Using computer simulations, US scientists have shown that the fluoroquinolone moxifloxacin may be superior to two other commonly used fluoroquinolones, in the treatment of thoberculosis (TB).
Treatment of tuberculosis involves a combination of several drugs, sometimes including drugs from a class known as fluoroquinolones. Tuberculosis infects millions of people every year and requires at least 6 months of treatment with multiple drugs. This treatment can include one of three fluoroquinolones: moxifloxacin (MOXI), levofloxacin (LEVO), or gatifloxacin (GATI). However, existing data from clinical trials and animal studies are inadequate to determine which is best or if all three are equivalent.
In the new study, Elsje Pienaar and colleagues at the University of Michigan Ann Arbor and Rutgers University developed a computer model to simulate the effects of the three drugs on granulomas – clusters of host cells and bacteria that develop in the lungs of tuberculosis patients. The computer model incorporates experimental data on the three drugs, as well as extensive knowledge on the chemistry of their activity.
The research team used the computer model to simulate treatment with each of the three drugs and compared them according to multiple criteria. In the simulations, MOXI appeared to be superior to both LEVO and GATI because it killed bacteria in granulomas more quickly, and it performed better when the simulated patients missed doses. LEVO killed bacteria more quickly than GATI. However, all three drugs were unable to kill bacteria at the very center of the granulomas.
"The exciting thing about this study is that we are able perform a side-by-side comparison of fluoroquinolones in identical infections," Pienaar says. "The potential practical application of our findings is to guide selection of individual fluoroquinolones for tuberculosis treatment."
Pienaar says that the predictions of the simulations are now being tested in animal experiments. The team is also enhancing the computer model to test fluoroquinolones alongside other tuberculosis drugs, which could help narrow down the best possible drug combinations for tuberculosis treatment.
Abstract
Granulomas are complex lung lesions that are the hallmark of tuberculosis (TB). Understanding antibiotic dynamics within lung granulomas will be vital to improving and shortening the long course of TB treatment. Three fluoroquinolones (FQs) are commonly prescribed as part of multi-drug resistant TB therapy: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, insufficient data are available to support selection of one FQ over another, or to show that these drugs are clinically equivalent. To predict the efficacy of MXF, LVX and GFX at a single granuloma level, we integrate computational modeling with experimental datasets into a single mechanistic framework, GranSim. GranSim is a hybrid agent-based computational model that simulates granuloma formation and function, FQ plasma and tissue pharmacokinetics and pharmacodynamics and is based on extensive in vitro and in vivo data. We treat in silico granulomas with recommended daily doses of each FQ and compare efficacy by multiple metrics: bacterial load, sterilization rates, early bactericidal activity and efficacy under non-compliance and treatment interruption. GranSim reproduces in vivo plasma pharmacokinetics, spatial and temporal tissue pharmacokinetics and in vitro pharmacodynamics of these FQs. We predict that MXF kills intracellular bacteria more quickly than LVX and GFX due in part to a higher cellular accumulation ratio. We also show that all three FQs struggle to sterilize non-replicating bacteria residing in caseum. This is due to modest drug concentrations inside caseum and high inhibitory concentrations for this bacterial subpopulation. MXF and LVX have higher granuloma sterilization rates compared to GFX; and MXF performs better in a simulated non-compliance or treatment interruption scenario. We conclude that MXF has a small but potentially clinically significant advantage over LVX, as well as LVX over GFX. We illustrate how a systems pharmacology approach combining experimental and computational methods can guide antibiotic selection for TB.
Authors
Elsje Pienaar, Jansy Sarathy, Brendan Prideaux, Jillian Dietzold, Véronique Dartois, Denise E Kirschner, Jennifer J Linderman
[link url="https://www.sciencedaily.com/releases/2017/08/170817141808.htm"]PLOS material[/link]
[link url="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005650"]PLOS Computational Biology abstract[/link]