A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment

Area of Science:

• Pharmacology
• Computational Biology
• Infectious Diseases

Background:

• Active tuberculosis (TB) remains a global health challenge due to complex factors hindering elimination.
• Developing optimal TB therapies is complicated by the vast possibilities for antibiotic doses, regimens, and combinations.
• Drug penetration into the lung granuloma, the site of TB infection, is a significant challenge.

Purpose of the Study:

• To integrate computational models of granuloma dynamics with pharmacokinetic and pharmacodynamic models for anti-TB antibiotics.
• To predict antibiotic behavior within granulomas and identify factors influencing treatment outcomes.
• To reduce the need for extensive pre-clinical and clinical trials by refining antibiotic strategies.

Main Methods:

• Integration of a computational model of granuloma formation and function with plasma and lung tissue pharmacokinetic/pharmacodynamic models.
• Calibration of the integrated model using animal data.
• Simulation of antibiotic concentrations and bacterial dynamics within granulomas for two first-line anti-TB drugs.

Main Results:

• Antibiotics frequently fall below effective concentrations within granulomas, allowing bacterial growth between doses.
• Concentration gradients of antibiotics are observed within granulomas, with lower levels in the center.
• Bacterial subpopulations during treatment are primarily intracellular or in non-replicative hypoxic areas.
• Pre-treatment granuloma severity predicts treatment success on an individual granuloma basis.

Conclusions:

• Antibiotic under-dosing within granulomas contributes to the prolonged treatment durations required for TB.
• Understanding granuloma drug penetration and spatial dynamics is essential for designing more effective TB therapies.
• Individual granuloma characteristics are key predictors of treatment response, suggesting personalized therapeutic approaches may be beneficial.