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

Optimization of binding affinities for antibody-drug conjugates (ADCs) is inextricably linked to their therapeutic efficacy and specificity, where the majority of ADCs are engineered to achieve equilibrium dissociation constants (Kd values) in the range of 10-9 to 10-10 M. Yet, there is a paucity of published data delineating the optimal binding affinity or its range that ensures improved therapeutic outcomes for ADCs. This study addresses this issue by integrating structural biophysics within a scalable in silico workflow to generate antigen-antibody binding affinity landscapes, with a focus on Trastuzumab, a monoclonal antibody employed in the treatment of HER2-positive breast cancer. By leveraging high-throughput computational techniques, including homology structural modeling and structural biophysics-based Kd calculations, this research puts forward a set of high-accuracy structural and intermolecular binding affinity data for Her2-Trastuzumab-Pertuzumab (PDB entry 6OGE). Beyond the design of Her2-targeting ADCs with enhanced efficacy and specificity, this scalable antigen-antibody binding affinity landscape also offers a technically feasible workflow for the high-throughput generation of synthetic structural and biophysical data with reasonable accuracy. Overall, in combination with artificial intelligence (e.g., deep learning) algorithms, this synthetic data approach aims to catalyze a paradigm shift in the discovery and design of antibodies and ADCs with improved efficacy and specificity. SIGNIFICANCEWith Trastuzumab as an example, this study presents a scalable computational biophysical generation of antigen-antibody binding affinity landscapes, serving two purposes: design of Her2-targeting ADCs with enhanced efficacy and specificity and continued accumulation of synthetic structural biophysics data for the development of useful AI-based drug discovery and design model in future. This scalable approach is broadly applicable to databases such as Protein Data Bank.