An Fc-Competent Anti-Human TIGIT Blocking Antibody Ociperlimab (BGB-A1217) Elicits Strong Immune Responses and Potent Anti-Tumor Efficacy in Pre-Clinical Models

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Abstract

TIGIT (T-cell immunoglobulin and ITIM domain) has emerged as a promising target in cancer immunotherapy. It is an immune “checkpoint” inhibitor primarily expressed on activated T cells, NK cells and Tregs. Engagement of TIGIT to its ligands PVR and PVR-L2 leads to inhibitory signaling in T cells, promoting functional exhaustion of tumor-infiltrating T lymphocytes. Here, we described the pre-clinical characterization of Ociperlimab (BGB-A1217), a novel humanized IgG1 anti-TIGIT antibody (mAb), and systemically evaluated the contribution of Fc functions in the TIGIT mAb-mediated anti-tumor activities. BGB-A1217 binds to the extracellular domain of human TIGIT with high affinity (KD = 0.135 nM) and specificity, and efficiently blocks the interaction between TIGIT and its ligands PVR or PVR-L2. Cell-based assays show that BGB-A1217 significantly enhances T-cell functions. In addition, BGB-A1217 induces antibody dependent cellular cytotoxicity (ADCC) against Treg cells, activates NK cells and monocytes, and removes TIGIT from T cell surfaces in an Fc-dependent manner, In vivo, BGB-A1217, either alone or in combination with an anti-PD-1 mAb elicits strong immune responses and potent anti-tumor efficacy in pre-clinical models. Moreover, the Fc effector function is critical for the anti-tumor activity of BGB-A1217 in a syngeneic human TIGIT-knock-in mouse model. The observed anti-tumor efficacy is associated with a pharmacodynamic change of TIGIT down-regulation and Treg reduction. These data support the selection of BGB-A1217 with an effector function competent Fc region for clinical development for the treatment of human cancers.

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Chen, X., Xue, L., Ding, X., Zhang, J., Jiang, L., Liu, S., … Zhang, T. (2022). An Fc-Competent Anti-Human TIGIT Blocking Antibody Ociperlimab (BGB-A1217) Elicits Strong Immune Responses and Potent Anti-Tumor Efficacy in Pre-Clinical Models. Frontiers in Immunology, 13. https://doi.org/10.3389/fimmu.2022.828319

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