Abstract 7176: Optimization of T cell co-stimulatory agonists: A semi-mechanistic PKPD model integrating drug properties and tumor-immune interactions

Abstract Background: Despite the success of some immune checkpoint inhibitors in the treatment of cancer, T-cell co-stimulation has faced limited clinical success due to the intricacies around the optimal engagement of agonistic antibodies to co-stimulatory receptors, and the absence of biomarkers for patient selection. Understanding agonist-binding characteristics, pharmacokinetics, T cell subpopulation activation and differentiation kinetics, T cell life span in the tumor microenvironment, and the impact of T cell/tumor interactions on tumor growth kinetics is crucial for the positive clinical outcomes of T cell agonists. Currently there is not a suitable model to describe these complex interactions. Methods: We investigated the pharmacokinetic (PK), pharmacodynamics (PD), and anti-tumor efficacy of an anti-TNFR2 agonist, HFB3-1, and an anti-OX40 agonist, HFB10-1, in syngeneic tumor models. Flow cytometry was employed to profile post-treatment tumor infiltrating lymphocytes (TILs) in dissociated tumor tissues. In vitro binding affinity was determined using Biolayer Interferometry. A semi-mechanistic model, integrating PK, tumor growth, and immune interaction networks (interactions among Teff, Treg, and tumor cells), was developed in Berkeley Madonna (version 10.5.1). This was subsequently adapted to a human tumor growth model with T cell interaction networks to assess the impact of doses and dosing regimens on tumor growth inhibition. Results: A semi-mechanistic PKPD model was successfully developed incorporating drug properties including binding affinity, agonistic effect, ADCC activity, and pharmacokinetics, and tumor growth characteristics including tumor growth kinetics, TIL composition, interaction, proliferation, and turnover. This model was applied to investigate the effect of FcγR, dose, and dosing frequency on in vivo efficacy. Following incorporation of human PK and human tumor microenvironment properties into the model, tumor growth characteristics were simulated under varying human dosing regimens. Simulation results indicated that lower and less frequent doses optimize T cell co-stimulation for optimal anti-tumor activity. Conclusion: Our semi-mechanistic PKPD model elucidates the intricate interactions between T cell co-stimulatory agonists and the tumor-immune cells in the tumor microenvironment. The model advocates for pulsatile agonism as the optimal anti-tumor activity for T cell co-stimulation agents. This model serves as a valuable tool for guiding dose and dose regimen optimization for T cell co-stimulatory agonists in clinical development. Citation Format: Jinping Gan, William Hedrich, Yun-Yueh Lu, Wenhua Xu, Robert H. Andtbacka, Francisco Adrian, Liang Schweizer. Optimization of T cell co-stimulatory agonists: A semi-mechanistic PKPD model integrating drug properties and tumor-immune interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7176.