Abstract 4531: Systems pharmacology to predict cellular biomarkers and optimize mono- and combination-therapy regimens: Focusing on immune checkpoint targets PD-1, PD-L1 and CTLA-4
Authors: Oleg Milberg, Chang Gong, Bing Wang, Paolo Vicini, Rajesh Narwal, Lorin Roskos, Aleksander Popel
Journal: Cancer Research (2017)

Abstract Cancer immunotherapy focuses on stimulating and promoting the immune system to recognize and eliminate cancer cells, with several FDA approvals in recent years. However, identifying patients best suited for specific immune therapies, and determining optimal treatment regimens continue to be a clinical challenge. Using a molecular-detailed computational systems pharmacology model to identify cellular biomarkers and optimize regimens, we may be able to predict the efficacy of regimens in specific patient populations, and expedite drug development for cancer treatment. We developed a cell/receptor-based multi-compartment systems pharmacology model focusing on the immune response against a growing tumor, with the intent to test the effects of immune checkpoint inhibitors against PD-1, PD-L1 and CTLA-4 administered as mono- and combination therapies. Additionally, the model also allows for testing of other immuno-therapies, such as adoptive cell therapies, which can be combined with the checkpoint inhibitors. The model was designed and developed using the SimBiology plug-in in MATLAB. Simulations were performed with parameters that define the immune response at particular tumor stages of melanoma and NSCLC. All results were qualitatively and quantitatively compared to experimental pre-clinical and clinical data for model validation, or used for the generation of predictions suitable for further experimental testing. In silico, we have identified that administrations of the prescribed doses of 1-10 mg/kg of anti-CLTA-4 (based on binding kinetics) effectively saturates the receptors on the T cells, and promotes both an extended life span of the antigen presenting cells (APCs), and the maximum attainable activation levels of the effector T cells. The model further predicts that the effectiveness of anti-CTLA-4 therapy is limited by the immunogenicity of the system (i.e., the antigen intensity level and number of APCs presenting the antigens) in a monotonic fashion. Furthermore, injecting activated APCs without therapy would show a temporary tumor response and a subsequent recovery by the tumor to its original growth trajectory, while raising the antigen intensity had a sustained effect on tumor response. Other simulations indicate that, despite the lack of apparent tumor response, a sustained immune attack may be ongoing in the body; however, the immune activity is proportionally limited by the tumor and regulatory cells. Lastly, several dose-responses and clinical trials were simulated for both combination and monotherapies, and correlated with published clinical trial data. Future work will focus on uncovering the cellular biomarkers responsible for such results, experimentally validating them, as well as simulating optimal combination treatment regimens for future evaluation. Citation Format: Oleg Milberg, Chang Gong, Bing Wang, Paolo Vicini, Rajesh Narwal, Lorin Roskos, Aleksander Popel. Systems pharmacology to predict cellular biomarkers and optimize mono- and combination-therapy regimens: Focusing on immune checkpoint targets PD-1, PD-L1 and CTLA-4 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4531. doi:10.1158/1538-7445.AM2017-4531