Analysis of combination drug therapy to develop regimens with shortened duration of treatment for tuberculosis

Abstract

Rationale: Tuberculosis remains a worldwide problem, particularly with the advent of multi-drug resistance. Shortening therapy duration for Mycobacterium tuberculosis is a major goal, requiring generation of optimal kill rate and resistance-suppression. Combination therapy is required to attain the goal of shorter therapy.

Objectives: Our objective was to identify a method for identifying optimal combination chemotherapy. We developed a mathematical model for attaining this end. This is accomplished by identifying drug effect interaction (synergy, additivity, antagonism) for susceptible organisms and subpopulations resistant to each drug in the combination.

Methods: We studied the combination of linezolid plus rifampin in our hollow fiber infection model. We generated a fully parametric drug effect interaction mathematical model. The results were subjected to Monte Carlo simulation to extend the findings to a population of patients by accounting for between-patient variability in drug pharmacokinetics.

Results: All monotherapy allowed emergence of resistance over the first two weeks of the experiment. In combination, the interaction was additive for each population (susceptible and resistant). For a 600 mg/600 mg daily regimen of linezolid plus rifampin, we demonstrated that >50% of simulated subjects had eradicated the susceptible population by day 27 with the remaining organisms resistant to one or the other drug. Only 4% of patients had complete organism eradication by experiment end.

Discussion: These data strongly suggest that in order to achieve the goal of shortening therapy, the original regimen may need to be changed at one month to a regimen of two completely new agents with resistance mechanisms independent of the initial regimen. This hypothesis which arose from the analysis is immediately testable in a clinical trial.

Conflict of interest statement

Competing Interests: The authors have no competing interests with respect to Pfizer, Inc. and this applies to employment, consultancy, patents, products in development, marketed products, etc. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Figure 1. Effect of Linezolid (LZD) or Rifampin (RIF) alone and in combination on the total colony counts of Mycobacterium tuberculosis (Mtb) (Panels A and B) and on the less susceptible subpopulations (Panels C–F) as determined in a Hollow Fiber Infection Model.

Figure 2. Plots of the α-values (an index of drug interaction for effect) for different combination regimens of linezolid plus rifampin for A. Fully-Susceptible Organisms; B. Linezolid-Resistant Organisms; C. Rifampin-Resistant Organisms.

Diamonds indicate positive α-values; Squares indicates negative α-values.

Figure 3. System simulation (1,000 iterate Monte Carlo simulation) for total colony counts (A), susceptible counts and subpopulations less-susceptible to the study drugs (LZD and RIF) from the Bayesian posterior parameter vectors (B).

In Panels A and B, the median values and the standard deviations are displayed. In Panels C–F, the box and whisker plots (median-line; 25th and 75th percentiles at the bottom and top of the box; 95th percentile is displayed at the top of the figure) show the distribution of colony counts for the total population (Panel C), the susceptible population (Panel D) and the less-susceptible populations for LZD (Panel E) and RIF (Panel F) simulated at the last day of the experiment.