Evidence map›Paper›PMID 33308139›Full record

ArticleBMC bioinformatics2020

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

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 and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.3field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
  2. Drug resistance mechanisms inFrontiers in pharmacology · 2026
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  10. TowardsFrontiers in bioengineering and biotechnology · 2021
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 8 institutions in 4 countries.

Giulia RussoDepartment of Drug Sciences, University of Catania, 95125, Catania, Italy.
Giuseppe SgroiDepartment of Mathematics and Computer Science, University of Catania, 95125, Catania, Italy.
Giuseppe Alessandro Parasiliti PalumboDepartment of Mathematics and Computer Science, University of Catania, 95125, Catania, Italy.
Marzio PennisiComputer Science Institute, DiSIT, University of Eastern Piedmont, 15121, Alessandria, Italy.
Miguel A JuarezSchool of Mathematics and Statistics, University of Sheffield, Sheffield, S3 7RH, UK.
Pere-Joan CardonaArchivel Farma, S.L., 08916, Badalona, Spain.
Santo MottaNational Research Council of Italy, 00185, Rome, Italy.
Kenneth B WalkerTuBerculosis Vaccine Initiative (TBVI), Lelystad, 8219, The Netherlands.
Epifanio FicheraEtna Biotech S.r.l., 95121, Catania, Italy.
Marco VicecontiDepartment of Industrial Engineering, University of Bologna, 40136, Bologna, Italy.
Francesco PappalardoDepartment of Drug Sciences, University of Catania, 95125, Catania, Italy. francesco.pappalardo@unict.it.
University of Catania · ITEtna Biotech (Italy) · ITInstitut d'Investigació en Ciències de la Salut Germans Trias i Pujol · ESNational Research Council · ITTuBerculosis Vaccine Initiative · NLUniversità degli Studi del Piemonte Orientale “Amedeo Avogadro” · ITUniversity of Bologna · ITUniversity of Sheffield · GB

Funding

Horizon 2020 Framework Programme 777123
6 · The paper itself

Abstract

backgroundIn 2018, about 10 million people were found infected by tuberculosis, with approximately 1.2 million deaths worldwide. Despite these numbers have been relatively stable in recent years, tuberculosis is still considered one of the top 10 deadliest diseases worldwide. Over the years, Mycobacterium tuberculosis has developed a form of resistance to first-line tuberculosis treatments, specifically to isoniazid, leading to multi-drug-resistant tuberculosis. In this context, the EU and Indian DBT funded project STriTuVaD-In Silico Trial for Tuberculosis Vaccine Development-is supporting the identification of new interventional strategies against tuberculosis thanks to the use of Universal Immune System Simulator (UISS), a computational framework capable of predicting the immunity induced by specific drugs such as therapeutic vaccines and antibiotics.

resultsHere, we present how UISS accurately simulates tuberculosis dynamics and its interaction within the immune system, and how it predicts the efficacy of the combined action of isoniazid and RUTI vaccine in a specific digital population cohort. Specifically, we simulated two groups of 100 digital patients. The first group was treated with isoniazid only, while the second one was treated with the combination of RUTI vaccine and isoniazid, according to the dosage strategy described in the clinical trial design. UISS-TB shows to be in good agreement with clinical trial results suggesting that RUTI vaccine may favor a partial recover of infected lung tissue.

conclusionsIn silico trials innovations represent a powerful pipeline for the prediction of the effects of specific therapeutic strategies and related clinical outcomes. Here, we present a further step in UISS framework implementation. Specifically, we found that the simulated mechanism of action of RUTI and INH are in good alignment with the results coming from past clinical phase IIa trials.

Indexed as

User-Computer InterfaceAntitubercular AgentsComputational BiologyImmune SystemIsoniazidTreatment OutcomeTuberculosisTuberculosis VaccinesAntitubercular AgentsIsoniazidTuberculosis VaccinesComputational modelingImmunityIn silico trialsIsoniazidRUTITherapeutic strategiesTuberculosis

Identifiers

PMID33308139
PMCPMC7733696
OpenAlexW3112837331

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.