Abstract LB-392: Optimizing dual immuno-oncology combinations with a virtual tumor

The purpose of the study was to investigate the application of a ‘virtual tumor9 (‘VT9) technology to optimizing the dosing and scheduling of dual immuno-oncology combinations in order to maximize the immunopotentiation and anti-tumor efficacy.The tumor microenvironment and local immune response play a key role in cancer biology, and immune resistance is a feature of many cancers. Immune checkpoints suppress the ability of the immune system to destroy cancer cells, and are a clear therapeutic target. However, response rates for such therapies are generally low. While anti-tumor efficacy can be improved by combining agents that target immune checkpoints with selected conventional anti-cancer therapies[1], the potential of immune-checkpoint blockade could be further unlocked through combination instead with other immunotherapies[2]. However, dosing and scheduling of immunotherapy combinations need to be optimized to take full advantage of a limited window for immunopotentiation[1]. Modelling can be used to deliver insights to help optimize such combination immunotherapies. We have developed a preclinical and a clinical VT technology that can predict how a tumor will respond to drug exposure[3]. This integrated PK/PD simulation platform can be used to optimize drug dosing and scheduling, and to design new combination therapies. The VT technology integrates pharmacokinetic and pharmacodynamic effects and models the way individual cells behave within a tumor population. These agent-based methods are particularly suitable for modeling not only tumor cells, but also other cell populations - such as those involved in the immune response - and also interactions between cells. Here we describe our recent development and application of the VT technology for modeling preclinical efficacy of novel immuno-oncology combinations.Building upon previous work, in which the VT platform was extended by the addition of a module that captured the synergistic interaction of PD(L)-1 blockade with conventional anticancer therapies[4], we have further expanded the model to allow simulation of immuno-checkpoint block with other immunotherapies. This module captures the mechanisms by which immunotherapies activate the innate antitumor immune response, either through immune-checkpoint block or by targeting immunosuppression by MDSC within the tumor microenvironment. Through a preclinical case study derived from the literature[5] and additional data available in the public domain, we demonstrate that the extended VT can be applied to simulate the efficacy of PD-1 blockade combined with an anti-C5a agent, and show that the combination treatment delays tumor progression. Furthermore, we demonstrate that the model can be applied to optimize the timing of the two therapies in order to maximize overall efficacy. Our enhanced VT capability represents a key step towards a strategic tool for optimizing dosing and scheduling of immuno-oncology combinations. [1] Ott, PA et al., Journal for ImmunoTherapy of Cancer5, 16 (2017). [2]. Galon, J. & Bruni, D. Nature Reviews Drug Discovery (2019) [3]. Orrell, D. & Fernandez, E. Innovations in Pharmaceutical Technology 60-62 (2011). [4]. Brightman, FA et al., Abstract 4866. Cancer Res75, 4866-4866 (2015). [5] Ajona, D. et al. Cancer Discov7, 694-703 (2017). Citation Format: Fernando Ortega, Frances A. Brightman, Christophe Chassagnole. Optimizing dual immuno-oncology combinations with a virtual tumor [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-392.