathology without affecting its beneficial effects in the immune system. In this review, we present the different approaches used by researchers to inhibit the action of IL-15, from most broad to the most selective. Indeed, it appears that it is important to selectively target the mode of action of the cytokine rather than the cytokine itself as they are involved in numerous biological processes.

Machine learning modeling to identify affinity improved biobetter anticancer drug trastuzumab and the insight of molecular recognition of trastuzumab towards its antigen HER2

In the present study, a machine learning (ML) model was developed to predict the epistatic phenomena of combination mutants to improve the anticancer antibody-drug trastuzumab's binding affinity towards its antigen human epidermal growth factor receptor 2 (HER2). An ML algorithm, Support Vector Regression (SVR) was used to develop ML models with a data set consists of 193 affinity values of single mutants of trastuzumab and its associated various amino acid sequence derived descriptors. The subset selection of descriptors and SVR hyperparameters were done using the Genetic Algorithm (GA) within the SVR and the wrapper approach called GA-SVR. A 100 evolutionary cycles of GA produced the best 100 probable GA-SVR models based on their fitness score (Q2) estimated using a stratified 5 fold cross-validation procedure. The final ML model found to be highly predictive of test data set of six combination mutants and one single mutant with Rpre2 = 0.71. The analysis of descriptors in the ML model highlighted the importance of mutant induced secondary structural variation causes the binding affinity variation of the trastuzumab. The same was verified using a short 20 ns and a long 100 ns in duplicate molecular dynamics simulation of a wild and mutant variant of trastuzumab. The secondary structure induced affinity change due to mutations in the CDR-H3 is a novel insight that came out of this study. That should help rational mutant selection to develop a biobetter trastuzumab with a multifold improved binding affinity into the market quickly.Communicated by Ramaswamy H. Sarma.

A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires

The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immune responses and the potential for future immunity. Analyses of TCR repertoires via deep learning (DL) methods have successfully diagnosed cancers and infectious diseases, including coronavirus disease 2019. However, few studies have used DL to analyze BCR repertoires. In this study, we collected IgG H chain Ab repertoires from 276 healthy control subjects and 326 patients with various infections. We then extracted a comprehensive feature set consisting of 10 subsets of repertoire-level features and 160 sequence-level features and tested whether these features can distinguish between infected individuals and healthy control subjects. Finally, we developed an ensemble DL model, namely, DL method for infection diagnosis (https://github.com/chenyuan0510/DeepID), and used this model to differentiate between the infected and healthy individuals. Four subsets of repertoire-level features and four sequence-level features were selected because of their excellent predictive performance. The DL method for infection diagnosis outperformed traditional machine learning methods in distinguishing between healthy and infected samples (area under the curve = 0.9883) and achieved a multiclassification accuracy of 0.9104. We also observed differences between the healthy and infected groups in V genes usage, clonal expansion, the complexity of reads within clone, the physical properties in the α region, and the local flexibility of the CDR3 amino acid sequence. Our results suggest that the Ab repertoire is a promising biomarker for the diagnosis of various infections.

The VH framework region 1 as a target of efficient mutagenesis for generating a variety of affinity-matured scFv mutants

In vitro affinity-maturation potentially generates antibody fragments with enhanced antigen-binding affinities that allow for developing more sensitive diagnostic systems and more effective therapeutic agents. Site-directed mutagenesis targeting “hot regions,” i.e., amino acid substitutions therein frequently increase the affinities, is desirable for straightforward discovery of valuable mutants. We here report two “designed” site-directed mutagenesis (A and B) targeted the N-terminal 1–10 positions of the V_H framework region 1 that successfully improved an anti-cortisol single-chain Fv fragment (K_a, 3.6 × 10⁸ M⁻¹). Mutagenesis A substituted the amino acids at the position 1–3, 5–7, 9 and 10 with a limited set of substitutions to generate only 1,536 different members, while mutagenesis B inserted 1–6 random residues between the positions 6 and 7. Screening the resulting bacterial libraries as scFv-phage clones with a clonal array profiling system provided 21 genetically unique scFv mutants showing 17–31-fold increased affinity with > 10⁹ M⁻¹K_a values. Among the mutants selected from the library A and B, scFv mA#18 (with five-residue substitutions) and mB_1-3#130 (with a single residue insertion) showed the greatest K_a value, 1.1 × 10¹⁰ M⁻¹.

