SMA-TB H2020-funded project repository
SMA-TB H2020-funded project repository
Open Access Repository to SMA-TB project outputs, which has received funding from the EC, through its European Union’s Horizon 2020 research and innovation programme under grant agreement No 847762 (Start date: 1st Jan 2020, End date: 30th June 2024).
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Background and Aim of SMA-TB
Tuberculosis (TB) is a chronic, life-threatening infectious disease which poses a tremendous challenge for physicians, researchers and Health Systems, which treatment is long, based only on the drug susceptibility of the responsible infective strain and very costly in drug-resistant cases (MDR-TB). The European Region still has the highest prevalence of MDR-TB in the world. Host-Directed Therapies (HDT) have been recently proposed to shorten treatment length and by to improve the patients’ outcomes while not increasing the risk of generating drug resistance.
As hyperinflammation is responsible of the lung damage associated to patients’ worse outcomes and sequelae, one of the approaches is to add an HDT with anti-inflammatory effect to the current drug regimen to cure the patients faster while having less permanent lung damage. Because TB has a wide range of clinical forms and severity stages, any therapeutic regimen needs to be studied in clinical trials (CT) as its benefit might differ among patients. No individualized personalized medicine is possible without stratifying the patients by integrating pathogen and host factors that will predict the course of the disease and the response to the intervention.
SMA-TB objectives are:
• To evaluate in a CT the potential impact of acetylsalicylic acid (ASA) and Ibuprofen (Ibu) (anti-inflammatory HDT) as adjuncts to standard therapy for drug sensitive (DS-) and MDR-TB. This potentially will reduce tissue damage, decrease the length of the treatment and the risk of bad outcomes.
• To identify and clinically validate host and pathogen biomarkers for further selection according to their relevance in terms of their ability to predict TB course and outcomes and response to treatment thanks to data science protocol.
• To generate a medical algorithm to stratify patients using network-based mathematical modelling for predicting the course of the disease and its response to the intervention, to be applied during clinical management to improve and personalize TB.
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