Abstract 4866: Modeling synergistic anti-PD-1 immunotherapy combinations with virtual tumor
Authors: Brightman Frances A. , Orrell David , Fernandez Eric , Chassagnole Christophe 
Published: August 2015

The tumor microenvironment and local immune response play a key role in cancer biology, and immune resistance is a feature of many cancers. For this reason, immunotherapy, which re-educates the immune system to recognize and target tumor cells, has recently developed into a highly active area of anticancer drug development. The field is dominated by immune-checkpoint blockers, which counteract the suppression of the immune response that is often observed in cancer. While early results for monotherapies are promising, with response rates exceeding those typically observed for chemotherapy, the real potential of immunotherapy agents could be in combining them together or with other anticancer treatments. However, there is currently no rational basis on which to select optimal dosing regimens or combination schedules, and hence there is a clear unmet need for predictive tools to aid this process[1,2].

Physiomics has developed a preclinical and a clinical ‘Virtual Tumour’ (“VT”) technology that can predict how a tumor will respond to drug exposure. 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 immune-checkpoint blockers, with a focus on anti-PD-1 agents.

The VT platform has been extended by the addition of an immunotherapy module, which has been developed and calibrated using qualitative and quantitative data taken from the literature. This module captures the mechanisms by which the immunotherapy activates the innate antitumor immune response and synergizes with other anticancer therapies. Through a preclinical case study also derived from the literature, we demonstrate that the extended VT can be applied to model the efficacy of immune-checkpoint blockers, both alone and in combination with other anticancer agents. Furthermore, we show that the VT accurately captures the synergy between an immunotherapy agent and conventional anticancer treatments.

Our enhanced VT capability represents the first step towards a ground-breaking tool for optimizing dosing and scheduling of immunotherapy, which is set to revolutionize cancer therapy in the near future, especially for patients with resistant disease.

1. Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252-264 (2012).

2. Lesterhuis, W. J., Haanen, J. B. A. G. & Punt, C. J. A. Cancer immunotherapy - revisited. Nat. Rev. Drug Discov. 10, 591-600 (2011).

Citation Format: Frances A. Brightman, David Orrell, Eric Fernandez, Christophe Chassagnole. Modeling synergistic anti-PD-1 immunotherapy combinations with virtual tumor. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4866. doi:10.1158/1538-7445.AM2015-4866