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- Title
Leveraging a physiologically-based quantitative translational modeling platform for designing B cell maturation antigen-targeting bispecific T cell engagers for treatment of multiple myeloma.
- Authors
Yoneyama, Tomoki; Kim, Mi-Sook; Piatkov, Konstantin; Wang, Haiqing; Zhu, Andy Z. X.
- Abstract
Bispecific T cell engagers (TCEs) are an emerging anti-cancer modality that redirects cytotoxic T cells to tumor cells expressing tumor-associated antigens (TAAs), thereby forming immune synapses to exert anti-tumor effects. Designing pharmacokinetically acceptable TCEs and optimizing their size presents a considerable protein engineering challenge, particularly given the complexity of intercellular bridging between T cells and tumor cells. Therefore, a physiologically-relevant and clinically-verified computational modeling framework is of crucial importance 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 formats of TCEs in tumor tissues. Our model incorporates a molecular size-dependent biodistribution using the two-pore theory, extravasation of T cells and hematologic cancer cells, mechanistic bispecific intercellular binding of TCEs, and competitive inhibitory interactions by shed targets. The biodistribution of TCEs 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 TAAs over shed targets. Notably, the model suggested a "sweet spot" for TCEs' CD3 binding affinity, which balanced the trapping of TCEs in T-cell-rich organs. The final model simulations indicated that the number of immune synapses is similar (~55/tumor cell) between two distinct clinical stage B cell maturation antigen (BCMA)-targeting TCEs, PF-06863135 in an IgG format and AMG420 in a BiTE format, at their respective efficacious doses in multiple myeloma patients. This result demonstrates the applicability of the developed computational modeling framework to molecular design optimization and clinical benchmarking for TCEs, thus suggesting that this framework can be applied to other targets to provide a quantitative means to facilitate model-informed best-in-class TCE discovery and development. Author summary: Cytotoxic T cells play a crucial role in eliminating tumor cells. However, tumor cells develop mechanisms to evade T cell recognition. Bispecific T cell engagers (TCEs) are designed to overcome this issue by bringing T cells into close proximity of tumor cells through simultaneous bispecific binding to both tumor-associated antigens and T cells. After successful regulatory approval of catumaxomab (an anti-EpCAM TCE, withdrawn in 2017) and blinatumomab (an anti-CD19 TCE), more than 40 TCEs are currently in clinical development with a variety of molecular sizes 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 tissues and other organs as well as subsequent bispecific intercellular bridging of T cells and tumor cells. The model simulation highlighted the importance of binding selectivity of TCEs to tumor cell surface targets over shed targets. 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, thus highlighting the usefulness of a developed computational modeling framework for best-in-class TCE discovery and development.
- Subjects
MULTIPLE myeloma; T cells; CYTOTOXIC T cells; SYNAPSES; B cells; CELLULAR recognition; POSITRON emission tomography; PROTEIN engineering
- Publication
PLoS Computational Biology, 2022, Vol 18, Issue 7, p1
- ISSN
1553-734X
- Publication type
Article
- DOI
10.1371/journal.pcbi.1009715