Table 1.
Ranking of MXF, LVX and GFX by different PK, PD or clinical metrics from literature and this work.
Fig 1.
Computational model structure and simulated granulomas.
(A) The model tracks plasma PK dynamics using a two-compartment model. The plasma PK model is linked to an agent-based model (ABM) representing spatial and temporal granuloma formation as well as tissue PK, i.e. antibiotic diffusion in the lung tissue and penetration into the granuloma. The model also tracks molecular level antibiotic dynamics such as cell uptake, and caseum binding. Finally the model calculates the antibacterial activity of antibiotics at specific locations in the granuloma using an Emax model based on local antibiotic concentration. Parameter definitions are in Table 3. (B) Emergent behavior of the model system is the formation of in silico granulomas that can represent the spectrum of granulomas observed in vivo, e.g. caseous and cellular granulomas shown. Art adapted from Servier Medical Art (http://servier.com/Powerpoint-image-bank) provided under a Creative Commons Attribution 3.0 Unported License.
Table 2.
Summary of experimental dataset types and where they are integrated into the computational model.
Fig 2.
Comparison of plasma PK in rabbits and simulations.
A two-compartment plasma PK model describes plasma dynamics in rabbits. To show variation in simulation outcomes we plot both baseline simulations (dark lines) and standard deviations (shaded) for 100 simulations. To show variation in rabbit data we plot individual measurements (connected circles) for between 3 and 7 rabbits.
Fig 3.
Comparison of average FQ concentrations in simulated granulomas (solid lines) recapitulate LCMS measurements in rabbit granulomas (data points).
Lines and data points show means and standard deviations for 100 in silico granulomas, and between 1 and 67 rabbit granulomas. Horizontal dotted lines show C50 values for intracellular (C50,BI), extracellular replicating (C50,BE) and extracellular non-replicating bacteria (C50,BN). Though not used for model calibration, dynamics in uninvolved lung are also in agreement between simulations and rabbit data (Figure in S2 Fig).
Fig 4.
Spatial distributions of FQs in simulated granulomas recapitulate MALDI-MS imaging in rabbit granulomas.
(A) Representative granulomas from rabbits (left) and simulations (right) showing different spatial distribution of GFX, MXF and LVX at 6 hours post dose. Simulations capture the qualitative differences between the three FQs. A quantitative comparison between our simulations and MALDI-MSI is not possible due to the semi-quantitative nature of the MALDI-MSI data. (B) Different distributions between GFX, MXF and LVX are consistent across all granulomas studied. Figures show average MALDI-MS abundance in rabbit granulomas (left) and concentrations in simulated granulomas (right) plotted as a function of distance from the edge of the granuloma (in μm). Solid lines show mean and dashed lines show standard deviation for 100 simulated granulomas and between 3 and 7 rabbit granulomas.
Table 3.
Model parameters by computational model component.
Fig 5.
Simulated bacterial CFU (A-D) and host immune dynamics (E-F) during treatment are similar between MXF and LVX with GFX killing fewer bacteria.
Dynamics in the total bacterial load (A) are dominated by the non-replicating Mtb population residing the caseum (D), which is not being cleared by any of the FQs. MXF clears the intracellular bacterial population (B) more quickly than LVX and GFX, and all three FQs clear the extracellular bacterial population within days (C). Metrics of inflammation (number of activated macrophages (E), and TNF-α:IL-10 ratio (F)) decline more quickly during MXF treatment compared to LVX and GFX. Lines show means of 210 in silico granulomas, with infection starting at day 0, and daily FQ treatment starting at day 380 (arrows) for 6 months (ending at day 560).
Fig 6.
Simulated average free FQ concentrations that each bacterial subpopulation (intracellular (A), extracellular (B) and nonreplicating (C)) is exposed to during one dosing period.
Solid lines show means and dashed lines show standard deviations for 210 in silico granulomas. Horizontal dotted lines show C50 values for intracellular (C50,BI), extracellular replicating (C50,BE) and extracellular non-replicating bacteria (C50,BN) for each FQ.
Fig 7.
Simulations show that MXF and LVX sterilize more granulomas than GFX.
Kaplan-Meier curves show percentage granulomas sterilized over 180 days of treatment with MXF, GFX or LVX (N = 210 granulomas).
Table 4.
EBA and extended EBA in clinical data and in simulations.
Fig 8.
Simulated effects of non-compliance on bacterial load following treatment.
Treatment simulations are repeated but programmed to randomly miss 20% of doses. Graph shows mean and standard error of total bacteria (CFU) after 180 days of treatment with MXF, GFX or LVX for 100% compliance (solid bars) or 20% missed doses (hashed bars). *: p-value < 0.05, **: p-value < 0.005.
Fig 9.
Simulated effects of non-compliance on granuloma sterilization.
Percentage granulomas sterilized over 180 days of treatment with MXF, GFX or LVX (N = 210 granulomas), comparing 100% compliance (solid lines) to 20% missed doses (dotted lines) for each FQ.
Fig 10.
Bacteria in all subpopulations increase more slowly following MXF treatment interruption, compared to GFX and LVX (A-D). Lines show means of 210 in silico granulomas, with infection starting at day 0, daily FQ treatment starting at day 380 (arrows). Treatment is interrupted after 10 days (vertical dotted lines), and the simulation is continued to day 560 without antibiotics. (E-F) Immune score (x-axes) and infection score (y-axes) decrease during complete treatment (E) and rebound following treatment interruption after 10 days (F). The start of treatment is located at the intersection of the dotted lines. Filled circles indicate the treatment phase, and open circles indicated progression following treatment interruption.
Table 5.
In vitro fluoroquinolone properties.
Table 6.
Summary of plasma and granuloma pharmacokinetic studies (dose size, number of samples, number of doses and time points) by FQ and experimental method.