Deterministic and Stochastic in-host Tuberculosis Models for Bacterium-directed and Host-directed Therapy Combination

TL;DR

This paper develops deterministic and stochastic in-host tuberculosis models to analyze therapy effects, revealing how immune responses and stochastic fluctuations influence disease outcomes and therapy success.

Contribution

It introduces novel stochastic differential equation models incorporating demographic and environmental variations to better understand TB progression and therapy impacts.

Findings

Stochastic fluctuations significantly affect T-cell and bacterial populations.

Immune responses can both promote and hinder disease progression.

Effective therapies can shift parameters into disease clearance regions.

Abstract

Mycobacterium tuberculosis infection can involve all immune system components and can result in different disease outcomes. The antibiotic TB drugs require strict adherence to prevent both disease relapse and mutation of drug- and multidrug-resistant strains. To overcome the constraints of pathogen-directed therapy, host-directed therapy has attracted more attention in recent years as an adjunct therapy to enhance host immunity to fight against this intractable pathogen. The goal of this paper is to investigate in-host tuberculosis models to provide insights into therapy development. Focusing on therapy-targeting parameters, the parameter regions for different disease outcomes are identified from an established ODE model. Interestingly, the ODE model also demonstrates that the immune responses can both benefit and impede disease progression, depending on the number of bacteria engulfed and released by macrophages. We then develop two It\^{o} SDE models, which consider the impact of demographic variations at the cellular level and environmental variations during therapies along with demographic variations. The SDE model with demographic variation suggests that stochastic fluctuations at the cellular level have significant influences on (1) the T-cell population in all parameter regions, (2) the bacterial population when parameters located in the region with multiple disease outcomes, and (3) the uninfected macrophage population in the parameter region representing active disease. Further, considering environmental variations from therapies, the second SDE model suggests that disease progression can slow down if therapies (1) can have fast return rates and (2) can bring parameter values into the disease clearance regions.