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    AI and Multiplex Cloning Redefine Aantibody Discovery

      The Latest Technological Advances in Antibody Discovery

      1. Single B Cell Isolation and Sequencing

      The isolation and sequencing of single B cells can help us gain a deeper understanding of the diversity and specificity of B cells. Through this method, we can clarify the relationship between the sequence and the cell, thus discovering antibodies with special properties. These antibodies may have high affinity for targets, can effectively counteract pathogens, or participate in the body's own immune response.


      2. Library Design for Display

      The library design of phage display and other display technologies is another method for antibody discovery, which significantly enhances the diversity of antibody structures and sequences and provides strong support for developing antibodies against difficult targets. Compared with the original library, libraries derived from immune donors can produce antibodies with higher expression levels, stronger affinity, and specificity for target antigens, injecting new vitality into the development of antibody drugs. At the same time, the generation of synthetic libraries allows us to control the antibody sequence entirely through de novo design. This innovation not only helps eliminate adverse sequences, but also significantly shortens the time for downstream engineering and optimization processes, improving R&D efficiency. The latest advances in library design mainly lie in sequence optimization. By precisely controlling the structure and properties of antibodies, such as stability and solubility, the developability of antibodies can be improved.


      3. Computer Simulation

      In recent years, computer simulation technology has been increasingly used in antibody research and is often used to screen, select, and optimize potential antibody sequences after discovery activities. This method can help us design antibody properties (such as binding sites, binding kinetics, etc.) more accurately, thereby enhancing antibody potency. In addition, it can predict the interaction between antibodies and the biological environment, aggregation, and stability. Therefore, the application of computer simulation is expected to reduce reliance on expensive and time-consuming trial-and-error experiments, promoting efficient progress in antibody research and development.


      Main Challenges in Antibody Discovery

      1. Difficult to Target Antigens and Novel Drug Targets

      Difficult-to-target antigens and novel drug targets are major challenges in antibody discovery and antibody drug design. Transmembrane protein receptors and other targets account for 20-30% of the human proteome and 60% of current drug targets. However, due to the complex multi-structure, large conformational variation, low immunogenicity, and lack of soluble forms of transmembrane receptors, it is difficult to develop functional antibodies against these receptors.


      2. Applicability of Animal Models

      In the process of antibody discovery, the use of in vivo models is often limited by the applicability of animal models, which can cause trouble for early development and subsequent testing. Especially when the host's own tissues express target antigens, the phenomenon of immune tolerance may hinder the stimulation of effective immune responses, thereby increasing research difficulty. In addition, cross-reactivity issues need to be particularly concerned in preclinical research and safety testing. Due to the similarity of protein structures, sequence homology, or expression patterns between different species, antibodies developed in animal models may accidentally cross-react with similar antigens.


      3. Immunogenicity

      Immunogenicity is also a problem that needs to be taken seriously in the process of antibody research and development, especially when using animal models to produce antibodies for humans, it may bring serious safety and efficacy issues. Due to differences in post-translational modifications, antibody structures, and amino acid sequences, it may stimulate immune responses against the therapeutic drugs themselves, thereby producing anti-drug antibodies, and may even lead to adverse reactions.


      4. Obtaining a Diversified Antibody Library

      Obtaining a diversified and functionally related antibody library is a major technical challenge in antibody discovery. Taking B-cell sequencing as an example, although 2-3% of the B-cells in the peripheral blood come from the bone marrow and spleen, this part of the cells only accounts for a very small part of the entire B-cell population. Among these circulating B cells, the proportion that can differentiate into plasma cells secreting antibodies is even smaller. Therefore, relying solely on B-cell sequencing to explore the antibody library will greatly limit our ability to fully access the entire antibody library.


      5. Discovering Antibodies with Specific Functions

      In antibody discovery, our ideal goal is to obtain diversified antibodies with different functions and capable of targeting multiple epitopes. Although the process of antibody discovery may produce binders against target antigens, it is indeed a challenging task to discover antibodies with specific functions (such as agonists or receptor blockers).


      From antibody discovery to final application, identifying antibodies is just a small part of the whole process. Although some antibodies show superior functionality, specificity, and affinity, they may not always be ideal candidates in the actual development process. Problems with antibody stability, such as degradation, aggregation in solution or during administration, and the complexity of formulation, are all problems that need to be overcome when successfully developing antibody candidate drugs. In addition, many antibody drug designs face a series of challenges, such as cost-effective characteristic analysis, developability assessment, and efficacy and safety prediction in the early stages.


      How to Reduce Risks Associated with Antibody Discovery

      1. Target Validation

      Strict target validation is crucial as it helps us accurately find the most promising therapeutic direction and concentrate resources on research. We should prioritize targets that have been thoroughly validated and have a strong biological rationale, not only to increase the chances of success but also to effectively reduce the risk of investing in targets with limited therapeutic potential.


