A mathematical model of immune response in infection induced by Mycobacteria tuberculosis.: Prediction of the disease course and outcomes at different treatment regimens

Motivation: A mathematical model to describe the immune response and different chemotherapeutic regimens for the treatment of tuberculosis (TB) has been developed. The model can be used as a tool for predicting the course of the infectious process and outcomes in individuals infected with Mycobacterium tuberculosis (MBT). To identify what chemotherapeutic regimen is the best, one should clearly understand how different anti-TB drugs work. One of the most promising approaches to elaborate optimal anti-TB chemotherapeutic treatment/regimen is the gene-network technology. Results: The model proposed herein consists of a set of ordinary differential equations for the dynamics of diverse populations of bacteria, macrophages, T lymphocytes, dendrite cells as well as for various concentrations of cytokines and antibacterial drugs. Different treatment regimens in individuals with different variants of the course of disease (latent and acute infection) were simulated, and so were different rates of drug inactivation (rapid and slow acetylators). Different regimes of the model, which yield different outcomes, correspond to different chemotherapeutic regimens, which either give recovery (adequate chemotherapy) or delay recovery and contribute to the emergence of drug-resistant strains (inadequate chemotherapy). A further progression of the model will be connected with optimization of the treatment regimens for TB. To serve the purpose, we have reconstructed the gene network for the mechanisms of anti-TB drugs and for the mechanisms underlying the emergence of drug resistance developed by MBT due to mutation in separate target genes.