Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach

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.

Dynamic balance of pro‐ and anti‐inflammatory signals controls disease and limits pathology

“…In an effort to characterize how caseum affects antibiotic distribution, there have been a number of studies that image the distribution of antibiotics in granulomas and quantify antibiotic molecular binding to caseum . Pienaar et al have utilized MALDI‐MS images and experimentally determined temporal granuloma antibiotic concentration measurements to compare efficacy of tuberculosis therapy with 3 different fluoroquinolones: moxifloxacin, levofloxacin, and gatifloxacin. The 3 fluoroquinolones display different distribution patterns in granulomas that GranSim can recapitulate.…”

“…In an effort to characterize how caseum affects antibiotic distribution, there have been a number of studies that image the distribution of antibiotics in granulomas and quantify antibiotic molecular binding to caseum. 130,132,133 Pienaar et al 134 Because active TB can be considered an immune response that is out of balance-too little pro-inflammatory mediators, or too many anti-inflammatory mediators, another approach to treatment is to modify the levels of 1 or both of the pro-and anti-inflammatory responses to achieve latency. The same idea could also be considered for clinically latent TB, though the idea would be to tip the distribution slightly toward pro-inflammatory responses and thus eliminate Mtb without inducing additional inflammation.…”

The Evolution of Fluoroquinolone-Resistance inMycobacterium tuberculosisis Modulated by the Genetic Background

“…(3-7). Meanwhile, biological factors not explored in this study, such as antibiotic type and 330 concentration (10-13, 46, 47), pharmacodynamic and pharmacokinetic features (58,59), and the 331 selective pressure of the host immune system (60), may also influence the evolution of FQ-R. 332…”

Drug permeation and metabolism in Mycobacterium tuberculosis: Prioritising local exposure as essential criterion in new TB drug development

“…Therefore, the PK profile in these regions is likely to be different from that measured in plasma. A reasonable PK/PD modelling approach in such cases is to use a hypothetical “effect compartment” (30,31) mimicking the concentration at the site of action which, while dependent on the concentration in plasma, is “delayed” by the drug traversing through the numerous layers of biological tissue, thus significantly smoothing out peak and trough concentrations. Also, the time over a certain concentration would be very different in this effect compartment.…”

“…Fortunately, the last decade has seen considerable efforts toward developing accurate methods (Table 1) to measure drug levels directly in Mtb-infected tissues (27)(28)(29). Through the application of advanced chromatographic, mass spectrometry, and imaging techniques including HPLC coupled to tandem mass spectrometry (LC-MS/MS) and MALDI mass spectrometry imaging (MALDI-MSI), the quantification of tissue and lesion distribution of known and experimental anti-TB drugs in animal models and clinical samples has become increasingly attainableprimarily through the work of Veronique Dartois and colleagues (4,7,8,28,54) -thus offering the prospect of informing a systems pharmacology approach to the design of antimycobacterial therapy (55). In addition to the well-established appreciation that TB drugs can be differentially active against bacilli in different metabolic states (56)(57)(58), these new analytical techniques have demonstrated that those same drugs can also possess distinct lesion-penetrating abilities (6,7,9,18).…”