Predicting the artificial immunity induced by RUTI (R) vaccine against tuberculosis using universal immune system simulator (UISS)
Por:
Pennisi, M, Russo, G, Sgroi, G, Bonaccorso, A, Palumbo, GAP, Fichera, E, Mitra, DK, Walker, KB, Cardona, PJ, Amat, M, Viceconti, M and Pappalardo, F
Publicada:
10 dic 2019
Ahead of Print:
10 dic 2019
Resumen:
Background: Tuberculosis (TB) represents a worldwide cause of mortality (it infects one third of the world's population) affecting mostly developing countries, including India, and recently also developed ones due to the increased mobility of the world population and the evolution of different new bacterial strains capable to provoke multidrug resistance phenomena. Currently, antitubercular drugs are unable to eradicate subpopulations of Mycobacterium tuberculosis (MTB) bacilli and therapeutic vaccinations have been postulated to overcome some of the critical issues related to the increase of drug-resistant forms and the difficult clinical and public health management of tuberculosis patients. The Horizon 2020 EC funded project "In Silico Trial for Tuberculosis Vaccine Development" (STriTuVaD) to support the identification of new therapeutic interventions against tuberculosis through novel in silico modelling of human immune responses to disease and vaccines, thereby drastically reduce the cost of clinical trials in this critical sector of public healthcare.
Results: We present the application of the Universal Immune System Simulator (UISS) computational modeling infrastructure as a disease model for TB. The model is capable to simulate the main features and dynamics of the immune system activities i.e., the artificial immunity induced by RUTI (R) vaccine, a polyantigenic liposomal therapeutic vaccine made of fragments of Mycobacterium tuberculosis cells (FCMtb). Based on the available data coming from phase II Clinical Trial in subjects with latent tuberculosis infection treated with RUTI (R) and isoniazid, we generated simulation scenarios through validated data in order to tune UISS accordingly to STriTuVaD objectives. The first case simulates the establishment of MTB latent chronic infection with some typical granuloma formation; the second scenario deals with a reactivation phase during latent chronic infection; the third represents the latent chronic disease infection scenario during RUTI (R) vaccine administration.
Conclusions: The application of this computational modeling strategy helpfully contributes to simulate those mechanisms involved in the early stages and in the progression of tuberculosis infection and to predict how specific therapeutical strategies will act in this scenario. In view of these results, UISS owns the capacity to open the door for a prompt integration of in silico methods within the pipeline of clinical trials, supporting and guiding the testing of treatments in patients affected by tuberculosis.
Filiaciones:
Pennisi, M:
Univ Catania, Dept Math & Comp Sci, I-95125 Catania, Italy
Russo, G:
Univ Catania, Dept Drug Sci, I-95125 Catania, Italy
Sgroi, G:
Univ Catania, Dept Math & Comp Sci, I-95125 Catania, Italy
Bonaccorso, A:
Univ Catania, Dept Drug Sci, I-95125 Catania, Italy
Palumbo, GAP:
Univ Catania, Dept Math & Comp Sci, I-95125 Catania, Italy
Fichera, E:
Etna Biotech Srl, I-95121 Catania, Italy
Mitra, DK:
All India Inst Med Sci, Dept Transplant Immunol & Immunogenet, New Delhi 110029, India
Walker, KB:
TB Vaccine Initiat TBVI, NL-8219 Lelystad, Netherlands
:
Archivel Farma SL, Badalona 08916, Spain
Univ Autonoma Barcelona, Fundacio Inst Germans Trias & Pujol IGTP, Expt TB Unit UTE, Badalona, Spain
Ctr Invest Biomed Red CIBER Enfermedades Respirat, Madrid, Spain
Amat, M:
Archivel Farma SL, Badalona 08916, Spain
Viceconti, M:
Univ Bologna, Dept Ind Engn, Alma Mater Studiorum, I-40136 Bologna, Italy
Pappalardo, F:
Univ Catania, Dept Drug Sci, I-95125 Catania, Italy
gold, Green Published
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