Abstract 104: Mechanistic insights and dose optimization for AZD3458, a novel selective PI3Kg immuno-modulator, using a quantitative systems approach
Authors: Pablo Morentin Gutierrez, Yuri Kosinsky, Kirill Peskov, Ivan Azarov, Lulu Chu, Veronika Voronova, Martin Johnson, Yingxue Chen, Larissa Carnevalli, Danielle Carroll, Michele Moschetta, Teresa Klinowska, Gabriel Helmlinger
Journal: Cancer Research (2019)

Abstract Objectives: PI3Kγ inhibition re-polarizes macrophages to an immuno-stimulatory phenotype, thereby activating a T-cell mediated tumor immune response. AZD3458 is a highly selective PI3Kγ inhibitor. Administration of AZD3458 in combination with checkpoint inhibitors such as α-PD-(L)1 antibodies had greater anti-tumor effects (TGI 26-86%) than checkpoint inhibitor alone in 4T1, LLC, CT-26 and MC-38 syngeneic mouse models. In these, AZD3458 remodeled the tumor microenvironment (TME), reducing immunosuppressive markers (e.g in 4T1 model there was a 20% decrease in total macrophages and 50% decrease in markers of immune suppression like CD206 by flow cytometry) and promoting cytotoxic T-cell activation (e.g. in CT-26 model there was a 2-fold increase in gzmB mRNA). We developed a predictive quantitative systems pharmacology (QSP) model, to quantitatively simulate TME effects and delineate mechanistic principles underlying AZD3458 and α-PD-(L)1 synergistic effects. Methods: The QSP model captures mechanistic, molecular and cellular interactions between PI3Kγ inhibition and checkpoint inhibitors, together with the pharmacokinetics acting on the respective targets. Features such as PI3Kγ inhibition-dependent tumor-associated macrophages, protein expression of immunosuppressive markers, reduction of MDSC activation and promotion of cytotoxic T-cell activation were included in the model. These immuno-changes were then linked to tumor cell death, resulting in macroscopic dynamic effects on tumor size. Some model parameters were taken from the literature and internal studies; some were estimated using NLME modeling of tumor size data. Results: The model adequately described individual and population tumor size patterns. Inter-animal variability was described using a random effect on a parameter related to the ability of T cells to infiltrate the tumor in response to systemic antigen. Additionally, the model incorporated in one quantitative framework data from 4 syngeneic tumors capturing respective changes in TME conditions. Simulations for the various treatments supported the mechanistic interpretation of the observed AZD3458 and α-PD-(L)1 synergistic effects. The model was further used to simulate treatment scenarios, to infer optimal dosing and scheduling for the combination and given underlying TME conditions. Conclusions: This study provides quantitative mechanistic insights into the links between PI3Kγ inhibition and anti-tumor immune responses, supporting our understanding of how AZD3458 may alleviate brakes in a myeloid immuno-suppressive TME and revert resistance to immunotherapy. This mechanistic understanding is critical when proceeding with dose escalation in an early clinical trial setting, as it allows to contextualize any potential compound-induced immuno-modulation in patients, for given doses and schedules. Citation Format: Pablo Morentin Gutierrez, Yuri Kosinsky, Kirill Peskov, Ivan Azarov, Lulu Chu, Veronika Voronova, Martin Johnson, Yingxue Chen, Larissa Carnevalli, Danielle Carroll, Michele Moschetta, Teresa Klinowska, Gabriel Helmlinger. Mechanistic insights and dose optimization for AZD3458, a novel selective PI3Kg immuno-modulator, using a quantitative systems approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 104.