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In Silico Trial for Tuberculosis Vaccine Development

Deliverables

Second dissemination report

Second dissemination report

Report on the modeling simulation framework able to simulate human immune system dynamics

The UNICT partner has developed through years a computational modeling infrastructure able to simulate the main features and dynamics of the immune system, named UISS (Universal Immune System Simulator). UISS is a multi-scale, multi-organ, three-dimensional agent based simulator of the immune system with an attached module able to simulate the dynamics of specific biological pathways at the molecular level. In this report we describe the extension and the enhancing of the UISS platform to take care of the human immune system at large scale, in order and include the organs that play important role in TB i.e., lungs and the lymph nodes around them.

Report on the tuning and refinement of the in silico models at time 12 and the creation of virtual patients for predictions

Report on the tuning and refinement of the in silico models at time 12 and the creation of virtual patients for predictions

Governance and meetings: the governance bodies are set. SC meetings organised: logistic, preparation of programme, invitation of experts

Governance and meetings: the governance bodies are set. Kick off meeting organised: logistic, preparation of programme, invitation of experts.

Report on the data collection and selection on TB disease and candidate vaccines/treatments

The purpose of this deliverable is to collect all publicly available data on the host-pathogen interaction for TB infection and disease as well as data from preclinical and clinical studies conducted with TB vaccine candidates in the pipeline.

First dissemination report

First dissemination report

Report on the creation of the subjects-specific and virtual patients libraries that will be used in the implementation of the in silico clinical trial

The purpose of this deliverable is to create a set of subjects-specific models with the aim to reproduce biological diversity of the subjects. Hence, the library of subjects created will be personalized with a “vector of features” that identifies a specific real patient with the final goal to to obtain a library of virtual M. tuberculosis infected patients that will be vaccinated and treated accordingly.

A coherent hierarchical Bayesian model encompassing the virtual and real data sources.

This deliverable brings real and virtual patients models together to relate in a coherent manner the outcome from the in silico and in vivo experiments in the (WP2).

Dissemination plan

Dissemination plan

An efficient computational implementation of the model, yielding information and measures of variability on the evaluation of the vaccines

In this deliverable we will present an efficient computational implementation of the model, yielding information and measures of variability on the evaluation of the vaccines

Report on the extensions implemented into the modeling simulation platform to reproduce the immune system – TBC – vaccines interactions and dynamics

The goal of this deliverable is to integrate into UISS modeling framework the capability to simulate the dynamics and the specific features of the tuberculosis mycobacterium infection. Moreover the UISS modeling framework will be enabled to simulate the artificial immunity induced by two vaccines i.e., Ruti vaccine and ID93 vaccine.

Public web site

Creation of a public web site (strituvad-isct.eu) .

Establishment of a web based information portal

A web site will be created that will contain links to all the significant Tuberculosis research sites, but more importantly, enabling the presentation of progress reports of the project to be seen publicly. At the same time, in a private channel each partner enabled with proper login and password could exchange confidential information in form of documents or data including reports, charts, updated financial plans, contact information; information about activities conducted by each group, protocols, Standard Operating Procedures (SOPs) IP-related documents etc.

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Publications

Predicting the artificial immunity induced by RUTI® vaccine against tuberculosis using universal immune system simulator (UISS)

Author(s): Marzio Pennisi, Giulia Russo, Giuseppe Sgroi, Angela Bonaccorso, Giuseppe Alessandro Parasiliti Palumbo, Epifanio Fichera, Dipendra Kumar Mitra, Kenneth B. Walker, Pere-Joan Cardona, Merce Amat, Marco Viceconti, Francesco Pappalardo
Published in: BMC Bioinformatics, 20/S6, 2019, ISSN 1471-2105
Publisher: BioMed Central
DOI: 10.1186/s12859-019-3045-5

How can we accelerate COVID-19 vaccine discovery?

Author(s): Giulia Russo; Valentina Di Salvatore; Filippo Caraci; Cristina Curreli; Marco Viceconti; Francesco Pappalardo
Published in: Expert Opinion on Drug Discovery, 3, 2021, ISSN 1746-0441
Publisher: Informa Healthcare
DOI: 10.1080/17460441.2021.1935861

Generation of digital patients for the simulation of tuberculosis with UISS-TB

Author(s): Miguel A. Juárez, Marzio Pennisi, Giulia Russo, Dimitrios Kiagias, Cristina Curreli, Marco Viceconti, Francesco Pappalardo
Published in: BMC Bioinformatics, 21/S17, 2020, ISSN 1471-2105
Publisher: BioMed Central
DOI: 10.1186/s12859-020-03776-z

Verification of an agent-based disease model of human Mycobacterium tuberculosis infection.

Author(s): Cristina Curreli; Francesco Pappalardo; Giulia Russo; Marzio Pennisi; Dimitrios Kiagias; Miguel A. Juárez; Marco Viceconti
Published in: International Journal for Numerical Methods in Biomedical Engineering, 37:7, 2021, Page(s) 1-15, ISSN 2040-7939
Publisher: John Wiley & Sons Ltd.
DOI: 10.1002/cnm.3470

A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets.

