Pennisi, M., Juarez, M.A. orcid.org/0000-0002-5128-0976, Russo, G. et al. (2 more authors) (2020) Generation of digital patients for the simulation of tuberculosis with UISS-TB. In: Yoo, I., Bi, J. and Hu, X., (eds.) 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 18-21 Nov 2019, San Diego CA, USA. Institute of Electrical and Electronics Engineers (IEEE) , pp. 2163-2167.
Abstract
EC funded STriTuVaD project aims to test, through a phase IIb clinical trial, two of the most advanced therapeutic vaccines against tuberculosis. In parallel, we have extended the Universal Immune System Simulator to include all relevant determinants of such clinical trial, to establish its predictive accuracy against the individual patients recruited in the trial, to use it to generate digital patients and predict their response to the HRT being tested, and to combine them to the observations made on physical patients using a new in silico-augmented clinical trial approach that uses a Bayesian adaptive design. This approach, where found effective could drastically reduce the cost of innovation in this critical sector of public healthcare. One of the most challenging task is to develop a methodology to reproduce biological diversity of the subjects that have to be simulated, i.e., provide an appropriate strategy for the generation of libraries of digital patients. This has been achieved through the the creation of the initial immune system repertoire in a stochastic way, and though the identification of a 'vector of features' that combines both biological and pathophysiological parameters that personalize the digital patient to reproduce the physiology and the pathophysiology of the subject.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Generation In Patients; Clinical Trials; Biological Parameters; Phase IIb Clinical Trial; Cohort Of Patients; Multidrug-resistant; Bayesian Model; Multi-agent; Active Phase; Alveolar Macrophages; Input Vector; Duration Of Therapy; Multidrug-resistant Tuberculosis; Rest Of The Features |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 777123 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Jun 2024 10:02 |
Last Modified: | 20 Jun 2024 11:16 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/bibm47256.2019.8983100 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213564 |