Leveraging a physiologically based quantitative translational modeling platform for designing bispecific T cell engagers for treatment of multiple myeloma

Bispecific T cell engager (TCE) is an emerging anti-cancer modality which redirects cytotoxic T cells to tumor cells expressing tumor-associated antigen (TAA) thereby forming immune synapses to exerts anti-tumor effects. Considering the protein engineering challenges in designing and optimizing size and pharmacokinetically acceptable TCEs in the context of the complexity of intercellular bridging between T cells and tumor cells, a physiologically relevant and clinically verified computational modeling framework is of crucial importance to guide the process to understand the protein engineering trade offs. In this study, we developed a quantitative, physiologically based computational framework to predict immune synapse formation for a variety of molecular format of TCEs in tumor tissue. Our model incorporated the molecular size dependent biodistribution using the two pore theory, extra-vascularization of T cells and hematologic cancer cells, mechanistic bispecific intercellular binding of TCEs and competitive inhibitory interaction by shed targets. The biodistribution of TCE was verified by positron emission tomography imaging of [89Zr]AMG211 (a carcinoembryonic antigen-targeting TCE) in patients. Parameter sensitivity analyses indicated that immune synapse formation was highly sensitive to TAA expression, degree of target shedding and binding selectivity to tumor cell surface TAA over shed target. Interestingly, the model suggested a "sweet spot" for TCEs CD3 binding affinity which balanced the trapping of TCE in T cell rich organs. The final model simulations indicated that the number of immune synapses is similar ([~]50/tumor cell) between two distinct clinical stage B cell maturation antigen (BCMA)-targeting TCEs, PF-06863135 in IgG format and AMG420 in BiTE format, at their respective efficacious dose in multiple myeloma patients, demonstrating the applicability of the developed computational modeling framework to molecular design optimization and clinical benchmarking for TCEs. This framework can be employed to other targets to provide a quantitative means to facilitate the model-informed best in class TCE discovery and development. Author summaryCytotoxic T cells play a crucial role in eliminating tumor cells. However, tumor cells develop mechanisms to evade from T cell recognition. Bispecific T cell engager (TCE) is designed to overcome this issue with bringing T cells to close proximity of tumor cells through simultaneous bivalent binding to both tumor-associated antigen and T cells. After successful regulatory approval of blinatumomab (anti-CD19 TCE), more than 40 TCEs are currently in clinical development with a variety of molecular size and protein formats. In this study, we developed a quantitative computational modeling framework for molecular design optimization and clinical benchmarking of TCEs. The model accounts for molecular size dependent biodistribution of TCEs to tumor tissue and other organs as well as following bispecific intercellular bridging of T cells and tumor cells. The model simulation highlighted the importance of binding selectivity of TCEs to tumor cell surface target over shed target. The model also demonstrated a good agreement in predicted immune synapse number for two distinct molecular formats of TCEs at their respective clinically efficacious dose levels, highlighting the usefulness of developed computational modeling framework for best in class TCE discovery and development.