A novel Stratified Medicine Algorithm to predict treatment responses to host dir...
A novel Stratified Medicine Algorithm to predict treatment responses to host directed therapy in TB patients.
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 responsib...
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.ver más
Seleccionando "Aceptar todas las cookies" acepta el uso de cookies para ayudarnos a brindarle una mejor experiencia de usuario y para analizar el uso del sitio web. Al hacer clic en "Ajustar tus preferencias" puede elegir qué cookies permitir. Solo las cookies esenciales son necesarias para el correcto funcionamiento de nuestro sitio web y no se pueden rechazar.
Cookie settings
Nuestro sitio web almacena cuatro tipos de cookies. En cualquier momento puede elegir qué cookies acepta y cuáles rechaza. Puede obtener más información sobre qué son las cookies y qué tipos de cookies almacenamos en nuestra Política de cookies.
Son necesarias por razones técnicas. Sin ellas, este sitio web podría no funcionar correctamente.
Son necesarias para una funcionalidad específica en el sitio web. Sin ellos, algunas características pueden estar deshabilitadas.
Nos permite analizar el uso del sitio web y mejorar la experiencia del visitante.
Nos permite personalizar su experiencia y enviarle contenido y ofertas relevantes, en este sitio web y en otros sitios web.