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

被引:28
|
作者
Russo, Giulia [1 ]
Pennisi, Marzio [2 ]
Fichera, Epifanio [3 ]
Motta, Santo [4 ]
Raciti, Giuseppina [1 ]
Viceconti, Marco [5 ]
Pappalardo, Francesco [1 ]
机构
[1] Univ Catania, Dept Drug Sci, I-95125 Catania, Italy
[2] Univ Piemonte Orientale, Comp Sci Inst, DiSIT, I-15125 Alessandria, Italy
[3] Etna Biotech SRL, I-95121 Catania, Italy
[4] Natl Res Council Italy, I-00185 Rome, Italy
[5] Alma Mater Studiorum Univ Bologna, Dept Ind Engn, I-40136 Bologna, Italy
关键词
Agent-based model; Human monoclonal antibodies; In silico trials; SARS-CoV-2; Vaccines; DENDRITIC CELLS; SARS-COV-2; PREDICT; MODELS;
D O I
10.1186/s12859-020-03872-0
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
BackgroundSARS-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.ResultsWe 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.ConclusionsIn 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.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] COVID-19 Vaccines
    Afzal, Saira
    Nasir, Mehreen
    ANNALS OF KING EDWARD MEDICAL UNIVERSITY LAHORE PAKISTAN, 2021, 27 (01): : 1 - 3
  • [22] Withania for COVID-19 An in-silico study
    不详
    CURRENT SCIENCE, 2021, 121 (04): : 469 - 470
  • [23] Effectiveness of COVID-19 vaccines against Omicron and Delta hospitalisation, a test negative case-control study
    Stowe, Julia
    Andrews, Nick
    Kirsebom, Freja
    Ramsay, Mary
    Bernal, Jamie Lopez
    NATURE COMMUNICATIONS, 2022, 13 (01)
  • [24] Effectiveness of COVID-19 vaccines against Omicron and Delta hospitalisation, a test negative case-control study
    Julia Stowe
    Nick Andrews
    Freja Kirsebom
    Mary Ramsay
    Jamie Lopez Bernal
    Nature Communications, 13
  • [26] COVID-19 Pandemic vaccines are about to face the real test
    Cohen, Jon
    SCIENCE, 2020, 368 (6497) : 1295 - 1296
  • [27] Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV): Designing Master Protocols for Evaluation of Candidate COVID-19 Therapeutics
    LaVange, Lisa
    Adam, Stacey J.
    Currier, Judith S.
    Higgs, Elizabeth S.
    Reineck, Lora A.
    Hughes, Eric A.
    Read, Sarah W.
    ANNALS OF INTERNAL MEDICINE, 2021, 174 (09) : 1293 - +
  • [28] Trial participants' rights after authorisation of COVID-19 vaccines
    Dal-Re, Rafael
    Orenstein, Walter
    Caplan, Arthur L.
    LANCET RESPIRATORY MEDICINE, 2021, 9 (04): : E30 - E31
  • [29] Optimal control of the spatial allocation of COVID-19 vaccines: Italy as a case study
    Lemaitre, Joseph Chadi
    Pasetto, Damiano
    Zanon, Mario
    Bertuzzo, Enrico
    Mari, Lorenzo
    Miccoli, Stefano
    Casagrandi, Renato
    Gatto, Marino
    Rinaldo, Andrea
    PLOS COMPUTATIONAL BIOLOGY, 2022, 18 (07)
  • [30] Competitive Intelligence in Pharmacovigilance-A Case Study on Two Covid-19 Vaccines
    Holle, L. V.
    DRUG SAFETY, 2022, 45 (10) : 1134 - 1134