Scalable Antigen-Antibody Binding Affinity Landscape: A Case Study with ENHERTU

Optimization of binding affinities for antibody-drug conjugates (ADCs) is crucial for their therapeutic efficacy and specificity, with most ADCs engineered to achieve equilibrium dissociation constants in the range of 0.1 to 1 nM. However, there is a lack of published data delineating the optimal binding affinity range that ensures improved therapeutic outcomes for ADCs. Therefore, this study integrates structural biophysics within a scalable in silico workflow to generate antigen-antibody binding affinity landscapes, focusing on ENHERTU, a monoclonal antibody employed in the treatment of HER2-positive breast cancer. Leveraging computational techniques, including homology structural modeling and structural biophysics-based binding affinity calculations, this article presents high-accuracy structural and intermolecular binding affinity data for the Her2-Trastuzumab-Pertuzumab complex. Beyond the design of Her2-targeting ADCs with enhanced efficacy and specificity, this scalable antigen-antibody binding affinity landscape offers a technically feasible workflow for the high-throughput generation of synthetic structural and biophysical data with reasonable accuracy. Combined with artificial intelligence (AI) algorithms, it is conceivable that this scalable in silico approach constitutes a catalyst for an AI-driven paradigm shift in the discovery and design of antibodies and ADCs with improved efficacy and specificity. Significance: This study presents a structural biophysics-based search engine tailored for ranking antigen-antibody binding affinities, with a specific focus on ENHERTU, a key monoclonal antibody in HER2-positive breast cancer treatment. By integrating advanced computational techniques like homology structural modeling and Kd calculations, the research generates accurate structural and binding affinity data. This scalable approach not only enhances the design of next-generation antibody-drug conjugates (ADCs) but also provides a practical method for generating synthetic structural and biophysical data efficiently. Combined with artificial intelligence algorithms, this scalable in silico approach aims to catalyze a paradigm shift in the discovery and design of antibodies and ADCs with improved efficacy and specificity.