ArticleBMC systems biology2015
In silico evaluation and exploration of antibiotic tuberculosis treatment regimens.
Article in BMC systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
37 citing papers in PubMed, 53 citations in OpenAlex.
- A Comparison of Mechanisms Driving Lesion Outcomes During Lung Tumor and Tuberculosis Granuloma Formation.Bulletin of mathematical biology · 2026Article
- Distinct mechanisms drive post-antibiotic Tuberculosis relapse post-cure versus post-treatment-failure.bioRxiv : the preprint server for biology · 2026Article
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
- A novel machine-learning based optimization: identifying new treatment regimens for tuberculosis.Numerical algebra, control and optimization · 2025Article
- Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden.Journal of theoretical biology · 2025Article
- GEODE: anFrontiers in pharmacology · 2025Article
- Extrapolation of lung pharmacokinetics of antitubercular drugs from preclinical species to humans using PBPK modelling.The Journal of antimicrobial chemotherapy · 2024Article
- 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
- In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models.PloS one · 2024Article
- Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens.PLoS computational biology · 2023Article
- Mathematical models to study the biology of pathogens and the infectious diseases they cause.iScience · 2022Review
- The within-host evolution of antimicrobial resistance in Mycobacterium tuberculosis.FEMS microbiology reviews · 2021Review
- RESTAMP - Rate estimates by sequence-tag analysis of microbial populations.Computational and structural biotechnology journal · 2021Article
- Mathematical model and tool to explore shorter multi-drug therapy options for active pulmonary tuberculosis.PLoS computational biology · 2020Article
- Spatial Organization and Recruitment of Non-Specific T Cells May Limit T Cell-Macrophage Interactions WithinFrontiers in immunology · 2020Article
- Both Pharmacokinetic Variability and Granuloma Heterogeneity Impact the Ability of the First-Line Antibiotics to Sterilize Tuberculosis Granulomas.Frontiers in pharmacology · 2020Article
- Integrating mathematical models with experimental data to investigate the within-host dynamics of bacterial infections.Pathogens and disease · 2019Review
- Fluoroquinolone Efficacy against Tuberculosis Is Driven by Penetration into Lesions and Activity against Resident Bacterial Populations.Antimicrobial agents and chemotherapy · 2019Article
- Tuberculosis drugs' distribution and emergence of resistance in patient's lung lesions: A mechanistic model and tool for regimen and dose optimization.PLoS medicine · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundImprovement in tuberculosis treatment regimens requires selection of antibiotics and dosing schedules from a large design space of possibilities. Incomplete knowledge of antibiotic and host immune dynamics in tuberculosis granulomas impacts clinical trial design and success, and variations among clinical trials hamper side-by-side comparison of regimens. Our objective is to systematically evaluate the efficacy of isoniazid and rifampin regimens, and identify modifications to these antibiotics that improve treatment outcomes.
resultsWe pair a spatio-temporal computational model of host immunity with pharmacokinetic and pharmacodynamic data on isoniazid and rifampin. The model is calibrated to plasma pharmacokinetic and granuloma bacterial load data from non-human primate models of tuberculosis and to tissue and granuloma measurements of isoniazid and rifampin in rabbit granulomas. We predict the efficacy of regimens containing different doses and frequencies of isoniazid and rifampin. We predict impacts of pharmacokinetic/pharmacodynamic modifications on antibiotic efficacy. We demonstrate that suboptimal antibiotic concentrations within granulomas lead to poor performance of intermittent regimens compared to daily regimens. Improvements from dose and frequency changes are limited by inherent antibiotic properties, and we propose that changes in intracellular accumulation ratios and antimicrobial activity would lead to the most significant improvements in treatment outcomes. Results suggest that an increased risk of drug resistance in fully intermittent as compared to daily regimens arises from higher bacterial population levels early during treatment.
conclusionsOur systems pharmacology approach complements efforts to accelerate tuberculosis therapeutic development.
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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.