Author(s): Giulia Russo; Valentina Di Salvatore; Giuseppe Sgroi; Giuseppe Alessandro Parasiliti Palumbo; Pedro A Reche; Francesco Pappalardo
Published in: Briefings in Bioinformatics, 3, 2022, ISSN 1477-4054
Publisher: Oxford University Press
DOI: 10.1093/bib/bbab403

Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

Author(s): Dimitrios Kiagias; Giulia Russo; Giuseppe Sgroi; Francesco Pappalardo; Miguel A. Juárez
Published in: Frontiers in Medical Technology, 3, 2021, Page(s) 1-11, ISSN 2673-3129
Publisher: Lausanne: Frontiers Media S.A., 2019-
DOI: 10.3389/fmedt.2021.719380

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility

Author(s): "Musuamba, Flora T; Skottheim Rusten, Ine; Lesage, Raphaë lle; Russo, Giulia; Bursi, Roberta; Emili, Luca; Wangorsch, Gaby; Manolis, Efthymios; Karlsson, Kristin E; Kulesza, Alexander; Courcelles, Eulalie; Boissel, Jean-Pierre; Rousseau, Cé cile F; Voisin, Emmanuelle M; Alessandrello, Rossana; Curado, Nuno; Dall' ara, Enrico; Rodriguez, Blanca; Pappalardo, Francesco; Geris, Liesbet"
Published in: CPT: Pharmacometrics & Systems Pharmacology, Vol 10, Iss 8, Pp 804-825 (2021), 2, 2021, ISSN 2163-8306
Publisher: Nature Publishing Group
DOI: 10.1002/psp4.12669

Parallelisation strategies for agent based simulation of immune systems

Author(s): Mozhgan Kabiri Chimeh, Peter Heywood, Marzio Pennisi, Francesco Pappalardo, Paul Richmond
Published in: BMC Bioinformatics, 20/S6, 2019, ISSN 1471-2105
Publisher: BioMed Central
DOI: 10.1186/s12859-019-3181-y

In silico clinical trials: concepts and early adoptions

Author(s): Francesco Pappalardo, Giulia Russo, Flora Musuamba Tshinanu, Marco Viceconti
Published in: Briefings in Bioinformatics, 20/5, 2018, Page(s) 1699-1708, ISSN 1467-5463
Publisher: Oxford University Press
DOI: 10.1093/bib/bby043

Credibility of In Silico Trial Technologies—A Theoretical Framing

Author(s): Marco Viceconti, Miguel A. Juarez, Cristina Curreli, Marzio Pennisi, Giulia Russo, Francesco Pappalardo
Published in: IEEE Journal of Biomedical and Health Informatics, 24/1, 2020, Page(s) 4-13, ISSN 2168-2194
Publisher: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/jbhi.2019.2949888

Possible Contexts of Use for In Silico Trials Methodologies: A Consensus-Based Review

Author(s): Marco Viceconti; Luca Emili; Payman Afshari; Eulalie Courcelles; Cristina Curreli; Nele Famaey; Liesbet Geris; Marc Horner; Maria Cristina Jori; Alexander Kulesza; Axel Loewe; Michael Neidlin; Markus Reiterer; Cécile F. Rousseau; Giulia Russo; Simon J. Sonntag; Emmanuelle M. Voisin; Francesco Pappalardo
Published in: IEEE Journal of Biomedical and Health Informatics, 25:10, 2021, Page(s) 3977-3982, ISSN 2168-2194
Publisher: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/jbhi.2021.3090469

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

Author(s): Giulia Russo, Marzio Pennisi, Epifanio Fichera, Santo Motta, Giuseppina Raciti, Marco Viceconti, Francesco Pappalardo
Published in: BMC Bioinformatics, 21/S17, 2020, ISSN 1471-2105
Publisher: BioMed Central
DOI: 10.1186/s12859-020-03872-0

Moving forward through the in silico modeling of tuberculosis: a further step with UISS-TB

Author(s): Giulia Russo, Giuseppe Sgroi, Giuseppe Alessandro Parasiliti Palumbo, Marzio Pennisi, Miguel A. Juarez, Pere-Joan Cardona, Santo Motta, Kenneth B. Walker, Epifanio Fichera, Marco Viceconti, Francesco Pappalardo
Published in: BMC Bioinformatics, 21/S17, 2020, ISSN 1471-2105
Publisher: BioMed Central
DOI: 10.1186/s12859-020-03762-5

Toward computational modelling on immune system function

Author(s): Francesco Pappalardo, Marzio Pennisi, Pedro A. Reche, Giulia Russo
Published in: BMC Bioinformatics, 20/S6, 2019, ISSN 1471-2105
Publisher: BioMed Central
DOI: 10.1186/s12859-019-3239-x

Toward computational modelling on immune system function

Author(s): Francesco Pappalardo, Giulia Russo, Pedro A. Reche
Published in: BMC Bioinformatics, 21/S17, 2020, ISSN 1471-2105
Publisher: BioMed Central
DOI: 10.1186/s12859-020-03897-5

An agent based modeling approach for the analysis of tuberculosis – immune system dynamics

Author(s): Francesco Pappalardo, Giulia Russo, Marzio Pennisi, Giuseppe Sgroi, Giuseppe Alessandro Parasiliti Palumbo, Santo Motta, Epifanio Fichera
Published in: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018, Page(s) 1386-1392, ISBN 978-1-5386-5488-0
Publisher: IEEE
DOI: 10.1109/bibm.2018.8621355