About SMA-TB project
Tuberculosis (TB) is a chronic, life-threatening infectious disease that 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). Host-Directed Therapies (HDT) have been recently proposed to shorten treatment length and to improve the patients’ outcomes while not increasing the risk of generating drug resistance.
As hyperinflammation is responsible for the lung damage associated with patients’ worse outcomes and sequelae, one of the approaches is to add an anti-inflammatory to the current drug regimen to cure the patients faster while having less permanent lung damage.
To evaluate in a CT the potential impact of aspirin and ibuprofen (anti-inflammatories HDT) as adjunct 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.