Abstract 2098: Quantitative modeling as a systematic approach for drug combination evaluation in immuno-oncology (IO)

Abstract

Abstract Objectives: Multiple strategies for eliciting and enhancing antitumor immunity are currently being evaluated. However, a more systematic approach is needed, to analyze and translate such results into clinic practice, while rationally designing combination therapies based on mechanistic understanding of potential synergistic effects (1). The objective of this study was to provide predictive simulations, via a quantitative systems pharmacology (QSP) model, capable of categorizing the types of synergistic effects that may arise from IO agent combinations, across realistic baseline conditions prevailing in the tumor microenvironment (TME). Methods: The QSP model was developed and qualified using in vivo mouse data published in the literature and from internal research. The following pharmacologic modalities were calibrated: PD-L1/PD-1, CTLA-4, CXCR2, A2AR inhibition, and OX40 agonism. Various combination scenarios were simulated for these modalities, at four baseline conditions prevailing in different syngeneic murine models. Results: Simulated efficacy results were highly dependent on the baseline conditions. Several combinations and monotherapies were effective only within a specific baseline TME phenotype. These findings were in agreement with experimental data (2). At baselines with higher levels of MDSC, best results were obtained for a PD-L1 mAb combined with either an OX40 agonist or a CXCR2 inhibitor, with 90% of complete responders. Anti (PD-L1 + CTLA-4) combinations showed high efficacy in Treg prevalence, but only moderate efficacy (22% complete responders), under baseline conditions of a dual (Treg + MDSC) immunosuppressive TME. Conclusion: This work provides a quantitative modeling framework to comparatively predict responses to IO combinations, based on realistic baseline conditions prevailing in the TME, while revealing mechanistic interactions underlying such responses in IO combinations.