Monoclonal antibody therapy for the treatment of asthma and chronic obstructive pulmonary disease with eosinophilic inflammation

Eosinophils have been linked with asthma for more than a century, but their role has been unclear. This review discusses the roles of eosinophils in asthma and chronic obstructive pulmonary disease (COPD) and describes therapeutic antibodies that affect eosinophilia. The aims of pharmacologic treatments for pulmonary conditions are to reduce symptoms, slow decline or improve lung function, and reduce the frequency and severity of exacerbations. Inhaled corticosteroids (ICS) are important in managing symptoms and exacerbations in asthma and COPD. However, control with these agents is often suboptimal, especially for patients with severe disease. Recently, new biologics that target eosinophilic inflammation, used as adjunctive therapy to corticosteroids, have proven beneficial and support a pivotal role for eosinophils in the pathology of asthma. Nucala® (mepolizumab; anti-interleukin [IL]-5) and Cinquair® (reslizumab; anti-IL-5), the second and third biologics approved, respectively, for the treatment of asthma, exemplifies these new treatment options. Emerging evidence suggests that eosinophils may contribute to exacerbations and possibly to lung function decline for a subset of patients with COPD. Here we describe the pharmacology of therapeutic antibodies inhibiting IL-5 or targeting the IL-5 receptor, as well as other cytokines contributing to eosinophilic inflammation. We discuss their roles as adjuncts to conventional therapeutic approaches, especially ICS therapy, when disease is suboptimally controlled. These agents have achieved a place in the therapeutic armamentarium for asthma and COPD and will deepen our understanding of the pathogenic role of eosinophils.

Characterization of the complex formed between a potent neutralizing ovine-derived polyclonal anti-TNFα Fab fragment and human TNFα

TNFα (tumour necrosis factor α) is an early mediator in the systemic inflammatory response to infection and is therefore a therapeutic target in sepsis. AZD9773 is an ovine-derived, polyclonal anti-TNFα Fab fragment derived from a pool of serum and currently being developed as a treatment for severe sepsis and septic shock. In the present study, we show that although AZD9773 has a modest affinity for TNFα in a binding assay, the K_i in a cell-based assay is approximately four orders of magnitude lower. We show using SEC (size exclusion chromatography) that the maximum size of the complex between AZD9773 and TNFα is consistent with approximately 12 Fabs binding to one TNFα trimer. A number of approaches were taken to map the epitopes recognized by AZD9773. These revealed that a number of different regions on TNFα are involved in binding to the polyclonal Fab. The data suggest that there are probably three epitopes per monomer that are responsible for most of the inhibition by AZD9773 and that all three can be occupied at the same time in the complex. We conclude that AZD9773 is clearly demonstrated to bind to multiple epitopes on TNFα and suggest that the polyclonal nature may account, at least in part, for the very high potency observed in cell-based assays.

Small antibody fusion proteins with complementarity-determining regions and lidamycin for tumor targeting therapy

Gelatinases are overexpressed in several types of maligancies and tumor stromal cells. Lidamycin is an enediyne antitumor antibiotic, which is composed of an apoprotein (LDP) and an active chromophore (AE). It is known that the heavy-chain complementarity-determining region-3 (CDR3) domain of scFv is important in antibody affinity. The aim of this study was to prepare the enediyne-energized fusion proteins with a heavy-chain CDR3 domain of anti-gelatinases scFv and lidamycin, and to evaluate their antitumor efficiency. Fusion proteins comprising the CDR3 domain and the lidamycin apoprotein were generated, and ELISA, immunofluorescence and FACS were used to analyze the binding of the fusion protein with antigen gelatinases. The purified fusion proteins were assembled with the lidamycin chromophore, and the antitumor effects were evaluated in vitro and in vivo. It was found that the CDR3-LDP and CDR3-LDP-CDR3 fusion proteins demonstrated high affinity towards antigen gelatinases. Following stimulation of CDR3-LDP with enediyne, the results of MTT showed potent cytotoxicity towards tumor cells; the IC50 values of CDR3-LDP-AE to HepG2 and Bel-7402 tumor cells were 1.05×10⁻¹¹ and 6.6×10⁻¹⁴ M, respectively. In addition, CDR3-LDP-AE displayed a potent antitumor effect in H22 cell xenografts in mice; the combination of CDR3-LDP (10 mg/kg) and CDR3-LDP-AE (0.25 and 0.5 mg/kg) revealed that the tumor inhibitory rates were 85.2 and 92.7%, respectively (P<0.05 compared with CDR3-LDP-AE). In conclusion, these results suggest that the CDR3-LDP fusion protein and its analog CDR3-LDP-AE may both be promising candidates for tumor targeting therapy.

Tailored amino acid diversity for the evolution of antibody affinity

Antibodies are a unique class of proteins with the ability to adapt their binding sites for high affinity and high specificity to a multitude of antigens. Many analyses have been performed on antibody sequences and structures to elucidate which amino acids have a predominant role in antibody interactions with antigens. These studies have generally not distinguished between amino acids selected for broad antigen specificity in the primary immune response and those selected for high affinity in the secondary immune response. By studying a large data set of affinity matured antibodies derived from in vitro directed evolution experiments, we were able to specifically highlight a subset of amino acids associated with affinity improvements. In a comparison of affinity maturations using either tailored or full amino acid diversification, the tailored approach was found to be at least as effective at improving affinity while requiring fewer mutagenesis libraries than the traditional method. The resulting sequence data also highlight the potential for further reducing amino acid diversity for high affinity binding interactions.