Generation of digital patients for the simulation of tuberculosis with UISS-TB.
Agent based model
In silico patient
Sequential sampling
Tuberculosis
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
14 Dec 2020
14 Dec 2020
Historique:
received:
16
09
2020
accepted:
22
09
2020
entrez:
14
12
2020
pubmed:
15
12
2020
medline:
5
1
2021
Statut:
epublish
Résumé
The STriTuVaD project, funded by Horizon 2020, aims to test through a Phase IIb clinical trial one of the most advanced therapeutic vaccines against tuberculosis. As part of this initiative, we have developed a strategy for generating in silico patients consistent with target population characteristics, which can then be used in combination with in vivo data on an augmented clinical trial. One of the most challenging tasks for using virtual patients is developing a methodology to reproduce biological diversity of the target population, ie, providing an appropriate strategy for generating libraries of digital patients. This has been achieved through the creation of the initial immune system repertoire in a stochastic way, and through the identification of a vector of features that combines both biological and pathophysiological parameters that personalise the digital patient to reproduce the physiology and the pathophysiology of the subject. We propose a sequential approach to sampling from the joint features population distribution in order to create a cohort of virtual patients with some specific characteristics, resembling the recruitment process for the target clinical trial, which then can be used for augmenting the information from the physical the trial to help reduce its size and duration.
Sections du résumé
BACKGROUND
BACKGROUND
The STriTuVaD project, funded by Horizon 2020, aims to test through a Phase IIb clinical trial one of the most advanced therapeutic vaccines against tuberculosis. As part of this initiative, we have developed a strategy for generating in silico patients consistent with target population characteristics, which can then be used in combination with in vivo data on an augmented clinical trial.
RESULTS
RESULTS
One of the most challenging tasks for using virtual patients is developing a methodology to reproduce biological diversity of the target population, ie, providing an appropriate strategy for generating libraries of digital patients. This has been achieved through the creation of the initial immune system repertoire in a stochastic way, and through the identification of a vector of features that combines both biological and pathophysiological parameters that personalise the digital patient to reproduce the physiology and the pathophysiology of the subject.
CONCLUSIONS
CONCLUSIONS
We propose a sequential approach to sampling from the joint features population distribution in order to create a cohort of virtual patients with some specific characteristics, resembling the recruitment process for the target clinical trial, which then can be used for augmenting the information from the physical the trial to help reduce its size and duration.
Identifiants
pubmed: 33308156
doi: 10.1186/s12859-020-03776-z
pii: 10.1186/s12859-020-03776-z
pmc: PMC7733699
doi:
Substances chimiques
Antibodies, Bacterial
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
449Subventions
Organisme : H2020 European Research Council
ID : 777123
Références
Cancer Res. 2010 Oct 15;70(20):7755-63
pubmed: 20924100
PLoS One. 2011;6(10):e26523
pubmed: 22028894
Front Immunol. 2019 Apr 30;10:894
pubmed: 31114572
Cell Immunol. 2006 Dec;244(2):137-40
pubmed: 17442286
Bioinformatics. 2008 Aug 1;24(15):1715-21
pubmed: 18556669
Bioinformatics. 2014 Jul 1;30(13):1884-91
pubmed: 24603984
J Immunol Methods. 2015 Dec;427:6-12
pubmed: 26343337
BMC Bioinformatics. 2017 Dec 28;18(Suppl 16):544
pubmed: 29297294
Bioinformatics. 2016 Sep 1;32(17):2672-80
pubmed: 27162187
Phys Life Rev. 2016 Jul;17:112-3
pubmed: 27185314
Biomed Res Int. 2014;2014:902545
pubmed: 25143952
BMC Bioinformatics. 2010 Oct 15;11 Suppl 7:S13
pubmed: 21106120
Nature. 2014 Jul 3;511(7507):99-103
pubmed: 24990750
BMC Bioinformatics. 2019 Dec 10;20(Suppl 6):504
pubmed: 31822272