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In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform

BACKGROUND: SARS-CoV-2 is a severe respiratory infection that infects humans. Its outburst entitled it as a pandemic emergence. To get a grip on this outbreak, specific preventive and therapeutic interventions are urgently needed. It must be said that, until now, there are no existing vaccines for c...

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Autores principales: Russo, Giulia, Pennisi, Marzio, Fichera, Epifanio, Motta, Santo, Raciti, Giuseppina, Viceconti, Marco, Pappalardo, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733700/
https://www.ncbi.nlm.nih.gov/pubmed/33308153
http://dx.doi.org/10.1186/s12859-020-03872-0
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author Russo, Giulia
Pennisi, Marzio
Fichera, Epifanio
Motta, Santo
Raciti, Giuseppina
Viceconti, Marco
Pappalardo, Francesco
author_facet Russo, Giulia
Pennisi, Marzio
Fichera, Epifanio
Motta, Santo
Raciti, Giuseppina
Viceconti, Marco
Pappalardo, Francesco
author_sort Russo, Giulia
collection PubMed
description BACKGROUND: SARS-CoV-2 is a severe respiratory infection that infects humans. Its outburst entitled it as a pandemic emergence. To get a grip on this outbreak, specific preventive and therapeutic interventions are urgently needed. It must be said that, until now, there are no existing vaccines for coronaviruses. To promptly and rapidly respond to pandemic events, the application of in silico trials can be used for designing and testing medicines against SARS-CoV-2 and speed-up the vaccine discovery pipeline, predicting any therapeutic failure and minimizing undesired effects. RESULTS: We present an in silico platform that showed to be in very good agreement with the latest literature in predicting SARS-CoV-2 dynamics and related immune system host response. Moreover, it has been used to predict the outcome of one of the latest suggested approach to design an effective vaccine, based on monoclonal antibody. Universal Immune System Simulator (UISS) in silico platform is potentially ready to be used as an in silico trial platform to predict the outcome of vaccination strategy against SARS-CoV-2. CONCLUSIONS: In silico trials are showing to be powerful weapons in predicting immune responses of potential candidate vaccines. Here, UISS has been extended to be used as an in silico trial platform to speed-up and drive the discovery pipeline of vaccine against SARS-CoV-2.
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spelling pubmed-77337002020-12-14 In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform Russo, Giulia Pennisi, Marzio Fichera, Epifanio Motta, Santo Raciti, Giuseppina Viceconti, Marco Pappalardo, Francesco BMC Bioinformatics Research BACKGROUND: SARS-CoV-2 is a severe respiratory infection that infects humans. Its outburst entitled it as a pandemic emergence. To get a grip on this outbreak, specific preventive and therapeutic interventions are urgently needed. It must be said that, until now, there are no existing vaccines for coronaviruses. To promptly and rapidly respond to pandemic events, the application of in silico trials can be used for designing and testing medicines against SARS-CoV-2 and speed-up the vaccine discovery pipeline, predicting any therapeutic failure and minimizing undesired effects. RESULTS: We present an in silico platform that showed to be in very good agreement with the latest literature in predicting SARS-CoV-2 dynamics and related immune system host response. Moreover, it has been used to predict the outcome of one of the latest suggested approach to design an effective vaccine, based on monoclonal antibody. Universal Immune System Simulator (UISS) in silico platform is potentially ready to be used as an in silico trial platform to predict the outcome of vaccination strategy against SARS-CoV-2. CONCLUSIONS: In silico trials are showing to be powerful weapons in predicting immune responses of potential candidate vaccines. Here, UISS has been extended to be used as an in silico trial platform to speed-up and drive the discovery pipeline of vaccine against SARS-CoV-2. BioMed Central 2020-12-14 /pmc/articles/PMC7733700/ /pubmed/33308153 http://dx.doi.org/10.1186/s12859-020-03872-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Russo, Giulia
Pennisi, Marzio
Fichera, Epifanio
Motta, Santo
Raciti, Giuseppina
Viceconti, Marco
Pappalardo, Francesco
In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform
title In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform
title_full In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform
title_fullStr In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform
title_full_unstemmed In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform
title_short In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform
title_sort in silico trial to test covid-19 candidate vaccines: a case study with uiss platform
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733700/
https://www.ncbi.nlm.nih.gov/pubmed/33308153
http://dx.doi.org/10.1186/s12859-020-03872-0
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