Francesco Pappalardo3, Epifanio Fichera2, Nicoletta Paparone3, Alessandro Lombardo3, Marzio Pennisi4, Giulia Russo5, Marco Leotta1, Francesco Pappalardo3, Alessandro Pedretti6, Francesco De Fiore7, Santo Motta4. 1. Department of Drug Sciences, University of Catania. 2. Etna Biotech S.R.L, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1. 3. Parco Scientifico E Tecnologico Della Sicilia, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1. 4. Department of Mathematics and Computer Science, University of Catania. 5. Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy. 6. Department of Pharmaceutical Sciences, University of Milan, Milan, Italy. 7. Softeco Sismat, Italy.
Abstract
MOTIVATION: 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. RESULTS: 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 CONTACT: francesco.pappalardo@unict.it; fp@francescopappalardo.net.
MOTIVATION: 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. RESULTS: 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 CONTACT: francesco.pappalardo@unict.it; fp@francescopappalardo.net.
Authors: Giulia Russo; Giuseppe Sgroi; Giuseppe Alessandro Parasiliti Palumbo; Marzio Pennisi; Miguel A Juarez; Pere-Joan Cardona; Santo Motta; Kenneth B Walker; Epifanio Fichera; Marco Viceconti; Francesco Pappalardo Journal: BMC Bioinformatics Date: 2020-12-14 Impact factor: 3.169