Tarunendu Mapder, n/a: No financial relationships to disclose
Objectives: PD-1/PD-L1 blockade is a key cancer immunotherapy, rejuvenating T cells for long-term remissions. However, its effectiveness against solid tumors is often limited by the tumor microenvironment. Regulatory T cells (Tregs), expressing PD-1 contribute to tumor progression and may affect the response to PD-1/PD-L1 therapy1. To develop a Quantitative Systems Pharmacology (QSP) model that incorporates mechanistic interactions and predicts the therapeutic outcomes of combining nivolumab with bispecific T cell engagers (BiTEs) for cancer treatment. The model aims to integrate biological and pharmacological data from the tumor microenvironment (TME) to optimize treatment strategies and improve clinical outcomes.
Methods: The QSP model was constructed to include detailed representations of the PD-1/PD-L1 pathway2, T cell activation, immunological synapse formation and the reversal of immune suppression and evasion3, ultimately leading to anti-tumor cytotoxicity in the TME. The model predicts pharmacodynamic (PD) effects, such as regulatory T cell depletion4, cytotoxic T cell activation, and tumor cell killing. It was calibrated and validated using clinical data from nivolumab studies to ensure predictive accuracy and can incorporate both preclinical and clinical data for the bispecific molecules.
Results: The QSP model demonstrated that combining nivolumab with bispecific T cell engagers enhances effector T cell activation and proliferation, driven by the blockade of the PD-1/PD-L1 interaction by nivolumab. BiTEs facilitate immunological synapse formation and mitigate immunosuppression mediated by regulatory T cells, enabling targeted tumor cell killing through cytotoxic granule release and cytokine secretion. The model predicted greater anti-tumor efficacy for the combination therapy compared to either agent alone. Key factors influencing treatment response include differential PD-L1 expression, an immunosuppressive TME and immune cell infiltration. Additionally, the model provided insights into optimizing dosing regimens and predicting patient-specific responses.
Conclusions: The QSP model of nivolumab in combination with bispecific T cell engagers offers a comprehensive framework for understanding and predicting the pharmacokinetics and pharmacodynamics of this therapeutic strategy. By integrating biological, pharmacological, clinical, and preclinical data, the model enhances our ability to optimize treatment strategies, improve clinical outcomes, and personalize therapy for patients with various types of cancer. The findings support the potential of this combination therapy to overcome tumor immune evasion mechanisms and provide a robust anti-tumor response. Ongoing research and model refinement will further elucidate the full potential and guide the optimal use of this combination therapy.
Citations: [1] Zhu WM, Middleton MR. Combination therapies for the optimisation of Bispecific T-cell Engagers in cancer treatment. Immunotherapy Advances. 2023 Jan 1;3(1):ltad013. [2] Anbari, S., Wang, H., Zhang, Y., Wang, J., Pilvankar, M., Nickaeen, M., Hansel, S. and Popel, A.S., 2023. Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager. Frontiers in Pharmacology, 14, p.1163432. [3] Ma H, Wang H, Sové RJ, Wang J, Giragossian C, Popel AS. Combination therapy with T cell engager and PD-L1 blockade enhances the antitumor potency of T cells as predicted by a QSP model. Journal for immunotherapy of cancer. 2020 Aug 27;8(2):e001141. [4] Serrano A, Zalba S, Lasarte JJ, Troconiz IF, Riva N, Garrido MJ. Quantitative Approach to Explore Regulatory T Cell Activity in Immuno-Oncology. Pharmaceutics. 2024 Nov 15;16(11):1461.
