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NCTLD

NATIONAL CENTER FOR TUBERCULOSIS AND LUNG DISEASES JSC
Country: Georgia
3 Projects, page 1 of 1
  • Funder: European Commission Project Code: 847465
    Overall Budget: 9,969,010 EURFunder Contribution: 9,969,010 EUR

    Tuberculosis is a leading cause of morbidity and mortality worldwide. Current TB treatments are inadequate, requiring patients closely adhere to multi-drug regimens that are long, complex, and often poorly tolerated. These concerns are greatly magnified in rifampicin-resistant (RIF-R) TB, an urgent global and EU public health priority. WHO estimates that only 54% of patients who began RIF-R TB treatment in 2016 were cured. In addition to these well-recognized shortcomings, current TB treatments, particularly those for RIF-R TB, leave a majority of cured patients with permanent, clinically significant lung impairment and radiographic evidence of bronchiectasis and fibrosis. This project will determine if two adjunctive host-directed therapies (HDTs) can prevent these poor outcomes. 330 patients with RIF-R TB and baseline risk factors for poor outcome will be enrolled in a randomized, controlled, 3-armed multi-centre trial, with clinical sites in Romania, Moldova, Georgia, Mozambique, and South Africa. All patients will receive standard multidrug therapy according to national guidelines. Those patients randomized to the experimental arms will in addition receive either CC-11050 or metformin. These selected HDT candidates represent 2 complementary HDT strategies: reducing inflammation vs inducing host cell anti-microbial activity, respectively. Both candidates are supported by data from preclinical and clinical studies. Co-primary efficacy endpoints will examine effects on lung function (measured by spirometry) and infection (measured as time to stable sputum culture conversion). A sub-study will examine quantitative change in lung radiodensity by CT scan. If successful, this ground-breaking project will increase Europe’s capacity to control RIF-R-TB by developing new treatments that increase the likelihood of cure and reduce the risk of life-long disability.

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  • Funder: European Commission Project Code: 883582
    Overall Budget: 2,500,000 EURFunder Contribution: 2,500,000 EUR

    Mycobacterium tuberculosis (Mtb), the etiologic agent of tuberculosis (TB), is the main cause of human deaths due to infection in general, and to antimicrobial resistance in particular. Little is known on the within-host evolution of Mtb. Theory predicts that in chronic infections like TB, short-sighted evolution operates in the light of a trade-off between virulence and transmission. This trade-off is particularly relevant in the context of drug resistance, given the fitness costs of resistance. However, the role of short-sighted evolution in TB has never been explored empirically. Theory and model systems further predict that phenotypic drug tolerance facilitates the emergence of drug resistance, but the relevance of phenotypic drug tolerance for drug resistance evolution in the clinic has not been established for TB or any other bacterial disease. To address these and related questions, I propose to build on my recent work on the transmission of drug-resistant Mtb with a new focus on the within-patient evolution of drug-resistant Mtb and its link to between-patient evolution during transmission. Specifically, I shall: 1) Define the genomic characteristics and evolutionary forces shaping multidrug-resistant Mtb populations in individual patients over time and across different body compartments; 2) Compare the genomic and phenotypic properties of multidrug-resistant Mtb populations in individual patients to those circulating within the corresponding patient population; 3) Determine the effect of suboptimal patient treatment and phenotypic drug tolerance on drug resistance evolution in Mtb inside patients. By combining population genomics of Mtb sampled sequentially and from surgical specimens with experimental evolution and phenotypic characterization of clinical and experimentally evolved strains, this project will generate new insights relevant to both basic science and global public health.

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  • Funder: European Commission Project Code: 847762
    Overall Budget: 6,388,100 EURFunder Contribution: 6,388,100 EUR

    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-inflammatoriesy 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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