Evaluation of Combination Strategies for the A2AR Inhibitor AZD4635 Across Tumor Microenvironment Conditions via a
Veronika Voronova1, Kirill Peskov1,2, Yuri Kosinsky1
1M&S Decisions LLC, Moscow, Russia.
Background:
Adenosine receptor type 2 (A2AR) inhibitor, AZD4635, has been shown to reduce immunosuppressive adenosine effects within the tumor microenvironment (TME) and to enhance the efficacy of checkpoint inhibitors across various syngeneic models. This study aims at investigating anti-tumor activity of AZD4635 alone and in combination with an anti-PD-L1-specific antibody (anti-PD-L1 mAb) across various TME conditions and at identifying, via mathematical quantitative modeling, a therapeutic combination strategy to further improve treatment efficacy.
Methods:
The model is represented by a set of ordinary differential equations capturing: 1) antigen-dependent T cell migration into the tumor, with subsequent proliferation and differentiation into effector T cells (Teff), leading to tumor cell lysis; 2) downregulation of processes mediated by A2AR or PD-L1, as well as other immunosuppressive mechanisms; 3) A2AR and PD-L1 inhibition by, respectively, AZD4635 and anti-PD-L1 mAb. Tumor size dynamics data from CT26, MC38, and MCA205 syngeneic mice treated with vehicle, anti-PD-L1 mAb, AZD4635, or their combination were used to inform model parameters. Between-animal and between-study variabilities (BAV, BSV) in treatment efficacy were quantified using a non-linear mixed-effects methodology.
Results:
The model reproduced individual and cohort trends in tumor size dynamics for all considered treatment regimens and experiments. BSV and BAV were explained by variability in T cell-to-immunosuppressive cell (ISC) ratio; BSV was additionally driven by differences in intratumoral adenosine content across the syngeneic models. Model sensitivity analysis and model-based preclinical study simulations revealed therapeutic options enabling a potential increase in AZD4635-driven efficacy; e.g., adoptive cell transfer or treatments affecting adenosine-independent immunosuppressive pathways.
Conclusions:
The proposed integrative modeling framework quantitatively characterized the mechanistic activity of AZD4635 and its potential added efficacy in therapy combinations, across various immune conditions prevailing in the TME. Such a model may enable further investigations, via simulations, of mechanisms of tumor resistance to treatment and of AZD4635 combination optimization strategies.
Insights
The adenosine receptor type 2 (A2AR) inhibitor AZD4635, combined with anti-PD-L1 therapy, shows anti-tumor activity by reducing immunosuppression. Mathematical modeling identified strategies to improve efficacy in various tumor microenvironment conditions.
Area of Science:
- Immunology
- Pharmacology
- Mathematical Biology
Background:
- Adenosine receptor type 2 (A2AR) inhibitor AZD4635 reduces tumor microenvironment (TME) immunosuppression.
- AZD4635 enhances checkpoint inhibitor efficacy in preclinical models.
- This study investigates AZD4635 alone and combined with anti-PD-L1 mAb.
Purpose of the Study:
- To evaluate the anti-tumor activity of AZD4635 and anti-PD-L1 mAb combination therapy.
- To explore efficacy across diverse TME conditions.
- To identify optimal therapeutic strategies using mathematical modeling.
Main Methods:
- Ordinary differential equations modeled T cell dynamics, immunosuppression, and drug inhibition.
- Tumor size data from CT26, MC38, and MCA205 syngeneic mice were used.
- Non-linear mixed-effects modeling quantified between-animal and between-study variability.
Main Results:
- The model accurately reproduced tumor size dynamics for all treatment groups.
- Variability in efficacy was linked to T cell-to-immunosuppressive cell ratios and adenosine levels.
- Simulations suggested strategies like adoptive cell transfer to improve AZD4635 efficacy.
Conclusions:
- An integrative modeling framework quantitatively assessed AZD4635's mechanistic activity and combination potential.
- The model can explore tumor resistance mechanisms and optimize combination strategies.
- This approach aids in understanding and improving cancer immunotherapy.
