Fig 1.
Antibiotic resistance emergence and selection over bacterial through population scales.
Resistance develops upon mutation conferring genetic resistance (A). Resistant bacteria grow (B) within the host, giving rise to hetero-resistance (C), which can spread resistant bacteria to others (D). Improper treatment (E) selects for resistant bacteria, resulting in drug-resistant TB (F), which can spread in a community. (G) The influence of bacterial and drug dynamics on resistance emergence and selection in the context of granulomas remains unclear. Arrows between scales represent a series of potential events, but influences can occur in both directions.
Fig 2.
Host immunity, PK and PD model structure in GranSim.
(A) Granuloma computational model (GranSim) simulates spatial and temporal host immunity dynamics and granuloma formation within a primate lung. Tissue pharmacokinetics (PK) that lead to an antibiotic distribution within a granuloma are also captured. (B) The plasma PK model predicts plasma dynamics following recommended dosing with INH and RIF. (C) PD is modeled using an Emax curve to calculate the bacterial killing rate constant for each antibiotic as a function of antibiotic concentration. The location of PD curve is determined by the bacterial resistance state, with resistant bacteria having higher effective concentrations (C50,Resist) compared to antibiotic-susceptible Mtb (C50,Susc). (D) Upon bacterial division, bacteria may mutate to acquire antibiotic resistance, incurring a fitness cost. Resistant bacteria have the opportunity to acquire compensatory mutations to overcome the fitness cost.
Fig 3.
Simulation protocols for resistance emergence (A) and resistance selection (B).
(A) Resistance emergence is tracked over 200 days of infection. (B) Resistance selection is simulated by stopping simulations at 200 days post infection, randomly selecting bacteria and changing them to be resistant (X: number of bacteria to be changed to resistant), and then resuming simulations in the presence of antibiotics.
Table 1.
Simulation outputs evaluated.
Table 2.
Drug resistance parameter values and ranges used for sensitivity analysis.
Fig 4.
Simulated granuloma dynamics and resistance emergence.
The formation of virtual granulomas is an emergent behavior of the model (A). A representative granuloma is shown 200 days post infection (no antibiotic treatment). Colors indicate the location of macrophage populations (resting, active, infected, chronically infected), T cell populations (regulatory, IFN-γ-producing, and cytotoxic), extracellular bacteria and caseum; (B) GranSim is calibrated to bacterial load dynamics observed in Mtb-infected non-human primates [10]. Solid line shows mean and dashed lines show quartiles for 353 simulated granulomas. Data points and error bars show mean and quartiles of between 3 and 84 non-human primates. (C) The granuloma resistance frequency (Box 1) follows bacterial trajectories, peaking at day 30 and leveling off after 60 days. (D) Granulomas that develop resistance have relatively low numbers of resistant bacteria after 200 days of infection, with a few granulomas having higher numbers of INH-R Mtb (up to 165). Resistance emergence is simulated for mutation rates 3x10-6 per base pair per day.
Fig 5.
PRCCs are shown between model parameters (mutation frequency, number of resistance mutations, relative fitness of resistant Mtb, and number of compensatory mutations) and model outputs of interest (Table 1) (Fraction of Granulomas with INH (A) or RIF (C) resistant Mtb; and the number of INH (B) or RIF (D) resistant Mtb per granuloma). Dotted lines show PRCCs with p-values of 0.05, PRCCs outside these lines are significant.
Fig 6.
Locations of INH-R, RIF-R and susceptible Mtb within granulomas.
Locations include intracellular, extracellular and non-replicating (in caseum) N = 353 granulomas. Locations for MDR Mtb are not shown since too few granulomas contained MDR to enable meaningful analysis.
Fig 7.
Treatment outcomes under INH- or RIF- monotherapy.
In each simulation, a set number of bacteria are selected to be changed to be resistant. Bacteria are selected from each bacterial sub-population (intracellular, extracellular, non-replicating). We include 0, 5, 20, 100 or ALL resistant Mtb into the simulation prior to treatment. Average bacterial load is shown when adding INH-R Mtb and treating with INH monotherapy in (A) or adding RIF-R Mtb and treating with RIF monotherapy in (B). Panels (C) and (D) contain Kaplan-Meier curves for the same simulations in (A) and (B), respectively, showing the fraction of granulomas that sterilized over time. N = 392 granulomas. *: p < 0.05.
Fig 8.
Predicted INH and RIF exposure relative to pharmacodynamic (PD) curves.
Solid lines show relative bacterial killing rate constants (kkill/Emax) as a function of concentration for INH susceptible Mtb (A-C) and RIF susceptible Mtb (D-F). Dotted lines show curves for INH- and RIF-resistant Mtb. Gray bars show the range of concentrations that intracellular (A,D), extracellular (B,E) or non-replicating (C,F) Mtb are exposed to over the first 7 days of treatment.
Table 3.
Ratio of AUCE of resistant Mtb vs susceptible Mtb for each bacterial subpopulation for INH or RIF*.
Fig 9.
Simulated treatment outcomes under INH and RIF combination therapy.
(A-C) The average number of total bacteria per granuloma is shown for INH and RIF combination therapy adding 0, 5, 20, 100 or All INH-R bacteria into granulomas. (A), RIF-R (B) or MDR (C). (D-F) Kaplan-Meier curves for the simulations in (A-C) respectively, showing the fraction of granulomas sterilized over time. N = 392 in silico granulomas.
Fig 10.
Number of treatment days before granulomas contain only resistant Mtb, when treatment is started with 5, 20 or 100 resistant Mtb present within granulomas.
Times are shown for INH-R, RIF-R or MDR bacteria during INH or RIF monotherapy or during combination therapy with INH and RIF. Data points and error bars show median +/- 5th and 95th percentiles for N = 392 in silico granulomas.