Tuberculosis (TB) is one of the top 10 causes of death worldwide, and the WHO’s EndTB strategy requires developing an effective vaccine. The H56 vaccine is a candidate currently in phase I/IIa trials as a boost to Bacillus Calmette-Guérin (BCG, the TB vaccine that is currently used in most countries world-wide). We build a multi-compartment, hybrid multi-scale model to 1) improve our understanding of immune response to H56 and 2) predict the role of T cells in preventing TB after vaccination.
First, we develop a two-compartment model of 31 non-linear ordinary differential equations (ODEs) that describe T-cell priming, proliferation, and differentiation in lymph nodes and blood. These ODEs allow us to track T cell response following vaccination. We calibrate our ODE model separately to human clinical trial data and non-human primate (NHP) experimental data to display differences in each species response to H56. Next, we couple our curated agent based model of granuloma formation in the lung, GranSim, to our blood and lymph node compartments to capture the host immune response to infection with Mycobacterium tuberculosis. This creation of a multi-scale and multi-compartment model allows us to represent a pseudo-whole-body response to both vaccination and infection. We use this whole-body model in the form of “virtual clinical trials” to retrospectively study the human and NHP datasets. In particular, we predict the role of T cells (induced through vaccination) throughout the course of infection in blood, lymph node, and lung granulomas. We use uncertainty and sensitivity analysis to compare and contrast immune response to vaccination and infection in NHPs and humans. We conjecture that vaccine dose may be critically important when evaluating H56 efficacy against Mycobacterium tuberculosis infection.