Generation of digital patients for the simulation of tuberculosis with UISS-TB
- PDF / 6,100,341 Bytes
- 8 Pages / 595.276 x 790.866 pts Page_size
- 81 Downloads / 128 Views
ESEARCH
Open Access
Generation of digital patients for the simulation of tuberculosis with UISS‑TB Miguel A. Juárez1* , Marzio Pennisi2, Giulia Russo3, Dimitrios Kiagias1, Cristina Curreli4, Marco Viceconti4 and Francesco Pappalardo3
From 3rd International Workshop on Computational Methods for the Immune System Function (CMISF 2019) San Diego, CA, USA. 18-21 November 2019 *Correspondence: [email protected] 1 School of Mathematics and Statistics, University of Sheffield, Sheffield S3 7RH, UK Full list of author information is available at the end of the article
Abstract 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: 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: 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. Keywords: Agent based model, In silico patient, Sequential sampling, Tuberculosis
Background It is estimated that one quarter of the world population is infected with (TB). Although the disease is preventable and treatable, about one and a half million people die annually from it, effectively placing TB as the first infectious cause of death. Due to person to person infection and treatment mismanagement, (MDR) TB continues to emerge, increasing the complexity in treatment and thus potentially worsening the transmission rate. There is a growing awareness that TB can be effectively fought only working globally, starting from countries like India, where the infection is endemic [1]. © The Author(s) 2020. Open Access This 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 articl
Data Loading...
