title: A computational model to predict the immune system activation by citrus-derived vaccine adjuvants.
abstract: Vaccines represent the most effective and cost-efficient weapons against a wide range of diseases. Nowadays new generation vaccines based on subunit antigens reduce adverse effects in high risk individuals. However, vaccine antigens are often poor immunogens when administered alone. Adjuvants represent a good strategy to overcome such hurdles, indeed they are able to: enhance the immune response; allow antigens sparing; accelerate the specific immune response; and increase vaccine efficacy in vulnerable groups such as newborns, elderly or immuno-compromised people. However, due to safety concerns and adverse reactions, there are only a few adjuvants approved for use in humans. Moreover, in practice current adjuvants sometimes fail to confer adequate stimulation. Hence, there is an imperative need to develop novel adjuvants that overcome the limitations of the currently available licensed adjuvants.
  
  
  
  We developed a computational framework that provides a complete pipeline capable of predicting the best citrus-derived adjuvants for enhancing the immune system response using, as a target disease model, influenza A infection. In silico simulations suggested a good immune efficacy of specific citrus-derived adjuvant (Beta Sitosterol) that was then confirmed in vivoAvailability: The model is available visiting the following URL: http://vaima.dmi.unict.it/AdjSim
  
  
  
  francesco.pappalardo@unict.it; fp@francescopappalardo.net.
authors.affiliation: Department of Drug Sciences, University of Catania.
authors.forename: Francesco
authors.initials: F
authors.lastname: Pappalardo
authors.affiliation: Etna Biotech S.R.L, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1.
authors.forename: Epifanio
authors.initials: E
authors.lastname: Fichera
authors.affiliation: Parco Scientifico E Tecnologico Della Sicilia, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1.
authors.forename: Nicoletta
authors.initials: N
authors.lastname: Paparone
authors.affiliation: Parco Scientifico E Tecnologico Della Sicilia, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Lombardo
authors.affiliation: Department of Mathematics and Computer Science, University of Catania.
authors.forename: Marzio
authors.initials: M
authors.lastname: Pennisi
authors.affiliation: Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy.
authors.forename: Giulia
authors.initials: G
authors.lastname: Russo
authors.affiliation: Department of Drug Sciences, University of Catania.
authors.forename: Marco
authors.initials: M
authors.lastname: Leotta
authors.affiliation: Parco Scientifico E Tecnologico Della Sicilia, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1.
authors.forename: Francesco
authors.initials: F
authors.lastname: Pappalardo
authors.affiliation: Department of Pharmaceutical Sciences, University of Milan, Milan, Italy.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Pedretti
authors.affiliation: Softeco Sismat, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: De Fiore
authors.affiliation: Department of Mathematics and Computer Science, University of Catania.
authors.forename: Santo
authors.initials: S
authors.lastname: Motta
mesh_terms: D000276:Adjuvants, Immunologic; D000368:Aged; D000941:Antigens; D002957:Citrus; D005544:Forecasting; D006801:Humans; D007107:Immune System; D016867:Immunocompromised Host; D007231:Infant, Newborn; D007252:Influenza Vaccines; D066230:Patient-Specific Modeling
chemical_list: D000276:Adjuvants, Immunologic; D000941:Antigens; D007252:Influenza Vaccines