Abstract LB-B010: Model-informed discovery and optimization of dose, regimen, and route for a binding-protein-resistant cytokine receptor agonist for oncology

Abstract Objective: To discover and optimize the dose, regimen, and route of a potent and selective binding-protein-resistant cytokine receptor antibody agonist for oncology by integrating PK/PD modeling with in vivo efficacy studies. Background: Cytokine receptor agonists represent a promising therapeutic class in oncology; however, endogenous binding proteins can limit their activity and complicate translational predictions. Our project aimed to overcome this challenge by developing a binding-protein-resistant receptor agonist and applying model-informed approaches to guide study design and dose optimization. Approach:Initial Modeling for PK Study Design: Competitor and benchmarking data were used to develop an initial PK model, enabling rational selection of dose levels for the exploratory mouse PK study. PK and Site-of-Action (SoA) Modeling: Using mouse PK study results and in vitro potency data, we developed a PK-SoA model incorporating physiological tissue compartments relevant to the expected mechanism of action. This model supported the prediction of three dose levels for the in vivo efficacy study, with primary focus on tumor growth control and secondary goals of identifying dose–response differentiation. PK–Tumor Growth Inhibition (TGI) Model and Dose Optimization: Upon completion of the mouse efficacy study, the data were integrated into a PK-TGI model. This refined model informed optimization of the dosing regimen, including route and schedule, to support a follow-up efficacy study and translational planning. Results: Model predictions were strongly aligned with experimental outcomes. The selected doses and routes achieved both primary and secondary endpoints in the mouse efficacy studies. The iterative modeling strategy enabled efficient dose and regimen refinement and provided quantitative support for progressing the molecule toward further development. Conclusion: Our integrated modeling framework was instrumental in guiding in vivo study design, optimizing dose and route selection, and accelerating the discovery of a novel binding-protein-resistant cytokine receptor agonist for oncology. This work demonstrates the value of model-informed approaches early in biologics discovery and their impact on translational readiness. Citation Format: Iman Samiee, Iman Samiee. Model-informed discovery and optimization of dose, regimen, and route for a binding-protein-resistant cytokine receptor agonist for oncology [abstract]. In: Proceedings of the AACR Immuno-Oncology Conference (AACR IO): Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2026 Feb 18-21; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2026;14(2 Suppl):Abstract nr LB-B010.