CellCelector™ as a platform in isolating primary B cells for antibody discovery

Background The most robust strategy in antibody discovery is the use of immunized animals and the ability to isolate and immortalize immune B-cells to hybridoma for further interrogation. However, capturing the full repertoire of an immunized animal is labor intensive, time consuming, and limited in throughput. Therefore, techniques to directly mine the antibody repertoire of primary B-cells are of great importance in antibody discovery. Methods In the current study, we present a method to isolate individual antigen specific primary B-cells using the CellCellector™ single-cell isolation platform from XenoMouse® (XM) immunized with a recombinant therapeutic protein, EGFR. We screened a subset of CD138+ B-cells and identified 238 potential EGFR specific B-cells from 1,189 antibody secreting cells (ASCs) and isolated 94 by CellCellector. Results We identified a diverse set of heavy chain CDR sequences and cloned and expressed 20 into a standard human IgG1 antibody format. We further characterized and identified 13 recombinant antibodies that engage soluble and native forms of EGFR. By extrapolating the method to all 400,000 CD138+ B-cells extracted from one EGFR immunized XM, a potential 1,196 unique EGFR-specific antibodies could be discovered. Conclusions CellCelector allows for interrogating the B-cell pool directly and isolating B-cells specific to the therapeutic target of interest. Furthermore, antibody sequences recovered from isolated B-cells engage the native and recombinant target, demonstrating the CellCellector can serve as a platform in antibody discovery.

A high-throughput, automated, cell-free expression and screening platform for antibody discovery

Antibody discovery is bottlenecked by the individual expression and evaluation of antigen- specific hits. Here, we address this gap by developing an automated workflow combining cell-free DNA template generation, protein synthesis, and high-throughput binding measurements of antibody fragments in a process that takes hours rather than weeks. We apply this workflow to 119 published SARS-CoV-2 neutralizing antibodies and demonstrate rapid identification of the most potent antibody candidates.

Introduction to Antibody-Drug Conjugates

Antibody-drug conjugates (ADCs) are innovative biopharmaceutical products in which a monoclonal antibody is linked to a small molecule drug with a stable linker. Most of the ADCs developed so far are for treating cancer, but there is enormous potential for using ADCs to treat other diseases. Currently, ten ADCs have been approved by the United States Food and Drug Administration (FDA), and more than 90 ADCs are under worldwide clinical development. Monoclonal antibodies have evolved from research tools to powerful therapeutics in the past 30 years. Tremendous strides have been made in antibody discovery, protein bioengineering, formulation, and delivery devices. This manuscript provides an overview of the biology, chemistry, and biophysical properties of each component of ADC design. This review summarizes the advances and challenges in the field to date, with an emphasis on antibody conjugation, linker-payload chemistry, novel payload classes, drug-antibody ratio (DAR), and product development. The review emphasizes the lessons learned in the development of oncology antibody conjugates and look towards future innovations enabling other therapeutic indications. The review discusses resistance mechanisms to ADCs, and give an opinion on future perspectives.

Novel Selection Approaches to Identify Antibodies Targeting Neoepitopes on the C5b6 Intermediate Complex to Inhibit Membrane Attack Complex Formation

The terminal pathway of complement is implicated in the pathology of multiple diseases and its inhibition is, therefore, an attractive therapeutic proposition. The practicalities of inhibiting this pathway, however, are challenging, as highlighted by the very few molecules in the clinic. The proteins are highly abundant, and assembly is mediated by high-affinity protein–protein interactions. One strategy is to target neoepitopes that are present transiently and only exist on active or intermediate complexes but not on the abundant native proteins. Here, we describe an antibody discovery campaign that generated neoepitope-specific mAbs against the C5b6 complex, a stable intermediate complex in terminal complement complex assembly. We used a highly diverse yeast-based antibody library of fully human IgGs to screen against soluble C5b6 antigen and successfully identified C5b6 neoepitope-specific antibodies. These antibodies were diverse, showed good binding to C5b6, and inhibited membrane attack complex (MAC) formation in a solution-based assay. However, when tested in a more physiologically relevant membrane-based assay these antibodies failed to inhibit MAC formation. Our data highlight the feasibility of identifying neoepitope binding mAbs, but also the technical challenges associated with the identification of functionally relevant, neoepitope-specific inhibitors of the terminal pathway.

Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences

Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design. High-throughput sequencing enables the identification of the multitude of antibodies elicited in response to dengue infection at the sequence level. Artificial intelligence can mine the complex data generated and has the potential to uncover patterns in entire antibody repertoires and detect signatures distinctive of single virus-binding antibodies. However, these machine learning have not been harnessed to determine the immune response to dengue virus. In order to enable the application of machine learning, we have benchmarked existing methods for encoding biological and chemical knowledge as inputs and have investigated novel encoding techniques. We have applied different machine learning methods such as neural networks, random forests, and support vector machines and have investigated the parameter space to determine best performing algorithms for the detection and prediction of antibody patterns at the repertoire and antibody sequence levels in dengue-infected individuals. Our results show that immune response signatures to dengue are detectable both at the antibody repertoire and at the antibody sequence levels. By combining machine learning with phylogenies and network analysis, we generated novel sequences that present dengue-binding specific signatures. These results might aid further antibody discovery and support vaccine design.

Accelerated antibody discovery targeting the SARS-CoV-2 spike protein for COVID-19 therapeutic potential

Background Rapid deployment of technologies capable of high-throughput and high-resolution screening is imperative for timely response to viral outbreaks. Risk mitigation in the form of leveraging multiple advanced technologies further increases the likelihood of identifying efficacious treatments in aggressive timelines. Methods In this study, we describe two parallel, yet distinct, in vivo approaches for accelerated discovery of antibodies targeting the severe acute respiratory syndrome coronavirus-2 spike protein. Working with human transgenic Alloy-GK mice, we detail a single B-cell discovery workflow to directly interrogate antibodies secreted from plasma cells for binding specificity and ACE2 receptor blocking activity. Additionally, we describe a concurrent accelerated hybridoma-based workflow utilizing a DiversimAb™ mouse model for increased diversity. Results The panel of antibodies isolated from both workflows revealed binding to distinct epitopes with both blocking and non-blocking profiles. Sequence analysis of the resulting lead candidates uncovered additional diversity with the opportunity for straightforward engineering and affinity maturation. Conclusions By combining in vivo models with advanced integration of screening and selection platforms, lead antibody candidates can be sequenced and fully characterized within one to three months.