A model of isoniazid treatment of tuberculosis

Lemmer, Yolandy; Grobler, Anne; Moody, Clint; Viljoen, Hendrik J.

doi:10.1016/j.jtbi.2014.07.024

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…A number of plasma pharmacokinetic (PK) models for anti-TB antibiotics are available and range from one-compartment models to more complex physiology-based models[15, 24–26]. Combined PK-pharmacodynamics (PD) models for TB antibiotics have been built for RIF[27] and INH[28, 29] and nonspecific antibiotics[30]. One previous model combining PK and PD of RIF with host-immunity has been published but does not capture the added complexity of the granuloma in terms of structure, organization and antibiotic distribution[31].…”

Section: Introductionmentioning

confidence: 99%

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

Pienaar

Cilfone

Lin

et al. 2015

Journal of Theoretical Biology

161

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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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Section: Introductionmentioning

confidence: 99%

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

Pienaar

Cilfone

Lin

et al. 2015

Journal of Theoretical Biology

161

View full text Add to dashboard Cite

show abstract

“…There is a rich literature of PK-PD computational models that include host immunity, antibiotic distribution into epithelial lung fluid or alveolar macrophages, for non-specific antibiotics as well as for INH and RIF. [115][116][117][118][119][120][121][122][123][124][125][126] None however examine the heterogeneous spatial distribution of antibiotics within granulomas, which we and others have shown to be critical to understanding and predicting antibiotic efficacy. 58,60,127,128 Fig.…”

Section: Discussionmentioning

confidence: 99%

A multi-scale approach to designing therapeutics for tuberculosis

Linderman

Cilfone

Pienaar

et al. 2015

Integrative Biology

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Approximately one third of the world’s population is infected with Mycobacterium tuberculosis. Limited information about how the immune system fights M. tuberculosis and what constitutes protection from the bacteria impact our ability to develop effective therapies for tuberculosis. We present an in vivo systems biology approach that integrates data from multiple model systems and over multiple length and time scales into a comprehensive multi-scale and multi-compartment view of the in vivo immune response to M. tuberculosis. We describe computational models that can be used to study (a) immunomodulation with the cytokines tumor necrosis factor and interleukin 10, (b) oral and inhaled antibiotics, and (c) the effect of vaccination.

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“…(2020) constructed a TB model with media impact on transmission rate. There are certainly more considerations in the mathematical modeling of TB, including vaccination ( Bhunu, Garira, Mukandavire, & Magombedze, 2008 ; Okuonghae & Omosigho, 2011 ), treatment and incomplete treatment ( Castillo-Chavez & Feng, 1997 ; Dye & Williams, 2000 ; Lemmer et al., 2014 ), fast and slow progression ( Aparicio et al., 2002 ; Cai et al., 2021 ; Gomes et al., 2007 ), relapse ( Ozcaglar et al., 2012 ; Ren, 2017 ), reinfection ( Aparicio et al., 2002 ; Chinnathambi et al., 2021 ), confection with HIV ( Bhunu et al., 2009 ), drug-resistant strains ( Dye & Williams, 2000 ; Trauer et al., 2014 ; Wang et al., 2023 ) and so on.…”

Section: Introductionmentioning

confidence: 99%

Modelling the preventive treatment under media impact on tuberculosis: A comparison in four regions of China

Zhang,

Takeuchi,

Dong

et al. 2024

Infectious Disease Modelling

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A model of isoniazid treatment of tuberculosis

Cited by 4 publications

References 33 publications

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

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

A multi-scale approach to designing therapeutics for tuberculosis

Modelling the preventive treatment under media impact on tuberculosis: A comparison in four regions of China

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