  EC funded STriTuVaD project aims to test, through a phase IIb clinical trial,
two of the most advanced therapeutic vaccines against tuberculosis. In
parallel, we have extended the Universal Immune System Simulator to include all
relevant determinants of such clinical trial, to establish its predictive
accuracy against the individual patients recruited in the trial, to use it to
generate digital patients and predict their response to the HRT being tested,
and to combine them to the observations made on physical patients using a new
in silico-augmented clinical trial approach that uses a Bayesian adaptive
design. This approach, where found effective could drastically reduce the cost
of innovation in this critical sector of public healthcare. One of the most
challenging task is to develop a methodology to reproduce biological diversity
of the subjects that have to be simulated, i.e., provide an appropriate
strategy for the generation of libraries of digital patients. This has been
achieved through the the creation of the initial immune system repertoire in a
stochastic way, and though the identification of a "vector of features" that
combines both biological and pathophysiological parameters that personalize the
digital patient to reproduce the physiology and the pathophysiology of the
subject.
