Optimizing combination therapy against drug resistance Mycobacterium tuberculosis: a modelling study

Area of Science:

• Mathematical Biology
• Immunology
• Infectious Diseases

Background:

• Co-infection with drug-sensitive and drug-resistant Mycobacterium tuberculosis strains presents unique challenges in tuberculosis treatment.
• The within-host dynamics and immune interactions during dual tuberculosis infections are not fully understood.

Purpose of the Study:

• To develop a comprehensive within-host mathematical model for Mycobacterium tuberculosis co-infection.
• To analyze the impact of bacterial strains, mutation, and immune responses on infection dynamics.
• To evaluate the efficacy of different treatment strategies for dual tuberculosis infections.

Main Methods:

• Development of a within-host mathematical model incorporating drug-sensitive and drug-resistant M. tuberculosis strains.
• Analysis of the basic reproduction number (R0) and its dependence on key immunological and bacterial parameters.
• Application of bifurcation analysis to understand complex threshold dynamics.
• Utilization of optimal control theory to assess treatment strategies.

Main Results:

• The basic reproduction number (R0) is significantly influenced by adaptive immune response rate, bacterial fitness cost, and macrophage engulfment rates.
• A backward bifurcation at R0=1 indicates complex dynamics and potential for persistent infection even when R0 is near 1.
• Combination therapy with at least 85% efficacy against both drug-sensitive and drug-resistant strains is predicted to effectively control tuberculosis co-infection.

Conclusions:

• Mathematical modeling provides critical insights into the complex dynamics of Mycobacterium tuberculosis co-infections.
• Understanding the interplay between bacterial strains and host immunity is essential for designing effective tuberculosis treatments.
• Targeted combination therapies are vital for overcoming drug resistance and achieving successful tuberculosis control.