ArticleJournal of theoretical biology2015
A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment.
Article in Journal of theoretical biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed, 77 citations in OpenAlex.
- A Comparison of Mechanisms Driving Lesion Outcomes During Lung Tumor and Tuberculosis Granuloma Formation.Bulletin of mathematical biology · 2026Article
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- Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden.Journal of theoretical biology · 2025Article
- Timing matters in macrophage/CD4+ T cell interactions: an agent-based model comparingmSystems · 2025Article
- GEODE: anFrontiers in pharmacology · 2025Article
- A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins.NPJ systems biology and applications · 2024Article
- Therapeutic developments for tuberculosis and nontuberculous mycobacterial lung disease.Nature reviews. Drug discovery · 2024Review
- A systematic efficacy analysis of tuberculosis treatment with BPaL-containing regimens using a multiscale modeling approach.CPT: pharmacometrics & systems pharmacology · 2024Article
- Mathematical model of oxygen, nutrient, and drug transport in tuberculosis granulomas.PLoS computational biology · 2024Article
- Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review.Microorganisms · 2023Review
- An agent-based modeling approach for lung fibrosis in response to COVID-19.PLoS computational biology · 2023Article
- Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens.PLoS computational biology · 2023Article
- Advances in the design of combination therapies for the treatment of tuberculosis.Expert opinion on drug discovery · 2023Review
- Anti-tuberculosis treatment strategies and drug development: challenges and priorities.Nature reviews. Microbiology · 2022Review
- Immunologic and imaging signatures in post tuberculosis lung disease.Tuberculosis (Edinburgh, Scotland) · 2022Review
- A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs.Scientific reports · 2021Article
- Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis.Annual review of pharmacology and toxicology · 2021Review
- Modelling the effects of environmental heterogeneity within the lung on the tuberculosis life-cycle.Journal of theoretical biology · 2020Article
- Systems biology predicts that fibrosis in tuberculous granulomas may arise through macrophage-to-myofibroblast transformation.PLoS computational biology · 2020Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.