      2. Combination of Multiple Strategies and Modes

      There is no "one-size-fits-all" method for antibody discovery. Strategies that have proven effective in other studies may not necessarily replicate the same successful results. Adopting a variety of strategies, such as applying multiple immunization methods in different species at the same time, can increase the likelihood of success. Also, adopting multiple discovery modes, such as polyclonal antibody sequencing, B-cell sequencing in conjunction with computer modeling, can also increase the chances of success. This not only integrates the advantages of various methods but also accelerates the early discovery stage of therapeutic development. In situations where computer simulation analysis and AI-driven antibody discovery run parallel to in vivo methods, we are expected to predict and prioritize potential antibody candidates. This method reduces the need for extensive experimental testing and simplifies the discovery process.


      3. Polyclonal Technology

      The use of REpAb polyclonal antibody sequencing provides a new avenue for antibody discovery, accelerating the discovery process. Once immunization is completed, we can quickly start the discovery process, collecting highly enriched and functionally significant antibodies from serum. Subsequently, we use proteomics-based methods to sequence these antibodies, and further carry out characteristic analysis and biophysical analysis through recombinant expression. This method eliminates the need for cell sorting, immortalization, and purification steps before characteristic analysis.


      4. Characterization of Antibodies

      Regular characterization of antibodies has proven to be a very effective strategy throughout the antibody discovery and development process. During the early stages of discovery, advanced technology is used to evaluate the biophysical and functional characteristics of antibodies, as well as cross-reactivity and performance, to ensure that the failure of candidate drugs in cell line development and formulation processes does not result in cost losses.


      5. High Throughput Screening

      High-throughput antibody screening assays are valuable for quickly evaluating a large number of potential antibodies and can screen for candidate antibodies with the desired characteristics. High-throughput methods for kinetic analysis using surface plasmon resonance (SPR) can quickly analyze the kinetic and affinity characteristics of a series of antibodies, thereby selecting antibodies with different affinities, binding rates, and dissociation rates to enrich the diversity of antibody candidates. Similarly, epitope grouping and epitope localization experiments through hydrogen-deuterium exchange mass spectrometry (HDX-MS) can provide information about different binding epitopes, providing strong support for downstream selection. Early cell line development work is often seen as "finding a needle in a haystack". To increase the chances of success, we need to use rapid analysis technology to strengthen sequence generation and PTM (such as glycosylation features) analysis. In this process, real-time peptide mapping and glycosylation analysis based on mass spectrometry are undoubtedly ideal tools.


      By combining these strategies, researchers can simplify and improve the efficiency of antibody discovery work, maximize the chances of successfully identifying therapeutic candidate antibodies, and lay a solid foundation for the smooth start of early production.


      Impact of Artificial Intelligence (AI) and Machine Learning (ML) on Antibody Discovery

      AI and ML have played a crucial role in enhancing all aspects of antibody discovery. In terms of target identification, AI-driven algorithms can analyze a large amount of biological data, such as proteomics, genomics, and disease pathways, to identify potential therapeutic targets. The rise of generative AI provides unprecedented opportunities for entirely new antibody designs, which are entirely generated based on algorithms and do not rely on experimental data or biological knowledge. However, due to the limited training data, the complexity of antibody structure and function, and the biological diversity and variability of the immune system, the effectiveness of this method has not yet been fully verified.


      Impact of Polyclonal Antibody Sequencing on Antibody Discovery

      Proteomics and mass spectrometry-based antibody discovery effectively address several challenges faced by other discovery technologies.


      REpAb polyclonal antibody sequencing is an important method for antibody discovery using proteomics and mass spectrometry. One of its main advantages is that it can use the natural immune system to expand the search for antibodies. Biologically, peripheral blood samples are a key bridge connecting germinal centers and bone marrow. Using proteomics-based methods to analyze serum immunoglobulins can directly analyze antibodies secreted from the bone marrow. These antibodies are often difficult to capture by traditional methods such as B cell sequencing because the differentiation characteristics of B cells make it extremely difficult to obtain samples representing the overall immune diversity.


      In addition, polyclonal sequencing is not restricted by the B cells obtained or the phage library and can analyze the entire antibody library at any given time point. Therefore, this method can analyze different antibody libraries and identify candidate antibodies with the desired function and specificity that may be missed using other methods.


      REpAb polyclonal antibody sequencing technology can also identify biologically significant antibody candidates. The immunoglobulins present in the serum have been extensively selected for affinity maturation and somatic hypermutation by the host immune system, producing functional antibodies. Since these immunoglobulins are produced in large quantities by natural hosts, concerns about production yields, post-translational modifications, stability, immunogenicity, and tendency to aggregate are usually minimal. However, in order to ensure the actual application effect of the antibody, comprehensive biophysical and performance characterization of it is still indispensable.


      REpAb polyclonal antibody sequencing technology adopts a de novo sequencing strategy, which can directly parse the amino acid sequence from the MS/MS spectrum without relying on any known protein or DNA sequence information. This method breaks the species limit and shows its unique advantages, especially when preparing antibodies against conserved proteins using different hosts. Antibodies undergo a wealth of somatic hypermutation during the process of affinity maturation in vivo, so sequence diversity is extremely high. In the absence of reference sequences, de novo sequencing of antibodies becomes the most effective method. In addition, when other methods (such as hybridoma technology) are not feasible for certain species, REpAb polyclonal antibody sequencing technology provides a new idea.

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