Population pharmacokinetics of rifampin in pulmonary tuberculosis patients, including a semimechanistic model to describe variable absorption

Abstract

This article describes the population pharmacokinetics of rifampin in South African pulmonary tuberculosis patients. Three datasets containing 2,913 rifampin plasma concentration-time data points, collected from 261 South African pulmonary tuberculosis patients aged 18 to 72 years and weighing 28.5 to 85.5 kg and receiving regular daily treatment that included administration of rifampin (450 to 600 mg) for at least 10 days, were pooled. A compartmental pharmacokinetic model was developed using nonlinear mixed-effects modeling. Variability in the shape of the absorption curve was described using a flexible transit compartment model, in which a delay in the onset of absorption and a gradually changing absorption rate were modeled as the passage of drug through a chain of hypothetical compartments, ultimately reaching the absorption compartment. A previously described implementation was extended to allow its application to multiple-dosing data. The typical population estimate of oral clearance was 19.2 liters x h(-1), while the volume of distribution was estimated to be 53.2 liters. Interindividual variability was estimated to be 52.8% for clearance and 43.4% for volume of distribution. Interoccasional variability was estimated for CL/F (22.5%) and mean transit time during absorption (67.9%). The use of single-drug formulations was found to increase both the mean transit time (by 104%) and clearance (by 23.6%) relative to fixed-dose-combination use. A strong correlation between clearance and volume of distribution suggested substantial variability in bioavailability, which could have clinical implications, given the dependence of treatment effectiveness on exposure. The final model successfully described rifampin pharmacokinetics in the population studied and is suitable for simulation in this context.

Figures

FIG. 1.

Diagrammatic representation of the final model for RIF pharmacokinetics. A1 = amount of RIF in the depot compartment (in milligrams); A2 = amount of RIF in the central compartment (mg); ktr = transit rate constant; n = estimated number of transit compartments.

FIG. 2.

Simulations illustrating the effect of the use of a SDF compared to the same model with FDC use assumed. The effects of the covariate relationship on CL/F and MTT alone are also represented. All other demographic parameters were assumed to represent the population median.

FIG. 3.

Goodness-of-fit plots for the final model for RIF pharmacokinetics for South African pulmonary TB patients. The broken line is a LOESS, while the solid line is identity or zero.

FIG. 4.

Plots of the observations (open circles), individual predictions (solid lines), and population predictions (dotted lines) from the final model, as used to illustrate goodness-of-fit for different classes of individual (typical individuals, individuals with low RIF exposure, and individuals with atypical absorption profiles. “Poor fit,” “Typical fit,” and “Best fit” denote goodness-of-fit, as categorized by inspection of IWRES and WRES.

FIG. 5.

A visual predictive check of the model's ability to predict the data. The predictive check was conditional on dose (450 mg, 480 mg, and 600 mg). The open circles represent the observed data points, solid lines are LOESS representations of the 5th, 50th, and 95th percentiles of the observations, the dashed line is a LOESS representation of the median of the simulations, and the dashed outer lines represent the 90% prediction intervals obtained from the simulations (also LOESS). Typically, one would expect the simulated data to match the observations—the central tendencies should match, and approximately 10% of the observations should be distributed equally above and below the 90% prediction limits.