Deterministic and stochastic in-host tuberculosis models for bacterium-directed and host-directed therapy...

Area of Science:

• Mathematical modeling of infectious diseases
• Immunology of tuberculosis
• Host-directed therapy development

Background:

• Tuberculosis (TB) infection involves complex immune interactions with varied outcomes.
• Standard antibiotic therapies require strict adherence to prevent resistance.
• Host-directed therapy (HDT) is emerging as an adjunct to boost host immunity against TB.

Purpose of the Study:

• Investigate in-host TB models to inform therapy development.
• Identify parameter regions for different TB disease outcomes using mathematical models.
• Explore the impact of cellular and environmental variations on TB progression and therapy.

Main Methods:

• Utilized an established ordinary differential equation (ODE) model to analyze TB disease outcomes based on therapy-targeting parameters.
• Developed two Itô stochastic differential equation (SDE) models to incorporate demographic and environmental variations.
• Analyzed the influence of stochastic fluctuations and therapy-induced environmental changes on immune and bacterial populations.

Main Results:

• ODE model showed immune responses can both aid and hinder TB progression depending on macrophage bacterial load.
• SDE model with demographic variation indicated significant effects of cellular fluctuations on T-cells, bacteria (in multi-outcome regions), and uninfected macrophages (in active disease regions).
• Second SDE model suggested therapies with fast return rates and ability to shift parameters to clearance regions can slow disease progression.

Conclusions:

• Mathematical modeling provides crucial insights into TB pathogenesis and therapy optimization.
• Host-directed therapies can be effective if they manage cellular stochasticity and environmental dynamics.
• Optimizing therapy parameters to promote disease clearance is key for successful TB treatment.