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Recent Advances in Therapeutic Antibody Screening

Floating antibodies
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In 1986, the first monoclonal antibody was approved by the United States Food and Drug Administration (FDA), and all through these years, substantial progress has been made in the field of therapeutic antibodies.

Advances in research and the use of computational strategies have enabled the development of therapeutic antibodies with the desired properties and these developments have made therapeutic antibodies one of the most rapidly growing classes of new drugs.

This article explores the various screening technologies used in the development of therapeutic antibodies and outlines the recent advances in this area.

Technologies for antibody screening

The screening process for therapeutic antibodies involves testing several potential candidates (antibody libraries) to identify those that exhibit high binding affinity and specificity to the target molecule. Screening of therapeutic antibodies has traditionally been done using humanization, phage display or transgenic mice.

The generation of humanized antibodies by the complementary-determining region (CDR) grafting technique is a popular technique that has accelerated the approval of therapeutic monoclonal antibodies (mAbs). The humanization of antibodies has enabled the use of a new class of biologics against life-threatening diseases such as cancer and autoimmune diseases.

The phage display technique is a commonly used technique for the rapid identification of peptides or antibody fragments that bind to several target molecules. Due to its ability to present large libraries, phage display is widely used in antibody screening.

Fully human mAbs can be developed using transgenic animals. This technique involves genetic modification of transgenic mouse lines wherein human immunoglobulin (Ig) genes are inserted to replace the endogenous Ig genes. After immunization, these animals can synthesize diverse human mAbs of high affinity.

Aside from the above techniques, several cell-based screening technologies have also emerged, such as mammalian cell display and yeast cell display. These techniques involve the multi-copy display of antibodies or antibody fragments on a cell surface in a functional form followed by high-throughput screening.

mRNA display is another screening technique that allows the selection of protein libraries with much higher diversity.

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Rational design strategy for therapeutic antibody development

Though useful, traditional methods for antibody development are cumbersome, expensive and time-consuming. Also, they may not necessarily be able to produce antibodies with the desired specificity, especially for difficult targets such as weakly immunogenic epitopes.

The rational design strategy enables one to obtain antibodies targeting a specific epitope and can be used to engineer proteins with better stability, binding affinity and catalytic activity. “The rational design strategy for protein engineering is a computational approach that involves designing and modifying protein structures using information on the protein's molecular properties, structure and function,” explains João Gonçalves, Full Professor at the Faculty of Pharmacy of the University of Lisbon. “This strategy is based on the understanding of the relationship between the protein's structure and function, as well as the underlying principles of protein folding and stability.”

“The rational design approach begins with the identification of the protein's structural and functional features, followed by the design and construction of novel protein sequences that optimize these properties. The strategy involves computational modeling techniques such as molecular dynamics simulations, homology modeling and protein-ligand docking, among others,” says Gonçalves.

High-throughput selection of computational designs

Recent advances in computational methods have led to significant advances in antibody design by providing better and faster results than traditional approaches. Philip M. Kim, a professor in the Department of Molecular Genetics at the University of Toronto, says, “Basically, if you're screening computational designs, you're starting at a much better starting point, as each design has a decent chance of binding the target. By contrast, for either combinatorial libraries (on phage or yeast) or B-cell approaches, you're essentially screening it blindly, so any affinity to your antigen will be serendipitous; this is usually counteracted by trying to screen huge libraries.”

The probability with which any given computational design will bind its target largely depends on the quality of the design approach taken. “In the past, we have shown that even with classical approaches, screening relatively small libraries (tens of thousands of variants in contrast to billions) yields binders. Now with modern machine learning approaches, the quality of designs is improving rapidly and with it, the number of variants that need to be screened is dropping,” says Kim.

Compared to the conventional screening strategies for therapeutic antibodies, high-throughput selection of computational designs offers several advantages. “There are multiple advantages, but a key one is that with design you can target particular interfaces or conformations at will, so you have much more precision,” Kim explains. “This also enables the targeting of difficult-to-hit antigens, such as G-protein coupled receptors (GPCRs) or other integral membrane proteins. Moreover, since you can target anywhere, you can also use the design approach to elucidate new biology by targeting multiple new, or even all, interfaces on a particular antigen. Also, with computational design, we can ensure good developability properties of all our antibodies from the get-go and can thus avoid the outcome of getting binding antibodies whose properties preclude further development or need multiple cycles to achieve both binding and good developability.”

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Language models to accelerate the screening process of therapeutic antibodies

The screening process for therapeutic antibodies can be time-consuming and resource-intensive, but there are several ways in which language models could potentially speed it up. Gonçalves enumerates the ways in which language models can accelerate the screening process of therapeutic antibodies:

  1. Predictive modeling: Language models can be trained on large datasets of antibody structures and binding data to predict the binding affinity and specificity of new candidates. This could potentially reduce the number of candidates that need to be experimentally tested, as those with low predicted affinity could be filtered out early in the process.
  2. Literature mining: Language models can be used to rapidly search through large volumes of scientific literature to identify antibodies that have already been characterized for a particular target. This could potentially save time by identifying candidates that have already been tested, rather than starting from scratch.
  3. Data analysis: Language models can help analyze the vast amounts of data generated in the screening process, identifying patterns and correlations that could help optimize the selection and design of new candidates.
  4. Automation: Language models can be used to automate certain aspects of the screening process, such as the design of experiments or the analysis of data, freeing up researchers to focus on other tasks.

The need for a time- and cost-efficient approach

Screening and then characterizing antibodies to find promising drug candidates can be a laborious and expensive process with several potentially useful candidates left unstudied. However, advanced screening technology can help. Computational approaches can be harnessed to discover specific and selective antibodies and reduce the research efforts during the lead identification stage. This time- and cost-efficient approach for screening therapeutic antibodies can accelerate the delivery of life-saving medicines to patients and improve patient outcomes.