VaccImm is a tool that simulates growth of a solid tumor treated with peptide vaccination therapy. It has an underlying agent-based model framework that stems from the C-ImmSim architecture. For the first time, the amino acid sequence of the cancer epitope, of the injected antigen and the MHC-genotype are incorporated into the calculations.

Peptide vaccination in cancer therapy is a promising alternative to conventional methods. However, the parameters for this personalized treatment are difficult to access experimentally. In this respect, in silico models can help to narrow down the parameter space or to explain certain phenomena at a systems level. Herein, we develop two empirical interaction potentials specific to B- cell and T-cell receptor complexes and validate their applicability in comparison to a more general potential. The interaction potentials are applied to the model VaccImm which simulates the immune response against solid tumors under peptide vaccination therapy. This multi-agent system is derived from another immune system simulator (C-ImmSim) and now includes a module that enables the amino acid sequence of immune receptors and their ligands to be taken into account. The multi-agent approach is combined with approved methods for prediction of major histocompatibility complex (MHC)-binding peptides and the newly developed interaction potentials. In the analysis, we critically assess the impact of the different modules on the simulation with VaccImm and how they influence each other. In addition, we explore the reasons for failures in inducing an immune response by examining the activation states of the immune cell populations in detail.

In summary, the present work introduces immune-specific interaction potentials and their application to the agent-based model VaccImm which simulates peptide vaccination in cancer therapy.

The system is available online at

A-L. Woelke, J. von Eichborn, M.S. Murgueitio, C.L. Worth, F. Castiglione, R. Preissner. Development of Immune-Specific Interaction Potentials and Their Application in the Multi-Agent-System VaccImm. PLoS ONE 6(8): e23257 (2011). doi:10.1371/journal.pone.0023257