title: Modeling the competition between lung metastases and the immune system using agents.
abstract: BACKGROUND
  The Triplex cell vaccine is a cancer cellular vaccine that can prevent almost completely the mammary tumor onset in HER-2/neu transgenic mice. In a translational perspective, the activity of the Triplex vaccine was also investigated against lung metastases showing that the vaccine is an effective treatment also for the cure of metastases. A future human application of the Triplex vaccine should take into account several aspects of biological behavior of the involved entities to improve the efficacy of therapeutic treatment and to try to predict, for example, the outcomes of longer experiments in order to move faster towards clinical phase I trials. To help to address this problem, MetastaSim, a hybrid Agent Based - ODE model for the simulation of the vaccine-elicited immune system response against lung metastases in mice is presented. The model is used as in silico wet-lab. As a first application MetastaSim is used to find protocols capable of maximizing the total number of prevented metastases, minimizing the number of vaccine administrations.
  
  
  RESULTS
  The model shows that it is possible to obtain "in silico" a 45% reduction in the number of vaccinations. The analysis of the results further suggests that any optimal protocol for preventing lung metastases formation should be composed by an initial massive vaccine dosage followed by few vaccine recalls.
  
  
  CONCLUSIONS
  Such a reduction may represent an important result from the point of view of translational medicine to humans, since a downsizing of the number of vaccinations is usually advisable in order to minimize undesirable effects. The suggested vaccination strategy also represents a notable outcome. Even if this strategy is commonly used for many infectious diseases such as tetanus and hepatitis-B, it can be in fact considered as a relevant result in the field of cancer-vaccines immunotherapy. These results can be then used and verified in future "in vivo" experiments, and their outcome can be used to further improve and refine the model.
authors.affiliation: Department of Mathematics and Computer Science, University of Catania, V,le A, Doria 6, Catania, Italy. mpennisi@dmi.unict.it
authors.forename: Marzio
authors.initials: M
authors.lastname: Pennisi
authors.forename: Francesco
authors.initials: F
authors.lastname: Pappalardo
authors.forename: Ariannna
authors.initials: A
authors.lastname: Palladini
authors.forename: Giordano
authors.initials: G
authors.lastname: Nicoletti
authors.forename: Patrizia
authors.initials: P
authors.lastname: Nanni
authors.forename: Pier-Luigi
authors.initials: PL
authors.lastname: Lollini
authors.forename: Santo
authors.initials: S
authors.lastname: Motta
mesh_terms: D000818:Animals; D019496:Cancer Vaccines / Q000276:immunology*; D019295:Computational Biology / Q000379:methods*; D005260:Female; D007107:Immune System / Q000276:immunology*; D008175:Lung Neoplasms / Q000517:prevention & control / Q000556:secondary*; D008325:Mammary Neoplasms, Experimental / Q000473:pathology* / Q000628:therapy; D051379:Mice; D008807:Mice, Inbred BALB C; D008822:Mice, Transgenic; D008954:Models, Biological; D009362:Neoplasm Metastasis / Q000517:prevention & control; D015203:Reproducibility of Results; D014611:Vaccination
chemical_list: D019496:Cancer Vaccines