A Computational Tool Integrating Host Immunity with Antibiotic Dynamics to Study Tuberculosis Treatment

dc.contributor.authorPienaar, Elsje
dc.contributor.authorClifone, Nicholas A.
dc.contributor.authorLin, Philana Ling
dc.contributor.authorDartois, Veronique
dc.contributor.authorMattila, Joshua
dc.contributor.authorButler, J. Russell
dc.contributor.authorFlynn, JoAnne L.
dc.contributor.authorKirschner, Denise E.
dc.contributor.authorLinderman, Jennifer J.
dc.description.abstractWhile 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.en_US
dc.identifier.citationPienaar, E., Cilfone, N. A., Lin, P. L., Dartois, V., Mattila, J. T., Butler, J. R., Flynn, J. L., Kirschner, D. E., & Linderman, J. J. (2015). A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment. Journal of Theoretical Biology, 367, 166-179. https://doi.org/10.1016/j.jtbi.2014.11.021en_US
dc.publisherJournal of Theoretical Biologyen_US
dc.relation.ispartofseriesVol. 367;166-179
dc.subjectPharmacodynamics, Pharmacokinetics, Agent based model, granuloma, Non-human primate, Rabbit, Isoniazid, Rifampicin, Antibiotic gradients, Suboptimal exposureen_US
dc.titleA Computational Tool Integrating Host Immunity with Antibiotic Dynamics to Study Tuberculosis Treatmenten_US
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
1.99 KB
Item-specific license agreed upon to submission