REVOLUTIONIZING ANTIBODY DISCOVERY INDUSTRY WITH HIGHLY EFFICIENT AND ACCURATE AI-BASED EPITOPE-SPECIFIC ANTIBODY DE NOVO DESIGN WORKFLOW

Abstract Background and significance The global antibody drug market is worth over $200 billion in 2021 and is expected to reach $380 billion by 2030. Antibody discovery is one of the most critical steps that determine the crucial properties of antibody drugs, such as efficacy, safety, and developability. Traditional methods based on mouse immunization have many drawbacks limiting drug discovery, which include long time periods, high costs, inability to target function-specific epitopes, unsuitable for low immunogenic and difficult-to-prepare antigens, the need to sacrifice mice, the need for further humanization to reduce immunogenicity, and so on. Here we report an antibody de novo design computational workflow that utilizes high-quality internally produced antibody data and advanced AI models. Using this workflow, we can de novo design antibodies that bind to user-specified functional epitopes with high affinity and specificity. Compared with classical wet-lab methods, the entire process is shortened from several months to several days and suitable for low immunogenicity and difficult-to-prepare antigens. It is particularly noteworthy that due to the use of humanized mouse-generated antibodies (Renlite bearing common light chain from Biocytogen) as training data for AI models, the designed antibodies have a high degree of humanization and good developability, effectively avoiding issues such as ADA and aggregation in subsequent processes. Methods First, with the help of Renlite, we comprehensively combined mouse immunization, B cell sorting with FACS, NGS single-cell sequencing, and bioinformatics analysis to internally generate a large amount of high-quality antibody sequence data. Second, we developed AI models for antigen-specific antibody selection and epitope prediction (bioRxiv, 2022: 2022.12. 22.521634.) to mine antigen-specific antibodies and corresponding antigen epitopes in the data. Based on the processed high-quality data, we trained an affinity prediction model that can accurately predict whether an antigen epitope and antibody sequence pair can bind to each other. Besides, using the sequence data, we trained an antibody sequence pre-training language model (bioRxiv, 2023: 2023.01. 19.524683.), which can generate high-quality antibody sequences to simulate the antibodies produced by mouse immunization. Finally, integrating the above AI models, we established an antibody de novo design computational workflow to simulate the biological process of antibody generation and affinity maturation in the mouse immune system, which can be seen as a “DigitalMouse”. Results In a test case, 1 million antibodies were designed aiming at binding to specific epitope of an antigen. 10 antibodies were selected and expressed. Binding affinity was determined using BLI. Two antibodies out of 10 had KD of 194 nM and 336 nM, respectively, with a concentration dependent signal increase on BLI. These antibodies have great potential as the starting point of candidate molecules for further in vitro, in vivo experimental validation and clinical trials. Conclusions The AI-based antibody de novo design workflow will revolutionize the antibody discovery industry paradigm, greatly shorten the antibody discovery phase, reduce R&D costs, and expand antibody discovery to more antigen targets that are difficult with animal immunization. The computational workflow will have a profound impact on the entire biopharmaceutical industry.