Generation of digital patients for the simulation of tuberculosis with UISS-TB

TL;DR

This paper extends the Universal Immune System Simulator to generate personalized digital patients for tuberculosis, aiming to improve clinical trial predictions and reduce healthcare innovation costs.

Contribution

It introduces a methodology to create diverse, personalized digital patients by modeling immune system variability and relevant biological parameters.

Findings

Successfully generated digital patient libraries with diverse immune profiles.

Validated the simulator's predictive accuracy against real patient data.

Demonstrated potential for in silico clinical trials to complement physical trials.

Abstract

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.