Fig 1.
Bacterial dynamics is illustrated in the Bacterial dynamics cutout and is modelled in each respective compartment. The immune system response is based on a model from literature[30] and is illustrated in blue. The immune system follows activation of macrophages by the initial infection, leading to a cascade of additional white cells in the lymph, finally leading to increased macrophage involvement and the formation of lesions. Drug therapy is illustrated in red and can be modelled with multiple drugs and their respective penetration into lesion, macrophages and lung. Drug resistant bacteria (dark green) is modelled using both natural and drug pressure mutation rates (kn and kd respectively). Bacteria killing is determined by both immune and drug related increase in bacterial elimination.
Table 1.
Rules for patient drug therapy outcomes.
Fig 2.
Bacterial dynamics of simulated TB infection.
Intra- (red) and extracellular (blue) bacteria count in lung tissue for TB infection without drug therapy, for 5000 patients, with infection starting on day 0. Respective shaded areas represent first and third quantiles of population.
Fig 3.
Population TB treatment outcome with standard drug therapy.
Summary of outcome of median standard drug treatment of TB infection, based on simulation of 5000 patients, with standard drug therapy (2HREZ/4HR). The TB infection occurs on day 0, and drug therapy is started on day 180.
Fig 4.
Intra- vs. extracellular bacterial load in TB infection.
Intra- and extracellular bacteria count in lung tissue for TB infection with standard drug therapy (2HREZ/4HR). The TB infection occurs on day -180, and drug therapy is started on day 0, triggering rapid intra- and extracellular bacterial death. The grey shaded block represents the cut-off of CFU/ml equal to 1 for a patient to be considered cleared of bacteria at the end of treatment.
Fig 5.
Comparison of therapy outcome for simulation vs. summary of previous clinical trials.
Comparison of simulated population consisting of 5000 patients (boxplot) therapy outcome vs. summary of previous clinical trials in terms of risk of relapse as percentage of overall population grouped by changes in duration and frequency of standard therapy. Clinical trial data with individual therapy simulations used for this figure is summarised in the Addendum B.
Fig 6.
Reduced successful therapy outcome with variations of standard drug therapy.
Comparison of patient outcomes for treatment of the median outcome of TB infection, with standard drug therapy (2HREZ/4HR) vs. median outcome of therapy excluding isoniazid, 1200 mg rifampin and reduced treatment time. Based on simulation of 5000 patients.
Fig 7.
Population TB treatment outcome for different treatment start scenarios.
Comparison of median patient outcomes for treatment of TB infection, with standard drug therapy (2HREZ/4HR) of earlier diagnosis and the impact on patient outcomes if treatment is started earlier. Based on simulation of 5000 patients. Shaded areas show the 95 percentile range of the simulations.
Fig 8.
Population TB treatment outcome for different adherence patterns.
Comparison of median patient outcomes for treatment of TB infection, with standard drug therapy (2HREZ/4HR) at 100% adherence, 80% adherence randomly spaced throughout treatment, 80% during intensive or continuation phase and simulations from medication event monitoring system (MEMS) data. Based on simulation of 5000 patients. Infection occurs 180 days before treatment.
Fig 9.
Population TB treatment outcome for reduced immune strength.
Comparison of median patient outcomes for treatment of TB infection, with standard drug therapy (2HREZ/4HR) and patients with reduced immune response calculated as a percentage of full immune system present. Based on simulation of 5000 patients. Infection occurs 180 days before treatment.
Fig 10.
Faster growth of drug-resistant bacterial strains due to incomplete drug therapy.
Comparison of growth of rifampin mono-resistant bacterial strain during treatment of TB infection, with standard drug therapy (2HREZ/4HR) vs. therapy excluding isoniazid. Based on simulation of 5000 patients. Infection occurs on day -180, and drug therapy is started on day 0.
Fig 11.
Number of drugs above MIC by compartment.
Number of drugs above MIC at steady state of standard therapy drugs at steady state over a 24h dosing interval for 5000 simulated patients. Each colored block represents one hour that a drug is above its MIC. The drugs are stacked for each hour with different colors representing different drugs. Empty squares show no drug on board. The percentage of drug onboard hours vs expected drug hours is shown above each plot to compare total compartment exposure in a 24h period.
Fig 12.
Sensitivity analysis of drug parameters.
Parameters were scaled by factor 0.1, 0.5, 2 and 10, and the final change in percentage of the population with TB (mean 5% under normal conditions) recorded to measure individual parameter impact on TB outcome.
Fig 13.
Screenshot of web application.
Graphical user interface of the TBsim tool. User can adjust regimen, drug parameters, adherence, and immune and resistance inputs. Outputs include pharmacokinetic profiles, bacterial load in intra and extracellular compartments, and immune system plots.
Fig 14.
Description of key aspects of the disease model. Bacterial infection occurs by aerosolized bacteria reaching the lungs. The bacteria remain in lung tissue, and may be absorbed into macrophages. With long-standing disease granulomatous lesions may form, which in-part may work to contain the bacteria, but also may limit effectiveness of drug therapy and thus form a partial reservoir of bacteria. Bacterial growth, killing, mutation, and drug effects are present for each model compartment. Dotted lines represent movement of immune cells and solid lines represent movement of bacteria.
Table 2.
Pharmacokinetics core model parameter values.
Fig 15.
Overview of pharmacokinetic model.
Description of the integrated pharmacokinetic (PK) model. Drug concentration in plasma and extracellular alveolar fluid was based on a one-compartment model, with first order absorption and linear elimination. Drug concentration inside macrophages and inside granuloma were modeled as separate effect compartments.
Table 3.
Pharmacokinetics effect compartment parameter values.
Fig 16.
Overview of pharmacodynamic model.
Description of the integrated pharmacodynamic (PD) model for each compartment of the simulation model, incorporating bacterial growth, death, exchange with other compartments, bacterial drug resistance, and killing by immune system effects and active drug concentrations.
Table 4.
Pharmacodynamics model parameter values.
Table 5.
Effect compartment parameter values.
Table 6.
Effective bacterial kill rates for tuberculosis drugs (outside/inside macrophages).
Table 7.
Resistant mutation rates.
Table 8.
Standard deviation of population patient parameter values.