Integrating computational simulation and high-throughput screening for the development of robust ultra-high concentration formulation of IgG1 antibody

High-viscosity monoclonal antibody (mAb) formulations impede injection, complicate manufacturing, and may affect drug stability and patient outcomes. Nevertheless, these issues can be effectively mitigated through the judicious selection of appropriate excipients and meticulous formulation optimization. In this study, we present an innovative approach that integrates computational simulation with high-throughput screening to enhance the efficiency of high-concentration mAb formulation development. Specifically, we introduce <i>in silico</i> modeling to predict mAb developability based on structural characteristics and reveal moderate risks in stability, solubility, viscosity, as well as hotspots in the complementarity-determining regions. Furthermore, by predicting protein-protein interactions and protein-excipient interactions and leveraging UNCLE, high-throughput protein stability analyzer, we successfully selected appropriate viscosity reducers, stabilizers, and buffer systems for drug formulation. The formulation confirmation studies demonstrated that the optimal formulation exhibited outstanding stability under various conditions, including high-temperature, accelerated conditions, long-term storage, light exposure, photosensitivity, oscillation, and freeze-thaw cycles, which can ensure the storage and transportation of the product. This approach not only addresses the current challenges associated with high-viscosity mAb formulations, but also offers a framework for future formulation optimization efforts in the biopharmaceutical industry.