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


Journal

BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194

Informations de publication

Date de publication:
14 Dec 2020
Historique:
received: 29 10 2020
accepted: 09 11 2020
entrez: 14 12 2020
pubmed: 15 12 2020
medline: 19 12 2020
Statut: epublish

Résumé

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. 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. 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.

Sections du résumé

BACKGROUND 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 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 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.

Identifiants

pubmed: 33308153
doi: 10.1186/s12859-020-03872-0
pii: 10.1186/s12859-020-03872-0
pmc: PMC7733700
doi:

Substances chimiques

COVID-19 Vaccines 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

527

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Auteurs

Giulia Russo (G)

Department of Drug Sciences, University of Catania, 95125, Catania, Italy.

Marzio Pennisi (M)

Computer Science Institute, DiSIT, University of Eastern Piedmont, 15125, Alessandria, Italy.

Epifanio Fichera (E)

Etna Biotech S.R.L., 95121, Catania, Italy.

Santo Motta (S)

National Research Council of Italy, 00185, Rome, Italy.

Giuseppina Raciti (G)

Department of Drug Sciences, University of Catania, 95125, Catania, Italy. racitigi@unict.it.

Marco Viceconti (M)

Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, 40136, Bologna, Italy.

Francesco Pappalardo (F)

Department of Drug Sciences, University of Catania, 95125, Catania, Italy. francesco.pappalardo@unict.it.

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Classifications MeSH