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

Journal Article · · Journal of Theoretical Biology
 [1];  [2];  [3];  [4];  [5];  [6];  [5];  [7];  [2]
  1. Univ. of Michigan, Ann Arbor, MI (United States); Univ. of Michigan Medical School, Ann Arbor, MI (United States)
  2. Univ. of Michigan, Ann Arbor, MI (United States)
  3. Children's Hospital of Pittsburgh of the Univ. of Pittsburgh Medical Center, Pittsburgh, PA (United States)
  4. The State Univ. of New Jersey, Newark, NJ (United States)
  5. Univ. of Pittsburgh, Pittsburgh, PA (United States)
  6. Adventist Univ. of Health Sciences, Orlando, FL (United States)
  7. Univ. of Michigan Medical School, Ann Arbor, MI (United States)
+ Show Author Affiliations

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. In this paper, 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. In conclusion, 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.

Research Organization:
Univ. of Michigan, Ann Arbor, MI (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1344478
Alternate ID(s):
OSTI ID: 1243356
Journal Information:
Journal of Theoretical Biology, Vol. 367; ISSN 0022-5193
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 44 works
Citation information provided by
Web of Science

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Cited By (23)

A computational model of invasive aspergillosis in the lung and the role of iron journal April 2016
Integrating Antimicrobial Therapy with Host Immunity to Fight Drug-Resistant Infections: Classical vs. Adaptive Treatment journal April 2016
Host-directed therapy targeting the Mycobacterium tuberculosis granuloma: a review journal October 2015
A Multi-Compartment Hybrid Computational Model Predicts Key Roles for Dendritic Cells in Tuberculosis Infection journal October 2016
Gene expression models based on a reference laboratory strain are poor predictors of Mycobacterium tuberculosis complex transcriptional diversity journal February 2018
Fluoroquinolone Efficacy against Tuberculosis Is Driven by Penetration into Lesions and Activity against Resident Bacterial Populations journal February 2019
Applying Optimization Algorithms to Tuberculosis Antibiotic Treatment Regimens journal August 2017
Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems journal January 2018
Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas journal May 2018
In silico evaluation and exploration of antibiotic tuberculosis treatment regimens journal November 2015
Modeling Granulomas in Response to Infection in the Lung journal March 2016
Low Levels of T Cell Exhaustion in Tuberculous Lung Granulomas journal June 2018
Spatiotemporal Dynamics of Virus Infection Spreading in Tissues journal December 2016
Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology journal August 2018
Agent‐based models of inflammation in translational systems biology: A decade later journal June 2019
A multi-scale approach to designing therapeutics for tuberculosis journal April 2015
Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach journal August 2017
Systems Pharmacology Approach Toward the Design of Inhaled Formulations of Rifampicin and Isoniazid for Treatment of Tuberculosis: Design of Inhaled Formulations for Treatment of TB journal March 2015
Facing the challenges of multiscale modelling of bacterial and fungal pathogen–host interactions journal February 2016
Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies journal January 2017
Systems biology predicts that fibrosis in tuberculous granulomas may arise through macrophage-to-myofibroblast transformation journal December 2020
Strategic Priming with Multiple Antigens can Yield Memory Cell Phenotypes Optimized for Infection with Mycobacterium tuberculosis: A Computational Study journal January 2016
Both Pharmacokinetic Variability and Granuloma Heterogeneity Impact the Ability of the First-Line Antibiotics to Sterilize Tuberculosis Granulomas journal March 2020

Figures / Tables (7)

Figure 1(p. 24)figure Figure 1
Figure 2(p. 25)figure Figure 2
Figure 3(p. 26)figure Figure 3
Figure 4(p. 27)figure Figure 4
Figure 5(p. 28)figure Figure 5
Figure 6(p. 29)figure Figure 6
Table 1(p. 30)table Table 1

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60 APPLIED LIFE SCIENCES
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pharmacodynamics
pharmacokinetics
agent based model
granuloma
antibiotic gradients