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

Abstract

While active tuberculosis (TB) is a treatable disease, many complex factors prevent its global elimination. Part of the difficulty in developing optimal therapies is the large design space of antibiotic doses, regimens and combinations. Computational models that capture the spatial and temporal dynamics of antibiotics at the site of infection can aid in reducing the design space of costly and time-consuming animal pre-clinical and human clinical trials. The site of infection in TB is the granuloma, a collection of immune cells and bacteria that form in the lung, and new data suggest that penetration of drugs throughout granulomas is problematic. Here we integrate our computational model of granuloma formation and function with models for plasma pharmacokinetics, lung tissue pharmacokinetics and pharmacodynamics for two first line anti-TB antibiotics. The integrated model is calibrated to animal data. We make four predictions. First, antibiotics are frequently below effective concentrations inside granulomas, leading to bacterial growth between doses and contributing to the long treatment periods required for TB. Second, antibiotic concentration gradients form within granulomas, with lower concentrations toward their centers. Third, during antibiotic treatment, bacterial subpopulations are similar for INH and RIF treatment: mostly intracellular with extracellular bacteria located in areas non-permissive for replication (hypoxic areas), presenting a slowly increasing target population over time. Finally, we find that on an individual granuloma basis, pre-treatment infection severity (including bacterial burden, host cell activation and host cell death) is predictive of treatment outcome.

Antibiotic efficacy — context matters

“…Mass spectrometry allows interrogation into spatial determinants of antibiotic efficacy [110,111] and synthetic biology has contributed novel tools for perturbing essential genes [112]. Current quantitative models are providing mechanistic insights into several nonlinear components of antibiotic lethality [56,113] and are useful for predicting multidrug treatment outcomes [114,115] and antibiotic drug synergies [116,117].…”

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions

“…Genes (and combinations of genes) that we identify to be important for in vivo survival can now be tested as potential new drug targets using existing drugs or KO strains in animal models. Combining GranSim-CBM with our existing model of antibiotic distribution and activity in the granuloma (40,41) could also help elucidate the contributions of bacterial heterogeneity, asymmetric division and growth, and lipid inclusion levels to treatment outcomes (5,111). Such insight can in turn inform new regimens and strategic drug design.…”

“…At the molecular level, the model accounts for secretion, diffusion, binding, and degradation of cytokines and chemokines. The model has been extensively calibrated to NHP data and successfully predicts granuloma outcomes for tumor necrosis factor alpha (TNF-␣), interleukin-10 (IL-10), and IFN-␥ knockouts (34)(35)(36)(37)(38)(39)(40)(41)61).…”

“…We have established computational models of granuloma formation (34)(35)(36)(37) to map host immune mechanisms from the molecular scale to the granuloma scale (34,35,38) and to explore new antibiotic treatment strategies (39)(40)(41). Other models have predicted hypoxic granuloma regions based on granuloma size (42) or have used oxygen and nitric oxide concentrations to determine M. tuberculosis growth and to predict M. tuberculosis dynamics (43).…”

Exploring the potential of adjunct therapy in tuberculosis

“…In addition, variable pathogen response to diverse host microenvironments poses a challenge [69]. Disease modeling of the granuloma, systems level analysis of the host immune system, and its interaction with Mtb that allow prediction of drug targets/pathways and prediction of treatment are needed [70].…”