  Tuberculosis (TB) is one of the deadliest diseases worldwide, with 1,5
million fatalities every year along with potential devastating effects on
society, families and individuals. To address this alarming burden, vaccines
can play a fundamental role, even though to date no fully effective TB vaccine
really exists. Current treatments involve several combinations of antibiotics
administered to TB patients for up to two years, leading often to financial
issues and reduced therapy adherence. Along with this, the development and
spread of drug-resistant TB strains is another big complicating matter. Faced
with these challenges, there is an urgent need to explore new vaccination
strategies in order to boost immunity against tuberculosis and shorten the
duration of treatment. Computational modeling represents an extraordinary way
to simulate and predict the outcome of vaccination strategies, speeding up the
arduous process of vaccine pipeline development and relative time to market.
Here, we present EU - funded STriTuVaD project computational platform able to
predict the artificial immunity induced by RUTI and ID93/GLA-SE, two specific
tuberculosis vaccines. Such an in silico trial will be validated through a
phase 2b clinical trial. Moreover, STriTuVaD computational framework is able to
inform of the reasons for failure should the vaccinations strategies against M.
tuberculosis under testing found not efficient, which will suggest possible
improvements.
